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Cytometer Detects Cancer Cell Subpopulations Concurrently

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A new flow cytometry system could help scientists develop more targeted, personalized strategies for treating cancer by enabling them to follow disease progression and therapeutic response in different cancer cell subpopulations simultaneously.

The technique, called diffuse in vivo flow cytometry (DiFC), was developed by researchers at Northeastern University and Dartmouth College. The method noninvasively detects fluorescent protein (FP)-expressing circulating tumor cell (CTC) subpopulations and tumor cell clusters (CTCCs) within the bloodstreams of small animals.

The researchers initially demonstrated that single-color DiFC can noninvasively detect single FP-expressing CTCs in mice, making it possible to monitor metastatic dissemination in the mice. The researchers proceeded to develop and validate a two-color DiFC system that can simultaneously detect CTCs expressing blue-green (GFP) or orange (tdTomato) FPs.
Diffuse <I>in vivo</I> flow cytometry (DiFC) detects cancer cells expressing fluorescent proteins (FPs) when the cells are excited by laser light as they move through a blood vessel. Fluorescent light is collected by the DiFC detection fibers and split between two detector arms (filters and photomultiplier tubes) for each fluorophore. Courtesy of Williams et al., doi 10.1117/1.JBO.29.6.065003.
Diffuse in vivo flow cytometry (DiFC) detects cancer cells expressing fluorescent proteins (FPs) when the cells are excited by laser light as they move through a blood vessel. Fluorescent light is collected by the DiFC detection fibers and split between two detector arms (filters and photomultiplier tubes) for each fluorophore. Courtesy of Williams et al., doi 10.1117/1.JBO.29.6.065003.

The two-color DiFC system uses a 488-nm laser coupled into two specially designed optical fiber probes. Each probe consists of a single source fiber surrounded by a ring of eight detection fibers. The probe tips have internal mounted filters to reduce fiber autofluorescence.

The eight detection fibers are grouped into two bundles of four which terminate on an output fiber coupler, emission band-pass filters, a second focusing lens, and photomultiplier tube. The two sets of detection fibers are interleaved in the probe tip. The tip can be aligned on the skin surface above a major blood vessel to excite and detect FP-expressing circulating cells.

With two-color DiFC, researchers can detect and study two populations of CTCs at the same time. Tumor development and response to therapies in different subpopulations within the same animal can be studied. Researchers can monitor the dynamics of cancer spread in real time and gain insight into the heterogeneity of cancer cell populations.

The Northeastern/Dartmouth team used two-color DiFC to detect CTCs and CTCCs containing both types of fluorophores in tissue-mimicking flow phantoms in vitro, and in multiple myeloma-bearing mice in vivo.

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In the phantoms, DiFC accurately differentiated between the GFP-expressing and tdTomato-expressing CTCs and CTCCs. In the tumor-bearing mice, DiFC revealed that CTC numbers expressing both FPs increased during disease. Most CTCCs (86.5%) expressed single FPs, with the remaining CTCCs expressing both FPs. These data points were supported by whole-body hyperspectral fluorescence cryo-imaging of the mice.

Traditionally, the study of CTCs has involved invasive methods such as blood draws and intravital microscopy. These approaches often fail to capture rare CTCs or multicellular CTCCs with high metastatic potential. The need to draw blood samples makes longitudinal studies difficult.

In contrast, DiFC uses highly scattered light to probe large, relatively deep blood vessels in bulk tissue. DiFC can scan much larger volumes of blood than microscopy-based methods. Hence, it allows for noninvasive sampling of large, peripheral blood volumes and the detection of rare cells to show, for example, that CTC numbers generally increase over the course of disease development in mouse metastasis models, but that these numbers can fluctuate significantly over 24-hour periods. In vivo measurements of CTCs offer the ability to sample larger volumes of blood over both short and long time periods to study CTC frequency and patterns.

Two-color DiFC could facilitate a range of experiments for studying two populations of cells. For example, anti-cluster therapies could be studied longitudinally. Researchers could observe CTC shedding patterns of two subpopulations of cancer cells in the same tumor, or in two tumors, to study the effect of therapies on treatment-resistant and treatment-responsive cancer cells within the same mouse. This would remove inter-mouse variability, which can occur when studying tumors in separate mice.

Although the current study focuses on the development and validation of a pre-clinical research system for monitoring cancer cells in small animals, the team is investigating the potential clinical translation of DiFC using molecularly-targeted contrast agents.

CTCs are rare, but their numbers are associated with overall patient prognosis and response to treatment. CTCCs are even more rare than single CTCs, but are purported to have 50 to 100x higher metastatic potential.

DiFC could become a valuable tool in the quest to conquer cancer. As the technology continues to evolve, it could contribute to more effective, personalized cancer treatments and, ultimately, could help mitigate the consequences of cancer.

The research was published in the Journal of Biomedical Optics (www.doi.org/10.1117/1.JBO.29.6.065003).

Published: June 2024
Glossary
flow cytometry
Flow cytometry is a powerful technique used in biology and medicine for the quantitative analysis of the physical and chemical characteristics of cells and particles suspended in a fluid. The method allows for the rapid measurement of multiple parameters simultaneously on a cell-by-cell basis. It is widely used in various fields, including immunology, microbiology, hematology, and cancer research. Here are the key components and features of flow cytometry: Sample preparation: Cells or...
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