ArticleBiosensors & bioelectronics2021
Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential.
Article in Biosensors & bioelectronics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- From spectroscopic analysis to clinical imaging: emerging applications and persistent challenges of raman spectroscopy in CTCs-based liquid biopsies.Annals of medicine · 2026Review
- Article
- Digital Holographic Microscopy for Phenotypic Profiling of Adherent Cells.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Label-Free Cancer Detection Methods Based on Biophysical Cell Phenotypes.Bioengineering (Basel, Switzerland) · 2025Review
- Cell-TIMP: Cellular Trajectory Inference Based on Morphological Parameters.Nano letters · 2025Article
- Investigating In Vivo Tumor Biomolecular Changes Following Radiation Therapy Using Raman Spectroscopy.ACS omega · 2024Article
- Holotomography and atomic force microscopy: a powerful combination to enhance cancer, microbiology and nanotoxicology research.Discover nano · 2024Review
- Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Hyperspectral Raman Imaging for Automated Recognition of Human Renal Amyloid.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2023Article
- Optical diffraction tomography and Raman spectroscopy reveal distinct cellular phenotypes during white and brown adipocyte differentiation.Biosensors & bioelectronics · 2023Article
- Vaccines in Breast Cancer: Challenges and Breakthroughs.Diagnostics (Basel, Switzerland) · 2023Review
- Targeted Enzyme Activity Imaging with Quantitative Phase Microscopy.Nano letters · 2023Article
- Effects of morphology and size of nanoscale drug carriers on cellular uptake and internalization process: a review.RSC advances · 2022Review
- Label-Free Morpho-Molecular Imaging for Studying the Differential Interaction of Black Phosphorus with Tumor Cells.Nanomaterials (Basel, Switzerland) · 2022Article
- Label-free discrimination of tumorigenesis stages using in vitro prostate cancer bone metastasis model by Raman imaging.Scientific reports · 2022Article
- Dynamic Tomographic Phase Microscopy by Double Six-Pack Holography.ACS photonics · 2022Article
- Label free identification of the different status of anemia disease using optimized double-slot cascaded microring resonator.Scientific reports · 2022Article
- Raman spectroscopy reveals phenotype switches in breast cancer metastasis.Theranostics · 2022Article
- Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia.Biosensors & bioelectronics · 2021Article
- Nanowire Assisted Mechanotyping of Cellular Metastatic Potential.Advanced functional materials · 2021Article
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6 authors.
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Abstract
Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging its molecular specificity, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also perform multivariate curve resolution alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.
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