Tomo-Seq Identifies SOX9 As a Key Regulator of Cardiac Fibrosis During Ischemic Injury

ORIGINAL RESEARCH ARTICLE

Tomo-Seq Identifies SOX9 as a Key Regulator of Cardiac Fibrosis During Ischemic Injury

Editorial, see p 1410 Grégory P.A. Lacraz, MSc, PhD*

Downloaded from BACKGROUND: Cardiac ischemic injury induces a pathological remodeling Jan Philipp Junker, MSc, response, which can ultimately lead to heart failure. Detailed mechanistic PhD* insights into molecular signaling pathways relevant for different aspects of Monika M. Gladka, MSc, cardiac remodeling will support the identification of novel therapeutic targets. PhD Bas Molenaar, MSc http://circ.ahajournals.org/ METHODS: Although genome-wide transcriptome analysis on diseased tissues Koen T. Scholman, MSc has greatly advanced our understanding of the regulatory networks that drive Marta Vigil-Garcia, MSc pathological changes in the heart, this approach has been disadvantaged by Danielle Versteeg, BS the fact that the signals are derived from tissue homogenates. Here we used Hesther de Ruiter, BS Marit W. Vermunt, MSc, tomo-seq to obtain a genome-wide gene expression signature with high spatial PhD resolution spanning from the infarcted area to the remote to identify new Menno P. Creyghton, MSc, regulators of cardiac remodeling. Cardiac tissue samples from patients by guest on February 6, 2018 PhD suffering from ischemic heart disease were used to validate our findings. Manon M.H. Huibers, MSc, PhD RESULTS: Tracing transcriptional differences with a high spatial resolution Nicolaas de Jonge, MD across the infarcted heart enabled us to identify gene clusters that share a Alexander van Oude- comparable expression profile. The spatial distribution patterns indicated a naarden, MSc, PhD separation of expressional changes for genes involved in specific aspects of Eva van Rooij, MSc, PhD cardiac remodeling, such as fibrosis, cardiomyocyte hypertrophy, and calcium handling (Col1a2, Nppa, and Serca2). Subsequent correlation analysis allowed for the identification of novel factors that share a comparable transcriptional regulation pattern across the infarcted tissue. The strong correlation between the expression levels of these known marker genes and the expression of *Drs. Lacraz and Junker the coregulated genes could be confirmed in human ischemic cardiac tissue contributed equally. samples. Follow-up analysis identified SOX9 as common transcriptional Correspondence to: Eva van regulator of a large portion of the fibrosis-related genes that become Rooij, MSc, PhD, Hubrecht activated under conditions of ischemic injury. Lineage-tracing experiments Institute, KNAW Department of indicated that the majority of COL1-positive fibroblasts stem from a pool of Cardiology, University Medical SOX9-expressing cells, and in vivo loss of Sox9 blunted the cardiac fibrotic Center Utrecht, Uppsalalaan 8, 3584CT Utrecht, The Netherlands. response on ischemic injury. The colocalization between SOX9 and COL1 E-mail [email protected] could also be confirmed in patients suffering from ischemic heart disease. Sources of Funding, see page 1408 CONCLUSIONS: Based on the exact local expression cues, tomo-seq can Key Words: fibrosis ◼ myocardial serve to reveal novel genes and key transcription factors involved in specific ischemia ◼ SOX9 transcription aspects of cardiac remodeling. Using tomo-seq, we were able to unveil the ◼ ventricular remodeling unknown relevance of SOX9 as a key regulator of cardiac fibrosis, pointing © 2017 American Heart to SOX9 as a potential therapeutic target for cardiac fibrosis. Association, Inc.

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transcriptional regulation pattern across the infarct- Clinical Perspective ed tissue. Subsequent functional annotation analy- ARTICLE sis indicated that these genes could be linked to the What Is New? known gene function of their reference gene. The strong correlation between the expression levels of • SOX9 is a key regulator of cardiac fibrosis after the marker genes and the expression of the coregu-

ischemic injury in mice by regulating the expression of many extracellular matrix-related proteins. lated genes could be confirmed in human ischemic • SOX9 is induced in cardiac tissue from patients suf- tissue samples. fering from ischemic heart disease and colocalizes Our data show that the high spatial resolution in gene with COL1 expression. expression signatures obtained by tomo-seq reveals new • Reduced levels of SOX9 lead to less cardiac fibrosis regulators, genetic pathways, and transcription factors after ischemic injury in mice. that are active in well-defined regions of the heart and • Tomo-seq can be used to identify new players in potentially involved in specific aspects of heart disease. cardiac biology and disease. Using this technique, we identified SOX9 as a potent regulator of many of the Col1a2 coregulated genes. In What Are the Clinical Implications? vivo loss of Sox9 reduced the expression of many ex- • Our data suggest that therapeutic inhibition of tracellular matrix (ECM) genes, which coincided with

Downloaded from SOX9 in the diseased heart could lead to a reduc- a blunted cardiac fibrotic response on ischemic injury. tion in cardiac fibrosis. These data unveil the currently unknown relevance of SOX9 as a key regulator of cardiac fibrosis and underscores that tomo-seq can be used to increase our mech- schemic heart disease induces a heterogeneous re- anistic insights into cardiac remodeling to help guide the

http://circ.ahajournals.org/ modeling response across the damaged area that in- identification of novel therapeutic candidates. Ivolves fibroblast activation, cardiomyocyte hypertrophy, and changes in calcium handling, all of which are METHODS eventually detrimental for cardiac function.1,2 Fibroblast An expanded Methods section is available in the online-only activation and cardiomyocyte hypertrophy occur as a Data Supplement. All reported gene expression data are avail- direct effect of the local stress signals caused by the able in the online-only Data Supplement. loss of viable tissue in the infarcted area. Subsequently,

by guest on February 6, 2018 a decline in contractility of the surviving cardiomyocytes occurs, caused by a change in metabolism and calcium- Ischemia Reperfusion Model handling genes.3 Animal experiments were performed in accordance with the Genome-wide transcriptome analysis on extracts institutional review committee at the Hubrecht Institute. Mice were randomly subjected to either sham or ischemia reperfu- from diseased tissues has significantly enhanced our sion surgery as previously described.7 Two weeks after sur- understanding of the gene regulatory networks that gery, cardiac tissues were collected for further analysis. drive these pathological changes in the heart.4,5 How- ever, to date, these approaches have been disadvantaged by the fact that the signals are derived from Tomo-Seq tissue homogenates, which inherently causes the loss Tomo-seq experiments were performed as described elsewhere.6 In short, 2.5-mm-wide portions of cardiac mouse of spatial information and dilutes out more localized tissue spanning from the infarct toward the remote region expression signatures. Recent developments in RNA of the left ventricular anterior wall were embedded in tis- amplification strategies provide the opportunity to sue-freezing medium, frozen on dry ice, and cryosectioned use small amounts of input RNA for genome-wide into 48 slices of 80 μm thickness. We extracted RNA from sequencing. Here we use tomo-seq6 to obtain a ge- individual slices and prepared barcoded Illumina sequenc- nome-wide gene expression signature with high spa- ing libraries according to the CEL-seq protocol.8 Paired-end tial resolution spanning from the infarcted area to reads obtained by Illumina sequencing were aligned to the remote. Tracing transcriptional differences across the transcriptome using Burrows-Wheeler Aligner’s Smith- the infarcted heart enabled us to identify clusters of Waterman alignment.9 The 5’ mate of each pair was used genes with a comparable gene expression profile. In for mapping, discarding all reads that mapped equally well these individual clusters, we recognized genes with to multiple loci. The 3’ mate was used for barcode information. Reads counts were normalized to the same number well-known functions in specific aspects of heart re- of total reads per section. Tomo-seq data analysis was per- modeling, such as Col1a2 for fibrosis, Nppa for car- formed in MATLAB (MathWorks) using custom-written code. diomyocyte hypertrophy, or Serca2 for contractility. For data analysis, we used an expression cutoff of >4 reads Correlation analyses using the spatial distribution in >1 section. In differential expression analysis (Figure 1C), patterns of these marker genes allowed for the iden- we determined the boundary between remote and infarcted tification of novel factors that share a comparable zone based on the spatial partitioning detected by pairwise

Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 October 10, 2017 1397 Lacraz et al

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Figure 1. High-resolution gene expression atlas of the infarcted heart by tomo-seq. A, Schematic representation of a mouse heart after sham surgery (Sham) and 14 days after ischemia reperfusion (14 dpIR). B, Pairwise correlation for all sections across all genes showing clusters of correlated sections 14 dpIR in 1 biological replicate. (Continued )

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comparison of sections across all genes in 1 biological rep- Table. Top 30 Genes Showing the Most Similar

licate (Figure 1B). For the infarcted zone, we used sections Expression Pattern to Col1a2 ARTICLE 1 to 26, and for the remote zone we used sections 29 to SOX9 Predicted SOX9 Validated 47. The border zone (sections 27–29) was omitted to reduce Col1a2 Similar Genes Binding Site Binding Site ambiguity in assignment of sections to zones. We then com- Col1a2 × × pared the sections within and outside the infarcted zone and Sparc × assessed statistical significance with Wilcoxon rank sum test. For this analysis, each section was considered as an indepen- Fstl1 × × dent measurement. Furthermore, filtering was applied for Serping1 × genes that showed a ≥2-fold expression difference between Pdgfrl × the remote and infarcted zones. For this analysis, the mean expression levels for each gene in the two zones were cal- Tmem45a × culated. Concerning the hierarchical clustering, expression Col3a1 × × traces of the genes that passed the differential expression Sfrp1 × × filter in Figure 1C were used for analysis. The data were Lox × × standardized by Z-score normalization (along rows of data) so that the mean expression is 0 and the standard deviation Ecrg4 is 1 to remove differences in the expression level between Dkk3 × genes. Euclidean distance was used as a distance metric. Col1a1 × Downloaded from The assignment of genes to clusters I through III (Figure 1D) Itgbl1 × was determined manually considering the similarity in gene expression pattern across the ischemic heart. Fn1 × × Thbs2 Cthrc1 ×

http://circ.ahajournals.org/ SOX9 Animal Models Sox9 (Sox9fl/fl) mutant mice harboring 2 loxP sites flank- Col8a1 × ing exons 2‒310 were crossed with Rosa26-CreERT2 mice Col5a2 × CreERT2 (R26 ) to obtain an inducible Sox9 loss of function model Lum × × (Sox9fl/+;R26CreERT2). For lineage-tracing studies, mice express- Fbln2 × ing CreERT2 under the control of the Sox9 promoter11 were bred with the Rosa26-tdTomato reporter mouse (R26RTdT) Gas1 × × to obtain Sox9CreERT2;R26RTdT mice. To induce the CreERT2 Antxr1 × ×

by guest on February 6, 2018 fl/+ CreERT2 CreERT2 TdT protein, Sox9 ;R26 and Sox9 ;R26R mice were Thbs1 × × injected with Tamoxifen (corn oil/ethanol) intraperitone- Ogn × ally (2 mg at the day of surgery and 2 and 4 days after injury). Control mice (referred to as Sox9fl/+;R26CreERT2 vehicle) Col16a1 × × received an equal volume of the vehicle that was used to Vim × × deliver Tamoxifen. Cxcl16 × Timp1 × × Pathway and Transcription Factor Rnase4 × × Binding Site Enrichment Ddah1 × × To investigate whether genes share a similar biological Genes that contain a predicted/validated SOX9 binding site in their promoter function, we searched for overrepresentation in the Kyoto region are marked. Encyclopedia of Genes and Genomes (KEGG) pathway using DAVID (Database for Annotation, Visualization and Integrated Detection of overrepresented conserved transcription fac- Discovery).12 The enriched genes in the KEGG pathway are tor binding sites in the set of genes spatially coregulated to shown as P values corrected for multiple hypothesis testing Col1a2 was determined using single-site analysis in oPOSSUM using the Benjamini-Hochberg method. 3.0 (online software). The enrichment of SOX9 binding sites

Figure 1 Continued. C, Pairwise correlation for all sections across genes exhibiting ≥2-fold and statistically significant differential expression between the infarct and remote zones 14 dpIR. D, Hierarchical clustering of expression traces for all genes that were found to be differentially expressed in C. E, Spatial expression pattern of 3 reference genes Col1a2, Nppa, and Serca2 in the hearts from Sham, 1 dpIR, and 14 dpIR mice. F, Validation of the expression pattern by in situ hybridization (ISH) 14 dpIR. Four-chamber view (left) and higher magnification (right) are shown. Scale bars, 1 mm (left) and 200 μm (right). G, Spatial expression traces of 10 coregulated genes 14 dpIR. Reference genes are shown in red, and the 10 most similar genes are shown in grey. Black bold traces show other known markers involved in fibrosis, hypertrophy, and contractility (Sparc, Nppb, and Pln, respectively). H, Validation of the coexpression pattern of Col1a2/Sparc, Nppa/Nppb, and Serca2/Pln by ISH. Scale bars, 200 μm. I through K, KEGG analysis showing the enriched pathways of the top 150 genes in which Col1a2 (I), Nppa (J), and Serca2 (K) coregulated genes are involved.

Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 October 10, 2017 1399 Lacraz et al

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Figure 2. Identification of novel genes involved in remodeling and function of the ischemic heart. A, Spatial expression traces of 3 selected novel genes coregulated with Col1a2 (Fstl1), Nppa (Pmepa1), or Serca2 (Chchd2) in mice 14 dpIR in 1 biological replicate. Expression traces were normalized by Z-score transformation. B, Validation of coexpression of Co- l1a2/Fstl1, Nppa/Pmepa1, and Serca2/Chchd2 by in situ hybridization (ISH) in mice 14 dpIR. Scale bars, 200 μm. C, Validation of the coexpression pattern of COL1A2/FSTL1, NPPA/PMEPA1, and SERCA2/CHCHD2 by ISH on human ischemic heart tissue. (Continued )

1400 October 10, 2017 Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 SOX9 as Key Regulator of Cardiac Fibrosis ORIGINAL RESEARCH

was determined using the Z-score, which uses the normal to ischemic injury, we collected cardiac tissue from approximation to the binomial distribution to compare the infarcted mice exposed to 1 hour of ischemia, fol- ARTICLE occurrence rate of a transcription factor binding site in the lowed by either 1 or 14 days of ischemia reperfusion set of target genes to the expected rate estimated from the (1 and 14 dpIR) and harvested tissues from sham-op- precomputed background set. erated mice as control (Sham) (Figure 1A and Figure I in the online-only Data Supplement).7 Histological Human Heart Samples and molecular analysis confirmed a classical cardiac Approval for studies on human tissue samples was obtained from remodeling response in our model of ischemic injury, the Medical Ethics Committee of the University Medical Center as exemplified by cardiac hypertrophy (hematoxylin Utrecht, The Netherlands (12#387). Written informed consent and eosin staining), fibrosis (Sirius Red staining), and was obtained or in certain cases waived by the ethics commit- a change in expression of cardiac markers (Figure I in tee when obtaining informed consent was not possible because the online-only Data Supplement).7 Using microdissec- of patient death. In this study, we included tissue from the left tion, a small portion of the anterior wall of the left ventricular free wall of patients with end-stage heart failure sec- ventricle spanning from the infarct toward the remote ondary to ischemic heart disease. This end-stage heart failure (2.5 mm wide and 4.0 mm long) was processed into tissue was obtained at explanation of the failing heart during heart transplantation or autopsy. For each case, 3 areas of the ≈50 consecutive cryosections with a thickness of 80 infarcted heart tissue were included: (1) infarct zone, (2) border μm (Figure 1A). Subsequent RNA extraction from indi-

Downloaded from zone, and (3) remote area. For in situ hybridization (ISH) analysis, vidual slices, followed by RNA amplification, barcoding 3 patients were included. From these patients, the border zone strategies, and RNA sequencing,6 provided genome- of the infarcted hearts was used for ISH to verify tomo-seq. Gene wide data about the spatial distribution in gene ex- expression values in infarct zone, border zone, and remote area pression across the injured heart (Databases I through obtained by real-time polymerase chain reaction (PCR) were plot- III in the online-only Data Supplement). A spatial par- ted for correlation analysis. Left ventricular free wall of nonfailing http://circ.ahajournals.org/ titioning between infarcted and remote area was vis- donor hearts that could not be transplanted for technical reasons ible at 1 and 14 dpIR but not in the sham-operated were used for comparison. In these cases, neither donor patient samples when performing pairwise comparison of sec- histories nor echocardiography revealed signs of heart disease. tions across all expressed genes (Figure 1B and Figure II in the online-only Data Supplement). The spatial Statistical Analysis separation became considerably more pronounced Values are presented as mean±standard error of the mean. after filtering for genes that showed a ≥2-fold and Previous studies were used to predetermine sample size.

by guest on February 6, 2018 statistically significant differential expression between Statistical analyses between 2 groups were conducted using the the infarct and remote zone by tomo-seq (Figure 1C 2-tailed unpaired or paired Student t test or a Mann-Whitney and Figure IIB in the online-only Data Supplement). The test when the normality assumption was not met. Comparison number of regulated genes was found to be the high- among >2 groups was performed using a 2-way analysis of est 14 dpIR, which included 2357 coding genes and variance with Bonferroni’s post hoc test. Pearson’s correlation coefficients were used to calculate gene pair correlation based 134 noncoding transcripts (Figure 1D and Figure IIC on gene expression in human samples. KEGG pathways are and Databases IV through VI in the online-only Data ranked by their respective P value corrected for multiple hypoth- Supplement. KEGG analysis on these regulated genes eses testing using the Benjamini-Hochberg method. P value showed enrichment for inflammatory pathway activa- <0.05 was interpreted to denote statistical significance. Prism tion at day 1 after injury, whereas pathways involved 6 (GraphPad Software, Inc.) was used for statistical analyses. in ECM, disease, and cardiomyocyte remodeling were found to be regulated 14 dpIR (Figure IID and Tables I and II in the online-only Data Supplement). RESULTS Tomo-Seq Performed on the Infarcted Gene Expression Patterns Reveal Mouse Heart Localized Remodeling Responses To obtain precise spatial information on local gene ex- Tracing transcriptional differences across the infarcted pression changes occurring in the heart in response heart enabled us to identify clusters of genes with a

Figure 2 Continued. Scale bars, 100 μm. D‒F, Real-time polymerase chain reaction (PCR) analysis of genes that are spatially coregulated in mice 14 dpIR (see Database VII in the online-only Data Supplement) using human cardiac tissues from patients with ischemic heart disease. Control hearts and remote, border-zone, and infarct zones from ischemic hearts are plotted. Data are presented as log 2-transformed values. Pearson correlation (r) and significance of coregulated gene expression are shown (n=27–34; P<0.05 is considered significant). G, Kernel density plot of Pearson r values of the correlation in expression among the 4 corresponding COL1A2, NPPA, and SERCA2coregulated genes (coregulated; n=12) versus genes that are not coregulated (ie, genes cross-referenced from different lists) (randomized; n=24) (see Figure VI in the online-only Data Supplement). Dotted lines depict the mean of the r values of all correlated and noncorrelated genes.

Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 October 10, 2017 1401 Lacraz et al Downloaded from http://circ.ahajournals.org/ by guest on February 6, 2018

Figure 3. Identification of Sox9 as a key regulator of fibrosis-related genes. A, Spatial coexpression of Col1a2 and Sox9 determined by tomo-seq in the heart 14 dpIR in 1 biological replicate. B, Valida- tion of the coexpression of Sox9 and Col1a2 in mice determined by in situ hybridization (ISH) 14 dpIR. Scale bars, 100 μm. C, polymerase chain reaction (PCR) analysis of Sox9 expression in infarct (I) and remote (R) cardiac regions. Data are presented as fold change over Sham-operated control hearts (n=5–6; *P<0.05 versus sham). D, Validation of the coexpression of SOX9 and COL1 in the infarct/border zone in mice determined by coimmunostaining 14 dpIR. Nuclei were (Continued )

1402 October 10, 2017 Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 SOX9 as Key Regulator of Cardiac Fibrosis ORIGINAL RESEARCH

comparable differential regulation throughout the in- sion profile throughout the infarcted tissue for Col1a2, farcted heart at 14 dpIR (Figure 1D). The individual Nppa, and Serca2, we used the Euclidean distance of ARTICLE clusters contained well-known marker genes for spe- Z-score-transformed spatial expression traces6 to mea- cific aspects of heart remodeling, Collagen type I alpha sure pattern similarity between genes 14 dpIR using 2 (Col1a2) (identified in cluster I), Natriuretic peptide Col1a2, Nppa, and Serca2 as reference genes. In doing A (Nppa) (identified in cluster II), and sarco- and en- so, we obtained a gene list that showed the greatest doplasmic reticulum Ca2+-ATPase (Serca2) (located in similarity in expressional differences across the infarcted cluster III). Col1a2 is expressed in activated fibroblasts tissue with our reference genes (Table, Figure 1G, and and important for cardiac fibrosis,13 whereas Nppa is Databases VII and VIII in the online-only Data Supple- a cardiomyocyte-specific stress marker involved in ment), a majority of which could be identified within myocyte hypertrophy.14 Cardiomyocyte contractility the corresponding gene cluster identified in Figure 1D. is regulated by calcium fluxes to and from the sarco- It is interesting to note that next to Col1a2, Nppa, and plasmic reticulum and is impaired during heart disease. Serca2, these lists also contained other well-known Serca2 is a key regulator of Ca2+ transfer into the sarco- genes related to the biological function of the refer- plasmic reticulum in muscle cells that is decreased dur- ence genes. Among the Col1a2 coexpressed genes, we ing heart failure, which contributes to the decline in recognized additional genes known for their function function.3 The expression traces for Col1a2, Nppa, and in ECM deposition (eg, Sparc and Col3a1),2 whereas

Downloaded from Serca2 confirmed a gene-specific differential regulation many of the genes coregulated with Nppa encode for from the infarcted area to the remote (Figure 1E). As proteins involved in cardiomyocyte hypertrophy (Nppb expected, Col1a2 and Nppa were more abundantly ex- and Myh7). The gene list for Serca2 contained Pln and pressed in the infarcted region 14 dpIR, whereas Serca2 Ryr2, both well known for their importance in cardiac actually showed a decrease in expression toward the calcium handling and contractility (Table and Databas- 3 http://circ.ahajournals.org/ infarcted region (Figure 1E). ISH on cardiac tissue 14 es VII and VIII in the online-only Data Supplement). dpIR confirmed theCol1a2 expression to originate from This coexpression of genes could be confirmed by ISH activated fibroblasts, whereas the transcriptional peaks and indicated the signals to stem from the same cell for Nppa stemmed from the stressed, hypertrophic car- population (Figure 1H). The connection between the diomyocytes immediately flanking the fibrotic regions spatially coregulated genes and their biological func- (Figure 1F). We observed a decline in Serca2 expression tion was underscored by KEGG pathway analysis. The more toward the injured area, which is likely because 150 genes with the highest similarity in expressional

by guest on February 6, 2018 of both a loss in cardiomyocytes and a decrease in tran- changes with either Col1a2, Nppa, or Serca2 through- scriptional activation because the Nppa signals clearly out the infarcted heart at 14 dpIR indicated enrich- indicate the presence of viable myocytes in this region ment for the cellular function known to be associated (Figure 1F). The reproducibility of the obtained gene with the reference genes (Figure 1I through 1K and expression profiles was confirmed on a second set of Tables III through V and Databases VII and VIII in the samples (Figure III in the online-only Data Supplement). online-only Data Supplement). The known biological link of several of the listed genes and the functional connection based on gene ontology analysis suggest Tomo-Seq Identifies Potential New that the correlation analysis can serve to identify genes Players for Cardiac Remodeling and that are functionally related to the biological function of the reference genes. Function RNA sequencing on whole-tissue homogenates from An important advantage of tomo-seq over the infarcted area from 3 independent mice 14 dpIR genome-wide sequencing techniques on tissue homog- showed a comparable directional regulation in gene enates is that the local information on gene regulation expression, with the Col1a2- and Nppa-related genes allows for correlation analysis to identify genes with a going up after infarct, whereas the Serca2-related comparable spatial distribution in transcriptional regu- genes are going down compared with sham-operated lation.6 Because we observed a gene-specific expres- mice (Figure IV in the online-only Data Supplement).

Figure 3 Continued. counterstained with 4',6-diamidino-2-phenylindole (DAPI). White arrows point to cells expressing SOX9 in their nuclei (purple). Scale bars, 50 μm. E, Schematic representation of the lineage tracing strategy of Sox9-expressing cells. Reporter mice conditionally expressing TdTomato driven by the Sox9 promotor (Sox9CreERT2;R26RTdT) were subjected to Sham surgery or IR, injected with Tamoxifen at days 0, 2, and 4 after surgery, and analyzed after 14 days. F, Fluorescence-activated cell sorting (FACS) quantification of cardiac SOX9-TdT+ cells in the hearts from Sham and 14 dpIR mice (n=3–4; *P<0.05 versus healthy Sham control hearts). G and H, Coimmunostaining against TdTomato (TdT) and α-actinin-2 (ACTN2) (G) or COL1 (H) in the hearts of Sham-operated mice and 14 dpIR. White stars in the Merge field indicate SOX9-TdT-positive regions. Scale bars, 1 mm (4-chamber view) and 50 μm (higher magnification).

Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 October 10, 2017 1403 Lacraz et al

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p = 0.0053 (F

10-4 Re 0 10-4 10-2 100 102 104 IR COL1A2 mRNA (log 2)

D DAPI SOX9 DAPI ACTN2 Merge Downloaded from Control http://circ.ahajournals.org/ IHD patient by guest on February 6, 2018

E DAPI SOX9 DAPI COL1 Merge Control IHD patient

Figure 4. SOX9 is expressed in the fibrotic region in human cardiac tissue. A, Pearson correlation of SOX9 and COL1A2 expression determined by real-time polymerase chain reaction (PCR) analysis on cardiac patient tissue (n=30). Data are presented as log 2-transformed values. B, Real-time PCR analysis of SOX9 expression in infarct (I) and remote (R) cardiac regions of ischemic tissue samples. Data are presented as fold change over (Continued )

1404 October 10, 2017 Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 SOX9 as Key Regulator of Cardiac Fibrosis ORIGINAL RESEARCH

However, in contrast with the data obtained by tomo- not shown to be coregulated (randomized) by tomo- seq, the changes observed by RNA sequencing on tis- seq (Figure 2G). ARTICLE sue homogenates failed to provide spatial information It remains to be determined which of the newly de- on coexpression of genes and showed smaller changes fined genes are relevant for cardiac remodeling. Yet with a high interanimal variation (Figure IV in the on- the functional link between the coregulated genes and line-only Data Supplement). Because tomo-seq analysis the fact that we can validate the coregulation in both is based on the correlation in gene expression within a mice and human implies that tomo-seq allows for the single sample, the variation among animals is of lesser identification of novel genes that are potentially rel- importance. evant for specific aspects of pathological remodeling Tomo-seq analysis for lncRNAs specifically showed of the infarcted heart. localized expression changes, albeit far less pronounced and specific than for coding genes (Figure V in the online-only Data Supplement), which is likely because of Tomo-Seq Identifies SOX9 as a Key the low abundance of lncRNA transcripts. Transcription Factor for Cardiac Fibrosis The overlap in differential expression throughout the The Correlation in Expression of infarcted heart triggered us to explore whether a com- Novel Genes Linked to Cardiac mon transcription factor could be responsible for the Downloaded from synchrony in transcriptional regulation of the different Remodeling and Function Is Conserved gene clusters. Using an in silico approach, we searched in Humans for transcription factors (using oPOSSUM 3.0) that con- Although multiple well-known markers of fibrosis, hy- tain ≥1 predicted binding site(s) in the promoter regions pertrophy, and calcium handling could be identified of the top 30 Col1a2 coregulated genes (Table and http://circ.ahajournals.org/ among the genes with a similar transcriptional activa- Table VI in the online-only Data Supplement). Among tion pattern, we also found multiple ill-studied genes these factors, we identified SOX9 as a potential candi- that so far have not been linked to aspects of cardiac re- date. SOX9 is a transcription factor recognized for its modeling (Databases VII and VIII in the online-only Data role in chondrocyte differentiation.10 Supplement). To confirm the correlation in transcription- Although so far unstudied in the adult heart, previ- al activation, we randomly chose 1 candidate from each ous work showed that SOX9 has a potent function in list to explore in more detail. The Z-score-transformed fibrosis.15 Expression trace analysis for Sox9 revealed a

by guest on February 6, 2018 expression traces at 14 dpIR indicated a close correla- strong spatial correlation with Col1a2 (Figure 3A). ISH tion in expressional regulation among Col1a2 with indicated Sox9 to be expressed in the same region of Fstl1, Nppa with Pmepa1, and Serca2 with Chchd2 (Fig- the infarcted area as Col1a2 although at a much lower ure 2A), which could be confirmed by ISH on murine level (Figure 3B). Real-time PCR on tissues from infarct- cardiac tissue 14 dpIR (Figure 2B). Further confirmation ed mouse heart further confirmed Sox9 upregulation for a correlation in expression of these novel factors in the infarct zone (Figure 3C). Based on the predict- with COL1A2, NPPA, and SERCA2 was obtained by ISH ed binding site(s) in the promoter regions of multiple on ischemic human heart tissues (Figure 2C). Col1a2 coregulated genes, its proposed function in The validity of using tomo-seq to identify genes that liver fibrosis, and the overlap in transcriptional regula- are expressionally linked was strengthened by the ob- tion with Col1a2 in the infarcted heart, we decided to servation that real-time PCR analysis on cardiac tissue further pursue SOX9 in cardiac fibrosis. The induction from patients suffering from ischemic heart disease in Sox9 expression was only observed 14 dpIR and re- confirmed the correlation between the expression stricted to the infarcted area (Figure VIIA and VIIB in the levels of COL1A2, NPPA, SERCA2, and the newly iden- online-only Data Supplement). Staining for both SOX9 tified genes (Figure 2D through 2F). The correlation and COL1 indicated SOX9 protein to be detectable in was strongly reduced when we cross-referenced genes the same region as COL1 (Figure 3D). from different lists (Figure VI in the online-only Data To start exploring the fate of SOX9-expressing cells in Supplement). The density plot for the cumulative Pear- the infarcted heart, we used a lineage-tracing approach son correlation coefficients validates the shift toward using a TdTomato reporter mouse model driven by the a higher correlation among genes that belong to the promoter of the Sox9 gene (Sox9CreERT2;R26RTdT) (Fig- same list (coregulated) compared with genes that were ure 3E). Fluorescence-activated cell sorting performed

Figure 4 Continued. healthy control hearts (n=3–10; *P<0.05). C, Validation of the coexpression pattern of SOX9/COL1A2 by in situ hybridization in human ischemic cardiac tissue. Scale bars, 100 μm (left) and 50 μm (right). D and E, Coimmunos- taining against SOX9 and α-actinin-2 (ACTN2) (D) or COL1 (E) in the hearts of control individuals or patients suffering from ischemic heart disease (IHD). White arrows point to cells expressing SOX9. Scale bars, 50 μm.

Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 October 10, 2017 1405 Lacraz et al Downloaded from http://circ.ahajournals.org/ by guest on February 6, 2018

Figure 5. Loss of Sox9 in mice protects against cardiac fibrosis. A, Schematic representation of the targeting strategy for conditional Sox9 deletion. Sox9fl/+;R26CreERT2 were subjected to Sham surgery or IR, injected with Tamoxifen at days 0, 2, and 4 after surgery, and analyzed after 14 days. B, Polymerase chain reaction (PCR) genotyping for Sox9 floxed-deleted allele (Sox9fl del) and Cre transgene. Genomic (Continued )

1406 October 10, 2017 Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 SOX9 as Key Regulator of Cardiac Fibrosis ORIGINAL RESEARCH

on single cells isolated from the left ventricle indicated R26CreERT2) (Figure 5A). Tamoxifen injection at the day a significant elevation of the SOX9-TdT+ cell popula- of surgery and 2 and 4 days after injury resulted in a ARTICLE tion 14 dpIR compared with Sham (Figure 3F). Immu- disruption of the Sox9 allele as confirmed by PCR on nostaining clearly showed a colocalization between genomic DNA (Figure 5B) and further quantified by SOX9-TdT+- and COL1-expressing cells, which were real-time PCR and immunofluorescence after IR in the surrounded by cardiomyocytes (marked by α-actinin-2) infarcted region (Figure 5C through 5E). Sox9 loss of (Figure 3G and 3H). A similar overlap in expression was function was accompanied with a profound reduction observed between SOX9-TdT+ and cells labeled with 2 in fibrosis, which was quantified by the amount of Sir- other fibroblast markers: periostin and vimentin (Figure ius Red staining in the infarcted region (Figure 5F and VIIIA and VIIIB in the online-only Data Supplement, re- 5G). Periostin, a protein marking activated fibroblasts,17 spectively).16,17 These data demonstrated that SOX9 is was also reduced in the infarcted Sox9fl/+;R26CreERT2 mice predominantly active in the fibroblast population that treated with Tamoxifen, further confirming the impor- repopulates the infarcted area after injury. tance of SOX9 as a key driver for fibrosis in the ischemic To further explore whether SOX9 is involved in the heart (Figure 5H). Expression analysis for 15 randomly transcriptional activation of the Col1a2-coregulated selected Col1a2-coexpressed genes showed an increase genes, we treated fibroblasts with transforming growth in expression in response to ischemic injury. Loss of factor β1 after we exposed them to either an siRNA Sox9 resulted in a significant reduction in expression for

Downloaded from against Sox9 or a control. In addition to a strong repres- 13 out of 15 genes 14 dpIR compared with control ani- sive effect on Sox9 expression, we also observed a sig- mals (Figure 5I and Database VII in the online-only Data nificant repressive effect on 6 out of 15 potential SOX9 Supplement, highlighted in yellow). targets listed as Col1a2-coregulated genes, with a gen- High expression levels of SOX9 have previously been eral downward trend for the remaining genes (Figure described in chondrocytes, and publically available 18 http://circ.ahajournals.org/ VIIC in the online-only Data Supplement). This was also SOX9 ChIP-seq data in this cell type showed that 15 true for additional fibrosis-related genes (Figure VIID in out of the 30 genes that were coexpressed with Col1a2 the online-only Data Supplement), indicating a global in our study have ≥1 of their predicted binding sites function for SOX9 in fibroblast activation. directly occupied by SOX9 (eg, Col1a2, Fn1, Lum, and Real-time PCR analysis in tissue samples from hu- Vim; Table and Figure IX in the online-only Data Sup- man ischemic hearts showed a significant correlation plement). It is important to note that these sites were between the levels of expression of COL1A2 and SOX9 found enriched for the histone mark H3K27ac in the

by guest on February 6, 2018 (Figure 4A). In agreement with our mouse data, real- adult mouse heart (ENCODE dataset), which further time PCR indicated an expressional increase in SOX9 demonstrates that these regions are active and open expression toward the infarcted area (Figure 4B), where for transcription factors such as SOX9 in vivo. Altogeth- the majority of the fibrosis is located. ISH showed that er these data demonstrate that SOX9 has the ability to a subpopulation of COL1A2-positive cells was also posi- occupy the promoter region of ECM-related genes and tive for SOX9 in human ischemic hearts (Figure 4C). Im- may actively regulate these genes in the heart. munostaining further confirmed that SOX9-expressing cells were also positive for COL1 (Figure 4D and 4E). DISCUSSION SOX9 Regulates Cardiac Fibrosis During Here we applied tomo-seq to obtain a genome-wide gene expression profile with a high spatial resolution Ischemia Reperfusion Injury throughout the mammalian heart after ischemic in- To examine the effect of SOX9 in vivo, we generated jury. Cardiac ischemia reperfusion damage induces inducible Sox9 heterozygous knockout mice (Sox9fl/+; a heterogeneous remodeling response that involves

Figure 5 Continued. DNA isolated from Sox9fl/+;R26CreERT2 treated with vehicle or Tamoxifen. Used forward (P1) and reverse (P2) primers are indicated as demi arrowheads in A. M indicates marker. C, Real-time PCR analysis of Sox9 in the hearts (infarct zone) from Sox9fl/+;R26CreERT2 mice 14 dpIR injected with either vehicle or Tamoxifen (n=4–7; *P<0.05 versus the indicated groups). D, Coimmunostaining against SOX9 and α-actinin-2 (ACTN2) on corresponding infarcted heart tissue 14 dpIR. Right, Same section including bright field (BF). Scale bars, 50 μm. E, Quantification of SOX9 expression 14 dpIR in the fibrotic region of the left ventricle or the corresponding region in Sham mice (n=3; **P<0.01; *P<0.05 versus Sham). F, Histological sections of infarcted hearts stained for Sirius Red (collagen) 14 dpIR. Scale bars, 1 mm (left) and 400 μm (right). G, Quantification of Sirius Red-positive area in infarcted area or corresponding Sham region (n=3–7; **P<0.01; *P<0.05 versus corresponding Sham). H, Western blot analysis of the fibrotic protein periostin (PSTN) in the hearts of Sox9fl/+;R26CreERT2 mice 14 dpIR. Glyceral- dehyde 3-phosphate dehydrogenase (GAPDH) was used as a loading control (n=4–5; *P<0.05 versus myocardial infarction [MI] vehicle-injected mice). I, Real-time PCR analysis of Col1a2-coexpressed genes in the mouse hearts 14 dpIR (n=6; **P<0.01; *P<0.05 versus Sham injected with vehicle).

Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 October 10, 2017 1407 Lacraz et al

several key processes, such as cardiac fibrosis, cardio- lated genes. SOX9 is a transcription factor essential myocyte hypertrophy, and a change in calcium han- for chrondrogenesis by the activation of many ECM dling within the heart muscle cells.2,3,13,14 Localized genes.21 In the heart, SOX9 is highly expressed during expressional differences of well-known marker genes development, where it promotes epithelial-to-mesen- for these remodeling processes allowed us to uncover chymal transition and ECM organization during heart novel genes that showed a comparable transcriptional valve development.22,23 In the adult heart, SOX9 has regulation and are linked to specific aspects of cardiac been shown to play a role in valve calcification.24,25 remodeling. Using this dataset, we identified SOX9 as Although SOX9 has been implicated in the fibrotic re- a key transcriptional regulator of ECM-related genes sponse of the liver,15 so far it was unknown to play a and showed that in vivo loss of Sox9 after myocar- role in cardiac fibrosis. We show that SOX9 is induced dial infarction blunted the cardiac fibrotic response on in response to ischemic injury and that in vivo loss of ischemic injury. SOX9 after myocardial infarction blunts the cardiac Although RNA sequencing techniques on tissue fibrotic response on damage, revealing a previously samples have been instrumental in defining genes unknown function for SOX9 in cardiac fibrosis. In relevant for cardiac remodeling and repair,4,5 so far addition, we show that SOX9 is mainly active in the these approaches have been disadvantaged by the fact fibroblast population that repopulates the infarcted that the signals are derived from tissue homogenates, area after injury.

Downloaded from which inherently causes the loss of spatial informa- In our efficacy studies, we make use of a reduction tion and dilutes out more localized expression signa- in SOX9 levels instead of complete deletion, which is tures. In addition, conventional methods for defining sufficient to cause an effect on cardiac fibrosis after localized changes in genes expression, such as ISH or injury. An equally profound phenotype in the heart immunohistochemistry, are limited to a defined set of has been reported by others on heterozygous dele-

http://circ.ahajournals.org/ candidate genes and do not allow for genome-wide tion of Rock1 and Klf6, 2 other key regulators of fi- screening for novel relevant gene candidates. Recent brosis.26,27 This finding suggests that the molecular developments in RNA amplification strategies provide mechanisms that drive cardiac fibrosis are sensitive to the opportunity to use small amounts of input RNA for small perturbations in gene expression. Because ther- genome-wide sequencing, as exemplified by tomo- apeutically targeting of SOX9 would also moderately seq.6 Although recent studies showed this method to lower expression levels, we think this genetic model provide insightful data for the developing and injured offers a good representation of what would happen 6,19

by guest on February 6, 2018 zebrafish heart, our study shows for the first time when using an inhibitor of SOX9 in the clinic as a the relevance for the mammalian heart after ischemic therapy for cardiac fibrosis. injury. Especially the transcriptional differences, intro- Here we show that the high spatial resolution in duced by the localized heterogeneity in remodeling gene expression signatures obtained by tomo-seq throughout an individual infarcted heart, appeared to reveals new regulators, genetic pathways, and tran- be valuable for the identification of clusters of genes scription factors that are active in well-defined re- that showed a comparable regulation in expression. gions of the heart and potentially involved in specific For this study, we focused on genes that showed an aspects of heart disease. This knowledge increases equivalent transcriptional regulation pattern across our mechanistic insights into cardiac remodeling and the infarcted tissue as well-known functions in fi- function and will help guide the identification of nov- brosis, cardiomyocyte hypertrophy, or contractility el therapeutic candidates. However, the applicability (Col1a2, Nppa, or Serca2).2,3,13,14 Based on subsequent of this approach is far greater than ischemic heart functional annotation analysis, expressional confirma- disease and the remodeling aspects we now focused tion in human ischemic tissue samples, and function- on, and it can serve to identify new relevant factors al in vitro and in vivo assays, we conclude that the for many different biological processes and disease high spatial resolution in gene expression signatures states. obtained by tomo-seq allows for the identification of new relevant factors for specific aspects of heart disease. While we were preparing our manuscript, ACKNOWLEDGMENTS it was also reported that Fstl1, 1 of our top Col1a2- The authors thank Jeroen Korving, Reinier van der Linden, coregulated genes, is important for cardiac fibroblast Stefan van der Elst, and Harry Begthel for technical assistance. activation,20 which further underscores the relevance of our approach for identifying new players in specific cardiac remodeling responses. SOURCES OF FUNDING Using our tomo-seq data, we identified SOX9 as This work was supported by grants from the European Re- common transcription factor able to regulate the search Council (AdG 294325 GeneNoiseControl and CoG expression of the majority of the Col1a2-coregu- 615708 MICARUS) and a grant from the Leducq Foundation.

1408 October 10, 2017 Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 SOX9 as Key Regulator of Cardiac Fibrosis ORIGINAL RESEARCH DISCLOSURES chondrocyte differentiation pathway and is required for expression of Sox5

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Circulation. 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 October 10, 2017 1409 Tomo-Seq Identifies SOX9 as a Key Regulator of Cardiac Fibrosis During Ischemic Injury Grégory P.A. Lacraz, Jan Philipp Junker, Monika M. Gladka, Bas Molenaar, Koen T. Scholman, Marta Vigil-Garcia, Danielle Versteeg, Hesther de Ruiter, Marit W. Vermunt, Menno P. Creyghton, Manon M.H. Huibers, Nicolaas de Jonge, Alexander van Oudenaarden and Eva van Rooij Downloaded from

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1 SUPPLEMENTAL MATERIAL

3 Tomo-seq identifies SOX9 as a key regulator of cardiac fibrosis

4 during ischemic injury

5 Grégory P.A. Lacraz, MSc, PhD; Jan Philipp Junker, MSc, PhD; Monika M. Gladka, MSc, 6 PhD; Bas Molenaar, MSc; Koen T. Scholman, MSc; Marta Vigil-Garcia, MSc; Danielle 7 Versteeg, BS; Hesther de Ruiter, BS; Marit W. Vermunt, MSc, PhD; Menno P. Creyghton, 8 MSc, PhD; Manon M.H. Huibers, MSc, PhD; Nicolaas de Jonge, MD; Alexander van 9 Oudenaarden, MSc, PhD and Eva van Rooij, MSc, PhD

1 1 Supplemental Methods

3 Ischemia-Reperfusion (IR) injury

4 Mice were weighed and injected IP with Ketamine/xylazine after which they were ventilated

5 with O2 containing 1.5–2.5% isoflurane using a micro-ventilator (UMV-03, UNO BV) for

6 anesthesia. The body temperature was maintained at 37°C with a heating pad. After removal

7 of the hair the skin was disinfected with iodine and 10% ethanol and incised on the left of the

8 midline and the heart was accessed via the third intercostal space. A branch of the left anterior

9 descending (LAD) coronary artery was identified and after placing a PE tubing the LAD was

10 ligated with a 7-0 silk suture. Ligation was confirmed by distal cyanosis. Following 1 hr of

11 ischemia, the PE tubing was removed and allowed for the removal of the ligature and

12 subsequent reperfusion. After closing the rib cage with a 5-0 silk suture and the skin with a

13 wound clip, the animals were disconnected from the ventilator and placed unrestrained on a

14 nose cone with 100% oxygen in a warm recovery cage until fully ambulatory, at which point

15 the oxygen was turned off. To alleviate pain or distress, buprenorphine was injected

16 subcutaneously as analgesic (0.05–0.1 mg/kg for mice), given once at completion of surgery.

17 Sham animals underwent an identical operation without induction of the infarct. At the

18 indicated time points after ischemia reperfusion or sham surgery, the animals were killed by

19 an overdose of isoflurane followed by cervical dislocation and tissues were snap-frozen or

20 collected in 4% paraformaldehyde for subsequent analysis.

22 Tomo-seq

23 Tomo-seq experiments were performed as described elsewhere. 1 In short, 2.5 mm wide

24 portions of cardiac mouse tissue spanning from the infarct towards the remote region of the left

25 ventricular anterior wall were embedded in tissue freezing medium, frozen on dry ice, and

2 1 cryosectioned into 48 slices of 80 μm thickness. We extracted RNA from individual slices and

2 prepared barcoded Illumina sequencing libraries according to the CEL-seq protocol.2 Paired-

3 end reads obtained by Illumina sequencing were aligned to the transcriptome using BWA.3 The

4 5’ mate of each pair was used for mapping, discarding all reads that mapped equally well to

5 multiple loci. The 3’ mate was used for barcode information. Read counts were normalized to

6 the same number of total reads per section. Tomo-seq data analysis was performed in

7 MATLAB (MathWorks) using custom-written code. For data analysis we used an expression

8 cut-off of >4 reads in >1 section. In differential expression analysis (Figure 1C), we determined

9 the boundary between remote and infarcted zone based on the spatial partitioning detected by

10 pairwise comparison of sections across all genes in one biological replicate (Figure 1B). For

11 the infarcted zone, we used sections 1-26, and for the remote zone we used sections 29-47. The

12 border zone (sections 27-29) was omitted in order to reduce ambiguity in assignment of

13 sections to zones. We then compared the sections within and outside the infarcted zone and

14 assessed statistical significance with Wilcoxon rank sum test. For this analysis, each section

15 was considered as an independent measurement. Furthermore, filtering was applied for genes

16 that showed at least a two-fold expression difference between remote and infarcted zone. For

17 this analysis, the mean expression levels for each gene in the two zones was calculated.

18 Concerning the hierarchical clustering, expression traces of the genes that passed the

19 differential expression filter in Figure 1C were used for analysis. The data was standardized by

20 Z-score normalization (along rows of data) so that the mean expression is zero and the standard

21 deviation is 1 in order to remove differences in expression level between genes. Euclidean

22 distance was used as distance metric. The assignment of genes to clusters I-III (Figure 1D) was

23 determined manually considering the similarity in gene expression pattern across the ischemic

24 heart.

3 1 In situ hybridization

2 ISH probe constructs were generated by PCR amplification of partial coding sequence from

3 cDNA of mouse and human heart tissue, and subsequent ligation into either pSPT18 or pSPT19

4 vectors (Roche). Primers used for partial coding sequence amplification can be found in

5 Supplemental Table 7 online. Probe plasmids were linearized and riboprobes were transcribed

6 from linearized template in the presence of digoxigenin-11-UTP. Mouse hearts were fixed in

7 formaldehyde overnight, and dehydrated stepwise into ethanol. Dehydrated hearts were

8 incubated in xylene for 2 hrs and overnight in paraffin at 55°C. Hearts were embedded in

9 paraffin blocks, which were solidified at room temperature. Hearts were sectioned transversally

10 using a microtome (Leica) and the sections (10 μm thick) were adhered to Starfrost slides.

11 Paraffin was dissolved in xylene and slides were rehydrated stepwise in ethanol. The slides

12 were prepared for hybridization by the following incubations: 0.2 M HCl for 15 min; 30 μg/ml

13 Proteinase K for 15/20 min for mouse/human slides at 37°C. Proteinase K was stopped by

14 rinsing the slides in 0.2% glycine. The slides were post-fixed for 10 min in 4% PFA, then in

15 acetic anhydride 5 min, and washed in SSC buffer. Slides were prehybridized in hybridization

16 buffer for 1 hr at 65°C. Probes were diluted in 1 μg/ml hybridization solution and denatured

17 by heating at 95°C followed cooling on ice. Hybridization buffer and probes were placed on

18 the slides and incubated for 24 hrs at 65°C, after which the hybridization buffer was removed

19 and slides were incubated at 65°C in 50% formamide/2× SSC buffer three times for 15 min.

20 Slides were washed in TBS-T followed by maleic acid buffer. Slides were blocked for at least

21 30 min at room temperature in blocking solution and incubated with 1:1500 anti-DIG-AP in

22 blocking solution for 3–4 hrs at room temperature. Slides were washed in maleid acid, TBS-T

23 and NTM buffers. Probe detection was carried out by incubation in NBT/BCIP substrate for

24 4–24 hrs in the dark. As soon as a signal could be detected the reaction was stopped by washing

4 1 the slides in NTM buffer and demi-water subsequently, after which they were mounted with

2 aquatex.

4 Pathway and transcription factor binding site enrichment

5 To investigate whether genes share a similar biological function, we searched for over-

6 representation in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway using

7 DAVID.4 The enriched genes in the KEGG pathway are shown as p values corrected for

8 multiple hypothesis testing using the Benjamini-Hochberg method.

9 Detection of over-represented conserved transcription factor binding sites in the set of genes

10 spatially co-regulated to Col1a2 was determined using single site analysis in oPOSSUM 3.0

11 (online software). The enrichment of SOX9 binding sites was determined using the Z-score,

12 which uses the normal approximation to the binomial distribution to compare the rate of

13 occurrence of a TFBS in the target set of genes to the expected rate estimated from the pre-

14 computed background set.

16 Human heart samples

17 Approval for studies on human tissue samples was obtained from the Medical Ethics

18 Committee of the University Medical Center Utrecht, The Netherlands (12#387). Written

19 informed consent was obtained or in certain cases waived by the ethics committee when

20 obtaining informed consent was not possible due to death of the patient. In this study, we

21 included tissue from the left ventricular free wall of patients with end-stage heart failure

22 secondary to ischemic heart disease. This end-stage heart failure tissue was obtained at

23 explanation of the failing heart during heart transplantation or at autopsy. For each case three

24 areas of the infarcted heart tissue were included; 1) infarct zone, 2) border zone and 3) remote

25 area. For ISH analysis, three patients were included. From these patients the border zone of the

5 1 infarcted hearts was used for ISH to verify tomo-seq and real-time PCR analysis. Left

2 ventricular free wall of non-failing donor hearts, that could not be transplanted for technical

3 reasons, were used for comparison. In these cases, neither donor patient histories, nor

4 echocardiography revealed signs of heart disease.

6 mRNA analysis

7 Total RNA from the infarct (containing the infarct and the peri-infarct zone) and the remote

8 zone (classified as myocardial tissue unaffected by the IR), or their equivalent tissue fraction

9 from sham operated hearts, or cultured cells was isolated using Trizol reagent (Invitrogen)

10 according to the manufacturer's instructions and quantified by optical density at 260 nm using

11 a Nanodrop 1000 spectrophotometer (Thermoscientific). For quantitative detection of mRNA

12 levels, complementary DNAs were synthesized by using iScript cDNA Synthesis Kit (Bio-

13 Rad). Real-time PCR analysis was carried out in a CFX96 Connect detection system (Bio-Rad)

14 using gene specific primers (provided in Supplemental Table 8 online) and iQ SYBR Green

15 Supermix (Bio-Rad). Reverse transcribed RNA (10 ng) was used as template for each reaction.

16 All reactions were run in duplicate with no template control. The PCR conditions were: 95ºC

17 for 15 min, followed by 40 cycles at 95ºC for 15 s, 60ºC for 30 s and 72ºC for 30 s.

18 Glyceraldehyde 3-phosphate dehydrogenase (Gapdh) was used as house-keeping gene.

20 Histology and immunofluorescence

21 Heart tissues from C57BL/6 and mutant mice or human patients were embedded either in

22 paraffin after fixation in 4% formalin for two consecutive days, or in OCT after fixation in PLP

23 buffer containing 0.1 M phosphate buffer containing 0.2 M L-lysine, pH 7.4, 2.12 mg/ml

24 NaIO4, and 4% paraformaldehyde overnight, washed in phosphate buffer, and dehydrated in

25 30% sucrose in phosphate buffer overnight. Hematoxylin and eosin (H&E) and Sirius Red

6 1 stains were performed using routine histology protocols. Images were acquired using a Leica

2 DM4000 M (Leica Microsystems) microscope. The Sirius Red-positive area was determined

3 as a percentage of the infarcted area using ImageJ. SOX9+ cells were quantitated in three

4 different fields (magnification × 20) of the fibrotic area 14dpIR (~1300 cells/heart) or the

5 corresponding region in Sham (~850 cells/heart) (n=3 mouse per group). For

6 immunofluorescence staining, paraffin sections were dewaxed in xylene for 5 min, rehydrated

7 in ethanol (2× 5 min in 100% ethanol, 2× 5 min in 96% ethanol, 2× 5 min in 70% ethanol), and

8 rinsed three times with demi water. This was followed by antigen retrieval boiling with citrate

9 buffer for 20 min. Sections were then washed in PBS, incubated for 45 min at room temperature

10 in a blocking solution containing 1% BSA, 0.01% Triton X-100, and 0.01% Tween20, and then

11 incubated overnight at 4℃ with a primary antibody specific for SOX9 (rabbit, 1:250, Millipore

12 #ab5535), type 1 collagen (mouse, 1:500, Abcam #ab6308), alpha-actinin (ACTN2, mouse,

13 1:400, Sigma #A7732), and TdTomato (goat, 1:1000, Sicgen #AB8181-200). Sections were

14 incubated for 60–90 min at room temperature with species specific secondary antibodies

15 conjugated with Alexa 488 (1:400, Life Technologies #A11001) or Alexa Fluor 568 (1:300,

16 Life Technologies #A10042 and #A11057). Nuclear counterstaining was performed using

17 DAPI prior to mounting (Prolong Gold antifade reagent, Life Technologies #P36934). Images

18 were acquired using a SPE (Leica Microsystems) microscope.

20 3T3-L1 cell culture

21 3T3-L1 cells were purchased from ATCC and cultured in Dulbecco’s Minimum GlutaMAX

22 (low glucose) (Gibco) supplemented with 10% FBS (Life technologies). Cells were serum-

23 starved for 16 hrs after which they were transfected for 24 hrs with 10 nM Sox9 siRNA

24 (Thermoscientific) or scrambled siRNA as control, using Lipofectamin 2000 reagent

25 (Invitrogen) in Opti-MEM media (Gibco). TGFβ1 (5 ng/ml; Preprotech) was then added and

7 1 the cells were harvested at the indicated time points for RNA extraction and subsequent real-

2 time PCR analysis.

4 SOX9 animals models

5 Sox9 (Sox9fl/fl) mutant mice harboring two loxP sites flanking the exons 2–3 6 were crossed with

6 Rosa26-CreERT2 mice (R26CreERT2) to obtain an inducible Sox9 loss-of-functional model

7 (Sox9fl/+;R26CreERT2). For lineage tracing studies, mice expressing CreERT2 under the control of

8 the Sox9 promoter 7 were bred with the Rosa26-tdTomato reporter mouse (R26RTdT) to obtain

9 Sox9CreERT2;R26RTdT mice. To induce the CreERT2 protein, Sox9fl/+;R26CreERT2 and

10 Sox9CreERT2;R26RTdT mice were injected with Tamoxifen (corn oil/ethanol) intraperitoneally (2

11 mg per day per mouse) at the day of surgery and 2 and 4 days after injury. Control mice

12 (referred to as Sox9fl/+;R26CreERT2 Vehicle) received an equal volume of the vehicle that was

13 used to deliver Tamoxifen. Primers used for genotyping are provided in Supplemental Table 8

14 online.

16 FACS analysis

17 Hearts (left ventricle) were digested by a medium containing 20 μg/ml DNAse I (Worthington

18 Biochemical #LK003172) and 26 U/ml LiberaseTM (Roche #5401020001) for 15 min at 37°C.

19 The dissociated single cells were collected by filtration through a 100-μm nylon mesh.

20 Expression of TdTomato was analysed by flow cytometry using a FACSAria III flow

21 cytometer (BD Biosciences) and the FlowJo software (Tree Star).

23 Western Blotting

24 Standard Western blot analysis was performed on homogenates from infarcted hearts of

25 Sox9fl/+;R26CreERT2 mice using Periostin antibody (Pierce #PA5-34641) and GAPDH (Merck

8 1 #mab374) as a loading control. Densitometry of the Western blots was performed using

2 ImageQuant TL Software (GE Healthcare Life Sciences software).

4 Statistical analysis

5 Values are presented as mean ± s.e.m. Previous studies were used to predetermine sample size.

6 Statistical analyses between two groups were conducted using the two-tailed unpaired or paired

7 Student's t-test or a Mann-Whitney test when the normality assumption was not met.

8 Comparison among groups was performed using a two-way ANOVA with Bonferroni’s post-

9 hoc test. Pearson's correlation coefficients were used to calculate gene pair correlation based

10 on gene expression in human samples. KEGG pathways are ranked by their respective p value

11 corrected for multiple hypothesis testing using the Benjamini-Hochberg method. p value <0.05

12 was interpreted to denote statistical significance. Prism 6 (GraphPad Software, Inc.) was used

13 for statistical analyses.

9 1 Supplemental Tables 2 3 Supplemental Table 1. Functional annotation chart of predicted pathways activated 1 4 dpIR using the list of differentially regulated genes determined by tomo-seq. 5 Fold KEGG term Genes Enrich P-value ment Biosynthesis of unsaturated fatty Acot1, Acot2, Acot9, Hadha, Hsd17b12, Elovl6, Scd2 3.6 1.7E-1 acids Fc gamma R- Fcgr1, Fcgr2b, Limk1, Rac2, Was, Cfl1, Dock2, Gsn, 2.8 1.7E-3 mediated Hck, Inpp5d, Map2k1, Ncf1, Pak1, Pik3r1, Pla2g6, phagocytosis Arpc5, Ptprc, Asap1, Vasp, Vav1 Leukocyte Rac2, Rock1, Actb, Actn4, Actn1, Cyba, Itga4, Itgal, 2.7 1.6E-3 transendothelial Itgam, Itgb2, Mmp9, Mapk11, Myl9, Ncf1, Ncf2, Ncf4, migration Pxn, Pik3r1, Actg1, Thy1, Vcam1, Vasp, Vav1 Hematopoietic Cd24a, Cd33, Cd44, Cd9, Fcgr1, Csf2ra, Gp1bb, H2- 2.7 2.2E-2 cell lineage Eb1, Itga4, Itgam, Il1b, Il1r1, Il1r2, Il11, Il4ra, Csf3r Regulation of Fgd3, Iqgap1, Limk1, Nckap1l, Rac2, Rock1, Was, Actb, 2.6 7.9E-6 actin Actn4, Actn1, Baiap2, Cfl1, Cyfip1, Cyfip2, Diap1, Enah, cytoskeleton Fn1, Gsn, Itga4, Itgal, Itgam, Itgax, Itgb2, Map2k1, Myh14, Myh9, Myl9, Pak1, Pak2, Pxn, Pik3r1, Pdgfra, Pdgfa, Arpc5, Actg1, Rras2, Apc, Mylk3, Tmsb4x, Vav1 Adherens Crebbp, Iqgap1, Rac2, Was, Actb, Actn4, Actn1, Baiap2, 2.6 5.3E-2 junction Insr, Actg-ps1, Ptpn1, Ptpn6, Snai1, Tgfbr1 Chemokine Fgr, Rac2, Rock1, Was, Arrb2, Xcr1, Ccl2, Ccl21a, Ccl3, 2.5 3.3E-4 signaling Ccl4, Ccl6, Ccl7, Ccl9, Ccr1, Ccr8, Cxcl16, Cxcr6, pathway Dock2, Elmo1, Hck, Cxcr2, Map2k1, Ncf1, Nfkbib, Pak1, Pxn, Pik3r1, Plcb1, Ccl27a, Ppbp, Cxcl5, Vav1 Pyrimidine Prim1, Entpd5, Pola1, Pole3, Pold4, Polr1c, Polr3a, Tk1, 2.2 1.4E-1 metabolism Rrm1, Txnrd1, Tyms, Umps, Upp1, Uck1, Uck2 Cytokine- Relt, Xcr1, Ccl2, Ccl21a, Ccl3, Ccl4, Ccl6, Ccl7, Ccl9, 2.1 2.0E-3 cytokine Ccr1, Ccr8, Cxcl16, Ccr6, Csf2ra, Csf2rb, Csf2rb2, Crlf2, receptor Il1b, Il1r1, Il1r2, Il10ra, Il11, Il15, Il17ra, Il4ra, Il8ra, interaction Pdgfra, Pdgfa, Csf3r, Il2rg, Ccl27a, Ppbp, Cxcl5, Tgfb2, Tgfbr1, Tnfrsf12a Focal adhesion Elk1, Rac2, Rock1, Vwf, Actb, Actn4, Actn1, Diap1, Fn1, 2.0 1.9E-2 Flnc, Itga4, Map2k1, Myl9, Pak1, Pak2, Parva, Pxn, Pik3r1, Pgf, Pdgfra, Pdgfa, Actg-ps1, Mylk3, Tnc, Thbs1, Vasp, Vav1, Zyx 6 7

10 1 Supplemental Table 2. Functional annotation chart of predicted pathways activated 14 2 dpIR using the list of differentially regulated genes determined by tomo-seq. 3 Fold KEGG term Genes Enrich P-value ment Valine, Hmgcs2, Hibch, Oxct1, Auh, Acat1, Acaa2, Acadm, Acadsb, leucine and Aldh6a1, Dbt, Dld, Ehhadh, Hadh, Ivd, Mccc1, Mccc2, Mcee, 3.5 1.0E-5 isoleucine Mut, Hibadh, Hadhb, Pcaa degradation Cd36, Agrn, Comp, Chad, Col1a1, Col1a2, Col3a1, Col4a2, ECM- Col5a1, Col5a2, Col6a1, Col6a2, Col11a1, Fn1, Itga11, Itga2b, receptor 3.1 7.9E-8 Itga6, Itgav, Itgb5, Itgb8, Lamc1, Sdc2, Sdc3, Sdc4, Tnc, Tnxb, interaction Thsb1, Thsb2, Thsb3, Thsb4, Vtn Serca2, Actc1, Actb, Ace, Cacna1c, Cacna2d1, Cacnb2, Des, Hypertrophic Igf1, Itga11, Itga2b, Itga6, Itgav, Itgb5, Itgb6, Itgb8, Lmna, cardiomyopa 2.5 2.8E-4 Myh7, Prkaa2, Prkag1, Ryr2, Sgcg, Ttn, Tgfb2, Tgfb3, Tnni3, thy (HCM) Tnnt2 Abca2, Atp6v0b, Atp6v0d1, Atp6ap1, Cd63, Cd68, Gm2a, Asah1, Ap1s1, Ap4s1, Naglu, Arsa, Ctsa, Ctsd, Ctsf, Ctsk, Lysosome Ctss, Ctsz, Clta, Fuca1, Glb1, Gns, Gba, Gusb, Hexa, Hexb, 2.4 6.9E-6 Lgmn, Lipa, Lamp1, Laptm5, Man2b1, Manba, Neu1, Pla2g15, Ap3s1, Slc11a1, Smpd1 Atp5b, Atp5j, Atp5f1, Atp5g3, Atp6v0b, Atp6v0d1, Atp6ap1, Ndufa1, Ndufa2, Ndufa4, Ndufa4l2, Ndufa5, Ndufa6, Ndufb3, Oxidative Ndufb4, Ndufb5, Ndufb6, Ndufab1, Ndufc2, Ndufs1, Ndufs4, phosphorylati 2.3 2.0E-5 Ndufs5, Cox7b, Cox7a2, Atp5l, Atp5g2, Atp5h, Atp5g1, Uqcrh, on Uqcrb, Atp5k, Ndufc1, Gm5457, Cox6c, Ndufs3, Sdha, Sdhb, Sdhd, Uqcrc2 Bcl2, Flt4, Rac2, Src, Actb, Actn4, Actn1, Bcar1, Figf, Comp, Chad, Col1a1, Col1a2, Col3a1, Col4a2, Col5a1, Col5a2, Col6a1, Col6a2, Col11a1, Egf, Egfr, Fn1, Flnc, Flna, Flnb, Igf1, Focal Itga11, Itga2b, Itga6, Itgav, Itgb6, Itgb8, Lama4, Lamb2, 2.3 1.4E-7 adhesion Lamc1, Mapk3, Myl9, Parva, Pik3cd, Pik3r2, Pip5k1c, Pdgfra, Pdgfrb, Myl12b, Prkcb, Ppp1ca, Tln1, Tnc, Tnxb, Thsb1, Thsb2, Thsb3, Thsb4, Vasp, Vav1, Vtn, Zyx Dilated Actc1, Actb, Adcy4, Adcy7, Cacna1c, Cacna2d1, Cacnb2, des, cardiomyopa Igf1, Itga11, Itga2b, Itga6, Itgav, Itgb5, Itgb6, Itgb8, Lmna, Pln, 2.2 1.3E-3 thy Ryr2, Sgcg, Tnt, Tgfb2, Tgfb3, Tpm2, Tnni3, Tnnt2 Atp5b, Atp5j, Atp5f1, Atp5g3, Bad, Ndufa1, Ndufa2, Ndufa4, Ndufa4l2, Ndufa5, Ndufa6, Ndufb3, Ndufb4, Ndufb5, Ndufb6, Ndufab1, Ndufc2, Ndufs1, Ndufs4, Ndufs5, App, Apoe, Bace2, Alzheimer's Cacna1c, Capn1, Casp9, Cdk5, Cox7b, Cox7a2, Eif2ak3, 2.2 2.6E-6 disease Gnaq, Itpr1, Lpl, Mapk3, Cycs, Atp5g2, Atp5h, Atp5g1, Uqcrh, Uqcrb, Ndufc1, Gapdh, Gm5457, Cox6c, Ndufs3, Sdha, Sdhb, Sdhd, Serca2, Tnfrsf1a, Uqcrc2 Atp5b, Atp5j, Atp5f1, Atp5g1, Atp5g2, Atp5g3, Atp5h, Ndufa1, Ndufa2, Ndufa4, Ndufa4l2, Ndufa5, Ndufa6, Ndufb3, Ndufb4, Parkinson's Ndufb5, Ndufb6, Ndufab1, Ndufc2, Ndufs1, Ndufs4, Ndufs5, 2.2 3.2E-5 disease Casp9, Cox7b, Cox7a2, Cycs, Uqcrb, Uqcrh, Ndufc1, Gm5457, Cox6c, Ndufs3, Slc25a4, Sdha, Sdhb, Sdhd, Uqcrc2, Uchl1, Ube2l6 Atp5b, Atp5j, Atp5f1, Atp5g3, Ndufa1, Ndufa2, Ndufa4, Huntington's Ndufa4l2, Ndufa5, Ndufa6, Ndufb3, Ndufb4, Ndufb5, Ndufb6, 2.1 6.5E-6 disease Ndufab1, Ndufc2, Ndufs1, Ndufs4, Ndufs5, Ap2b1, Ap2s1, Casp9, Clta, Cox7b, Cox7a2, Dctn1, Dctn4, Gnaq, Ap2a2,

11 Itpr1, Ppargc1a, Polr2f, Cycs, Atp5g2, Atp5h, Atp5g1, Uqcrh, Uqcrb, Ndufc1, Gm5457, Cox6c, Gm7511, Ap2m1, Ndufs3, Hdac1, Slc25a4, Sdha, Sdhb, Sdhd, Tfam, Uqcrc2 Regulation of Cd14, Rras, Iqgap1, Nckap1l, Rac2, Arhgef6, Arhgef1, 1.8 5.5E-4 actin Arhgef7, Tiam1, Was, Arpc1b, Arpc4, Actb, Actn4, Actn1, cytoskeleton Baiap2, Bcar1, Csk, Chrm2, Egf, Egfr, Fgf1, Fgf13, Fgfr1, Fn1, Itga11, Itga2b, Itga6, Itgam, Itgav, Itgb2, Itgb5, Itgb6, Itgb8, Mapk3, Mras, Myh9, Myl9, Pik3cd, Pik3r2, Pip5k1b, Pip5k1c, Pdgfra, Pdgfrb, Myl12b, Arpc2, Gm5637, Ppp1ca, Nras, Slc9a1, Vav1 1

12 1 Supplemental Table 3. Functional annotation chart of predicted pathways activated 14 2 dpIR using the one hundred fifty most similar genes to the cardiac stress gene Col1a2 3 determined by tomo-seq. 4 Fold KEGG term Genes P-value Enrichment Col1a1, Col1a2, Col3a1, Col5a1, Col5a2, Fn1, ECM-receptor interaction 16.9 2.0E-7 Itgb1, Thbs1, Thbs4 TGF-beta signaling Dcn, Id2, Id3, Thbs1, Thbs4,Tgfb3 11.3 5.0E-4 pathway Actn1, Col1a1, Col1a2, Col3a1, Col5a1, Focal adhesion 8.5 1.7E-6 Col5a2, Fn1, Igf1, Itgb1, Thbs1, Thbs2, Thbs4 Antigen processing and Ctsl, Ctss, H2-Ab1, H2-Eb1, Lgmn 7.7 5.0E-2 presentation 5 6

13 1 Supplemental Table 4. Functional annotation chart of predicted pathways activated 14 2 dpIR using the one hundred fifty most similar genes to the cardiac stress gene Nppa 3 determined by tomo-seq. 4 Fold KEGG term Genes P-value Enrichment Prion diseases C1qa, C1qb, Lamc1 10.9 2.6E-1 Rpl11, Rps21, Rps14, Rpsa, RplP0, Fau, Ribosome 10.0 2.7E-3 Rpl37 Hypertrophic Actb, Itgb5, Lmna, Myh7 6.1 2.9E-1 cardiomyopathy ECM-receptor Comp, Itgb5, Lamb2, Lamc1 6.1 3.6E-1 interaction Dilated Actb, Itgb5, Lmna, Myh7 5.5 2.5E-1 cardiomyopathy Focal adhesion Actb, Comp, Flna, Itgb5, Lamb2, Lamc1, Rhoa 4.5 9.2E-2 Tight junction Rras, Actb, Myh7, Rhoa 3.8 4.8E-1 5 6

14 1 Supplemental Table 5. Functional annotation chart of predicted pathways activated 14 2 dpIR using the one hundred fifty most similar genes to the cardiac stress gene Serca2 3 determined by tomo-seq. 4

Fold KEGG term Genes P-value Enrichment

Fatty acid elongation in Acaa2, Hadh, Hadhb 32.1 1.6E-2 mitochondria

Valine, leucine and Oxct1, Auh, Acat1, Acaa2, Acadm, Dbt, Dld, 20.5 6.0E-10 isoleucine degradation Hadh, Ivd, Hibadh, Hadhb

Atp5b, Atp5j, Atp5f1, Atp5g3, Ndufa2, Ndufa5, Ndufa6, Ndufb3, Ndufb5, Ndufb6, Oxidative phosphorylation Ndufc2, Ndufs1, Ndufs4, Cox7b, Cox7a2, 17.1 1.7E-23 Atp5h, Uqcrh, Uqcrb, Atp5k, Ndufc1, Cox6c, Ndufs3, Sdha, Sdhb, Sdhd, Uqcrc2 Atp5b, Atp5j, Atp5f1, Atp5g3, Ndufa2, Ndufa5, Ndufa6, Ndufb3, Ndufb5, Ndufb6, Ndufc2, Ndufs1, Ndufs4, Cox7b, Cox7a2, Parkinson's disease 16.7 1.5E-23 Atp5h, Uqcrh, Uqcrb, Ndufc1, Cox6c, Ndufs3, Slc25a4, Sdha, Sdhb, Sdhd, Uqcrc2 Atp5b, Atp5j, Atp5f1, Atp5g3, Ndufa2, Ndufa5, Ndufa6, Ndufb5, Ndufb6, Ndufc2, Alzheimer's disease Ndufs1, Ndufs4, Cox7b, Cox7a2, Atp5h, 12.2 6.8E-23 Uqcrh, Uqcrb, Ndufc1, Cox6c, Ndufs3, Slc25a4, Sdha, Sdhb, Sdhd, Uqcrc2

Atp5b, Atp5j, Atp5f1, Atp5g3, Ndufa2, Ndufa5, Ndufa6, Ndufb5, Ndufb6, Ndufc2, Huntington's disease Ndufs1, Ndufs4, Cox7b, Cox7a2, Atp5h, 12.2 3.1E-20 Uqcrh, Uqcrb, Ndufc1, Cox6c, Ndufs3, Slc25a4, Sdha, Sdhb, Sdhd, Uqcrc2 Acat1, Acaa2, Acsl1, Acadl, Acadm, Hadh, Fatty acid metabolism 13.3 6.7E-5 Hadhb Cs, Dlat, Dld, Fh1, Idh2, Mdh1, Pdha1, Citrate cycle (TCA cycle) 11.0 1.1E-8 Sdha, Sdhb, Sdhd, Sucla2

Cardiac muscle Serca2, Actc1, Cacna1c, Cox7b, Cox7a2, 11.0 1.4E-6 contraction Uqcrh, Uqcrb, Cox6c, Ryr2, Uqcrc2 Cd36, Acsl1, Acadl, Acadm, Fabp3, Fabp4, PPAR signaling pathway 7.6 1.5E-3 Lpl 5 6

15 1 Supplemental Table 6. Conserved transcription factor binding sites (TFBS) for the set of 2 30 genes showing the greatest similarity in expressional regulation to Col1a2 14 dpIR in 3 mice. 4 Transcription Target Class Family Z-score factor gene hits NFATC2 Ig-fold Rel 28 15.631 RUNX1 Ig-fold Runt 28 11.582 FOXO3 Winged Helix-Turn-Helix Forkhead 27 6.786 Nkx2-5 Helix-Turn-Helix Homeo 28 6.461 YY1 Zinc-coordinating BetaBetaAlpha-zinc finger 28 5.285 KLF4 Zinc-coordinating BetaBetaAlpha-zinc finger 25 5.200 SOX17 Other Alpha-Helix High Mobility Group 28 4.838 SOX9 Other Alpha-Helix High Mobility Group 27 4.227 Arnt::Ahr Zipper-Type Helix-Loop-Helix 26 3.659 PRXX2 Helix-Turn-Helix Homeo 27 3.022 SPI1 Winged Helix-Turn-Helix Ets 29 2.876 EBF1 Zipper-Type Helix-Loop-Helix 25 2.842 TBP Beta-sheet TATA-binding 26 1.089 ELK1 Winged Helix-Turn-Helix Ets 25 -2.576 ZEB1 Zinc-coordinating BetaBetaAlpha-zinc finger 29 -3.273 5 6 Results are sorted by Z-score for the detected transcription factors regulating ≥25 Col1a2 co- 7 regulated genes. 8

16 1 Supplemental Table 7. List of primer sequences used to create in situ hybridization 2 probes. 3 Gene Organism Sequence Fw SalI_GTCGACCTTATTGGTTGCCCGGTGAG Chchd2 Mus Musculus Rv BamH1_GGATCCCAAGGAGGCCAAACACTTCC Fw XbaI_GCTCTAGACCTTGACATTGCACCTCTGG Col1a2 Mus Musculus Rv SacI_GCGAGCTCATGAAGAGAGAACCTGGGGC Fw XbaI_GCTCTAGAGACGCCCTCATTGAACTGTC Fstl1 Mus Musculus Rv SacI_GCGAGCTCCTCCATGGCGCTTGAAGTAC Nppa Fw XbaI_GCTCTAGAAGCAAACATCAGATCGTGCC Mus Musculus Rv SacI_GCGAGCTCATATGCAGAGTGGGAGAGGC Fw XbaI_GCTCTAGAAGGAAATGGCCCAGAGACAG Nppb Mus Musculus Rv SacI_GCGAGCTCCAAAAGCAGGAAATACGCTATGT Fw XbaI_GCTCTAGAAGTGCAATACCTCACTCGCT Pln Mus Musculus Rv SacI_GCGAGCTCCATGTACACAGCCCTTGAGC Fw SalI_GCGTCGACCTCTTCCCCTTTCCATCTCC Pmepa1 Mus Musculus Rv BamH1_GCGGATCCCTCACCAAGCTAGGCACCTC Fw XbaI_GCTCTAGAGGTTCGAGGAAGGGGAAGAA Serca2 Mus Musculus Rv SacI_GCGAGCTCTGTGGTAAGTGTGCCTGTCT Fw XbaI_GCTCTAGAACATGGAGGACGATTGGGAGA Sox9 Mus Musculus Rv SacI_GCGAGCTCAGGGTGATAGTCTGAGCAGGC Fw XbaI_GCTCTAGACATAAGCTCACCGTCCACAAG Sparc Mus Musculus Rv SacI_GCGAGCTCGCAAGAACCTGAAAGCCCAA Fw XbaI_GCTCTAGAGCTTAGCTCTTCGGTGGTTG CHCHD2 Homo sapiens Rv SacI_ GCGAGCTCTTTGCCCAGTCCCAGATTCT Fw XbaI_GCTCTAGACAAGGTTTCCAAGGACCTGC COL1A2 Homo sapiens Rv SacI_GCGAGCTCGAAGACCACGAGAACCAGGA Fw XbaI_GCTCTAGACCCCACTCACTACCCTGTTT FSTL1 Homo sapiens Rv SacI_GCGAGCTCACATTCAGACTGGTCCACGT Fw XbaI_GCTCTAGAGACAGACGTAGGCCAAGAGA NPPA Homo sapiens Rv_Kpnl_GCGGTACCTAATGCATGGGGTGGGAGAG Fw Sal1_GCGTCGACCTCTTCCCCTTTCCATCTCC PMEPA1 Homo sapiens Rv Bamh1_GCGGATCCCTCACCAAGCTAGGCACCTC Fw XbaI_GCTCTAGAATGGTGGTTCATTGCTGCTG SERCA2 Homo sapiens Rv SacI_GCGAGCTCTGGAGAGGGATCTGGCTACT Fw XbaI_GCTCTAGAAGGAAGTCGGTGAAGAACGG SOX9 Homo sapiens Rv Sacl_GCGAGCTCCCGTAGCTGCCCGTGTAG Fw XbaI_GCTCTAGAAGTACATCGCCCTGGATGAG SPARC Homo sapiens Rv SacI_GCGAGCTCGTCCGTGCTCCCAAAAGTTT 4

17 1 Supplemental Table 8. List of primer sequences used for real-time and genotyping PCR.

Accession Gene Organism Sequence number Mus Forward ACCCACCCAGAGTGGAGAAG Acta2 NM_007392.2 Musculus Reverse AGCATCATCACCAGCGAAG Mus Forward GGCCAAGTGGTGTATGTCTTC Cdrap NM_019394.3 Musculus Reverse GCTGCCAGGTCTCCATAGTAAC Mus Forward AATGCAATGAAGAACTGGACTG Col1a1 NM_007742.3 Musculus Reverse CCCTCGACTCCTACATCTTCTG Mus Forward CAAGGACCTGCTGGTGAAC Col1a2 NM_007743.2 Musculus Reverse TGGTCCAACGACTCCTCTC Mus Forward GCAAAGATGGCTCTAATGGAA Col2a1 NM_031163.3 Musculus Reverse GACTTCCAGGGGGACCAA Mus Forward GATGGCAAAGATGGATCACCTGG Col3a1 NM_009930.2 Musculus Reverse GACCCTTTTCTCCTGGGATGC Mus Forward GAGAGACAGGACCCTTTGGAC Col4a1 NM_009931.2 Musculus Reverse GGTCATCTGTTGTCTGACTATGC Mus Forward GAAAGGCTGGTGATCAAGGT Col5a2 NM_007737.2 Musculus Reverse TTTCTCCCCGAGGTCCTAAT Mus Forward GCCAGCCAAGCCTAAATGT Col8a1 NM_007739.2 Musculus Reverse CAGAGTTCAGGGAAATGATGAA Mus Forward CAGATTGTGACTTAACATCCAAGG Col11a1 NM_007702.2 Musculus Reverse CTCGATTATATCCTCAGGTGCAT Mus Forward CAGCCTAGCAGATGGCAAAT Col11a2 NM_009926.1 Musculus Reverse CAGTCCACAATGAGAGTGACAGA Mus Forward GCATTGCAGGAGAAAATGGT Col16a1 NM_028266.5 Musculus Reverse CCATCTTGCCATAACCTGGA Mus Forward TGACCTGGAGGAAAACATTAAGA Ctgf NM_010217.2 Musculus Reverse AGCCCTGTATGTCTTCACACTG Mus Forward TGTGTTGTGCCTTCCAAAGA Dkk3 NM_015814.2 Musculus Reverse GTTCCCAGGTGATGAGATCC Mus Forward AAGTGGAAGGGTCCTTAGCAA Ecm1 NM_007899.2 Musculus Reverse TCGATGAAGGCCAGTTTCTC Mus Forward TGCATCAATACAGTGGGCTCT Fbln1 NM_010180.2 Musculus Reverse CCAGTCTCACATTCGTCAATATCT Mus Forward CAGGTGGCCTCTAACACCAT Fbln2 NM_007992.2 Musculus Reverse TTGCAGGGTCCATTGTCTTT Mus Forward CCACTGTGGAGTACGTGGTTAG Fn1 NM_010233.1 Musculus Reverse AAGCAATTTTGATGGAATCGAC Mus Forward CAGCCATCAACATCACCACT Fstl1 NM_008047.5 Musculus Reverse ATGAGGGCGTCAACACAGA Mus Forward TGTCGTGGAGTCTACTGGTG Gapdh NM_008084.2 Musculus Reverse ACACCCATCACAAACATGG Mus Forward TTGCCACGATTGTAATCCAC Htra3 NM_030127.2 Musculus Reverse GGTGTTCTGCAGGGCAAA

18 Mus Forward CACCTTCACCAGCTCCACCAC Igf1 NM_001111276.1 Musculus Reverse CAGTCTCCTCAGATCACAGCTC Mus Forward GGTAGGATTGACAGGATTGGAG Nppa NM_008725.2 Musculus Reverse GCTTAGGATCTTTTGCGATCTG Mus Forward TGCAGAGACCTCAAAAGGTG Pdgfrl NM_026840.3 Musculus Reverse CCTGATCTTCCTCCCAGAAA Mus Forward TGGTGATATAGTGGAAATTGCTG Serca2 NM_009722.3 Musculus Reverse GAGTTGTAGACTTGATGGATGTCAA Mus Forward GTGGTTCAAGATGTGCTCCA Sfrp1 NM_013834.3 Musculus Reverse TCAGAGCAGCCAACATGC Mus Forward TATCTTCAAGGCGCTGCAA Sox9 NM_011448.4 Musculus Reverse TCGGTTTTGGGAGTGGTG Mus Forward CCATTGGCGAGTTTGAGAA Sparc NM_022316.2 Musculus Reverse GTGCACTTGGTGGCAAAGA Mus Forward CCCGGTGAAAGTGACTGATT Spp1 NM_001204201.1 Musculus Reverse TTCTTCAGAGGACACAGCATTC Mus Forward CATATTTGTGGCCATTGAAGG Tcf4 NM_013685.2 Musculus Reverse CAGCTCTTTGTCCGTCCCTA Mus Forward CCCTGGACACCAATTACTGC Tgfb3 NM_009368.3 Musculus Reverse TCAATATAAAGGGGGCGTACA Mus Forward TCGGACCTCAAGTATGAGTGC Thbs2 NM_011581.3 Musculus Reverse TCTAAGAAGGGGTGTTTGCAG Mus Forward AACATTATTACGTCGGACAGAGATT Tmem45a NM_019631.3 Musculus Reverse AGATAAACTGTTCACCAGCTATACCA Homo Forward GGTCGCTTTTGGGATTACCT APOE NM_000041.2 sapiens Reverse CATGGTCTCGTCCATCAGC Homo Forward GGTGATATTGTAGAAATTGCTGTTG ATP5G3 NM_001689.4 sapiens Reverse TGACTGGTCAACTCTTAGTGTGGT Homo Forward TGATGGAAAAGGAGTTGGACTT COL1A2 NM_000089.3 sapiens Reverse CAGGTCCTTGGAAACCTTGA Homo Forward GCTGAAACAGTGCCGACTT CHCHD2 NM_016139.2 sapiens Reverse AACTTAGTTATGAGAGCTGATTTTCCA Homo Forward GCAACAAGGAGGTGTACTTCG CRIP1 NM_001311.4 sapiens Reverse CACATTTCTCGCACTTCAGG Homo Forward GAGGACACGCAGCACAAAT DKK3 NM_015881.5 sapiens Reverse TTTGCCAGGTTCACTTCTGA Homo Forward CAGGTGGCCTCTAACACCAT FBLN2 NM_001004019.1 sapiens Reverse TTGCAGGGTCCATTGTCTTT Homo Forward GCCATCAATATTACAACGTATCCA FSTL1 NM_007085.4 sapiens Reverse TCAATGAGAGCATCAACACAGA Homo Forward TCAGCAGACTCAACTATCATGGA FXYD5 NM_144779.2 sapiens Reverse GGGCTGGAGTTCTGTGTAGACT Homo Forward GGGTCATCATCTCTGCCCC GAPDH NM_002046.3 sapiens Reverse GGTCATGAGTCCTTCCACGA Homo Forward AGCTACAGAATGGGGACTCCT HTRA3 NM_053044.3 sapiens Reverse AGCAACAACACAGGGAGCTT

19 Homo Forward GGTGTGCTGAAGAAGACCACT NDUFA5 NM_005000.2 sapiens Reverse TTGTGTACAATATTCTTAGCCTCTCG Homo Forward CCGTGAGCTTCCTCCTTTTA NPPA NM_006172.3 sapiens Reverse CCAAATGGTCCAGCAAATTC Homo Forward CTGCACGGTCCTTCATCAG PMEPA1 NM_020182.3 sapiens Reverse TTGCCTGACACTGTGCTCTC Homo Forward GGTGATATTGTAGAAATTGCTGTTG SERCA2 NM_001681.3 sapiens Reverse TGACTGGTCAACTCTTAGTGTGGT Homo Forward TCTTGTATAGCAATATGCTTGGAGA SLC25A3 NM_213611.2 sapiens Reverse GGGCAATGTCAGCAAAGAAT Homo Forward GTACCCGCACTTGCACAAC SOX9 NM_000346.3 sapiens Reverse TCTCGCTCTCGTTCAGAAGTC

Genotyping Forward GTCATATTCACGCCCCCATT Transgene Sox9fl primers Reverse AGACTCTGGGCAAGCTCTGG Genotyping Forward TGGTAATGAGTCATACACAGTAC Transgene Sox9fl del primers Reverse GTCAAGCGACCCATGAACGC Genotyping Forward GCGGTCTGGCAGTAAAAACTATC Transgene Sox9creERT2 primers Reverse GTGAAACAGCATTGCTGTCACTT Genotyping Forward GAAGCAACTCATCGATTGATTTACG Transgene Cre primers Reverse CACTATCCAGGTTACGGATATAGTTC Genotyping Forward CTGTTCCTGTACGGCATGG Transgene TdTomato primers Reverse GGCATTAAAGCAGCGTATCC 1

20 Supplemental Figure 1

A Sham 1 dpIR 14 dpIR H&E Sirius Red

B Col1a2 Nppa Serca2 8 25 2.0 I I I 20 6 R R 1.5 R sham)

vs 15 4 1.0 ** 10

2 0.5 ** ** 5 **

(Fold change **

Relative mRNA abundance 0 0 0.0 Sham 1 dpIR 14 dpIR Sham 1 dpIR 14 dpIR Sham 1 dpIR 14 dpIR Supplemental Figure 2

Whole genome Diff. expressed genes

A 1 dpIR B 1 dpIR C 1 dpIR -score) Z -score) Z Counts ( Infarct-to-remote position Pearson correlation ( Infarct-to-remote position Infarct-to-remote position expressed genes 1371 diff. Infarct-to-remote position

D 1 dpIR Regulation of actin cytoskeleton Chemokine signaling pathway Leukocyte transendothelial migration Fc gamma R-mediated phagocytosis Cytokine-cytokine receptor interaction Hematopoietic cell lineage Focal adhesion Adherens junction Biosynthesis of unsaturated fatty acids Pyrimidine metabolism 10-6 10-5 10-4 10-3 10-2 10-1 100 P-values (log 10)

14 dpIR ECM-receptor interaction Focal adhesion Alzheimer's disease Huntington's disease Lysosome Regulation of actin cytoskeleton Branched-chain amino acids Oxidative phosphorylation Parkinson's disease Hypertrophic cardiomyopathy Dilated cardiomyopathy 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1 100 P-values (log 10) Supplemental Figure 3

A Col1a2 Col1a2 2000 3 14 dpIR #1 14 dpIR #1 14 dpIR #2 2 14 dpIR #2 1500

1 1000

-score 0 Counts Z 500 -1

0 -2 0 10 20 30 40 50 0 10 20 30 40 50 Infarct-to-remote position Infarct-to-remote position

B Nppa Nppa 1000 3 14 dpIR #1 14 dpIR #1 800 14 dpIR #2 2 14 dpIR #2

600 1

400 -score 0 Counts Z

200 -1

0 -2 0 10 20 30 40 50 0 10 20 30 40 50 Infarct-to-remote position Infarct-to-remote position

C Serca2 Serca2 1500 3 14 dpIR #1 14 dpIR #1 2 1200 14 dpIR #2 14 dpIR #2 1 900 0

600 -score Counts Z -1

300 -2

0 -3 0 10 20 30 40 50 0 10 20 30 40 50 Infarct-to-remote position Infarct-to-remote position

4 4 4 D Nppa Col1a2 Serca2 similar genes similar genes similar genes 2 2 2

0 -score -score -score Z Z 0 Z 0 -2

-2 -2 -4 0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50 Infarct-to-remote position Infarct-to-remote position Infarct-to-remote position

E r = 0.88 p < 10-20 14 dpIR #1

14 dpIR #2 Supplemental Figure 4

A B C Sham 14 dpIR Sham 14 dpIR Sham 14 dpIR

Col1a2 Nppa Serca2 Sparc Crip1 Pln Fstl1 Gpx3 Atp5g3 Serping1 C1qa Slc25a4 Pdgfrl Fxyd5 Hadhb Tmem45a Capg Lpl Col3a1 Pmepa1 Fabp3 Sfrp1 Rpl34-ps1 Gbas Lox Col14a1 Ndufa5 Ecrg4 Comp Got1 Dkk3 Mfge8 Acaa2 Col1a1 Lmna Acadl Itgbl1 Zbtb7a Acadm Fn1 Slc48a1 Atp5k Thbs2 Myh7 Txlnb Cthrc1 Sec61a1 Uqcrh Col8a1 Dact3 Pdha1 Col5a2 Ssbp4 Mpc2 Lum Junb Hadh Fbln2 S1pr2 Mlf1 Gas1 Atf5 Sod2 Antxr1 Rps14 Ndufc1 Thbs1 Apoe Cox7b Ogn Htra1 Ndufb3 Col16a1 Hexa Fhl2 Vim Cyb5r3 Uqcrc2 Cxcl16 Rhoc Gm16089 Timp1 C1qb mt-Cytb Rnase4 Gm5805 Hsdl2 Ddah1 Dnajb1 Ryr2 Gpc6 Sod3 Atp5f1 Htra3 Pdlim7 Hopx Prelp Tapbp Uqcrb Fam198b Tagln Slc25a3 Dcn Lamp1 mt-Co2 Igf1 Cnppd1 Acat1 Ctss Fmod Ppp1r14c Mfap5 Vmac Trdn Sulf2 Ctsd Cox6c Fcrls Ap2b1 Minos1 Mmp2 Cnn2 mt-Nd1 Marcks Lgals1 Mdh1 Tgfb3 Ssr2 Mylk4 Lrp1 Clec3b mt-Nd4 Ptgfrn Ninj1 Ccng1 Crtap Efemp2 Cisd1 Txndc5 Ckb Ndufc2 Slc1a5 Hspg2 Fh1 Sfrp2 Plvap Gm21987 C1qc Fam129b Asb11 Serpina3n Gabarap Abcc9 Frzb Mxd4 Dnajc28 Pcolce2 Rpsa Art3 Wbp5 Actb Mrpl18 Postn Mxra8 Usmg5 Itm2a Mfsd10 Cd36 Prdx4 Clic1 Ndufs1 Gpm6b C1qtnf3 Pdhx Fcer1g Bet1l Tcea3 Sertad4 Rpl37 Auh Serpinh1 Ltbp4 Kcnj2 Mdk Rps15-ps2 Slc25a42 Dpt Triobp Dld Fbln5 Gm8730 Grb14 Pamr1 Rps21 Ttn Ahnak2 Slc35c2 Phyh Angptl7 Pi16 Fam134b Ctsl Gm10076 Etfdh Fxyd6 Lamb2 Ppm1k Ctsk Itgb5 Gm6265 Ctgf Fam195b Ces1d Nid1 Kdelr1 Camta1 Cd9 Dram1 Opa1 Sptan1 Emr1 Vldlr Tnfsf12 Fibin Sdhb Arap1 Cox7a2 Serpine2 Rpl3-ps1 Atp5j Plxdc2 Ap2m1 Oxct1 Id3 Fkbp10 mt-Nd2 Fgl2 Erp44 Nnt Cd302 Gm9843 Cacna1c Selplg Lamc1 Chchd3 Bgn Mvp Yipf7 Nedd9 Foxp1 Gm15543 Maged1 Ssc5d Ndufa6 Zfp185 Smarcb1 Idh2 Matn2 Coro1a Slirp Col4a5 Abhd12 Cs Myh10 Rps15a-ps5 Ndufs3 S100a10 Osbpl5 Tmem126a Pdlim3 Vat1 Fabp4 Thbs4 Tmbim1 Ivns1abp Lmo4 Flna Acot13 Abi3bp Gnb2 Rbfox1 Adamts2 Cfdp1 Eno3 Islr Clec11a Gm10012 Ssr1 Rhoj Mgme1 Fndc3b C4b Asb15 Nucb1 Ptgis mt-Co3 Mgp Tagln2 Acsl1 Kcne4 Tkt Adhfe1 Crispld2 Qsox1 Slc25a12 Tgfbi Ptma Chchd2 Clmp Mfap4 Eci2 Adcy7 Copg1 Ppip5k2 Mdfic Cygb Acyp2 Colec12 Eef2 Dbt Basp1 Eif5a Ivd Lyz2 Plec Gm15776 Sh3d19 Selm Pkia Rin2 Oaf Actc1 Mrc2 Rras Ndufb6 Pmp22 Snx17 Mrpl30 Pcolce Rpl10a-ps1 Atp5h Id2 Tmem176a Nfs1 Tgif1 Ncf1 Gm13186 Mmp14 Npc2 Prdx3 H2-Eb1 Zdhhc8 Gstk1 Ski Ecm1 Ndufs4 Tm9sf3 Stat3 Abcd3 Ccdc80 Crlf1 Dlat Cd53 Cabin1 Sdhd Csf1r Adamtsl2 Ckmt2 S100a4 Ptprf Sdha Col8a2 Tspo Hibadh Itgb1 Bicc1 Tbc1d4 Gpr153 Gm10288 Cox5a Fbln1 Syde1 Cryab Dap Trpc4ap Cmya5 Lgmn Clu Sucla2 Cfh Aplnr Dhrs4 Anxa1 Sat1 Atp5b Loxl1 Bcap31 Ak4 H2-Ab1 Rhoa mt-Atp6 Actn1 Emp3 Zfp106 Tyrobp Myoc Chpt1 Dpysl3 Tbrg1 Ghitm Serpinf1 Rps3 Cox14 Lcp1 Ccdc109b 2310002L09Rik Gxylt2 Esyt1 Lrtm1 Fkbp7 Cotl1 Gm12338 Col5a1 Abhd16a Kitl Cilp Emilin1 mt-Nd3 Rbp1 Axl Gm11716 Tns3 Atf6b Rgcc Rhou Slc44a2 Ndufb5 Timp2 Glmp mt-Nd5 Z-score Unc93b1 Fbln1 Apoo -2 2 Chsy1 Nppb Rnf128 Dab2 Tuba1a Ndufa2 Supplemental Figure 5

A Sham 1 dpIR 14 dpIR Infarct-to-remote position Infarct-to-remote position Infarct-to-remote position Pearson correlation (z score) Pearson correlation (z score) Pearson correlation (z score) Infarct-to-remote position Infarct-to-remote position Infarct-to-remote position

B Sham 1 dpIR 14 dpIR Counts (Z score) Counts (Z score) Counts (Z score) 39 regulated genes 85 regulated genes 87 regulated genes

Infarct-to-remote position Infarct-to-remote position Infarct-to-remote position C Sham 1 dpIR 14 dpIR 300 300 300

200 200 200 Counts Counts 100 Counts 100 100

0 0 0 0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50 Infarct-to-remote position Infarct-to-remote position Infarct-to-remote position Supplemental Figure 6

3 6 2 104 A 10 r = 0.7512 10 r = 0.5093 10 r = 0.1390 r = 0.7159 p < 0.0001 p = 0.0056 p = 0.4982 p < 0.0001 102 104 102 101 101 102 100 mRNA (log 2) (log mRNA 0 mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA

10 10) (log mRNA 100 100 10-2 10-1 CRIP1 APOE APOE FXYD5 FXYD5 PMEPA1 PMEPA1 -4 10-2 10-2 10-1 10 -4 -2 0 2 4 -4 -2 0 2 4 -4 -2 0 2 4 10 10 10 10 10 10 10 10 10 10 10-1 100 101 10 10 10 10 10 COL1A2 mRNA (log 2) COL1A2 mRNA (log 2) COL1A2 mRNA (log 10) COL1A2 mRNA (log 2)

6 106 6 15 B 10 r = !0.1395 r = 0.0391 10 r = 0.3660 10 r = 0.0804 p = 0.4465 p = 0.8318 p = 0.0394 10 p = 0.6616 104 10 104 104 105 102 102 100 mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA 100 2) (log mRNA 102 10-5 0 -2 10 10 10-10 ATP5G3 ATP5G3 NDUFA5 CHCHD2 SLC25A3 SLC25A3 10-4 100 10-2 10-15 10-4 10-2 100 102 104 10-4 10-2 100 102 104 10-4 10-2 100 102 104 10-4 10-2 100 102 104 COL1A2 mRNA (log 2) COL1A2 mRNA (log 2) COL1A2 mRNA (log 2) COL1A2 mRNA (log 2)

4 8 6 1 C 10 r = 0.5142 10 r = 0.4280 10 r = 0.4011 10 r = 0.6368 p = 0.0037 p = 0.1832 4 p = 0.0579 0 p = 0.0002 106 10 10 102 102 10-1 104 100 100 10-2 2 mRNA (log 2) (log mRNA

10 2) (log mRNA mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA 10-2 10-3 -2 10 0 10 10-4 10-4 DKK3 FSTL1 FBLN2 HTRA3 10-4 10-2 10-6 10-5 10-10 10-8 10-6 10-4 10-2 100 102 10-10 10-5 100 105 10-10 10-8 10-6 10-4 10-2 100 102 10-10 10-8 10-6 10-4 10-2 100 102 NPPA mRNA (log 2) NPPA mRNA (log 2) NPPA mRNA (log 2) NPPA mRNA (log 2)

D 106 106 106 15 r = 0.1089 r = !0.0255 r = 0.2126 10 r = !0.0299 p = 0.5666 p = 0.2594 p = 0.8937 1010 p = 0.8754 104 104 104 105

102 102 100 mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA 102 10-5 100 100 10-10 ATP5G3 ATP5G3 NDUFA5 CHCHD2 SLC25A3 SLC25A3 10-2 100 10-2 10-15 10-10 10-8 10-6 10-4 10-2 100 102 10-10 10-8 10-6 10-4 10-2 100 102 10-10 10-8 10-6 10-4 10-2 100 102 10-10 10-8 10-6 10-4 10-2 100 102 NPPA mRNA (log 2) NPPA mRNA (log 2) NPPA mRNA (log 2) NPPA mRNA (log 2)

4 6 6 1 E 10 r = 0.7917 10 r = 0.3050 10 r = 0.1693 10 r = 0.3027 p < 0.0001 p = 0.1077 p = 0.3624 0 p = 0.0979 104 10 102 104 10-1 102 100 102 10-2 0

10 2) (log mRNA mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA mRNA (log 2) (log mRNA 10-3 -2 0 10 10 -2 10 10-4 DKK3 FSTL1 FBLN2 HTRA3 10-4 10-2 10-4 10-5 10-2 100 102 104 10-2 10-1 100 101 102 103 10-6 10-4 10-2 100 102 104 10-6 10-4 10-2 100 102 104 SERCA2 mRNA (log 2) SERCA2 mRNA (log 2) SERCA2 mRNA (log 2) SERCA2 mRNA (log 2)

3 6 6 6 F 10 r = 0.2452 10 r = 0.2265 10 r = 0.1310 10 r = 0.4674 p = 0.1915 p = 0.2374 p = 0.0172 p = 0.0106 102 104 104 104

101 102 102 102 mRNA (log 2) (log mRNA

0 2) (log mRNA 0 mRNA (log 2) (log mRNA 10 2) (log mRNA 10

100 100 10-1 10-2 CRIP1 APOE APOE FXYD5 FXYD5

PMEPA1 PMEPA1 -4 10-2 10-2 10-2 10 -6 -4 -2 0 2 4 -2 0 2 4 10-6 10-4 10-2 100 102 104 10-6 10-4 10-2 100 102 104 10 10 10 10 10 10 10 10 10 10 SERCA2 mRNA (log 2) SERCA2 mRNA (log 2) SERCA2 mRNA (log 2) SERCA2 mRNA (log 2) Supplemental Figure 7

A B Sox9 Sox9 Sham 3 1 dpIR I ** 14 dpIR R sham) 2 vs ts oun

C 1 (Fold change

Relative mRNA abundance 0 Sham 1 dpIR 14 dpIR

Infarct-to-remote position C Col1a2 similar genes in vitro

1) 3 Control siRNA ! Sox9 siRNA

p<0.06 *** * p<0.06 1 * * * * * (Fold change upon TGF Relative mRNA abundance 0

Fn1 Sox9 Fstl1 Sfrp1 Dkk3 Fbln2 Col1a2 Sparc Pdgfrl Col3a1 Col1a1 Thbs2Col8a1Col5a2 Col16a1 Tmem45a D Fibrotic genes in vitro

1) 25 Control siRNA ! Sox9 siRNA 15

5 3

2 p<0.06 p<0.07 p<0.07 1 * ** (Fold change upon TGF Relative mRNA abundance 0

Ctgf Tcf4 Tgfb3 Spp1 Acta2 Ecm1 Cdrap Col2a1 Col4a1 Col11a1 Col11a2 Density Supplemental Figure S8

A DAPI TdT DAPI POSTN Merge * * * * * * * * Infarct * * * Remote

B DAPI TdT DAPI VIM Merge * * * * * * * Infarct * * * * * Remote Supplemental Figure 9

Chr6; mm9 Chr1; mm9

20 kb 20 kb 5 5 heart Mouse H3K27ac 5 5 cytes SOX9 Chondro- 5 5 cytes H3K27ac Chondro-

Col1a2 Fn1

Chr10; mm9 Chr2; mm9

20 kb 20 kb 5 5 heart Mouse H3K27ac 5 5 cytes SOX9 Chondro- 5 5 cytes H3K27ac Chondro-

Lum Vim 1 Supplemental Figure Legends

2 Supplemental Figure 1. Cardiac ischemia-reperfusion injury. A, Histological sections of

3 hearts stained for Hematoxylin and Eosin (H&E) or Sirius Red from Sham, 1, and 14 dpIR

4 mice. Scale bars, 1 mm. B, Real-time PCR analysis of three reference genes associated to

5 cardiac fibrosis (Col1a2), remodeling (Nppa), and contractility/calcium handling (Serca2) in

6 infarct (I) and remote (R) zones in Sham, 1, and 14 dpIR mice (n=6 per group; **p<0.01 vs

7 infarct).

9 Supplemental Figure 2. High resolution gene expression atlas of the infarcted heart by

10 tomo-seq. A, Pairwise correlation for all sections across all genes expressed at >4 reads in >1

11 section analysed by tomo-seq 1 dpIR in one biological replicate. B, Pairwise correlation for all

12 sections across genes exhibiting at least two-fold and statistically significant differential

13 expression between the infarct and remote zones by tomo-seq at 1 dpIR. C, Hierarchical

14 clustering of expression traces for all genes exhibiting at least two-fold and statistically

15 significant differential expression between the infarct and remote zones, using Euclidean

16 distance as metric at 1 dpIR. D, KEGG analysis showing the enriched pathways the genes

17 exhibiting a two-fold differential expression between the infarct and remote zones at 1 or 14

18 dpIR are involved in. Pathways are ranked by their respective p value corrected by the

19 Benjamini-Hochberg method.

21 Supplemental Figure 3. Confirmation of tomo-seq data. A through C, Spatial expression

22 pattern of Col1a2 (A), Nppa (B), and Serca2 (C) determined by tomo-seq 14 dpIR in two

23 distinct biological replicates (14 dpIR #1 and #2). Traces show gene expression as the number

24 of read counts (left) or after Z-score normalization (right). Z-score normalization removes

25 overall expression differences between genes by setting the mean to 0 and the standard

21 1 deviation to 1. D, Spatial expression traces of ten co-regulated genes 14 dpIR in the second

2 biological replicate based on the data obtained in the first biological replicate. Reference genes

3 are shown in red, and ten most similar genes are shown in grey. E, Spearman correlation

4 between the two sets of samples. Data are shown as total number of reads (summed over all

5 sections) for each gene.

7 Supplemental Figure 4. Validation of tomo-seq by RNA-seq. Validation of the differential

8 gene expression for the one hundred thirty most similar genes to Col1a2 (A), Nppa (B), and

9 Serca2 (C) between Sham and 14 dpIR hearts by RNA-seq performed on the complete injured

10 area or a corresponding region in the Sham hearts (n=3 per group).

12 Supplemental Figure 5. High resolution lncRNA expression atlas of the infarcted heart

13 by tomo-seq. A, Pairwise correlation for all sections across all non-coding genes detected at

14 greater than four reads in more than one section by tomo-seq in the hearts from Sham, 1, and

15 14 dpIR mice. Especially at 14 dpIR, anatomical regions of the infarcted heart i.e., infarct and

16 remote zones are detectable as clusters of correlated sections. B, Hierarchical clustering of

17 expression traces for all genes exhibiting a peak of expression using Euclidean distance as

18 metric. C, Spatial expression traces of detected non-coding genes by tomo-seq in the hearts of

19 Sham, 1, and 14 dpIR mice. Traces are shown as read counts.

21 Supplemental Figure 6. Lack of correlation between genes from different reference lists.

22 Real-time PCR analysis on human ischemic heart tissue indicating the lack of correlation when

23 cross-referencing genes from different lists. A, COL1A2 expression correlation with the

24 expression of NPPA co-regulated genes. B, COL1A2 expression correlation with the expression

25 of SERCA2 co-regulated genes. C, NPPA expression correlation with the expression of

22 1 COL1A2 co-regulated genes. D, NPPA expression correlation with the expression of SERCA2

2 co-regulated genes. E, SERCA2 expression correlation with the expression of COL1A2 co-

3 regulated genes. F, SERCA2 expression correlation with the expression of NPPA co-regulated

4 genes. Control hearts and remote, border-zone and infarct zones from ischemic hearts are

5 plotted. Data are presented as log 2 transformed values. Pearson correlation (r) and significance

6 of co-regulated gene expression is shown (n=27–34; p<0.05 is considered as significant).

8 Supplemental Figure 7. Regulation of ECM-related genes. A, Spatial expression pattern of

9 Sox9 determined by tomo-seq in the hearts from Sham, 1, and 14 dpIR mice. B, Real-time PCR

10 analysis of Sox9 in the infarct (I) and remote (R) zones in the hearts from Sham, 1, and 14 dpIR

11 mice. Data are displayed as fold change vs matched Sham hearts (n=5–6 per group; **p<0.01).

12 C and D, Real-time PCR analysis of Col1a2 co-expressed genes and other fibrotic genes

13 including SOX9 target genes in 3T3-L1 fibroblasts activated with TGFβ1 (5 ng/ml) for 3 hrs

14 after knock-down of Sox9 using a siRNA (10 nM) or a control siRNA (n=5 in two independent

15 experiments; *p<0.05; **p<0.01; ***p<0.001 vs control siRNA).

17 Supplemental Figure 8. Lineage tracing of SOX9-expressing cells. Co-immunostaining for

18 TdTomato (TdT) and Periostin (POSTN) (A) or Vimentin (VIM) (B) in the hearts from sham-

19 operated mice and 14 dpIR. White stars in the Merge field indicate SOX9-TdT-positive

20 regions. Scale bars, 200 μm.

22 Supplemental Figure 9. Peak intensity plots of ChIP-seq reads for SOX9 and histone

23 modification marks (H3K27ac) in four ECM related genes induced in the heart 14 dpIR.

24 Normalized read signals from each ChIP-seq were plotted on a 20-kb window flanking the

25 SOX9 binding sites predicted by oPOSSUM 6.0 (cf. Supplemental Table 6). ChIP-seq signals

23 1 for H3K27Ac obtained in the heart (ENCODE dataset) were mapped to the corresponding

2 SOX9 peak regions obtained in chondrocytes.8

24 1 Supplemental References

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