US 201602897.62A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2016/0289762 A1 KOH et al. (43) Pub. Date: Oct. 6, 2016

(54) METHODS FOR PROFILIING AND Publication Classification QUANTITATING CELL-FREE RNA (51) Int. Cl. (71) Applicant: The Board of Trustees of the Leland CI2O I/68 (2006.01) Stanford Junior University, Palo Alto, (52) U.S. Cl. CA (US) CPC ...... CI2O 1/6883 (2013.01); C12O 2600/112 (2013.01); C12O 2600/118 (2013.01); C12O (72) Inventors: Lian Chye Winston KOH, Stanford, 2600/158 (2013.01) CA (US); Stephen R. QUAKE, Stanford, CA (US); Hei-Mun Christina FAN, Fremont, CA (US); Wenying (57) ABSTRACT PAN, Stanford, CA (US) The invention generally relates to methods for assessing a (21) Appl. No.: 15/034,746 neurological disorder by characterizing circulating nucleic acids in a blood sample. According to certain embodiments, (22) PCT Filed: Nov. 6, 2014 methods for S. a Nial disorder include (86). PCT No.: PCT/US2O14/064355 obtaining RNA present in a blood sample of a patient Suspected of having a neurological disorder, determining a S 371 (c)(1), level of RNA present in the sample that is specific to brain (2) Date: May 5, 2016 tissue, comparing the sample level of RNA to a reference O O level of RNA specific to brain tissue, determining whether a Related U.S. Application Data difference exists between the sample level and the reference (60) Provisional application No. 61/900,927, filed on Nov. level, and indicating a neurological disorder if a difference 6, 2013. is determined. Patent Applica US 2016/02897.62 A1

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METHODS FOR PROFLING AND circulating nucleic acid that are specific to brain tissue. The QUANTITATING CELL-FREE RNA present invention recognizes that abnormal deviations in circulating nucleic acid result from tissue-specific nucleic RELATED APPLICATIONS acid being released into the blood in large amounts as tissue 0001. This application claims the benefit of and priority begins to fail and degrade. By focusing on genes the to U.S. Provisional No. 61/900,927, filed Nov. 6, 2013, and expression of which is highly specific to brain tissue, meth is a continuation-in-part of U.S. Non-Provisional Ser. No. ods of the invention allow one to characterize the extent of 13/752,131, filed Jan. 28, 2013, which claims the benefit of brain degradation based on statistically-significant levels of and priority to U.S. Provisional No. 61/591,642, filed on Jan. circulating brain-specific transcripts; and use that character 27, 2012. The entirety of each foregoing application is ization to diagnose and determine the stage of the neuro incorporated herein by reference. logical disease. Accordingly, methods of the invention allow one to characterize neurological disorders without focusing TECHNICAL FIELD on Small Subset of known biomarkers, but rather focusing on the extent to which nucleic acid is released into blood from 0002 The present invention relates to assessing neuro brain tissue affected by disease. Methods of the invention are logical disorders based on nucleic acid specific to brain particularly useful in diagnosing and determining the stage tissue. of Alzheimer's disease. 0007. In particular embodiments, methods of the inven BACKGROUND tion include obtaining RNA from a blood sample of a patient 0003. Dementia is a catchall term used to characterize Suspected of having a neurological disorder, and determin cognitive declines that interfere with one’s ability to perform ing a level of the sample RNA that originated from brain everyday activities. Signs of dementia include declines in tissue. In certain embodiments, the RNA is converted to the following mental functions: memory, communication cDNA. The level of the sample RNA specific to brain tissue and language, ability to focus and pay attention, reasoning, is then compared to a reference level of RNA that is specific judgment, motor skills, and visual perception. While there to brain tissue. The reference level may be derived from a are several neurological disorders that cause dementia, Subject or patient population having a neurological disorder Alzheimer's disease is the most common, accounting for 60 or from a normal/control Subject or patient population. to 80 percent of all dementia cases. Depending on the reference level chosen, similarities or 0004 Alzheimer's disease is a progressive disease that variances between the level of sample RNA and the refer gradually destroys memory and mental functions in patients. ence level of RNA are indicative of the neurological disor Symptoms manifest initially as a decline in memory fol der, the type of neurological disorder and/or the stage of the lowed by deterioration of other cognitive functions as well neurological disorder. In certain embodiments, only simi as by abnormal behavior. Individuals with Alzheimer's larities or variances of statistical significance are indicative disease usually begin to show dementia symptoms later in of the neurological disorder. Whether a variance is signifi life (e.g., 65 years or older), but a Small percentage of cant depends upon the chosen reference population. individuals in their 40s and 50s experience early onset 0008. Additional aspects of the invention involve assess Alzheimer's disease. Alzheimer's disease is associated with ing a neurological disorder using a set of predictive variables the damage and degeneration of neurons in several regions correlated with a neurological disorder. In Such aspects, of the brain. The neuropathic characteristics of Alzheimer's methods of the invention involve detecting RNA present in disease include the presence of plaques and tangles, synaptic a biological sample obtained from a patient Suspected of loss, and selective neuronal cell death. Plaques are abnormal having a neurological disorder. In certain embodiments, the levels of protein fragments called beta-amyloid that accu RNA is converted to cDNA. Sample levels of one or more mulate between nerve cells. Tangles are twisted fibers of a RNA transcripts that are specific to brain tissue are deter protein known as tau that accumulate within nerve cells. mined, and the sample levels of RNA transcripts specific to 0005 While the above-described neuropathic character brain tissue are compared to a set of predictive variables istics are hallmarks of the disease, the exact cause of correlated with a neurological disorder. The predictive vari Alzheimer's disease is unknown and there are no specific ables may include reference levels of RNA transcripts that tests that confirm whether an individual has Alzheimer's are specific to brain tissue and correspond to one or more disease. For diagnosis of Alzheimer's, clinicians assess a stages of the neurological disorders. In certain embodiments, combination of clinical criteria, which may include a neu the predictive variables may include brain-specific reference rological exam, mental status tests, and brain imaging. levels of transcripts that correlate to other factors such as Efforts are being made to determine the genetic causes in age, sex, environmental exposure, familial history of demen order to help definitively diagnose Alzheimer's disease. tia, dementia symptoms. The stage of a neurological disor However, only a handful of genetic markers associated with der of the patient may be indicated based on variances or Alzheimer's have been characterized to date, and diagnostic similarities between the level of sample RNA and the tests for those markers require invasive brain biopsies. predictive variables. 0009 RNA obtained from the blood sample may be SUMMARY converted into synthetic cDNA. In such instances, the 0006. The present invention provides methods for assess sample levels of cDNA that correspond to RNA originating ing neurological conditions using circulating nucleic acid from brain tissue may be compared to reference levels of (such as DNA or RNA) that is specific to brain tissue. In RNA or references levels of cDNA that correspond to RNA particular embodiments, the invention involves a compara originating from brain tissue. For example, methods of the tive analysis of levels of circulating nucleic acid in a patient invention may include the steps of detecting circulating that are specific to brain tissue with reference levels of RNA in a sample obtained from a patient suspected of US 2016/02897.62 A1 Oct. 6, 2016

having a neurological disorder and converting the circulat of the apotopic rate of that tissue. The normal cell-free ing RNA from the sample into cDNA. The next steps involve transcriptome serves as a baseline or reference level to determining levels of the sample cDNA that correspond to assess tissue health of other individuals. The invention RNA originating from brain tissue, and comparing the includes a comparative measurement of the cell-free tran determined levels of the cDNA to a reference level of Scriptome of a sample to the normal cell free transcriptome cDNA. The reference level of cDNA may also correspond to to assess the sample levels of tissue-specific transcripts RNA originating from brain tissue. The neurological con circulating in plasma and to assess the health of tissues dition of the patient may then be indicated based similarities contributing to the cell-free transcriptome. or differences between the patient cloNA levels and the 0014. In addition to cell-free transcriptomes reference reference cDNA levels. levels of normal patient populations, methods of the inven 0010 Methods of the invention are also useful to identify tion also utilize reference levels for cell-free transcriptomes one or more biomarkers associated with a neurological specific to other patient populations. Using methods of the disorder. In Such aspects, brain-specific transcripts of an invention one can determine the relative contribution of individual or patient population Suspected of having or tissue-specific transcripts to the cell-free transcriptome of actually having a neurological disorder (e.g. exhibiting maternal Subjects, fetus Subjects, and/or Subjects having a impaired cognitive functions) are compared to a reference condition or disease. (e.g. brain-specific transcripts of a healthy, normal popula 0015. By analyzing the health of tissue based on tissue tion). The brain-specific transcripts of the individual or specific transcripts, methods of the invention advanta patient population that are differentially expressed as com geously allow one to assess the health of a tissue without pared to the reference may then be identified as biomarkers relying on disease-related protein biomarkers. In certain of the neurological disorder. In certain embodiments, only aspects, methods of the invention assess the health of a differentially expressed brain-specific transcripts that are tissue by comparing a sample level of RNA in a biological statistically significant are identified as biomarkers. Methods sample to a reference level of RNA specific to a tissue, of determining statistical significance are known in the art. determining whether a difference exists between the sample 0011. The reference level of RNA or cDNA specific to level and the reference level, and characterizing the tissue as brain tissue may pertain to a patient population having a abnormal if a difference is detected. For example, if a particular condition or pertain to a normal/control patient patients RNA expression levels for a specific tissue differs population. In one embodiment, the reference level of RNA from the RNA expression levels for the specific tissue in the or cDNA specific to brain tissue may be levels of RNA or normal cell-free transcriptome, this indicates that patients cDNA specific to brain tissue in a normal patient population. tissue is not functioning properly. In another embodiment, the reference level of RNA or 0016. In certain aspects, methods of the invention involve cDNA may be the level of RNA or cDNA specific to brain assessing health of a tissue by characterizing the tissue as tissue in a patient population having a certain neurological abnormal if a specified level of RNA is present in the blood. disorder. The certain neurological disorder may be mild The method may further include detecting a level of RNA in cognitive impairment or moderate-to-Severe cognitive a blood sample, comparing the sample level of RNA to a impairment. The various levels of cognitive impairment may reference level of RNA specific to a tissue, determining be indicative of a stage of Alzheimer's disease. In further whether a difference exists between the sample level and the embodiments, the reference level of RNA or cDNA may be reference level, and characterizing the tissue as abnormal if the level of RNA or cDNA specific to brain tissue having a the sample level and the reference level are the same. certain neurological disorder at a certain age. Other embodi 0017. The present invention also provides methods for ments may include reference levels that correspond to a comprehensively profiling fetal specific cell-free RNA in variety of predictive variables, including type of neurologi maternal plasma and deconvoluting the cell-free transcrip cal disorder, stage of neurological disorder, age, sex, envi tome of fetal origin with relative proportion to different fetal ronmental exposure, familial history of dementia, dementia tissue types. Methods of the invention involve the use of symptoms. next-generation sequencing technology and/or microarrays 0012 Methods of the invention involve assaying biologi to characterize the cell-free RNA transcripts that are present cal samples for circulating nucleic acid (RNA or DNA). in maternal plasma at different stages of pregnancy. Quan Suitable biological samples may include blood, blood frac tification of these transcripts allows one to deduce changes tions, plasma, saliva, sputum, urine, semen, transvaginal of these genes across different trimesters, and hence pro fluid, and cerebrospinal fluid. Preferably, the sample is a vides a way of quantification of temporal changes in tran blood sample. The blood sample may be plasma or serum. Scripts. 0013 The present invention also provides methods for 0018 Methods of the invention allow diagnosis and profiling the origin of the cell-free RNA to assess the health identification of the potential for complications during or of an organ or tissue. Deviations in normal cell-free tran after pregnancy. Methods also allow the identification of Scriptomes are caused when organ/tissue-specific transcripts pregnancy-associated transcripts which, in turn, elucidates are released in to the blood in large amounts as those maternal and fetal developmental programs. Methods of the organs/tissue begin to fail or are attacked by the immune invention are useful for preterm diagnosis as well as eluci system or pathogens. As a result inflammation process can dation of transcript profiles associated with fetal develop occur as part of body's complex biological response to these mental pathways generally. Thus, methods of the invention harmful stimuli. The invention, according to certain aspects, are useful to characterize fetal development and are not utilizes tissue-specific RNA transcripts of healthy individu limited to characterization only of disease states or compli als to deduce the relative optimal contributions of different cations associated with pregnancy. Exemplary embodiments tissues in the normal cell-free transcriptome, with each of the methods are described in the detailed description, tissue-specific RNA transcript of the sample being indicative claims, and figures provided below. US 2016/02897.62 A1 Oct. 6, 2016

BRIEF DESCRIPTION OF THE DRAWINGS at two concentration levels. FIG. 11B depicts the same 0019 FIG. 1 depicts a listing of the top detected female results segmented across the two subjects labeled as P53 & pregnancy associated differentially expressed transcripts. P54. 0020 FIG. 2 shows plots of the two main principal 0031 FIG. 12 illustrates relative expression of fetal liver components for cell free RNA transcript levels obtained in genes across maternal time points (first trimester, second Example 1. trimester, third trimester, and post partum). FIG. 12 is split 0021 FIG. 3A depicts a heatmap of the top 100 cell free across FIGS. 12A and 12B, as indicated by the graphical transcript levels exhibiting different temporal levels in pre figure outline. In FIG. 12A, relative expression fold changes term and normal pregnancy using microarrays. The heat map of each trimester as compared to post-partum for the panel of FIG. 3A is split across FIG. 3A-1 and FIG. 3A-2, as of Fetal Liver genes. Plotted are the results for two subjects indicated by the graphical figure outline. done at two different concentrations each, each point rep 0022 FIG. 3B depicts heatmap of the top 100 cell free resent one Subject sampled at a particular trimester, and the transcript levels exhibiting different temporal levels in pre cell free RNA went through the described protocol at two term and normal pregnancy using RNA-Seq. The heat map concentration levels. FIG. 12B depicts the same results of FIG. 3B is split across FIG. 3B-1 and FIG. 3B-2, as segmented across the two subjects labeled as P53 & P54. indicated by the graphical figure outline. 0032 FIG. 13 illustrates the relative composition of 0023 FIG. 4 depicts a ranking of the top 20 transcripts different organs contribution towards a plasma adult cell free differentially expressed between pre-term and normal preg transcriptome. nancy. 0033 FIG. 14 illustrates a decomposition of decomposi 0024 FIG. 5 depicts results of a Gene Ontology analysis tion of organ contribution towards a plasma adult cell free on the top 20 common RNA transcripts of FIG. 4, showing transcriptome using RNA-seq data. those transcripts enriched for proteins that are attached 0034 FIG. 15 shows a heat map of the tissue specific (integrated or loosely bound) to the plasma membrane or on transcripts of Table 2 of Example 3, being detectable in the the membranes of the platelets. cell free RNA. 0025 FIG. 6 depicts that the gene expression profile for 0035 FIG. 16 depicts a flow-diagram of a method of the PVALB across the different trimesters shows the premature invention according to certain embodiments. births highlighted in blue has higher levels of cell free 0036 FIG. 17 illustrates identifying brain-specific cell RNA transcripts found as compared to normal pregnancy. free RNA transcripts that differ between Alzheimer's sub 0026 FIG. 7 outlines exemplary process steps for deter jects and control Subjects. mining the relative tissue contributions to a cell-free tran 0037 FIG. 18 illustrates an experimental design compar scriptome of a sample. FIG. 7 is split across FIGS. 7A and ing microarray, RNA-Seq and quantitative PCR for a cus 7B, as indicated by the graphical figure outline. tomized bioinformatics pipeline. In the experiment, 11 preg 0027 FIG. 8 depicts the panel of selected fetal tissue nant women and 4 non-pregnant control Subjects were specific transcripts generated in Example 2. FIG. 8 is split recruited. For all the pregnant patients, blood was drawn at across FIGS. 8A and 8B, as indicated by the graphical figure 1st, 2nd, 3rd trimester and postpartum. The cell-free plasma outline. RNA were then extracted, amplified and characterized by 0028 FIGS. 9A and 9B depict the raw data of parallel Affymetrix microarray, Illumina sequencer and quantitative quantification of the fetal tissue-specific transcripts showing PCR. changes across maternal time-points (first trimester, second 0038 FIG. 19 illustrates a heat map of temporal varying trimester, third trimester, and post partum) using the actual genes obtained from microarray analysis. Unsupervised cell free RNA as well as the cDNA library of the same cell clustering was performed on genes across different time free RNA. points. Cluster of genes belongs to the CGB family of genes 0029 FIG. 10 illustrates relative expression of placental which are known to be expressed at high levels during the genes across maternal time points (first trimester, second first trimester exhibited corresponding high levels of RNA in trimester, third trimester, and post partum). FIG. 10 is split the first trimester. across FIGS. 10A and 10B, as indicated by the graphical 0039 FIG. 20 illustrates another heat map of temporal figure outline. In FIG. 10, relative expression fold changes varying genes obtained from microarray analysis. Unsuper of each trimester as compared to post-partum for the panel vised clustering was performed on genes across different of placental genes. Plotted are the results for two subjects time points. Cluster of genes belongs to the CGB family of done at two different concentrations each, each point rep genes which are known to be expressed at high levels during resent one Subject sampled at a particular trimester, and the the first trimester exhibited corresponding high levels of cell free RNA went through the described protocol at two RNA in the first trimester. concentration levels. FIG. 10B depicts the same results 0040 FIG. 21 illustrates a list of genes identified with segmented across the two subjects labeled as P53 & P54. fetal SNPs using the experimental design of FIG. 18. List of 0030 FIG. 11 illustrates relative expression of fetal brain identified Gene Transcripts with identified fetal SNPs and genes across maternal time points (first trimester, second the captured temporal dynamics. The barplot reflects the trimester, third trimester, and post partum). FIG. 11 is split relative contribution of fetal SNPs as reflected in the across FIGS. 11A and 11B, as indicated by the graphical sequencing data. The red color bar reflects the extent of the figure outline. In FIG. 11A, relative expression folds relative Fetal SNP contribution. changes of each trimester as compared to post-partum for 0041 FIG. 22 identifies placental specific transcripts the panel of Fetal Brain genes. Plotted are the results for two measured by qPCR in the experimental design of FIG. 18. Subjects done at two different concentrations each, each As shown in FIG. 22, the time course of placental specific point represent one subject sampled at a particular trimester, genes is measured by qPCR. Plot showing the Delta Ct value and the cell free RNA went through the described protocol with respect to the housekeeping gene ACTB across the US 2016/02897.62 A1 Oct. 6, 2016 different trimesters of pregnancy including after birth. Gen ferential detection of transcripts is achieved, in part, by eral trends show elevated levels during the trimesters with a isolating and amplifying plasma RNA from the maternal decline to low levels after the baby is born. blood throughout the different stages of pregnancy, and 0042 FIG. 23 identifies fetal brain specific transcripts quantitating and characterizing the isolated transcripts via measured byq. As shown in FIG. 23, the time course of fetal microarray and RNA-Seq. brain specific genes is measured by qPCR. Plot showing the 0053 Methods and materials specific for analyzing a Delta Ct value with respect to the housekeeping gene ACTB biological sample containing RNA (including non-maternal, across the different trimesters of pregnancy including after maternal, maternal-fetus mixed) as described herein, are birth. General trends show elevated levels during the tri merely one example of how methods of the invention can be mesters with a decline to low levels after the baby is born. applied and are not intended to limit the invention. Methods 0043 FIG. 24 identifies fetal liver specific transcripts of the invention are also useful to screen for the differential measured by qPCR. As shown in FIG. 24, the time course of expression of target genes related to cancer diagnosis, pro fetal liver specific genes is measured by qPCR. Plot showing gression and/or prognosis using cell-free RNA in blood, the Delta Ct value with respect to the housekeeping gene stool, sputum, urine, transvaginal fluid, breast nipple aspi ACTB across the different trimesters of pregnancy including rate, cerebrospinal fluid, etc. after birth. General trends show elevated levels during the 0054. In certain embodiments, methods of the invention trimesters with a decline to low levels after the baby is born. generally include the following steps: obtaining a biological 0044 FIG. 25 illustrates tissue composition of the adult sample containing genetic material from different genomic cell free transcriptome in typical adult plasma as a Summa Sources, isolating total RNA from the biological sample tion of RNAs from different tissue types. containing biological sample containing a mixture of genetic 004.5 FIG. 26 illustrates decomposition of Cell-free RNA material from different genomic sources, preparing ampli transcriptome of normal adult into their respective tissues fied cDNA from total RNA, sequencing amplified cDNA, types using microarray data and quadratic programming. and digital counting and analysis, and profiling the amplified 0046 FIG. 27 depicts a Principle Component Analysis cDNA (PCA) space reflecting the unsupervised clustering of the 0055 Methods of the invention also involve assessing the patients using the gene expression data from the 48 genes health of a tissue contributing to the cell-free transcriptome. assay. In certain embodiments, the invention involves assessing the 0047 FIG. 28 depicts the measured APP levels in cell-free transcriptome of a biological sample to determine patients. The left panel shows the levels of APP transcripts tissue-specific contributions of individual tissues to the across different age groups in the study. The right panel cell-free transcriptome. According to certain aspects, the shows the different levels of the APP transcripts of the invention assesses the health of a tissue by detecting a combined population of patients. sample level of RNA in a biological sample, comparing the 0048 FIG. 29 depicts the measured MOBP levels in sample level of RNA to a reference level of RNA specific to patients. The left panel shows the levels of the MOBP the tissue, and characterizing the tissue as abnormal if a transcripts across different age groups in the study. The right difference is detected. This method is applicable to charac panel shows the different levels of the MOBP transcripts of terize the health of a tissue in non-maternal Subjects, preg the combined population of patients. nant subjects, and live fetuses. FIG. 16 depicts a flow 0049 FIG. 30 depicts classification results using com diagram of this method according to certain embodiments. bined Z-scores. 0056. In certain aspects, methods of the invention employ a deconvolution of a reference cell-free RNA transcriptome DETAILED DESCRIPTION to determine a reference level for a tissue. Preferably, the 0050 Methods and materials described herein apply a reference cell-free RNA transcriptome is a normal, healthy combination of next-generation sequencing and microarray transcriptome, and the reference level of a tissue is a relative techniques for detecting, quantitating and characterizing level of RNA specific to the tissue present in the blood of RNA present in a biological sample. In certain embodi healthy, normal individuals. Methods of the invention ments, the biological sample contains a mixture of genetic assume that apoptotic cells from different tissue types material from different genomic sources, i.e. pregnant release their RNA into plasma of a subject. Each of these female and a fetus. tissues expresses a specific number of genes unique to the 0051. Unlike other methods of digital analysis in which tissue type, and the cell-free RNA transcriptome of a subject the nucleic acid in the sample is isolated to a nominal single is a Summation of the different tissue types. Each tissue may target molecule in a small reaction volume, methods of the express one or more numbers of genes. In certain embodi present invention are conducted without diluting or distrib ments, the reference level is a level associated with one of uting the genetic material in the sample. Methods of the the genes expressed by a certain tissue. In other embodi invention allow for simultaneous screening of multiple ments, the reference level is a level associated with a transcriptomes, and provide informative sequence informa plurality of genes expressed by a certain tissue. It should be tion for each transcript at the single-nucleotide level, thus noted that a reference level or threshold amount for a providing the capability for non-invasive, high throughput tissue-specific transcript present in circulating RNA may be screening for a broad spectrum of diseases or conditions in Zero or a positive number. a Subject from a limited amount of biological sample. 0057 For healthy, normal subjects, the relative contribu 0052. In one particular embodiment, methods of the tions of circulating RNA from different tissue types are invention involve analysis of mixed fetal and maternal RNA relatively stable, and each tissue-specific RNA transcript of in the maternal blood to identify differentially expressed the cell-free RNA transcriptome for normal subjects can transcripts throughout different stages of pregnancy that may serve as a reference level for that tissue. Applying methods be indicative of a preterm or pathological pregnancy. Dif of the invention, a tissue is characterized as unhealthy or US 2016/02897.62 A1 Oct. 6, 2016

abnormal if a sample includes a level of RNA that differs mization method to deduce the relative optimal contribu from a reference level of RNA specific to the tissue. The tions of different organs/tissues towards the cell-free tran tissue of the sample may be characterized as unhealthy if the Scriptome of the sample. actual level of RNA is statistically different from the refer 0062 One or more databases of genetic information can ence level. Statistical significance can be determined by any be used to identify a panel of tissue-specific transcripts. method known in the art. These measurements can be used Accordingly, aspects of the invention provide systems and to screen for organ health, as diagnostic tool, and as a tool methods for the use and development of a database. Par to measure response to pharmaceuticals or in clinical trials ticularly, methods of the invention utilize databases contain to monitor health. ing existing data generated across tissue types to identify the 0058 If a difference is detected between the sample level tissue-specific genes. Databases utilized for identification of of RNA and the reference level of RNA, such difference tissue-specific genes include the Human 133A/GNF1H Suggests that the associated tissue is not functioning prop Gene Atlas and RNA-Seq Atlas, although any other database erly. The change in circulating RNA may be the precursor to or literature can be used. In order to identify tissue-specific organ failure or indicate that the tissue is being attacked by transcripts from one or more databases, certain embodi the immune system or pathogens. If a tissue is identified as ments employ a template-matching algorithm to the data abnormal, the next step(s), according to certain embodi bases. Template matching algorithms used to filter data are ments, may include more extensive testing of the tissue (e.g. known in the art, see e.g., Pavlidis P. Noble W S (2001) invasive biopsy of the tissue), prescribing course of treat Analysis of Strain and regional variation in gene expression ment specific to the tissue, and/or routine monitoring of the in mouse brain. Genome Biol 2:research0042.1-0042.15. tissue. 0063. In certain embodiments, quadratic programming is 0059 Methods of the invention can be used to infer organ used as a constrained optimization method to deduce relative health non-invasively. This non-invasive testing can be used optimal contributions of different organs/tissues towards the to screen for appendicitis, incipient diabetes and pathologi cell-free transcriptome in a sample. Quadratic programming cal conditions induced by diabetes such as nephropathy, is known in the art and described in detail in Goldfarb and neuropathy, retinopathy etc. In addition, the invention can be A. Idnani (1982). Dual and Primal-Dual Methods for Solv used to determine the presence of graft versus host disease ing Strictly Convex Quadratic Programs. In J. P. Hennart in organ transplants, particularly in bone marrow transplant (ed.), Numerical Analysis, Springer-Verlag, Berlin, pages recipients whose new immune system is attacking the skin, 226-239, and D. Goldfarb and A. Idnani (1983). A numeri GI tract or liver. The invention can also be used to monitor cally stable dual method for solving strictly convex qua the health of Solid organ transplant recipients such as heart, dratic programs. Mathematical Programming, 27, 1-33. lung and kidney. The methods of the invention can assess 0064 FIG. 7 outlines exemplary process steps for deter likelihood of prematurity, preeclampsia and anomalies in mining the relative tissue contributions to a cell-free tran pregnancy and fetal development. In addition, methods of Scriptome of a sample. Using information provided by one the invention could be used to identify and monitor neuro or more tissue-specific databases, a panel of tissue-specific logical disorders (e.g. multiple Sclerosis and Alzheimer's genes is generated with a template-matching function. A disease) that involve cell specific death (e.g. of neurons or quality control function can be applied to filter the results. A due to demyelination) or that involve the generation of blood sample is then analyzed to determine the relative plaques or protein aggregation. contribution of each tissue-specific transcript to the total RNA of the sample. Cell-free RNA is extracted from the 0060 A cell-free transcriptome for purposes of determin sample, and the cell-free RNA extractions are processed ing a reference level for tissue-specific transcripts can be the using one or more quantification techniques (e.g. standard cell-free transcriptome of one or more normal Subjects, mircoarrays and RNA-sequence protocols). The obtained maternal Subjects, Subjects having a certain conditions and gene expression values for the sample are then normalized. diseases, or fetus Subjects. In the case of certain conditions, This involves rescaling of all gene expression values to the the reference level of a tissue is a level of RNA specific to housekeeping genes. Next, the sample's total RNA is the tissue present in blood of one or more Subjects having a assessed against the panel of tissue-specific genes using certain disease or condition. In such aspect, the method quadratic programming in order to determine the tissue includes detecting a level of RNA in a blood, comparing the specific relative contributions to the sample's cell-free tran sample level of RNA to a reference level of RNA specific to Scriptome. The following constraints are employed to obtain a tissue, determining whether a difference exists between the the estimated relative contributions during the quadratic sample level and the reference level, and characterizing the programming analysis: a) the RNA contributions of different as abnormal if the sample level and the reference level are tissues are greater than or equal to Zero, and b) the Sum of the same. all contributions to the cell-free transcriptome equals one. 0061. A deconvolution of a cell-free transcriptome is 0065 Method of the invention for determining the rela used to determine the relative contribution of each tissue tive contributions for each tissue can be used to determine type towards the cell-free RNA transcriptome. The follow the reference level for the tissue. That is, a certain population ing steps are employed to determine the relative RNA of Subjects (e.g., maternal, normal, cancerous, Alzheimer's contributions of certain tissues in a sample. First, a panel of (and various stages thereof)) can be subject to the decon tissue-specific transcripts is identified. Second, total RNA in volution process outlined in FIG. 7 to obtain reference levels plasma from a sample is determined using methods known of tissue-specific gene expression for that patient population. in the art. Third, the total RNA is assessed against the panel When relative tissue contributions are considered individu of tissue-specific transcripts, and the total RNA is consid ally, quantification of each of these tissue-specific transcripts ered a Summation these different tissue-specific transcripts. can be used as a measure for the reference apoptotic rate of Quadratic programming can be used as a constrained opti that particular tissue for that particular population. For US 2016/02897.62 A1 Oct. 6, 2016

example, blood from one or more healthy, normal individu 0071 Alternatively, fetal specific RNA may be concen als can be analyzed to determine the relative RNA contri trated by known methods, including centrifugation and bution of tissues to the cell-free RNA transcriptome for various enzyme inhibitors. The RNA is bound to a selective healthy, normal individuals. Each relative RNA contribution membrane (e.g., silica) to separate it from contaminants. The of tissue that makes up the normal RNA transcriptome is a RNA is preferably enriched for fragments circulating in the reference level for that tissue. plasma, which are less than less 300 bp. This size selection 0066. According to certain embodiments, an unknown is done on an RNA size separation medium, Such as an sample of blood can be subject to process outlined in FIG. electrophoretic gel or chromatography material. 7 to determine the relative tissue contributions to the cell 0072 Flow cytometry techniques can also be used to free RNA transcriptome of that sample. The relative tissue enrich for fetal cells in maternal blood (Herzenberg et al., contributions of the sample are then compared to one or PNAS 76: 1453-1455 (1979); Bianchi et al., PNAS 87: more reference levels of the relative contributions to a 3279-3283 (1990); Bruch et al., Prenatal Diagnosis 11: reference cell-free RNA transcriptome. If a specific tissue 787-798 (1991)). U.S. Pat. No. 5,432,054 also describes a shows a contribution to the cell-free RNA transcriptome in technique for separation of fetal nucleated red blood cells, the sample that is greater or less than the contribution of the using a tube having a wide top and a narrow, capillary specific tissue in a reference cell-free RNA transcriptome, bottom made of polyethylene. Centrifugation using a vari then the tissue exhibiting differential contribution may be able speed program results in a stacking of red blood cells characterized accordingly. If the reference cell-free tran in the capillary based on the density of the molecules. The Scriptome represents a healthy population, a tissue exhibit density fraction containing low-density red blood cells, ing a differential RNA contribution in a sample cell-free including fetal red blood cells, is recovered and then differ transcriptome can be classified as unhealthy. entially hemolyzed to preferentially destroy maternal red 0067. The biological sample can be blood, saliva, spu blood cells. A density gradient in a hypertonic medium is tum, urine, semen, transvaginal fluid, cerebrospinal fluid, used to separate red blood cells, now enriched in the fetal red Sweat, breast milk, breast fluid (e.g., breast nipple aspirate), blood cells from lymphocytes and ruptured maternal cells. stool, a cell or a tissue biopsy. In certain embodiments, the The use of a hypertonic solution shrinks the red blood cells, samples of the same biological sample are obtained at which increases their density, and facilitates purification multiple different time points in order to analyze differential from the more dense lymphocytes. After the fetal cells have transcript levels in the biological sample over time. For been isolated, fetal RNA can be purified using standard example, maternal plasma may be analyzed in each trimes techniques in the art. ter. In some embodiments, the biological sample is drawn 0073. Further, an agent that stabilizes cell membranes blood and circulating nucleic acids, such as cell-free RNA. may be added to the maternal blood to reduce maternal cell The cell-free RNA may be from different genomic sources lysis including but not limited to aldehydes, urea formalde is found in the blood or plasma, rather than in cells. hyde, phenol formaldehyde, DMAE (dimethylaminoetha 0068. In a particular embodiment, the drawn blood is nol), cholesterol, cholesterol derivatives, high concentra maternal blood. In order to obtain a sufficient amount of tions of magnesium, vitamin E, and vitamin E derivatives, nucleic acids for testing, it is preferred that approximately calcium, calcium gluconate, taurine, niacin, hydroxylamine 10-50 mL of blood be drawn. However, less blood may be derivatives, bimoclomol. Sucrose, astaxanthin, glucose, ami drawn for a genetic screen in which less statistical signifi triptyline, isomer A hopane tetral phenylacetate, isomer B cance is required, or in which the RNA sample is enriched hopane tetral phenylacetate, citicoline, inositol, Vitamin B, for fetal RNA. Vitamin B complex, cholesterol hemisuccinate, Sorbitol, 0069 Methods of the invention involve isolating total calcium, coenzyme Q, ubiquinone, Vitamin K. Vitamin K RNA from a biological sample. Total RNA can be isolated complex, menaquinone, Zonegran, Zinc, ginkgo biloba from the biological sample using any methods known in the extract, diphenylhydantoin, perforan, polyvinylpyrrolidone, art. In certain embodiments, total RNA is extracted from phosphatidylserine, tegretol, PABA, disodium cromglycate, plasma. Plasma RNA extraction is described in Enders et al., nedocromil Sodium, phenyloin, Zinc citrate, mexitil, dilantin, “The Concentration of Circulating Corticotropin-releasing Sodium hyaluronate, or polaxamer 188. Hormone mRNA in Maternal Plasma Is Increased in Preec 0074 An example of a protocol for using this agent is as lampsia.” Clinical Chemistry 49: 727-731, 2003. As follows: The blood is stored at 4°C. until processing. The described there, plasma harvested after centrifugation steps tubes are spun at 1000 rpm for ten minutes in a centrifuge is mixed Trizol LS reagent (Invitrogen) and chloroform. The with braking power set at Zero. The tubes are spun a second mixture is centrifuged, and the aqueous layer transferred to time at 1000 rpm for ten minutes. The supernatant (the new tubes. Ethanol is added to the aqueous layer. The plasma) of each sample is transferred to a new tube and spun mixture is then applied to an RNeasy mini column (Qiagen) at 3000 rpm for ten minutes with the brake set at Zero. The and processed according to the manufacturer's recommen supernatant is transferred to a new tube and stored at -80° dations. C. Approximately two milliliters of the “buffy coat, which 0070. In the embodiments where the biological sample is contains maternal cells, is placed into a separate tube and maternal blood, the maternal blood may optionally be pro Stored at -80° C. cessed to enrich the fetal RNA concentration in the total 0075 Methods of the invention also involve preparing RNA. For example, after extraction, the RNA can be sepa amplified cDNA from total RNA. cDNA is prepared and rated by gel electrophoresis and the gel fraction containing indiscriminately amplified without diluting the isolated circulatory RNA with a size of corresponding to fetal RNA RNA sample or distributing the mixture of genetic material (e.g., <300 bp) is carefully excised. The RNA is extracted in the isolated RNA into discrete reaction samples. Prefer from this gel slice and eluted using methods known in the ably, amplification is initiated at the 3' end as well as art. randomly throughout the whole transcriptome in the sample US 2016/02897.62 A1 Oct. 6, 2016

to allow for amplification of both mRNA and non-polyade devices, or any other medium which can be used to store the nylated transcripts. The double-stranded cDNA amplifica desired information and which can be accessed by a com tion products are thus optimized for the generation of puting device. sequencing libraries for Next Generation Sequencing plat I0082 In certain aspects, methods of the invention can be forms. Suitable kits for amplifying cDNA in accordance used to determine cell-free RNA transcripts specific to the with the methods of the invention include, for example, the certain tissue, and use those transcripts to diagnose disorders Ovation(R) RNA-Seq System. and diseases associated with that tissue. In certain embodi 0076 Methods of the invention also involve sequencing ments, methods of the invention can be used to determine the amplified cDNA. While any known sequencing method cell-free RNA transcripts specific to the brain, and use those can be used to sequence the amplified cDNA mixture, single transcripts to diagnose neurological disorders (such as molecule sequencing methods are preferred. Preferably, the Alzheimer's disease). For example, methods of profiling amplified cDNA is sequenced by whole transcriptome shot cell-free RNA described herein can be used to differentiate gun sequencing (also referred to herein as (“RNA-Seq). Subjects with neurological disorders from normal Subjects Whole transcriptome shotgun sequencing (RNA-Seq) can be because cell-free RNA transcripts associated with certain accomplished using a variety of next-generation sequencing neurological disorders present at statistically-significant dif platforms such as the Illumina Genome Analyzer platform, ferent levels than the same cell-free RNA transcripts in ABI Solid Sequencing platform, or Life Science's 454 normal healthy populations. As a result, one is able to utilize Sequencing platform. levels of those RNA transcripts for clear and simple diag 0077 Methods of the invention further involve subject nostic tests. ing the cDNA to digital counting and analysis. The number 0083. In accordance with certain embodiments, cell-free of amplified sequences for each transcript in the amplified RNA transcripts that source from brain tissue can be further sample can be quantitated via sequence reads (one read per examined as potential biomarkers for neurological disorders. amplified Strand). Unlike previous methods of digital analy In certain embodiments, once a brain-specific cell-free RNA sis, sequencing allows for the detection and quantitation at transcript is determined, levels of the brain-specific cell-free the single nucleotide level for each transcript present in a RNA transcripts in normal patients are compared to patients biological sample containing a genetic material from differ with certain neurological disorders. In instances where the ent genomic sources and therefore multiple transcriptomes. levels of brain specific cell-free RNA transcript consistently exhibit a statistically significant difference between subjects 0078. After digital counting, the ratios of the various with a certain neurological disorder and normal subjects, amplified transcripts can compared to determine relative then that brain-specific cell-free RNA transcript can be used amounts of differential transcript in the biological sample. as a biomarker for that neurological disorder. For example, Where multiple biological samples are obtained at different the inventors have found that measurements of PSD3 and time-points, the differential transcript levels can be charac APP cell-free RNA transcript levels in plasma for Alzheimer terized over the course of time. disorder patients are statistically different from the levels of 0079. Differential transcript levels within the biological PSD3 and APP cell-free RNA in normal subjects. sample can also be analyzed using via microarray tech 0084. According to certain aspects, a neurological disor niques. The amplified cDNA can be used to probe a microar der is indicated in a patient based on a comparison of the ray containing gene transcripts associated with one or con patient’s circulating nucleic acid that is specific to brain ditions or diseases, such as any prenatal condition, or any tissue and circulating nucleic acid of a reference or multiple type of cancer, inflammatory, or autoimmune disease. references that is specific to brain tissue. In particular, the 0080. It will be understood that methods and any flow circulating nucleic acid is RNA, but may also be DNA. In diagrams disclosed herein can be implemented by computer certain embodiments, levels of brain-specific circulating program instructions. These program instructions may be RNA present in a reference population are used as thresholds provided to a computer processor, Such that the instructions, that are indicative with a condition. The condition may be a which execute on the processor, create means for imple normal healthy condition or may be a diseased condition menting the actions specified in the flowchart blocks or (e.g. neurological disorder, Alzheimer's disease generally or described in methods for assessing tissue disclosed herein. particular stage of Alzheimer's disease). When the threshold The computer program instructions may be executed by a is indicative of a diseased condition, the patients transcript processor to cause a series of operational steps to be per levels that are underexpressed or overexpressed in compari formed by the processor to produce a computer implemented son to the threshold may indicate that the patient does not process. The computer program instructions may also cause have the disease. When the threshold is indicative of normal at least Some of the operational steps to be performed in condition, the patient's transcript levels that are underex parallel. Moreover, some of the steps may also be performed pressed or overexpressed in comparison to the threshold across more than one processor, Such as might arise in a may indicate that the patient has the disease. multi-processor computer system. In addition, one or more I0085. Reference RNA levels (e.g. levels of circulating processes may also be performed concurrently with other RNA) may be obtained by statistically analyzing the brain processes or even in a different sequence than illustrated specific transcript levels of a defined patient population. The without departing from the scope or spirit of the invention. reference levels may pertain to a healthy patient population 0081. The computer program instructions can be, stored or a patient population with a particular neurological disor on any suitable computer-readable medium including, but der. In further examples, the references levels may be not limited to, RAM, ROM, EEPROM, flash memory or tailored to a more specific patient population. For example, other memory technology, CD-ROM, digital versatile disks a reference level may correlate to a patient population of a (DVD) or other optical storage, magnetic cassettes, mag certain age and/or correspond to a patient population exhib netic tape, magnetic disk storage or other magnetic storage iting symptoms associated with a particular stage of a US 2016/02897.62 A1 Oct. 6, 2016 neurological disorder. Other factors for tailoring the patient individual will have trouble performing everyday complex population for reference levels may include sex, familial tasks, such as managing financings and planning Social history, environmental exposure, and/or phenotypic traits. gatherings, will have trouble remembering their own per I0086 Brain-specific genes or transcripts may be deter Sonal history, and becomes moody or withdrawn. Stage 5 mined by deconvolving the cell-free transcriptome as involves moderately severe cognitive decline, in which gaps described above and outlined in FIG. 7. Brain-specific genes in memory and thinking are noticeable and the individual or transcripts may also be determined by directly analyzing will begin to need help with certain activities. In Stage 5, brain tissue. In addition, Tables 1 and 2, as listed in Example individuals will be confused about the day, will have trouble 4 below, provide genes whose expression profiles are unique with recalling particular details (such as phone number and to certain tissue types. Particularly, Tables 1 and 2 list street address), but will be able to remember significant brain-specific genes corresponding with hypothalamus as details about themselves and their loved ones. Stage 6 well as genes corresponding with the whole brain (e.g. most involves severe cognitive decline, as the individuals brain tissue), prefrontal cortex, thalamus, etc. In certain memory continues to worsen. Individuals in Stage 6 will embodiments, brain-specific genes or transcripts include likely need extensive help with daily activities because they APP, PSD3, MOBP, MAG, SLC2A1, TCF7L2, CDH22, lose awareness of their Surroundings and while they often CNTF, and PAQR6. remember certain tasks, they forget how to complete them or 0087. The brain-specific transcripts used in methods of make mistakes (e.g. wearing pajamas during the day, for the invention may correspond to cell-free transcripts getting to rinse after shampooing, wearing shoes on wrong released from certain types of brain tissue. The types of brain side of the foot). Stage 7 involves very severe cognitive tissue include the pituitary, hypothalamus, thalamus, corpus decline and is the final stage of Alzheimer's disease. In Stage callosum, cerebrum, cerebral cortex, and combinations 7, individuals lose their ability to respond to the environ thereof. In particular embodiments, the brain-specific tran ment, remember others, carry on a conversation, and control Scripts correspond with the hypothalamus. The hypothala movement. Individuals need help with daily care, eating, mus is bounded by specialized brain regions that lack an dressing, using the bathroom, and have abnormal reflexes effective blood/brain barrier, and thus transcripts released and tense muscles. Individuals may still be verbal, but will from the hypothalamus are likely to be introduced into blood not make sense or relate to the present. or plasma. 0091. In certain embodiments, methods for assessing a 0088 FIG. 19 illustrates the difference in levels of PSD3 neurological disorder involve a comparison of one or more and APP cell-free RNA between subjects with Alzheimer's brain-specific transcripts of an individual to a set of predic and normal subjects. Measurements of PSD3 and APP cell tive variables correlated with the neurological disorder. The free RNA transcripts levels in plasma shows that the levels set of predictive variables may include a variety of reference of these two transcripts are elevated in AD patients and can levels that are brain specific. For instance, the set of pre be used to cleanly group the AD patients from the normal dictive variables may include brain-specific transcript levels patients. Shown in the figure are only two potential tran of a plurality of references. For example, one reference level Scripts showing significant diagnostic potential. High may correspond to a normal patient population and another throughput microfluidics chip allow for simultaneous mea reference level may correspond to a patient population with Surements of other brain specific transcripts which can the neurological disorder. In further examples, the references improve the classification process. may correspond to more specific patient populations. For 0089. In particular aspects, brain-specific transcripts are example, each reference level may correlate to a patient used to characterize and diagnose neurological disorders. population of a certain age and/or correspond to a patient The neurological disorder characterized may include degen population exhibiting symptoms associated with a particular erative neurological disorders, such as Alzheimer's disease, stage of a neurological disorder. Other factors for tailoring Parkinson's disease, Huntington's disease, and some types the patient population for reference levels may include sex, of multiple Sclerosis. The most common neurological dis familial history, environmental exposure, and/or phenotypic order is Alzheimer's disease. In some instances, the neuro traits. logical disorder is classified by the extent of cognitive 0092 Statistical analyses can be used to determine brain impairment, which may include no impairment, mild impair specific reference levels of certain patient populations (such ment, moderate impairment, and severe impairment. as those discussed above). Statistical analyses for identify 0090 Alzheimer's disease is characterized into stages ing trends in patient populations and comparing patient based on the cognitive symptoms that occur as the disease populations are known in the art. Suitable statistical analyses progresses. Stage 1 involves no impairment (normal func include, but are not limited to, clustering analysis, principle tion). The person does not experience any memory problems component analysis, non-parametric statistical analyses (e.g. or signs of dementia. Stage 2 involves a very mild decline Wilcoxon tests), etc. in cognitive functions. During Stage 2, a person may expe 0093. In addition, statistical analyses may be used to rience mild memory loss, but cognitive impairment is not statistically significant deviations between the individuals likely noticeable by friends, family, and treating physicians. circulating nucleic specific to brain tissue and that of a Stage 3 involves a mild cognitive decline, in which friends, reference. When the reference is based on a diseased popu family, and treating physicians may notice difficulties in the lation, statistically significant deviations of the individuals individual’s memory and ability to perform tasks. For brain-specific circulating RNA to those of the diseased example, trouble identifying certain words, noticeable dif population are indicative of no neurological disorder. When ficulty in performing tasks in Social or work settings, for the reference is based on a normal population, statistically getting just-read materials. Stage 4 involves moderate cog significant deviations of the individual’s brain-specific cir nitive decline, which is noticeable and causes a significant culating RNA to those of the normal population are indica impairment on the individual’s daily life. In Stage 4, the tive of a neurological disorder. Methods of determining US 2016/02897.62 A1 Oct. 6, 2016

statistical significance are known in the art. P-values and which were then centrifuged at 16000 g for 10 min at 4°C. odds ratio can be used for statistical inference. Logistic to remove residual cells. Supernatants were then stored in regression models are common statistical classification 1.5 ml microcentrifuge tubes at -80° C. until use. models. In addition, Chi-Square tests and T-test may also be used to determine statistical significance. RNA Extraction and Amplification 0094 Methods of the invention can also be used to identify one or more biomarkers associated with a neuro 0100. The cell-free maternal plasma RNAs was extracted logical disorder. In Such aspects, brain-specific transcripts of by Trizol LS reagent. The extracted and purified total RNA an individual or patient population Suspected of having or was converted to cDNA and amplified using the RNA-Seq actually having a neurological disorder (e.g. exhibiting Ovation Kit (NuGen). (The above steps were the same for impaired cognitive functions) are compared to reference both Microarray and RNA-Seq sample preparation). brain-specific transcript (e.g. a healthy, normal control). The 0101 The cDNA was fragmented using DNase I and brain-specific transcripts of the individual or patient popu labeled with Biotin, following by hybridization to Affyme lation that are differentially expressed as compared to the trix GeneChip ST 1.0 microarrays. The Illumina sequencing reference may then be identified as biomarkers of the platform and standard Illumina library preparation protocols neurological disorder. In certain embodiments, only differ were used for sequencing. entially expressed brain-specific transcripts that are statisti cally significant are identified as biomarkers. Data Analysis: 0.095. In certain embodiments, methods of the invention provide recommend a course of treatment based on the Correlation Between Microarray and RNA-Seq clinical indications determined by comparing of the patients 0102 The RMA algorithm was applied to process the raw circulating brain-specific RNA and the reference. Depending microarray data for background correction and normaliza on the diagnosis, the course of treatment may include tion. RPKM values of the sequenced transcripts were medicinal therapy, behavioral therapy, sleep therapy, and obtained using the CASAVA 1.7 pipeline for RNA-seq. The combinations thereof. The course of treatment and diagnosis RPKM in the RNA-Seq and the probe intensities in the may be provided in a read-out or a report. microarray were converted to log 2 scale. For the RNA-Seq data, to avoid taking the log of 0, the gene expressions with EXAMPLES RPKM of 0 were set to 0.01 prior to taking logs. Correlation coefficients between these two platforms ranges were then Example 1 calculated. Profiling Maternal Plasma Cell-Free RNA by RNA Differential Expression of RNA Transcripts Levels Using Sequencing-A Comprehensive Approach RNA-Seq Overview 0103 Differential gene expression analysis was per 0096. The plasma RNA profiles of 5 pregnant women formed using edgeR, a set of library functions which are were collected during the first trimester, second trimester, specifically written to analyze digital gene expression data. post-partum, as well as those of 2 anon-pregnant female Gene Ontology was then performed using DAVID to iden donors and 2 male donors using both microarray and RNA tify for significantly enriched GO terms. Seq. 0097. Among these pregnancies, there were 2 pregnan Principle Component Analysis & Identification of cies with clinical complications such as premature birth and Significant Time Varying Genes one pregnancy with bi-lobed placenta. Comparison of these 0104 Principle component analysis was carried out using pregnancies against normal cases reveals genes that exhibit a custom script in R. To identify time varying genes, the time significantly different gene expression pattern across differ course library of functions in R were used to implement ent temporal stages of pregnancy. Application of Such tech empirical Bayes methods for assessing differential expres nique to samples associated with complicated pregnancies sion in experiments involving time course which in our case may help identify transcripts that can be used as molecular are the different trimesters and post-partum for each indi markers that are predictive of these pathologies. vidual patients.

Study Design and Methods: Results and Discussion Subjects 0105 RNA-Seq Reveals that Pregnancy-Associated Transcripts are Detected at Significantly Different Levels 0098 Samples were collected from 5 pregnant women Between Pregnant and Non Pregnant Subjects. were during the first trimester, second trimester, third tri 0106. A comparison of the transcripts level derived using mester, and post-partum. As a control, blood plasma samples RNA-Seq and Gene Ontology Analysis between pregnant were also collected from 2 non-pregnant female donors and and non-pregnant Subjects revealed that transcripts exhibit 2 male donors. ing differential transcript levels are significantly associated Blood Collection and Processing with female pregnancy, Suggesting that RNA-Seq are enabling observation of real differences between these two 0099 Blood samples were collected in EDTA tube and class of transcriptome due to pregnancy. The top rank centrifuged at 1600 g for 10 min at 4° C. Supernatant were significantly expressed gene is PLAC4 which has also been placed in 1 ml aliquots in a 1.5 ml microcentrifuge tube known as a target in previous studies for developing RNA US 2016/02897.62 A1 Oct. 6, 2016

based test for trisomy 21. A listing of the top detected female Example 2 pregnancy associated differentially expressed transcripts is shown in FIG. 1. Quantification of Tissue-Specific Cell-Free RNA 0107 Principle Component Analysis (PCA) on Plasma Exhibiting Temporal Variation During Pregnancy Cell Free RNA Transcripts Levels in Maternal Plasma Distinguishes Between Pre-Mature and Normal Pregnancy Overview 0108. Using the plasma cell free transcript level profiles 0118 Cell-free fetal DNA found in maternal plasma has as inputs for Principle Component Analysis, the profile from been exploited extensively for non-invasive diagnostics. In each patient at different time points clustered into different contrast, cell-free fetal RNA which has been shown to be pathological clusters Suggesting that cell free plasma RNA similarly detected in maternal circulation has yet been transcript profile in maternal plasma may be used to distin applied widely as a form of diagnostics. Both fetal cell-free guish between pre-term and non-preterm pregnancy. RNA and DNA face similar challenges in distinguishing the 0109 Plasma Cell free RNA levels were quantified using fetal from maternal component because in both cases the both microarray and RNA-Seq Transcripts expression lev maternal component dominates. To detect cell-free RNA of els profile from microarray and RNA-Seq from each patient fetal origin, focus can be placed on genes that are highly are correlated with a Pearson correlation of approximately expressed only during fetal development, which are Subse 0.7. Plots of the two main principal components for cell free quently inferred to be of fetal in origin and easily distin RNA transcript levels is shown in FIG. 2. guished from background maternal RNA. Such a perspec 0110) Identification of Cell Free RNA Transcripts in tive is collaborated by studies that has established that Maternal Plasma Exhibiting Significantly Different Time cell-free fetal RNA derived from genes that are highly Varying Trends Between Pre-Term and Normal Pregnancy expressed in the placenta are detectable in maternal plasma Across all Three Trimesters and Post Partum during pregnancy. 0111. A heatmap of the top 100 cell free transcript levels 0119) A significant characteristic that set RNA apart from exhibiting different temporal levels in preterm and normal DNA can be attributed to RNA transcripts dynamic nature pregnancy using microarrays is shown in FIG. 3A. A heat which is well reflected during fetal development. Life begins map of the top 100 cell free transcript levels exhibiting as a series of well-orchestrated events that starts with different temporal levels in preterm and normal pregnancy fertilization to form a single-cell Zygote and ends with a using RNA-Seq is shown in FIG. 3B. multi-cellular organism with diverse tissue types. During 0112 Common Cell Free RNA Transcripts Identified by pregnancy, majority of fetal tissues undergoes extensive Microarray and RNA-Seq which Exhibit Significantly Dif remodeling and contain functionally diverse cell types. This ferent Time Varying Trends Between Pre-Term and Normal underlying diversity can be generated as a result of differ Pregnancy Across all Three Trimesters and Post-Partum ential gene expression from the same nuclear repertoire; 0113. A ranking of the top 20 transcripts differentially where the quantity of RNA transcripts dictate that different expressed between pre-term and normal pregnancy is shown cell types make different amount of proteins, despite their in FIG. 4. These top 20 common RNA transcripts were genomes being identical. The human genome comprises analyzed using Gene Ontology and were shown to be approximately 30,000 genes. Only a small set of genes are enriched for proteins that are attached (integrated or loosely being transcribed to RNA within a particular differentiated bound) to the plasma membrane or on the membranes of the cell type. These tissue specific RNA transcripts have been platelets (see FIG. 5). identified through many studies and databases involving developing fetuses of classical animal models. Combining Gene Expression Profiles for PVALB known literature available with high throughput data gen 0114. The protein encoded by PVALB gene is a high erated from samples via sequencing, the entire collection of affinity calcium ion-binding protein that is structurally and RNA transcripts contained within maternal plasma can be functionally similar to calmodulin and troponin C. The characterized. encoded protein is thought to be involved in muscle relax 0120 Fetal organ formation during pregnancy depends ation. As shown in FIG. 6, the gene expression profile for on Successive programs of gene expression. Temporal regu PVALB across the different trimesters shows the premature lation of RNA quantity is necessary to generate this pro births highlighted in blue has higher levels of cell free gression of cell differentiation events that accompany fetal RNA transcripts found as compared to normal pregnancy. organ genesis. To unravel similar temporal dynamics for cell free RNA, the expression profile of maternal plasma cell free Conclusion: RNA, especially the selected fetal tissue specific panel of genes, as a function across all three trimesters during preg 0115 Results from quantification and characterization of nancy and post-partum were analyzed. Leveraging high maternal plasma cell-free RNA using RNA-Seq strongly throughput qPCR and sequencing technologies capability Suggest that pregnancy associated transcripts can be for simultaneous quantification of cell free fetal tissue detected. specific RNA transcripts, a system level view of the spec 0116 Furthermore, both RNA-Seq and microarray meth trum of RNA transcripts with fetal origins in maternal ods can detect considerable gene transcripts whose level plasma was obtained. In addition, maternal plasma was showed differential time trends that has a high probability of analyzed to deconvolute the heterogeneous cell free tran being associated with premature births. scriptome of fetal origin a relative proportion of the different 0117 The methods described herein can be modified to fetal tissue types. This approach incorporated physical con investigate pregnancies of different pathological situations straints regarding the fetal contributions in maternal plasma, and can also be modified to investigate temporal changes at specifically the fraction of contribution of each fetal tissues more frequent time points. were required to be non-negative and Sum to one during all US 2016/02897.62 A1 Oct. 6, 2016

three trimesters of the pregnancy. These constraints on the 0129 RNA Extraction data set enabled the results to be interpreted as relative 0.130 Cell free RNA extractions were carried using Trizol proportions from different fetal organs. That is, a panel of followed by Qiagen's RNeasy Mini Kit. To ensure that there previously selected fetal tissue-specific RNA transcripts are no contaminating DNA, DNase digestion is performed exhibiting temporal variation can be used as a foundation for after RNA elution using RNase free DNase from Qiagen. applying quadratic programing in order to determine the Resulting cell free RNA from the pregnant subjects was then relative tissue-specific RNA contribution in one or more processed using standard microarrays and Illumina RNA samples. seq protocols. These steps generate the sequencing library 0121 When considered individually, quantification of that we used to generate RNA-seq data as well as the each of these fetal tissue specific transcripts within the microarray expression data. The remaining cell free RNA maternal plasma can be used as a measure for the apoptotic are then used for parallel qPCR. rate of that particular fetal tissue during pregnancy. Normal I0131 Parallel qPCR of Selected Transcripts fetal organ development is tightly regulated by cell division 0.132. Accurate quantification of these fetal tissue specific and apoptotic cell death. Developing tissues compete to transcripts was carried out using the Fluidigm BioMark Survive and proliferate, and organ size is the result of a system (See e.g. Spurgeon, S. L., Jones, R. C. & Ramakrish balance between cell proliferation and death. Due to the nan, R. High throughput gene expression measurement with close association between aberrant cell death and develop real time PCR in a microfluidic dynamic array. PloS One 3, mental diseases, therapeutic modulation of apoptosis has e1662 (2008)). This system allows for simultaneous query of become an area of intense research, but with this comes the a panel of fetal tissue specific transcripts. Two parallel forms demand for monitoring the apoptosis rate of specific. Quan of inquiry were conducted using different starting Source of tification of fetal cell-free RNA transcripts provide such material. One was using the cDNA library from the Illumina prognostic value, especially in premature births where the sequencing protocol and the other uses the eluted RNA incidence of apoptosis in various organs of these preterm directly. Both sources of material were amplified with infants has been have been shown to contribute to neurode evagreen primers targeting the genes of interest. Both velopmental deficits and cerebral palsy of preterm infants. sources, RNA and cDNA, were preamplified. cDNA is Sample Collection and Study Design preamplifed using evagreen PCR Supermix and primers. 0122) RNA source is preamplified using the CellsDirect One-Step (0123 Selection of Fetal Tissue Specific Transcript Panel qRT-PCR kit from Invitrogen. Modifications were made to 0124. To detect the presence of these fetal tissue-specific the default One-Step qRT-PCR protocol to accomodate a transcripts, a list of known fetal tissue specific genes was longer incubation time for reverse transcription. 19 cycles of prepared from known literature and databases. The speci preamplification were conducted for both sources and the ficity for fetal tissues was validated by cross referencing collected PCR products were cleaned up using Exonuclease between two main databases: TISGeD (Xiao, S.-J., Zhang, I Treatment. To increase the dynamic range and the ability C. & Ji, Z.-L. TiSGeD: a Database for Tissue-Specific to quantify the efficiency of the later qPCR steps, serial Genes. Bioinformatics (Oxford, England) 26, 1273-1275 dilutions were performed on the PCR products from 5 fold, (2010)) and BioGPS (Wu, C. et al. BioGPS: an extensible 10 fold and 10 fold dilutions. Each of the collected maternal and customizable portal for querying and organizing gene plasma from individual pregnant women across the time annotation resources. Genome biology 10, R130 (2009); Su, points went through the same procedures and was loaded A. I. et al. A gene atlas of the mouse and human protein onto 48x48 Dynamic Arrary Chips from Fluidigm to per encoding transcriptomes. Proceedings of the National Acad form the qPCR. For positive control, fetal tissue specific emy of Sciences of the United States of America 101, 6062-7 RNA from the various fetal tissue types were bought from (2004)). Most of these selected transcripts are associated Agilent. Each of these RNA from fetal tissues went through with known fetal developmental processes. This list of genes the same preamplification and clean-up steps. A pool sample was overlapped with RNA sequencing and microarray data with equal proportions of different fetal tissues was created to generate the panel of selected fetal tissue-specic tran as well for later analysis to deconvolute the relative contri scripts shown in FIG. 8. bution of each tissue type in the pooled samples. All Subjects collected data from the Fluidigm BioMark system were 0125 pre-processed using Fluidigm Real Time PCR Analysis 0126 Samples of maternal blood were collected from software to obtain the respective Ct values for each of the normal pregnant women during the first trimester, second transcript across all samples. Negative controls of the trimester, third trimester, and post-partum. For positive experiments were performed with the entire process with controls, fetal tissue specific RNA from the various fetal water, as well as with samples that did not undergoes the tissue types were bought from Agilent. Negative controls for reverse transcription process. the experiments were performed with the entire process with water, as well as with samples that did not undergoes the I0133) Data Analysis: reverse transcription process. 0.134 Fetal tissue specific RNA transcripts clear from the maternal peripheral bloodstream within a short period after O127 Blood Collection and Processing birth. That is, the post-partum cell-free RNA transcriptome 0128. At each time-point, 7 to 15 mL of peripheral blood of maternal blood lacks fetal tissue specific RNA transcripts. was drawn from each subject. Blood was centrifuged at 1600 As a result, it is expected that the quantity of these fetal g for 10 mins and transferred to microcentrifuge tubes for tissue-specific transcripts to be higher before than after birth. further centrifugation at 16000 g for 10 mins to remove The data of interest were the relative quantitative changes of residual cells. The above steps were carried out within 24 the tissue specific transcripts across all three trimesters of hours of the blood draw. Resulting plasma is stored at -80 pregnancy as compared to this baseline level after the baby Celsius for subsequent RNA extractions. is born. As described the methods, the fetal tissue-specific US 2016/02897.62 A1 Oct. 6, 2016 transcripts were quantified in parallel both using the actual Huntington's disease, and amyotrophic lateral Sclerosis. In cell-free RNA as well as the cDNA library of the same Such a scenario, the methodology described herein will cell-free RNA. An example of the raw data obtained is allow for close monitoring for disease progression and shown in FIGS. 9A and 9B. The qPCR system gave a better possibly an ideal dosage according to the progression. quality readout using the cell-free RNA as the initial source. 0.141. Deducing Relative Contributions of Different Fetal Focusing on the qPCR results from the direct cell-free RNA Tissue Types: Source, the analysis was conducted by comparing the fold changes level of each of these fetal tissue specific transcripts 0142. Differential rate of apoptosis of specific tissues across all three trimesters using the post-partum level as the may directly correlate with certain developmental diseases. baseline for comparison. The Delta-Delta Ct method was That is, certain developmental diseases may increase the employed (Schmittgen, T. D. & Livak, K. J. Analyzing levels of a particular specific RNA transcripts being real-time PCR data by the comparative CT method. Nature observed in the maternal transcriptome. Knowledge of the Protocols 3, 1101-1108 (2008)). Each of the transcript relative contribution from various tissue types will allow for expression level was compared to the housekeeping genes to observations of these types of changes during the progres get the delta Clt value. Subsequently, to compare each sion of these diseases. The quantified panel of fetal tissue trimesters to after birth, the delta-delta Ct method was specific transcripts during pregnancy can be considered as a applied using the post-partum data as the baseline. summation of the contributions from the various fetal tissues 0135 Results and Discussion: (See FIG. 25). 0136. As shown in FIGS. 10, 11, and 12, the tissue 0.143 Expressing, specific transcripts are generally found to be at a higher level during the trimesters as compared to after-birth. In particu lar, the tissue-specific panel of placental, fetal brain and fetal liver specific transcripts showed the same bias, where these transcripts are typically found to exist at higher levels during pregnancy then compared to after birth. Between the differ ent trimesters, a general trend showed that the quantity of where Y is the observed transcript quantity in maternal these transcripts increase with the progression into preg plasma for gene i, X is the known transcript quantity for nancy. gene i in known fetal tissue and e the normally distributed 0137 Biological Significance of Quantified FetalTissue error. Additional physical constraints includes: Specific RNA: 0144. 1. Summation of all fraction contributing to the 0138 Most of the transcripts in the panel were involved observed quantification is 1, given by the condition: in fetal organ development and many are also found within X = 1 the amniotic fluid. Once such example is ZNF238. This 0145 2. All the contribution from each tissue type has to transcript is specific to fetal brain tissue and is known to be greater than or equal Zero. There is no physical meaning Vital for cerebral cortex expansion during embryogenesis to having a negative contribution. This is given by te0, when neuronal layers are formed. Loss of ZNF238 in the since L is defined as the fractional contribution of each central nervous system leads to severe disruption of neuro genesis, resulting in a striking postnatal Small-brain pheno tissue types. type. Using methods of the invention, one can determine 0146 Consequently to obtain the optimal fractional con whether ZNF238 is presenting in healthy, normal levels tribution of each tissue type, the least-square error is mini according to the stage of development. mized. The above equations are then solved using quadratic 0139 Known defects due to the loss of ZNF238 include programming in R to obtain the optimal relative contribu a striking postnatal Small-brain phenotype: microcephaly, tions of the tissue types towards the maternal cell free RNA agenesis of the corpus callosum and cerebellar hypoplasia. transcripts. In the workflow, the quantity of RNA transcripts Microcephaly can sometimes be diagnosed before birth by are given relative to the housekeeping genes in terms of Ct prenatal ultrasound. In many cases, however, it might not be values obtained from qPCR. Therefore, the Ct value can be evident by ultrasound until the third trimester. Typically, considered as a proxy of the measured transcript quantity. diagnosis is not made until birth or later in infancy upon An increase in Ct value of one is similar to a two-fold change finding that the baby's head circumference is much smaller in transcript quantity, i.e. 2 raised to the power of 1. The than normal. Microcephaly is a life-long condition and process beings with normalizing all of the data in CT relative currently untreatable. A child born with microcephaly will to the housekeeping gene, and is followed by quadratic require frequent examinations and diagnostic testing by a programming. doctor to monitor the development of the head as he or she 0147 As a proof of concept for the above scheme, grows. Early detection of ZNf238 differential expression different fetal tissue types (Brain, Placenta, Liver, Thymus, using methods of the invention provides for prenatal diag Lung) were mixed in equal proportions to generate a pool nosis and may hold prognostic value for drug treatments and sample. Each fetal tissue types (Brain, Placenta, Liver, dosing during course of treatment. Thymus, Lung) along with the pooled sample were quanti 0140 Beyond ZNF238, many of the characterized tran fied using the same Fluidigm Biomark System to obtain the Scripts may hold diagnostic value in developmental diseases Ct values from qPCR for each fetal tissue specific transcript involving apoptosis, i.e., diseases caused by removal of across all tissues and the pooled sample. These values were unnecessary neurons during neural development. Seeing that used to perform the same deconvolution. The resulting fetal apoptosis of neurons is essential during development, one fraction of each of the fetal tissue organs (Brain, Placenta, could extrapolate that similar apoptosis might be activated in Liver, Thymus, Lung) was 0.109, 0.206, 0.236, 0.202 & neurodegenerative diseases such as Alzheimer's disease, 0.245 respectively. US 2016/02897.62 A1 Oct. 6, 2016

0148 Conclusion: 0153. Apoptotic cells from different tissue types release 0149. In summary, the panel of fetal specific cell free their RNA into the cell-free RNA component in plasma. transcripts provides valuable biological information across Each of these tissues expresses a number of genes unique to different fetal tissues at once. Most particularly, the method their tissue type, and the observed cell-free RNA transcrip can deduce the different relative proportions of fetal tissue tomes can be considered as a Summation of contributions specific transcripts to total RNA, and, when considered from these different tissue types. Using expression data of individually, each transcript can be indicative of the apop different tissue types available in public databases, the totic rate of the fetal tissue. Such measurements have cell-free RNA transcriptome from our four nonpregnant numerous potential applications for developmental and fetal Subjects were deconvoluted using quadratic programming to medicine. Most human fetal development studies have relied reveal the relative contributions of different tissue types mainly on postnatal tissue specimens or aborted fetuses. (FIG. 26). These contributions identified different tissue Methods described herein provide quick and rapid assay of types which are consistent among different control Subjects. the rate of fetal tissue/organ growth or death on live fetuses Whole blood, as expected, is the major contributor (~40%) with minimal risk to the pregnant mother and fetus. Similar toward the cell-free RNA transcriptome. Other major con methods may be employed to monitor major adult organ tributing tissue types include the bone marrow and lymph tissue systems that exhibit specific cell free RNA transcripts nodes. One also sees consistent contributions from Smooth in the plasma. muscle, epithelial cells, thymus, and hypothalamus. 0154 Results and Discussion Example 3 (O155 Within the cohort, about 100 genes were analyzed whose RNA transcripts contained paternal SNPs that were Additional Study for Quantification of distinct from the maternal inheritance to explicitly demon Tissue-Specific Cell-Free RNA Exhibiting Temporal strate that the fetus contributes a substantial amount of RNA Variation During Pregnancy to the mother's blood (See FIG. 21). To accurately quantify and verify the relative fetal contribution, the following were 0150 High-throughput methods of microarray and next genotyped: a mother and her fetus and inferred paternal generation sequencing were used to characterize the land genotype. The weighted average fraction of fetal-originated scape of cell-free RNA transcriptome of healthy adults and cell-free RNA was quantified using paternal SNPs. Cell-free of pregnant women across all three trimesters of pregnancy RNA fetal fraction depends on gene expression and varies and post-partum. The results confirm the study presented in greatly across different genes. In general, the fetal fraction of Example 2, by showing that it is possible to monitor the gene cell-free RNA increases as the pregnancy progress and expression status of many tissues and the temporal expres decreases after delivery. The weighted average fetal fraction sion of certain genes can be measured across the stages of started at 0.4% in the first trimester, increased to 3.4% in the human development. The study also investigated the role of second trimester, and peaked at 15.4% in the third trimester. cell-free RNA in adults suffering from neurodegenerative Although fetal RNA should be cleared after delivery, there disorder Alzheimer's and observed a marked increase of was still 0.3% of fetal RNA as calculated, which can be neuron-specific transcripts in the blood of affected individu attributed to background noise arising from misalignment als. Thus, this study shows that the same principles of and sequencing errors. observing tissue-specific RNA to assess development can 0156. In addition to monitoring fetal tissue-specific also be applied to assess the deterioration of brain tissue mRNA, noncoding transcripts present in the cell-free com associated with neurological disorders. partment across pregnancy were identified. These noncoding 0151. Overview transcripts include long noncoding RNAS (IncRNAS), as 0152. An additional study following the guidance of well as circular RNAs (circRNA). Additional PCR assays Example 2 was conducted to illustrate the temporal variation were designed to specifically amplify and validate the pres among tissue-specific cell-free RNA across trimesters. FIG. ence of these circRNA in plasma. circRNAs have recently 18 outlines the experimental design for this study, which been shown to be widely expressed in human cells and have examined cell-free plasma samples of 15 subjects, of which greater stability than their linear counterparts, potentially 11 were pregnant and 4 were not pregnant (2 males; 2 making them reliable biomarkers for capturing transient females). The blood samples were taken over several time events. Several of the circRNA species appear to be spe points: 1st, 2nd, and 3rd Trimester and Post-Partum. The cifically expressed during different trimesters of pregnancy. cell-free plama RNA were then extracted, amplified, and The identification of these cell-free noncoding RNAs during characterized by Affymetrix microarray, IIlumina pregnancy improve our ability to monitor the health of the Sequencer, and quantitative PCR. For each plasma sample, mother and fetus. ~20 million sequencing reads were generated, ~80% of 0157. There is a general increase in the number of genes which could be mapped against the human reference detected across the different trimesters followed by a steep genome (hg19). As the plasma RNA is of low concentration drop after the pregnancy. Such an increase in the number of and Vulnerable to degradation, contamination from the genes detected Suggests that unique transcripts are expressed plasma DNA is a concern. To assess the quality of the specifically during particular time intervals in the develop sequencing library, the number of reads assigned to different ing fetus. FIGS. 18 and 19 show the heatmap of genes whose regions was counted: 34% mapped to exons, 18% mapped to level changed over time during pregnancy, as detected by introns, and 24% mapped to ribosomal RNA and tRNA. microarray. ANOVA was applied to identify genes that Therefore, dominant portion of the reads originated from varied in expression in a statistically significant manner RNA transcripts rather than DNA contamination. To validate across different trimesters. An additional condition filtering the RNA-Seq measurements, all of the plasma samples were for transcripts that were expressed at low levels in both the also analyzed with gene expression microarrays. postpartum plasma of pregnant Subjects and in nonpregnant US 2016/02897.62 A1 Oct. 6, 2016 controls. Using these conditions, 39 genes from RNA-seq throughout pregnancy until term in the placenta. It is and 34 genes from microarray were identified, of which anchored to the plasma membrane of the syncytiotropho there were 17 genes in common. Gene Ontology (GO) blast and to a lesser extent of cytotrophoblastic cells. This performed on the identified genes using Database for Anno enzyme is also released into maternal serum, and variations tation, Visualization and Integrated Discovery (DAVID) of its concentration are related with several clinical disorders revealed that the identified gene list is enriched for the such as preterm delivery. Another gene in the panel, BACE2, following GO terms: female pregnancy (Bonferroni-cor encoded the B site APP-cleaving enzyme, which generates rected P=5.5x10), extracellular region (corrected P=6.6x amyloid-fi protein by endoproteolytic processing. Brain 10), and hormone activity (corrected P=6.3x10). These deposition of amyloid-fi protein is a frequent complication RNA transcripts show a general trend of having low expres of Down syndrome patients, and BACE-2 is known to be sion postpartum and the highest expression during the third overexpressed in Down syndrome. trimester. Most of these transcripts are specifically expressed 0.161. Other transcripts in our placental assay are known in the placenta, and their levels reach a maximum in the later to be transcribed at high levels in the placenta, and levels of stages of pregnancy. these mRNAs are important for normal placental function 0158 Other nonplacental transcripts that share similar and development in pregnancy. TAC3 is mainly expressed in temporal trends. Two Such significant transcripts were the placenta and is significantly elevated in preeclamptic RAB6B and MARCH2, which are known to be expressed human placentas at term. Similarly, PLAC1 is essential for specifically in CD71+ erythrocytes. Erythrocytes enriched normal placental development. PLAC1 deficiency results in for CD71+ have been shown to contain fetal hemoglobin a hyperplastic placenta, characterized by an enlarged and and are interpreted to be of fetal origin. The presence of dysmorphic junctional Zone. An increase in cell-free mRNA transcripts with known specificity to different fetal tissue of PLAC1 has been suggested to be correlated with the types reflects the fact that the cell-free transcriptome during occurrence of preeclampsia. the period of pregnancy can be considered as a Summation 0162. On the fetal liver tissue-specific panel, one of the of transcriptomes from various different fetal tissues on top characterized transcripts is AFP. AFP encodes for C.-feto of a maternal background. protein and is transcribed mainly in the fetal liver. AFP is the 0159. This analysis detected the presence of numerous most abundant plasma protein found in the human fetus. transcripts that are specifically expressed in several other Clinically, AFP protein levels are measured in pregnant fetal tissues, although the available sequencing depth women in either maternal blood or amniotic fluid and serve resulted in limited concordance between samples. To verify as a screening marker for fetal aneuploidy, as well as neural the presence of these and other potential fetal tissue-specific tube and abdominal wall defects. Other fetal liver-specific transcripts, a panel of fetal tissue-specific transcripts was transcripts that were characterized are highly involved in devised for detailed quantification using the more sensitive metabolism. An example is fetal liver-specific monooxy method of quantitative PCR (qPCR). Three main sources genase CYP3A7, which catalyzes many reactions involved were focused on, which are of interest to fetal neurodevel in synthesis of cholesterol and steroids and is responsible for opment and metabolism: placenta, fetal brain, and fetal liver. the metabolism of more than 50% of all clinical pharma In FIGS. 22-24, the levels of these groups of fetal tissue ceuticals. In drug-treated diabetic pregnancies in which specific transcripts at different trimesters were systemati glucose levels in the woman are uncontrolled, neural tube cally compared to the level seen in maternal serum after and cardiac defects in the early developing brain, spine, and delivery. To illustrate the temporal trends, housekeeping heart depend on functional GLUT2 carriers, whose tran genes as the baseline were used as a baseline, and ACt scripts are well characterized in the panel. Mutations in this analysis was applied to find the level of relative expression gene results in Fanconi-Bickel syndrome, a congenital these fetal tissue-specific transcripts with respect to the defect of facilitative glucose transport. Monitoring of fetal housekeeping genes. Many of these tissue-specific tran liver-specific transcripts during the drug regime may enable Scripts expressed at Substantially higher levels during the analysis of the fetuses response to drug therapy that the pregnancy compared with postpartum. There was a general mother is undergoing. trend of an increase in the quantity of these transcripts across Example 4 advancing gestation. 0160 The placental qPCR assay focused on genes that Deconvolution of Adult Cell-Free Transcriptome are known to be highly expressed in the placenta, many of which encode for proteins that have been shown to be Overview present in the maternal blood. The serum levels of these 0163 The plasma RNA profiles of 4 healthy, normal proteins are known to be involved in pregnancy complica adults were analyzed. Based on the gene expression profile tions such as preeclampsia and premature births. Examples of different tissue types, the methods described quantify the in our panel includes ADAM12, which encodes for disinte relative contributions of each tissue type towards the cell grin, and metalloproteinase domain-containing protein 12. free RNA component in a donor's plasma. For quantifica These proteinases are highly expressed in human placenta tion, apoptotic cells from different tissue types are assumed and are present at high concentrations in maternal serum as to release their RNA into the plasma. Each of these tissues early as the first trimester. ADAM12 serum concentrations expressed a specific number of genes unique to the tissue are known to be significantly reduced in pregnancies com type, and the observed cell-free RNA transcriptome is a plicated by fetal trisomy 18 and trisomy 21 and may Summation of these different tissue types. therefore be of potential use in conjunction with cell-free DNA for the detection of chromosomal abnormalities. Simi Study Design and Methods: larly, placental alkaline phosphatase, encoded by the ALPP 0164. To determine the contribution of tissue-specific gene, is a tissue-specific isoform expressed increasingly transcripts to the cell-free adult transriptome, a list of known US 2016/02897.62 A1 Oct. 6, 2016 tissue-specific genes was prepared from known literature which, only relative contributions from the hypothalamus and databases. Two database sources were utilized: Human and spleen were observed, as shown in FIG. 14. U133A/GNF1H Gene Atlas and RNA-Seq Atlas. Using the 0171 A list of 84 tissue-specific genes (as provided in raw data from these two database, tissue-specific genes were Table 2) was further selected for verification with qPCR. identified by the following method. A template-matching The Fluidigm BioMark Platform was used to perform the process was applied to data obtained from the two databases qPCR on RNA derived from the following tissues: Brain, for the purpose of identifying tissue-specific gene. The list of Cerebellum, Heart, Kidney, Liver and Skin. Similar qPCR tissue specific genes identified by the method is provided in workflow was applied to the cell free RNA component as Table 1 below. The specificity and sensitivity of the panel is well. The delta Ct values by comparing with the housekeep constrained by the number of tissue samples in the database. ing genes: ACTB was plotted in the heatmap format in FIG. For example, the Human U133A/GNF1H Gene Atlas dataset 15, which shows that these tissue specific transcripts are includes 84 different tissue samples, and a panel's specificity detectable in the cell free RNA. from that database is constrained by the 84 sample sets. Similarly, for the RNA-seq atlas, there are 11 different tissue samples and specificity is limited to distinguishing between Tables for Example 4 these 11 tissues. After obtaining a list of tissue-specific 0172. The following table lists the tissue-specific genes transcripts from the two databases, the specificity of these for Example 4 that was obtained using raw data from the transcripts was verified with literature as well as the TisCED Human U133A/GNF1H Gene Atlas and RNA-Seq Atlas database. databases. 0.165. The adult cell-free transcriptome can be considered as a Summation of the tissue-specific transcripts obtained TABLE 1. from the two databases. To quantitatively deduce the relative proportions of the different tissues in an adult cell-free List of Tissue-Specific Genes Determined by transcriptome, quadratic programming is performed as a Deconvolution of Adult Transcriptone constrained optimization method to deduce the relative Gene Tissue optimal contributions of different organs/tissues towards the cell free-transcriptome. The specificity and accuracy of this A4GALT Uterus Corpus A4GNT Superior Cervical Ganglion process is dependent on the table of genes (Table 2 below) AADAC Small intestine and the extent by which that they are detectable in RNA-seq AASS Ovary and microarray. ABCA12 Tonsil 0166 Subjects: Plasma samples were collected from 4 ABCA4 retina ABCB4 CD19 Bcells neg. sel. healthy, normal adults. ABCB6 CD71 Early Erythroid ABCB7 CD71 Early Erythroid Initial Results: ABCC2 Pancreatic Islet ABCC3 Adrenal Cortex 0167 Deconvolution of our adult cell-free RNA tran ABCC9 Dorsal Root Ganglion Scriptome from microarray using the above methods ABCF3 Adrenal gland revealed the relative contributions of the different tissue and ABCG1 Lung ABCG2 CD71 Early Erythroid organs are tabulated in FIG. 13. ABHD4 Adipocyte 0168 FIG. 13 shows that the normal cell free transcrip ABHDS Whole Blood tome for adults is consistent across all 4 subjects. The ABHD6 pineal night ABHD8 Whole Brain relative contributions between the 4 subjects do not differ ABO Heart greatly, Suggesting that the relative contributions from dif ABT X721 B lymphoblasts ferent tissue types are relatively stable between normal ABTB2 Placenta adults. Out of the 84 tissue types available, the deduced ACAA1 Liver ACACB Adipocyte optimal major contributing tissues are from whole blood and ACAD8 Kidney bone marrow. ACADL Thyroid 0169. An interesting tissue type contributing to circulat ACADS Liver ing RNA is the hypothalamus. The hypothalamus is bounded ACADSB Fetal liver ACAN Trachea by specialized brain regions that lack an effective blood ACBD4 Liver brain barrier; the capillary endothelium at these sites is ACCN3 Prefrontal Cortex fenestrated to allow free passage of even large proteins and ACE2 Testis Germ Cell other molecules which in our case we believed that RNA ACHE CD71 Early Erythroid ACLY Adipocyte transcripts from apoptotic cells in that region could be ACOT1 Adipocyte released into the plasma cell free RNA component. ACOX2 Liver 0170 The same methods were performed on the subjects ACP2 Liver ACP5 Lung using RNA-seq. The results described herein are limited due ACP6 CD34 to the amount of tissue-specific RNA-Seq data available. ACPP Prostate However, it is understood that tissue-specific data is expand ACR Testis Intersitial ing with the increasing rate of sequencing of various tissue ACRV1 Testis Intersitial ACSBG2 Testis Intersitial rates, and future analysis will be able to leverage those ACSF2 Kidney datasets. For RNA-seq data (as compared to microarray), ACSL4 Fetal liver whole blood nor the bone marrow samples are not available. ACSLS Small intestine The cell free transcriptome can only be decomposed to the ACSL6 CD71 Early Erythroid available 11 different tissue types of RNA-seq data. Of US 2016/02897.62 A1 Oct. 6, 2016 16

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue

ACSM3 Leukemia chronic ALG3 Liver Myelogenous K562 ALOX12 Whole Blood ACSMS Liver ALOX12B Tonsil ACSS3 Adipocyte ALOX1SB Prostate ACTA1 Skeletal Muscle ALPI Small intestine ACTC1 Heart ALPK3 Skeletal Muscle ACTG1 CD71 Early Erythroid ALPL Whole Blood ACTL7A Testis Intersitial ALPP Placenta ACTL7B Testis Intersitial ALPPL2 Placenta ACTN3 Skeletal Muscle ALX1 Superior Cervical Ganglion ACTR8 Superior Cervical Ganglion ALX4 Superior Cervical Ganglion ADA Leukemia lymphoblastic AMBN pineal day MOLT 4 AMDHD2 BDCA4 Dentritic Cells ADAM12 Placenta AMELY Subthalamic Nucleus ADAM17 CD33 Myeloid AMHR2 Heart ADAM2 Testis Intersitial AMPD1 Skeletal Muscle ADAM21 Appendix AMPD2 pineal night ADAM23 Thalamus AMPD3 CD71 Early Erythroid ADAM28 CD19 Bcells neg. sel. ANAPC1 X721 B lymphoblasts ADAM3O Testis Germ Cell ANG Liver ADAMSP Testis Intersitial ANGEL2 CD8 T cells ADAM7 Testis Leydig Cell ANGPT1 CD35 ADAMTS12 Atrioventricular Node ANGPT2 Ciliary Ganglion ADAMTS2O Appendix ANGPTL2 Uterus Corpus ADAMTS3 CD105 Endothelial ANGPTL3 Fetal liver ADAMTS8 Lung ANK CD71 Early Erythroid ADAMTS9 Dorsal Root Ganglion ANKFY1 CD8 T cells ADAMTSL2 Ciliary Ganglion ANKH Cerebellum Peduncles ADAMTSL3 retina ANKLE Testis ADAMTSL4 Atrioventricular Node ANKRD1 Skeletal Muscle ADARB2 Skeletal Muscle ANKRD2 Skeletal Muscle ADAT1 CD71 Early Erythroid ANKRD34C Thalamus ADCK4 Ciliary Ganglion ANKRDS Skeletal Muscle ADCY1 Fetal brain ANKRDS3 Skeletal Muscle ADCY9 Lung ANKRDS7 Bronchial Epithelial Cells ADCYAP1 Pancreatic Islet ANKS1B Superior Cervical Ganglion ADH7 Tongue ANTXR1 Uterus Corpus ADIPOR1 Bone marrow ANXA13 Small intestine ADM2 Pituitary ANXA2P1 Bronchial Epithelial Cells ADORA3 Olfactory Bulb ANXA2P3 Bronchial Epithelial Cells ADRA1D Skeletal Muscle AOC2 retina ADRA2A Lymph node Testis Germ Cell ADRA2B Superior Cervical Ganglion Kidney ADRB1 pineal night Heart AFF3 Trigeminal Ganglion Dorsal Root Ganglion AFF4 Testis Intersitial Whole Blood AGPAT2 Adipocyte Superior Cervical Ganglion AGPAT3 CD33 Myeloid PC Fetal brain AGPAT4 CD71 Early Erythroid PEX2 Colorectal adenocarcinoma AGPS Testis Intersitial PIP Trachea AGR2 Trachea POA1 Liver AGRN Colorectal adenocarcinoma POA4 Small intestine AGRP Superior Cervical Ganglion POB48R Whole Blood AGXT Liver POBEC1 Small intestine AIFM1 X721 B lymphoblasts POBEC2 Skeletal Muscle AIM2 CD19 Bcells neg. sel. POBEC3B Colorectal adenocarcinoma AJAP1 BDCA4 Dentritic Cells POC4 Liver AKAP10 CD33 Myeloid POF Liver AKAP3 Testis Intersitial POL5 Bone marrow AKAP6 Medulla Oblongata POOL Superior Cervical Ganglion AKAP7 Fetal brain QP2 Kidney AKAP8L, CD71 Early Erythroid Testis Intersitial AKR1C4 Liver Adipocyte AKR7A3 Liver R Liver AKT2 Thyroid RCN1 Trigeminal Ganglion ALAD CD71 Early Erythroid RFGAP1 Lymphoma burkitts Raji ALDH3D2 Tongue RG1 Fetal liver ALDH6A1 Kidney RHGAP11A Trigeminal Ganglion ALDHAA1 Ovary RHGAP19 Olfactory Bulb ALDOA Skeletal Muscle RHGAP22 CD36 ALG12 CD4 T cells RHGAP28 Testis Intersitial ALG13 CD19 Bcells neg. sel. RHGAP6 Prostate US 2016/02897.62 A1 Oct. 6, 2016 17

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue ARHGEF1 CD4 T cells BALAP2L2 Superior Cervical Ganglion ARHGEFS Pancreas BAMBI Colorectal adenocarcinoma ARHGEF7 Thymus BANK1 CD19 Bcells neg. sel. ARID3A Placenta BARD1 X721 B lymphoblasts ARID3B X721 B lymphoblasts BARX1 Atrioventricular Node ARL15 Uterus Corpus BATF3 X721 B lymphoblasts ARMC4 Superior Cervical Ganglion BBOX1 Kidney ARMC8 CD71 Early Erythroid BBS4 pineal day ARMCXS Small intestine BCAM Thyroid ARR3 retina BCAR3 Placenta ARSA Liver BCAS3 X721 B lymphoblasts ARSB Superior Cervical Ganglion BCKDK Liver ARSE Liver BCL10 Colon ARSF Globus Pallidus BCL2L1 CD71 Early Erythroid ART1 Cardiac Myocytes BCL2L10 Trigeminal Ganglion ART3 Testis BCL2L13 pineal day ART4 CD71 Early Erythroid BCL2L14 Testis ASB1 Trigeminal Ganglion BCL3 Whole Blood ASB7 Globus Pallidus BDH1 Liver ASB8 Superior Cervical Ganglion BDKRB1 Smooth Muscle ASCC2 CD71 Early Erythroid BDKRB2 Smooth Muscle ASCL2 Superior Cervical Ganglion BDNF Smooth Muscle ASCL3 Superior Cervical Ganglion BECN Ciliary Ganglion ASF1A CD71 Early Erythroid BEST1 retina ASIP BDCA4 Dentritic Cells BET1L. Superior Cervical Ganglion ASL Liver BHLHB9 pineal night ASPN Uterus BIRC3 CD19 Bcells neg. sel. ASPSCR1 Colorectal adenocarcinoma BLK CD19 Bcells neg. sel. ASTE1 CD8 T cells BLVRA CD105 Endothelial ASTN2 pineal day BMP1 Placenta ATF5 Liver BMP2K CD71 Early Erythroid ATG4A CD71 Early Erythroid BMP3 Temporal Lobe ATG7 CD14 Monocytes BMP5 Trigeminal Ganglion ATN1 Prefrontal Cortex BMP8A Fetal Thyroid ATOH1 Superior Cervical Ganglion BMP8B Superior Cervical Ganglion ATP1 OA CD56 NK Cells BMPR1B Skeletal Muscle ATP1 OD Placenta BNC1 Bronchial Epithelial Cells ATP11A Superior Cervical Ganglion BNC2 Uterus ATP12A Trachea BNIP3L CD71 Early Erythroid ATP13A3 Smooth Muscle BOK Thalamus ATP1B3 Adrenal Cortex BPHL Kidney ATP2C2 Colon BPI Bone marrow ATP4A Adrenal gland BPY2 Adrenal gland ATP4B Parietal Lobe BRAF Superior Cervical Ganglion ATPSG1 Heart BRAP Testis Intersitial ATP5G3 Heart BRE Adrenal gland ATP5J2 Superior Cervical Ganglion BRS3 Skeletal Muscle ATP6V0A2 CD37 BRSK2 Cerebellum Peduncles ATP6V1B1 Kidney BSDC1 CD71 Early Erythroid ATP7A CD71 Early Erythroid BTBD2 Prefrontal Cortex ATRIP CD14 Monocytes BTD Superior Cervical Ganglion ATXN3L Superior Cervical Ganglion BTN2A3 Appendix ATXN7L1 Skeletal Muscle BTN3A1 CD8 T cells AURKC Testis Seminiferous Tubule BTRC CD71 Early Erythroid AVE Bronchial Epithelial Cells BUB1 X721 B lymphoblasts AVIL Dorsal Root Ganglion BYSL Leukemia chronic AVP Hypothalamus Myelogenous K563 AXIN1 CD56 NK Cells C10orf118 Testis Leydig Cell AXL Cardiac Myocytes C10orf119 CD33 Myeloid AZI1 CD71 Early Erythroid C10orf28 Superior Cervical Ganglion B3GALNT1 Amygdala C10orf57 Ciliary Ganglion B3GALTS CD105 Endothelial C10orf72 Adrenal Cortex B3GNT2 CD71 Early Erythroid C10orf76 CD19 Bcells neg. sel. B3GNT3 Placenta C10orf&1 Dorsal Root Ganglion B3GNTL1 CD38 C10orf&4 Superior Cervical Ganglion BAAT Liver C10orf&8 Testis Seminiferous Tubule BACH2 Lymphoma burkitts Daudi C10orf5 Superior Cervical Ganglion BAD Whole Brain C11orfA1 Fetal brain BAG2 Uterus C11orfA8 Adipocyte BAG4 Superior Cervical Ganglion C11orf57 Appendix BAI1 Cingulate Cortex C11orf67 Skeletal Muscle BAIAP2 Liver C11orf71 Thyroid US 2016/02897.62 A1 Oct. 6, 2016 18

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue C11orf&O Leukemia lymphoblastic C2Orf34 pineal day MOLTS C2Orfa Testis C12orfa. CD71 Early Erythroid C2Orfa-3 X721 B lymphoblasts C12orf23 Whole Brain C2Orfs4 Trigeminal Ganglion C12Orfalf CD8 T cells C3AR1 CD14 Monocytes C12orf29 CD56 NK Cells C3orf57 Lymphoma burkitts Daudi C13orf23 Placenta C3orf64 pineal day C13orf27 Testis Leydig Cell C4orf19 Placenta C13orf54 CD71 Early Erythroid C4orf23 Superior Cervical Ganglion C14orf106 CD33 Myeloid C4Orf6 Superior Cervical Ganglion C14orf118 Superior Cervical Ganglion C5 Fetal liver C14orf138 CD19 Bcells neg. sel. CSAR1 Whole Blood C14orf162 Cerebellum CSOrf23 CD39 C14orf169 Testis CSOrf28 Thyroid C14orf56 Superior Cervical Ganglion CSOrfa. CD71 Early Erythroid C15 orf2 Cerebellum CSOrfa2 Superior Cervical Ganglion C15 orf29 Fetal brain C6orf103 Testis Intersitial C15Crf39 Whole Blood C6orf1 OS Colon C15orf244 Testis C6orf108 Lymphoma burkitts Raji C15Crfs Superior Cervical Ganglion C6orf124 Fetal brain C16orf Dorsal Root Ganglion C6orf162 Pituitary C16orf53 pineal day C6orf208 Superior Cervical Ganglion C16orf59 CD71 Early Erythroid C6orf25 Superior Cervical Ganglion C16orf68 Testis C6orf27 Superior Cervical Ganglion C16orf71 Testis Seminiferous Tubule C6orf5 Appendix C17orfa2 X721 B lymphoblasts C6orf54 Skeletal Muscle C17orf53 Dorsal Root Ganglion C6orf64 Testis C17orf59 Dorsal Root Ganglion C7orf10 Bronchial Epithelial Cells C17orf68 CD8 T cells C7orf25 Superior Cervical Ganglion C17orf73 Cardiac Myocytes C7orf58 Leukemia chronic C17orf30 Testis Germ Cell Myelogenous K567 C17orf1 Testis Intersitial C8G Liver C17orf3S BDCA4 Dentritic Cells C8orf17 Superior Cervical Ganglion C17orf38 Superior Cervical Ganglion C8orf241 Leukemia lymphoblastic C19Crf29 Leukemia chronic MOLT 7 Myelogenous K564 C9 Liver C19Crf61 Leukemia lymphoblastic C9Crf116 Testis MOLT 6 C9orf27 Trigeminal Ganglion C1GALT1C1 Superior Cervical Ganglion C9orf3 Uterus C1orf103 Leukemia chronic C9orf38 Superior Cervical Ganglion Myelogenous K565 C9Crfa-O CD71 Early Erythroid C1orf1 OS Testis Intersitial C9Crfaô Bronchial Epithelial Cells C1orf106 Small intestine C9Crf68 Skeletal Muscle C1orf114 Testis Intersitial C9Crf86 CD71 Early Erythroid C1orf13S Testis C9orf Testis Intersitial C1orf14 Testis Leydig Cell CA1 CD71 Early Erythroid C1orf156 CD19 Bcells neg. sel. CA12 Kidney C1orf75 Testis Intersitial CA3 Thyroid C1orf222 Testis CA4 Lung C1orf25 CD71 Early Erythroid CASA Liver C1orf27 pineal night CASB Superior Cervical Ganglion C1orf5 CD71 Early Erythroid CA6 Salivary gland C1orf50 Testis CA7 Atrioventricular Node C1orf66 Leukemia chronic CA9 Skin Myelogenous K566 CAB39L. Prostate C1orf68 Liver CABPS retina C1orf&9 Atrioventricular Node CABYR Testis Intersitial C1orf CD71 Early Erythroid CACNA1B Superior Cervical Ganglion C1OTNF1 Smooth Muscle CACNA1D Pancreas C1OTNF3 Spinal Cord CACNA1E Superior Cervical Ganglion C2 Liver CACNA1F pineal day C20orf191 Superior Cervical Ganglion CACNA1G Cerebellum C20orf29 Superior Cervical Ganglion CACNA1H Adrenal Cortex C21orfas CD105 Endothelial CACNA1I Prefrontal Cortex C21orf7 Whole Blood CACNA1S Skeletal Muscle C21orf1 Testis Intersitial CACNA2D1 Superior Cervical Ganglion C22orf24 Superior Cervical Ganglion CACNA2D3 CD14 Monocytes C22orf26 Ciliary Ganglion CACNB1 Skeletal Muscle C22Orf30 Trigeminal Ganglion CACNG2 Cerebellum Peduncles C22Orf31 Uterus Corpus CACNG4 Skeletal Muscle C2CD2 Adrenal Cortex CADM4 Prostate Cerebellum CADPS2 Cerebellum Peduncles US 2016/02897.62 A1 Oct. 6, 2016 19

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue CALCA Dorsal Root Ganglion CCS CD71 Early Erythroid CALCRL Fetal lung CCT4 Superior Cervical Ganglion CALMLS Skin CD160 CD56 NK Cells CAMK1G Whole Brain CD18O CD19 Bcells neg. sel. CAMK4 Testis Intersitial CD1C Thymus CAMTA2 pineal night CD2O7 Appendix CAND2 Heart CD209 Lymph node CANT1 Prostate CD22 Lymphoma burkitts Raji CAPNS Colon CD226 Superior Cervical Ganglion CAPN6 Placenta CD244 CD56 NK Cells CAPN7 Superior Cervical Ganglion CD248 Adipocyte CARD14 CD71 Early Erythroid CD32O Heart CASP10 CD4 T cells CD3EAP Dorsal Root Ganglion CASP2 Leukemia lymphoblastic CD3G Thymus MOLT 8 CD4 BDCA4 Dentritic Cells CASP9 Adrenal Cortex CD40 Lymphoma burkitts Raji CASQ2 Heart CD4OLG CD41 CASR Kidney CDSL CD105 Endothelial CASS4 Cingulate Cortex CD79B Lymphoma burkitts Raji CATSPERB Superior Cervical Ganglion CD8O X721 B lymphoblasts CAV3 Superior Cervical Ganglion CD81 CD71 Early Erythroid CBFA2T3 BDCA4 Dentritic Cells CDC14A Testis CBL Testis Germ Cell CDC25C Testis Intersitial CBLC Bronchial Epithelial Cells CDC27 CD71 Early Erythroid CBX2 Trachea CDC34 CD71 Early Erythroid CCBP2 Superior Cervical Ganglion CDC42EP2 Smooth Muscle CCDC132 Trigeminal Ganglion CDC6 Colorectal adenocarcinoma CCDC19 Testis Intersitial CDC73 Colon CCDC21 CD71 Early Erythroid CDCA4 CD71 Early Erythroid CCDC25 CD33 Myeloid CDCP1 Bronchial Epithelial Cells CCDC28B Lymphoma burkitts Raji CDH13 Uterus CCDC33 Superior Cervical Ganglion CDH1S Cerebellum CCDC41 CD40 CDH18 Subthalamic Nucleus CCDC46 Testis Intersitial CDH2O Superior Cervical Ganglion CCDC51 Leukemia promyelocytic CDH22 Cerebellum Peduncles HL60 CDH3 Bronchial Epithelial Cells CCDC6 Colon CDH4 Amygdala CCDC64 CD8 T cells CDHS Placenta CCDC68 Fetal lung CDH6 Trigeminal Ganglion CCDC76 CD8 T cells CDHT Skeletal Muscle CCDC81 Superior Cervical Ganglion CDKSR2 Whole Brain CCDC87 Testis CDK6 CD42 CCDC88A BDCA4 Dentritic Cells CDK8 Colorectal adenocarcinoma CCDC88C CD56 NK Cells CDKL2 Superior Cervical Ganglion CCDC99 Leukemia lymphoblastic CDKL3 Superior Cervical Ganglion MOLT 9 CDKLS Superior Cervical Ganglion CCHCR1 Testis CDKN2D CD71 Early Erythroid CCIN Testis Intersitial CDON Tonsil CCKAR Uterus Corpus CDR1 Cerebellum CCL11 Smooth Muscle CDS1 Small intestine CCL13 Small intestine CDSN Skin CCL18 Thymus CDX4 Superior Cervical Ganglion CCL2 Smooth Muscle CDYL CD71 Early Erythroid CCL21 Lymph node CEACAM21 Bone marrow CCL22 X721 B lymphoblasts CEACAM3 Whole Blood CCL24 Uterus Corpus CEACAMS Colon CCL27 Skin CEACAM7 Colon CCL3 CD33 Myeloid CEACAM8 Bone marrow CCL4 CD56 NK Cells CEBPA Liver CCL7 Smooth Muscle CEBPE Bone marrow CCND1 Colorectal adenocarcinoma CELSR3 Fetal brain CCNF CD71 Early Erythroid CEMP1 Skeletal Muscle CCNJ Ciliary Ganglion CENPE CD71 Early Erythroid CCNTL Atrioventricular Node CENPI Appendix CCNL2 CD4 T cells CENPQ Trigeminal Ganglion CCNO Testis CENPT CD71 Early Erythroid CCR1O X721 B lymphoblasts CEP170 Fetal brain CCR3 Whole Blood CEP55 X721 B lymphoblasts CCR5 CD8 T cells CEP63 Whole Blood CCR6 CD19 Bcells neg. sel. CEP76 CD71 Early Erythroid CCRL2 CD71 Early Erythroid CER1 Superior Cervical Ganglion CCRN4L Appendix CES1 Liver US 2016/02897.62 A1 Oct. 6, 2016 20

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue CES2 Liver CLNS Thyroid CES3 Colon CLN6 pineal day CETN1 Testis CLPB Testis Intersitial CFEHR4 Liver CLTCL1 Testis CFEHRS Liver CLUL1 retina CFI Fetal liver CMA1 Adrenal Cortex CGB Placenta CMAH Uterus CGRRF1 Testis Intersitial CMAS CD71 Early Erythroid CHAD Trachea CMKLR1 BDCA4 Dentritic Cells CHAF1A Leukemia lymphoblastic CNGA1 Uterus Corpus MOLT 10 CNIH3 Amygdala CHAF1B Leukemia lymphoblastic CNNM1 Prefrontal Cortex MOLT 11 CNNM4 pineal day CHAT Uterus Corpus CNR1 Fetal brain CHD3 Fetal brain CNR2 Uterus Corpus CHD8 Trigeminal Ganglion CNTFR Cardiac Myocytes CHI3L1 Uterus Corpus CNTLN Trigeminal Ganglion CHIA Lung CNTN2 Thalamus CHIT1 Lymph node COBLL1 Placenta CHKA Testis Intersitial COG7 Prostate CHML Superior Cervical Ganglion COL11A1 Adipocyte CHMP1B Superior Cervical Ganglion COL13A1 Cardiac Myocytes CHMP6 Heart COL14A1 Uterus CHODL Testis Germ Cell COL17A1 Bronchial Epithelial Cells CHPF Colorectal adenocarcinoma COL19A1 Trigeminal Ganglion CHRM2 Skeletal Muscle COL7A1 Skin CHRM3 Prefrontal Cortex COL8A2 retina CHRM4 Superior Cervical Ganglion COL9A1 pineal night CHRMS Skeletal Muscle COL9A2 retina CHRNA2 Heart COLEC10 Appendix CHRNA4 Skeletal Muscle COLEC11 Liver CHRNAS Appendix COMP Adipocyte CHRNA6 Temporal Lobe COMT Liver CHRNA9 Appendix COQ4 Thyroid CHRNB3 Superior Cervical Ganglion COQ6 Testis CHST10 Whole Brain CORIN Superior Cervical Ganglion CHST12 CD56 NK Cells CORO1B CD14 Monocytes CHST3 Testis Germ Cell CORO2A Bronchial Epithelial Cells CHST4 Uterus Corpus COX6B1 Superior Cervical Ganglion CHST7 Ovary CP Fetal liver CHSY1 Placenta CPA3 CD44 CIB2 BDCA4 Dentritic Cells CPM Adipocyte CIDEA Ciliary Ganglion CPN2 Liver CIDEB Liver CPNE6 Amygdala CIDEC Adipocyte CPNET Leukemia chronic CISH Leukemia chronic Myelogenous K569 Myelogenous K568 CPOX Fetal liver CKAP2 CD71 Early Erythroid CPT1A X721 B lymphoblasts CKM Skeletal Muscle CPZ Placenta CLCA4 Colon CR Whole Blood CLCF1 Uterus Corpus CREBZF CD8 T cells CLCN1 Skeletal Muscle CRH Placenta CLCN2 Olfactory Bulb CRHR1 Cerebellum Peduncles CLCNS Appendix CRIM1 Placenta CLCN6 Whole Brain CRISP2 Testis Intersitial CLCNKA Kidney CRLF1 Adipocyte CLCNKB Kidney CRLF2 Skeletal Muscle CLDN10 Kidney CRTAC1 Lung CLDN11 Heart CRTAP Adipocyte CLDN15 Small intestine CRY2 pineal night CLDN4 Colorectal adenocarcinoma CRYAA Kidney CLDN7 Colon CRYBA2 Pancreatic Islet CLDN8 Salivary gland CRYBA4 Superior Cervical Ganglion CLEC11A CD43 CRYBB1 Superior Cervical Ganglion CLEC16A Lymphoma burkitts Raji CRYBB2 retina CLEC4M Lymph node CRYBB3 Superior Cervical Ganglion CLECSA CD33 Myeloid CSAD Fetal brain CLGN Testis Intersitial CSAG2 Leukemia chronic CLIC2 CD71 Early Erythroid Myelogenous K570 CLICS Skeletal Muscle CSDC2 Heart CLMN Testis Intersitial CSF2 Colorectal adenocarcinoma CLN3 Placenta CSF2RA BDCA4 Dentritic Cells US 2016/02897.62 A1 Oct. 6, 2016 21

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue CSF3 Smooth Muscle DCBLD2 Trigeminal Ganglion CSF3R Whole Blood DCC Testis Seminiferous Tubule CSN3 Salivary gland DCHS2 Cerebellum CSNK1G3 CD19 Bcells neg. sel. DCI Liver CSPG4 Trigeminal Ganglion DCLRE1A X721 B lymphoblasts CST2 Salivary gland DCP1A CD4 T cells CST4 Salivary gland DCT retina CSTS Salivary gland DCUN1D1 CD71 Early Erythroid CST7 CD56 NK Cells DCUN1D2 Heart CSTF2T CD105 Endothelial DCX Fetal brain CTAG2 X721 B lymphoblasts DDX10 Leukemia promyelocytic CTBS Whole Blood HL61 CTDSPL Colorectal adenocarcinoma DDX17 Heart CTF1 Superior Cervical Ganglion DDX23 Thymus CTLA4 Superior Cervical Ganglion DDX25 Testis Leydig Cell CTNNA3 Testis Intersitial DDX28 CD14 Monocytes CTPS2 Ciliary Ganglion DDX31 Superior Cervical Ganglion CTSD Lung DDX43 Testis Seminiferous Tubule CTSG Bone marrow DDX5 Liver CTSK Uterus Corpus DDX51 BDCA4 Dentritic Cells CTTNBP2NL CD8 T cells DDX52 Colorectal adenocarcinoma CUBN Kidney DECR2 Liver CUEDC1 BDCA4 Dentritic Cells DEFA4 Bone marrow CUL1 Testis Intersitial DEFAS Small intestine CULT Smooth Muscle DEFA6 Small intestine CXCL1 Smooth Muscle DEFB126 Testis Germ Cell CXCL3 Smooth Muscle DEGS1 Skin CXCLS Smooth Muscle DENND1A X721 B lymphoblasts CXCL6 Smooth Muscle DENND2A Atrioventricular Node CXCR3 BDCA4 Dentritic Cells DENND3 CD33 Myeloid CXCRS CD19 Bcells neg. sel. DENND4A pineal night CXorf1 pineal day DEPDC5 Lymphoma burkitts Raji CXorfA0A Adrenal Cortex DES Skeletal Muscle CXorf56 Superior Cervical Ganglion DGAT1 Small intestine CXorf57 Hypothalamus DGCR14 Testis Intersitial CYB561 Prostate DGCR6L Trigeminal Ganglion CYLC1 Testis Seminiferous Tubule DGCR8 Leukemia chronic CYLD CD4 T cells Myelogenous K571 CYorf15B CD4 T cells DGKA CD4 T cells CYP19A1 Placenta DGKB Caudate nucleus CYP1A1 Lung DGKE Superior Cervical Ganglion CYP1A2 Liver DGKG Cerebellum CYP20A1 BDCA4 Dentritic Cells DGKQ Superior Cervical Ganglion CYP26A1 Fetal brain DHDDS pineal day CYP27A1 Liver DHODH Liver CYP27B1 Bronchial Epithelial Cells DHRS1 Liver CYP2A6 Liver DHRS12 Liver CYP2A7 Liver DHRS2 Colorectal adenocarcinoma CYP2B7P1 Superior Cervical Ganglion DHRS9 Trachea CYP2C19 Atrioventricular Node DHTKD1 Liver CYP2C8 Liver DHX29 CD71 Early Erythroid CYP2C9 Liver DHX35 Leukemia lymphoblastic CYP2D6 Liver MOLT 12 CYP2E1 Liver DHX38 CD56 NK Cells CYP2F1 Superior Cervical Ganglion DHX57 Testis Seminiferous Tubule CYP2W1 Skin DIAPH2 Testis Germ Cell CYP3A43 Liver DIDO1 CD8 T cells CYP3A5 Small intestine DIO2 Thyroid CYP3A7 Fetal liver DIO3 Cerebellum Peduncles CYP4F11 Liver DKFZP434L187 Atrioventricular Node CYP4F2 Liver DKK2 Ciliary Ganglion CYP4F8 Prostate DKK4 Pancreas CYP7B1 Ciliary Ganglion DLAT Adipocyte DACT1 Fetal brain DLEU2 CD71 Early Erythroid DAGLA Amygdala DLG3 Fetal brain DAO Kidney DLK2 Testis Leydig Cell DAPK2 Atrioventricular Node DLL3 Fetal brain DAZ1 Testis Leydig Cell DLX2 Fetal brain DAZL Testis DLX4 Placenta DB CD71 Early Erythroid DLXS Placenta DBNDD1 Trigeminal Ganglion DMC1 Superior Cervical Ganglion DBP Thyroid DMD Olfactory Bulb US 2016/02897.62 A1 Oct. 6, 2016 22

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue DMPK Heart EDIL3 Occipital Lobe DMWD Atrioventricular Node EDN2 Superior Cervical Ganglion DNA2 X721 B lymphoblasts EDN3 retina DNAH17 Testis EDNRA Uterus DNAH2 trioventricular Node EFCAB1 Superior Cervical Ganglion DNAH9 Cardiac Myocytes FHC1 Testis Intersitial DNAI1 Testis FHC2 Appendix DNAI2 Testis FNA4 Prostate DNAJC1 CD56 NK Cells FNB1 Colorectal adenocarcinoma DNAJC9 CD71 Early Erythroid EFNB3 Fetal brain DNAL4 Testis EGF Kidney DNALI1 Testis Intersitial EGFR Placenta DNASE1L1 CD14 Monocytes EGLN1 Whole Bloo DNASE1L2 Tonsil EIF1AY CD71 Early Erythroid DNASE1L3 BDCA4 Dentritic Cells EIF2AK1 CD71 Early Erythroid DNASE2B Salivary gland EIF2B4 Testis DND1 Testis EIF2C2 CD71 Early Erythroid DNM2 BDCA4 Dentritic Cells EIF2C3 Pituitary DNMT3A Superior Cervical Ganglion EIF3K Superior Cervical Ganglion DNMT3B Leukemia chronic EIF4G2 Liver Myelogenous K572 EIFSA2 Ciliary Ganglion DNMT3L Liver ELF3 Colon DOC2B Adrenal gland ELL2 Pancreatic Islet DOCKS Superior Cervical Ganglion ELMO3 CD71 Early Erythroid DOCK6 Lung ELOVL6 Adipocyte DOK2 CD14 Monocytes ELSPBP1 Testis Leydig Cell DOK3 Superior Cervical Ganglion ELTD1 Smooth Muscle DOK4 Fetal brain EMID1 Fetal brain DOKS Fetal brain EMILIN2 Superior Cervical Ganglion DOLK Testis EML.1 Fetal brain DOPEY2 Skeletal Muscle EMR3 Whole Blood DOT1L, Superior Cervical Ganglion EMX2 Uterus DPAGT1 X721 B lymphoblasts EN1 Adipocyte DPEP3 Testis ENDOG Liver DPF3 Cerebellum ENO3 Skeletal Muscle DPH2 Skeletal Muscle ENOX1 Fetal brain DPM2 CD71 Early Erythroid ENPP1 Thyroid DPP4 Smooth Muscle ENTPD1 X721 B lymphoblasts DPPA4 CD45 ENTPD2 Superior Cervical Ganglion DPT Adipocyte ENTPD3 Caudate nucleus DPY19L2P2 Leukemia lymphoblastic ENTPD4 Smooth Muscle MOLT 13 ENTPD7 Bone marrow DRD2 Caudate nucleus EPB41 CD71 Early Erythroid DSC Skin EPB41L4A Trigeminal Ganglion DSG Skin EPHA1 Liver DTL CD105 Endothelial EPHA3 Fetal brain DTX2 Skeletal Muscle EPHAS Fetal brain DTYMK CD105 Endothelial EPN2 CD71 Early Erythroid DUSP10 X721 B lymphoblasts EPN3 Thalamus DUSP26 Skeletal Muscle EPS15L1 Appendix DUSP4 Placenta EPS8L1 Placenta DUSP7 Bronchial Epithelial Cells EPS8L3 Pancreas DVL3 Placenta EPX Bone marrow DYNC2H1 Pituitary EPYC Placenta DYRK2 CD8 T cells ERCC1 Heart DYRK4 Testis Intersitial ERCC4 Superior Cervical Ganglion DYSF Whole Blood ERCC6 Ovary E2F1 CD71 Early Erythroid ERCC8 Uterus Corpus E2F2 CD71 Early Erythroid EREG CD46 E2F4 CD71 Early Erythroid ERF Ciliary Ganglion E2F5 Lymphoma burkitts Daudi ERG CD47 E2F8 CD71 Early Erythroid ERICH1 Superior Cervical Ganglion E4F1 CD4 T cells ERLIN2 Thyroid EAF2 CD19 Bcells neg. sel. ERMAP CD71 Early Erythroid EBI3 Placenta ERMP1 CD56 NK Cells ECHDC1 Adipocyte ERN1 Liver ECHS1 Liver ERO1LB Pancreatic Islet ECM1 Tongue ESM1 CD105 Endothelial ECSIT Heart ESR1 Uterus EDA Trigeminal Ganglion ETFB Liver EDA2R Superior Cervical Ganglion ETNK1 Colon EDC3 Testis ETNK2 Liver US 2016/02897.62 A1 Oct. 6, 2016 23

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue ETV3 Superior Cervical Ganglion FCRL2 CD19 Bcells neg. sel. ETV4 Colorectal adenocarcinoma FECH CD71 Early Erythroid EVPL Tongue FEM1B Testis Intersitial EXOSC1 Trigeminal Ganglion FEM1C Cerebellum EXOSC2 X721 B lymphoblasts FER1L4 Trigeminal Ganglion EXOSC4 Testis FETUB Liver EXOSCS X721 B lymphoblasts FEZF2 Amygdala EXPHS Placenta FFAR2 Whole Blood EXT2 Smooth Muscle FFAR3 Temporal Lobe EXTL3 Subthalamic Nucleus FGD1 Fetal brain EYA3 Cardiac Myocytes FGD2 CD33 Myeloid EYA4 Skin FGF12 Occipital Lobe F10 Liver FGF14 Cerebellum F11 Pancreas FGF17 Cingulate Cortex F12 Liver FGF2 Smooth Muscle F13B Fetal liver FGF22 Ovary F2R Cardiac Myocytes FGF23 Superior Cervical Ganglion F2RL1 Colon FGF3 Colorectal adenocarcinoma FAAH pineal night FGF4 Olfactory Bulb FABP6 Small intestine FGF5 Superior Cervical Ganglion FABP7 Fetal brain FGF8 Superior Cervical Ganglion FADS1 Adipocyte FGF9 Cerebellum Peduncles FAH Liver FGFR1OP Testis Intersitial FAIM Colorectal adenocarcinoma FGFR4 Liver FAM10SA BDCA4 Dentritic Cells FGL1 Fetal liver FAM106A Atrioventricular Node FGL2 CD14 Monocytes FAM108B1 Whole Brain FHIT CD4 T cells FAM11OB Trigeminal Ganglion FHL3 Skeletal Muscle FAM118A CD33 Myeloid FHLS Testis Intersitial FAM119B Uterus Corpus FILIP1L, Uterus FAM120C Ovary FKBP10 Smooth Muscle FAM12SB Spinal Cord FKBP14 Smooth Muscle FAM127B Thyroid FKBP6 Testis FAM13SA Appendix FKBPL CD105 Endothelial FAM149A pineal day FKRP Superior Cervical Ganglion FAM48A Testis Intersitial FLG Skin FAMSOB Whole Brain FLJ2O712 Temporal Lobe FAMSSD Colon FLNC Skeletal Muscle FAMSC Amygdala FLOT2 Whole Blood FAM63A Whole Blood FLT1 Superior Cervical Ganglion FAM86A Pituitary FLT4 Placenta FAM86B1 Skeletal Muscle FMO2 Lung FAM86C Leukemia promyelocytic FMO3 Liver HL62 FMO6P Appendix EANCE Lymphoma burkitts Daudi FN3K Superior Cervical Ganglion EANCG Leukemia lymphoblastic FNBP1L Fetal brain MOLT 14 FNDC8 Testis Intersitial FARP2 Testis FOLH1 Prostate FARS2 Heart FOSL1 Colorectal adenocarcinoma FAS Whole Blood FOXA1 Prostate FASLG CD56 NK Cells FOXA2 Pancreatic Islet FASTK Heart FOXB1 Superior Cervical Ganglion FASTKD2 X721 B lymphoblasts FOXC1 Salivary gland FAT4 Fetal brain FOXC2 Superior Cervical Ganglion FBLN2 Adipocyte FOXD3 Superior Cervical Ganglion FBN2 Placenta FOXD4 Globus Pallidus FBP Liver FOXE1 Thyroid FBP2 Skeletal Muscle FOXE Superior Cervical Ganglion FBXL12 Thymus FOXK2 Adrenal Cortex FBXL15 Whole Brain FOXL1 Liver FBXL4 CD71 Early Erythroid FOXN1 Superior Cervical Ganglion FBXL6 Pancreas FOXN2 Appendix FBXL8 X721 B lymphoblasts FOXP3 Adrenal Cortex FBXO17 Leukemia chronic FPGS Ovary Myelogenous K573 FPGT pineal day FBXO38 CD8 T cells FPR2 Whole Blood FBXO4 Trigeminal Ganglion FPR3 Superior Cervical Ganglion FBXO46 X721 B lymphoblasts FRAT1 Whole Blood FCGR2A Whole Blood FRAT2 Whole Blood FCGR2B Placenta FRK Superior Cervical Ganglion FCHO1 Lymphoma burkitts Raji FRMD8 Superior Cervical Ganglion FCN2 Liver FRS2 Pituitary US 2016/02897.62 A1 Oct. 6, 2016 24

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue

FRS3 Testis GDPD3 Colon FRZB retina GE Uterus Corpus FSHB Pituitary GEMIN4 Testis Intersitial FSHR Superior Cervical Ganglion GEMIN8 Skeletal Muscle FST Bronchial Epithelial Cells GFOD2 Superior Cervical Ganglion FSTL3 Placenta GFRA3 Liver FSTL4 Appendix GFRA4 Pons FTCD Liver GGTLC1 Lung FTSJ1 Bronchial Epithelial Cells GH2 Placenta FXC1 Superior Cervical Ganglion GHRHR Pituitary FXN CD105 Endothelial GHSR Superior Cervical Ganglion FXYD2 Kidney GIF Superior Cervical Ganglion FYCO1 Tongue GIMAP4 Whole Blood FZD4 Adipocyte GINS4 X721 B lymphoblasts FZD5 Colon GIP Small intestine FZD7 Cerebellum GIPC2 Small intestine FZD8 Superior Cervical Ganglion GJA3 Superior Cervical Ganglion FZD9 Appendix GA4 Lung FZR1 CD71 Early Erythroid GUAS Superior Cervical Ganglion G6PC Liver GJA8 Skeletal Muscle G6PC2 Superior Cervical Ganglion GB1 Liver GAB1 Superior Cervical Ganglion GJB3 Bronchial Epithelial Cells GABRA4 Caudate nucleus GBS Bronchial Epithelial Cells GABRAS Amygdala GJC1 Superior Cervical Ganglion GABRB2 Skin GJC2 Spinal Cord GABRE Placenta GK Whole Blood GABRG3 Subthalamic Nucleus GK2 Testis Intersitial GABRP Tonsil GK3P Testis Germ Cell GABRQ Skeletal Muscle GKN1 Small intestine GAD2 Caudate nucleus GLE1 Testis Intersitial GADD45G Placenta GLI1 Atrioventricular Node GADD45GIP1 Heart GLMN Skeletal Muscle GAL3ST1 Spinal Cord GLP2R Superior Cervical Ganglion GALK1 Liver GLRA1 Superior Cervical Ganglion GAL2 Leukemia chronic GLRA2 Uterus Corpus Myelogenous K574 GLS2 Liver GALNS CD33 Myeloid GLT8D2 Smooth Muscle GALNT12 Colon GLTP Tonsil GALNT14 Kidney GLTPD1 Heart GALNT4 CD71 Early Erythroid GMDS Colon GALNT6 CD71 Early Erythroid GMEB1 CD56 NK Cells GALNT8 Trigeminal Ganglion GML Trigeminal Ganglion GALR2 Superior Cervical Ganglion GNA13 BDCA4 Dentritic Cells GALT Liver GNA14 Superior Cervical Ganglion GAMT Liver GNAT1 retina GAPDHS Testis Intersitial GNAZ Fetal brain GAPVD1 CD71 Early Erythroid GNB1L Leukemia chronic GARNL3 Appendix Myelogenous K576 GAST Cerebellum GNG4 Superior Cervical Ganglion GATA4 Heart GNLY CD56 NK Cells GATAD1 Leukemia chronic GNRHR Pituitary Myelogenous K575 GOLT1B Smooth Muscle GATC Superior Cervical Ganglion GON4L Leukemia chronic GBA Placenta Myelogenous K577 GBX1 Bone marrow GP5 Trigeminal Ganglion GCAT Liver GP6 Superior Cervical Ganglion GCDH Liver GP9 Whole Blood GCGR Liver GPATCH1 CD8 T cells GCHFR Liver GPATCH2 Testis Seminiferous Tubule GCKR Liver GPATCH3 CD14 Monocytes GCLC CD71 Early Erythroid GPATCH4 Atrioventricular Node GCLM CD71 Early Erythroid GPATCH8 CD56 NK Cells GCM1 Placenta GPC4 Pituitary GCM2 Skeletal Muscle GPC5 pineal day GCNT1 CD19 Bcells neg. sel. GPD1 Adipocyte GCNT2 CD71 Early Erythroid GPI CD71 Early Erythroid GDAP1L1 Fetal brain GPKOW CD71 Early Erythroid GDF11 retina GPR124 retina GDF15 Placenta GPR137 Testis GDF2 Subthalamic Nucleus GPR143 retina GDF5 Fetal liver GPR153 Fetal brain GDF9 Testis Leydig Cell GPR157 Globus Pallidus US 2016/02897.62 A1 Oct. 6, 2016 25

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue GPR161 Uterus HAPLN1 Cardiac Myocytes GPR17 Whole Brain HAPLN2 Spinal Cord GPR172B Placenta HAS2 Skeletal Muscle GPR176 Smooth Muscle HBE1 Leukemia chronic GPR18 CD19 Bcells neg. sel. Myelogenous K578 GPR182 Superior Cervical Ganglion HBQ1 CD71 Early Erythroid GPR2O Trigeminal Ganglion HBS1L CD71 Early Erythroid GPR21 Globus Pallidus HBXIP Kidney GPR31 Superior Cervical Ganglion HCCS CD71 Early Erythroid GPR32 Superior Cervical Ganglion HCFC2 Testis Intersitial GPR35 Pancreas HCG4 Superior Cervical Ganglion GPR37L1 Amygdala HCG9 Liver GPR39 Superior Cervical Ganglion HCN4 Testis Leydig Cell GPR4 Lung HCRT Hypothalamus GPR44 Thymus HCRTR1 Bone marrow GPR50 Superior Cervical Ganglion HCRTR2 Atrioventricular Node GPR52 Superior Cervical Ganglion HDAC11 Testis GPR6 Caudate nucleus HDGF CD71 Early Erythroid GPR64 Testis Leydig Cell HEATR6 Atrioventricular Node GPR65 CD56 NK Cells HECTD3 CD71 Early Erythroid GPR68 Skeletal Muscle HECW1 Atrioventricular Node GPR87 Bronchial Epithelial Cells HEPH Leukemia chronic GPR98 Medulla Oblongata Myelogenous K579 GPRIN2 Superior Cervical Ganglion HEXIM1 CD71 Early Erythroid GPT Liver HEY2 retina GPX5 Testis Leydig Cell HGC6.3 Skeletal Muscle GRAMD1C Appendix HGF Smooth Muscle GRB7 Liver HGFAC Liver GREM1 Smooth Muscle HHAT BDCA4 Dentritic Cells GRID2 Superior Cervical Ganglion HHIPL2 Testis Intersitial GRIK3 Superior Cervical Ganglion HHLA1 Adrenal gland GRIK4 Olfactory Bulb HHLA3 Liver GRIN2A Subthalamic Nucleus HIC Superior Cervical Ganglion GRIN2B Skeletal Muscle HIC2 Leukemia chronic GRIN2C Thyroid Myelogenous K580 GRIN2D Superior Cervical Ganglion HIF3A Superior Cervical Ganglion GRIP1 Superior Cervical Ganglion HIGD1B Lung GRIP2 CD48 HIP1R CD19 Bcells neg. sel. GRK1 Superior Cervical Ganglion HIPK3 CD33 Myeloid GRK4 Testis HIST1H1E Leukemia chronic GRM1 Cerebellum Myelogenous K581 GRM2 Heart HIST1H1T Dorsal Root Ganglion GRM4 Cerebellum Peduncles HIST1H2AB CD19 Bcells neg. sel. GRRP1 Globus Pallidus HIST1H2BC Leukemia chronic GRTP1 Superior Cervical Ganglion Myelogenous K582 GSR X721 B lymphoblasts HIST1H2BG CD8 T cells GSTCD Atrioventricular Node HIST1H2B Ciliary Ganglion GSTM1 Liver HIST1H2BM Superior Cervical Ganglion GSTM2 Liver HIST1H2BN Small intestine GSTM4 Small intestine HIST1H3F Uterus Corpus GSTT2 Whole Brain HIST1H3I Cardiac Myocytes GSTTP1 Testis Intersitial HIST1H3J Atrioventricular Node GSTZ1 Liver HIST1H4A CD71 Early Erythroid GTF2IRD1 Colorectal adenocarcinoma HIST1H4E Superior Cervical Ganglion GTF3C5 Heart HIST1H4G Skeletal Muscle GTPBP1 CD71 Early Erythroid HIST3H2A Leukemia chronic GUCY1A2 Superior Cervical Ganglion Myelogenous K583 GUCY1B2 Superior Cervical Ganglion HIVEP2 Fetal brain GUCY2C Colon HKDC1 pineal night GUCY2D BDCA4 Dentritic Cells GUF1 Superior Cervical Ganglion HLA-DOB CD19 Bcells neg. sel. GULP1 Placenta HLCS Thyroid GYG2 Adipocyte HMBS CD71 Early Erythroid GYPE CD71 Early Erythroid HMGA2 Bronchial Epithelial Cells GYS1 Heart HMGB3 Placenta GZMK CD8 T cells HMGCL Liver H2AFB1 Testis HMGCS2 Liver HAAO Liver HMHB1 Skeletal Muscle HAL Fetal liver HNF4G Ovary HAMP Liver HNRNPA2B1 Liver HAO1 Liver HOOK1 Testis Intersitial HAO2 Kidney HOOK2 Thyroid US 2016/02897.62 A1 Oct. 6, 2016 26

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue

HOXA1 Leukemia chronic DH3G Heart Myelogenous K584 ER3IP1 Smooth Muscle HOXA10 Uterus FI44 CD33 Myeloid HOXA3 Superior Cervical Ganglion FIT1 Whole Blood HOXA6 Kidney FIT2 Whole Blood HOXA7 Adrenal Cortex FITS Whole Blood HOXA9 Colorectal adenocarcinoma FNA21 Testis Seminiferous Tubule HOXB1 Cingulate Cortex FNA4 Dorsal Root Ganglion HOXB13 Prostate FNAS Superior Cervical Ganglion HOXBS Colorectal adenocarcinoma FNA6 Superior Cervical Ganglion HOXB6 Colorectal adenocarcinoma FNAR1 Superior Cervical Ganglion HOXB7 Colorectal adenocarcinoma FNG CD56 NK Cells HOXB8 Superior Cervical Ganglion FNW1 Ovary HOXC11 Superior Cervical Ganglion FT140 Thyroid HOXCS Liver FT52 CD71 Early Erythroid HOXC8 Skeletal Muscle FT81 Testis Leydig Cell HOXD1 Trigeminal Ganglion GF1R Prostate HOXD10 Uterus GF2AS Subthalamic Nucleus HOXD11 Appendix GFALS Liver HOXD12 Skeletal Muscle GLL1 CD49 HOXD3 Uterus GLV6-57 Lymph node HOXD4 Uterus HH Heart HOXD9 Uterus KZF3 CD8 T cells HP Liver KZFS CD8 T cells HPGD Placenta L10 Atrioventricular Node HPN Liver L11 Smooth Muscle HPR Liver L11RA CD4 T cells HPS1 CD71 Early Erythroid L12A Uterus Corpus HPS4 CD105 Endothelial L12RB2 CD56 NK Cells HR pineal day L13 Testis Intersitial HRC Heart L13RA2 Testis Intersitial HRG Liver L15 pineal night HRK CD19 Bcells neg. sel. L17B Olfactory Bulb HS1BP3 CD14 Monocytes L17RA CD33 Myeloid HS3ST1 Ovary L17RB Kidney HS3ST3B1 Heart L18RAP CD56 NK Cells HS6ST1 Superior Cervical Ganglion L.19 Trachea HSD11B1 Liver L1B Smooth Muscle HSD17B1 Placenta L1F6 Superior Cervical Ganglion HSD17B2 Placenta L1F7 Skeletal Muscle HSD17B6 Liver L1F9 Superior Cervical Ganglion HSD17B8 Liver L1RAPL1 Prefrontal Cortex HSD3B1 Placenta L1RAPL2 Superior Cervical Ganglion HSF1 Heart L1RL1 Placenta HSFX1 Cardiac Myocytes L2 Heart HSP90AA1 Heart L2ORA Ciliary Ganglion HSPA1L, Testis Intersitial L21 Superior Cervical Ganglion HSPA4L Testis Intersitial L22 Superior Cervical Ganglion HSPA6 Whole Blood L24 Smooth Muscle HSPB2 Heart L2S Pons HSPB3 Heart L2RA Superior Cervical Ganglion HSPC159 Superior Cervical Ganglion L2RB CD56 NK Cells HTN1 Salivary gland L3RA BDCA4 Dentritic Cells HTR1A Liver L4 Atrioventricular Node HTR1B Heart L4R CD19 Bcells neg. sel. HTR1D Skeletal Muscle L5 Atrioventricular Node HTR1E pineal night LSRA Ciliary Ganglion HTR1F Appendix L9 Leukemia promyelocytic HTR2A Prefrontal Cortex HL63 HTR2C Caudate nucleus L9R Testis Intersitial HTR3A Dorsal Root Ganglion LVBL Heart HTR3B Skin MPG1 retina HTRSA Skeletal Muscle NCENP Leukemia lymphoblastic HTR7 Cardiac Myocytes MOLT 15 HTRA2 CD71 Early Erythroid NE1 Atrioventricular Node HUS1 Superior Cervical Ganglion NG1 CD19 Bcells neg. sel. HYAL2 Lung NHA Testis Germ Cell HYAL4 Superior Cervical Ganglion NHBA Placenta CAM4 CD71 Early Erythroid NHBE Liver CAMS Amygdala NPP5E X721 B lymphoblasts COSLG Skeletal Muscle NSIG2 X721 B lymphoblasts Testis Germ Cell NSL4 Placenta US 2016/02897.62 A1 Oct. 6, 2016 27

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue NSL6 Superior Cervical Ganglion KCNV2 retina NSRR Superior Cervical Ganglion KCTD14 Adrenal gland NTS12 BDCA4 Dentritic Cells KCTD15 Kidney NTS5 Liver KCTD17 pineal day PO8 CD4 T cells KCTD2O CD71 Early Erythroid QCB1 Lymphoma burkitts Daudi KCTDS BDCA4 Dentritic Cells RF2 Whole Blood KCTD7 pineal night RF6 Bronchial Epithelial Cells KDELC1 Cardiac Myocytes RS4 Skeletal Muscle KDELR3 Smooth Muscle RX4 Skin KDSR Olfactory Bulb RXS Lung KIAAOO40 CD19 Bcells neg. sel. SCA1 CD71 Early Erythroid KIAAO087 Trigeminal Ganglion SL1 Pancreatic Islet KIAAOO90 Placenta SOC2 Liver KIAAO1OO BDCA4 Dentritic Cells SYNA1 Testis Germ Cell KIAAO141 Superior Cervical Ganglion TCH Testis Intersitial KIAAO196 CD14 Monocytes TFG2 CD4 T cells KIAAO319 Fetal brain TGA2 Bronchial Epithelial Cells KIAAO556 pineal day TGA3 Bronchial Epithelial Cells KIAAO586 Testis Intersitial TGA9 Testis Seminiferous Tubule KIAA1024 Adrenal Cortex TGB1BP3 Heart KIAA1199 Smooth Muscle TGB5 Colorectal adenocarcinoma KIAA1310 Uterus Corpus TGB6 Bronchial Epithelial Cells KIAA1324 Prostate TGB8 Appendix KIAA1539 CD71 Early Erythroid TGBL1 Adipocyte KIAA1609 Bronchial Epithelial Cells TIEH4 Liver KIAA1751 Superior Cervical Ganglion TIHS Placenta KIF17 Cingulate Cortex TM2B X721 B lymphoblasts KIF18A X721 B lymphoblasts TPKA Whole Brain KIF18B Leukemia lymphoblastic TSN1 CD71 Early Erythroid MOLT 16 VL Tongue KIF21B Fetal brain AKMIP2 Prefrontal Cortex KIF22 CD71 Early Erythroid MDS Liver KIF2S Superior Cervical Ganglion PH2 Superior Cervical Ganglion KIF26B Ciliary Ganglion KAL1 Spinal Cord KIFSA Whole Brain KAZALD1 Skeletal Muscle KIFC1 CD71 Early Erythroid KCNA Superior Cervical Ganglion KIR2DL2 CD56 NK Cells KCNA10 Skeletal Muscle KIR2DL3 CD56 NK Cells KCNA2 Skeletal Muscle KIR2DL4 CD56 NK Cells KCNA3 Dorsal Root Ganglion KIR2DS4 CD56 NK Cells KCNA4 Superior Cervical Ganglion KIR3DL1 CD56 NK Cells KCNAB1 Caudate nucleus KIR3DL2 CD56 NK Cells KCNAB3 Subthalamic Nucleus KIRREL Superior Cervical Ganglion KCNB2 Trigeminal Ganglion KISS1 Placenta KCNC3 Lymphoma burkitts Daudi KL Kidney KCND Thyroid KLF12 CD8 T cells KCND2 Cerebellum Peduncles KLF15 Liver KCNE Pancreas KLF3 CD71 Early Erythroid KCNE1L, Superior Cervical Ganglion KLF8 Spinal Cord KCNE Uterus Corpus KLHDC4 CD56 NK Cells KCNG CD19 Bcells neg. sel. KLHL11 Temporal Lobe KCNG2 Superior Cervical Ganglion KLHL12 Testis Intersitial KCNH Appendix KLHL18 CD105 Endothelial KCNH2 CD105 Endothelial KLHL21 Heart KCNH4 Superior Cervical Ganglion KLHL2S Atrioventricular Node KCN1 Kidney KLHL26 Whole Brain KCNJ 10 Occipital Lobe KLHL29 Uterus Corpus KCNJ13 Superior Cervical Ganglion KLHL3 Cerebellum KCNJ14 Appendix KLHL4 Fetal brain KCNV2 Whole Blood KLK10 Tongue KCNJ3 Superior Cervical Ganglion KLK12 Tongue KCNJ6 Cingulate Cortex KLK13 Tongue KCNJ9 Cerebellum KLK14 Atrioventricular Node KCNK10 BDCA4 Dentritic Cells KLK15 Pancreas KCNK12 Olfactory Bulb KLK2 Prostate KCNK2 Atrioventricular Node KLK3 Prostate KCNK7 Superior Cervical Ganglion KLKS Testis Intersitial KCNMA1 Uterus KLK7 Pancreas KCNMB3 Testis Intersitial KLK8 Tongue KCNN2 Adrenal gland KLRC3 CD56 NK Cells KCNN4 CD71 Early Erythroid KLRF1 CD56 NK Cells KCNS3 Lung KLRK1 CD8 T cells US 2016/02897.62 A1 Oct. 6, 2016 28

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue KNTC1 Leukemia lymphoblastic LMCD1 Skeletal Muscle MOLT 1.7 LMF1 Liver KPNA4 X721 B lymphoblasts LMO1 retina KPTN Cerebellum LMTK2 Superior Cervical Ganglion KRT1 Skin LMX1B Superior Cervical Ganglion KRT10 Skin LOC1720 Superior Cervical Ganglion KRT12 Liver LOC388796 Lymphoma burkitts Raji KRT17 Tongue LOC390S61 Uterus Corpus KRT2 Skin LOC390940 Superior Cervical Ganglion KRT23 Colorectal adenocarcinoma LOC399904 Temporal Lobe KRT3 Superior Cervical Ganglion LOC441204 Appendix KRT33A Superior Cervical Ganglion LOC442421 Superior Cervical Ganglion KRT34 Skin LOCS1145 Appendix KRT36 Superior Cervical Ganglion LOC93432 Ovary KRT38 Atrioventricular Node LOH3CR2A Appendix KRT6B Tongue LOR Skin KRT84 Superior Cervical Ganglion LPAL2 Uterus Corpus KRT86 Placenta LPAR3 Testis Germ Cell KRT9 Superior Cervical Ganglion LPIN2 CD71 Early Erythroid KRTAP1-1 Superior Cervical Ganglion LRAT Pons KRTAP1-3 Ciliary Ganglion LRCH3 CD8 T cells KRTAP4-7 Superior Cervical Ganglion LRDD Pancreas KRTAPS-9 Superior Cervical Ganglion LRFN3 Superior Cervical Ganglion L1TD1 Dorsal Root Ganglion LRFN4 Fetal brain L2HGDH Superior Cervical Ganglion LRIT1 Superior Cervical Ganglion LACTB2 Small intestine LRP1B Amygdala LAD1 Bronchial Epithelial Cells LRP2 Thyroid LAIR1 BDCA4 Dentritic Cells LRPSL Superior Cervical Ganglion LAIR2 CD56 NK Cells LRRC16A Testis Germ Cell LALBA Ovary LRRC17 Smooth Muscle LAMA2 Adipocyte LRRC2 Thyroid LAMA3 Bronchial Epithelial Cells LRRC2O Skeletal Muscle LAMA4 Smooth Muscle LRRC3 Skeletal Muscle LAMAS Colorectal adenocarcinoma LRRC31 Colon LAMB3 Bronchial Epithelial Cells LRRC32 Lung LAMC2 Bronchial Epithelial Cells LRRC36 Testis Intersitial LANCL2 Testis LRRC37A4 Cerebellum LAT CD4 T cells LRRK1 Lymphoma burkitts Daudi LAX CD4 T cells LST Whole Bloo LCAT Liver LST-3TM12 Fetal liver LCMT2 CD105 Endothelial LTB4R CD33 Myeloid LCT Trigeminal Ganglion LTB4R2 Temporal Lobe LDB CD105 Endothelial LTBP4 Thyroid LDB3 Skeletal Muscle LTC4S Lung LDHAL6B Testis LTK BDCA4 Dentritic Cells LDHB Liver LUC7L Whole Bloo LDLR Adrenal Cortex LY6D Tongue LECT1 CD105 Endothelial LY6E Lung LE Thymus LY6G5C CD71 Early Erythroid LEFTY1 Colon LY6G6D Pancreas LEFTY2 Uterus Corpus LY6G6E Ovary LENE Salivary gland LY6E Amygdala LE Placenta LY96 Whole Bloo LETM1 Thymus LYL1 CD71 Early Erythroid LFNG Liver LYPD1 Smooth Muscle LGALS13 Placenta LYST Whole Bloo LGALS14 Placenta LY VE1 Fetal lung LGR4 Colon LYZL6 Testis Intersitial LHB Pituitary LZTFL1 Leukemia lymphoblastic MOLT LHCGR. Superior Cervical Ganglion 19 R S.C.Cerical Ganglion LZTS1 Skeletal Muscle LHX6 Fetal brain MACROD1 Heart LIG3 Leukemia lymphoblastic MOLT MAF Small intestine 8 MAFF Placenta LILRB4 BDCA4 Dentritic Cells MAFK Superior Cervical Ganglion LILRBS Skeletal Muscle MAGEA1 X721 B lymphoblasts LIM2 CD56 NK Cells MAGEA2 Leukemia chronic Myelogenous LIMS2 Uterus KS85 LIPF Small intestine MAGEAS X721 B lymphoblasts LIPG Thyroid MAGEA8 Placenta LIPT1 CD8 T cells MAGEB1 Testis Germ Cell US 2016/02897.62 A1 Oct. 6, 2016 29

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue MAGEC1 Leukemia chronic Myelogenous MGCS590 Cardiac Myocytes KS86 MGMT Liver MAGEC2 Skeletal Muscle MGST3 Lymphoma burkitts Daudi MAGED4 Fetal brain MIA2 Superior Cervical Ganglion MAGEL2 Hypothalamus MIA3 BDCA4 Dentritic Cells MAGI1 Globus Pallidus MICALL2 Colorectal adenocarcinoma MAGIX Superior Cervical Ganglion MIER2 Lung MAGOHB CD105 Endothelial MIPEP Kidney MALL Small intestine MITF Uterus MAML3 Ovary MKS1 Superior Cervical Ganglion MAMLD1 Testis Germ Cell MLANA retina MAN1A2 Placenta MILF1 Testis Intersitial MAN1C1 Placenta MLE3 Whole Blood MAN2C1 CD8 T cells MLL2 Liver MAP2K3 CD71 Early Erythroid MLLT1 Superior Cervical Ganglion MAP2KS Globus Pallidus MLLT10 Dorsal Root Ganglion MAP2K7 Atrioventricular Node MLLT3 CD8 T cells MAP3K12 Cerebellum MLN Liver MAP3K14 CD19 Bcells neg. sel. MLNR Superior Cervical Ganglion MAP3K6 Lung MMACHC Liver MAP4K2 X721 B lymphoblasts MME Adipocyte MAPK4 Skeletal Muscle MMP10 Uterus Corpus MAPK7 CD56 NK Cells MMP11 Placenta MAPKAP1 X721 B lymphoblasts MMP12 Tonsil MAPKAPK3 Heart MMP15 Thyroid MARK2 Globus Pallidus MMP24 Cerebellum Peduncles MARK3 CD71 Early Erythroid MMP26 Skeletal Muscle MAS1 Appendix MMP28 Lung MASP1 Heart MMP3 Smooth Muscle MASP2 Liver MMP8 Bone marrow MAST1 Fetal brain MMP9 Bone marrow MATK CD56 NK Cells MN1 Fetal brain MATN1 Trachea MINDA Whole Blood MATN4 Lymphoma burkitts Raji MOBKL3 Adrenal Cortex MBNL3 CD71 Early Erythroid MOCOS Adrenal gland MBTPS1 pineal night MOCS3 Atrioventricular Node MBTPS2 Dorsal Root Ganglion MOGAT2 Liver MC2R Adrenal Cortex MON1B Prostate MC3R Superior Cervical Ganglion MORC4 Placenta MC4R, Superior Cervical Ganglion MORF4L2 Heart MCCC2 X721 B lymphoblasts MORN1 Cingulate Cortex MCF2 pineal day MOS Superior Cervical Ganglion MCM10 CD105 Endothelial MOSC2 Kidney MCM9 CD19 Bcells neg. sel. MOSPD2 CD33 Myeloid MCOLN3 Adrenal Cortex MPL Skeletal Muscle MCPH1 Thymus MPP3 Cerebellum MCTP1 Caudate nucleus MPP5 Placenta MCTP2 Whole Blood MPP6 Testis Germ Cell ME Adipocyte MPPED1 Fetal brain MECR Heart MPPED2 Thyroid MED1 Thymus MPZL.1 Smooth Muscle MED15 CD8 T cells MPZL2 Colorectal adenocarcinoma MED22 CD19 Bcells neg. sel. MRAS Heart MED31 Cerebellum MREG pineal day MED7 Testis Intersitial MRPL17 X721 B lymphoblasts MEGF6 Lung MRPL46 X721 B lymphoblasts MEGF8 Skeletal Muscle MRPS18A Heart MEOX2 Fetal lung MRPS18C Atrioventricular Node MEP1B Small intestine MRS2 X721 B lymphoblasts ME Bronchial Epithelial Cells MRTO4 Leukemia promyelocytic HL64 METTL4 CD8 T cells MS4A12 Colon METTL8 CD19 Bcells neg. sel. MS4A2 Ciliary Ganglion MEX3D Subthalamic Nucleus MS4A4A Placenta MFAPS Adipocyte MS4AS Testis Intersitial MFI2 Uterus Corpus MSC X721 B lymphoblasts MFN1 Lymphoma burkitts Raji MSH4 Uterus Corpus MFSD7 Ovary MSLN Lung MGA CD8 T cells MSRA Kidney MGAT4A CD8 T cells MST1 Liver MGATS Temporal Lobe MST1R Colorectal adenocarcinoma MGC295.06 Thymus MSX1 Colorectal adenocarcinoma MGC4294 Superior Cervical Ganglion MT4 Lymphoma burkitts Raji US 2016/02897.62 A1 Oct. 6, 2016 30

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue

MTERFD1 CD105 Endothelial DUFB7 Heart MTERFD2 CD8 T cells ECAB2 Caudate nucleus MTF1 CD33 Myeloid EIL3 Leukemia lymphoblastic MOLT MTHFSD Testis 21 MTMR1O CD71 Early Erythroid K11 Uterus Corpus MTMR12 CD71 Early Erythroid Pancreas MTMR3 CD71 Early Erythroid Testis Germ Cell MTMR4 Placenta Colorectal adenocarcinoma MTMRA Superior Cervical Ganglion Whole Brain MTMR8 Skeletal Muscle Olfactory Bulb MTNR1A Superior Cervical Ganglion Fetal brain MTNR1B Superior Cervical Ganglion Atrioventricular Node MTTP Small intestine Fetal brain MUC1 Lung Superior Cervical Ganglion MUC13 Pancreas CD19 Bcells neg. sel. MUC16 Trachea Thymus MUC2 Colon CD71 Early Erythroid MUCSB Trachea Colorectal adenocarcinoma MUM1 Testis Lymphoma burkitts Raji MUSK Skeletal Muscle Testis MUTYH. Leukemia lymphoblastic MOLT Atrioventricular Node 2O BDCA4 Dentritic Cells MVD Adipocyte Cardiac Myocytes MXD1 Whole Blood CD71 Early Erythroid MYBPC1 Skeletal Muscle Ciliary Ganglion MYBPC3 Heart Colorectal adenocarcinoma MYBPH Superior Cervical Ganglion Hypothalamus MYCN Fetal brain Whole Blood MYCT1 Trigeminal Ganglion Superior Cervical Ganglion MYF5 Superior Cervical Ganglion Fetal brain MYF6 Skeletal Muscle Spinal Cord MYH1 Skeletal Muscle Heart MYH13 Skeletal Muscle Superior Cervical Ganglion MYH15 Appendix Colon MYHTB Superior Cervical Ganglion Skeletal Muscle MYL7 Heart Lymphoma burkitts Raji MYNN Trigeminal Ganglion Superior Cervical Ganglion MYO16 Fetal brain Bronchial Epithelial Cells MYO1A Small intestine Testis Intersitial MYO1B Bronchial Epithelial Cells Leukemia chronic Myelogenous MYOSA Superior Cervical Ganglion KS87 MYOSC Salivary gland NMUR1 CD56 NK Cells MYO7B Liver NOC2L Lymphoma burkitts Raji MYOC retina NOC3L X721 B lymphoblasts MYST2 Testis NOC4L Testis MYT1 pineal night NOL10 Superior Cervical Ganglion N4BP1 Whole Blood NOL3 Heart N6AMT1 Trigeminal Ganglion NOS1 Uterus Corpus NAALAD2 Pituitary NOS3 Placenta NAALADL1 Liver NOTCH1 Leukemia lymphoblastic MOLT NAB2 Cerebellum 22 NAPG Superior Cervical Ganglion Colon NARF CD71 Early Erythroid CD105 Endothelial NAT1 Colon Kidney NAT2 Colon Smooth Muscle NAT8 Kidney CD8 T cells NAT8B Kidney Fetal liver NAV2 Fetal brain Subthalamic Nucleus NAV3 Fetal brain CD50 NBE Fetal brain Kidney NBEAL2 Lymphoma burkitts Raji Bronchial Epithelial Cells NCAM2 Superior Cervical Ganglion Heart NCAPG2 CD71 Early Erythroid Heart NCBP1 X721 B lymphoblasts Superior Cervical Ganglion NCLN BDCA4 Dentritic Cells Skeletal Muscle NCOA2 Whole Blood Prostate NCR1 CD56 NK Cells Fetal brain NCR2 Lymphoma burkitts Raji Superior Cervical Ganglion NCR3 CD56 NK Cells Kidney NDP Amygdala Liver NDUFA4L2 Pancreas pineal day NDUFB2 Heart Lung US 2016/02897.62 A1 Oct. 6, 2016 31

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue NR1EH4 Fetal liver OR11A1 Superior Cervical Ganglion NR13 Liver OR1A1 Superior Cervical Ganglion NR2C1 Superior Cervical Ganglion OR2B2 Superior Cervical Ganglion NR2C2 Testis Leydig Cell OR2B6 Superior Cervical Ganglion NR2E1 Amygdala OR2C1 Superior Cervical Ganglion NR2E3 retina OR2H1 Skeletal Muscle NR4A1 Adrenal Cortex OR 23 Superior Cervical Ganglion NR4A2 Adrenal Cortex OR2S2 Uterus Corpus NR4A3 Adrenal Cortex OR2W1 Superior Cervical Ganglion NRSA1 Globus Pallidus OR3A2 Superior Cervical Ganglion NR6A1 Testis ORS2A1 Testis Seminiferous Tubule NRAP Heart ORSI1 Lymphoma burkitts Raji NRAS BDCA4 Dentritic Cells OR6A2 Superior Cervical Ganglion NRBF2 Whole Blood OR7AS Appendix NRG2 Superior Cervical Ganglion OR7C1 Testis Seminiferous Tubule NRIP2 Olfactory Bulb OR7E19P Superior Cervical Ganglion NRL retina ORAI2 CD19 Bcells neg. sel. NRP2 Skeletal Muscle ORM1 Liver NRTN Superior Cervical Ganglion OSBP2 CD71 Early Erythroid NRXN3 Cerebellum Peduncles OSBPL10 CD19 Bcells neg. sel. NSUN3 CD71 Early Erythroid OSBPL3 Colorectal adenocarcinoma NSUN6 CD4 T cells OSBPL7 Tonsil NTSDC3 Fetal brain OSGEPL1 CD4 T cells NTSM CD71 Early Erythroid OSM CD71 Early Erythroid NTAN1 CD71 Early Erythroid OSR2 Uterus NTHL1 Liver OTUD3 Prefrontal Cortex NTN1 Superior Cervical Ganglion OTUD7B Heart NTNG1 Uterus Corpus OXCT2 Testis Intersitial NTSR1 Colorectal adenocarcinoma OXSM X721 B lymphoblasts NUDT1 CD71 Early Erythroid OXT Hypothalamus NUDT15 Colorectal adenocarcinoma P2RX2 Superior Cervical Ganglion NUDT18 CD19 Bcells neg. sel. P2RX3 CD71 Early Erythroid NUDT4 CD71 Early Erythroid P2RX6 Skeletal Muscle NUDT6 Leukemia lymphoblastic MOLT P2RY1O CD19 Bcells neg. sel. 23 P2RY2 Bronchial Epithelial Cells NUDTT Superior Cervical Ganglion P2RY4. Superior Cervical Ganglion NUFIP1 CD105 Endothelial PADI3 Pons NUMB Whole Blood PAEP Uterus NUP155 Testis Intersitial PAFAH2 Thymus NUPL1 Fetal brain PAGE X721 B lymphoblasts NUPL2 Colorectal adenocarcinoma PAK1IP1 Prostate NXPH3 Cerebellum PAK7 Fetal brain OAS CD14 Monocytes PALB2 X721 B lymphoblasts OAS2 Lymphoma burkitts Daudi PALMD Fetal liver OAS3 CD33 Myeloid PANK4 Lymphoma burkitts Raji OASL Whole Blood PANX1 Bronchial Epithelial Cells OAZ3 Testis Intersitial PAPOLG Fetal brain OBFC2A Uterus Corpus PAPPA2 Placenta OBSCN Temporal Lobe PAQR3 Testis Germ Cell OCEL1 CD14 Monocytes PARD3 Bronchial Epithelial Cells OCLM Superior Cervical Ganglion PARG Superior Cervical Ganglion OCLN Skeletal Muscle PARN X721 B lymphoblasts ODF Testis Intersitial PARP11 Appendix ODZ.4 Fetal brain PARP16 Atrioventricular Node OGFRL1 Whole Blood PARP3 X721 B lymphoblasts OLAH Placenta PART1 Prostate OLFM4 Small intestine PAWR Uterus OLFML3 Adipocyte PAX Thymus OLR Placenta PAX2 Kidney OMD Superior Cervical Ganglion PAX4 Superior Cervical Ganglion OMP Superior Cervical Ganglion PAX7 Atrioventricular Node ONECUT1 Liver PCCA Colon OPA3 Colorectal adenocarcinoma PCDH1 Placenta OPLAH Heart PCDH11X Fetal brain OPN1LW retina PCDH17 Testis Intersitial OPN1SW Superior Cervical Ganglion PCDHT Prefrontal Cortex OPRD1 Thalamus PCDHB1 Superior Cervical Ganglion OPRL1 Lymphoma burkitts Raji PCDHB11 Uterus Corpus OR1OC1 Superior Cervical Ganglion PCDHB13 Pancreatic Islet OR1OH1 Trigeminal Ganglion PCDH63 Testis OR1OH3 Pons PCDHB6 Superior Cervical Ganglion OR1OJ1 Superior Cervical Ganglion PCK2 Liver US 2016/02897.62 A1 Oct. 6, 2016 32

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne

Gene Tissue Gene Tissue

PCNP Liver BF1 Testis Intersitial PCNT Skeletal Muscle CK1 Cerebellum Peduncles PCNX CD8 T cells GB X721 B lymphoblasts PCNXL2 Prefrontal Cortex GL Colorectal adenocarcinoma PCOLCE Liver GR Trachea PCOLCE2 Adipocyte Testis PCSK1 Pancreatic Islet Pancreas PCYOX1 Adipocyte Thymus PCYT1A Testis CD8 T cells PDC retina Fetal brain PDCD1 Pons CD56 NK Cells PDCD1LG2 Superior Cervical Ganglion CD71 Early Erythroid PDE10A Caudate nucleus Liver PDE1B Caudate nucleus R Bronchial Epithelial Cells PDE1C pineal night TPNM3 Superior Cervical Ganglion PDE3B CD8 T cells TX1 Tongue PDE6A retina TX2 retina PDE6G retina TX3 Adrenal gland PDE7B Trigeminal Ganglion KD2 Uterus PDE9A Prostate KDREJ CD14 Monocytes PDGFRL Fetal Thyroid KLR Liver PDHA2 Testis Intersitial KMYT1 CD71 Early Erythroid PDLA2 Pancreas KP2 Colon PDK3 X721 B lymphoblasts LA1A X721 B lymphoblasts PDLIM3 Skeletal Muscle LA2G12A CD105 Endothelial PDLIM4 Colorectal adenocarcinoma LA2G2E Superior Cervical Ganglion PDPN Placenta LA2G2F Trigeminal Ganglion PDPR Superior Cervical Ganglion LA2G3 Skeletal Muscle PDSS1 Leukemia lymphoblastic MOLT LA2G4A Smooth Muscle 24 LA2G7 CD14 Monocytes PDX1 Heart LAA X721 B lymphoblasts PDXP CD14 Monocytes LAC1 Placenta PDZD3 Superior Cervical Ganglion LAC4 Placenta PDZK1P1 Kidney LAG1 Trigeminal Ganglion PDZRN4 Atrioventricular Node LAGL2 Testis PECR Liver LCB2 CD14 Monocytes PEPD Kidney LCB3 Small intestine PER3 retina LCB4 Thalamus PET112L Heart LCXD1 X721 B lymphoblasts PEX11A Prostate LD1 X721 B lymphoblasts PEX13 Testis Intersitial LEK2 Bronchial Epithelial Cells PEX19 Adipocyte LEKHA2 Superior Cervical Ganglion PEX3 X721 B lymphoblasts LEKHA6 Placenta PEXSL Superior Cervical Ganglion LEKHA8 CD56 NK Cells PF4 Whole Blood LEKHF2 CD19 Bcells neg. sel. PF4V1 Whole Blood LEKHH3 Superior Cervical Ganglion PFKFB1 Liver LK1 X721 B lymphoblasts PFKFB2 Pancreatic Islet CD33 Myeloid PFKFB3 Skeletal Muscle CD71 Early Erythroid PGA3 Small intestine LN Uterus PGAM1 CD71 Early Erythroid LOD2 Smooth Muscle PGAP1 Adrenal Cortex LS1 Colon PGGT1B Ciliary Ganglion LSCR2 Testis Intersitial PGK2 Testis Intersitial LUNC Trachea PGLYRP4 Superior Cervical Ganglion LXNA1 Fetal brain PGM3 Smooth Muscle LXNC1 Whole Blood PGPEP1 Kidney PMCH Hypothalamus PGR Uterus PMCHL1 Hypothalamus PHACTR4 X721 B lymphoblasts PMEPA1 Prostate PHC1 Testis Germ Cell PNMT Adrenal Cortex PHEX BDCA4 Dentritic Cells PNPLA2 Adipocyte PHF7 Testis Intersitial PNPLA3 Atrioventricular Node PHKG1 Superior Cervical Ganglion PNPLA4 Bronchial Epithelial Cells PHKG2 Testis POF1B Skin PHILDA2 Placenta POFUT2 Smooth Muscle PHOX2A Uterus Corpus POLE2 Leukemia lymphoblastic MOLT PI15 Testis Leydig Cell 25 PI3 Tonsil POLL CD71 Early Erythroid PI4K2A CD71 Early Erythroid POLM CD19 Bcells neg. sel. PIAS2 Testis Intersitial POLQ Lymphoma burkitts Daudi PLAS3 pineal day POLR1C Leukemia promyelocytic HL65 PLAS4 Whole Brain POLR2D Testis US 2016/02897.62 A1 Oct. 6, 2016 33

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue POLR2 Trigeminal Ganglion PRM2 Testis Leydig Cell POLR3B X721 B lymphoblasts PRMT3 Leukemia promyelocytic HL67 POLR3C CD71 Early Erythroid PRMT7 BDCA4 Dentritic Cells POLR3D X721 B lymphoblasts PRND Testis Germ Cell POLR3G Leukemia promyelocytic HL66 PRO1768 Trigeminal Ganglion POLRMT Testis PRO2012 Appendix POM121L2 Superior Cervical Ganglion PROC Liver POMC Pituitary PROCR Placenta POMGNT1 Heart PROL1 Salivary gland POMT1 Testis PROP1 Trigeminal Ganglion POMZP3 Testis Germ Cell PROZ Superior Cervical Ganglion PON3 Liver PRPS2 Ovary POP1 Dorsal Root Ganglion PRR3 Leukemia lymphoblastic MOLT POPDC2 Heart 26 POSTN Cardiac Myocytes PRR5 CD71 Early Erythroid POU2F3 Trigeminal Ganglion PRR7 X721 B lymphoblasts POU3F3 Superior Cervical Ganglion PRRC1 BDCA4 Dentritic Cells POU3F4 Ciliary Ganglion PRRG1 Spinal Cord POU4F2 Superior Cervical Ganglion PRRG2 Parietal Lobe POUSF1 Pituitary PRRG3 Salivary gland POUSF1P3 Uterus Corpus PRRX1 Adipocyte POUSF1P4 Ciliary Ganglion PRSS12 Superior Cervical Ganglion PP14571 Placenta PRSS16 Thymus PPA1 Heart PRSS21 Testis PPARD Placenta PRSS8 Placenta PPARG Adipocyte PSCA Prostate PPARGC1A Salivary gland PSD Subthalamic Nucleus PPAT X721 B lymphoblasts PSG1 Placenta PPBPL2 Superior Cervical Ganglion PSG11 Placenta PPCDC X721 B lymphoblasts PSG2 Placenta PPEF2 retina PSG3 Placenta PPFIA2 pineal day PSG4 Placenta PPFIBP1 Colorectal adenocarcinoma PSG5 Placenta PPIL2 Leukemia chronic Myelogenous PSG6 Placenta KS88 PSG7 Placenta PPIL6 Liver PSG9 Placenta PSKH1 Testis PPM1H Cerebellum PSMB4 Superior Cervical Ganglion PPOX CD71 Early Erythroid PSMD5 Leukemia chronic Myelogenous PPP1R12B Uterus KS90 PPP1R13B Thyroid PSPH Lymphoma burkitts Raji PPP1R3D Whole Blood PSPN Trigeminal Ganglion PPP2R2D Whole Brain PSTPIP2 Bone marrow PPP3R1 Whole Blood PTCH2 Fetal brain PPP5C X721 B lymphoblasts PTDSS2 Lymphoma burkitts Raji PPRC1 CD105 Endothelial PTER Kidney PPT2 Olfactory Bulb PTGDR CD56 NK Cells PPY Pancreatic Islet PTGER2 CD56 NK Cells PPY2 Superior Cervical Ganglion PTGES2 X721 B lymphoblasts POLC2 Skeletal Muscle PTGES3 Superior Cervical Ganglion PRAME Leukemia chronic Myelogenous PTGFR Uterus KS89 PTGIR CD14 Monocytes PRDM1 Superior Cervical Ganglion PTGS Smooth Muscle PRDM11 CD52 PTGS2 Smooth Muscle PRDM12 Cardiac Myocytes PTH2R Superior Cervical Ganglion PRDM13 Superior Cervical Ganglion PTHLH Bronchial Epithelial Cells PRDM16 Superior Cervical Ganglion PTK7 BDCA4 Dentritic Cells PRDMS Skeletal Muscle PTPLA CD53 PRDM8 Superior Cervical Ganglion PTPN CD19 Bcells neg. sel. PRE X721 B lymphoblasts PTPN21 Testis PRF1 CD56 NK Cells PTPN3 Thalamus PRG3 Bone marrow PTPN9 Appendix PRICKLE3 X721 B lymphoblasts PTPRG Adipocyte PRKAA1 Testis Intersitial PTPRH Pancreas PRKAB1 CD71 Early Erythroid PTPRS BDCA4 Dentritic Cells PRKAB2 Dorsal Root Ganglion PURG Skeletal Muscle PRKCG Superior Cervical Ganglion PUS3 Skeletal Muscle PRKCH CD56 NK Cells PUS7L Superior Cervical Ganglion PRKRIP1 Colorectal adenocarcinoma PVALB Cerebellum PRKY CD4 T cells PVRL3 Placenta PRL Pituitary PXDN Smooth Muscle PRLH Trigeminal Ganglion PXMP2 Liver US 2016/02897.62 A1 Oct. 6, 2016 34

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue PXMP4 Lung RENBP Kidney PYGM Skeletal Muscle RERGL Uterus PYGO1 Skeletal Muscle RETSAT Adipocyte PYHIN1 Superior Cervical Ganglion REV3L Uterus PYY Colon REXO4 CD19 Bcells neg. sel. PZP Skin RFC1 Leukemia lymphoblastic MOLT QPRT Liver 28 QRSL1 CD19 Bcells neg. sel. RFC2 X721 B lymphoblasts QTRT1 Thyroid RFNG Liver RAB11B Thyroid RFPL3 Superior Cervical Ganglion RAB11FIP3 Kidney RFWD3 CD105 Endothelial RAB17 Liver RFX1 Superior Cervical Ganglion RAB23 Uterus RFX3 Trigeminal Ganglion RAB2S Tongue RFXAP Pituitary RAB30 Liver RGN Adrenal gland RAB33A Whole Brain RGPD5 Testis Intersitial RAB38 Bronchial Epithelial Cells RGR retina RAB3D Atrioventricular Node RGS14 Caudate nucleus RAB40A Dorsal Root Ganglion RGS17 Pancreatic Islet RAB40C Superior Cervical Ganglion RGS3 Heart RAB4B BDCA4 Dentritic Cells RGS6 pineal night RABL2A Fetal brain RGS9 Caudate nucleus RAC3 Whole Brain RHAG CD71 Early Erythroid RAD51L1 Superior Cervical Ganglion RHBDF1 Olfactory Bulb RADS2 Lymphoma burkitts Raji RHBDL1 Lymphoma burkitts Raji RAD9A CD105 Endothelial RHBG Atrioventricular Node RAG1 Thymus RHCE CD71 Early Erythroid RALGPS1 Fetal brain RHD CD71 Early Erythroid RAMP1 Uterus RHO retina RAMP2 Lung RHOBTB1 Placenta RAMP3 Lung RHOBTB2 Lung RANBP10 CD71 Early Erythroid RHOD Bronchial Epithelial Cells RANBP17 Colorectal adenocarcinoma RIBC2 Testis Intersitial RAP2C Uterus RIC3 Cingulate Cortex RAPGEF1 Uterus Corpus RIC8B Caudate nucleus RAPGEF4 Amygdala RIN3 CD14 Monocytes RAPGEFL1 Whole Brain RINT1 Superior Cervical Ganglion RAPSN Skeletal Muscle RIOK2 Smooth Muscle RARA Whole Blood RIT1 Whole Blood RARB Superior Cervical Ganglion RIT2 Fetal brain RARS2 Uterus Corpus RLBP1 retina RASA1 Placenta RLN1 Prostate RASA2 CD8 T cells RLN2 Superior Cervical Ganglion RASA3 CD56 NK Cells RMI1 X721 B lymphoblasts RASAL1 Lymphoma burkitts Raji RMND1 Trigeminal Ganglion RASGRF1 Cerebellum RMINDSA CD71 Early Erythroid RASGRP3 CD19 Bcells neg. sel. RMINDSB Testis RASSF7 Pancreas RNASE3 Bone marrow RASSF8 Testis Intersitial RNASEH2B Leukemia lymphoblastic MOLT RASSF9 Appendix 29 RAVER2 Ciliary Ganglion RNASEL Whole Bloo RAX Cerebellum Peduncles RNF10 CD71 Early Erythroid RBBPS CD14 Monocytes RNF121 Subthalamic Nucleus RBM19 Superior Cervical Ganglion RNF123 CD71 Early Erythroid RBM4B Fetal brain RNF125 CD8 T cells RBM7 Whole Blood RNF14 CD71 Early Erythroid RBMY1A1 Testis RNF141 Testis Intersitial RBP4. Liver RNF17 Testis Intersitial RBPJL Pancreas RNF170 Thyroid REX1 CD71 Early Erythroid RNF185 Superior Cervical Ganglion RC3H2 BDCA4 Dentritic Cells RNF19A CD71 Early Erythroid RCAN3 Prostate RNF32 Testis Intersitial RCBTB2 Leukemia lymphoblastic MOLT RNF40 CD71 Early Erythroid 27 RNFT1 Testis Leydig Cell RCN3 Smooth Muscle RNMTL1 Testis RDH11 Prostate ROBO1 Fetal brain RDH16 Liver ROPN1 Testis Intersitial RDH8 retina ROR Adipocyte RECQL4 CD105 Endothelial RORB Superior Cervical Ganglion RECQL5 Skeletal Muscle RORC Liver RELB Lymphoma burkitts Raji RP2 Whole Bloo REN Ovary RPA4 Superior Cervical Ganglion US 2016/02897.62 A1 Oct. 6, 2016 35

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue RPAIN Lymphoma burkitts Daudi SDPR. Fetal lung RPE Leukemia promyelocytic HL68 SDS Liver RPE65 retina SEC14L3 Trigeminal Ganglion RPGRIP1 Testis Intersitial SEC14L4 CD71 Early Erythroid RPGRIP1L, Superior Cervical Ganglion SEC22B Placenta RPH3AL Pancreatic Islet SECTM1 Whole Blood RPL1OL Testis SEL.1L. Pancreas RPL3L Skeletal Muscle SELE retina RPP38 Testis Germ Cell SELP Whole Blood RPRM Fetal brain SEMA3A Appendix RPS6KA4 Pons SEMA3B Placenta RPS6KA6 Appendix SEMA3D Trigeminal Ganglion RPS6KB1 CD4 T cells SEMA4G Fetal liver RPS6KC1 Testis Intersitial SEMASA Olfactory Bulb RRAD Skeletal Muscle SEMA7A Superior Cervical Ganglion RRAGB Superior Cervical Ganglion SEMG1 Prostate RREH retina SEMG2 Prostate RRN3 CD56 NK Cells SENP2 Testis Intersitial RRP12 CD33 Myeloid SEPHS1 Leukemia lymphoblastic MOLT RRP9 X721 B lymphoblasts 30 RS1 retina SERPINA10 Liver RSAD2 CD71 Early Erythroid SERPINAf Fetal liver RSF1 Uterus SERPINB13 Tongue RTDR1 Testis SERPINB3 Trachea RTN2 Skeletal Muscle SERPINB4 Superior Cervical Ganglion RUNX1T1 Fetal brain SERPINB8 CD33 Myeloid RUNX2 Pons SERPINE1 Cardiac Myocytes RWDD2A Testis Germ Cell SERPINF2 Liver RXFP3 Superior Cervical Ganglion SETD4 Testis RYR2 Prefrontal Cortex SETD8 CD71 Early Erythroid S100A12 Bone marrow SETMAR Atrioventricular Node S100A2 Bronchial Epithelial Cells SF3A3 Leukemia chronic Myelogenous S100A3 Colorectal adenocarcinoma KS92 S1 OOAS Liver SFMBT1 Testis Germ Cell S1OOG Uterus Corpus SFRP5 retina S1PR5 CD56 NK Cells SFTPA2 Lung SAA1 Salivary gland SFTPD Lung SAA3P Skin SGCA Heart SAA4 Liver SGCB Olfactory Bulb SAC3D1 Testis SGPL1 Colorectal adenocarcinoma SAG retina SGPP1 Placenta SAMHD1 CD33 Myeloid SGTA Heart SAMSN1 Leukemia chronic Myelogenous SH2D1A Leukemia lymphoblastic MOLT K591 31 SAR1B Small intestine SH2D3C Thymus SARDEH Liver SH3BGR Skeletal Muscle SATB2 Fetal brain SH3TC1 Thymus SBNO1 Appendix SH3TC2 Placenta SCAMP3 Atrioventricular Node SHANK1 CD56 NK Cells SCAND2 Superior Cervical Ganglion SHC2 Pancreatic Islet SCAPER Fetal brain SHC3 Prefrontal Cortex SCARA3 Uterus Corpus SHH Superior Cervical Ganglion SCGB1D2 Skin SHOX2 Thalamus SCGB2A2 Skin SHQ1 Leukemia lymphoblastic MOLT SCGN Pancreatic Islet 32 SCIN Trigeminal Ganglion SHROOM2 pineal night SCLY Liver S Small intestine SCN3A Fetal brain SIAH1 Placenta SCN4A Skeletal Muscle SIAH2 CD71 Early Erythroid SCNSA Heart SIGLEC1 Lymph node SCN8A Superior Cervical Ganglion SIGLECS Superior Cervical Ganglion SCNN1B Lung SIGLEC6 Placenta SCNN1D Superior Cervical Ganglion SILV retina SCO2 CD33 Myeloid SIM1 Superior Cervical Ganglion SCRIB Heart SIM2 Skeletal Muscle SCRT1 Superior Cervical Ganglion SIRPB1 Whole Blood SCT BDCA4 Dentritic Cells SIRT1 CD19 Bcells neg. sel. SCUBE3 Superior Cervical Ganglion SIRT4 Superior Cervical Ganglion SCYL2 BDCA4 Dentritic Cells SIRTS Heart SCYL3 BDCA4 Dentritic Cells SIRT7 CD33 Myeloid SDCCAG3 Lymphoma burkitts Raji SIX1 Pituitary SDF2 Whole Blood SIX2 Pituitary US 2016/02897.62 A1 Oct. 6, 2016 36

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne

Gene Tissue Gene Tissue

SIX3 retina Colorectal adenocarcinoma SIX5 Superior Cervical Ganglion Prostate SKAP1 CD8 T cells X721 B lymphoblasts SLAMF1 X721 B lymphoblasts Liver SLC10A1 Liver Liver SLC10A2 Small intestine Fetal liver SLC12A1 Kidney CD105 Endothelial SLC12A2 Trachea Prefrontal Cortex SLC12A6 Testis Intersitial Prostate SLC12A9 CD14 Monocytes Kidney SLC13A2 Kidney Testis SLC13A3 Kidney retina SLC13A4 pineal night Adrenal Cortex SLC14A1 CD71 Early Erythroid CD71 Early Erythroid SLC15A1 Superior Cervical Ganglion Heart SLC16A10 Superior Cervical Ganglion Small intestine SLC16A4 Placenta Kidney SLC16A8 retina Superior Cervical Ganglion SLC17A1 Superior Cervical Ganglion Thyroid SLC17A3 Kidney Placenta SLC17A4 Superior Cervical Ganglion Skeletal Muscle SLC17AS Placenta Kidney SLC18A1 Skeletal Muscle Fetal lung SLC18A2 Uterus Bronchial Epithelial Cells SLC19A2 Adrenal Cortex Trigeminal Ganglion SLC19A3 Placenta pineal night SLC1AS Colorectal adenocarcinoma Superior Cervical Ganglion SLC1A6 Cerebellum CD71 Early Erythroid SLC1A7 Trigeminal Ganglion Placenta SLC20A2 Thyroid Superior Cervical Ganglion SLC22A1 Liver Prefrontal Cortex SLC22A13 Superior Cervical Ganglion CD33 Myeloid SLC22A18AS Lymphoma burkitts Raji Liver SLC22A2 Kidney Ciliary Ganglion SLC22A3 Prostate X721 B lymphoblasts SLC22A4 CD71 Early Erythroid CD33 Myeloid SLC22A6 Kidney Leukemia lymphoblastic MOLT SLC22A7 Liver 34 SLC22A8 Kidney LIT3 Adipocyte SLC24A1 retina LITRK3 Subthalamic Nucleus SLC24A2 Ciliary Ganglion LMO1 Superior Cervical Ganglion SLC24A6 Adrenal gland LURP1 Tongue SLC25A10 Liver SMC2 Leukemia lymphoblastic MOLT SLC25A11 Heart 35 SLC25A17 X721 B lymphoblasts SMCHD1 Whole Blood SLC25A21 Leukemia chronic Myelogenous SMCP Testis Intersitial KS93 SMG6 SLC25A28 BDCA4 Dentritic Cells SMR3A SLC25A31 Testis SMR3B SLC25A37 Bone marrow SMURF1 SLC25A38 CD71 Early Erythroid SMYD3 SLC25A4 Skeletal Muscle SLC25A42 Superior Cervical Ganglion SMYD5 SLC26A2 Colon SNAPC1 Testis Intersitial SLC26A3 Colon SNAPC4 Testis SLC26A4 Thyroid SNCAIP SLC26A6 Leukemia lymphoblastic MOLT SNIP1 33 SNX1 Fetal Thyroid SLC27A2 Kidney SNX16 Trigeminal Ganglion SLC27AS Liver SNX19 Superior Cervical Ganglion SLC27A6 Olfactory Bulb SNX2 CD19 Bcells neg. sel. SLC28A3 Pons SNX24 Spinal Cord SLC29A1 CD71 Early Erythroid SOAT1 Adrenal gland SLC2A11 pineal day SOAT2 Fetal liver SLC2A14 Colorectal adenocarcinoma SOCS1 Lymphoma burkitts Raji SLC2A2 Fetal liver SOCS2 Leukemia chronic Myelogenous SLC2A6 CD14 Monocytes KS95 SLC30A10 Fetal liver SOCS6 Colon SLC31A1 CD105 Endothelial SOD3 Thyroid SLC33A1 BDCA4 Dentritic Cells SOHLH2 X721 B lymphoblasts SLC34A1 Kidney SOS1 Adipocyte SLC35A3 Colon SOSTDC1 retina US 2016/02897.62 A1 Oct. 6, 2016 37

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue SOX1 Superior Cervical Ganglion STYK1 Trigeminal Ganglion SOX11 Fetal brain SUCLG1 Kidney SOX12 Fetal brain SULT1A3 Ciliary Ganglion SOX18 Superior Cervical Ganglion SULT2A1 Adrenal gland SOX5 Testis Intersitial SULT2B1 Tongue SP140 CD19 Bcells neg. sel. SUOX Liver SPA17 Testis Intersitial SUPT3H Testis Seminiferous Tubule SPAG1 Appendix SUPV3L1 Leukemia promyelocytic HL70 SPAG11B Testis Leydig Cell SURF2 Testis Germ Cell SPAG6 Testis SUV39H1 CD71 Early Erythroid SPANXB1 Testis Seminiferous Tubule SVEP1 Placenta SPAST Fetal brain SYCP1 Testis Intersitial SPATA2 Testis SYCP2 Testis Leydig Cell SPATASL1 Leukemia promyelocytic HL69 SYDE1 Placenta SPATA6 Testis Intersitial SYF2 Skeletal Muscle SPC25 Leukemia chronic Myelogenous SYN3 Skeletal Muscle K596 SYNGR4 estis SPCS3 BDCA4 Dentritic Cells SYNPO2L Heart SPDEF Prostate SYP pineal night SPEG Uterus SYT12 Trigeminal Ganglion SPIB Lymphoma burkitts Raji X721 B lymphoblasts SPINT3 Testis Germ Cell TAAR3 Superior Cervical Ganglion SPO11 Trigeminal Ganglion TAARS Superior Cervical Ganglion SPPL2B CDS4 TAC1 Caudate nucleus SPR Liver TAC3 Placenta SPRED2 Thymus TACR3 Pancreas SRDSA1 Fetal brain TAF4 Leukemia lymphoblastic MOLT SRDSA2 Liver 37 SREBF1 Adrenal Cortex TAFSL CD71 Early Erythroid SRF CD71 Early Erythroid TAF7L Testis Germ Cell SRR Superior Cervical Ganglion TAL1 CD71 Early Erythroid SSH3 Bronchial Epithelial Cells TANC2 Superior Cervical Ganglion SSR3 Prostate TAP2 CD56 NK Cells SSSCA1 CD105 Endothelial TARBP1 CD55 SST Pancreatic Islet TAS2R1 Globus Pallidus SSTR1 Atrioventricular Node TAS2R14 Superior Cervical Ganglion SSTR4 Ciliary Ganglion TAS2R7 Superior Cervical Ganglion SSTRS Subthalamic Nucleus TAS2R9 Subthalamic Nucleus SSX2 Superior Cervical Ganglion TASP1 Superior Cervical Ganglion SSX5 Liver TAT Liver ST3GAL1 CD8 T cells TBC1D12 Spinal Cord ST6GALNAC4 CD71 Early Erythroid TBC1D13 Kidney ST7 X721 B lymphoblasts TBC1D16 Adipocyte ST7L Ovary TBC1D22A CD19 Bcells neg. sel. ST8SIA2 Superior Cervical Ganglion TBC1D22B CD71 Early Erythroid ST8SIA4 Whole Bloo TBC1D29 Dorsal Root Ganglion ST8SIAS Adrenal gland TBC1D8B Pituitary STAB2 Lymph node TBCA Superior Cervical Ganglion STAC Ciliary Ganglion TBCD Leukemia lymphoblastic MOLT STAG3L4 Appendix 38 STAM2 Testis Intersitial TBCE CDS6 STARD13 X721 B lymphoblasts TBL1Y Superior Cervical Ganglion STARDS Uterus Corpus TBL2 Testis STAT2 BDCA4 Dentritic Cells TBP Testis Intersitial STATSA Leukemia lymphoblastic MOLT TBRG4 Lymphoma burkitts Raji 36 TBX10 Skeletal Muscle STBD1 Pancreatic Islet TBX19 Pituitary STC Smooth Muscle TBX21 CD56 NK Cells STEAP1 Prostate TBX3 Adrenal gland STEAP3 CD71 Early Erythroid TBX4 Temporal Lobe STIL Trigeminal Ganglion TBX5 Superior Cervical Ganglion STK11 CD71 Early Erythroid TCHH Placenta STK16 X721 B lymphoblasts TCL1B Atrioventricular Node STMN3 Amygdala TCL6 Cardiac Myocytes STON1 Uterus TCN2 Kidney STRN Ciliary Ganglion TCP11 Testis Intersitial STRN3 Uterus TDP1 Testis Intersitial STS Placenta TEAD3 Placenta STX17 Superior Cervical Ganglion TEAD4 Colorectal adenocarcinoma STX2 CD8 T cells TEC Liver STX3 Whole Blood TECTA Superior Cervical Ganglion STX6 Whole Blood TESK2 CD19 Bcells neg. sel. US 2016/02897.62 A1 Oct. 6, 2016 38

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue

TEX13B Skeletal Muscle TMEMTO Skeletal Muscle TEX14 Testis Seminiferous Tubule TMLHE Superior Cervical Ganglion TEX15 Testis Seminiferous Tubule TMPRSS2 Prostate TEX28 Testis TMPRSS3 Small intestine TFAP2A Placenta TMPRSS5 Olfactory Bulb TFAP2B Skeletal Muscle TMPRSS6 Liver TFAP2C Placenta TNFAIP6 Smooth Muscle TFB1M Leukemia promyelocytic HL71 TNFRSF10C Whole Blood TFB2M Leukemia chronic Myelogenous TNFRSF1OD Cardiac Myocytes K597 TNFRSF11A Appendix TFCP2L1 Salivary gland TNFRSF11B Thyroid TFDP1 CD71 Early Erythroid TNFRSF14 Lymphoma burkitts Raji TFDP3 Superior Cervical Ganglion TNFRSF25 CD4 T cells TFEC CD33 Myeloid TNFRSF4 Lymph node TFF3 Pancreas TNFRSF8 X721 B lymphoblasts TFR2 Liver TNFRSF9 Ciliary Ganglion TGDS Pancreas THFSF11 Lymph node TGFB11 Uterus TNFSF14 X721 B lymphoblasts TGM2 Placenta THFSF8 CD4 T cells TGM3 Tongue TNFSF9 Leukemia promyelocytic HL72 TGM4 Prostate TNIP2 Lymphoma burkitts Raji TGM5 Liver TNN pineal night TGS1 CD105 Endothelial TNNI1 Skeletal Muscle THADA CD4 T cells TNNI3 Heart THAP10 Whole Brain TNNI3K Superior Cervical Ganglion THAP3 Lymphoma burkitts Raji TNNT1 Skeletal Muscle THEBS3 Testis TNNT2 Heart THG1L, CD105 Endothelial TNP1 Testis Intersitial THNSL2 Liver TNP2 Testis Intersitial THRB Superior Cervical Ganglion TNR Skeletal Muscle THSD1 Pancreas TNS4 Colorectal adenocarcinoma THSD4 Superior Cervical Ganglion TNXA Adrenal Cortex THSD7A Placenta TNXB Adrenal Cortex THUMPD2 Leukemia lymphoblastic MOLT TOM1L1 Bronchial Epithelial Cells 39 TOMM22 X721 B lymphoblasts TIMM22 Whole Brain TOP3B Leukemia chronic Myelogenous TIMMSO Skin K598 TIMM8B Heart TOX3 Colon TIMP2 Placenta TOX4 Superior Cervical Ganglion TLE3 Whole Blood TP53BP1 pineal night TLE6 CD71 Early Erythroid TP73 Skeletal Muscle TLL1 Superior Cervical Ganglion TPPP3 Placenta TLL2 Heart TPSAB1 Lung TLR3 Testis Intersitial TRABD BDCA4 Dentritic Cells TLR7 BDCA4 Dentritic Cells TRADD CD4 T cells TLX3 Cardiac Myocytes TRAF1 X721 B lymphoblasts TM4SF2O Small intestine TRAF2 Lymphoma burkitts Raji TM4SFS Liver TRAF3IP2 Bronchial Epithelial Cells TM7SF2 Adrenal gland TRAF6 Leukemia chronic Myelogenous TMCC1 Pancreas KS99 TMCC2 CD71 Early Erythroid TRAK1 CD19 Bcells neg. sel. TMCO3 Smooth Muscle TRAK2 CD71 Early Erythroid TMEM104 Skin TRDMT1 Superior Cervical Ganglion TMEM11 CD71 Early Erythroid TRDN Tongue TMEM110 Liver TRE Kidney TMEM121 CD14 Monocytes TREML2 Placenta TMEM13S Adipocyte TRH Hypothalamus TMEM140 Whole Blood TRIM10 CD71 Early Erythroid TMEM149 BDCA4 Dentritic Cells TRIM13 Testis Intersitial TMEM159 Heart TRIM15 Pancreas TMEM186 X721 B lymphoblasts TRIM17 Ciliary Ganglion TMEM187 Lung TRIM21 Whole Blood TMEM19 Superior Cervical Ganglion TRIM23 Amygdala TMEM2 Placenta TRIM2S Placenta TMEM209 Superior Cervical Ganglion TRIM29 Tongue TMEM39A Pituitary TRIM31 Skeletal Muscle TMEM4SA Skin TRIM32 Cerebellum TMEM48 X721 B lymphoblasts TRIM36 Amygdala TMEM53 Liver TRIM4.6 CD71 Early Erythroid TMEM57 CD71 Early Erythroid TRIM68 CD56 NK Cells TMEM62 Cingulate Cortex TRIO Fetal brain TMEM63A CD4 T cells TRIP10 Skeletal Muscle US 2016/02897.62 A1 Oct. 6, 2016 39

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult TranscriptOne Gene Tissue Gene Tissue TRIP11 Testis Intersitial UQCRFS1 Superior Cervical Ganglion TRMT12 CD105 Endothelial URM1 Heart TRMU CD8 T cells UROD CD71 Early Erythroid TRPA1 Superior Cervical Ganglion USH2A pineal day TRPC5 Superior Cervical Ganglion USP10 Whole Blood TRPM1 retina USP12 CD71 Early Erythroid TRPM2 BDCA4 Dentritic Cells USP13 Skeletal Muscle TRPM8 Skeletal Muscle USP18 X721 B lymphoblasts TRPV4 Superior Cervical Ganglion USP19 Trigeminal Ganglion TRRAP Leukemia lymphoblastic MOLT USP2 Testis Germ Cell 40 USP27X Superior Cervical Ganglion TSGA10 Testis Intersitial USP29 Superior Cervical Ganglion TSHB Pituitary USP32 Testis Intersitial TSKS Testis Intersitial USP6NL Atrioventricular Node TSPAN1 Trachea UTRN Testis Intersitial TSPAN15 Olfactory Bulb UTS2 CD56 NK Cells TSPAN32 CD8 T cells JTY Ciliary Ganglion TSPANS CD71 Early Erythroid UVRAG CD19 Bcells neg. sel. TSPAN9 Heart WAC14 Skeletal Muscle TSSC4 Heart WARS X721 B lymphoblasts TSTA3 CD105 Endothelial WASH1 pineal night TTC15 Testis Intersitial WASH2 Fetal brain TTC22 Superior Cervical Ganglion WASP Whole Blood TTC23 Lymphoma burkitts Raji WAV2 CD19 Bcells neg. sel. TTC27 Leukemia chronic Myelogenous WAV3 Placenta K6OO WAX2 Superior Cervical Ganglion TTC28 Fetal brain VCPIP1 CD33 Myeloid TTC9 Fetal brain VENTX CD33 Myeloid TTLL12 CD105 Endothelial VGF Pancreatic Islet TTLL4 Testis WGLL1 Placenta TTLL5 Testis Intersitial WGLL3 Placenta TTPA Atrioventricular Node WILL Colon TTTY9A Superior Cervical Ganglion VIPR1 Lung TUBA4B Lymphoma burkitts Raji VLDLR Pancreatic Islet TUBA8 Superior Cervical Ganglion VNN2 Whole Blood TUBAL3 Small intestine VNN3 CD33 Myeloid TUBB4Q Skeletal Muscle VPRBP Testis Intersitial TUBD1 Superior Cervical Ganglion VPREB1 CD57 TUFM Superior Cervical Ganglion VPS13B CD8 T cells TUFT1 Skin VPS33B Testis TWSG1 Smooth Muscle VPS45 pineal day TYR retina VPS53 Skin TYRP1 retina WSIG4 Lung U2AF1 Superior Cervical Ganglion VSX1 Superior Cervical Ganglion UAP1L1 X721 B lymphoblasts VTCN1 Trachea UBA Superior Cervical Ganglion WARS2 X721 B lymphoblasts UBE2D1 Whole Blood WASL Colon UBE2D4 Liver WDR18 X721 B lymphoblasts UBFD1 CD105 Endothelial WDR25 Lung UBQLN3 Testis Intersitial WDR43 Lymphoma burkitts Daudi UCN pineal night WDR55 CD4 T cells UCP Fetal Thyroid WDRSB Superior Cervical Ganglion UFC Trigeminal Ganglion WDR60 Testis Intersitial UGT2A1 Atrioventricular Node WDR67 CD56 NK Cells UGT2B15 Liver WDR70 BDCA4 Dentritic Cells UGT2B17 Appendix WDR78 Testis Seminiferous Tubule ULBP1 Cerebellum WDR8 Lymphoma burkitts Raji ULBP2 Bronchial Epithelial Cells WDR91 X721 B lymphoblasts UMOD Kidney WHSC1L1 Ovary UNC119 Lymphoma burkitts Raji WHSC2 Lymphoma burkitts Raji UNC5C Superior Cervical Ganglion WIPI1 CD71 Early Erythroid UNC93A Fetal liver WISP1 Uterus Corpus UNC93B1 BDCA4 Dentritic Cells WISP3 Superior Cervical Ganglion UPB1 Liver WNT11 Uterus Corpus UPF1 Prostate WNT2B retina UPK1A Prostate WNT3 Superior Cervical Ganglion UPK1B Trachea WNT4 Pancreatic Islet UPK3A Prostate WNTSA Colorectal adenocarcinoma UPK3B Lung WNTSB Prostate UPP1 Bronchial Epithelial Cells WNT6 Colorectal adenocarcinoma UQCC Lymphoma burkitts Raji WNTTA Bronchial Epithelial Cells UQCRC1 Heart WNTTB Skeletal Muscle US 2016/02897.62 A1 Oct. 6, 2016 40

TABLE 1-continued TABLE 1-continued List of Tissue-Specific Genes Determined by List of Tissue-Specific Genes Determined by Deconvolution of Adult TranscriptOne Deconvolution of Adult Transcriptone Gene Tissue Gene Tissue

WNT8B Skin ZNF224 CD8 T cells WRNIP1 Trigeminal Ganglion ZNF226 pineal night WT1 Uterus ZNF23 CD71 Early Erythroid WWC3 CD19 Bcells neg. sel. ZNF235 Superior Cervical Ganglion XCL1 CD56 NK Cells ZNF239 Testis Seminiferous Tubule XK CD71 Early Erythroid ZNF250 Skin XPNPEP2 Kidney ZNF253 Superior Cervical Ganglion XPO4 pineal day ZNF259 Testis XPO6 Whole Blood ZNF264 CD4 T cells XPO7 CD71 Early Erythroid ZNF267 Whole Blood XRCC3 Colorectal adenocarcinoma ZNF273 Skin YAF2 Skeletal Muscle ZNF274 CD19 Bcells neg. sel. YBX2 Testis ZNF28OB Testis Intersitial YIF1A Liver ZNF286A Superior Cervical Ganglion YIPF6 CD71 Early Erythroid ZNF304 Superior Cervical Ganglion YWHAQ Skeletal Muscle ZNF318 X721 B lymphoblasts YY2 Uterus Corpus ZNF323 Superior Cervical Ganglion ZAK Dorsal Root Ganglion ZNF324 Thymus ZAP70 CD56 NK Cells ZNF331 Adrenal Cortex ZBED4 Dorsal Root Ganglion ZNF34 Fetal Thyroid ZBTB10 Superior Cervical Ganglion ZNF343 Ciliary Ganglion ZBTB17 Lymphoma burkitts Raji ZNF345 Superior Cervical Ganglion ZBTB24 Skin ZNF362 Atrioventricular Node ZBTB3 Superior Cervical Ganglion ZNF385D Superior Cervical Ganglion ZBTB33 Superior Cervical Ganglion ZNF391 Testis Intersitial ZBTB40 CD4 T cells ZNF415 Testis Intersitial ZBTB43 CD33 Myeloid ZNF430 CD8 T cells ZBTBS CD19 Bcells neg. sel. ZNF434 Globus Pallidus ZBTB6 Superior Cervical Ganglion ZNF443 Trigeminal Ganglion ZBTB7B Ovary ZNF446 Superior Cervical Ganglion ZC3H12A Smooth Muscle ZNF45 CD60 ZC3H14 Testis Intersitial ZNF451 CD71 Early Erythroid ZCCHC2 Salivary gland ZNF460 Trigeminal Ganglion ZCWPW1 Testis Germ Cell ZNF467 Whole Blood ZDHHC13 X721 B lymphoblasts ZNF468 CD56 NK Cells ZDHHC14 Lymphoma burkitts Raji ZNF471 Skeletal Muscle ZDHHC18 Whole Blood ZNF484 Atrioventricular Node ZDHHC3 Testis Intersitial ZNF507 Fetal liver ZER CD71 Early Erythroid ZNF510 Appendix ZFHX4 Smooth Muscle ZNF516 Uterus ZFP2 Superior Cervical Ganglion ZNF550 Temporal Lobe ZFP30 Ciliary Ganglion ZNF556 Ciliary Ganglion ZFPM2 Cerebellum ZNF557 Ciliary Ganglion ZFR2 Trigeminal Ganglion ZNF587 Superior Cervical Ganglion ZFYVE9 Cingulate Cortex ZNF589 Superior Cervical Ganglion ZG16 Colon ZNF606 Fetal brain ZGPAT Liver ZNF672 CD71 Early Erythroid ZIC3 Cerebellum ZNF696 Trigeminal Ganglion ZKSCAN 1 Pancreas ZNF Skeletal Muscle ZKSCANS CD19 Bcells neg. sel. ZNF711 Testis Germ Cell ZMATS Liver ZNF717 Appendix ZMYM1 Superior Cervical Ganglion ZNF74 Dorsal Root Ganglion ZMYND10 Testis ZNF770 Skeletal Muscle ZNF124 Uterus Corpus ZNF771 Atrioventricular Node ZNF132 Skin ZNF78OA Superior Cervical Ganglion ZNF133 CD58 ZNF79 Leukemia lymphoblastic MOLT ZNF135 CD59 41 ZNF136 CD8 T cells ZNF8 Superior Cervical Ganglion ZNF14 Trigeminal Ganglion ZNF8O Trigeminal Ganglion ZNF140 Superior Cervical Ganglion ZNF157 Trigeminal Ganglion ZNF804A Lymphoma burkitts Daudi ZNF167 Appendix ZNF821 Testis Intersitial ZNF175 Leukemia chronic Myelogenous ZNHIT2 Testis K6O1 ZP2 Cerebellum ZNF177 Testis Seminiferous Tubule ZPBP Testis Intersitial ZNF185 Tongue ZSCAN16 CD19 Bcells neg. sel. ZNF193 Ovary ZSCAN2 Skeletal Muscle ZNF2OO Whole Blood ZSWIM1 Ciliary Ganglion ZNF208 Liver ZW10 Superior Cervical Ganglion ZNF214 Superior Cervical Ganglion ZXDB Ciliary Ganglion ZNF215 Dorsal Root Ganglion ZZZ3 CD61 ZNF223 Ciliary Ganglion US 2016/02897.62 A1 Oct. 6, 2016

(0173 The following table (Table 2) lists panel of 94 TABLE 2-continued tissue-specific genes in Example 4 that were verified with qPCR. Panel of 94 tissue-specific genes in Example a that were verified with qPCR. TABLE 2 Gene Tissue Panel of 94 tissue-specific genes in Example CYP1A2 Liver 4 that were verified with qPCR. CYP2C8 Liver CYP2D6 Liver Gene Tissue CYP2E1 Liver ITIEH4 Liver PMCH Amygdala HRG Liver HAPLN1 Bronchial epithelial cells FTCD Liver PRDM12 Cardiac myocytes IGFALS Liver ARPP-21 Caudate nucleus RDH16 Liver GPR88 Caudate nucleus SDS Liver PDE10A Caudate nucleus SLC22A1 Liver CBLN1 Cerebellum TBX3 Liver CDH22 Cerebellum SLC27AS Liver DGKG Cerebellum KCNK12 Olfactory bulb CDR1 Cerebellum MPZ Olfactory bulb FAT2 Cerebellum C21 ORF7 Whole blood GABRA6 Cerebellum FFAR2 Whole blood KCNJ12 Cerebellum FCGR3A Whole blood KIAAO802 Cerebellum EMR2 Whole blood NEUROD1 Cerebellum FAMSB Whole blood NRXN3 Cerebellum FCGR3B Whole blood PPFIA4 Cerebellum FPR2 Whole blood ZIC1 Cerebellum MLE3 Whole blood SAA4 Cervix PF4 Whole blood SERPINC1 Cervix PF4V1 Whole blood CALML4 Colon PPBP Whole blood DSC2 Colon TLR1 Whole blood ACTC1 Heart TNFRSF10C Whole blood NKX2-5 Heart ZDHHC18 Whole blood CASQ2 Heart CKMT2 Heart HRC Heart HSPB3 Heart Example 5 HSPB7 Heart ITGB1BP3 Heart MYL3 Heart Using Tissue-Specific Cell-Free RNA to Assess MYL7 Heart Alzheimer's MYOZ2 Heart NPPB Heart CSRP3 Heart 0.174. The analysis of fetal brain-specific transcripts, in MYBPC3 Heart Examples 2 and 3, leads to the assessment of brain-specific PGAM2 Heart transcripts for neurological disorder. Particularly, the qPCR TNNI3 Heart brain panel detected fetal brain-specific transcripts in mater SLC4A3 Heart TNNT2 Heart nal blood, whereas the whole transcriptome deconvolution SYNPO2L Heart analysis in our nonpregnant adult samples, in Examples 2 AVP Liver and 3, revealed that the hypothalamus is a significant con ACTB Housekeeping tributor to the whole cell-free transcriptome. Since the GAPDH Housekeeping MAB21L2 Housekeeping hypothalamus is bounded by specialized brain regions that HCRT Hypothalamus lack an effective blood-brain barrier, cell-free DNA in the OXT Hypothalamus blood was examined in the current study to measure neu BBOX1 Kidney ronal death. qPCR was used to measure the expression levels AQP2 Kidney KCN1 Kidney of selected brain transcripts in the plasma of both Alzheim FMO1 Kidney er's patients and age-matched normal controls. These mea NAT8 Kidney Surements were made for a cohort of 16 patients: 6 diag XPNPEP2 Kidney nosed as Alzheimer's and 10 normal subjects. FIG. 17 PDZK1P1 Kidney PTH1R Kidney depicts the measurements of PSD3 and APP cell-free RNA SLC12A1 Kidney transcript levels in plasma. As provided in FIG. 17, the SLC13A3 Kidney levels of PSD3 and APP cell-free RNA transcripts are SLC22A6 Kidney elevated in Alzheimer's (AD) patients as compared to nor SLC22A8 Kidney SLC7A9 Kidney mal patients and can be used to characterize the different UMOD Kidney patient populations. SLC17A3 Kidney (0175. The APP transcript encodes for the precursor mol AKR1C4 Liver C8G Liver ecule whose proteolysis generates B amyloid, which is the APOF Liver primary component of amyloid plaques found in the brain of AQP9 Liver Alzheimer's disease patients. Preliminary measurements of CYP2A6 Liver the plasma APP transcript corroborate the known biology behind progression of Alzheimer's disease and showed a US 2016/02897.62 A1 Oct. 6, 2016 42 significant increase in patients with Alzheimer's disease transcripts, two of them are elevated in Alzheimer patients: compared with normal Subjects, suggesting that plasma APP APP and PSD3. Another 7 transcripts were below normal mRNA levels may be a good marker for diagnosing levels at a significant level: MOBP: MAG; SLC2A1: Alzheimer's disease. Similarly, the gene PSD3, which is TCF7L2: CDH22: CNTF and PAQR6. FIG. 28 shows the highly expressed in the nervous system and localized to the boxplot of the different levels of APP transcripts across the postsynaptic density based on sequence similarities, shows different patient groups and the corrected P-value indicating an increase in the plasma of patients with Alzheimer's the significance of the transcripts in distinguishing Alzheim disease. By plotting the ACt values of APP against PSD3. er's. FIG. 29 illustrates the alternate trends where the levels AD patients were clustered away from the normal patients. of the measure brain transcript MOBP were lower in the In light of the cluster variants, cell-free RNA may serve as Alzheimer population as compared to the normal popula a blood-based diagnostic test for Alzheimer's disease and tion. MOBP is a myelin-associated oligodendrocyte protein other neurodegenerative disorders. coding gene which is known to play a role in compacting or stabilizing the myelin sheath. Example 6 0.184 Methods of Normalization for Comparison Across Sample Batches Assessing Neurological Disorders with 0185. Considerable heterogeneity may be present Brain-Specific Transcripts between different batches of samples collected. A normal ization scheme may be deployed to allow for valid com Overview parison across samples from different batches, and Such 0176 This study expands upon Example 5 and was scheme was deployed in the present study. For each gene designed to determine brain-specific tissue transcripts that assay within each batch, the deltact values of each sample correlate with the various stages of Alzheimer's disease. The was used to generate a Z-score by using the mean and study examined a cohort of patients from different centers standard deviation inferred from the population of normal that have previously collected Alzheimer's patents and age samples within the batch. This z-score is then used to as the controlled references. There were a total of 254 plasma normalized expression value for downstream analysis, as samples available from the different centers. Cell free RNA discussed below. was extracted from each of the samples. The extracted cell 0186 Classification Results. Using Combined Z-Scores free RNA from each of these samples were then assayed (See FIG. 30) using high throughput qPCR on the Biomark Fluidigm 0187. To incorporate the different measurements across system. Each of the samples was assayed using a panel of 48 the brain specific genes into a single distinct measure for genes of which 43 genes are known to be brain specific. The classification of the patients, the method of combined resulting measurements from each of the samples were put Z-scores was employed. The combined Z-scores measure the through a very stringent quality control process. The first deviation of the brain specific transcripts from the mean step includes measuring the distribution of housekeeping expected value of the normal controls and combine these genes: ACTB and GAPDH. By observing the levels of deviations into a single measure for distinguishing Alzheim housekeeping genes across the sample from different er's. To analyze the utility of Such a measure in distinguish batches, batches with significantly lower levels of house ing Alzheimer's, a receiver-operator analysis was performed keeping genes were removed from downstream analysis. and achieved an area under curve (AUC) of 0.79 (See FIG. The next step in quality control is by the number of failed 30). gene assays in each of the patient sample. Sample where 8 or more assays failed to amplify are removed. This results in INCORPORATION BY REFERENCE 125 good quality samples: 0188 References and citations to other documents, such (0177. I. 27 Alzheimers Patients (AD) as patents, patent applications, patent publications, journals, (0178 II. 52 Mild Cognitive Impairment Patients (MCI) books, papers, web contents, have been made throughout (0179 III. 46 Normal patients. this disclosure. All Such documents are hereby incorporated 0180 IV. herein by reference in their entirety for all purposes. 0181 Analysis and Results 0182 An unsupervised method of Principle Component EQUIVALENTS Analysis (PCA) was applied to the qPCR gene expression of the 43 brain-specific transcripts in order to differentiate 0189 The invention may be embodied in other specific between Alzheimer's and Normal patients. FIG. 27 illus forms without departing from the spirit or essential charac trates the PCA space reflecting the unsupervised clustering teristics thereof. The foregoing embodiments are therefore of the patients using the gene expression data from the to be considered in all respects illustrative rather than 48-gene assay. As shown in FIG. 27 two different popula limiting on the invention described herein. Scope of the tions are formed which correspond to the neurological invention is thus indicated by the appended claims rather disease state of the patients. than by the foregoing description, and all changes which 0183. Additionally, a Wilcox non-parametric statistical come within the meaning and range of equivalency of the test was performed between Alzheimer's and normal claims are therefore intended to be embraced therein. patients for each of the brain specific transcripts. The What is claimed is: resulting p-values were bonferroni corrected for multiple 1. A method for characterizing a neurological disorder of testing. Brain specific transcripts whose p-values that are a patient, the method comprising: significant at the 0.05 levels were cataloged as transcripts obtaining RNA from a blood sample of a patient sus that high distinguishing power between alzheimer's and pected of having a neurological disorder, normal patients. Amongst all the assayed brain specific converting the RNA obtained in the sample into cDNA; US 2016/02897.62 A1 Oct. 6, 2016 43

determining a level of the sample cDNA that corresponds 14. The method of claim 11, further comprising monitor to RNA originating from brain tissue; ing progression of the neurological disorder by repeating the comparing the level of the sample cDNA to a reference detecting step through the indicating step at a future time. level of circulating RNA originating from brain tissue; 15. The method of claim 11, wherein the stages are and selected from the group consisting of no cognitive impair indicating a neurological disorder based upon a statisti ment, mild cognitive impairment, moderate cognitive cally-significant deviation between the level of sample impairment, and severe cognitive impairment. cDNA and the reference level. 16. The method of claim 11, wherein the neurological 2. The method of claim 1, further comprising the step of disorder is Alzheimer's disease. determining a stage of the indicated neurological disorder. 17. The method of claim 11, wherein the blood sample is 3. The method of claim 2, wherein the stage is selected plasma or serum. from the group consisting of no cognitive impairment, mild 18. The method of claim 11, wherein the determining step cognitive impairment, moderate cognitive impairment, and is performed via a sequencing technique, a microarray severe cognitive impairment. technique, or both. 4. The method of claim 1, wherein the neurological 19. A method of characterizing a neurological disorder, disorder is Alzheimer's disease. comprising the steps of 5. The method of claim 1, wherein the level of the sample obtaining RNA from a blood sample of a patient sus cDNA and the reference level correspond to an amount of pected of having a neurological disorder, circulating RNA released from brain tissue selected from the determining a level of RNA present in the sample that is group consisting of spinal cord, pituitary, hypothalamus, specific to brain tissue; thalamus, corpus callosum, cerebrum, cerebral cortex, and comparing the sample level of RNA to a reference level combinations thereof. of RNA specific to brain tissue: 6. The method of claim 1, further comprising the step of determining whether a difference exists between the monitoring progression of the neurological disorder by sample level and the reference level; and repeating the steps of obtaining through comparing. indicating a neurological disorder if a difference is deter 7. The method of claim 1, wherein the reference level mined. comprises a level of cDNA corresponding to a patient 20. The method of claim 19, further comprising the step population without cognitive impairment. of determining a stage of the indicated neurological disorder. 21. The method of claim 19, wherein the stage is selected 8. The method of claim 1, wherein the reference level from the group consisting of no cognitive impairment, mild comprises a level of cDNA corresponding to a patient cognitive impairment, moderate cognitive impairment, and population diagnosed with a neurological disorder. severe cognitive impairment. 9. The method of claim 1, wherein the blood sample is 22. The method of claim 19, wherein the neurological plasma or serum. disorder is Alzheimer's disease. 10. The method of claim 1, wherein the determining step 23. The method of claim 19, wherein the level of sample is performed via a sequencing technique, a microarray RNA and the reference level of RNA correspond to an technique, or both. amount of circulating RNA released from brain tissue 11. A method for characterizing a neurological disorder of selected from the group consisting of pituitary, hypothala a patient, the method comprising: mus, thalamus, corpus callosum, cerebrum, cerebral cortex, obtaining RNA from a blood sample of a patient sus and combinations thereof. pected of having a neurological disorder, 24. The method of claim 19, further comprising the step converting the RNA obtained in the sample into cDNA; of monitoring progression of the neurological disorder by determining a level of the sample cDNA that corresponds repeating the steps of obtaining through comparing at a to RNA originating from brain tissue; future time. comparing the level of the sample cDNA to a set of 25. The method of claim 19, wherein the reference level variables correlated with a neurological disorder, of RNA corresponds to a patient population diagnosed with wherein the variables comprise reference levels of a neurological disorder. cDNA that correspond to circulating RNA originating 26. The method of claim 19, wherein the blood sample is from brain tissue and to one or more stages of the plasma or serum. neurological disorder, and 27. The method of claim 19, wherein the determining step indicating a stage of a neurological disorder of the patient is performed via a sequencing technique, a microarray based upon a statistically significant deviation between technique, or both. the level of the sample cDNA and the set of variables 28. A method for identifying one or more biomarkers correlated with a neurological disorder. associated with a neurological disorder, the method com 12. The method of claim 11, wherein the reference levels prising of cDNA further correspond to patient populations of certain obtaining RNA present in a blood sample of a patient ageS. Suspected of having a neurological disorder; 13. The method of claim 11, wherein the level of the converting the RNA in the sample into cDNA; sample cDNA and reference levels of cDNA correspond to determining levels of the sample cDNA that corresponds an amount of circulating RNA released from brain tissue that to RNA originating from brain tissue; is selected from the group consisting of pituitary, hypothala comparing the levels of the sample cDNA to one or more mus, thalamus, corpus callosum, cerebrum, cerebral cortex, reference levels that correspond to circulating RNA and combinations thereof. originating from brain tissue; and US 2016/02897.62 A1 Oct. 6, 2016 44

identifying as a biomarker for a neurological disorder a level of sample cDNA that is statistically different from a reference level.

k k k k k