The DOI matches its Crossref record.
Hydrodynamic stretching of single cells for large population mechanical phenotyping
Open accessAbstract
Cell state is often assayed through measurement of biochemical and biophysical markers. Although biochemical markers have been widely used, intrinsic biophysical markers, such as the ability to mechanically deform under a load, are advantageous in that they do not require costly labeling or sample preparation. However, current techniques that assay cell mechanical properties have had limited adoption in clinical and cell biology research applications. Here, we demonstrate an automated microfluidic technology capable of probing single-cell deformability at approximately 2,000 cells/s. The method uses inertial focusing to uniformly deliver cells to a stretching extensional flow where cells are deformed at high strain rates, imaged with a high-speed camera, and computationally analyzed to extract quantitative parameters. This approach allows us to analyze cells at throughputs orders of magnitude faster than previously reported biophysical flow cytometers and single-cell mechanics tools, while creating easily observable larger strains and limiting user time commitment and bias through automation. Using this approach we rapidly assay the deformability of native populations of leukocytes and malignant cells in pleural effusions and accurately predict disease state in patients with cancer and immune activation with a sensitivity of 91% and a specificity of 86%. As a tool for biological research, we show the deformability we measure is an early biomarker for pluripotent stem cell differentiation and is likely linked to nuclear structural changes. Microfluidic deformability cytometry brings the statistical accuracy of traditional flow cytometric techniques to label-free biophysical biomarkers, enabling applications in clinical diagnostics, stem cell characterization, and single-cell biophysics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Mechanical forces direct stem cell behaviour in development and regeneration
Vining, K. H., & Mooney, D. J. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.108 · Cited by 1,656
Fundamentals and applications of inertial microfluidics: a review
Zhang, J., Yan, S., Yuan, D., Alici, G., Nguyen, N.-T., Ebrahimi Warkiani, M., & Li, W. (2016) · Lab on a Chip · doi:10.1039/c5lc01159k · Cited by 1,017
Single-cell RNA-seq: advances and future challenges
Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku555 · Cited by 847
“Recent advances in engineering have allowed researchers to go beyond using molecular markers by specifying cells based on physiological and biophysical features such as cell size (69), deformation (70,71), and electric (72) or magnetic properties (73).”
Functional and Biomimetic Materials for Engineering of the Three-Dimensional Cell Microenvironment
Huang, G., Li, F., Zhao, X., Ma, Y., Li, Y., Lin, M., Jin, G., Lu, T. J., Genin, G. M., & Xu, F. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00094 · Cited by 825
Amini, H., Lee, W., & Di Carlo, D. (2014) · Lab on a Chip · doi:10.1039/c4lc00128a · Cited by 722
Self-citation
Metadata
- Authors
- Gossett, D. R., Tse, H. T. K., Lee, S. A., Ying, Y., Lindgren, A. G., Yang, O. O., Rao, J., Clark, A. T., & Di Carlo, D.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2012-04-30
- Volume
- 109
- Issue
- 20
- Pages
- 7630-7635
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2016983464
- Cited by
- 775
- References
- 0
- FWCI
- 28.08
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hydrodynamic stretching of single cells for large population mechanical phenotyping. https://app.cochat.ai/papers/10.1073/pnas.1200107109
- The paper
- Gossett, D. R., Tse, H. T. K., Lee, S. A., Ying, Y., Lindgren, A. G., Yang, O. O., Rao, J., Clark, A. T., & Di Carlo, D. (2012). Hydrodynamic stretching of single cells for large population mechanical phenotyping. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1200107109
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.