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. 2019 Feb 5;116(6):2232-2236.
doi: 10.1073/pnas.1814102116. Epub 2019 Jan 23.

Optofluidic real-time cell sorter for longitudinal CTC studies in mouse models of cancer

Affiliations

Optofluidic real-time cell sorter for longitudinal CTC studies in mouse models of cancer

Bashar Hamza et al. Proc Natl Acad Sci U S A. .

Abstract

Circulating tumor cells (CTCs) play a fundamental role in cancer progression. However, in mice, limited blood volume and the rarity of CTCs in the bloodstream preclude longitudinal, in-depth studies of these cells using existing liquid biopsy techniques. Here, we present an optofluidic system that continuously collects fluorescently labeled CTCs from a genetically engineered mouse model (GEMM) for several hours per day over multiple days or weeks. The system is based on a microfluidic cell sorting chip connected serially to an unanesthetized mouse via an implanted arteriovenous shunt. Pneumatically controlled microfluidic valves capture CTCs as they flow through the device, and CTC-depleted blood is returned back to the mouse via the shunt. To demonstrate the utility of our system, we profile CTCs isolated longitudinally from animals over 4 days of treatment with the BET inhibitor JQ1 using single-cell RNA sequencing (scRNA-Seq) and show that our approach eliminates potential biases driven by intermouse heterogeneity that can occur when CTCs are collected across different mice. The CTC isolation and sorting technology presented here provides a research tool to help reveal details of how CTCs evolve over time, allowing studies to credential changes in CTCs as biomarkers of drug response and facilitating future studies to understand the role of CTCs in metastasis.

Keywords: GEMM; circulating tumor cells; metastasis; microfluidic; single-cell RNA-Seq.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Microfluidic sorter for longitudinal CTC studies in GEMMs. (A) Peristaltic pump withdraws blood from a surgically implanted cannula in the carotid artery of a mouse at a flow rate of 30 μL⋅min−1. The blood is directed into the main flow channel of the CTC sorter chip. For tdTomato-positive cells, a green (532-nm) laser illuminates two points along the main flow channel of the CTC chip separated by a known distance. Thus, fluorescent CTCs emit two red-shifted pulses of light, which are detected by a photomultiplier tube (PMT). Based on the timing of the pulses, a LabVIEW program computes the velocity of the cells and operates computer-controlled pneumatic valves to redirect fluorescent CTCs toward a collection tube. After exiting the chip, CTC-depleted blood returns to the jugular vein of the mouse via a second surgically implanted cannula. (B) Top-view image of the CTC sorter microfluidic chip showing the inlet, outlets, and valve actuation lines (V1 and V2). (C) Illustration of the CTC detection mechanism using the two excitation laser lines. A low-pass filter is applied to the raw data for determining true peaks. (D) Outlet by which blood is returned to the mouse is briefly sealed while the opposite outlet is opened to allow for CTC isolation in real time. (E) After collection, CTCs are further enriched by a secondary CTC sorting chip designed with a parallel channel to flush CTCs into wells containing cell lysis buffer for downstream scRNA-Seq.
Fig. 2.
Fig. 2.
scRNA-Seq of captured CTCs demonstrates the utility of intramouse CTC profiling. The tSNE of all CTCs collected across three JQ1-treated mice is colored by time point posttreatment (A), mouse (B), and cluster of assignment based on k-nearest neighbors clustering (C). (Top Right) Pie charts show the fractional representation of each cluster in each treated mouse. Boxplots of the first PC of CTC transcriptomes from PCAs were obtained from longitudinally following the same treated mouse [D, correlation (Corr) = 0.56] or untreated mouse (E, Corr = −0.05). Each point represents a CTC. (F) Boxplots of the first PC from three different “mock terminal bleed” permutations across three treated mice (SI Appendix, Supplementary Information Text and Fig. S9).
Fig. 3.
Fig. 3.
scRNA-Seq of end-point primary tumors demonstrates heterogeneity in phenotype. (A) The tSNE of primary tumor cells across treated and untreated mice, colored by mouse called from k-nearest neighbors clustering. (B) tSNE of primary tumor cells across treated and untreated mice, colored by clusters. (C) Computational cluster assignments (SI Appendix, Supplementary Information Text) for 96-h CTCs next to their matched primary for a representative treated mouse and untreated mouse plotted as bar plots (n = 18 and n = 82 cells for treated mouse 1 96-h CTCs and tumor cells, respectively; n = 52 and n = 84 cells for untreated mouse 1 96-h CTCs and tumor cells, respectively). UA, unassigned. Neither pairing is significantly different (P = 0.99 and P = 0.66 for treated mouse 1 and untreated mouse 1, respectively, by Fisher’s exact test).

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