TY - JOUR
T1 - Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential
AU - Paidi, Santosh Kumar
AU - Shah, Vaani
AU - Raj, Piyush
AU - Glunde, Kristine
AU - Pandey, Rishikesh
AU - Barman, Ishan
N1 - Funding Information:
S.K.P. acknowledges the support of the SLAS Graduate Education Fellowship Grant. I.B. acknowledges the support from the National Cancer Institute ( R01 CA238025 ), the National Institute of Biomedical Imaging and Bioengineering ( 2-P41-EB015871-31 ) and the National Institute of General Medical Sciences ( DP2GM128198 ). K.G. acknowledges the support from the National Cancer Institute ( R01 CA213428 , R01 CA213492 ). The authors thank Tomocube Inc for use of the 3D ODT system. The schematic in Fig. 1 A was partially created with BioRender.com .
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - 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.
AB - 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.
KW - Breast cancer
KW - Metastasis
KW - Optical diffraction tomography
KW - Raman spectroscopy
KW - Random forests
KW - Single-cell phenotyping
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U2 - 10.1016/j.bios.2020.112863
DO - 10.1016/j.bios.2020.112863
M3 - Article
C2 - 33272866
AN - SCOPUS:85097066778
SN - 0956-5663
VL - 175
JO - Biosensors and Bioelectronics
JF - Biosensors and Bioelectronics
M1 - 112863
ER -