Fast secretome profiling reveals infection-linked immune signatures

Cells communicate and coordinate their behaviour by secreting proteins into their surroundings, a process central to health and disease. When secretion goes wrong it can cause widespread physiological dysfunction, but studying this “secretome” has been limited by traditional tests. Immunoassay formats that utilise secondary antibody readouts are the current gold standard because they are specific and sensitive, but they depend on predefined panels and so can miss new biology. In work led by Rachel E. Heap, researchers developed a scalable, mass spectrometry-based workflow to map secreted proteins more broadly and quickly. The approach combines data-independent acquisition with ion mobility and parallel fragmentation — an approach marketed and known as dia-PASEF — to deliver rapid, global profiling. The team used a translationally relevant human iPSC-derived macrophage model to test the method, aiming to capture a wide swath of secreted proteins, resolve temporal changes after stimulation, and compare what the secretome reveals versus targeted assays or the intracellular proteome.

The workflow produced deep protein lists very quickly: the authors report identification of over 1200 proteins in under 15 minutes of acquisition time, with exceptional reproducibility across a large sample set. They applied dia-PASEF profiling to pro-inflammatory phenotypes and confirmed robust identification of key cytokines and chemokines while also uncovering non-canonical immune responses that were absent from both targeted panels and the intracellular proteome. Notably, the analysis revealed a unique cholesterol efflux signature marked by secretion of APOA1 and PON1 in response to Mycobacterium Tuberculosis. The team also performed temporal profiling of macrophage responses to lipopolysaccharide over 24 hours and resolved dynamic secretion trajectories that distinguish acute from chronic inflammatory states. Early secretion of TNFα and IL6 was observed to initiate downstream signalling cascades that resulted in delayed secretion of chemokines such as CXCL10 and CCL8.

Taken together, these results establish a robust, scalable platform for global characterisation of secretory networks. By moving beyond predefined antibody panels, dia-PASEF profiling can reveal unexpected players in immune responses and infection, such as the APOA1 and PON1 secretion linked to Mycobacterium Tuberculosis, and can map how secretion changes over time during inflammation. Beyond the macrophage studies shown here, the workflow offers broad utility for biomarker discovery, mechanistic studies of disease progression, and evaluation of new therapeutic interventions. Because it is rapid and reproducible across large sample sets, this platform could become a powerful tool for researchers aiming to connect secreted protein patterns to specific disease processes and to advance precision medicine.