Quantitative phase microscopy spatial signatures of cancer cells

Abstract

We present cytometric classification of live healthy and cancerous cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cells and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow. After acquisition, the optical path delay maps of the cells were extracted and then used to calculate 15 parameters derived from the cellular 3D morphology and texture. Upon analyzing tens of cells in each group, we found high statistical significance in the difference between the groups in most of the parameters calculated, with the same trends for all statistically significant parameters. Furthermore, a specially designed machine learning algorithm, implemented on the phase map extracted features, classified the correct cell type (healthy/cancer/metastatic) with 81-93% sensitivity and 81-99% specificity. The quantitative phase imaging approach for liquid biopsies presented in this paper could be the basis for advanced techniques of staging freshly isolated live cancer cells in imaging flow cytometers. © 2017 International Society for Advancement of Cytometry.

Quantitative Phase Imaging for Label-Free Analysis of Cancer Cells—Focus on Digital Holographic Microscopy

“…In the case of building a model for clinical use, this means that each model must be possible to train on samples from one set of patients with known cancer types, while still retaining its classification accuracy on samples from other patients with unknown diagnosis. Achieving this robustness is crucial for any use of machine learning outside of the lab, and although several studies have shown the potential of various classification approaches to distinguish between cells of known types when trained on cells from the same individual [54] or on known cell lines [51], few studies so far have explicitly addressed this problem. Changes detected by image analysis were already visible on day 1 for 0.25 µM etoposide, whereas effects on viability, as detected with spectrophotometry assays, were detected only on day 3 for 5 µM etoposide concentration, leading to the conclusion that the morphological changes observed occur before and at lower concentrations than the reduction seen in cell metabolic activity or viability assays [51].…”

“…Moreover, Roitshtain et al used a low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow. After acquisition, the optical path delay maps of the cells were extracted and then used to calculate 15 parameters derived from the cellular 3D morphology and texture [54]. In a very recent study, activated macrophages were analyzed and information of both cellular morphology and molecular content were collected with the help of the combination of Quantitative Phase Microscopy (QPM), Raman spectroscopy, and auto fluorescence imaging [101].…”

Evaluation of holographic imaging cytometer holomonitor M4® motility applications

“…Recently, several approaches for quantitative phase imaging (QPI) have been developed for live-imaging of cancer cells in culture (8)(9)(10)(11)(12)(13)(14)(15)(16)(17). As a label-free imaging technology that is non-cytotoxic even with prolonged exposure, QPI is a noninvasive method for single cell analysis of adherent cell behavior.…”

Automated analysis of individual sperm cells using stain‐free interferometric phase microscopy and machine learning

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