Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

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 underscoring the need to marry emerging imaging techniques with tumor biology. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a labelfree 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 the molecular specificity of Raman spectroscopy, 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 leverage 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.

Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods

“…NFL has the potential to be an alternative optical tool for medical armamentarium 59 . Compared to other techniques for detection of metastatic potential of cancer cells such as those by Paidi et al 60 and Bendau et al 61 , the NFL technique can not only be used for measurements with cells in vitro and fresh tissue specimens ex vivo, but also for in vivo measurements. Since it does not involve spatial scanning (mapping), it works faster than those that require mapping 60 , 61 , and can potentially be implemented for real-time diagnosis during surgery.…”

Label free identification of the different status of anemia disease using optimized double-slot cascaded microring resonator

“…The optical-based label-free biosensors have been attracting great attention in recent years due to their exclusive features like high sensitivity, convenient price, miniature size and immunity to environmental condition like humidity, spark and electromagnetic (EM) interference. Due to these characteristics, these biosensors have found lots of interests in bacteria detection 12 – 14 , drug screening 15 detection of different cancers 11 , 16 and quantitative monitoring of body performance 17 . The increasing tendency in the optical based biosensors is to design a miniaturized, high sensitive and cost effective sensor that provides fully-automated biological analysis and clinical diagnostics on a chip that known as lab-on-a-chip devices 18 .…”

Dynamic Tomographic Phase Microscopy by Double Six-Pack Holography

“…Yet, this 3D data contains valuable information pertaining to the cell phenotype, such as the metastatic potential of cancer cells. 2 Furthermore, as tomographic imaging tools become more readily available, 3 , 4 there is increasing demand for improved tools to enable the 3D study of not only fast flowing cells but also dynamic living cells. The technique presented in this paper is aimed toward the eventual goal of making high-accuracy dynamic tomography a reality.…”