A novel machine learning based approach for iPS progenitor cell identification

doi:10.1371/journal.pcbi.1007351

	A novel machine learning based approach for iPS progenitor cell identification
	Zhang, Haishan 1,2; Shao, Ximing 3; Peng, Yin 4; Teng, Yanning 1,5; Saravanan, Konda Mani 1; Zhang, Huiling 1; Li, Hongchang 3; Wei, Yanjie 1
	2019-12-01
发表期刊	PLOS COMPUTATIONAL BIOLOGY
ISSN	1553-734X
卷号	15 期号:12 页码:19
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.
DOI	10.1371/journal.pcbi.1007351
收录类别	SCI
语种	英语
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
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS记录号	WOS:000507310800030
出版者	PUBLIC LIBRARY SCIENCE
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15023
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.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 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Inst Biomed & Biotechnol, Shenzhen Key Lab Mol Biol Neural Dev, Shenzhen, Guangdong, Peoples R China 4.Shenzhen Univ, Dept Pathol, Sch Med, 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.