scLASER: a robust framework for simulating and detecting time-dependent single-cell dynamics in longitudinal studies.

Area of Science:

• Single-cell biology
• Computational biology
• Clinical research

Background:

• Longitudinal single-cell studies track cellular dynamics but lack robust methods for temporal modeling and power estimation.
• Existing methods struggle with rare cell types and complex temporal patterns.

Purpose of the Study:

• Introduce scLASER, a novel framework for analyzing time-dependent cellular neighborhood dynamics.
• Enable simulation of longitudinal single-cell datasets for accurate power estimation.
• Enhance the sensitivity and robustness of single-cell data analysis in clinical studies.

Main Methods:

• scLASER framework for detecting time-dependent cellular neighborhood dynamics.
• Simulation of longitudinal single-cell datasets for power estimation.
• Application to benchmark datasets and clinical cohorts (Inflammatory Bowel Disease, COVID-19).

Main Results:

• scLASER demonstrates higher sensitivity than traditional cluster-based approaches, especially for rare cell types and non-linear temporal patterns.
• Identified treatment-responsive NOTCH3+ stromal cell trajectories in Inflammatory Bowel Disease data (AUC > 0.92).
• Discovered three distinct T cell activity axes in COVID-19 progression, including cytotoxic effector and interferon-stimulated gene programs.

Conclusions:

• scLASER provides a robust method for longitudinal single-cell analysis.
• The framework optimizes study design for clinical single-cell research.
• Enables deeper insights into cellular dynamics during disease progression and treatment response.