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Morphological profiling by high-throughput single-cell biophysical fractometry

Communications Biology 2023 21 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 45 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Ziqi Zhang, Ziqi Zhang, Kelvin C. M. Lee, Dickson M. D. Siu, Michelle C. K. Lo, Queenie T. K. Lai, Edmund Y. Lam, Kevin K. Tsia

Summary

Researchers developed a high-speed imaging technique called single-cell biophysical fractometry that measures the complex, irregular geometry of individual cells at a rate of about 10,000 cells per second. This tool can detect subtle structural differences between cancer cell subtypes and track how cells respond to drugs, offering a more detailed picture of cell health than standard methods.

Complex and irregular cell architecture is known to statistically exhibit fractal geometry, i.e., a pattern resembles a smaller part of itself. Although fractal variations in cells are proven to be closely associated with the disease-related phenotypes that are otherwise obscured in the standard cell-based assays, fractal analysis with single-cell precision remains largely unexplored. To close this gap, here we develop an image-based approach that quantifies a multitude of single-cell biophysical fractal-related properties at subcellular resolution. Taking together with its high-throughput single-cell imaging performance (~10,000 cells/sec), this technique, termed single-cell biophysical fractometry, offers sufficient statistical power for delineating the cellular heterogeneity, in the context of lung-cancer cell subtype classification, drug response assays and cell-cycle progression tracking. Further correlative fractal analysis shows that single-cell biophysical fractometry can enrich the standard morphological profiling depth and spearhead systematic fractal analysis of how cell morphology encodes cellular health and pathological conditions.

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