Evaluation of the metastatic potential of malignant cells by image processing of digital holographic microscopy data

Abstract

The cell refractive index has been proposed as a putative cancer biomarker of great potential, being correlated with cell content and morphology, cell division rate and membrane permeability. We used digital holographic microscopy to compare the refractive index and dry mass density of two B16 murine melanoma sublines of different metastatic potential. Using statistical methods, the distribution of phase shifts within the reconstructed quantitative phase images was analyzed by the method of bimodality coefficients. The observed correlation of refractive index, dry mass density and bimodality profile with the metastatic potential of the cells was validated by real time impedance‐based assay and clonogenic tests. We suggest that the refractive index and bimodality analysis of quantitative phase image histograms could be developed as optical biomarkers useful in label‐free detection and quantitative evaluation of cell metastatic potential.

Quantitative phase imaging unravels new insight into dynamics of mesenchymal and amoeboid cancer cell invasion

“…Such recognition was achieved by exploitation of the possibility to simultaneously measure cell migration and growth by evaluating gain of cell dry mass. A different study demonstrates that digital holographic microscopy can distinguish the metastatic potential of melanoma cells 62 .…”

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

“…28,29 The effects of cell seeding density, 4 exposure to anticancer drugs, 30,31 and other influences on cell phenotype [32][33][34] have been robustly evaluated with QPI. Quantitative imaging and machine learning have the potential to save time, labor, and reduce human error in phenotypic profiling, which could help pathologists and scientists to accurately detect circulating tumor cells, 35 classify cancer cells, 36,37 evaluate the metastatic potential of cancer cells, 38 and assess cancer drug resistance. 39 Thus, machine learning-assisted QPI has great power to aid in interpreting large-scale and high-dimensionality data from cells, potentially enhancing cancer diagnosis and treatment.…”

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

“…Ее преимущества перед фазово-контрастной и широкопольной оптической микроскопией заключаются в возможности получения численных значений морфологических и оптических параметров клеток при использовании малой плотности облучения, что делает этот метод исследования практически неинвазивным. 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].…”