Abstract

There is great interest in the application of advanced proteomic techniques to characterize decellularized tissues in order to develop a deeper understanding of the effects of the complex extracellular matrix (ECM) composition on the cellular response to these pro-regenerative bioscaffolds. However, the identification of proteins in ECM-derived bioscaffolds is hindered by the high abundance of collagen in the samples, which can interfere with the detection of lower-abundance constituents that may be important regulators of cell function. To address this limitation, we developed a novel multi-enzyme digestion approach using treatment with a highly-purified collagenase derived from Clostridium Histolyticum to selectively deplete collagen from ECM-derived protein extracts, reducing its relative abundance from up to 90% to below 10%. Moreover, we applied this new method to characterize the proteome of human decellularized adipose tissue (DAT), human decellularized cancellous bone (DCB), and commercially-available bovine tendon collagen (BTC). We successfully demonstrated with all three sources that collagenase treatment increased the depth of detection and enabled the identification of a variety of signaling proteins that were masked by collagen in standard digestion protocols with trypsin/LysC, increasing the number of proteins identified in the DAT by ∼2.2 fold, the DCB by ∼1.3 fold, and the BTC by ∼1.6 fold. In addition, quantitative proteomics using label-free quantification demonstrated that the DAT and DCB extracts were compositionally distinct, and identified a number of adipogenic and osteogenic proteins that were consistently more highly expressed in the DAT and DCB respectively. Overall, we have developed a new processing method that may be applied in advanced mass spectrometry studies to improve the high-throughput proteomic characterization of bioscaffolds derived from mammalian tissues. Further, our study provides new insight into the complex ECM composition of two human decellularized tissues of interest as cell-instructive platforms for regenerative medicine.

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