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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Single-cell foundation models: bringing artificial intelligence into cell biology.

Seungbyn Baek¹, Kyungwoo Song², Insuk Lee^3,4

¹Department of Biotechnology, College of Life Science & Biotechnology, Yonsei University, Seoul, Republic of Korea.

Experimental & Molecular Medicine

|October 1, 2025

Summary

This summary is machine-generated.

Foundation models are being adapted for single-cell genomics (scFMs) to analyze complex cellular data. While promising, challenges in data integration, interpretability, and computational cost need addressing for scFMs to advance single-cell analysis.

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Area of Science:

Genomics
Artificial Intelligence
Computational Biology

Background:

Foundation models, large deep learning models, excel at self-supervised learning for diverse tasks.
Single-cell genomics requires unified frameworks for analyzing vast, expanding datasets.
Single-cell foundation models (scFMs) leverage foundation model advances for single-cell data analysis.

Purpose of the Study:

To provide an overview of single-cell foundation models (scFMs).
To highlight key concepts and applications of scFMs in downstream tasks.
To critically assess current limitations and propose future research directions for scFMs.

Main Methods:

scFMs typically employ transformer architectures.
These models integrate diverse omics data.
They extract latent patterns at cell and gene levels for heterogeneity and network analysis.

Main Results:

scFMs show promise in analyzing cellular heterogeneity and regulatory networks.
Current scFMs face challenges including nonsequential data, quality inconsistencies, and computational intensity.
Interpreting biological relevance of latent embeddings and model representations is nontrivial.

Conclusions:

Addressing challenges in robustness, interpretability, and scalability is crucial for scFMs.
scFMs have the potential to become pivotal tools in single-cell genomics.
Overcoming limitations will unlock deeper insights into cellular function and disease mechanisms.