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scLTNN: an innovative tool for automatically visualizing single-cell trajectories
Cencan Xing1, Zehua Zeng1, Lei Hu1,2
1Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology, Beijing, Beijing 100083, China.
Bioinformatics Advances
|March 10, 2025
Summary
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.
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.
