Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems

Area of Science:

• Developmental Biology
• Computational Biology
• Genomics

Background:

• Organ function arises from diverse cell types, with cellular heterogeneity originating during development.
• Multipotent progenitor cells make fate decisions, transitioning through intermediate states to form specific cell types.
• Single-cell technologies now enable detailed interrogation of cell types and transitional states within tissues.

Purpose of the Study:

• To review computational tools for reconstructing cell-state transition trajectories from single-cell data.
• To discuss the assumptions and limitations of pseudotemporal ordering algorithms.
• To propose a workflow for single-cell trajectory analysis beneficial for experimental biologists.

Main Methods:

• Review of existing computational tools for single-cell trajectory inference.
• Analysis of the assumptions and caveats associated with these algorithms.
• Development of a generalized workflow for single-cell trajectory analysis.

Main Results:

• Identification and categorization of various pseudotemporal ordering algorithms.
• Evaluation of the strengths and weaknesses of current trajectory inference methods.
• A proposed framework for applying these computational tools in biological research.

Conclusions:

• Pseudotemporal ordering algorithms are powerful but require careful consideration of their limitations.
• A standardized workflow can enhance the reliability and interpretability of single-cell trajectory analysis.
• This review aims to guide experimental biologists in effectively utilizing these computational approaches for studying cell development.