Trajectory Inference for Unraveling Dynamic Biological Processes from Raman Spectral Data

Area of Science:

• Biomedical research
• Molecular biology
• Spectroscopy

Background:

• Cell heterogeneity complicates understanding of biomedical processes.
• Single-cell RNA sequencing (scRNA-seq) uses trajectory inference to analyze cell state dynamics.
• Adipocyte differentiation is a complex biological process involving dynamic cell state changes.

Purpose of the Study:

• To adapt trajectory inference methods for Raman spectroscopy data.
• To investigate the dynamics of adipocyte differentiation using label-free Raman spectroscopy.
• To explore cell heterogeneity in differentiation pathways.

Main Methods:

• Application of trajectory inference algorithms to Raman spectroscopy datasets.
• Label-free molecular profiling of cells during differentiation.
• Analysis of spectral data to reconstruct cellular pathways.

Main Results:

• Successfully adapted trajectory inference for Raman spectroscopy.
• Identified distinct molecular trajectories during adipocyte differentiation.
• Demonstrated the capability of Raman spectroscopy to capture dynamic cell state transitions.

Conclusions:

• Trajectory inference is a viable method for analyzing Raman spectroscopy data.
• Raman spectroscopy can provide label-free insights into cell differentiation dynamics.
• This approach enhances the study of cell heterogeneity in biological processes.