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

Gene expression profiling (GEP) provides critical insights into the cellular states, phenotypes, and biological behaviors of biological systems. However, the traditional GEP acquisition methods have significant time limitations, which directly hinder the real-time monitoring of dynamic processes, such as cell development and disease progression. In this study, we propose the biological omics sparse sensing (BoSS) framework, which effectively resolves the compatibility issue between dimensionality-reduced sampling theory and biological reaction by introducing sparse sampling strategies and noise regularization constraints. Furthermore, we have first presented rapid capture and accurate reconstruction of GEP using multiplex RT-qPCR, increasing the efficiency of transcriptome data acquisition by 60-fold. The experimental results demonstrate that when evaluating transcriptional heterogeneity in cancer tissue blocks, BoSS exhibits excellent specificity and sensitivity in clustering analysis, while fully preserving key information related to biological states, thus ensuring that the accuracy of downstream data analysis is comparable to that of ribonucleic acid (RNA)-seq. This achievement indicates that the BoSS framework is expected to play an important role in scenarios, such as intraoperative assessment of cancer progression, personalized therapy, and predictive control of biological systems.