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      Identification of rare post-mitotic cell states induced by injury and required for whole-body regeneration in Schmidtea mediterranea

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          Abstract

          Regeneration requires coordination of stem cells, their progeny, and distant differentiated tissues. Here, we present a comprehensive atlas of whole-body regeneration in Schmidtea mediterranea and identify wound-induced cell states. Analysis of 299,998 single-cell transcriptomes captured from regeneration-competent and regeneration-incompetent fragments identified transient regeneration-activated cell states (TRACS) in the muscle, epidermis, and intestine. TRACS were stem-cell-division-independent with distinct spatiotemporal distributions and RNAi depletion of TRACS-enriched genes produced regeneration defects. Muscle expression of notum, follistatin, evi/wls, glypican-1, and junctophilin-1 was required for tissue polarity. Epidermal expression of agat-1/2/3, cyp3142a1, zfhx3, and atp1a1 was important for stem cell proliferation. Finally, expression of spectrinβ and atp12a in intestinal basal cells and lrrk2, cathepsinB, myosin1e, polybromo-1, and talin-1 in intestinal enterocytes regulated stem cell proliferation and tissue remodeling. respectively. Our results identify cell types and molecules important for regeneration, indicating that regenerative capacity can emerge from coordinated transcriptional plasticity across all three germ layers.

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          Most cited references79

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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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            Is Open Access

            Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

            Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.
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              Dimensionality reduction for visualizing single-cell data using UMAP

              Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.
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                Author and article information

                Journal
                100890575
                21417
                Nat Cell Biol
                Nat Cell Biol
                Nature cell biology
                1465-7392
                1476-4679
                17 July 2021
                September 2021
                02 September 2021
                02 March 2022
                : 23
                : 9
                : 939-952
                Affiliations
                [1 ]Stowers Institute for Medical Research, Kansas City, MO, USA.
                [2 ]Howard Hughes Institute for Medical Research, Kansas City, MO, USA.
                Author notes

                Author Contributions Statement: Conceptualization and data interpretation (BWBP, ASA), data analysis (BWBP, CEB, SC), acquisition of data (BWBP, AMK, ARS, ACB), cloning of planarian gene transcripts (FGM), writing of original manuscript (BWBP), supervision and funding acquisition (ASA), and revision and editing of manuscript (all authors).

                Article
                NIHMS1724709
                10.1038/s41556-021-00734-6
                8855990
                34475533
                bdc4a9eb-d9df-44e2-b55f-304a26e65faa

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                Cell biology
                Cell biology

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