Identifying heterogeneity in cell culture through machine learning-enabled lens-free microscopy

Martin Alice; Florian Lemarchand; Kiran Padmanabhan; Lionel Hervé; Olivier Cioni; Chiara Paviolo

doi:10.1117/12.3021685

18 June 2024 Identifying heterogeneity in cell culture through machine learning-enabled lens-free microscopy

Martin Alice, Florian Lemarchand, Kiran Padmanabhan, Lionel Hervé, Olivier Cioni, Chiara Paviolo

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Proceedings Volume PC12996, Unconventional Optical Imaging IV; PC129960Y (2024) https://doi.org/10.1117/12.3021685
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Cellular heterogeneity is the hallmark of many cancers, referring to the co-existence of different phenotypes with very distinct biological behaviours in single isolates. Automatically detecting single-cell heterogeneity is therefore critical, and can provide important information on cancer initiation. We present a clustering algorithm that allows identifying heterogeneity in cell culture from time-lapses of lensless microscopic images. A preliminary segmentation and tracking pipeline extract quantitative features (morphology, motility and reproduction cycle) for each cell. An unsupervised learning algorithm then clusters the time-series of the cell tracks measurements, in two steps. We validate our approach on co-cultures of mixed cells lines, and on murine fibroblasts isolated from genetically modified mice, where the modified genome promotes the establishment of cancers and heterogeneous cell morphologies and behaviours

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Martin Alice, Florian Lemarchand, Kiran Padmanabhan, Lionel Hervé, Olivier Cioni, and Chiara Paviolo "Identifying heterogeneity in cell culture through machine learning-enabled lens-free microscopy", Proc. SPIE PC12996, Unconventional Optical Imaging IV, PC129960Y (18 June 2024); https://doi.org/10.1117/12.3021685

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KEYWORDS

Machine learning

Microscopy

Cancer

Cancer detection

Algorithm development

Cell phenotyping

Holography

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