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Analyzing Dendritic Morphology in Columns and Layers

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Published on: March 23, 2017

Inferring Tree-Shaped Single-Cell Trajectories with Totem.

António G G Sousa¹, Johannes Smolander¹, Sini Junttila¹

¹Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.

Methods in Molecular Biology (Clifton, N.J.)

|July 27, 2024

Summary

Totem software infers complex tree-shaped cell development trajectories from single-cell transcriptomics data. User-friendly protocols enable reproducible analysis of cell differentiation, such as in human bone marrow stem cells.

Keywords:

Bioinformatics Cell connectivity Data analysis Pseudotime Single-cell RNA-seq Totem Trajectory inference Tree-shaped topology

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Area of Science:

Computational Biology
Genomics
Cell Biology

Background:

Single-cell transcriptomics reveals cellular heterogeneity.
Trajectory inference methods order cells to model dynamic processes like differentiation.
Inferring complex, tree-like cellular structures remains a challenge.

Purpose of the Study:

To present user-friendly protocols for inferring tree-shaped single-cell trajectories using the Totem method.
To provide reproducible analysis of cell differentiation pathways.
To demonstrate the utility of Totem for complex topology prediction.

Main Methods:

Development and presentation of two protocols: QuickStart and GuidedStart for Totem.
Application of Totem to single-cell transcriptomics data.
Case study using human bone marrow CD34+ cells to analyze erythroid, lymphoid, and myeloid lineage branching.

Main Results:

Totem successfully infers both linear and nonlinear trajectories with flexibility.
The provided protocols facilitate easy and reproducible analysis.
Demonstrated ability to resolve branching trajectories in a complex biological system.

Conclusions:

Totem offers a robust and usable solution for inferring complex single-cell trajectories.
The reproducible protocols and Docker image lower the barrier for analyzing cell development.
This work advances the computational analysis of cell differentiation and heterogeneity.