Endogenous light scattering as an optical signature of circulating tumor cell clusters

Abstract

Circulating tumor cell clusters (CTCCs) are significantly more likely to form metastases than single tumor cells. We demonstrate the potential of backscatter-based flow cytometry (BSFC) to detect unique light scattering signatures of CTCCs in the blood of mice orthotopically implanted with breast cancer cells and treated with an anti-ADAM8 or a control antibody. Based on scattering detected at 405, 488, and 633 nm from blood samples flowing through microfluidic devices, we identified 14 CTCCs with large scattering peak widths and intensities, whose presence correlated strongly with metastasis. These initial studies demonstrate the potential to detect CTCCs via label-free BSFC.

Label-free flow cytometry of rare circulating tumor cell clusters in whole blood

“…Following a previously established data analysis workflow, signals from the three scattering channels were summed together, while the green fluorescence signal was processed independently (Fig. 3 c) 9 . Peak locations were determined using the built-in MATLAB function findpeaks.m in the same step.…”

“…To date, the only FDA-approved platform for CTC detection is CellSearch, which uses magnetic-activated cell sorting for positive detection and isolation of CTCs and CTCCs 2 , 7 , 9 , 10 . While CellSearch has become the diagnostic standard for CTCs, the platform’s reliance on targeting the epithelial cell adhesion molecule (which is not always present on all CTCs and CTCCs), inability to detect CTCs in ~ 30–35% of metastatic breast cancer patients, and poor correlation with prognosis limits its application 2 , 9 , 11 , 12 .…”

“…Previously, our group demonstrated through in vitro label-free flow cytometry that CTCCs have unique scattering signatures in comparison to white blood cells 9 . In this study, we build on our work with backscatter flow cytometry (BSFC) to assess the potential of detecting unique endogenous scattering signatures of CTCCs in whole blood.…”

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

“…Time domain statistics parameters such as Mean (M), Standard Deviation (SD), Root Mean Square (RMS), Skewness (Skew), Kurtosis (Kurt), Interquartile Range (IQR) and Entropy (E) were used, given its adequacy in differentiating types of periodic signals providing from different origins, from synthetic to biological sources [ 1 , 51 , 61 , 62 ]. Statistical time-domain parameters have been used to differentiate pathological from healthy electrocardiogram (ECG) and pulse waveform samples [ 11 , 45 , 61 , 62 ], to identify tumor cell clusters in cell lines [ 63 ], to differentiate between aggregates and isolated particles [ 1 ] or to identify different objects also through the back-scattered signal in underwater conditions [ 51 , 52 , 53 ]. High order moment-based features such as SD, Kurtosis and Skewness have been considered robust to aspects such as size or shape [ 51 ].…”

Assessment of internal refractive index profile of stochastically inhomogeneous nuclear models via analysis of two-dimensional optical scattering patterns

“…Some notable efforts include assessment of abnormalities in morphological, structural, and biochemical properties of red blood cells in diseases such as malaria or anemia, 1 – 4 optical oximetry at the capillary level to assess local tissue oxygenation and metabolic function, 5 detection of virus infections, 6 dynamical monitoring of apoptosis, 7 identification and quantification of cellular or subcellular changes associated with precancer progression or field carcinogenesis, 8 – 12 and testing for the presence of circulating tumor cell clusters which can have strong metastatic potential. 13 …”