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A novel machine learning based approach for iPS progenitor cell identification

文献类型：期刊论文

作者Zhang, Haishan2,3; Shao, Ximing4; Peng, Yin1; Teng, Yanning2,5; Saravanan, Konda Mani2; Zhang, Huiling2; Li, Hongchang4; Wei, Yanjie2
刊名PLOS COMPUTATIONAL BIOLOGY
出版日期2019-12-01
卷号15期号:12页码:19
ISSN号1553-734X
DOI10.1371/journal.pcbi.1007351
英文摘要Author summary Identification of induced pluripotent stem (iPS) progenitor cells could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only after about 6 days of induction, iPS cells can be experimentally determined via fluorescent probes. What is more, the percentage of the progenitor cells during the early induction period is below 5%, too low to capture experimentally in early stage. In this work, we proposed an approach for the identification of iPS progenitor cells, the iPS forming cells, based on machine learning and microscopic image analysis. The aim is to help biologists to enrich iPS progenitor cells during the early stage of induction, which allows experimentalists to select iPS progenitor cells with much higher probability, and furthermore to study the biomarkers which trigger the reprogramming process. Identification of induced pluripotent stem (iPS) progenitor cells, the iPS forming cells in early stage of reprogramming, could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only about 6 days after reprogramming initiation, iPS cells can be experimentally determined via fluorescent probes. What is more, the ratio of progenitor cells during early reprograming period is below 5%, which is too low to capture experimentally in the early stage. In this paper, we propose a novel computational approach for the identification of iPS progenitor cells based on machine learning and microscopic image analysis. Firstly, we record the reprogramming process using a live cell imaging system after 48 hours of infection with retroviruses expressing Oct4, Sox2 and Klf4, later iPS progenitor cells and normal murine embryonic fibroblasts (MEFs) within 3 to 5 days after infection are labeled by retrospectively tracing the time-lapse microscopic image. We then calculate 11 types of cell morphological and motion features such as area, speed, etc., and select best time windows for modeling and perform feature selection. Finally, a prediction model using XGBoost is built based on the selected six types of features and best time windows. Our model allows several missing values/frames in the sample datasets, thus it is applicable to a wide range of scenarios. Cross-validation, holdout validation and independent test experiments show that the minimum precision is above 52%, that is, the ratio of predicted progenitor cells within 3 to 5 days after viral infection is above 52%. The results also confirm that the morphology and motion pattern of iPS progenitor cells is different from that of normal MEFs, which helps with the machine learning methods for iPS progenitor cell identification.
资助项目National Key Research and Development Program of China[2018YFB0204403] ; National Key Research and Development Program of China[2016YFB0201305] ; National Science Foundation of China[U1435215] ; National Science Foundation of China[61433012] ; Shenzhen Basic Research Fund[JCYJ20160331190123578] ; Shenzhen Basic Research Fund[JCYJ20170413093358429] ; Shenzhen Basic Research Fund[GGFW2017073114031767] ; Chinese Academy of Sciences[2019VBA0009] ; Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence, Youth Innovation Promotion Association, CAS
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
出版者PUBLIC LIBRARY SCIENCE
WOS记录号WOS:000507310800030
源URL[http://119.78.100.204/handle/2XEOYT63/15022]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Hongchang; Wei, Yanjie
作者单位1.Shenzhen Univ, Dept Pathol, Sch Med, Shenzhen, Guangdong, Peoples R China
2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Joint Engn Res Ctr Hlth Big Data Intelligent Anal, Ctr High Performance Comp, Shenzhen, Guangdong, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Inst Biomed & Biotechnol, Shenzhen Key Lab Mol Biol Neural Dev, Shenzhen, Guangdong, Peoples R China
5.China Merchants Bank Network Technol Hangzhou Co, Hangzhou, Zhejiang, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Haishan,Shao, Ximing,Peng, Yin,et al. A novel machine learning based approach for iPS progenitor cell identification[J]. PLOS COMPUTATIONAL BIOLOGY,2019,15(12):19.
APA Zhang, Haishan.,Shao, Ximing.,Peng, Yin.,Teng, Yanning.,Saravanan, Konda Mani.,...&Wei, Yanjie.(2019).A novel machine learning based approach for iPS progenitor cell identification.PLOS COMPUTATIONAL BIOLOGY,15(12),19.
MLA Zhang, Haishan,et al."A novel machine learning based approach for iPS progenitor cell identification".PLOS COMPUTATIONAL BIOLOGY 15.12(2019):19.

入库方式： OAI收割

来源：计算技术研究所

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