Mouse kidney nuclear isolation and library preparation for single-cell combinatorial indexing RNA sequencing.
Mouse kidney nuclear isolation and library preparation for single-cell combinatorial indexing RNA sequencing.
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DOI:
10.1016/j.xpro.2022.101904
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发表时间:
2022-12-16
期刊:
影响因子:
--
通讯作者:
Humphreys, Benjamin D.
中科院分区:
文献类型:
--
作者:
Li, Haikuo;Humphreys, Benjamin D.
Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq3) enables high-throughput single-nucleus transcriptomic profiling of multiple samples in one experiment. Here, we describe an optimized protocol of mouse kidney nuclei isolation and sci-RNA-seq3 library preparation. The use of a dounce tissue homogenizer enables nuclei extraction with high yield. Fixed nuclei are processed for sci-RNA-seq3, and self-loaded transposome Tn5 is used for tagmentation in library generation. The step-by-step protocol allows researchers to generate scalable single-cell transcriptomic data with common laboratory supplies at low cost. For complete details on the use and execution of this protocol, please refer to Li et al. (2022). Optimized nuclei isolation protocol for mouse kidneys with high efficiency Tn5 assembly with annealed oligonucleotides to make functional transposome Transposome activity titration test to determine optimal working concentration Performing a small-scale sci-RNA-seq3 experiment as a proof of principle Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq3) enables high-throughput single-nucleus transcriptomic profiling of multiple samples in one experiment. Here, we describe an optimized protocol of mouse kidney nuclei isolation and sci-RNA-seq3 library preparation. The use of a dounce tissue homogenizer enables nuclei extraction with high yield. Fixed nuclei are processed for sci-RNA-seq3, and self-loaded transposome Tn5 is used for tagmentation in library generation. The step-by-step protocol allows researchers to generate scalable single-cell transcriptomic data with common laboratory supplies at low cost.
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影响因子:
14.8
作者:
Martin, Beth K.;Qiu, Chengxiang;Nichols, Eva;Phung, Melissa;Green-Gladden, Rula;Srivatsan, Sanjay;Blecher-Gonen, Ronnie;Beliveau, Brian J.;Trapnell, Cole;Cao, Junyue;Shendure, Jay
通讯作者:
Shendure, Jay
影响因子:
64.8
作者:
Cao, Junyue;Spielmann, Malte;Shendure, Jay
通讯作者:
Shendure, Jay
影响因子:
16.6
作者:
Zheng GX;Terry JM;Belgrader P;Ryvkin P;Bent ZW;Wilson R;Ziraldo SB;Wheeler TD;McDermott GP;Zhu J;Gregory MT;Shuga J;Montesclaros L;Underwood JG;Masquelier DA;Nishimura SY;Schnall-Levin M;Wyatt PW;Hindson CM;Bharadwaj R;Wong A;Ness KD;Beppu LW;Deeg HJ;McFarland C;Loeb KR;Valente WJ;Ericson NG;Stevens EA;Radich JP;Mikkelsen TS;Hindson BJ;Bielas JH
通讯作者:
Bielas JH
DOI:
10.3791/3564
发表时间:
2012-07-30
期刊:
Journal of visualized experiments : JoVE
影响因子:
--
作者:
Gage, Gregory J;Kipke, Daryl R;Shain, William
通讯作者:
Shain, William
影响因子:
29
作者:
Li, Haikuo;Dixon, Eryn E.;Wu, Haojia;Humphreys, Benjamin D.
通讯作者:
Humphreys, Benjamin D.