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Machine learning-driven single-cell phenotyping in size-controlled microenvironments via parallel deterministic

Sangmin Lee1,2,3,4,5, Steven O'Donnell4,5, Zhangli Peng5

  • 1Department of Biologic and Materials Sciences & Prosthodontics, University of Michigan, Ann Arbor, MI, 48109, USA. shinjw@umich.edu.

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|March 25, 2026
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Summary
This summary is machine-generated.

This study introduces a microfluidic platform for precisely encapsulating single cells in varied microgel sizes. This innovation enables detailed analysis of cell behavior in controlled physical microenvironments, advancing mechanobiology research.

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Area of Science:

  • Mechanobiology
  • Microfluidics
  • Cellular Engineering

Background:

  • Understanding cellular responses to physical microenvironments is crucial for regenerative medicine and tissue engineering.
  • Existing single-cell encapsulation methods face limitations like random cell distribution and fixed microenvironment sizes, hindering detailed analysis.

Purpose of the Study:

  • To develop a droplet microfluidic platform for deterministic single-cell encapsulation within size-controlled microgels.
  • To enable simultaneous generation of multiple, size-specific microenvironments for comparative cellular studies.

Main Methods:

  • Utilized parallelized flow-focusing and cell-selective gelation in a droplet microfluidic system.
  • Employed machine learning algorithms to analyze 3D cell morphology and cytoskeletal features.
  • Generated distinct microgel size regimes from a single precursor stream for simultaneous cell encapsulation.

Main Results:

  • Achieved deterministic single-cell encapsulation in microgels of multiple sizes, minimizing empty compartments.
  • Revealed heterogeneous, size-dependent cellular phenotypic responses using machine learning analysis.
  • Demonstrated that cellular phenotypes can predict microgel confinement over time.

Conclusions:

  • Established a data-driven framework for mapping single-cell responses across engineered microenvironments.
  • Provided a scalable platform for predictive studies of mechanosensitive behavior in diverse cellular niches.
  • Advanced the capability to study cell behavior in precisely controlled, variable physical contexts.