Kate, S. | Search bioRxiv

bioRxiv2026

Subcellular Localization Constrains Protein Detectability and Reveals Systematic RNA-Protein Discordance Across Cancers

Transcript abundance is widely used as a proxy for protein expression in cancer studies; however, mRNA levels often fail to predict protein detectability due to post-transcriptional and compartment-specific regulatory processes. Here, we present a machine learning framework that integrates RNA expression, gene-level attributes, and subcellular localization to model protein detectability across human cancers. Leveraging transcriptomic data from TCGA, TARGET, and GTEx, and protein annotations from the Human Protein Atlas, we constructed a dataset comprising over 100,000 gene-cancer pairs across seven tumor types. Models based on RNA features alone achieved moderate predictive performance (ROC-AUC ~0.71), whereas incorporating subcellular localization significantly improved accuracy (ROC-AUC ~0.82). Paired bootstrap analysis confirmed that these gains were statistically robust. We further identify a substantial set of genes with high transcript abundance yet absent protein detection, revealing widespread RNA-protein decoupling. These discordant genes are enriched in mitochondrial, metabolic, and translational regulatory pathways, suggesting that discordance reflects structured biological processes rather than stochastic variation. Together, our results demonstrate that cellular context, particularly subcellular localization, is a key determinant of protein detectability and underscore the limitations of transcript-centric interpretations in cancer genomics.

bioinformatics↗

bioRxiv2025

Hebb's Vision: The Structural Underpinnings of Hebbian Assemblies

1In 1949, Donald Hebb proposed that groups of neurons that activate stereotypically form the organizational building blocks of perception, cognition, and behavior. Finding the structural underpinning of such assemblies has been technically challenging, due to a lack of large-scale structure-activity maps. Here, we analyze this relation using a novel dataset that links in vivo optical physiology to connectivity using postmortem elec-tron microscopy (EM). From the fluorescence traces, we extract neural assemblies from higher-order correlations in neural activity. Physiologically, we show that these assemblies exhibit properties consistent with Hebbs theory, including more reliable responses to repeated natural movie inputs than size-matched random ensembles and superior decoding of visual stimuli. Structurally, we find that neurons that participate in assemblies are significantly more integrated into the structural network than those that do not. Contrary to Hebbs original prediction, we do not observe a marked increase in the strength of monosynaptic excitatory connections between cells participating in the same assembly. However, we find significantly stronger indirect feed-forward inhibitory connections targeting cells in other assemblies. These results show that assemblies can be useful components of perception, and, surprisingly, they are delineated by mutual inhibition.

neuroscience↗