An integrated multiomic and quantitative label-free microscopy-based approach to study pro-fibrotic signalling in ex vivo human precision-cut lung slices.

Fibrosis can affect any organ, resulting in the loss of tissue architecture and function with often life-threatening consequences. Pathologically, fibrosis is characterised by the expansion of connective tissue due to excessive deposition of extracellular matrix (ECM) proteins, including the fibrillar forms of collagen. A significant limitation for discovering cures for fibrosis is the availability of suitable human models and techniques to quantify mature fibrillar collagen deposition as close as possible to human physiological conditions.Here we have extensively characterised an ex vivo cultured human lung tissue-derived, precision-cut lung slices (hPCLS) model using label-free second harmonic generation (SHG) light microscopy to quantify fibrillar collagen deposition and mass spectrometry-based techniques to obtain a proteomic and metabolomic fingerprint of hPCLS in ex vivo culture.We demonstrate that hPCLS are viable and metabolically active, with mesenchymal, epithelial, endothelial and immune cell types surviving for at least 2 weeks in ex vivo culture. Analysis of hPCLS-conditioned supernatants showed a strong induction of pulmonary fibrosis-related ECM proteins upon transforming growth factor-ß1 (TGF-ß1) stimulation. This upregulation of ECM proteins was not translated into an increased deposition of fibrillar collagen. In support of this observation, we revealed the presence of a pro-ECM degradation activity in our ex vivo cultures of hPCLS, inhibition of which by a metalloproteinase inhibitor resulted in increased collagen deposition in response to TGF-ß1 stimulation.Together the data show that an integrated approach of measuring soluble pro-fibrotic markers alongside quantitative SHG-based analysis of fibrillar collagen is a valuable tool for studying pro-fibrotic signalling and testing anti-fibrotic agents.

Coupling proteomics and metabolomics for the unsupervised identification of protein-metabolite interactions in Chaetomium thermophilum.

Protein-metabolite interactions play an important role in the cell's metabolism and many methods have been developed to screen them in vitro. However, few methods can be applied at a large scale and not alter biological state. Here we describe a proteometabolomic approach, using chromatography to generate cell fractions which are then analyzed with mass spectrometry for both protein and metabolite identification. Integrating the proteomic and metabolomic analyses makes it possible to identify protein-bound metabolites. Applying the concept to the thermophilic fungus Chaetomium thermophilum, we predict 461 likely protein-metabolite interactions, most of them novel. As a proof of principle, we experimentally validate a predicted interaction between the ribosome and isopentenyl adenine.