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Identification of gene expression signatures across different types of neural stem cells with the Monte-Carlo feature selection method.

Author information

Affiliations

  • 1. Schoolof Life Sciences, Shanghai University, Shanghai, P.R. China.
      Authors
    • Chen L 1
    • Li J 1
    • Wang S 1
    • Cai YD 1
    (4 authors)
  • 2. Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China.
      Authors
    • Zhang YH 2
    • Huang T 2
    • Kong X 2
    (3 authors)
  • 3. Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou, Guangdong, P.R. China.
      Authors
    • Feng K 3
    (1 author)
  • 4. Anhui province key lab of Farmland Ecological Conversation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, P.R. China.
      Authors
    • Zhang Y 4
    (1 author)

ORCIDs linked to this article

Journal of Cellular Biochemistry, 29 Dec 2017, 119(4):3394-3403
https://doi.org/10.1002/jcb.26507 PMID: 29130544 

Abstract 

Adult neural stem cells (NSCs) are a group of multi-potent, self-renewing progenitor cells that contribute to the generation of new neurons and oligodendrocytes. Three subtypes of NSCs can be isolated based on the stages of the NSC lineage, including quiescent neural stem cells (qNSCs), activated neural stem cells (aNSCs) and neural progenitor cells (NPCs). Although it is widely accepted that these three groups of NSCs play different roles in the development of the nervous system, their molecular signatures are poorly understood. In this study, we applied the Monte-Carlo Feature Selection (MCFS) method to identify the gene expression signatures, which can yield a Matthews correlation coefficient (MCC) value of 0.918 with a support vector machine evaluated by ten-fold cross-validation. In addition, some classification rules yielded by the MCFS program for distinguishing above three subtypes were reported. Our results not only demonstrate a high classification capacity and subtype-specific gene expression patterns but also quantitatively reflect the pattern of the gene expression levels across the NSC lineage, providing insight into deciphering the molecular basis of NSC differentiation.

Full text links 

Read article at publisher's site: https://doi.org/10.1002/jcb.26507

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Funding 

Funders who supported this work.

National Natural Science Foundation of China (2)

Natural Science Foundation of Shanghai (1)

Youth Innovation Promotion Association of the Chinese Academy of Sciences (1)