Investigating evolutionary perspective of carcinogenesis with single-cell transcriptome analysis.
Investigating evolutionary perspective of carcinogenesis with single-cell transcriptome analysis.
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DOI：
10.5732/cjc.012.10291
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发表时间：
2013-12
影响因子：
--
通讯作者：
Zhong JF
中科院分区：
文献类型：
--
作者：
Zhang X;Zhang C;Li Z;Zhong J;Weiner LP;Zhong JF
We developed phase-switch microfluidic devices for molecular profiling of a large number of single cells. Whole genome microarrays and RNA-sequencing are commonly used to determine the expression levels of genes in cell lysates (a physical mix of millions of cells) for inferring gene functions. However, cellular heterogeneity becomes an inherent noise in the measurement of gene expression. The unique molecular characteristics of individual cells, as well as the temporal and quantitative information of gene expression in cells, are lost when averaged among all cells in cell lysates. Our single-cell technology overcomes this limitation and enables us to obtain a large number of single-cell transcriptomes from a population of cells. A collection of single-cell molecular profiles allows us to study carcinogenesis from an evolutionary perspective by treating cancer as a diverse population of cells with abnormal molecular characteristics. Because a cancer cell population contains cells at various stages of development toward drug resistance, clustering similar single-cell molecular profiles could reveal how drug-resistant sub-clones evolve during cancer treatment. Here, we discuss how single-cell transcriptome analysis technology could enable the study of carcinogenesis from an evolutionary perspective and the development of drug-resistance in leukemia. The single-cell transcriptome analysis reported here could have a direct and significant impact on current cancer treatments and future personalized cancer therapies.
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DOI：
10.1073/pnas.1100509108
发表时间：
2011-08-02
影响因子：
11.1
作者：
Li, Shengwen Calvin;Jin, Yangsun;Zhong, Jiang F.
通讯作者：
Zhong, Jiang F.
影响因子：
158.5
作者：
Druker, Brian J.;Guilhot, Francois;Larson, Richard A.
通讯作者：
Larson, Richard A.
影响因子：
158.5
作者：
Kantarjian, Hagop;Giles, Francis;Ottmann, Oliver G.
通讯作者：
Ottmann, Oliver G.
影响因子：
11.8
作者：
Guo, Guoji;Huss, Mikael;Robson, Paul
通讯作者：
Robson, Paul
影响因子：
20.3
作者：
Jorgensen, Heather G.;Allan, Elaine K.;Holyoake, Tessa L.
通讯作者：
Holyoake, Tessa L.