Dataset Information

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow.

ABSTRACT: Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.

SUBMITTER: Constantinou I

PROVIDER: S-EPMC6563144 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow.

Constantinou Iordania I Jendrusch Michael M Aspert Théo T Görlitz Frederik F Schulze André A Charvin Gilles G Knop Michael M

Micromachines 20190509 5

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, hi ...[more]

PMID: 31075890

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Project description:Granzyme B (GrB) is an essential cytotoxic effector in cancer immunotherapy as it can be a potential biomarker to predict the efficacy of immunotherapies including checkpoint inhibitors. Monitoring the Granzyme B activity in cells would help determine a patient's clinical response to treatment and lead to better treatment strategies by preventing administration of ineffective therapies and avoid adverse events resulting in a delay in subsequent treatment. Methods: A microfluidic device with hydrodynamic traps and pneumatic valving system was fabricated using photo and soft lithography. Single cell Granzyme B (GrB) activity was detected and measured fluorometrically using a commercial assay kit with a peptide substrate containing GrB recognition sequence (Ac-IEPD-AFC) and AFC (7-Amino-4-trifluoromethylcoumarin) label. Fluorescence was observed and measured using a confocal microscope with CSU-W1 scanner unit and CCD camera as well as an inverted microscope with photodetector. Model cells (NK-92, GrB-transduced Jurkat, and THP1 cells) and human PBMCs from healthy donor and lung cancer patients including an anti-PD-1 antibody treated patient were profiled of its GrB activity as proof of concept. Results: GrB expression from the model cells was found to be markedly different. NK-92 cells were found to have higher GrB activity than the GrB-transduced Jurkat cells. THP-1 was found to have relatively no significant activity. A marked increase in GrB expression was also observed in anti-PD-1 treated lung cancer patient sample in comparison to PBMC from a healthy donor. TCR+ Ig-G4+ PBMC cells were found to have high activity which signifies a clear response to PD-1 blockade. Conclusion: As proof of concept, we have shown the capability of a microfluidic platform to measure GrB production through a single cell enzymatic activity assay. Our platform might be a promising tool for evaluating the sensitivity of immunotherapies and identifying specific T cell subset responsible for the anti-tumor response.

Dataset Information

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow.

Publications

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow.

Similar Datasets

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