Quantitative assessment of changes in cellular morphology at photodynamic treatment in vitro by means of digital holographic microscopy

Abstract

Changes in morphological characteristics of cells from two cultured cancer cell lines, HeLa and A549, induced by photodynamic treatment with Radachlorin photosensitizer have been monitored using digital holographic microscopy. The observed dose-dependent post-treatment dynamics of phase shift variations demonstrated several scenarios of cell death. In particular the phase shift increase at low doses can be associated with apoptosis while its decrease at high doses can be associated with necrosis. Two cell types were shown to be differently responsive to treatment at the same doses. Although the sequence of death scenarios with increasing irradiation dose was demonstrated to be the same, each specific scenario was realized at substantially different doses. Results obtained by holographic microscopy were confirmed by confocal fluorescence microscopy with the commonly used test assay.

Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images

“…The results obtained are in a good agreement with the predominant cell death pathways, demonstrated using confocal fluorescence microscopy at similar doses of PDTr and our previous findings on HeLa cells resistance to PDTr with Radachlorin photosensitizer [37,41,42]. Therefore, the developed classification algorithm allows for robust distinguishing between different cell states and types.…”

“…Thus trained classification algorithm based on the SVM classifier with quadratic kernel functions operating with the set of 10 cell parameters was applied to analysis of the dynamics of cell response to PDTr with different doses. As we have previously shown [36,42], different doses of PDTr, provided by variation of either irradiation power density or irradiation duration or both can cause different pathways of cell death and different death dynamics. In experiments we varied the power density keeping constant the irradiation duration.…”

“…Phase unwrapping using the Goldstein algorithm [40] and cell segmentation on the obtained phase images were performed afterwards for further calculation of cellular characteristics. The detailed description of the optical setup and workflow process can be found in our recent papers [37,41,42].…”

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

“…62 Quantitative sorting of adherent cells based on morphology is of potential utility in phenotypic screening and basic studies linking gene expression to phenotype and functional behavior. In phenotypic screening, DHM followed by assignment of a machine learning prediction score to individual cells would allow the detection of subtle morphological shifts in response to various treatments, 63 a task of increasing importance in drug discovery. 64 In this study, two breast cancer cell lines, MCF-7 and MDA-MB-231, were scored on an EM axis by linear SVM, consistent with their morphological appearance.…”

Голографическое исследование отклика клеток линии HeLa на фотодинамическое воздействие с эндогенно генерируемым протопорфирином IX

“…С помощью голографической микроскопии проводились исследования динамики морфологии клеток крови [2][3][4][5], эндотелиальных клеток [6], нейронов [7,8], культур раковых клеток [9][10][11] и их отклика на противоопухолевую терапию [12][13][14][15][16], бактериальной инфекции клеток [17]. Была показана возможность определения не только морфологических и оптических параметров клеток, но и их сухой массы [18], метастатического потенциала [19] и типа клеточной гибели при внешнем воздействии [20]. Также была показана перспективность использования голографической микроскопии для исследования гистологических срезов тканей [21][22][23].…”