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[Preprint]. 2024 Mar 2:2023.10.31.564995.
doi: 10.1101/2023.10.31.564995.

Integrated single-nuclei and spatial transcriptomic analysis reveals propagation of early acute vein harvest and distension injury signaling pathways following arterial implantation

Marina E Michaud  1 , Lucas Mota  2 , Mojtaba Bakhtiari  1 , Beena E Thomas  1 , John Tomeo  2 , William Pilcher  3 , Mauricio Contreras  2 , Christiane Ferran  2   4 , Swati Bhasin  1   5 , Leena Pradhan-Nabzdyk  2 , Frank W LoGerfo  2 , Patric Liang  2 , Manoj K Bhasin  1   5   3
Affiliations

Integrated single-nuclei and spatial transcriptomic analysis reveals propagation of early acute vein harvest and distension injury signaling pathways following arterial implantation

Marina E Michaud et al. bioRxiv. .

Abstract

Background: Vein graft failure (VGF) following cardiovascular bypass surgery results in significant patient morbidity and cost to the healthcare system. Vein graft injury can occur during autogenous vein harvest and preparation, as well as after implantation into the arterial system, leading to the development of intimal hyperplasia, vein graft stenosis, and, ultimately, bypass graft failure. While previous studies have identified maladaptive pathways that occur shortly after implantation, the specific signaling pathways that occur during vein graft preparation are not well defined and may result in a cumulative impact on VGF. We, therefore, aimed to elucidate the response of the vein conduit wall during harvest and following implantation, probing the key maladaptive pathways driving graft failure with the overarching goal of identifying therapeutic targets for biologic intervention to minimize these natural responses to surgical vein graft injury.

Methods: Employing a novel approach to investigating vascular pathologies, we harnessed both single-nuclei RNA-sequencing (snRNA-seq) and spatial transcriptomics (ST) analyses to profile the genomic effects of vein grafts after harvest and distension, then compared these findings to vein grafts obtained 24 hours after carotid-cartoid vein bypass implantation in a canine model (n=4).

Results: Spatial transcriptomic analysis of canine cephalic vein after initial conduit harvest and distention revealed significant enrichment of pathways (P < 0.05) involved in the activation of endothelial cells (ECs), fibroblasts (FBs), and vascular smooth muscle cells (VSMCs), namely pathways responsible for cellular proliferation and migration and platelet activation across the intimal and medial layers, cytokine signaling within the adventitial layer, and extracellular matrix (ECM) remodeling throughout the vein wall. Subsequent snRNA-seq analysis supported these findings and further unveiled distinct EC and FB subpopulations with significant upregulation (P < 0.00001) of markers related to endothelial injury response and cellular activation of ECs, FBs, and VSMCs. Similarly, in vein grafts obtained 24 hours after arterial bypass, there was an increase in myeloid cell, protomyofibroblast, injury-response EC, and mesenchymal-transitioning EC subpopulations with a concomitant decrease in homeostatic ECs and fibroblasts. Among these markers were genes previously implicated in vein graft injury, including VCAN (versican), FBN1 (fibrillin-1), and VEGFC (vascular endothelial growth factor C), in addition to novel genes of interest such as GLIS3 (GLIS family zinc finger 3) and EPHA3 (ephrin-A3). These genes were further noted to be driving the expression of genes implicated in vascular remodeling and graft failure, such as IL-6, TGFBR1, SMAD4, and ADAMTS9. By integrating the ST and snRNA-seq datasets, we highlighted the spatial architecture of the vein graft following distension, wherein activated and mesenchymal-transitioning ECs, myeloid cells, and FBs were notably enriched in the intima and media of distended veins. Lastly, intercellular communication network analysis unveiled the critical roles of activated ECs, mesenchymal transitioning ECs, protomyofibroblasts, and VSMCs in upregulating signaling pathways associated with cellular proliferation (MDK, PDGF, VEGF), transdifferentiation (Notch), migration (ephrin, semaphorin), ECM remodeling (collagen, laminin, fibronectin), and inflammation (thrombospondin), following distension.

Conclusions: Vein conduit harvest and distension elicit a prompt genomic response facilitated by distinct cellular subpopulations heterogeneously distributed throughout the vein wall. This response was found to be further exacerbated following vein graft implantation, resulting in a cascade of maladaptive gene regulatory networks. Together, these results suggest that distension initiates the upregulation of pathological pathways that may ultimately contribute to bypass graft failure and presents potential early targets warranting investigation for targeted therapies. This work highlights the first applications of single-nuclei and spatial transcriptomic analyses to investigate venous pathologies, underscoring the utility of these methodologies and providing a foundation for future investigations.

Keywords: Vein graft distension; coronary artery bypass grafting (CABG); endothelial injury; implantation injury; single-nuclei transcriptomics; spatial transcriptomics.

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Conflict of interest statement

Disclosures The authors declare no competing interests.

Figures

Figure 1.
Figure 1.. Overview of the workflow used for sample analysis.
(A) Canine vein graft samples were retrieved following distension, then analyzed via single-nuclei RNA-seq (snRNA-seq) and spatial transcriptomics (ST) through the 10x Genomics platform. Single-nuclei-based transcriptomics enables the sequencing of difficult-to-dissociate samples while preserving detailed individual cellular resolution. As a complementary approach, ST provides insight into the spatial gene expression of the tissue, with the transcriptomes of 1-10 cells captured per barcoded spot of the sample slide. Combined, these approaches illuminate the unique cellular architecture and transcriptomic landscape of the tissue. (B) Varying permeabilization times were assessed for fluorescent cDNA footprint to optimize tissue permeabilization. The left panel shows the brightfield images (H&E), and the right panel shows fluorescent images (Cy3, Cy5 Multispectral Imaging (MSI)). (C) Images of H&E strained veins. Notably, the lower panel illustrates the expansion of the lumen following distension. (D) Overview of the developed method for adding histological annotations to ST samples for targeted downstream analyses. Capture spots are projected onto the H&E image as voxels, generating a spatial plot that can be annotated with STannotate and used for analysis.
Figure 2.
Figure 2.. Spatial transcriptomic analysis of distended veins.
(A) Top: Spatial plots displaying capture spots (voxels) projected onto H&E images of control (left) and distended (right) veins from the same canine, annotated using our ST annotation tool. Bottom: Volcano plot of significantly differentially expressed genes (P < 0.00001) within the distended vein compared to the control vein, wherein each gene is colored according to the vein wall layer with the highest expression of the marker. (B) Enriched pathways (via Reactome database) within each layer of the distended veins compared to control veins, based on significantly upregulated genes between each layer (P < 0.05). (C) Network of key differentially expressed genes and associated pathways (P < 0.05) between control and distended veins, derived from clusterProfiler analysis using the Reactome pathways database.
Figure 3.
Figure 3.. Spatial clustering of control and distended veins.
(A) UMAP displaying the clustering of spatial transcriptomic voxels (containing 1-10 cells each) derived from control and distended veins. Clusters are derived based on similar transcriptomic profiles correlating to similar cell type compositions. Cluster labels were designated by the predominant localization of associated voxels to either the intima (I), media (M), or adventitia (A) of the vein samples. The spatial distribution of these clusters was then mapped onto the vein samples. (B) Mapping of spatial clusters onto a control vein sample. (C) Mapping of spatial clusters onto a distended vein sample from the same canine. (D) Relative proportions of each spatial cluster present within control vein and distended vein groups. (E) Heatmap of marker genes associated with different spatial clusters. Columns represent individual voxels grouped by spatial cluster, while rows display individual genes. Horizontal colored bars above the heatmap indicate the different spatial cellular clusters. Relative gene expression is shown in pseudo color, where blue represents low expression and red represents high expression. Enriched biological pathways (via clusterProfiler using Reactome database) based on differentially upregulated genes between the (F) M1 and M2 clusters, as well as the (G) A1 and A2 clusters (P adjusted < 0.05). Gene ratio is defined as the proportion of upregulated genes present in the cluster that overlap with the respective pathway. The multiple test correction for the P value has been performed using the Benjamini-Hochberg (BH) approach.
Figure 4.
Figure 4.. Single-nuclei transcriptomic analysis of control and distended veins.
(A) UMAP displaying the clustering of nuclei derived from control and distended veins. Canonical cell types are based on the expression of marker genes (Figure S3). (B) Relative proportions of each cluster present within control vein and distended vein groups. (C) Volcano plot of differentially expressed genes within the distended vein compared to the control vein (P < 0.00001), wherein each gene is colored according to the cluster with the highest expression of the marker. (D) Heatmap of top marker genes associated with different cell types. Columns represent individual cells grouped by cell types, while rows display individual genes. Horizontal colored bars above the heatmap indicate the different cell types. Relative gene expression is shown in pseudo color, where blue represents low expression and red represents high expression. Top marker genes for clusters are defined by the average log2 fold-change of genes expressed in greater than 30% of nuclei within a cluster and P < 0.05. (E) Network of key differentially enriched pathways between clusters, derived from clusterProfiler analysis using the Reactome database (P < 0.05). Each node depicts the proportion of each cluster displaying enrichment of the associated pathway and the cumulative number of genes enriched in the pathway between the represented clusters.
Figure 5.
Figure 5.. Characterization of fibroblast subpopulations.
(A) Volcano plot of differentially expressed genes within fibroblast sub-clusters of the distended vein compared to the control vein (P < 0.001), wherein each gene is colored according to the fibroblast subpopulation with the highest expression of the marker. (B) Heatmap of top marker genes associated with different fibroblast sub-clusters. Columns represent individual cells grouped by cell types, while rows display individual genes. Horizontal colored bars above the heatmap indicate the different cell types. Relative gene expression is shown in pseudo color, where blue represents low expression and red represents high expression. Top markers for each fibroblast cluster are defined by the average log2 fold-change and P < 0.05. (C) Relative proportions of each fibroblast subpopulation present within control vein and distended vein groups. (D) Dot plot of the top markers representing the phenotype of each fibroblast subpopulation. The relative expression and percent of cells expressing specific markers are shown by shades of blue and the dot size, respectively.
Figure 6.
Figure 6.. Characterization of endothelial cell subpopulations.
(A) Volcano plot of differentially expressed genes within endothelial cells of the distended vein compared to the control vein, wherein each gene is colored according to the endothelial cell subpopulation with the highest expression of the marker. (B) Heatmap of top marker genes associated with different endothelial sub-clusters. Columns represent individual cells grouped by cell types, while rows display individual genes. Horizontal colored bars above the heatmap indicate the different cell types. Relative gene expression is shown in pseudo color, where blue represents low expression and red represents high expression. Top marker genes between endothelial clusters are defined by the average log2 fold-change and P < 0.05. (C) Relative proportions of each endothelial cell subpopulation present within control and distended vein groups. (D) Dot plot of the top markers representing the phenotype of each endothelial cell subpopulation. The relative expression and percent of cells expressing specific markers are shown by shades of blue and the dot size, respectively.
Figure 7.
Figure 7.. Deconvolution of spatial transcriptomic data through the integration of single-nuclei data.
(A) Summary of distinct fibroblast and endothelial subpopulations identified in control and distended veins via snRNA-seq analysis along with expression ok key markers. (B) Heatmap displaying the differential enrichment scores of cellular subpopulations across layers of vein wall following distension. An increased differential score (red) indicates increased gene signature expression associated with the respective sub-population in the distended veins relative to control veins, and blue indicates increased enrichment in control veins. (C) Heatmap of neighborhood enrichment Z-scores generated via Squidpy, illustrating the proximity of cellular populations to one another based on the dominant cell type assigned to each voxel. Red indicates enriched proximity between two populations, whereas blue indicates depleted proximity. (D) Spatial plots of representative control and distended vein samples displaying the dominant cell type within each voxel predicted by model-based deconvolution of the spatial transcriptomic data using the single-nuclei dataset via Cell2location. (E) Validation of the enrichment score (left) and model-based (panel D) integration approaches using RNAscope (right).
Figure 8.
Figure 8.. Intercellular communication analysis.
Network displaying the number of intercellular communication interactions between subpopulations for control (A) and distended veins (B). (C) The proportion of key signaling molecules active in control versus distended veins. (D) Heatmap of the differential signaling pattern (both incoming and outgoing signaling per cell type or subpopulation) in distended veins (red) compared to control veins (blue). (E) Chord diagrams illustrate intercellular communication networks for distended veins, with the exception of BMP, which is illustrated for the control vein. The outer ring and arrows of the diagram represent the signaling produced by each cellular subpopulation, while the inner ring represents which subpopulations are receiving the outgoing signals. (F) Network depicting the interactions of key signaling molecules and differentially expressed genes across pathways implicated in distension injury.
Figure 9.
Figure 9.. Relationship of pre- and post-implantation transcriptomic profiles and select protein expression.
(A) Relative proportions of each fibroblast subpopulation present within control vein and distended vein groups. (B) Spatial plot of grafted vein sample displaying the dominant cell type within each voxel based on deconvolution of the spatial transcriptomic data using the single-nuclei dataset via Cell2location. (C) Temporally resolved multilayer gene network analysis illustrating the effects of key genes (circled) at the time of vein harvesting and distension (left) to 24 hours post-implantation (right). (D) Immunohistochemistry staining of control, distended, and grafted veins with VCAN, GLIS3, or FBN with quantification values (left).
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References

    1. Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, et al. Heart Disease and Stroke Statistics-2022 Update: A Report from the American Heart Association. Circulation. 2022;145(8):E153–E639. - PubMed
    1. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MSV, Evenson KR, Ferguson JF, Gupta DK, Khan SS, et al. Heart Disease and Stroke Statistics - 2021 Update: A Report From the American Heart Association. Circulation. 2021;143(8):E254–E743. - PubMed
    1. Almasri J, Adusumalli J, Asi N, Lakis S, Alsawas M, Prokop LJ, Bradbury A, Kolh P, Conte MS, Murad MH. A systematic review and meta-analysis of revascularization outcomes of infrainguinal chronic limb-threatening ischemia. Journal of Vascular Surgery. 2019;69(6):126S–136S. - PubMed
    1. Martínez-González B, Reyes-Hernández CG, Quiroga-Garza A, Rodríguez-Rodríguez VE, Esparza-Hernández CN, Elizondo-Omaña RE, Guzmán-López S. Conduits used in coronary artery bypass grafting: A review of morphological studies. Annals of Thoracic and Cardiovascular Surgery. 2017;23(2):55–65. - PMC - PubMed
    1. Widimsky P, Straka Z, Stros P, Jirasek K, Dvorak J, Votava J, Lisa L, Budesinsky T, Kolesar M, Vanek T, Brucek P. One-year coronary bypass graft patency: A randomized comparison between off-pump and on-pump surgery angiographic results of the PRAGUE-4 trial. Circulation. 2004;110(22):3418–3423. - PubMed

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