2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Organization of the human intestine at single-cell resolution

      research-article
      1 , 2 , 2 , 2 , 3 , 2 , 1 , 2 , 2 , 2 , 2 , 2 , 2 , 1 , 1 , 1 , 4 , 1 , 5 , 6 , 5 , 7 , 3 , 2 , 7 , 7 , 5 , 8 , 9 , 1 , 3 , 8 , 1 , , 2 , , 2 ,
      Nature
      Nature Publishing Group UK
      Functional genomics, Gene regulation, Single-cell imaging, Multicellular systems

      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.

          Abstract

          The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health 1 . The intesting has a length of over nine metres, along which there are differences in structure and function 2 . The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.

          Abstract

          Intestinal cell types are organized into distinct neighbourhoods and communities within the healthy human intestine, with distinct immunological niches.

          Related collections

          Most cited references94

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
            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

                Contributors
                gnolan@stanford.edu
                wjg@stanford.edu
                mpsnyder@stanford.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                19 July 2023
                19 July 2023
                2023
                : 619
                : 7970
                : 572-584
                Affiliations
                [1 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Pathology, , Stanford School of Medicine, ; Stanford, CA USA
                [2 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Genetics, , Stanford School of Medicine, ; Stanford, CA USA
                [3 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Biomedical Data Science, , Stanford School of Medicine, ; Stanford, CA USA
                [4 ]GRID grid.411544.1, ISNI 0000 0001 0196 8249, Department of Pathology and Neuropathology, , University Hospital and Comprehensive Cancer Center Tübingen, ; Tübingen, Germany
                [5 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Computer Science, , Stanford University, ; Stanford, CA USA
                [6 ]GRID grid.5333.6, ISNI 0000000121839049, School of Computer and Communication Sciences, , École Polytechnique Fédérale de Lausanne, ; Lausanne, Switzerland
                [7 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Statistics and Data Science, , University of Pennsylvania, ; Pennsylvania, PA USA
                [8 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Surgery, , Washington University, ; St Louis, MO USA
                [9 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Medicine, , University of Washington, ; Seattle, WA USA
                Author information
                http://orcid.org/0000-0001-9961-7673
                http://orcid.org/0000-0001-7876-5060
                http://orcid.org/0000-0003-2165-9456
                http://orcid.org/0000-0001-9652-4885
                http://orcid.org/0000-0002-1625-3083
                http://orcid.org/0000-0001-9944-8769
                http://orcid.org/0000-0002-3580-1348
                http://orcid.org/0000-0002-1792-1768
                http://orcid.org/0000-0002-1120-1778
                http://orcid.org/0000-0003-3680-7562
                http://orcid.org/0000-0002-0880-5749
                http://orcid.org/0000-0003-2401-0177
                http://orcid.org/0000-0003-4979-9573
                http://orcid.org/0000-0002-0317-7608
                http://orcid.org/0000-0002-8862-9043
                http://orcid.org/0000-0003-1409-3095
                http://orcid.org/0000-0003-0784-7987
                Article
                5915
                10.1038/s41586-023-05915-x
                10356619
                37468586
                85c82320-0443-40d0-b555-1abe5d94d036
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 November 2021
                : 2 March 2023
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                functional genomics,gene regulation,single-cell imaging,multicellular systems
                Uncategorized
                functional genomics, gene regulation, single-cell imaging, multicellular systems

                Comments

                Comment on this article