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      scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation

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          Abstract

          Background

          The Human Cell Atlas is a large international collaborative effort to map all cell types of the human body. Single-cell RNA sequencing can generate high-quality data for the delivery of such an atlas. However, delays between fresh sample collection and processing may lead to poor data and difficulties in experimental design.

          Results

          This study assesses the effect of cold storage on fresh healthy spleen, esophagus, and lung from ≥ 5 donors over 72 h. We collect 240,000 high-quality single-cell transcriptomes with detailed cell type annotations and whole genome sequences of donors, enabling future eQTL studies. Our data provide a valuable resource for the study of these 3 organs and will allow cross-organ comparison of cell types.

          We see little effect of cold ischemic time on cell yield, total number of reads per cell, and other quality control metrics in any of the tissues within the first 24 h. However, we observe a decrease in the proportions of lung T cells at 72 h, higher percentage of mitochondrial reads, and increased contamination by background ambient RNA reads in the 72-h samples in the spleen, which is cell type specific.

          Conclusions

          In conclusion, we present robust protocols for tissue preservation for up to 24 h prior to scRNA-seq analysis. This greatly facilitates the logistics of sample collection for Human Cell Atlas or clinical studies since it increases the time frames for sample processing.

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          Most cited references26

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            From Louvain to Leiden: guaranteeing well-connected communities

            Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.
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              Quantitative single-cell RNA-seq with unique molecular identifiers.

              Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.
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                Author and article information

                Contributors
                em17@sanger.ac.uk
                aw24@sanger.ac.uk
                rjudicemiragaia@gmail.com
                ks10014@cam.ac.uk
                ktam2@cam.ac.uk
                ng395@cam.ac.uk
                pippa.m.harding@gmail.com
                kp9@sanger.ac.uk
                nh3@sanger.ac.uk
                kpn25@mrc-cu.cam.ac.uk
                rcf29@mrc-cu.cam.ac.uk
                kevinloudon@doctors.org.uk
                jrf58@cam.ac.uk
                mrc38@medschl.cam.ac.uk
                anthitsingene@gmail.com
                svd@sanger.ac.uk
                md18@sanger.ac.uk
                mp20@sanger.ac.uk
                mjt.stubbington@gmail.com
                st9@sanger.ac.uk
                stegle@ebi.ac.uk
                km16@sanger.ac.uk
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                31 December 2019
                31 December 2019
                2020
                : 21
                : 1
                Affiliations
                [1 ]ISNI 0000 0004 0606 5382, GRID grid.10306.34, Wellcome Sanger Institute, ; Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA UK
                [2 ]ISNI 0000 0000 9709 7726, GRID grid.225360.0, European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), ; Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD UK
                [3 ]GRID grid.454369.9, Department of Surgery, , University of Cambridge and NIHR Cambridge Biomedical Research Centre, ; Cambridge, CB2 0QQ UK
                [4 ]ISNI 0000000121885934, GRID grid.5335.0, MRC Cancer Unit, Hutchison-MRC Research Centre, , University of Cambridge, ; Cambridge, CB2 0XZ UK
                [5 ]ISNI 0000000121901201, GRID grid.83440.3b, Molecular Immunology Unit, Department of Medicine, ; Cambridge, CB2 0QQ UK
                [6 ]GRID grid.498512.3, 10x Genomics Inc., ; 6230 Stoneridge Mall Road, Pleasanton, CA 94588 USA
                Author information
                http://orcid.org/0000-0001-5906-1498
                Article
                1906
                10.1186/s13059-019-1906-x
                6937944
                31892341
                9118e224-6824-44d6-9da6-2f6c1d4f9070
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 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.

                History
                : 9 August 2019
                : 28 November 2019
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Genetics
                single-cell rna sequencing,human,spleen,esophagus,lung,ischemic time
                Genetics
                single-cell rna sequencing, human, spleen, esophagus, lung, ischemic time

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