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Related Experiment Video

Updated: Jul 21, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
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Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon.

Dominik Otto1,2,3, Cailin Jordan1,2,3,4, Brennan Dury1,2,3

  • 1Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA.

Biorxiv : the Preprint Server for Biology
|July 28, 2023
PubMed
Summary

We developed Mellon, a new algorithm to map cell states using single-cell data. It reveals rare cell states during differentiation and identifies regulatory mechanisms driving these transitions.

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Area of Science:

  • Computational Biology
  • Single-cell Genomics
  • Developmental Biology

Background:

  • Cell-state density is key to understanding cell differentiation, regeneration, and disease.
  • Current methods struggle to accurately capture the nuances of cell-state distributions.

Conclusions:

  • Cell-state density provides critical insights into differentiation processes.
  • Mellon offers a powerful tool for dissecting cellular dynamics and regulatory mechanisms.
  • The algorithm facilitates a deeper understanding of cell fate decisions in development and disease.