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      Single-Cell Sequencing Technology in Oncology: Applications for Clinical Therapies and Research

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          Abstract

          Cellular heterogeneity is a fundamental characteristic of many cancers. A lack of cellular homogeneity contributes to difficulty in designing targeted oncological therapies. Therefore, the development of novel methods to determine and characterize oncologic cellular heterogeneity is a critical next step in the development of novel cancer therapies. Single-cell sequencing (SCS) technology has been recently employed for analyzing the genetic polymorphisms of individual cells at the genome-wide level. SCS requires (1) precise isolation of the single cell of interest; (2) isolation and amplification of genetic material; and (3) descriptive analysis of genomic, transcriptomic, and epigenomic data. In addition to targeted analysis of single cells isolated from tumor biopsies, SCS technology may be applied to circulating tumor cells, which may aid in predicting tumor progression and metastasis. In this paper, we provide an overview of SCS technology and review the current literature on the potential application of SCS to clinical oncology and research.

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          The clonal evolution of tumor cell populations.

          P C Nowell (1976)
          It is proposed that most neoplasms arise from a single cell of origin, and tumor progression results from acquired genetic variability within the original clone allowing sequential selection of more aggressive sublines. Tumor cell populations are apparently more genetically unstable than normal cells, perhaps from activation of specific gene loci in the neoplasm, continued presence of carcinogen, or even nutritional deficiencies within the tumor. The acquired genetic insta0ility and associated selection process, most readily recognized cytogenetically, results in advanced human malignancies being highly individual karyotypically and biologically. Hence, each patient's cancer may require individual specific therapy, and even this may be thwarted by emergence of a genetically variant subline resistant to the treatment. More research should be directed toward understanding and controlling the evolutionary process in tumors before it reaches the late stage usually seen in clinical cancer.
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            The technology and biology of single-cell RNA sequencing.

            Aleksandra A. Kolodziejczyk, Jong Kim, Valentine Svensson … (2015)
            The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.
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              Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

              Saiful Islam, Una Kjällquist, Annalena Moliner … (2011)
              Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Anal Cell Pathol (Amst)
                Journal ID (iso-abbrev): Anal Cell Pathol (Amst)
                Journal ID (publisher-id): ACP
                Title: Analytical Cellular Pathology (Amsterdam)
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 2210-7177
                ISSN (Electronic): 2210-7185
                Publication date (Print): 2016
                Publication date (Electronic): 25 May 2016
                Volume: 2016
                Electronic Location Identifier: 9369240
                Affiliations
                1Department of Hematology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China
                2Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China
                3Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
                4Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China
                5Central Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China
                Author notes

                Academic Editor: Catherine Alix-Panabières

                Article
                DOI: 10.1155/2016/9369240
                PMC ID: 4897661
                PubMed ID: 27313981
                SO-VID: 54b8670b-d349-43e0-9a11-23f35628b9af
                Copyright © Copyright © 2016 Baixin Ye et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 18 February 2016
                Date accepted : 12 May 2016
                Categories
                Subject: Review Article

                Data availability:

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