23
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Endothelial Reprogramming by Disturbed Flow Revealed by Single-Cell RNA and Chromatin Accessibility Study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          SUMMARY

          Disturbed flow (d-flow) induces atherosclerosis by regulating gene expression in endothelial cells (ECs). For further mechanistic understanding, we carried out a single-cell RNA sequencing (scRNA-seq) and scATAC-seq study using endothelial-enriched single cells from the left- and right carotid artery exposed to d-flow (LCA) and stable-flow ( s-flow in RCA) using the mouse partial carotid ligation (PCL) model. We find eight EC clusters along with immune cells, fibroblasts, and smooth muscle cells. Analyses of marker genes, pathways, and pseudotime reveal that ECs are highly heterogeneous and plastic. D-flow induces a dramatic transition of ECs from atheroprotective phenotypes to pro-inflammatory cells, mesenchymal (EndMT) cells, hematopoietic stem cells, endothelial stem/progenitor cells, and an unexpected immune cell-like (EndICLT) phenotypes. While confirming KLF4/KLF2 as an s-flow-sensitive transcription factor binding site, we also find those sensitive to d-flow (RELA, AP1, STAT1, and TEAD1). D-flow reprograms ECs from atheroprotective to proatherogenic phenotypes, including EndMT and potentially EndICLT.

          Graphical Abstract

          In Brief

          To determine the effect of proatherogenic disturbed flow on transcriptomic and epigenomic chromatin accessibility profiles in endothelial cells at single-cell resolution, Andueza et al. perform scRNA-seq and scATAC-seq analyses using mouse carotid arteries following the partial carotid ligation. Disturbed flow reprograms endothelial cells to proatherogenic phenotypes, including EndMT and endothelial-to-immune cell-like transition.

          Related collections

          Most cited references71

          • Record: found
          • Abstract: found
          • Article: not found

          NIH Image to ImageJ: 25 years of image analysis

          For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fast, sensitive, and accurate integration of single cell data with Harmony

              The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
                Bookmark

                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                28 December 2020
                15 December 2020
                12 January 2021
                : 33
                : 11
                : 108491
                Affiliations
                [1 ]Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Emory University, Atlanta, GA, USA
                [2 ]These authors contributed equally
                [3 ]Lead Contact
                Author notes
                [* ]Correspondence: hjo@ 123456emory.edu

                AUTHOR CONTRIBUTIONS

                Conceptualization, A.A., S.K., J.K., and H.J.; Investigation, A.A., S.K., J.K., N.V.-R., D.-W.K., H.L.M., and J.I.P.; Writing, A.A., S.K., J.K., and H.J.; Funding Acquisition, H.J.; Supervision, S.K. and H.J.

                Article
                NIHMS1657048
                10.1016/j.celrep.2020.108491
                7801938
                33326796
                57cc1906-ef83-4343-9a9f-27ecbff7e26b

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Article

                Cell biology
                Cell biology

                Comments

                Comment on this article