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Welter EM, Benavides S, Archer TK, Kosyk O, Zannas AS. Machine learning-based morphological quantification of replicative senescence in human fibroblasts. GeroScience 2024; 46:2425-2439. [PMID: 37985642 PMCID: PMC10828145 DOI: 10.1007/s11357-023-01007-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 10/28/2023] [Indexed: 11/22/2023] Open
Abstract
Although aging has been investigated extensively at the organismal and cellular level, the morphological changes that individual cells undergo along their replicative lifespan have not been precisely quantified. Here, we present the results of a readily accessible machine learning-based pipeline that uses standard fluorescence microscope and open access software to quantify the minute morphological changes that human fibroblasts undergo during their replicative lifespan in culture. Applying this pipeline in a widely used fibroblast cell line (IMR-90), we find that advanced replicative age robustly increases (+28-79%) cell surface area, perimeter, number and total length of pseudopodia, and nuclear surface area, while decreasing cell circularity, with phenotypic changes largely occurring as replicative senescence is reached. These senescence-related morphological changes are recapitulated, albeit to a variable extent, in primary dermal fibroblasts derived from human donors of different ancestry, age, and sex groups. By performing integrative analysis of single-cell morphology, our pipeline further classifies senescent-like cells and quantifies how their numbers increase with replicative senescence in IMR-90 cells and in dermal fibroblasts across all tested donors. These findings provide quantitative insights into replicative senescence, while demonstrating applicability of a readily accessible computational pipeline for high-throughput cell phenotyping in aging research.
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Affiliation(s)
- Emma M Welter
- Department of Psychiatry, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA
| | - Sofia Benavides
- Department of Psychiatry, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA
| | - Trevor K Archer
- Chromatin and Gene Expression Section, Epigenetics and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, Durham, NC, 27709, USA
| | - Oksana Kosyk
- Department of Psychiatry, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA
| | - Anthony S Zannas
- Department of Psychiatry, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA.
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Welter EM, Kosyk O, Zannas AS. An open access, machine learning pipeline for high-throughput quantification of cell morphology. STAR Protoc 2023; 4:101947. [PMID: 36527712 PMCID: PMC9792532 DOI: 10.1016/j.xpro.2022.101947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/07/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Cell morphology is influenced by many factors and can be used as a phenotypic marker. Here we describe a machine-learning-based protocol for high-throughput morphological measurement of human fibroblasts using a standard fluorescence microscope and the pre-existing, open access software ilastik for cell body identification, ImageJ/Fiji for image overlay, and CellProfiler for morphological quantification. Because this protocol overlays nuclei with their cell bodies and relies on coloration differences, it can be broadly applied to other cell types beyond fibroblasts. For details on the use and execution of this protocol, please also refer to Leung et al. (2022).1.
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Affiliation(s)
- Emma M Welter
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
| | - Oksana Kosyk
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Anthony S Zannas
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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