Year: 2026
Type: preprint
Abstract: Cellular secretome profiling is essential for decoding intercellular communication, identifying disease biomarkers, and discovering therapeutic targets. Although mass spectrometry-based proteomics is the gold standard for discovery-driven analysis, deep secretome profiling remains technically challenging because low-abundance signaling molecules are masked by the vast dynamic range of high-background serum supplements. To overcome these limitations, we develop DeepSec, a robust and streamlined method that uses a small-molecule–modulated nanoparticle protein corona to selectively deplete bovine serum albumin from conditioned media. Using an endogenous lipopolysaccharide (LPS) dose-response series, DeepSec preserved biological fold changes relative to a non-depleted workflow and orthogonal ELISA, establishing protein-corona enrichment as a quantitatively faithful strategy for dynamic secretome proteomics. In parallel, DeepSec increased the number of significantly secreted proteins by three-fold. We demonstrate its utility for profiling secretomes from cultured cells and host–pathogen interaction models. Using pathway-specific inhibitors, we further resolve proteins secreted through ER–Golgi- and autophagy-dependent routes following LPS stimulation. Finally, we show the translational utility of DeepSec by profiling patient-derived 3D liver microtissues modeling metabolic dysfunction-associated steatohepatitis, capturing distinct, drug-specific metabolic and extracellular matrix rewiring patterns in response to clinical therapeutics. These results establish DeepSec as a robust platform for interrogating complex extracellular signaling networks across diverse models of health and disease.
Source: Research Square
Authors: Amir Ata Saei, Xueyao Wang, Konstantinos Fragkoulis, Aleksandra Wielento, Sabine Willems, Marie-Stéphanie Aschtgen, Anna Kirk, Seyed Majed Modaresi, Hassan Gharibi, Mohieddin JAFARI, Danilo Ritz, Alexander Schmidt, Hojatollah Vali, Jutta Jalkanen, Mikael Rydén, Niklas Mejhert, Volker Lauschke, Babak Borhan, Morteza Mahmoudi, Sylvain Peuget
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7169858613