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      Spatial transcriptomics using multiplexed deterministic barcoding in tissue

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          Abstract

          Spatially resolved transcriptomics of tissue sections enables advances in fundamental and applied biomedical research. Here, we present Multiplexed Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm 2 with a 50 µm resolution. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared to previous reports. Furthermore, the introduction of alignment markers allowed seamless registration of images and spatial transcriptomic spots. Together with technological advances, we provide an open-source computational pipeline to prepare raw sequencing data for downstream analysis. The functionality of xDBiT was demonstrated by acquiring 16 spatially resolved transcriptomic datasets from five different murine organs, including the cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of spatial transcriptomes allowed for in-depth characterization of the murine kidney.

          Abstract

          Examining the spatially resolved transcriptome of tissue sections promises advances in biomedical research. Here, the authors present xDBiT, a versatile, microfluidics-based approach to cost-effectively measure the spatial transcriptome of multiple tissue sections in parallel.

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          Most cited references71

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Fiji: an open-source platform for biological-image analysis.

            Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Contributors
                celia.martinez@helmholtz-muenchen.de
                matthias.meier@helmholtz-muenchen.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 March 2023
                18 March 2023
                2023
                : 14
                : 1523
                Affiliations
                [1 ]Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany
                [2 ]GRID grid.6936.a, ISNI 0000000123222966, TUM School of Medicine, , Technical University of Munich, ; Munich, Germany
                [3 ]GRID grid.9647.c, ISNI 0000 0004 7669 9786, Center for Biotechnology and Biomedicine, , University of Leipzig, ; Leipzig, Germany
                Author information
                http://orcid.org/0000-0002-2216-3332
                http://orcid.org/0000-0002-9534-6201
                http://orcid.org/0000-0001-9455-4538
                Article
                37111
                10.1038/s41467-023-37111-w
                10024691
                36934108
                4993f654-0176-41a2-bf91-455f0977c73b
                © 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 September 2022
                : 2 March 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 772646
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                gene expression profiling,rna sequencing,transcriptomics
                Uncategorized
                gene expression profiling, rna sequencing, transcriptomics

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