Profiling Transcriptional Heterogeneity with Seq-Well S 3 : A Low-Cost, Portable, High-Fidelity Platform for Massively Parallel Single-Cell RNA-Seq.

التفاصيل البيبلوغرافية
العنوان:	Profiling Transcriptional Heterogeneity with Seq-Well S ³ : A Low-Cost, Portable, High-Fidelity Platform for Massively Parallel Single-Cell RNA-Seq.
المؤلفون:	Drake RS; Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA.; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA., Villanueva MA; Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA. mav@mit.edu.; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA. mav@mit.edu.; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. mav@mit.edu.; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. mav@mit.edu.; Broad Institute of MIT and Harvard, Cambridge, MA, USA. mav@mit.edu.; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. mav@mit.edu., Vilme M; Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA.; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA., Russo DD; Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA.; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA., Navia A; Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA.; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.; Broad Institute of MIT and Harvard, Cambridge, MA, USA., Love JC; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. clove@mit.edu.; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. clove@mit.edu.; Broad Institute of MIT and Harvard, Cambridge, MA, USA. clove@mit.edu.; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. clove@mit.edu., Shalek AK; Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA. shalek@mit.edu.; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. shalek@mit.edu.; Broad Institute of MIT and Harvard, Cambridge, MA, USA. shalek@mit.edu.
المصدر:	Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2023; Vol. 2584, pp. 57-104.
نوع المنشور:	Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة:	English
بيانات الدورية:	Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE
أسماء مطبوعة:	Publication: Totowa, NJ : Humana Press Original Publication: Clifton, N.J. : Humana Press,
مواضيع طبية MeSH:	Single-Cell Analysis/methods , Single-Cell Gene Expression Analysis, Sequence Analysis, RNA/methods ; Transcriptome ; Reverse Transcription ; Gene Expression Profiling/methods ; High-Throughput Nucleotide Sequencing/methods
مستخلص:	Seq-Well is a high-throughput, picowell-based single-cell RNA-seq technology that can be used to simultaneously profile the transcriptomes of thousands of cells (Gierahn et al. Nat Methods 14(4):395-398, 2017). Relative to its reverse-emulsion-droplet-based counterparts, Seq-Well addresses key cost, portability, and scalability limitations. Recently, we introduced an improved molecular biology for Seq-Well to enhance the information content that can be captured from individual cells using the platform. This update, which we call Seq-Well S ³ (S ³ : Second-Strand Synthesis), incorporates a second-strand-synthesis step after reverse transcription to boost the detection of cellular transcripts normally missed when running the original Seq-Well protocol (Hughes et al. Immunity 53(4):878-894.e7, 2020). This chapter provides details and tips on how to perform Seq-Well S ³ , along with general pointers on how to subsequently analyze the resultant single-cell RNA-seq data. (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
References:	Gierahn TM, Wadsworth MH 2nd, Hughes TK, Bryson BD, Butler A, Satija R, Fortune S, Love JC, Shalek AK (2017) Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 14(4):395–398. https://doi.org/10.1038/nmeth.4179. (PMID: 10.1038/nmeth.4179) Hughes TK, Wadsworth MH 2nd, Gierahn TM, Do T, Weiss D, Andrade PR, Ma F, de Andrade Silva BJ, Shao S, Tsoi LC, Ordovas-Montanes J, Gudjonsson JE, Modlin RL, Love JC, Shalek AK (2020) Second-Strand synthesis-based massively parallel scRNA-Seq reveals cellular states and molecular features of human inflammatory skin pathologies. Immunity 53(4):878–894.e7. https://doi.org/10.1016/j.immuni.2020.09.015. (PMID: 10.1016/j.immuni.2020.09.015) Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5):1187–1201. https://doi.org/10.1016/j.cell.2015.04.044. (PMID: 10.1016/j.cell.2015.04.044) Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA, Sanes JR, Shalek AK, Regev A, McCarroll SA (2015) Highly parallel genome-wide expression profiling of individual cells using Nanoliter droplets. Cell 161(5):1202–1214. https://doi.org/10.1016/j.cell.2015.05.002. (PMID: 10.1016/j.cell.2015.05.002) Montoro DT, Haber AL, Biton M, Vinarsky V, Lin B, Birket SE, Yuan F, Chen S, Leung HM, Villoria J, Rogel N, Burgin G, Tsankov AM, Waghray A, Slyper M, Waldman J, Nguyen L, Dionne D, Rozenblatt-Rosen O, Tata PR, Mou H, Shivaraju M, Bihler H, Mense M, Tearney GJ, Rowe SM, Engelhardt JF, Regev A, Rajagopal J (2018) A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560(7718):319–324. https://doi.org/10.1038/s41586-018-0393-7. (PMID: 10.1038/s41586-018-0393-7) Ordovas-Montanes J, Dwyer DF, Nyquist SK, Buchheit KM, Vukovic M, Deb C, Wadsworth MH 2nd, Hughes TK, Kazer SW, Yoshimoto E, Cahill KN, Bhattacharyya N, Katz HR, Berger B, Laidlaw TM, Boyce JA, Barrett NA, Shalek AK (2018) Allergic inflammatory memory in human respiratory epithelial progenitor cells. Nature 560(7720):649–654. https://doi.org/10.1038/s41586-018-0449-8. (PMID: 10.1038/s41586-018-0449-8) Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G, Graham DB, Herbst RH, Rogel N, Slyper M, Waldman J, Sud M, Andrews E, Velonias G, Haber AL, Jagadeesh K, Vickovic S, Yao J, Stevens C, Dionne D, Nguyen LT, Villani AC, Hofree M, Creasey EA, Huang H, Rozenblatt-Rosen O, Garber JJ, Khalili H, Desch AN, Daly MJ, Ananthakrishnan AN, Shalek AK, Xavier RJ, Regev A (2019) Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178(3):714–730.e22. https://doi.org/10.1016/j.cell.2019.06.029. (PMID: 10.1016/j.cell.2019.06.029) Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Polański K, Goncalves A, Gardner L, Holmqvist S, Henriksson J, Zou A, Sharkey AM, Millar B, Innes B, Wood L, Wilbrey-Clark A, Payne RP, Ivarsson MA, Lisgo S, Filby A, Rowitch DH, Bulmer JN, Wright GJ, Stubbington MJT, Haniffa M, Moffett A, Teichmann SA (2018) Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563(7731):347–353. https://doi.org/10.1038/s41586-018-0698-6. (PMID: 10.1038/s41586-018-0698-6) Monian B, Tu AA, Ruiter B, Morgan DM, Petrossian PM, Smith NP, Gierahn TM, Ginder JH, Shreffler WG, Love JC (2022) Peanut oral immunotherapy differentially suppresses clonally distinct subsets of T helper cells. J Clin Invest 132(2):e150634. https://doi.org/10.1172/JCI150634. (PMID: 10.1172/JCI150634) Genshaft AS, Subudhi S, Keo A, Sanchez Vasquez JD, Conceição-Neto N, Mahamed D, Boeijen LL, Alatrakchi N, Oetheimer C, Vilme M, Drake R, Fleming I, Tran N, Tzouanas C, Joseph-Chazan J, Villanueva MA, van de Werken HJG, van Oord GW, Groothuismink ZMA, Beudeker BJ, Osmani Z, Nkongolo S, Mehrotra A, Feld J, Chung RT, de Knegt RJ, Janssen HLA, Aerssens J, Bollekens J, Hacohen N, Lauer GM, Boonstra A, Shalek AK, Gehring A (2021) Clinical implementation of single-cell RNA sequencing using liver fine needle aspirate tissue sampling and centralized processing captures compartment specific immuno-diversity. BioRxiv. https://doi.org/10.1101/2021.11.30.470634. Morgan DM, Ruiter B, Smith NP, Tu AA, Monian B, Stone BE, Virk-Hundal N, Yuan Q, Shreffler WG, Love JC (2021) Clonally expanded, GPR15-expressing pathogenic effector TH2 cells are associated with eosinophilic esophagitis. Sci Immunol 6(62):eabi5586. https://doi.org/10.1126/sciimmunol.abi5586. (PMID: 10.1126/sciimmunol.abi5586) Kotliar D, Lin AE, Logue J, Hughes TK, Khoury NM, Raju SS, Wadsworth MH 2nd, Chen H, Kurtz JR, Dighero-Kemp B, Bjornson ZB, Mukherjee N, Sellers BA, Tran N, Bauer MR, Adams GC, Adams R, Rinn JL, Melé M, Schaffner SF, Nolan GP, Barnes KG, Hensley LE, McIlwain DR, Shalek AK, Sabeti PC, Bennett RS (2020) Single-cell profiling of Ebola virus disease in vivo reveals viral and host dynamics. Cell 183(5):1383–1401.e19. https://doi.org/10.1016/j.cell.2020. (PMID: 10.1016/j.cell.2020) Trombetta JJ, Gennert D, Lu D, Satija R, Shalek AK, Regev A (2014) Preparation of single-cell RNA-Seq libraries for next generation sequencing. Curr Protoc Mol Biol 107:4.22.1–17. https://doi.org/10.1002/0471142727.mb0422s107. (PMID: 10.1002/0471142727.mb0422s107) Figure “Created with BioRender.com. Illumina (2016) Optimizing cluster density on illumina sequencing systems ( https://www.illumina.com/content/dam/illumina-marketing/documents/products/other/interview_cmason.pdf ). Illumina (2019) bcl2fastq2 Conversion Software v2.20 Software Guide ( https://support.illumina.com/content/dam/illumina-support/documents/documentation/software_documentation/bcl2fastq/bcl2fastq2-v2-20-software-guide-15051736-03.pdf ). Sarah Teichmann's group at EMBL-EBI (2022) Seq-Well S3 library structure ( https://teichlab.github.io/scg_lib_structs/methods_html/SeqWell_S3.html ). Illumina (2016) Troubleshooting demultiplexing issues ( https://support.illumina.com/bulletins/2016/08/troubleshooting-demultiplexing-issues-using-basespace-sequence-hub-and-bclfastq-v.html ). The Broad Institute, Inc. and The General Hospital Corporation (2019) Drop-seq Pipeline ( https://cumulus-doc.readthedocs.io/en/0.12.0/drop_seq.html ). Zhang MJ, Ntranos V, Tse D (2020) Determining sequencing depth in a single-cell RNA-seq experiment. Nat Commun. https://doi.org/10.1038/s41467-020-14482-y. Luecken MD, Theis FJ (2019) Current best practices in single‐cell RNA‐SEQ analysis: a tutorial. Mol Syst Biol. https://doi.org/10.15252/msb.20188746. Chung NC, Storey JD (2015) Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics 31(4):545–554. https://doi.org/10.1093/bioinformatics/btu674. (PMID: 10.1093/bioinformatics/btu674) Xi NM, Li JJ (2021) Benchmarking computational doublet-detection methods for single-cell RNA sequencing data. Cell Syst. https://doi.org/10.1016/j.cels.2020.11.008. Young MD, Behjati S (2020) SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. GigaScience. https://doi.org/10.1093/gigascience/giaa151. Sun S, Zhu J, Ma Y, Zhou X (2019) Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol. https://doi.org/10.1186/s13059-019-1898-6. Hie B, Peters J, Nyquist SK et al (2020) Computational methods for single-cell RNA sequencing. Ann Rev Biomed Data Sci 3:339–364. https://doi.org/10.1146/annurev-biodatasci-012220-100601. (PMID: 10.1146/annurev-biodatasci-012220-100601) Becht E, McInnes L, Healy J et al (2018) Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 37:38–44. https://doi.org/10.1038/nbt.4314. (PMID: 10.1038/nbt.4314) Kotliar D, Veres A, Nagy MA et al (2019) Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-seq. elife. https://doi.org/10.7554/elife.43803. Ziegler CGK, Miao VN, Owings AH, Navia AW, Tang Y, Bromley JD, Lotfy P, Sloan M, Laird H, Williams HB, George M, Drake RS, Christian T, Parker A, Sindel CB, Burger MW, Pride Y, Hasan M, Abraham GE 3rd, Senitko M, Robinson TO, Shalek AK, Glover SC, Horwitz BH, Ordovas-Montanes J (2021) Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19. Cell 184(18):4713–4733.e22. https://doi.org/10.1016/j.cell.2021.07.023. (PMID: 10.1016/j.cell.2021.07.023)
معلومات مُعتمدة:	R01 AR074302 United States AR NIAMS NIH HHS; R01 HL095791 United States HL NHLBI NIH HHS; R01 AR040312 United States AR NIAMS NIH HHS; P01 AI039671 United States AI NIAID NIH HHS; R01 HL126551 United States HL NHLBI NIH HHS; RM1 HG006193 United States HG NHGRI NIH HHS; U19 AI089992 United States AI NIAID NIH HHS; F30 AI143160 United States AI NIAID NIH HHS; P30 AR075043 United States AR NIAMS NIH HHS; N01AI30025 United States AI NIAID NIH HHS; U24 AI118672 United States AI NIAID NIH HHS; R01 AR056802 United States AR NIAMS NIH HHS; R01 DA046277 United States DA NIDA NIH HHS; P30 CA014051 United States CA NCI NIH HHS
فهرسة مساهمة:	Keywords: Picowells; RNA-Seq; Seq-Well; Single-cell RNA sequencing; Single-cell genomics; Systems biology; Transcriptomics; scRNA-seq
تواريخ الأحداث:	Date Created: 20221210 Date Completed: 20221216 Latest Revision: 20230322
رمز التحديث:	20240628
DOI:	10.1007/978-1-0716-2756-3_3
PMID:	36495445
قاعدة البيانات:	MEDLINE

الوصف
تدمد:	1940-6029
DOI:	10.1007/978-1-0716-2756-3_3