An Implementable Compressed Sensing Framework for Gene Expression Profiling via Multiplex PCR

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Traditional gene expression profiling (GEP) methods are time-consuming, limiting real-time monitoring of dynamic biological processes like disease progression.
• Existing GEP techniques face challenges in efficiently capturing and analyzing complex biological data, hindering timely clinical applications.

Purpose of the Study:

• To introduce the biological omics sparse sensing (BoSS) framework for rapid and accurate GEP.
• To overcome the limitations of traditional GEP methods by enabling efficient transcriptome data acquisition and analysis.

Main Methods:

• Developed the BoSS framework integrating sparse sampling strategies and noise regularization.
• Utilized multiplex RT-qPCR for rapid capture and reconstruction of GEP data, achieving a 60-fold increase in efficiency.
• Applied BoSS to evaluate transcriptional heterogeneity in cancer tissue blocks.

Main Results:

• BoSS demonstrated a 60-fold increase in transcriptome data acquisition efficiency.
• The framework achieved excellent specificity and sensitivity in clustering analysis of cancer tissues.
• BoSS preserved key biological state information, yielding accuracy comparable to RNA-seq for downstream analysis.

Conclusions:

• The BoSS framework significantly enhances the speed and accuracy of GEP.
• BoSS is a promising tool for real-time monitoring of dynamic biological systems and intraoperative cancer assessment.
• This advancement supports personalized therapy and predictive control in biological systems.