Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT.
Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT.
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DOI:
10.1038/s41596-018-0058-x
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发表时间:
2018-11
期刊:
影响因子:
14.8
通讯作者:
Schier AF
中科院分区:
文献类型:
--
作者:
Raj B;Gagnon JA;Schier AF
Lineage relationships among the large number of heterogeneous cell types generated during development are difficult to reconstruct in a high-throughput manner. We recently established a method, scGESTALT, that combines cumulative editing of a lineage barcode array by CRISPR-Cas9 with large-scale transcriptional profiling using droplet-based single-cell RNA sequencing. The technique generates edits in the barcode array over multiple timepoints using Cas9 and pools of single-guide RNAs introduced during early and late zebrafish embryonic development, which distinguishes it from similar Cas9 lineage tracing methods. The recorded lineages are captured along with thousands of cellular transcriptomes to build lineage trees with hundreds of branches representing relationships among profiled cell types. Here we provide details for (i) generating transgenic zebrafish; (ii) performing multi-timepoint barcode editing; (iii) building single-cell RNA-seq libraries from brain tissue; and (iv) concurrently amplifying lineage barcodes from captured single cells. Generating transgenic lines takes 6 months while performing barcode editing and generating single-cell libraries involve 7 days of hands-on time. scGESTALT provides a scalable platform to map lineage relationships between cell types in any system that permits genome editing during development, regeneration or disease. This protocol describes how to generate transgenic zebrafish expressing a barcode array that can be edited by CRISPR/Cas9 at multiple developmental stages. Single cell RNA sequencing of edited barcodes and cellular transcriptomes allows reconstruction of lineage relationships.
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影响因子:
64.8
作者:
Halpern KB;Shenhav R;Matcovitch-Natan O;Toth B;Lemze D;Golan M;Massasa EE;Baydatch S;Landen S;Moor AE;Brandis A;Giladi A;Avihail AS;David E;Amit I;Itzkovitz S
通讯作者:
Itzkovitz S
影响因子:
9.3
作者:
Baron M;Veres A;Wolock SL;Faust AL;Gaujoux R;Vetere A;Ryu JH;Wagner BK;Shen-Orr SS;Klein AM;Melton DA;Yanai I
通讯作者:
Yanai I
影响因子:
25
作者:
Hrvatin S;Hochbaum DR;Nagy MA;Cicconet M;Robertson K;Cheadle L;Zilionis R;Ratner A;Borges-Monroy R;Klein AM;Sabatini BL;Greenberg ME
通讯作者:
Greenberg ME
影响因子:
64.5
作者:
Dixit, Atray;Pamas, Oren;Li, Biyu;Chen, Jenny;Fulco, Charles P.;Jerby-Amon, Livnat;Marjanovic, Nemanja D.;Dionne, Danielle;Burks, Tyler;Raychowdhury, Raktima;Adamson, Britt;Norman, Thomas M.;Lander, Eric S.;Weissman, Jonathan S.;Friedman, Nir;Regev, Aviv
通讯作者:
Regev, Aviv
影响因子:
48
作者:
Habib N;Avraham-Davidi I;Basu A;Burks T;Shekhar K;Hofree M;Choudhury SR;Aguet F;Gelfand E;Ardlie K;Weitz DA;Rozenblatt-Rosen O;Zhang F;Regev A
通讯作者:
Regev A