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Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon.

Dominik J Otto^1,2,3, Cailin Jordan^1,2,3,4, Brennan Dury^1,2,3

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

|June 18, 2024

Summary

Mellon, a new algorithm, estimates cell-state densities from single-cell data. It reveals rare cell states during differentiation, offering insights into biological development.

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Area of Science:

Computational Biology
Single-Cell Genomics
Developmental Biology

Background:

Cell-state density describes cell distribution in phenotypic landscapes.
Understanding cell-state density is key to deciphering biological process mechanisms.

Purpose of the Study:

To introduce Mellon, an algorithm for estimating cell-state densities from high-dimensional single-cell data.
To analyze cell-state dynamics during differentiation and developmental processes.

Main Methods:

Developed and applied the Mellon algorithm for cell-state density estimation.
Utilized high-dimensional single-cell data representations.
Performed temporal interpolation on time-series data.

Main Results:

Identified distinct patterns of high-density regions (major cell types) and low-density regions (rare transitory states) in differentiating systems.
Provided evidence linking enhancer priming and master regulator activation to the emergence of transitory cell states.
Demonstrated Mellon's scalability and applicability across various single-cell data modalities.

Conclusions:

Cell-state density is critical for understanding differentiation processes.
Mellon provides a powerful tool for analyzing cell-state dynamics and uncovering mechanisms guiding biological trajectories.
The algorithm facilitates detailed views of developmental processes and rare cell state identification.