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scLTNN: an innovative tool for automatically visualizing single-cell trajectories.

Cencan Xing¹, Zehua Zeng¹, Lei Hu^1,2

¹Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology, Beijing, Beijing 100083, China.

Bioinformatics Advances

|March 10, 2025

Summary

This summary is machine-generated.

A new tool, scRNA-seq latent time neural network (scLTNN), efficiently infers cell fate trajectories from single-cell RNA sequencing data. This method requires minimal computational resources and no prior biological knowledge for accurate cell developmental path reconstruction.

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Area of Science:

Computational Biology
Genomics
Developmental Biology

Background:

Cellular state identification and trajectory inference are crucial for simulating cell fate dynamics using single-cell RNA sequencing (scRNA-seq) data.
Current methods for constructing cell fate trajectories often require significant computational resources or prior knowledge of developmental processes, limiting their accessibility and broad application.

Purpose of the Study:

To develop a novel, efficient, and broadly applicable computational tool for inferring cell fate trajectories from scRNA-seq data.
To overcome the limitations of existing methods by reducing computational demands and eliminating the need for prior biological knowledge.

Main Methods:

The study introduces the scRNA-seq latent time neural network (scLTNN), a tool combining an artificial neural network with a distribution model.
scLTNN leverages the consistent expression distribution of highly variable genes and is pre-trained for automated analysis.
The method was implemented and validated on diverse biological systems, including human bone marrow cells, mouse pancreatic endocrine lineage, and zebrafish axial mesoderm.

Main Results:

scLTNN accurately infers the origin and terminal states of cells, and reconstructs developmental trajectories with high fidelity.
The tool demonstrates minimal computational resource and time requirements.
Successful reconstruction of cell fate trajectories was achieved across human, mouse, and zebrafish datasets, showcasing its cross-species applicability.

Conclusions:

scLTNN offers a straightforward and efficient approach for illustrating cell fate trajectories from scRNA-seq data.
The tool's ability to function without prior biological knowledge makes it a versatile resource for various research applications.
scLTNN represents a significant advancement in computational tools for understanding cell differentiation and development.