Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy.

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) generates valuable gene expression data but faces high costs for large-scale studies.
• The inherent sparsity of single-cell expression profiles allows for data compression, presenting a potential solution to reduce costs.

Purpose of the Study:

• To develop and validate a computational method for compressing and inferring single-cell expression profiles.
• To reduce the cost of large-scale scRNA-seq experiments while preserving data accuracy and sensitivity.

Main Methods:

• Computational simulation and experimental validation using 54 single cells.
• Overlapped pooling strategy to assign cells into multiple groups.
• Compressed sensing techniques to infer individual cell expression profiles from pooled data.

Main Results:

• Expression profiles can be accurately inferred from pooled data using the proposed overlapped pooling and compressed sensing strategy.
• Combining the method with plate-based scRNA-seq maintains gene detection sensitivity and individual cell identity.
• The approach achieves approximately 50% reduction in library costs compared to traditional methods.

Conclusions:

• This novel pooling and compressed sensing method offers a cost-effective solution for large-scale scRNA-seq.
• The technique preserves essential data quality metrics like sensitivity and individual identity.
• This approach has implications for improving measurement, storage, and computation of other compressible biological signals.