研究目的
To enable longitudinal, in-depth studies of CTC biology in GEMMs and other murine cancer models by developing an optofluidic system for continuous collection and analysis of CTCs from the same mouse over time, eliminating biases from intermouse heterogeneity.
研究成果
The optofluidic system successfully enables longitudinal CTC collection from the same mouse, eliminating intermouse heterogeneity biases. scRNA-Seq analysis shows that CTC profiles change over time with treatment, and matched primary tumor data suggest CTCs can serve as surrogates for tumor biology. This platform facilitates studies on CTC evolution, drug response biomarkers, and metastasis mechanisms.
研究不足
The system may collect non-target cells (e.g., white and red blood cells) along with CTCs, potentially complicating downstream analysis. Sensitivity is sufficient but not absolute, as only the brightest fluorescent cells are reliably detected. The method requires surgical implantation of shunts, which could introduce complications. Future work is needed to optimize capture efficiency and reduce background noise.
1:Experimental Design and Method Selection:
The system uses a microfluidic CTC sorter chip with fluorescence detection and pneumatic valves for real-time sorting. Blood is withdrawn from a mouse via an arteriovenous shunt at 30 μL/min, and CTCs are captured based on fluorescence signals.
2:Sample Selection and Data Sources:
Genetically engineered mouse models (GEMMs) of small cell lung cancer (SCLC) with tdTomato fluorescence markers are used. Blood samples are spiked with fluorescent beads or cells for validation.
3:List of Experimental Equipment and Materials:
Includes a polydimethylsiloxane-based microfluidic chip, photomultiplier tube (PMT), lasers (532 nm), pneumatic valves, peristaltic pump, and equipment for scRNA-Seq (Smart-Seq2 protocol).
4:Experimental Procedures and Operational Workflow:
Blood is continuously withdrawn, CTCs are detected and sorted into collection tubes, enriched via magnetic-activated cell sorting and secondary sorting chips, and processed for scRNA-Seq. Longitudinal studies involve collecting CTCs over multiple days post-drug treatment (JQ1).
5:1). Data Analysis Methods:
5. Data Analysis Methods: Data are analyzed using principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), k-nearest neighbors clustering, differential expression analysis, and statistical tests (e.g., Spearman correlation, Student's t test, hypergeometric test).
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