Quantitative phase microscopy spatial signatures of cancer cells

TL;DR

This study uses label-free quantitative phase microscopy to classify live healthy, primary tumor, and metastatic cancer cells based on their morphological and textural features, achieving high accuracy and potential for liquid biopsy applications.

Contribution

It introduces a novel method combining quantitative phase imaging with machine learning for accurate classification of live cancer cells in flow conditions.

Findings

Achieved 81%-93% sensitivity in cell classification.

Achieved 81%-99% specificity in cell classification.

Identified significant morphological and textural differences between cell groups.

Abstract

We present cytometric classification of live healthy and cancer 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 cell 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 used to calculate 15 parameters derived from cellular 3-D 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.