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      Single-cell RNA sequencing of mid-to-late stage spider embryos: new insights into spider development

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          Abstract

          Background

          The common house spider Parasteatoda tepidariorum represents an emerging new model organism of arthropod evolutionary and developmental (EvoDevo) studies. Recent technical advances have resulted in the first single-cell sequencing (SCS) data on this species allowing deeper insights to be gained into its early development, but mid-to-late stage embryos were not included in these pioneering studies.

          Results

          Therefore, we performed SCS on mid-to-late stage embryos of Parasteatoda and characterized resulting cell clusters by means of in-silico analysis (comparison of key markers of each cluster with previously published information on these genes). In-silico prediction of the nature of each cluster was then tested/verified by means of additional in-situ hybridization experiments with additional markers of each cluster.

          Conclusions

          Our data show that SCS data reliably group cells with similar genetic fingerprints into more or less distinct clusters, and thus allows identification of developing cell types on a broader level, such as the distinction of ectodermal, mesodermal and endodermal cell lineages, as well as the identification of distinct developing tissues such as subtypes of nervous tissue cells, the developing heart, or the ventral sulcus (VS). In comparison with recent other SCS studies on the same species, our data represent later developmental stages, and thus provide insights into different stages of developing cell types and tissues such as differentiating neurons and the VS that are only present at these later stages.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-023-09898-x.

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          Most cited references198

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          MITOS: improved de novo metazoan mitochondrial genome annotation.

          About 2000 completely sequenced mitochondrial genomes are available from the NCBI RefSeq data base together with manually curated annotations of their protein-coding genes, rRNAs, and tRNAs. This annotation information, which has accumulated over two decades, has been obtained with a diverse set of computational tools and annotation strategies. Despite all efforts of manual curation it is still plagued by misassignments of reading directions, erroneous gene names, and missing as well as false positive annotations in particular for the RNA genes. Taken together, this causes substantial problems for fully automatic pipelines that aim to use these data comprehensively for studies of animal phylogenetics and the molecular evolution of mitogenomes. The MITOS pipeline is designed to compute a consistent de novo annotation of the mitogenomic sequences. We show that the results of MITOS match RefSeq and MitoZoa in terms of annotation coverage and quality. At the same time we avoid biases, inconsistencies of nomenclature, and typos originating from manual curation strategies. The MITOS pipeline is accessible online at http://mitos.bioinf.uni-leipzig.de. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Spatial reconstruction of single-cell gene expression

            Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
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              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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                Author and article information

                Contributors
                brenda.medina@geo.uu.se
                ralf.janssen@geo.uu.se
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                7 February 2024
                7 February 2024
                2024
                : 25
                : 150
                Affiliations
                Department of Earth Sciences, Palaeobiology, Uppsala University, ( https://ror.org/048a87296) Villavägen 16, 75236 Uppsala, Sweden
                Article
                9898
                10.1186/s12864-023-09898-x
                10848406
                38326752
                19c59c97-645d-4aca-a573-7c5394c3fb5f
                © The Author(s) 2024

                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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 29 June 2023
                : 12 December 2023
                Funding
                Funded by: European Union’s Horizon 2020 Research and Innovation programme
                Award ID: 766053
                Award ID: 766053
                Award ID: 766053
                Award Recipient :
                Funded by: Uppsala University
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Genetics
                single-cell sequencing,spider development,nervous system,genetic fingerprint,parasteatoda tepidariorum

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