Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects
Xiao-Yan Zhang,Sadie L. Marjani,Zhaoyang Hu,Sherman M. Weissman,Xinghua Pan,Xinghua Pan,Shixiu Wu +6 more
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TLDR
The current methods for single cancer-cell sequencing are discussed, with a strong focus on those practically used or potentially valuable in cancer research, including single-cell isolation, whole genome and transcriptome amplification, epigenome profiling, multi-dimensional sequencing, and next-generation sequencing and analysis.Abstract:
Advances in genomic technology have enabled the faithful detection and measurement of mutations and the gene expression profile of cancer cells at the single-cell level. Recently, several single-cell sequencing methods have been developed that permit the comprehensive and precise analysis of the cancer-cell genome, transcriptome, and epigenome. The use of these methods to analyze cancer cells has led to a series of unanticipated discoveries, such as the high heterogeneity and stochastic changes in cancer-cell populations, the new driver mutations and the complicated clonal evolution mechanisms, and the novel identification of biomarkers of variant tumors. These methods and the knowledge gained from their utilization could potentially improve the early detection and monitoring of rare cancer cells, such as circulating tumor cells and disseminated tumor cells, and promote the development of personalized and highly precise cancer therapy. Here, we discuss the current methods for single cancer-cell sequencing, with a strong focus on those practically used or potentially valuable in cancer research, including single-cell isolation, whole genome and transcriptome amplification, epigenome profiling, multi-dimensional sequencing, and next-generation sequencing and analysis. We also examine the current applications, challenges, and prospects of single cancer-cell sequencing.read more
Citations
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Journal ArticleDOI
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Simone Picelli,Åsa K. Björklund,Åsa K. Björklund,Omid R. Faridani,Sven Sagasser,Sven Sagasser,Gösta Winberg,Gösta Winberg,Rickard Sandberg,Rickard Sandberg +9 more
TL;DR: Smart-seq2 as discussed by the authors improved reverse transcription, template switching and preamplification to increase both yield and length of cDNA libraries generated from individual cells, which have improved detection, coverage, bias and accuracy compared to Smart-seq libraries and are generated with off-the-shelf reagents at lower cost.
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Single Cell Isolation and Analysis.
TL;DR: This review focuses on the recent developments in single cell isolation and analysis, which include technologies, analyses and main applications and summarizes the historical background, limitations, applications, and potential of singlecell isolation technologies.
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Molecular heterogeneity in breast cancer: State of the science and implications for patient care.
Rachel E. Ellsworth,Heather L. Blackburn,Craig D. Shriver,Patrick Soon-Shiong,Darrell L. Ellsworth +4 more
TL;DR: Current information about genomic variability within primary breast carcinomas, between primary tumors and regional/distant metastases, among circulating tumor cells and disseminated tumor cells, and in cell-free nucleic acids in circulation are presented.
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Mechanisms that drive inflammatory tumor microenvironment, tumor heterogeneity, and metastatic progression
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TL;DR: This review summarizes recent progress, mechanistic understanding, and options for metastasis-targeted therapy.
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High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery
TL;DR: Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery, therefore, (sc)RNA- sequencing has broader application prospects, especially in the field of drug discovery.
References
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TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.
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Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.
Marco Gerlinger,Andrew Rowan,Stuart Horswell,James Larkin,David Endesfelder,Eva Grönroos,Pierre Martinez,Nicholas Matthews,Aengus Stewart,Patrick S. Tarpey,Ignacio Varela,Benjamin Phillimore,Sharmin Begum,Neil Q. McDonald,Adam Butler,David T. Jones,Keiran Raine,Calli Latimer,Claudio R. Santos,Mahrokh Nohadani,Aron Charles Eklund,Bradley Spencer-Dene,Graham Clark,Lisa Pickering,Gordon Stamp,Martin Gore,Zoltan Szallasi,Zoltan Szallasi,Julian Downward,P. Andrew Futreal,Charles Swanton +30 more
TL;DR: Intratumor heterogeneity can lead to underestimation of the tumor genomics landscape portrayed from single tumor-biopsy samples and may present major challenges to personalized-medicine and biomarker development.
Journal ArticleDOI
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
TL;DR: Drop-seq as discussed by the authors analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin, and identifies 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes.
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