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D-MAINS: A Deep-Learning Model for the Label-Free Detection of Mitosis, Apoptosis, Interphase, Necrosis, and Senescence in Cancer Cells.

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  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101600052 Publication Model: Electronic Cited Medium: Internet ISSN: 2073-4409 (Electronic) Linking ISSN: 20734409 NLM ISO Abbreviation: Cells Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: Basel, Switzerland : MDPI
    • الموضوع:
      Mitosis* ; Deep Learning* ; Apoptosis* ; Necrosis* ; Cellular Senescence* ; Interphase*; Humans ; Cell Line, Tumor ; Neoplasms/pathology ; Neoplasms/metabolism ; Image Processing, Computer-Assisted/methods
    • نبذة مختصرة :
      Background: Identifying cells engaged in fundamental cellular processes, such as proliferation or living/death statuses, is pivotal across numerous research fields. However, prevailing methods relying on molecular biomarkers are constrained by high costs, limited specificity, protracted sample preparation, and reliance on fluorescence imaging.
      Methods: Based on cellular morphology in phase contrast images, we developed a deep-learning model named Detector of Mitosis, Apoptosis, Interphase, Necrosis, and Senescence (D-MAINS).
      Results: D-MAINS utilizes machine learning and image processing techniques, enabling swift and label-free categorization of cell death, division, and senescence at a single-cell resolution. Impressively, D-MAINS achieved an accuracy of 96.4 ± 0.5% and was validated with established molecular biomarkers. D-MAINS underwent rigorous testing under varied conditions not initially present in the training dataset. It demonstrated proficiency across diverse scenarios, encompassing additional cell lines, drug treatments, and distinct microscopes with different objective lenses and magnifications, affirming the robustness and adaptability of D-MAINS across multiple experimental setups.
      Conclusions: D-MAINS is an example showcasing the feasibility of a low-cost, rapid, and label-free methodology for distinguishing various cellular states. Its versatility makes it a promising tool applicable across a broad spectrum of biomedical research contexts, particularly in cell death and oncology studies.
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    • Grant Information:
      MIG-2023-2-1018 Ovarian Cancer Research Alliance; R37 CA240625 United States CA NCI NIH HHS; P50 CA272218 United States CA NCI NIH HHS; R35 GM150509 United States GM NIGMS NIH HHS; T32GM133332 United States NH NIH HHS; R01 CA259111 United States CA NCI NIH HHS; P30 CA047904 United States CA NCI NIH HHS; T32 GM133332 United States GM NIGMS NIH HHS; DP2GM146320, P30 CA047904, P50 CA272218, R35 GM150509 ,R37CA240625, R01CA259111 United States NH NIH HHS; DP2 GM146320 United States GM NIGMS NIH HHS
    • Contributed Indexing:
      Keywords: cell death; image processing; interphase; label free; machine learning; mitosis; senescence
    • الموضوع:
      Date Created: 20240626 Date Completed: 20240626 Latest Revision: 20240628
    • الموضوع:
      20240628
    • الرقم المعرف:
      PMC11205186
    • الرقم المعرف:
      10.3390/cells13121004
    • الرقم المعرف:
      38920634