Cellular proliferation biases clonal lineage tracing and trajectory inference

Area of Science:

• Computational Biology
• Genomics
• Developmental Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity in development and disease.
• scRNA-seq aims to provide unbiased views of tissue cellular architecture.
• Sampling bias can distort scRNA-seq datasets, misrepresenting true cellular composition.

Purpose of the Study:

• To identify and characterize a novel form of sampling bias in scRNA-seq data.
• To investigate the impact of this bias on lineage tracing and trajectory inference.
• To develop methods for robust trajectory inference resilient to sampling bias.

Main Methods:

• Probabilistic modeling of sampling bias in growing cell populations.
• Simulation studies to validate theoretical derivations.
• Analysis of a real-world time-course scRNA-seq dataset of T-cell development.

Main Results:

• Demonstrated a novel sampling bias linked to relative cell growth rates and repeated sampling.
• Showed that this bias can significantly affect fate probability predictions in lineage tracing.
• Identified specific statistical phenomena influencing sample representation in heterogeneous populations.

Conclusions:

• Sampling bias is an inherent challenge in scRNA-seq, particularly in dynamic systems.
• Cellular growth dynamics critically influence data representation and downstream analysis.
• New computational approaches are needed to mitigate bias and improve the accuracy of trajectory inference.