Spatiotemporal single-cell analysis reveals critical roles of mechano-sensing genes at the border zone in remodeling after myocardial infarction

doi:10.21203/rs.3.rs-620498/v1

Download PDF

Article

Spatiotemporal single-cell analysis reveals critical roles of mechano-sensing genes at the border zone in remodeling after myocardial infarction

https://doi.org/10.21203/rs.3.rs-620498/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 17 Nov, 2022

Read the published version in Nature Cardiovascular Research →

Version 1

posted

You are reading this latest preprint version

The left ventricular remodeling after myocardial infarction (MI) causes heart failure, but its underlying mechanisms remain largely unknown. Here, we performed an integrative analysis of spatial transcriptomics and single cell RNA-seq in murine MI model and found that mechanical stress-response genes are expressed at the border zone and play a critical role in left ventricular remodeling after MI. Single-cardiomyocyte RNA-seq of the heart after MI divided cardiomyocytes into 8 clusters. We identified the cell cluster which was specifically derived from the ischemic area in an early stage after MI and expressed mechano-sensing genes such as Csrp3, Ankrd1 and Ankrd2. Spatial transcriptomic analysis demonstrated that cardiomyocytes with this gene expression profile were specifically localized at the border zone in post-MI day 1. AAV9-mediated gene silencing of Csrp3 exacerbated cardiac dilatation and dysfunction after MI. Collectively, our datasets and findings not only provide an insight into spatiotemporal molecular pathogenesis of myocardial infarction but also highlight mechano-sensing genes at the border zone as an adaptive regulator of left ventricular remodeling.

Cardiac & Cardiovascular Systems

Molecular Biology

spatiotemporal single-cell analysis

myocardial infarction

left ventricular remodeling.

Despite decades of intensive research and therapeutic development, ischemic heart diseases are still a leading cause of death worldwide. Myocardial infarction (MI) causes various complications such as arrhythmia, valvular disease and heart failure¹. In particular, the number of patients with heart failure has recently been increasing and becoming a more serious problem in many countries^2,3. MI induces changes of left ventricular (LV) size, shape and function which were known as LV remodeling, leading to the development of heart failure^4–6. Therefore, it is an urgent need to clarify molecular mechanisms of LV remodeling after MI to inhibit the development of heart failure and improve the prognosis of patients with MI^7–10. However, cellular and molecular behaviors change spatially and temporally after MI, it is difficult to precisely understand the molecular mechanisms of LV remodeling.

The infarcted heart is usually roughly divided into three zones such as an infarct zone (IZ), an ischemic border zone (BZ; defined as the sectors immediately adjacent to IZ) and a remote zone (RZ)¹¹. Although previous studies showed transcriptional differences between myocardium proximal and distal to the IZ^11–13, the precise characteristics of cardiomyocytes in each region have not been well explored. Furthermore, previous studies examined gene expressions using mRNA isolated from bulk samples and did not separate cardiomyocytes and non-cardiomyocytes. Here, we identified and characterized the distinctive transcriptional properties in each region of infarcted hearts by integrative analysis of single-cardiomyocyte RNA-seq and spatial transcriptomics. We unveiled a novel molecular adaptation of cardiomyocytes in the BZ characterized by transcriptional activation of mechano-sensing genes such as Csrp3 (also known as MLP, muscle LIM protein), Ankrd1 and Ankrd2.

Single-cardiomyocyte RNA-seq identifies spatiotemporally distinct cell clusters after MI

We made a murine MI model by ligating the left anterior descending artery. We separately isolated cardiomyocytes from ischemic region (IR, area including infarct zone and 2 mm of its lateral margin) and remote region (RR, area other than IR) and conducted single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic analysis by Visium (10X Genomics) (Fig. 1a). We sequenced 775 cardiomyocytes from 7 murine hearts and analyzed 761 cardiomyocytes (sham, 88 cardiomyocytes; IR in post MI (pMI) day 1, 169 cardiomyocytes; RR in pMI day 1, 237 cardiomyocytes; IR in pMI day 7, 151 cardiomyocytes; RR in pMI day 7, 130 cardiomyocytes) in which >2500 genes were detected. Using clustering analysis for scRNA-seq data, cardiomyocytes derived from five different conditions (sham, pMI day 1 IR, pMI day 1 RR, pMI day 7 IR and pMI day 7 RR) were classified into eight cell clusters (Fig. 1b). We statistically analyzed the proportion of each cluster and found that there were some clusters only appeared in the specific conditions (Fig. 1c). For example, cluster 6 accounts for the majority of sham cardiomyocytes and highly expressed genes associated with mitochondrial functions and cardiomyocyte metabolism (Fig. 1c-e). By contrast, cluster 0 and cluster 1, mainly derived from cardiomyocytes of pMI day 1 and day 7, expressed genes associated with collagen production and inflammatory responses (inflammatory cytokine production), respectively (Fig. 1c-e). Although the proportions of many clusters were very similar between IR and RR, only cluster 7 was specifically derived from the cardiomyocytes of IR at pMI day 1 (Fig. 1c). Differential gene expression analysis showed that one of the key features in the cluster 7 was high expression of genes encoding for mechano-sensing proteins such as Csrp3, Ankrd1 and Ankrd2 (Fig. 1d, e). The cluster 7 cardiomyocytes occupied about 30% of all cardiomyocytes in IR of pMI day1, but they were scarcely observed in other four conditions (Fig. 1c).

Integrative analysis of spatial transcriptomics and scRNA-seq identifies spatiotemporally-regulated cell clusters after MI

To know the specific positional information of these cardiomyocyte clusters seen in our scRNA-seq analysis, we performed spatial transcriptomic analysis using Visium (10X Genomics). A mean of 1,974.4 spots (1,153-3,154 spots) from seven murine hearts (sham, n=1; day1, n=2; day7, n=2; day14, n=2) were sequenced and a median of 3,627 genes per spot (interquartile range 2,073-5,425) were detected. Fig. 2a showed both hematoxylin and eosin (H&E) staining of the heart at each time point after MI surgery and spot region clusters revealed by clustering analysis of Visium data (Fig. 2b). Although the size of infarct regions was slightly different among mice, the characteristic of each cluster’s distribution was very similar and reproducible (Extended Data Fig. 1a). We also performed differential gene expression and gene ontology enrichment analysis for each cluster (Fig. 2c, d). Spot regions of cluster Green, characteristic for genes involved in mitochondrial functional pathway, occupied the majority of sham heart and RZ of infarcted hearts. Cluster Brown, characterized by expression of collagens and extracellular matrix genes, was specifically localized in IZ (Fig. 2a, c, d). Spot regions of cluster Red were mainly seen in BZ of the infarcted hearts (Fig. 2a). Almost all marker genes of cluster Red were shared with those of cluster 7 in single-cardiomyocyte RNA-seq analysis (Fig. 1d, e, 2c, d). Genes encoding mechano-sensing proteins such as Csrp3 and Ankrd1 were highly expressed in cluster Red, which were uniquely localized in BZ of the infarcted hearts in acute phase of MI (Fig. 2e). Spatially-regulated expression of these genes in cardiomyocytes was validated by RNA in situ hybridization (Extended Data Fig. 1b–e). Subsequently, we determined cell types composing each spot region by integrative analysis of scRNA-seq data from both cardiomyocytes and non-cardiomyocytes (Extended Data Fig. 2) and Visium data. Cardiomyocytes occupied the majority of the spot regions of cluster Green and Red, whereas cardiac fibroblasts accounted a large part of cluster Brown (Fig. 2f).

Weighted gene co-expression network analysis of spatial transcriptomics reveals mechano-sensing genes as a distinct key factor expressed in BZ after MI

To clarify the transcriptional network of BZ, we performed weighted gene co-expression network analysis (WGCNA) on the Visium data. WGCNA identified nine gene modules (Fig. 3a, Extended Data Fig. 3) and each module contained 40-954 genes. Among nine modules, expressions of Module 1(M1) genes encoding collagens and extracellular matrix proteins were up-regulated selectively in IZ especially in pMI day 7 (Extended Data Fig. 3). The M2 genes were widely seen in the sham hearts and RZ of the infarcted hearts (Extended Data Fig. 3). By contrast, the M3 genes were specifically expressed in IZ of pMI day 1 hearts (Extended Data Fig. 3). Expression patterns of M8 genes were unique and up-regulated only in BZ of the pMI day1 hearts (Fig. 3b). WGCNA of M8 identified mechano-sensing genes such as Csrp3 and Ankrd1 as its central genes (Fig. 3c, d). M8 genes were highly overlapped with the marker genes of cluster Red (Extended Data Fig. 4). Furthermore, single-cardiomyocyte RNA-seq analysis revealed that the cardiomyocytes with lower Csrp3 expression had high expression levels of p53 signaling associated genes such as Cdkn1a, Bax and Fas (Fig. 3e). p53 signaling in cardiomyocytes was shown to induce heart failure both after MI and after pressure overload^14–16, suggesting that Csrp3 in cardiomyocytes adaptively functions against the induction of failing cardiomyocytes.

Csrp3 expressed in cardiomyocytes of BZ adaptively prevents adverse LV remodeling after MI

To clarify the roles of the mechano-sensing genes specifically upregulated in BZ of the pMI day 1 heart, we generated knockdown AAV9 vectors to deliver shRNA against Csrp3, which was identified as one of the key genes in M8 (Fig. 3c). We injected the knock-down vectors (Extended Data Fig. 5a) into mice through their tail veins two weeks before MI surgery and performed transthoracic echocardiography to analyse their cardiac function (Fig. 4a). The delivered shRNA vectors significantly reduced expressions of Csrp3 at mRNA and protein levels in the heart (Fig. 4b, c). Compared with controls, the AAV9-shCsrp3 injected mice showed more enlarged left ventricles and reduced LV contraction after MI (Fig. 4d−f). Although there was no statistical significance, the AAV9-shCsrp3 injected mice also tended to die after MI as compared with control mice (Extended Data Fig. 5b).

In the present study, we comprehensively investigated the spatiotemporal gene expression of the heart after MI and identified several gene programs which were precisely regulated in a spatiotemporal manner. It has been well known that LV remodeling after MI including expansion of the BZ causes LV dysfunction and dilatation, resulting in heart failure^17,18. However, the cellular and molecular mechanisms underlying remodeling processes in the BZ remain unclear. Previous studies examined expressions of RNA or proteins using myocardial tissues around the BZ dissected from infarcted hearts and reported that expressions of genes associated with angiogenesis, inflammation, apoptotic response and B-type natriuretic peptide (BNP) were highly up-regulated in the BZ^11,19–21. However, it was difficult to precisely define spatiotemporal molecular behaviors after MI at the single-cell level due to the ambiguous spatial localization of the BZ and the analysis using bulk tissues. In this study, we combined spatial transcriptomics and single-cell RNA-seq to obtain spatiotemporal expression profiles after MI at the single-cell level, and identified the mechano-sensing gene Csrp3, expressed in the BZ at an early phase after MI, as an adaptive regulator of LV remodeling. Our integrative datasets provide a publicly available resource to further deepen the understanding of pathogenesis of myocardial infarction.

Our spatial transcriptomic analysis clearly revealed that expressions of mechano-sensing genes were upregulated in an acute phase after MI in the BZ. In a recent study using echocardiography for a porcine MI model, the end-diastolic mean wall stress and myocardial stiffness of the BZ lineally increased throughout 28 days after MI²², suggesting that cardiomyocytes activate gene expression in response to mechanical stresses during the very acute phase of MI. Csrp3 (MLP) has been reported to be a mechanostress sensor localized at the Z disc^23,24 and its expression in cardiomyocytes is upregulated by mechanical stretch²⁵ as well as pressure overload on the heart^16,26. It has been reported that Csrp3 induces expression of cardiac hypertrophic genes such as Nppb, Acta1 and Myh7 in response to pressure overload and phenylephrine stimulation through the activation of calcineurin/NFAT pathway^23,27,28. Rcan1, whose expression is correlated with the activity of calcineurin/NFAT pathway²⁹, was also up-regulated in the cardiomyocytes with high expression levels of Csrp3 in our study (Fig. 3e). By contrast, DNA damage and p53 signaling pathway, an inducer of heart failure^14–16, was activated in cardiomyocytes with low expression levels of Csrp3 (Fig. 3e), suggesting that activation of Csrp3 in the BZ after MI inhibits DNA damage and p53 signaling activation to prevent the induction of failing cardiomyocytes. In skeletal muscle, Csrp3 has also been reported to regulate autophagy and protect against muscular dystrophy^30,31. Since autophagy is one of the important cardioprotective mechanisms during post-infarction cardiac remodeling^32–35, Csrp3 may also protect against adverse cardiac remodeling through the regulation of autophagy. In this study, mice with reduced levels of Csrp3 showed more LV dilatation and dysfunction, indicating exacerbation of LV remodeling after MI. These results suggest that the upregulation of Csrp3 as an acute response to mechanical stress in the BZ soon after MI is important to inhibit LV remodeling and may be a novel target of heart failure.

Animal models

All the animal experiments were approved by the University of Tokyo Ethics Committee for Animal Experiments and strictly adhered to the guidelines for animal experiments of the University of Tokyo (Approved Number P14-106). All wild type C57BL/6 male mice were purchased from CLEA JAPAN. All mice were housed in separate cages at a maximum of 6 mice per cage in a specific-pathogen–free, temperature-controlled vivarium under a 12-h light/dark cycle with ad libitum access to food and water.

Operation of myocardial infarction model and echocardiography

Myocardial infarction (MI) was induced as previously described³⁶. Age of mice operated and used for experiments were 9 week old male mice. Mice were anaesthetized by inhalation of 2% isoflurane. MI was induced by ligation of the left anterior descending artery. Mice that failed to develop MI or died within one day after the operation were excluded from the analysis. Transthoracic echocardiography was performed on conscious mice, using a Vevo 2100 imaging system (Visualsonics, Inc.). To minimize variation in the data, cardiac function was assessed only when the heart rate was 600–700 beats per minute. M-mode echocardiographic images were obtained from a longitudinal view to measure the size and function of the left ventricle.

Single-cardiomyocyte RNA-seq analysis of mouse samples

Cardiomyocytes were isolated using Langendorff perfusion from the left ventricle as previously described¹⁶. Enzymatic dissociation using Langendorff perfusion was performed with 35 mL enzyme solution (type 2 collagenase 1 mg/mL [Worthington], protease type XIV 0.05 mg/mL [Sigma-Aldrich], NaCl 130 mM, KCl 5.4 mM, MgCl₂ 0.5 mM, NaH₂PO₄ 0.33 mM, D-glucose 22 mM, HEPES 25 mM; pH 7.4) pre-heated to 37 °C, at a rate of 3 mL/min. To neutralize enzymatic activity, fetal bovine serum (FBS) was added to the solution to a final concentration of 0.2% (v/v). After perfusion of digestion buffer, left ventricle was cut according to each sectional region (IR, RR) and cardiomyocytes were subsequently isolated. Cell suspensions were filtered through a 100-μm nylon mesh cell strainer and centrifuged at 100 g for 2 min, after which the supernatant was discarded. To prevent hypercontraction, the cardiomyocytes were resuspended in medium (NaCl 130 mM, KCl 5.4 mM, MgCl₂ 0.5 mM, NaH₂PO₄ 0.33 mM, D-glucose 22 mM, HEPES 25 mM, FBS 0.2%; pH 7.4) containing a low concentration of calcium (0.1 mM). Rod-shaped live cardiomyocytes (viability of cardiomyocytes was ≥80% for all time points) were collected immediately with a 0.2–2-µL pipette (sample volume, 0.5 µL) and incubated in lysis buffer.

Single-cardiomyocyte cDNA libraries were generated using the Smart-seq2 protocol³⁷ and the efﬁciency of reverse transcription was assessed by examining the cycle threshold (Ct) values of control genes (Tnnt2) from quantitative real-time polymerase chain reaction (qRT-PCR) using a CFX96 Real-Time PCR Detection System (Bio-Rad), and the distribution of cDNA fragment lengths was assessed using LabChip GX (Perkin Elmer) and/or TapeStation 2200 (Agilent Technologies). The following primer sets were used for qRT-PCR: Tnnt2 mRNA forward: TCCTGGCAGA GAGGAGGAAG; Tnnt2 mRNA reverse: TGCAGGTCGA ACTTCTCAGC. A Ct value of 25 was set as the threshold. The sequencing libraries were subjected to paired-end 51-bp RNA sequencing on a HiSeq 2500 (Illumina) in rapid mode.

For the isolation and collection of non-cardiomyocytes, hearts were minced and enzymatically dissociated using 2 mg/mL type 2 collagenase (Worthington), 1 mg/mL dispase (Roche), and 20 U/mL DNase I (Roche), with 5 cycles of digestion for a total 40 min at 37 °C. After using a 40-μm cell strainer (Greiner) to remove the cardiomyocytes, 5,000 cells were prepared to a concentration of 1000 cells/µL and loaded into the Chromium Controller and single-cell cDNA libraries were generated using the Chromium 3′ v3 chemistry kit (10x Genomics) according to the manufacturer’s instruction. Libraries were sequenced on a NovaSeq 6000 System (Illumina) using a NovaSeq S4 Reagent Kit (200 cycles, 20027466, Illumina).

Single-cardiomyocyte RNA-seq data processing

Raw sequencing reads from single-cardiomyocyte RNA sequencing libraries were trimmed to remove adapter sequences and low-quality bases using fastp-0.20.0³⁸ with the parameters “--cut_tail --cut_tail_window_size 1 --cut_tail_mean_quality 30 --length_required 30”. The reference transcript data and the gene annotation file for mouse was downloaded from GENCODE (release 23, https://www.gencodegenes.org/)³⁹. The clean reads were aligned to the mouse genome (mm10) using STAR (version 2.7.2b)⁴⁰.　The reads aligned to exon were counted using featureCounts⁴¹. TPM normalization was calculated with reads mapped to the nuclear genome. We removed the low-quality cardiomyocytes with less than 2500 detected genes, which were used for the downstream analysis.

Identification of cell clusters and DEGs in single-cardiomyocyte RNA-seq

log₂(TPM+1) counts were used as the input of Seurat R package (version 4.0.0)⁴². Each dataset was merged, and the merged data were used for dimensionality reduction and cluster detection. Differentially expressed genes (DEGs) were detected by using FindMarkers in Seurat (log2fc.threshold > 0.25 and p_val_adj < 0.05). Top DEGs were presented according to the log₂ fold-change of the average expression (avg.log2FC). Finally, GO enrichment analysis was performed using Metascape with GO biological processes⁴³.

Spatial transcriptome analysis of mouse MI model

Frozen samples were embedded in OCT (TissueTek Sakura) and cryosectioned at −14ºC (Leica CM1860). Sections were placed on chilled Visium Spatial Gene Expression Slides (2000233, 10X Genomics), and adhered by warming the back of the slide. Tissue sections were then fixed in chilled methanol and stained according to the Visium Spatial Gene Expression User Guide (CG000239 Rev D, 10X Genomics). For gene expression samples, tissue was permeabilized for 15 minutes, which was selected as the optimal time based on tissue optimization time course experiments (CG000238 Rev D, 10X Genomics). Brightfield histology images of Hematoxylin and Eosin stain were taken using a 10X objective on a BZ-X700 microscope (Keyence Corporation, Itasca, Illinois). Raw images were stitched together using BZ-X analyzer software (Keyence Corporation) and exported as TIFF files.

Libraries were prepared according to the Visium Spatial Gene Expression User Guide (CG000239 Rev D, 10X Genomics) and sequenced on a NovaSeq 6000 System (Illumina) using a NovaSeq S4 Reagent Kit (200 cycles, 20027466, Illumina), at a sequencing depth of approximately 250-400M read-pairs per sample. Sequencing was performed using the following read protocol: read 1, 28 cycles; i7 index read, 10 cycles; i5 index read, 10 cycles; read 2, 91 cycles.

Spatial transcriptome data processing

Raw FASTQ files and histology images were processed by sample with the Space Ranger software (ver 1.2.1, 10X Genomics), against the Cell Ranger mm10 reference genome “refdata-gex-mm10-2020-A”, available at: https://cf.10xgenomics.com/supp/spatial-exp/refdata-gex-mm10-2020-A.tar.gz. Raw counts were used as the input of Seurat R package (version 4.0.0), log normalization was implemented separately for each dataset and integrated them. The integrated data were used for dimensionality reduction and cluster detection. Differentially expressed genes were detected by using FindMarkers in Seurat (log2fc.threshold > 0.25 and p_val_adj < 0.05). Top DEGs were presented according to the log2 fold-change of the average expression (avg.log2FC). Finally, GO enrichment analysis was performed using Metascape with GO biological processes⁴³.

Weighted gene co-expression analysis

CPM (counts per million) counts were used as the input of WGCNA analysis (version 1.69)⁴⁴. All genes expressed at more than 700 spots were used for analysis. The soft power threshold was analyzed with the “pickSoftThreshold” function and was applied to construct a signed network and calculate module eigengene expression using the “blockwiseModules” function. Modules with <30 genes were merged to their closest larger neighboring module. To visualize the weighted co-expression networks, Cytoscape (version 3.8.0)⁴⁵ with the “prefuse force-directed layout” was used. Signed eigengene-based connectivity of a gene in a module reflected node size.

AAV9-shCsrp3 infection

The AAV vectors were prepared by VectorBuilder Inc. (https://en.vectorbuilder.com) according to established procedures⁴⁶. Briefly, AAV vectors of serotypes 2 and 9 were generated in HEK293T cells, using triple-plasmid co-transfection for packaging. Viral stocks were obtained by CsCl2-gradient centrifugation. Titration of AAV viral particles was performed by real-time PCR quantification of the number of viral genomes, measured as CMV copy number. The viral preparations had a titer between 1×10¹² and 5×10¹² genome copy (GC)/mL. Viruses were administered in 100-μL saline via tail-vein injection. 3×10¹¹ GC doses of AAV9-eGFP or 3×10¹¹ GC doses of AAV9-shCsrp3 were administered to the uninjured mice 2 week before MI surgery.

RNA in situ hybridization

For RNA fluorescent in situ hybridization, the RNAscope system (Advanced Cell Diagnostics) was used with a probe against murine Csrp3 and Tnnt2 mRNA as previously described⁴⁷. Frozen sections (10 µm) were fixed in PBS containing 4% paraformaldehyde for 5 min at room temperature, dehydrated by serial immersion in 50%, 70%, and 100% ethanol for 5 min at room temperature, and treated with protease for 30 min at room temperature. The probe was then hybridized for 2 h at 40 °C, followed by RNAscope amplification, and co-stained with DAPI to detect nuclei. Images were obtained using each 20X or 40X objective on a BZ-X700 microscope (Keyence Corporation, Itasca, Illinois).

Tissue histology

For histological analysis, mice were anaesthetized by isoflurane inhalation and sacrificed by cervical dislocation. The chest was opened, and the heart was flushed with cold PBS via cardiac apical insertion of a 25-gauge needle. The right atrium was cut to allow drainage of blood from the heart, and the mice were briefly perfused with cold fixative (4% paraformaldehyde in PBS) through the apex of the heart. Tissues were excised, flushed with fixative, incubated in fixative for 12 h at 4 °C with gentle rotation, and finally embedded in paraffin. Paraffin-embedded heart tissues were sectioned into 4-μm slices using an SM2010 R Sliding Microtome (Leica Biosystems), and sections were stained with Picrosirius Red/Fast Green dyes. Brightfield histology images were taken using a 10X objective on a BZ-X700 microscope (Keyence Corporation, Itasca, Illinois).

qRT-PCR

Expression of Csrp3 mRNA was evaluated by qRT-PCR using a CFX96 Real-Time PCR Detection System, and the relative expression levels of the target genes were normalized to the expression of an internal control gene, using the comparative Ct method. The following primer sets were used for qRT-PCR.

Rps18 mRNA forward, CTTAGAGGGACAAGTGGCG

Rps18 mRNA reverse, ACGCTGAGCCAGTCAGTGTA

Csrp3 mRNA forward, TGAGAAGGTCATGGGAGGTG

Csrp3 mRNA reverse, CTTGCTGTGTAAGCCCTCCA

Western blotting

Heart tissue was homogenized and lysed with RIPA buffer containing 10 mM Tris-HCl, 150 mM NaCl, 5 mM EDTA, 1% Triton X-100, 1% sodium deoxycholate, and 0.1% sodium dodecyl sulfate with protease and phosphatase inhibitor cocktails for 30 min on ice. Lysates were centrifuged at 15,000 × g for 30 min and the supernatants were used as whole-cell extracts. Total protein concentrations in the supernatants were measured using a bicinchoninic acid assay (Pierce BCA Protein Assay Kit; Thermo Scientific). For immunoblot analysis, extracted protein samples were separated on 5%–20% Mini-PROTEAN TGX precast gradient gels (Bio-Rad) and transferred onto nitrocellulose membranes (BioRad). Membranes were blocked with 5% FBS in Tris-buffered saline plus 0.05% Tween and incubated overnight at 4 °C with primary antibodies, followed by horseradish peroxidase-conjugated secondary antibodies and ECL Plus (GE Healthcare). Immunoreactive signals were detected using a LAS 4000 analyzer (GE Healthcare). The following antibodies were used for western blotting: anti-Csrp3 monoclonal antibody (ab173301, Abcam, 1:2000), HRP-linked horse anti-rabbit IgG antibody (#7074, Cell Signaling Technology, 1:3000), HRP-linked horse anti-mouse IgG antibody (#7076, Cell Signaling Technology, 1:3000) and anti-Actin monoclonal Antibody (#MA5-11869, Invitrogen, 1:5000).

Data Availability

The authors declare that all data are available in the article file and Extended Data information files or available from the authors upon reasonable request. The sequencing and alignment metrics of single-cardiomyocyte RNA-seq and Visium are provided as Extended data tables. The source data underlying Figures. 1e, 2c, d, 3c, d, 4b, c, f and Extended Data Fig. 3b are provided as data source tables. Single-cardiomyocyte, single-cell RNA sequencing and spatial transcriptomic data have been deposited　in　GSE176092 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176092). The secure token is “ersbeoycldebpgd”).

Acknowledgements

We thank K. Shiina for next-generation sequencing support and Y. Yokota and N. Furukawa for experimental support. This work was supported by grants from a Grant-in-Aid for Young Scientists (to T.K., M.K. and M.I.), the Japan Foundation for Applied Enzymology (to S.N. and T.K.), the SENSHIN Medical Research Foundation (to S.N. and T.K.), the Kanae Foundation for the Promotion of Medical Science (to S.N.), MSD Life Science Foundation (to S.N. and T.K.), The Tokyo Biomedical Research Foundation (to S.N.), Astellas Foundation for Research on Metabolic Disorders (to S.N.), The Novartis Foundation (Japan) for the Promotion of Science (to S.N.), the Japanese Circulation Society (to S.N.), the Takeda Science Foundation (to S.N.), The Cell Science Research Foundation (to S.N.), a Grant-in-Aid for Scientiﬁc Research (B) (to S.N.), a Grant-in-Aid for Scientific Research (A) (to I.K.), and AMED (JP21ek0210152, JP21gm6210010, JP20bm0704026, JP21ek0210141, JP21ek0109440, JP21ek0109487, JP21gm0810013, JP21km0405209, JP21ek0210118, JP21ek0109406, JP21ek0109543) (to S.N., H.A., and I.K.).

Author contributions

S.Y., T.K., S.N., H.A., and I.K. conceived the project, designed the study, and interpreted the results; S.Y., B.Z. and T.K. collected single cells and generated the single-cell sequencing data; S.Y. and S.H. performed computational analyses; H.A. provided support for computational analyses; T.K., B.Z, and S.Y. performed MI procedure, conducted functional analysis, performed biochemical experiments, and analyzed the data; B.Z., Z.D., S.I., K.S., T.Y., T.S., M.K., K.F., M.K., M.I., M.H., H.T., N.T., H.M., H.A., and I.K. provided experimental and analytical support; S.Y., T.K., S.H., S.N., H.A., and I.K. wrote the manuscript with input from all authors.

Competing Interests

There are no competing interests and the authors declare no competing interests.

1. Virani, S. S. et al. Heart disease and stroke statistics—2020 update: A report from the American Heart Association. Circulation (2020). doi:10.1161/CIR.0000000000000757

2. Prabhakaran, D. et al. Cardiovascular, respiratory, and related disorders: key messages from Disease Control Priorities, 3rd edition. Lancet 391, 1224–1236 (2018).

3. Reiner, Ž. Hypertriglyceridaemia and risk of coronary artery disease. Nat. Rev. Cardiol. 14, 401–411 (2017).

4. Cui, M. et al. Dynamic Transcriptional Responses to Injury of Regenerative and Non-regenerative Cardiomyocytes Revealed by Single-Nucleus RNA Sequencing. Dev. Cell 53, 102-116.e8 (2020).

5. Bahit, M. C., Kochar, A. & Granger, C. B. Post-Myocardial Infarction Heart Failure. JACC Hear. Fail. 6, 179–186 (2018).

6. Leach, J. P. et al. Hippo pathway deficiency reverses systolic heart failure after infarction. Nature 550, 260–264 (2017).

7. Mann, D. L., Bogaev, R. & Buckberg, G. D. Cardiac remodeling and myocardial recovery: Lost in translation? Eur. J. Heart Fail. 12, 789–796 (2010).

8. Fu, X. et al. Specialized fibroblast differentiated states underlie scar formation in the infarcted mouse heart. J. Clin. Invest. 128, 2127–2143 (2018).

9. Vagnozzi, R. J. et al. An acute immune response underlies the benefit of cardiac stem cell therapy. Nature 577, 405–409 (2020).

10. Martini, E. et al. Single-Cell Sequencing of Mouse Heart Immune Infiltrate in Pressure Overload-Driven Heart Failure Reveals Extent of Immune Activation. Circulation 140, 2089–2107 (2019).

11. Van Duijvenboden, K. et al. Conserved NPPB + Border Zone Switches from MEF2- to AP-1-Driven Gene Program. Circulation 140, 864–879 (2019).

12. Ounzain, S. et al. Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs. Eur. Heart J. 36, 353–368 (2015).

13. Lacraz, G. P. A. et al. Tomo-Seq Identifies SOX9 as a Key Regulator of Cardiac Fibrosis during Ischemic Injury. Circulation 136, 1396–1409 (2017).

14. Shukla, P. C. et al. BRCA1 is an essential regulator of heart function and survival following myocardial infarction. Nat. Commun. 2, 511–593 (2011).

15. Sano, M. et al. p53-induced inhibition of Hif-1 causes cardiac dysfunction during pressure overload. Nature 446, 444–448 (2007).

16. Nomura, S. et al. Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure. Nat. Commun. 9, 4435 (2018).

17. Tada, Y. et al. Myocardial viability of the peri-infarct region measured by T1 mapping post manganese-enhanced MRI correlates with LV dysfunction. Int. J. Cardiol. 281, 8–14 (2019).

18. Yan, A. T. et al. Characterization of the peri-infarct zone by contrast-enhanced cardiac magnetic resonance imaging is a powerful predictor of post-myocardial infarction mortality. Circulation 114, 32–39 (2006).

19. Yang, L. et al. Quantitative Proteomics and Immunohistochemistry Reveal Insights into Cellular and Molecular Processes in the Infarct Border Zone One Month after Myocardial Infarction. J. Proteome Res. 16, 2101–2112 (2017).

20. Meng, Q. et al. Expression profiles of long noncoding RNAs and messenger RNAs in the border zone of myocardial infarction in rats. Cell. Mol. Biol. Lett. 24, 1–13 (2019).

21. Zimmermann, M. et al. Analysis of region specific gene expression patterns in the heart and systemic responses after experimental myocardial ischemia. Oncotarget 8, 60809–60825 (2017).

22. Torres, W. M. et al. Regional and temporal changes in left ventricular strain and stiffness in a porcine model of myocardial infarction. Am. J. Physiol. - Hear. Circ. Physiol. 315, H958–H967 (2018).

23. Knöll, R. et al. The cardiac mechanical stretch sensor machinery involves a Z disc complex that is defective in a subset of human dilated cardiomyopathy. Cell 111, 943–955 (2002).

24. Makarewich, C. A. et al. The DWORF micropeptide enhances contractility and prevents heart failure in a mouse model of dilated cardiomyopathy. Elife 7, 1340–1342 (2018).

25. Kuhn, C. et al. Cardiac remodeling is not modulated by overexpression of muscle LIM protein (MLP). Basic Res. Cardiol. 107, (2012).

26. Buyandelger, B. et al. MLP (muscle LIM protein) as a stress sensor in the heart. Pflugers Arch. Eur. J. Physiol. 462, 135–142 (2011).

27. Heineke, J. et al. Attenuation of cardiac remodeling after myocardial infarction by muscle LIM protein-calcineurin signaling at the sarcomeric Z-disc. Proc. Natl. Acad. Sci. U. S. A. 102, 1655–1660 (2005).

28. Boateng, S. Y., Senyo, S. E., Qi, L., Goldspink, P. H. & Russell, B. Myocyte remodeling in response to hypertrophic stimuli requires nucleocytoplasmic shuttling of muscle LIM protein. J. Mol. Cell. Cardiol. 47, 426–435 (2009).

29. Crabtree, G. R. & Olson, E. N. NFAT signaling: Choreographing the social lives of cells. Cell 109, 67–79 (2002).

30. Rashid, M. M. et al. Muscle LIM protein/CSRP3: a mechanosensor with a role in autophagy. Cell Death Discov. 1, 1–12 (2015).

31. Cui, C. et al. The autophagy regulatory molecule CSRP3 interacts with LC3 and protects against muscular dystrophy. Int. J. Mol. Sci. 21, (2020).

32. Liu, C. Y. et al. LncRNA CAIF inhibits autophagy and attenuates myocardial infarction by blocking p53-mediated myocardin transcription. Nat. Commun. 9, (2018).

33. Wu, D., Zhang, K. & Hu, P. The role of autophagy in acute myocardial infarction. Front. Pharmacol. 10, (2019).

34. Sciarretta, S. et al. Trehalose-Induced Activation of Autophagy Improves Cardiac Remodeling After Myocardial Infarction. J. Am. Coll. Cardiol. 71, 1999–2010 (2018).

35. Kanamori, H. et al. The role of autophagy emerging in postinfarction cardiac remodeling. Cardiovasc. Res. 91, 330–339 (2011).

36. Toko, H. et al. ATF6 is important under both pathological and physiological states in the heart. J. Mol. Cell. Cardiol. 49, 113–120 (2010).

37. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

38. Chen, S., Zhou, Y., Chen, Y. & Gu, J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

39. Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

40. Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

41. Liao, Y., Smyth, G. K. & Shi, W. FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

42. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 1–15 (2021).

43. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, (2019).

44. Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

45. Paul Shannon, 1 et al. Cytoscape: A Software Environment for Integrated Models. Genome Res. 13, 2498–2504 (2003).

46. Ruozi, G. et al. AAV-mediated in vivo functional selection of tissue-protective factors against ischaemia. Nat. Commun. 6, 7388 (2015).

47. Satoh, M. et al. High-throughput single-molecule RNA imaging analysis reveals heterogeneous responses of cardiomyocytes to hemodynamic overload. J. Mol. Cell. Cardiol. 128, 77–89 (2019).

There is NO Competing Interest.

20210612nreditorialpolicychecklist.pdf
Editorial policy checklist
table.xlsx
Table S1, Table S2, Table S3, Table S4, Table S5, Table S6
20210612nrreportingsummary.pdf
Reporting summary
ExtendedData.pdf
Extended Data Fig. 1: Clustering of spatial transcriptomics of the heart after MI. a, Visualization of cell clusters at each time point (Sham, n=1; day 1, n=2; day 7, n=2; day 14, n=2). Each spot is colored with each cluster color. The area surrounded by dotted lines indicate infarcted zone. b, H&E staining and RNA fluorescent in situ hybridization of Csrp3 and Tnnt2 using the post-MI day7 heart. Tissues of each IZ, BZ and RZ in the boxes are enlarged and showed in c–e. Scale bar, 1000 μm. c–e, RNA fluorescent in situ hybridization of Csrp3 and Tnnt2. Scale bar, 50 μm.
Extended Data Fig. 2: UMAP plot of reference single cell RNA-seq data a, The Uniform Manifold Approximation and Projection (UMAP) plot of single cell RNA seq of hearts after myocardial infarction colored by cell type. CM stands for cardiomyocytes; EC, endothelial cells; MP, macrophage; FB, fibroblast; SMC, smooth muscle cells; DC, Dendritic cell. b, UMAP plot of single cell RNA seq divided by each time point. Abbreviation and legend are same as a. c, Feature plot of known cell markers used for cell type annotation. Tnnt2 is a marker for cardiomyocyte; Cdh5, endothelial cell; Fcgr1, macrophage; Col1a1, fibroblast, S100a9, granulocyte; Acta2, smooth muscle cell; Cd3e, T lymphocyte; Ms4a1, B lymphocyte; Cd209a, DC-like cell.
Extended Data Fig. 3: Weighted gene co-expression network analysis (WGCNA) of spatial transcriptomic data. a, Visualization of module expression level of each gene module other than M8 at each time point. The area surrounded by dotted line indicates infarcted zone. b, Bar plot showing gene ontology (GO) enrichment in each module.
Extended Data Fig. 4: Overlaps of gene modules and cluster marker genes in spatial transcriptome. Matrix showing the percentage of overlap between top 100 marker genes identified by clustering in Fig. 2 and top 100 characteristic genes for each module identified by WGCNA in Fig. 3. All marker and module genes were used if clusters and modules contain less than 100 genes.
Extended Data Figure 5: Analysis of Csrp3 knock-down mouse. a, The design of shCsrp3 knock-down vector. Each shRNA was designed using “The RNAi consortium database” (by Broad Institute) and #1 was chosen in this study. b, Survival curves comparing Control (n=13) and shCsrp3-AAV9 injected mice (n=14) after MI surgery. Statistical analysis was performed by Gehan–Breslow–Wilcoxon test.
Extended data table 1: The sequencing and alignment metrics of single-cardiomyocyte RNA-seq Extended data table 2: The sequencing and alignment metrics produced by Space Ranger

Download PDF

Journal Publication

published 17 Nov, 2022

Read the published version in Nature Cardiovascular Research →

Version 1

posted

You are reading this latest preprint version

Spatiotemporal single-cell analysis reveals critical roles of mechano-sensing genes at the border zone in remodeling after myocardial infarction

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

Discussion

Methods

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1