A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

We present multimodal confocal Raman micro-spectroscopy (RS) and tomographic phase microscopy (TPM) for quick morpho-chemical phenotyping of human breast cancer cells (MDA-MB-231). Leveraging the non-perturbative nature of these advanced microscopy techniques, we captured detailed morpho-molecular data from living, label-free cells in their native physiological environment. Human bias-free data processing pipelines were developed to analyze hyperspectral Raman images (spanning Raman modes from 600 cm<sup>-1</sup> to 1800 cm<sup>-1</sup>, which uniquely characterize a wide range of molecular bonds and subcellular structures), as well as morphological data from three-dimensional refractive index tomograms (providing measurements of cell volume, surface area, footprint, and sphericity at nanometer resolution, alongside dry mass and density). By systematically breaking down the rich single-cell details that RS and TPM deliver, we demonstrate, in a quantitative manner, the advantage of such a multimodal microscopy method for phenotyping tumoral breast cancer cells. Our tools also provide further insight into the subcellular information without the use of any labels. Finally, we study and discuss any unique or correlated information that RS- and TPM-derived datasets feature. We believe our tools and quantitative data analysis pipelines can revolutionize phenotyping tasks in biomedical research when in need of a rapid and non-perturbative method on living cells in culture, with the potential for future translation into clinical and diagnostic applications.

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Publication Details

Journal
Journal of Visualized Experiments
Published
2025-08-22
DOI
https://doi.org/10.3791/68498
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Leonardo Bianchi, Peter T. C. So, Paola Saccomandi, Arianna Bresci et al.
Journal of Visualized Experiments
Radiomics and Machine Learning in Medical Imaging
article

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Leonardo Bianchi, Peter T. C. So, Paola Saccomandi, Arianna Bresci, Koseki J. Kobayashi-Kirschvink, Jeon Woong Kang, Gabriela Paroni
article en

Abstract

We present multimodal confocal Raman micro-spectroscopy (RS) and tomographic phase microscopy (TPM) for quick morpho-chemical phenotyping of human breast cancer cells (MDA-MB-231). Leveraging the non-perturbative nature of these advanced microscopy techniques, we captured detailed morpho-molecular data from living, label-free cells in their native physiological environment. Human bias-free data processing pipelines were developed to analyze hyperspectral Raman images (spanning Raman modes from 600 cm<sup>-1</sup> to 1800 cm<sup>-1</sup>, which uniquely characterize a wide range of molecular bonds and subcellular structures), as well as morphological data from three-dimensional refractive index tomograms (providing measurements of cell volume, surface area, footprint, and sphericity at nanometer resolution, alongside dry mass and density). By systematically breaking down the rich single-cell details that RS and TPM deliver, we demonstrate, in a quantitative manner, the advantage of such a multimodal microscopy method for phenotyping tumoral breast cancer cells. Our tools also provide further insight into the subcellular information without the use of any labels. Finally, we study and discuss any unique or correlated information that RS- and TPM-derived datasets feature. We believe our tools and quantitative data analysis pipelines can revolutionize phenotyping tasks in biomedical research when in need of a rapid and non-perturbative method on living cells in culture, with the potential for future translation into clinical and diagnostic applications.

Journal of Visualized Experiments(222)
Broad Institute (US), Mario Negri Institute for Pharmacological Research (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Massachusetts Institute of Technology (US), Politecnico di Milano (IT)
Openalex Percentile: Top 69%
Radiomics and Machine Learning in Medical Imaging
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