ArticleScientific reports2016
Multiparameter mechanical and morphometric screening of cells.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 58 citations in OpenAlex.
- Creating an atlas of variant effects to resolve variants of uncertain significance and guide cardiovascular medicine.Nature reviews. Cardiology · 2026Review
- Spatial Segregation Across Travelling Fronts in Individual-Based and Continuum Models for the Growth of Heterogeneous Cell Populations.Bulletin of mathematical biology · 2025Article
- Conditions for a microfluidic creep experiment for microparticles using a cross-slot extensional flow device.Biomicrofluidics · 2025Article
- Environmental community transcriptomics: strategies and struggles.Briefings in functional genomics · 2025Review
- Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference.PloS one · 2025Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Unknown cell class distinction via neural network based scattering snapshot recognition.Biomedical optics express · 2023Article
- A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning.Microsystems & nanoengineering · 2023Article
- A Progress Report and Roadmap for Microphysiological Systems and Organ-On-A-Chip Technologies to Be More Predictive Models in Human (Knee) Osteoarthritis.Frontiers in bioengineering and biotechnology · 2022Review
- Deep learning assisted mechanotyping of individual cells through repeated deformations and relaxations in undulating channels.Biomicrofluidics · 2022Article
- A survey of physical methods for studying nuclear mechanics and mechanobiology.APL bioengineering · 2021Review
- Laser tweezers as a biophotonic tool to investigate the efficacy of living sickle red blood cells in response to optical deformation.Biophysical reviews · 2021Article
- Development and validation of a cellular host response test as an early diagnostic for sepsis.PloS one · 2021Article
- Elongation Index as a Sensitive Measure of Cell Deformation in High-Throughput Microfluidic Systems.Biophysical journal · 2020Article
- Physical Biomarkers of Disease Progression: On-Chip Monitoring of Changes in Mechanobiology of Colorectal Cancer Cells.Scientific reports · 2020Article
- Mapping inertial migration in the cross section of a microfluidic channel with high-speed imaging.Microsystems & nanoengineering · 2020Article
- Effect of Nuclear Stiffness on Cell Mechanics and Migration of Human Breast Cancer Cells.Frontiers in cell and developmental biology · 2020Article
- Visco-Node-Pore Sensing: A Microfluidic Rheology Platform to Characterize Viscoelastic Properties of Epithelial Cells.iScience · 2019Article
- Cells Under Stress: An Inertial-Shear Microfluidic Determination of Cell Behavior.Biophysical journal · 2019Article
- Developments in label-free microfluidic methods for single-cell analysis and sorting.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
We introduce a label-free method to rapidly phenotype and classify cells purely based on physical properties. We extract 15 biophysical parameters from cells as they deform in a microfluidic stretching flow field via high-speed microscopy and apply machine-learning approaches to discriminate different cell types and states. When employing the full 15 dimensional dataset, the technique robustly classifies individual cells based on their pluripotency, with accuracy above 95%. Rheological and morphological properties of cells while deforming were critical for this classification. We also show the application of this method in accurate classifying cells based on their viability, drug screening and detecting populations of malignant cells in mixed samples. We show that some of the extracted parameters are not linearly independent, and in fact we reach maximum classification accuracy by using only a subset of parameters. However, the informative subsets could vary depending on cell types in the sample. This work shows the utility of an assay purely based on intrinsic biophysical properties of cells to identify changes in cell state. In addition to a label-free alternative to flow cytometry in certain applications, this work, also can provide novel intracellular metrics that would not be feasible with labeled approaches (i.e. flow cytometry).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.