Integrative Co-Registration of Elemental Imaging and Histopathology for Enhanced Spatial Multimodal Analysis of Tissue Sections through TRACE.

Summary: Elemental imaging provides detailed profiling of metal bioaccumulation, offering more precision than bulk analysis by targeting specific tissue areas. However, accurately identifying comparable tissue regions from elemental maps is challenging, requiring the integration of hematoxylin and eosin (H&E) slides for effective comparison. Facilitating the streamlined co-registration of Whole Slide Images (WSI) and elemental maps, TRACE enhances the analysis of tissue regions and elemental abundance in various pathological conditions. Through an interactive containerized web application, TRACE features real-time annotation editing, advanced statistical tools, and data export, supporting comprehensive spatial analysis. Notably, it allows for comparison of elemental abundances across annotated tissue structures and enables integration with other spatial data types through WSI co-registration. Availability and Implementation: Available on the following platforms- GitHub: jlevy44/trace_app , PyPI: trace_app , Docker: joshualevy44/trace_app , Singularity: joshualevy44/trace_app . Contact: joshua.levy@cshs.org. Supplementary information: Supplementary data are available.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging localized at 50-micron resolution, may facilitate the development of algorithms which better appreciate the morphological and molecular underpinnings of carcinogenesis. Here, we explore the utility of leveraging spatial transcriptomics data with a contrastive crossmodal pretraining mechanism to generate deep learning models that can extract molecular and histological information for graph-based learning tasks. Performance on cancer staging, lymph node metastasis prediction, survival prediction, and tissue clustering analyses indicate that the proposed methods bring improvement to graph based deep learning models for histopathological slides compared to leveraging histological information from existing schemes, demonstrating the promise of mining spatial omics data to enhance deep learning for pathology workflows.

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity.

Background: Spatial transcriptomics involves studying the spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. While spatial gene expression is commonly amalgamated from 1-10 cells across 50-micron spots, recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. However, elucidating such information from histology alone presents a significant challenge but if solved can better permit spatial molecular analysis at cellular resolution for instances where Visium data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in whole slide images collected from a cohort of stage pT3 colorectal cancer patients. A cell graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA patterns through optimal transport methods, facilitating the analysis of cellular groupings and gene relationships. This approach leveraged spot-level expression as an intermediary to co-map histological and transcriptomic information at the single-cell level. Results: Our study demonstrated that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within a spot. Furthermore, our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional patch-based computer vision methods as well as other methods which did not incorporate single cell expression during the model fitting procedures. Topological nuances of single-cell expression within a Visium spot were preserved using the developed methodology. Conclusion: This innovative approach augments the resolution of spatial molecular assays utilizing histology as a sole input through synergistic co-mapping of histological and transcriptomic datasets at the single-cell level, anchored by spatial transcriptomics. While initial results are promising, they warrant rigorous validation. This includes collaborating with pathologists for precise spatial identification of distinct cell types and utilizing sophisticated assays, such as Xenium, to attain deeper subcellular insights.

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows.

The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.

Therapeutic potential and mechanism of Chinese herbal medicines in treating fibrotic liver disease.

Liver fibrosis is a pathological condition characterized by replacement of normal liver tissue with scar tissue, and also the leading cause of liver-related death worldwide. During the treatment of liver fibrosis, in addition to antiviral therapy or removal of inducers, there remains a lack of specific and effective treatment strategies. For thousands of years, Chinese herbal medicines (CHMs) have been widely used to treat liver fibrosis in clinical setting. CHMs are effective for liver fibrosis, though its mechanisms of action are unclear. In recent years, many studies have attempted to determine the possible mechanisms of action of CHMs in treating liver fibrosis. There have been substantial improvements in the experimental investigation of CHMs which have greatly promoted the understanding of anti-liver fibrosis mechanisms. In this review, the role of CHMs in the treatment of liver fibrosis is described, based on studies over the past decade, which has addressed the various mechanisms and signaling pathways that mediate therapeutic efficacy. Among them, inhibition of stellate cell activation is identified as the most common mechanism. This article provides insights into the research direction of CHMs, in order to expand its clinical application range and improve its effectiveness.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Current challenges, including limited focus on dermal elastosis variations and reliance on self-reported measures, can introduce subjectivity and inconsistency. Spatial transcriptomics offer an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene on photoaging and prevent cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and inter-patient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal and squamous keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging localized at 50-micron resolution, may facilitate the development of algorithms which better appreciate the morphological and molecular underpinnings of carcinogenesis. Here, we explore the utility of leveraging spatial transcriptomics data with a contrastive crossmodal pretraining mechanism to generate deep learning models that can extract molecular and histological information for graph-based learning tasks. Performance on cancer staging, lymph node metastasis prediction, survival prediction, and tissue clustering analyses indicate that the proposed methods bring improvement to graph based deep learning models for histopathological slides compared to leveraging histological information from existing schemes, demonstrating the promise of mining spatial omics data to enhance deep learning for pathology workflows.

Cardiac Piezo1 Exacerbates Lethal Ventricular Arrhythmogenesis by Linking Mechanical Stress with Ca2+ Handling After Myocardial Infarction.

Ventricular arrhythmogenesis is a key cause of sudden cardiac death following myocardial infarction (MI). Accumulating data show that ischemia, sympathetic activation, and inflammation contribute to arrhythmogenesis. However, the role and mechanisms of abnormal mechanical stress in ventricular arrhythmia following MI remain undefined. We aimed to examine the impact of increased mechanical stress and identify the role of the key sensor Piezo1 in ventricular arrhythmogenesis in MI. Concomitant with increased ventricular pressure, Piezo1, as a newly recognized mechano-sensitive cation channel, was the most up-regulated mechanosensor in the myocardium of patients with advanced heart failure. Piezo1 was mainly located at the intercalated discs and T-tubules of cardiomyocytes, which are responsible for intracellular calcium homeostasis and intercellular communication. Cardiomyocyte-conditional Piezo1 knockout mice (Piezo1Cko) exhibited preserved cardiac function after MI. Piezo1Cko mice also displayed a dramatically decreased mortality in response to the programmed electrical stimulation after MI with a markedly reduced incidence of ventricular tachycardia. In contrast, activation of Piezo1 in mouse myocardium increased the electrical instability as indicated by prolonged QT interval and sagging ST segment. Mechanistically, Piezo1 impaired intracellular calcium cycling dynamics by mediating the intracellular Ca2+ overload and increasing the activation of Ca2+-modulated signaling, CaMKII, and calpain, which led to the enhancement of phosphorylation of RyR2 and further increment of Ca2+ leaking, finally provoking cardiac arrhythmias. Furthermore, in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), Piezo1 activation remarkably triggered cellular arrhythmogenic remodeling by significantly shortening the duration of the action potential, inducing early afterdepolarization, and enhancing triggered activity.This study uncovered a proarrhythmic role of Piezo1 during cardiac remodeling, which is achieved by regulating Ca2+ handling, implying a promising therapeutic target in sudden cardiac death and heart failure.

HiTAIC: hierarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation.

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

Single-Cell Transcriptomics Reveals the Difference of Aortic Atherosclerosis Response to Phytosterols and Oxidation Products of Sterols.

SCOPE: Phytosterols (PS) and sterol oxidation products are key dietary factors influencing atherosclerosis besides cholesterol, although the mechanisms remain elusive. Recently, single-cell RNA sequencing (scRNA-seq) has revealed the heterogeneity of multiple cell types associated with complex pathogenesis in atherosclerosis development. METHODS AND RESULTS: Here, scRNA-seq is performed to investigate the alterations in the aortic cells from ApoE-/- mice induced by diet-derived PS or two sterol oxidation products, phytosterols oxidation products (POPs), and cholesterol oxidation products (COPs). The study identifies four fibroblast subpopulations with different functions, and immunofluorescence demonstrates their spatial heterogeneity, providing evidence that suggests the transformation of smooth muscle cells (SMCs) and fibroblasts in atherosclerosis. The composition and gene expression profiles of aortic cells change broadly in response to PS/COPs/POPs exposure. Notably, PS exhibits an atheroprotective effect where different gene expressions are mainly found in B cells. Exposure to COPs accelerates atherosclerosis and results in marked alternations in myofibroblast subpopulations and T cells, while POPs only alter fibroblast subpopulations and B cells. CONCLUSION: The data elucidate the effects of dietary PS/COPs/POPs on aortic cells during atherosclerosis development, especially on the newly identified fibroblast subpopulations.

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes.

Current Procedural Terminology Codes is a numerical coding system used to bill for medical procedures and services and crucially, represents a major reimbursement pathway. Given that pathology services represent a consequential source of hospital revenue, understanding instances where codes may have been misassigned or underbilled is critical. Several algorithms have been proposed that can identify improperly billed CPT codes in existing datasets of pathology reports. Estimation of the fiscal impacts of these reports requires a coder (i.e., billing staff) to review the original reports and manually code them again. As the re-assignment of codes using machine learning algorithms can be done quickly, the bottleneck in validating these reassignments is in this manual re-coding process, which can prove cumbersome. This work documents the development of a rapidly deployable dashboard for examination of reports that the original coder may have misbilled. Our dashboard features the following main components: (1) a bar plot to show the predicted probabilities for each CPT code, (2) an interpretation plot showing how each word in the report combines to form the overall prediction, and (3) a place for the user to input the CPT code they have chosen to assign. This dashboard utilizes the algorithms developed to accurately identify CPT codes to highlight the codes missed by the original coders. In order to demonstrate the function of this web application, we recruited pathologists to utilize it to highlight reports that had codes incorrectly assigned. We expect this application to accelerate the validation of re-assigned codes through facilitating rapid review of false-positive pathology reports. In the future, we will use this technology to review thousands of past cases in order to estimate the impact of underbilling has on departmental revenue.

Fujian cytoplasmic male sterility and the fertility restorer gene OsRf19 provide a promising breeding system for hybrid rice.

Cytoplasmic male sterility (CMS) determined by mitochondrial genes and restorer of fertility (Rf) controlled by nuclear-encoded genes provide the breeding systems of many hybrid crops for the utilization of heterosis. Although several CMS/Rf systems have been widely exploited in rice, hybrid breeding using these systems has encountered difficulties due to either fertility instability or complications of two-locus inheritance or both. In this work, we characterized a type of CMS, Fujian Abortive cytoplasmic male sterility (CMS-FA), with stable sporophytic male sterility and a nuclear restorer gene that completely restores hybrid fertility. CMS is caused by the chimeric open reading frame FA182 that specifically occurs in the mitochondrial genome of CMS-FA rice. The restorer gene OsRf19 encodes a pentatricopeptide repeat (PPR) protein targeted to mitochondria, where it mediates the cleavage of FA182 transcripts, thus restoring male fertility. Comparative sequence analysis revealed that OsRf19 originated through a recent duplication in wild rice relatives, sharing a common ancestor with OsRf1a/OsRf5, a fertility restorer gene for Boro II and Hong-Lian CMS. We developed six restorer lines by introgressing OsRf19 into parental lines of elite CMS-WA hybrids; hybrids produced from these lines showed equivalent or better agronomic performance relative to their counterparts based on the CMS-WA system. These results demonstrate that CMS-FA/OsRf19 provides a highly promising system for future hybrid rice breeding.

MGP Regulates Perivascular Adipose-Derived Stem Cells Differentiation Toward Smooth Muscle Cells Via BMP2/SMAD Pathway Enhancing Neointimal Formation.

Perivascular adipose-derived stem cells (PV-ADSCs) could differentiate into smooth muscle cells (SMCs), participating in vascular remodeling. However, its underlying mechanism is not well explored. Our previous single-cell RNA-sequencing dataset identified a unique expression of matrix Gla protein (MGP) in PV-ADSCs compared with subcutaneous ADSCs. MGP involves in regulating SMC behaviors in vascular calcification and atherosclerosis. In this study, we investigated MGP's role in PV-ADSCs differentiation toward SMCs in vitro and in vascular remodeling in vivo. PV-ADSCs were isolated from perivascular regions of mouse aortas. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR), Western blot, and immunofluorescence confirmed higher MGP expression in PV-ADSCs. The MGP secretion increased along PV-ADSCs differentiation toward SMCs in response to transforming growth factor-beta 1 (TGF-ß1). Lentivirus knockdown of MGP markedly promoted the bone morphogenetic protein 2 (BMP2) expression and phosphorylation of SMAD1/5/8 in PV-ADSCs, subsequently inhibiting its differentiation toward SMCs. Such inhibition could be partially reversed by further application of BMP2 inhibitors. On the contrary, exogenous MGP inhibited BMP2 expression and SMAD1/5/8 phosphorylation in PV-ADSCs, thereby promoting its differentiation toward SMCs. Transplantation of cultured PV-ADSCs, which was pretreated by MGP knockdown, in mouse femoral artery guide-wire injury model significantly alleviated neointimal hyperplasia. In conclusion, MGP promoted the differentiation of PV-ADSCs toward SMCs through BMP2/SMAD-mediated signaling pathway. This study offers a supplement to the society of perivascular tissues and PV-ADSCs.

Distinct Characteristics Between Perivascular and Subcutaneous Adipose-Derived Stem Cells.

Adipose-derived stem cells (ADSCs) can differentiate into vascular lineages and participate in vascular remodeling. Perivascular ADSCs (PV-ADSCs) draw attention because of their unique location. The heterogeneity of subcutaneous (SUB) and abdominal ADSCs were well addressed, but PV-ADSCs' heterogeneity has not been investigated. In this study, we applied single-cell analysis to compare SUB-ADSCs and PV-ADSCs regarding their subpopulations, functions, and cell fates. We uncovered four subpopulations of PV-ADSCs (Dpp4+, Col4a2+/Icam1+, Clec11a+/Cpe+, and Sult1e1+ cells), among which the Clec11a+ subpopulation potentially participated in and regulated PV-ADSC differentiation toward a smooth muscle cell (SMC) phenotype. Distinct characteristics between PV-ADSCs and SUB-ADSCs were revealed.

TBX20 Contributes to Balancing the Differentiation of Perivascular Adipose-Derived Stem Cells to Vascular Lineages and Neointimal Hyperplasia.

BACKGROUND: Perivascular adipose-derived stem cells (PVASCs) can contribute to vascular remodeling, which are also capable of differentiating into multiple cell lineages. The present study aims to investigate the mechanism of PVASC differentiation toward smooth muscle cells (SMCs) and endothelial cells (ECs) as well as its function in neointimal hyperplasia. METHODS: Single-cell sequencing and bulk mRNA sequencing were applied for searching key genes in PVASC regarding its role in vascular remodeling. PVASCs were induced to differentiate toward SMCs and ECs in vitro, which was quantitatively evaluated using immunofluorescence, quantitative real-time PCR (QPCR), and Western blot. Lentivirus transfections were performed in PVASCs to knock down or overexpress TBX20. In vivo, PVASCs transfected with lentivirus were transplanted around the guidewire injured femoral artery. Hematoxylin-eosin (H&E) staining was performed to examine their effects on neointimal hyperplasia. RESULTS: Bulk mRNA sequencing and single-cell sequencing revealed a unique expression of TBX20 in PVASCs. TBX20 expression markedly decreased during smooth muscle differentiation while it increased during endothelial differentiation of PVASCs. TBX20 knockdown resulted in the upregulation of SMC-specific marker expression and activated Smad2/3 signaling, while inhibiting endothelial differentiation. In contrast, TBX20 overexpression repressed the differentiation of PVASCs toward smooth muscle cells but promoted endothelial differentiation in vitro. Transplantation of PVASCs transfected with TBX20 overexpression lentivirus inhibited neointimal hyperplasia in a murine femoral artery guidewire injury model. On the contrary, neointimal hyperplasia significantly increased in the TBX20 knockdown group. CONCLUSION: A subpopulation of PVASCs uniquely expressed TBX20. TBX20 could regulate SMC and EC differentiation of PVASCs in vitro. Transplantation of PVASCs after vascular injury suggested that PVASCs participated in neointimal hyperplasia via TBX20.