Microfluidic active loading of single cells enables analysis of complex clinical specimens
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Author(s)
Malinowski, Seth W.
Touat, Mehdi
Ligon, Keith L.
Calistri, Nicholas L
Kimmerling, Robert John
Stevens, Mark M.
Olcum, Selim A.
Manalis, Scott R
Date Issued
November 2018
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Calistri, Nicholas L., Robert J. Kimmerling, Seth W. Malinowski, Mehdi Touat, Mark M. Stevens, Selim Olcum, Keith L. Ligon, and Scott R. Manalis. “Microfluidic Active Loading of Single Cells Enables Analysis of Complex Clinical Specimens.” Nature Communications 9, no. 1 (November 14, 2018). © 2018 The Authors
Version
Final published version
Abstract
A fundamental trade-off between flow rate and measurement precision limits performance of many single-cell detection strategies, especially for applications that require biophysical measurements from living cells within complex and low-input samples. To address this, we introduce ‘active loading’, an automated, optically-triggered fluidic system that improves measurement throughput and robustness by controlling entry of individual cells into a measurement channel. We apply active loading to samples over a range of concentrations (1–1000 particles μL[superscript −1]), demonstrate that measurement time can be decreased by up to 20-fold, and show theoretically that performance of some types of existing single-cell microfluidic devices can be improved by implementing active loading. Finally, we demonstrate how active loading improves clinical feasibility for acute, single-cell drug sensitivity measurements by deploying it to a preclinical setting where we assess patient samples from normal brain, primary and metastatic brain cancers containing a complex, difficult-to-measure mixture of confounding biological debris.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Koch Institute for Integrative Cancer Research at MIT
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-018-07283-x
