ArticleChinese journal of cancer2013
Investigating evolutionary perspective of carcinogenesis with single-cell transcriptome analysis.
Article in Chinese journal of cancer, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Review
- Article
- Single-cell Transcriptome Study as Big Data.Genomics, proteomics & bioinformatics · 2016Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
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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Registered trials
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