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Single-cell and spatial transcriptomics of the infarcted heart define the dynamic onset of the border zone in response to mechanical destabilization

Nature Cardiovascular Research volume 1, pages 1039–1055 (2022)Cite this article

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Abstract

The border zone (BZ) of the infarcted heart is a geographically complex and biologically enigmatic interface separating poorly perfused infarct zones (IZs) from remote zones (RZs). The cellular and molecular mechanisms of myocardial BZs are not well understood because microdissection inevitably combines them with uncontrolled amounts of RZs and IZs. Here, we use single-cell/nucleus RNA sequencing, spatial transcriptomics and multiplexed RNA fluorescence in situ hybridization to redefine the BZ based on cardiomyocyte transcriptomes. BZ1 (Nppa+Xirp2−) forms a hundreds-of-micrometer-thick layer of morphologically intact cells adjacent to RZs that are detectable within an hour of injury. Meanwhile, BZ2 (Nppa+Xirp2+) forms a near-single-cell-thick layer of morphologically distorted cardiomyocytes at the IZ edge that colocalize with matricellular protein-expressing myofibroblasts and express predominantly mechanotransduction genes. Surprisingly, mechanical injury alone is sufficient to induce BZ genes. We propose a ‘loss of neighbor’ hypothesis to explain how ischemic cell death mechanically destabilizes the BZ to induce its transcriptional response.

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Fig. 1: CM transcripts redefine the ischemic BZ.
Fig. 2: BZ2 CMs form a thin boundary between surviving and ischemic myocardium.
Fig. 3: Cardiac immune niches after ischemic injury.
Fig. 4: Activated fibroblasts localize with BZ2 CMs.
Fig. 5: The transcriptional BZ emerges rapidly after ischemia.
Fig. 6: Morphological features of the emerging BZ.
Fig. 7: Ischemic injury is not necessary and mechanical trauma is sufficient to elicit BZ biology.

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Data availability

sc/snRNA-seq data and spatial transcriptomic sequencing data have been deposited to the Gene Expression Omnibus under accession no. GSE214611. All other data supporting the findings in this study are included in the main article and associated files.

Code availability

The code used to process this data is publicly available at Zenodo (https://zenodo.org/record/7055957#.Y00deezMIqu)

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Acknowledgements

This publication includes data generated at the UC San Diego IGM Genomics Center utilizing an Illumina NovaSeq 6000 that was purchased with funding from a National Institutes of Health SIG grant (#S10 OD026929). We thank IGM and the Nikon Imaging Center at UCSD for technical assistance. The work was funded by National Institutes of Health (NIH) grants NIH UL1TR001442 (UCSD), AHA17IRG33410543 (K.R.K.), NIH R00HL129168 (K.R.K.), NIH DP2AR075321 (K.R.K.), NIH HL142251 (F.S.) and NIH T32HL105373 (D.M.C.), National Heart Lung and Blood Institute (NHLBI) grants NHLBI T32HL007444 (V.K.N.) and NHLBI HL162369 (F.S.) and Department of Defense grant no. W81XWH1810380 (F.S.).

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Author notes

  1. These authors contributed equally: D.M. Calcagno, N. Taghdiri.

Authors and Affiliations

  1. Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA

    D. M. Calcagno, N. Taghdiri, J. M. Mesfin, A. Toomu, R. Sehgal, J. Lee, K. L. Christman, K. Zhang, Z. Fu & K. R. King

  2. Division of Cardiology and Cardiovascular Institute, Department of Medicine, University of California San Diego, La Jolla, CA, USA

    V. K. Ninh, Y. Liang, J. M. Duran, E. Adler, F. Sheikh & K. R. King

  3. Sanford Consortium for Regenerative Medicine, La Jolla, CA, USA

    J. M. Mesfin & K. L. Christman

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Contributions

D.M.C., N.T., V.K.N. and K.R.K. designed and performed the experiments, analyzed the data and wrote the manuscript. J.M.M., A.T., R.S., J.L. and Y.L. performed the experiments. J.M.D. and E.A. provided the human tissue samples. K.L.C., K.Z. and F.S. provided guidance on experimental design. Z.F. performed all mouse surgeries and developed new methods including the NP models. K.R.K. conceived the project and provided funding. All authors reviewed the results and commented on the manuscript.

Corresponding author

Correspondence to K. R. King.

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Competing interests

F.S. is a cofounder and has an equity interest in Papillon Therapeutics; he is a consultant and has equity interest and a research grant from LEXEO Therapeutics. The other authors declare no competing interests.

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Nature Cardiovascular Research thanks Rafael Kramann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Integration of single cell and nuclei RNA-seq heart datasets during acute responses to MI.

(a) Overall experimental design and integration of sc/snRNA-seq data with whole transcriptome spatial data. Hearts were harvested at several time points following experimental MI and collected for snRNA-seq. The resulting data matrices were integrated with available scRNA-seq data. (b) Experimental timepoints post-MI that were examined in our study with a summary table of total numbers of cells, nuclei and spatial pixels analyzed to support the robustness of our claims across biological replicates. (c) Gating strategy to isolate nuclei using DAPI and FACS. (d) Mitochondrial QC metrics of samples and replicates for both single nuclei, single cell and integrated sn/sc data. (e) UMAP plots annotated by major cell types (left) and subsets (right) after removing nuclei and cells that have more than 5% mitochondrial counts. (f) UMAP plots displaying composition of single nuclei (left) and single cell (right) derived samples. (g) Subcluster composition as derived from UMAPs shown in (f). (h) UMAP plots split by timepoint and across biological replicates. (i) Average subcluster composition displayed in (h). (l) QC metric of samples and replicates for both single nuclei and single cell represented in counts per sample (nCounts) and features per sample (nGenes).

Extended Data Fig. 2 Integration and quantification of spatial transcriptomic datasets.

(a) UMAP plots split by sample. (b) Quantification of cluster classification as a percentage of total pixels captured by sample (data presented as mean values ± SEM; n = 2, sham; n = 1, 1 hr and 4hrs; n = 3, 72 hrs and 168hrs post-MI). (c) Quality control metrics, split by sample. (d) Spatial plot showing results of clustering across different time points after MI and replicates.

Extended Data Fig. 3 High resolution clustering of CMs at 24 hrs and 72 hrs post-MI.

(a) UMAP plots of subset and reclustered CMs as shown in Fig. 1b with higher resolution (top) and annotated based on spatial mapping (bottom). (b) Heatmap of resulting DEGs based on resolution. (f) Heatmap of DEGs based on spatial mapping. (c) Select gene ontology terms enriched in BZ2 CMs related to Ras/Rho signaling. (d) Average scaled expression of guanine nucleotide exchange factors (GEFs) and GTPase activating proteins (GAPs) in CM subsets. (e) Spatial feature plots of representative Ras/Rho related genes showing distribution in BZ.

Extended Data Fig. 4 Mapping of snRNA-seq derived CM subsets to space.

(a) Strategy for mapping labels (IZ, RZ, BZ1 and BZ2) from CM nuclei to spatial clusters (see methods). (b) UMAP plot of integrated dataset composed of 34,116 pixels in 16 samples (summarized in Extended Data Fig. 1b, see Extended Data fig. 8). (c) DEGs based on high-resolution clustering (top) and post-classification regions (bottom). (d) Histogram of counts from representative samples (green, sham; red, 72 hrs post-MI). (e) Histogram of BZ1 scores. (f) Dot plot of BZ1 and BZ2 scores. (g,h) Feature plots (g) and violin plots (h) of CM, BZ1 and BZ2 scores. (i) Results of ROC analysis (AUC, area under the curve) and stepwise label-mapping (reference cluster indicated above). (j) Classification results shown in UMAP space. (k) AUC as a function of gene-set length.

Extended Data Fig. 5 Spatial transcriptomics of human STEMI tissue.

(a) H&E of cross section of a single human heart sample from a patient presenting with anterior wall STEMI. (b) Spatial transcriptomic clustering results based on DEG analysis and assessment of BZ marker genes shown in space in (c) and by violin plots in (d). (e) Heatmap of cluster defining DEGs. LQ, low quality.

Extended Data Fig. 6 Segmentation, classification and quantification of mFISH data.

(a) After mFISH imaging, slides were stained for WGA to label the CM membrane. (b) Areas with well-defined perimeters were converted to regions-of-interest (shown in white). (c) Expression of BZ1 markers (Nppa, Clu) and BZ2 markers (Xirp2, Flnc) across ROIs. Scatter plot was visually inspected to establish thresholds and classify ROIs as RZ, BZ1, or BZ2 (blue, purple, red). (d) Quantification of neighbor composition in 25-pixel radius (n = 625 cells, and 1008 cells examined over 1 sample for BZ1 and BZ2, respectively). (e) Quantification of minimum distance to the IZ border defined by Tnnt2 staining. (f) To quantify direct contact of CMs, the percentage of Tnnt2 + pixels were calculated in ROI perimeters with various degrees of thickness (n = 625 cells, and 1008 cells examined over 1 sample for BZ1 and BZ2, respectively). (g) Contiguous regions of respective CM subsets were measured. Images shown are representative of 2 separate experiments. Scale bars indicate 500 μm. Boxplots presented with mean; box: 25th-75th%; whiskers: 2.5th-97.5th %. **** P-value < .0001; Mann-Whitney Test, two-sided.

Extended Data Fig. 7 Colocalization of immune and fibroblast subsets across time.

(a) Spatial plots showing subclustering results (shown in 3c) across 72 hrs post-MI and 168 hrs post-MI replicates. (b) Colocalized immune subsets in 72 hrs post-MI replicate. (c) Spatially correlated and clustered gene-set scores. (d) Spatially correlated and clustered genes based on spearman rank correlation analysis for all regions (left) and infarct zone (right) of 72 hrs post-MI sample. (e) All gene-set scores projected to clusters. (f) Volcano plot comparing gene-set scores in BZ1 and BZ2 (Wilcoxon Ranked Sum Test, two-sided).

Extended Data Fig. 8 Spatial transcriptomics on mechanical injury models.

(a) H&E sections at low magnification (lower panel) and high magnification (top panel). Representative of at least 2 separate experiments. (b) Clustering results of spatial transcriptomic data based on integrated dataset as shown in Extended Data Fig. 3. (c) Hif1 scores applied to representative 72 hrs post-MI sample (left) and NP (right) with quantifications plotted per pixel in (d). In comparison to post-MI hearts, needle pass injuries have a significantly lower Hif1 score compared to the positive control (72 hr MI) samples and is unchanged from negative controls in sham samples. (e) Experimental design of fluorescent dye assay to assess regional perfusion. Wheat germ agglutinin (WGA) conjugated to AlexaFluor 488 was injected directly into LV 10 min or 72 hrs post NP injury and hearts were harvested 20 minutes after injection for imaging and quantification (data presented as mean values ± SEM n = 3 biologically independent samples). The representative image in the left panel designates the visualization of the needle pass area designated as the negative control and the reference site designated as neighbors, and the right panel represents a positive control area remote from the site of injury. Quantification of samples harvested 30 minutes and 72 hrs post-NP injury reveal that in both time courses, Neighbors and Positive controls are significantly increased as compared to the Negative Control (Needle pass) sites and are not different from each other (f) Representative H&E-stained sections of NP injuries highlighting area of injury in dash lines and directions of adjacent myocyte bundles in arrows used to quantify gene expression. Representative of 2 separate experiments, each of which had tissue sections aligned with the long axis of the needle insertion. (g) Anisotropy ratios in each sample with non-injured area (right) showing no significant anisotropy in the borderzones of needle pass injuries (n = 2 biologically independent samples). Data presented as mean values ± SEM. *P-value < 0.05, **P-value < 0.01, ***P-value < 0.001, ****P-value < 0.0001; One-way ANOVA with Tukey’s post-hoc analysis.

Supplementary information

Reporting Summary

Supplementary Table 1

Differentially expressed genes of major cell types: (1.1) cardiomyocytes; (1.2) immune cells; (1.3) and stromal cells. P values were derived from Seurat and FDR-adjusted for multiple hypothesis testing

Supplementary Table 2

Differentially expressed genes of cell type subsets: (2.1–2.7) monocyte and macrophage subsets; (2.8) dendritic cell subsets; (2.9 and 2.10) neutrophil subsets; (2.11–2.18) endothelial cell subsets; (2.19–2.25) fibroblast subsets; (2.26–2.30) cardiomyocyte subsets. P values were derived from Seurat and FDR-adjusted for multiple hypothesis testing

Supplementary Table 3

Differentially expressed genes derived from spatial transcriptomics after label mapping. P values were derived from Seurat and FDR-adjusted for multiple hypothesis testing

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Calcagno, D.M., Taghdiri, N., Ninh, V.K. et al. Single-cell and spatial transcriptomics of the infarcted heart define the dynamic onset of the border zone in response to mechanical destabilization. Nat Cardiovasc Res 1, 1039–1055 (2022). https://doi.org/10.1038/s44161-022-00160-3

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