Deep learning models for image and data processes of intracellular calcium ions.

Journal: Cellular signalling
PMID: 34954391

Abstract

Intracellular calcium ion (Ca) in cytoplasm as an intracellular second messenger is involved in almost all important cellular activities of organisms. Generally its concentration ([Ca]) is tested by live imaging followed image and data processes, in which much tedious and subjective manual work is involved. Here we show a computational approach of Deep Calcium following the principles of deep learning to predict the cytoplasmic Ca ranges and calcium peaks in calcium curve of objective cells. To validate Deep Calcium, chondrocytes, bone marrow stromal cells (BMSCs) and osteoblastic like cells (MC3T3-E1) from both the tissue and cell samples as well as from spontaneous and mechanical stimulated calcium response patterns are used. The good performance comparing with other relative machine learning models, as well as consistency biological results with human experts are demonstrated. Deep Calcium provides references for other image and data processes of intracellular range determination and curve peak identification.

Authors

  • Jin Zhou
  • Huan Wu
    SILC Business School, Shanghai University, Shanghai 201800, China.
  • Xusen Zhang
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering, Chongqing University, Chongqing, China.
  • Guoqing Xia
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering, Chongqing University, Chongqing, China.
  • Xiaoyuan Gong
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering, Chongqing University, Chongqing, China.
  • Dangyang Yue
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering, Chongqing University, Chongqing, China.
  • Yijuan Fan
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering, Chongqing University, Chongqing, China.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Guixue Wang
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State Key Laboratory of Mechanical Transmission, State and Local Joint Engineering Laboratory for Vascular Implants, Bioengineering College of Chongqing University , Chongqing, China.
  • Yong Li
    Department of Surgical Sciences, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, United States.
  • Jun Pan
    Key Laboratory for Biorheological Science and Technology of Ministry of Education, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering, Chongqing University, Chongqing, China. Electronic address: panj@cqu.edu.cn.

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