The Choices Cells Make: Single- Cell Techniques for Stem-Cell Research

BD Stem Cell Resource Markers of Self-Renewal and Differentiation

23-9953-02 Self Renewing Pathway

TGF-β/Activin Sperm Liver Thymus Pancreas Thyroid Lung Intestine Wnt BMP-SMad Erk-MAPK Skin and Hair JAK-STAT Wnt TGF-β/Activin BMP4 bFGF LIF LIFR gp130 Egg

p p p Jak β-catenin Smads 175 MEK/ERK

Stat3 C-myc Sox2 Oct4 Nanog Smads 2/3 Stem-cell gene expression and differentiation Neural Crest Primordial Germ Cell Stem Cell Neural Stem Cell Self Renewal Ectoderm Embryonic Stem Cell or Induced Pluripotent Endoderm Neurons, Glia Stem Cell Smooth Muscle Cardiac Tissue Chondrocytes Glial Restricted Progenitor Osteocytes

Neuronal Type 1 Astrocyte Oligodendrocyte Restricted Progenitor Progenitor Mesoderm Mesenchymal The ability for humans and other organisms to Stem Cell Type 2 Astrocyte Endothelium Oligodendrocyte Neuron Heart Skeletal Muscle Kidney Smooth Muscle Myoblast Adipocyte (Fat) Chondrocyte Fibroblast Osteoblast (Bone) Hemangioblast (Cartilage)

Myotube (Muscle) Plasmacytoid Hematopoietic Stem Cell Dendritic Cell develop specialized cells that compose diverse Committed Lymphoid Progenitor

Monoblast Megakaryoblast Proerythroblast Pre-NK Cell Thymocyte Pre B Cell Myeloblast

Erythroblast Progranulocyte Monocyte Myeloid Dendrtitic Cell Megakaryocyte NK Lymphoblast T-Lymphoblast B-Lymphoblast tissues such as muscle, liver, brain, and others, Normoblast Neutrophilic Eosinophilic Basophilic Myelocyte Myelocyte Myelocyte NK Cell T Cell B Cell Macrophage Thrombocytes Reticulocyte Neutrophilic Eosinophilic Basophilic (Platelets) Band Cell Band Cell Band Cell Figure 1: Markers of self-renewal and differentiation Plasma Cell

Erythrocyte Neutrophil Eosinophil Basophil relies on their development from a small pool of (Red Blood Cell)

White colored markers are available from BD Biosciences 1 Ectoderm Markers Embryonic Stem Cells Mesoderm Markers Endoderm Markers Stem Cell Relevant Signaling Astrocytes Neuronal Markers Embryonic Stem Cell Markers Early Mesoderm Markers Heart - Cardiogenesis Markers Hematopoietic Stem Cell Markers Mesenchymal Stem Cell Differentiation Markers Mesenchymal Stem Cell Markers (Fat) Definitive Endoderm Markers Erk-MAPK Signaling TGF Beta Signaling A2B5 GFAP α-Synuclein MAP2B Alkaline Phosphatase Nanog (Human) BMP2 Cryptic α-Actinin CD309 (VEGFR2, KDR) Negative Markers Chondrocyte (Cartilage) Markers Osteoblast (Bone) Markers Negative Markers B-Catenin HNF-1β (TCF-2) basic FGF Mek1 BMPR-II Id3 CD44 Ran-2 α-Synuclein (pY125) MASH1 CD9 Nanog (Mouse) BMP4 MIXL1 Annexin VI CD325 (N-Cadherin) CD184 (CXCR4) HNF-3β (FoxA2) C-Raf Mek1 (pS298) CD119 (IFN-γ receptor α-chain) p16 INK4 FGFR3 S100B β-Tubulin (Tuj1) mGluR1 CD15 (SSEA-1, Lewis X) Nucleostemin Brachyury NF-YA Cardiac specific myosin Connexin-43 CD2 CD24 Aggrecan Matrillin-4 Alkaline Phosphatase IGFBP-3 CD11b (Integrin αM chain) CD79a CD325 (N-Cadherin) Sox17 ERK Mek1/2 (pS222) c-Myc p107 CD24 mGluR1α CD24 Oct3/4 CD31 (PECAM1) Nodal Cardiac Troponin I Desmin CD3 CD33 CD44 Osteonectin Collagen 1 Osteocalcin CD14 CD117 (SCF R, c-kit) GATA4 ERK1 Mek2 DP-1 p300 Early Ectoderm Markers CD56 (NCAM) Nestin CD29 (Integrin β1) Oct3/4A CD34 (Mucosialin, gp 105-120) Sca-1 (Ly6A/E) Cardiac Troponin T Flk-1 (KDR, VEGF-R2, Ly-73) CD4 CD36 CD151 Sox9 Fibronectin RUNX2 CD19 HLA-DR ERK1/2 (pT202/pY204) p38 MAPK (PT180/pY182) E2F-1 PP2A Catalytic α Nestin TP63 CD81 (TAPA-1) NeuroD1 CD30 Podocalyxin CD325 (N-Cadherin) Snail Caveolin-2 GATA4 CD7 CD38 Collagen II CD45 (Leukocyte Common Hepatic Endoderm Markers ERK2 Ras E2F-2 ROCK-I undifferentiated cells, called stem cells. Despite Notch1 CD90 (Thy-1) Neurofilament NF-M CD49f (Integrin α6) Podocalyxin Associated Caveolin-3 GATA6 CD8 CD45 (Leukocyte Common Antigen, Ly-5) ERK3 E2F-3 ROCK-II Myoblast (Muscle) Markers α-Fetoprotein CD90 (Thy-1) StudyingCD90.1 (Thy-1.1) Neuron-restricted progenitors CD133 (AC133, Prominin-1) Keratin SulfateheterogeneityEndothelial Cell Markers CD13 HAND1 CD10 Antigen, Ly-5) Id1 Smad2/3 Positive Markers Albumin CD326 (EpCAM) Neural Crest Cell Markers CD90.2 (Thy-1.2) Neuropilin-2 CD194 (CCR4) PRC2 (Suz12) CD31 (PECAM1) HAND2 CD11b CD45R (B220) Acetylcholinesterase Lactate Dehydrogenase (LDH) Jak-STAT Signaling Id2 SP1 CD14 CD151 ASGPR 1 GATA4 CD112 Nicastrin CD324 (E-Cadherin) Rex-1 CD34 (Mucosialin, gp 105-120) Islet 1 CD13 CD48 Actin Myf-5 CD9 CD73 (Ecto-5’-nucleotidase) CD49d (Integrin α4) MASH1 CD15 (SSEA-1, Lewis X) CD152 (CTLA-4) CD26 HNF-1α CD130 (gp130) STAT4 CD271 (p75, NGFR/NTR) p150 Glued CD326 (EpCAM) Ronin CD66 M-Cadherin CD14 CD56 (NCAM) B-Enolase (ENO-3) MyoD CD10 CD90 (Thy-1) Wnt Signaling CD57 (HNK-1) Neurogenin 3 CD18 (Integrin β2 chain, CR3/CR4) CD157 CD29 (Integrin β1) HNF-1β (TCF-2) STAT1 (C-TERMINUS) STAT4 (pY693) CD325 (N-Cadherin) Pax-5 CD338 (ABCG2) SFRP2 CD66c MLC2a CD15 (SSEA-1, Lewis X) CD97 CD146 (MCAM, MUC18) Pax3 CD13 CD105 (Endoglin) CD271 (p75, NGFR/NTR) Notch1 CD29 (Integrin β1) CD166 (ALCAM) CD44H (Pgp-1, H-CAM) LDLR STAT1 (pY701) STAT5 (MGF) 14-3-3 GSK-3β CD15s (Sialyl Lewis x) CD114 (G-CSF Receptor) CD325 (N-Cadherin) Pax7 CD29 (Integrin β1) CD106 (VCAM-1) ChAT P-glycoprotein c-Myc Smad2/3 CD31 (PECAM1) CD184 (CXCR4) CD106 (VCAM-1) MLC2v STAT3 STAT5 (MGF) (PY694) α-Catenin GSK-3β (pY216) CD16 CD138 (Syndecan-1) Desmin PK-M CD44 CD146 (MCAM, MUC18) CD49f (Integrin α6) Tat-SF1 Neural Stem Cell Markers Contactin PSD-95 Cripto Sox2 CD34 (Mucosialin, gp 105-120) CD192 (CCR2) CD117 (SCF R, c-kit) Myogenin STAT3 (pS727) STAT6 β-Catenin HDAC3 Doublecortin Serotonin Receptor 5-HT 2AR DPPA4 SSEA-3 CD140a (PDGFR ) Myosin Heavy Chain CD19 CD235a CD49d (Integrin α4) CD166 (ALCAM) A2B5 CD349 (Frizzled-9) CD36 CD201 (EPCR) α STAT3 (pY705) STAT6 (pY641) δ-Catenin Jagged1 GABA B Receptor Serotonin Receptor 5-HT 2BR DPPA5 (ESG1) SSEA-4 CD144 (VE-cadherin) NF-YA CD20 GATA3 CD49e (Integrin α5) Fibronectin Intestinal Stem Cell Markers BMI-1 Ki-67 CD45 (Leukocyte Common CD202b (TIE2) Mesenchymal Stem Cell Markers (Bone Marrow) γ-Catenin Lgr5 Gad65 SMN FGF-4 SSEA-5 CD166 (ALCAM) Nkx2.5 CD54 (ICAM-1) HLA I BMI-1 Lgr5 CD15 (SSEA-1, Lewis X) MSI1 (Musashi-1) Antigen, Ly-5) CD202b (TIE2) (pY992) Negative Markers Positive Markers Notch Signaling Akt Neurogenin 3 GAP-43 (Neuromodulin) Synapsin I FoxD3 STAT3 CD172a (SIRPA) P-glycoprotein (MDR) Positive Markers CD55 Vimentin EphB2 Sox9 CD15s (Sialyl Lewis x) MSl2 (Musashi-2) CD45R (B220) CD202b (TIE2) (pY1102) CBP (CREB-Binding Protein) Notch1 CaM Kinase II Nicastrin GluR δ 2 Synaptophysin Frizzled-5 STAT3 (pS727) CD202b (TIE2) SSEA-1 CD11a (Integrin αL chain) CD34 (Mucosialin, gp 105-120) CD10 CD117 (SCF R, c-kit) CD59 CD24 Nanog (Mouse) CD49d (Integrin α4) CD209 BMI-1 CDw93 (C1qRp) Jagged1 Notch2 Casein Kinase I ε Notch1 GluR2 Synaptotagmin GCNF (NR6A1) STAT3 (pY705) CD202b (TIE2) (pY992) Vimentin CD11b (Integrin αM chain) CD45 (Leukocyte Common CD13 CD120a (TNF Receptor Type I) Pancreatic Endoderm Markers CD29 (Integrin β1) Nestin CD49e (Integrin α5) CD209a (CIRE, DC-SIGN) CaM Kinase IV CDw338 (ABCG2) Jagged2 Notch3 Casein Kinase II α PP2A Catalytic α GluR5/6/7 Syntaxin GCTM-2 STAT3-interacting protein 1 CD202b (TIE2) (pY1102) CD14 Antigen, Ly-5) CD14 CD120b (TNF Receptor Type II) Skeletal Muscle Markers CD49f (Integrin 6) Neurogenin 3 CD49f (Integrin α6) Noggin CD49f (Integrin α6) CD213a1 CD31 (PECAM1) DAZL α Neurogenin 3 Notch4 Casein Kinase II β SNAI2 (Slug) Glutamine Synthetase Tau GDF-3 TRA-1-60 Antigen CD19 CD79a CD15 (SSEA-1, Lewis X) CD146 α-Actinin Myf5 CD142 Neuropilin-2 CD54 (ICAM-1) Notch1 CD54 (ICAM-1) CD213a2 CD34 (Mucosialin, gp 105-120) Flk-1 (KDR, VEGF-R2, Ly-73) Nicastrin CBP (CREB-Binding Protein) Wnt16 HNF-3 (FoxA2) TrkB Hsp27 TRA-1-81 Antigen Hemangioblast Markers CD81 (TAPA-1) Pax-6 β CD56 (NCAM) CD249 CD40f (Integrin α6 chain) FOXO1 CD31 (PECAM1) HLA-DR CD29 (Integrin β1) CD166 Actin MyoD Chromogranin A Nkx2.2 Islet-1 Tubby Lefty-A UTF-1 CD95 (Fas/APO-1) Prominin-2 CD62E (E-Selectin) CD252 (OX-40 Ligand) ACE CD324 (E-Cadherin) CD44 G-CSF CD44 (Pgp-1, H-CAM) CD166 (ALCAM) B-Enolase (ENO-3) Myogenin Gad65 Nkx6.1 Jagged1 Tyrosine Hydroxylase Lin-28 Zic3 inCD133 (AC133, Prominin-1) PSA-NCAM stem-cellCD62L (L-Selectin) biologyCD253 (TRAIL) Brachury EphB4 CD59 Gfi-1 CD49d (Integrin α4) CD271 (p75, NGFR/NTR) CD146 (MCAM, MUC18) Myosin heavy chain Glucagon Pancreatic polypeptide MAP2 (a+b) Vimentin CD49e (Integrin 5) Flk-1 (KDR, VEGF-R2, Ly-73) CD146 (MCAM, MUC18) Sox1 Induced Pluripotency Markers CD62P (P-Selectin) CD262 (TRAIL-R2, DR5) CD31 (PECAM1) Flk-1 (KDR, VEGF-R2, Ly-73) CD90 (Thy-1) HOXB4 α CD325 (N-Cadherin) Myosin light chain Glut2 Pax-6 their essential role in development and CD102 CD322 CD34 (Mucosialin, gp 105-120) GATA1 CD90.1 (Thy-1.1) Jak2 CD51 (Integrin αV) GD2 Desmin Titin Insulin PDX-1 CD184 (CXCR4) Sox2 Oligodendrocyte Markers CD271 (p75, NGFR/NTR) Vimentin c-Myc Nanog CD105 (Endoglin) CD325 (N-Cadherin) CD133 (AC133, Prominin-1) LMO-2 CD105 (Endoglin) Lmo2 CD54 (ICAM-1) Ly-6A/E (Sca-1) M-Cadherin Islet-1 PTF1A CD338 (ABCG2) CD44 O1 KLF4 Oct3/4 CD106 (VCAM-1) CDw93 (C1qRp) CD144 (VE-cadherin) Podocalyxin CD106 (VCAM-1) Ly-6A/E (Sca-1) CD56 (NCAM) Nucleostemin Islet-2 Somatostatin CD44H (Pgp-1, H-CAM) O4 Lin-28 Sox2 CD109 CDw145 CD202b (TIE2) Runx1 (CBFA2) CD117 (SCF R, c-kit) MRP1 CD71 (Transferrin Receptor) SSEA-4 Smooth Muscle Markers NeuroD1 Synaptophysin CD73 (Ecto-5’-nucleotidase) STRO-1 CD140a (PDGFR α) Olig1 CD112 Flk-1 (KDR, VEGF-R2, Ly-73) CD202b (TIE2) (pY992) Tal (SCL) CD133 (AC133, Prominin-1) NF-YA Calmodulin Smooth muscle myosin CD90 (Thy-1) TAZ Primitive Gut Tube Markers Galactocerebroside C Olig2 Primordial Germ Cell Markers CD116 (GM-CSF Receptor) HIF-1α CD202b (TIE2) (pY1102) CD164 Notch1 Desmin Vimentin MBP RIP CD117 (SCF R, c-kit) IP-10 CD184 (CXCR4) P-glycoprotein CD105 (Endoglin) Vimentin CDX2 HNF-1β (TCF-2) Blimp-1 Nanos CD120a (TNF Receptor Type I) Ly-6A/E (Sca-1) CD201 (EPCR) Runx1 (CBFA2) CD106 (VCAM-1) CD15 (SSEA-1, Lewis X) Oct3/4 Skin Precursor Cell Markers CD120b (TNF Receptor Type II) Nestin CD202b (TIE2) Sox7 CD117 (SCF R, c-kit) Piwi/Hiwi CD121a (IL-1 Receptor, Type I/p80) STAT3 CD202b (TIE2) (pY992) Tal (SCL) CRABP2 Sca-1 (Ly6A/E) DAZL Steel CD124 (IL-4 Receptor ) STAT3 (pS727) CD202b (TIE2) (pY1102) WASP (Wiskott-Aldrich Fibronectin Vimentin FABP9 Stella (Dppa3) α CD141 (Thrombomodulin) STAT3 (pY705) CD325 (N-Cadherin) Syndrome Protein) Nestin Fragilis VASA (DDX4) CD144 (VE-cadherin) STAT3-interacting protein 1 Visual System Trophoblast Markers CD146 (MCAM, MUC18) Vimentin CD147 (Neurothelin) UnderstandingCD68 Pax-6 CDX2 Sox7 the patterns of stem- CD325 (N-Cadherin) Recoverin Cytokeratin 7 Trop-2

For Research Use Only. Not for use in diagnostic or therapeutic procedures. homeostasis, stem-cell populations are typically BD, BD Logo and all other trademarks are property of Becton, Dickinson and Company. © 2015 BD bdbiosciences.com/stemcell cell gene expression is one of the central small in number and are intermixed with goals for stem-cell biologists and one differentiated and intermediate cell types within that holds great therapeutic potential. The ability to understand how a pool a specific tissue, creating a heterogeneous of stem cells becomes a liver, heart, or distribution of cells.2 In addition to this spatial brain cell could allow researchers to grow organs and tissues in a laboratory, heterogeneity within a tissue, there is also a eliminating the necessity for organ temporal heterogeneity that occurs as stem cells transplants.2 turn on and off gene expression patterns as a Studying these patterns has proven difficult as tools for analyzing gene function of time and enter the beginning, expression in rare cells have been limited intermediate, and final stages of differentiation.2 to bulk analysis of spatially and temporally heterogeneous cell effectors of cellular function are proteins.5 Understanding how populations, like those found in tissues.2 Bulk analysis of gene they are expressed, modified, and change the phenotype of a expression averages data across an entire population of cells, cell is critical to stem-cell research.5 Therefore, being able to making it impossible to deduce what rare stem cells, that can quantify protein expression in individual cells on a large scale, make up less than 0.01% of a cell population, are doing at the sensitively and quickly, is important for advancing research.5 transcriptomic or proteomic level.2,3 Flow cytometry Over the past decade, single-cell “omics” techniques have advanced, making it possible to collect single-cell data at the One of the tried-and-true methods for characterizing, analyzing, genomic, transcriptomic, proteomic, and epigenomic level.2 and isolating single stem cells based on protein markers is More recently, the use of single-cell sequencing in stem-cell flow cytometry. Historically, researchers have discovered biology has allowed the analysis of multiple types of molecules characteristic cell-surface biomarkers and intracellular from a single cell, in parallel.4 These techniques have incredible biomarkers to distinguish certain types of stem cells and their potential for uncovering previously unappreciated cell diversity, differentiation into specialized cell types. Flow cytometry uses tracing cell lineages, identifying new cell subpopulations, and antibodies conjugated to fluorophores for detecting cells that uncovering novel regulatory mechanisms.4 are expressing these specific, distinguishing biomarkers. Here, the value that single-cell techniques have already provided This method is high-throughput and can be used to isolate ™ to advancing the stem-cell field will be reviewed and the live cells through fluorescence-activated cell sorting, or FACS . potential that multi-omics techniques hold for furthering Based on the detection of a fluorescent signal, cells can be stem-cell research is discussed. deposited in bulk or as single cells, making this method amenable to the use of downstream single-cell Single-cell sequencing in stem-cell biology sequencing techniques. ™ Over the years, specific subpopulations of stem cells in Use of FACS in stem-cell research is widespread and with good various stages of differentiation have been identified. These reason: this method is preferable for the isolation of very low subpopulations express distinct genes that can be used as frequency cell types as a large number of cells can be sorted biomarkers to denote various stages along the path of in a relatively short period of time. With the discovery of new differentiation. Stem-cell researchers have used these markers, subpopulations and stem-cell markers, the need for high- through detection at the RNA or protein level, to analyze and/or parameter analyses has become critical for stem cell research. isolate these specific cell sub-types in bulk. However, even these Addressing this need, flow cytometry has evolved in recent “pure” cell types can be heterogeneous due to their existence in years allowing researchers to utilize and analyze as many as 5,6 discrete microenvironments within a tissue, cell-cycle differences, 30 parameters, simultaneously. and stochasticity. In addition, there may be more distinct Mass cytometry populations of cells within these subpopulations that are difficult to detect without the use of single-cell sequencing technologies.2 Another method of protein profiling, called mass cytometry, has been used to analyze stem cells and identify unique cell Single-cell RNA-seq and stem cells subtypes.7 This method has been used to analyze muscle stem In the past decade, single-cell RNA-seq techniques have been cells and hematopoietic stem cells, which give rise to all the cells 3,7,8 used to study many types of cells, including embryonic, in the blood. The principle of this method is similar to flow developing, and adult tissue-specific stem cells. For instance, cytometry, though instead of using fluorescent dye-conjugated several studies with stem cells from different tissues of origin antibodies, they are conjugated to heavy metal ions, allowing have revealed developmental intermediates between what were antibody binding to be detected with the precision of mass 8 thought to be two distinct cell types, allowing researchers to spectrometry. trace stem-cell lineages and reconstruct a more accurate One of the major benefits to this method is that it can pathway of how stem cells differentiate. In addition, single-cell potentially allow the multiplexing of over 100 antibodies.5,8 RNA-seq studies have also revealed the role that cell cycle plays In practice, however, between 30 and 45 parameters can be in tissue-resident stem cells making the choice to differentiate measured in a single mass-cytometry experiment without into specialized cells or not.2 having to do any additional data correction.8 Single cells that are studied using mass cytometry are atomized and ionized, Single-cell proteomics and stem cells making the recovery of live cells for downstream analysis, like While single-cell RNA-seq has been powerful for understanding single-cell RNA-seq, an impossibility.8 the transcriptome of stem cells, the most direct and primary The power of single-cell multi-omics Combining multiple single-cell “omics” techniques holds a lot of potential for researchers in the stem-cell field. The ability to simultaneously analyze genomics, epigenomics, transcriptomics, and/or proteomics, in a single cell, with one workflow could provide an incredible amount of data to uncover novel biological processes, gene-regulatory Figure 2: General workflow for single-cell profiling of both transcripts and mechanisms, and cell subpopulations. Ultimately, this could proteins using antibody-DNA conjugates. Cell sample is stained with antibody-DNA lead to the ability to gain a deep understanding into the oligonucleotide conjugates. After cells are stained using protocols similar to those used in flow cytometry, single-cell isolation, cDNA library preparation (labeled link between cell genotype and phenotype and to track RNA-derived library), and extension of antibody-derived DNA oligonucleotides individual cell lineages with exquisite resolution, a major can be carried out with a previously validated single-cell RNA-seq workflows. goal of developmental and stem-cell biology.2,4 Biology.9 The DNA oligonucleotides conjugated to antibodies While integration of all of these “omics” workflows may seem contain a unique sequence for each distinct antibody and a decades away, methods for simultaneous coupling of high- common sequence that can be used for amplification by PCR parameter protein profiling with unbiased, high-throughput (often called a PCR handle).9 After cells are labeled with sequencing methods like single-cell RNA-seq, have recently antibody and single cells are isolated, cDNA library preparation been developed. These methods overcome many of the and extension of antibody-derived DNA can be done limitations to high-parameter protein profiling discussed above simultaneously using validated single-cell RNA-seq workflows.9,11,12 by using antibodies conjugated to DNA oligonucleotides.5 CITE-seq was able to identify immune cell subpopulations and Protein profiling of cell-surface markers using antibody- quantify differences in cell surface protein expression.9 DNA conjugates Furthermore, using 13 monoclonal antibodies against cell Using antibody-DNA conjugates, Adam Abate’s group at the surface antigens typically used for cell profiling, CITE-seq was University of California, San Francisco, developed a method called able to accurately differentiate specific cell subpopulations with Abseq, that could theoretically allow for the profiling of greater greater resolution than single-cell RNA-seq analysis alone.8 5 than 100 proteins. The use of antibody-DNA conjugates allows REAP-seq uses a nearly identical approach to that of CITE-seq, researchers to use a unique “barcoding” sequence for each unique though REAP-seq allowed for the quantification of 82 distinct 5 antibody. Thus, the use of a 10-nucleotide sequence can create cell surface proteins and >20,000 genes in a single workflow.10 greater than 1,000,000 unique sequences, allowing for extremely This demonstrated the power of REAP-seq to measure high high-parameter protein profiling, on a scale that is far greater number of parameters in a way that can easily integrate with 5 than the number of total human proteins. single-cell RNA-seq workflows. The use of DNA tags can also help researchers detect low- The major advances of CITE-seq and REAP-seq for stem abundance surface proteins which may be difficult to detect cell biology are their capability to measure a high number using other techniques. DNA tags can be amplified by PCR and of parameters that can integrate with single-cell RNA-seq thus, hypothetically, it would be possible to detect a single copy protocols. Additionally, these methods enable simultaneous of a distinct surface protein. and high-parameter examination of RNA and protein in a In a proof-of-principle experiment, Abate’s group showed that this single workflow. method can accurately profile immortalized T cells and B cells in ™ a single-cell sample using antibody-DNA conjugates that bind to two Synergies of FACS and single- cell-surface biomarkers.5 While not explicitly tested or validated, cell multi-omics the use of high-throughput sequencing as a method for quantifying The successful profiling of mRNA and protein in a single protein expression gives this new method the potential to integrate workflow holds extreme promise for answering many with high-throughput single-cell RNA-seq workflows.5 unanswered questions in stem-cell biology and could help CITE-seq and REAP-seq researchers understand new mechanisms of regulating gene expression and identify subtle, yet important, steps in stem cell Months following Abate’s publication, two methodologies for differentiation.5 In combination with FACS™, which can be used integrating both mRNA and protein expression profiling into a to enrich for low-abundance stem cell populations before single workflow were published.9,10 The first, a method called single-cell sequencing, these multi-omics techniques can CITE-seq, was developed by laboratories at the New York facilitate the identification of rare cell types and hold the Genome Center and NYU Center for Genomics and Systems potential to gain insight into novel gene regulatory mechanisms The BD AbSeq assay combines genomics and immunology involved in stem-cell differentiation. Conversely, the discovery of technologies to facilitate even deeper single-cell analysis new biomarkers that come with single cell multi-omic analysis insights. Utilizing the BD AbSeq technology with mRNA analysis shows promise in streamlining flow cytometry panel design. can help elucidate complex biological systems with distinct Future breakthroughs in multi-omics may allow additional clustering of different cell subsets. The assay enables high- characterization of the genome and epigenome in single stem parameter interrogation of cell-surface protein markers in a cells.5 With the current multi-omics techniques in use, and for single panel. those in development, tracking the flow of information in BD offers a full single-cell multi-omics system solution including individual stem cells has direct applicability to identifying the a catalog of assays (mRNA-seq, protein detection, and role that stem cells play in the development of tumors, in multiplexing) for library preparation, secondary analysis, discovering how embryonic and tissue-resident stem cells can and data visualization. be used therapeutically for regenerative medicine, and in figuring out how differentiated cells can be reprogrammed Summary into pluripotent states. As additional methods for measuring As single-cell analysis becomes more widely used in basic mRNA and protein expression are developed, the barriers for research, the appreciation for the heterogeneity of cells in implementation into basic research will be lowered and these biological systems will continue to grow, leading to the potential techniques may become more widely used to understand for development of new diagnostics and treatments for a number complex, heterogeneous biological systems. of disease states. BD’s current offerings for single-cell analysis, BD’s continuing commitment to cutting- from flow cytometry to multi-omics, can help researchers advance their understanding of cell heterogeneity by offering the edge multi-omics technology right research tools for a broad range of experimental questions. BD’s commitment to making innovative and transformative research tools available to the broader, global research Disclaimer community includes a portfolio of trusted antibodies and The method developed by Adam Abate’s laboratory8 at the demonstrated success in providing platforms for single-cell University of California, San Francisco has been named Abseq ™ analysis. One of these innovations, the newly available BD but is not to be confused with BD AbSeq technology. The two AbSeq assay, utilizes antibody-oligonucleotide conjugates. methods were developed independently and have significant ™ When used in combination with the BD Rhapsody single-cell differences in overall methodology. Discussion of Dr. Abate’s work analysis platform provides researchers with an accessible, within this paper are included and discussed to provide the reader off-the-shelf, system solution that can simultaneously analyze with appropriate background, context, and understanding of RNA and proteins in thousands of individual cells and develop a current multi-omics techniques being used for single-cell profiling. more complete picture of the role genes and proteins play in biological systems.

References: 1. Stem Cell Information. National Institutes of Health website: https://stemcells.nih.gov/info/basics/1.htm. Accessed July 11, 2018. 2. Wen L, Tang F. Single-cell sequencing in stem cell biology. Genome Biol. 2016;17:71. 3. Proserpio V, Lönnberg T. Single-cell technologies are revolutionizing the approach to rare cells. Immunol Cell Biol. 2016;94:225-229. 4. Hu Y, An Q, Sheu K, et al. Single cell multi-omics technology: methodology and application. Front Cell Dev Biol. 2018;6:28. 5. Shahi P, Kim SC, Haliburton JR, Garner ZJ, Abate AR. Abseq: Ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci Rep. 2017;7:4447. 6. Bendall SC, Simonds EF, Qiu P, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science. 2011;332(6030):687-696. 7. Propiglia E, Samusik N, Tri Van Ho A, et al. High-resolution myogenic lineage mapping by single-cell mass cytometry. Nat Cell Biol. 2017;19(5):558-567. 8. Spitzer MH, Nolan GP. Mass cytometry: single cells, many features. Cell. 2016;165(4):780-791. 9. Stoeckius M, Hafemeister C, Stephenson W, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14(9):865-868. 10. Peterson VM, Zhang KX, Kumar N, et al. Multiplexed quantification of proteins and transcripts in single cells. Nature Biotechnol. 2017; 35(10):936-939. 11. Fan HC, Fu GK, Fodor SPA. Combinatorial labeling of single cells for gene expression cytometry. Science. 2015;347(6222): 1258367. 12. Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202-1214.

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