Cleo-Aron Weis

Early detection of acute lung injury (ALI) remains challenging, as conventional diagnostics rarely capture initial molecular changes. We therefore examined whether extracellular vesicles (EVs) in bronchoalveolar lavage fluid (BALF) can detect early regional injury and distinguish initial stress mechanisms in a porcine ALI model. Unilateral ALI was induced using Triton X-100, followed by six hours of mechanical ventilation with either fixed positive end-expiratory pressure (PEEP) at 5 cmH₂O or transpulmonary-pressure (TPP)-guided PEEP. Spatially separated BALF sampling allowed direct comparison between injured and contralateral mechanically stressed lungs. EV concentration and size distribution were quantified by nano-flow cytometry, while vesicular identity was confirmed by transmission electron microscopy and tetraspanin dot blot analyses. Furthermore, EV metrics were related to corresponding histologic injury scores. Across both PEEP strategies, EV concentration was markedly higher in both directly injured and contralateral mechanically stressed lungs compared with sham controls, indicating alveolar stress beyond the primary injury site. Median EV size differed significantly by injury type and correlated inversely with histologic injury, particularly with alveolar neutrophil infiltration. BALF-derived EV concentration and size may therefore provide complementary, spatially resolved molecular readouts of early ALI.

The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy.