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      Single cell genomics as a transformative approach for aquaculture research and innovation

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          Abstract

          Single cell genomics encompasses a suite of rapidly maturing technologies that measure the molecular profiles of individual cells within target samples. These approaches provide a large up‐step in biological information compared to long‐established ‘bulk’ methods that profile the average molecular profiles of all cells in a sample, and have led to transformative advances in understanding of cellular biology, particularly in humans and model organisms. The application of single cell genomics is fast expanding to non‐model taxa, including aquaculture species, where numerous research applications are underway with many more envisaged. In this review, we highlight the potential transformative applications of single cell genomics in aquaculture research, considering barriers and potential solutions to the broad uptake of these technologies. Focusing on single cell transcriptomics, we outline considerations for experimental design, including the essential requirement to obtain high quality cells/nuclei for sequencing in ectothermic aquatic species. We further outline data analysis and bioinformatics considerations, tailored to studies with the under‐characterized genomes of aquaculture species, where our knowledge of cellular heterogeneity and cell marker genes is immature. Overall, this review offers a useful source of knowledge for researchers aiming to apply single cell genomics to address biological challenges faced by the global aquaculture sector though an improved understanding of cell biology.

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          Robust enumeration of cell subsets from tissue expression profiles

          We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              SCANPY : large-scale single-cell gene expression data analysis

              Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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                Author and article information

                Contributors
                daniel.macqueen@roslin.ed.ac.uk
                Journal
                Rev Aquac
                Rev Aquac
                10.1111/(ISSN)1753-5131
                RAQ
                Reviews in Aquaculture
                John Wiley and Sons Inc. (Hoboken )
                1753-5123
                1753-5131
                07 March 2023
                September 2023
                : 15
                : 4 ( doiID: 10.1111/raq.v15.4 )
                : 1618-1637
                Affiliations
                [ 1 ] The Roslin Institute and Royal (Dick) School of Veterinary Studies The University of Edinburgh Midlothian UK
                Author notes
                [*] [* ] Correspondence

                Daniel J. Macqueen, The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK.

                Email: daniel.macqueen@ 123456roslin.ed.ac.uk

                Author information
                https://orcid.org/0000-0002-6702-5304
                https://orcid.org/0000-0002-9616-5912
                https://orcid.org/0000-0001-8050-7722
                Article
                RAQ12806
                10.1111/raq.12806
                10946576
                38505116
                e11c99b4-4fcf-4b82-a441-ae8b4baf9384
                © 2023 The Authors. Reviews in Aquaculture published by John Wiley & Sons Australia, Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 February 2023
                : 26 October 2022
                : 16 February 2023
                Page count
                Figures: 2, Tables: 2, Pages: 20, Words: 19061
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council , doi 10.13039/501100000268;
                Award ID: BBS/E/D/10002071
                Award ID: BBS/E/D/20002174
                Award ID: BB/W005859/1
                Award ID: BB/V009818/1
                Funded by: Norwegian Seafood Research Fund , doi 10.13039/501100010197;
                Award ID: 901631
                Funded by: Scottish Universities Life Sciences Alliance
                Funded by: University of Edinburgh Data Driven Innovation Initiative
                Categories
                Review
                Reviews
                Custom metadata
                2.0
                September 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.9 mode:remove_FC converted:18.03.2024

                aquaculture,bioinformatic pipelines,cell isolation,nuclei isolation,single cell genomics,transformative applications

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