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      Human lymph node fibroblastic reticular cells maintain heterogeneous characteristics in culture

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          Summary

          Fibroblastic reticular cells (FRCs) are mesenchymal stromal cells in human lymph nodes (LNs) playing a pivotal role in adaptive immunity. Several FRC subsets have been identified, yet it remains to be elucidated if their heterogeneity is maintained upon culture. Here, we established a protocol to preserve and culture FRCs from human LNs and characterized their phenotypic profile in fresh LN suspensions and upon culture using multispectral flow cytometry. We found nine FRC subsets in fresh human LNs, independent of donor, of which four persisted in culture throughout several passages. Interestingly, the historically FRC-defining marker podoplanin (PDPN) was not present on all FRC subsets. Therefore, we propose that CD45 negCD31 neg human FRCs are not restricted by PDPN expression, as we found CD90, BST1, and CD146/MCAM to be more widely expressed. Together, our data provide insight into FRC heterogeneity in human LNs, enabling further investigation into the function of individual FRC subsets.

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          Highlights

          • Improved isolation and culture of human lymph node-derived FRCs

          • Nine heterogeneous clusters of FRCs found in ex vivo CD45 neg CD31 neg cells

          • Four out of nine clusters are preserved in vitro, while four new clusters emerge

          • Donor homogeneity indicates that not all FRCs are PDPN +

          Abstract

          Biological sciences; Molecular biology; Cell biology

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          Most cited references43

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          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.
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            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.
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              Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

              Background Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Results We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. Conclusions Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression. Electronic supplementary material The online version of this article (10.1186/s12864-018-4772-0) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                04 June 2024
                19 July 2024
                04 June 2024
                : 27
                : 7
                : 110179
                Affiliations
                [1 ]Amsterdam UMC location Vrije Universiteit Amsterdam, Molecular Cell Biology & Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
                [2 ]Amsterdam Institute for Immunology and Infectious diseases, Amsterdam, the Netherlands
                [3 ]Cancer Center Amsterdam, Cancer Biology & Immunology, Amsterdam, the Netherlands
                [4 ]Erasmus MC Transplant Institute, University Medical Center Rotterdam, Department of Surgery, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands
                Author notes
                []Corresponding author c.m.dewinde@ 123456amsterdamumc.nl
                [∗∗ ]Corresponding author r.mebius@ 123456amsterdamumc.nl
                [5]

                These authors equally contributed

                [6]

                Lead contact

                Article
                S2589-0042(24)01404-4 110179
                10.1016/j.isci.2024.110179
                11233964
                38989462
                e75c3505-bd59-4453-9b6a-eabefcf5a38a
                © 2024 The Author(s)

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

                History
                : 22 December 2023
                : 16 April 2024
                : 31 May 2024
                Categories
                Article

                biological sciences,molecular biology,cell biology
                biological sciences, molecular biology, cell biology

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