Quantification of Extracellular Matrix Proteins from a Rat Lung Scaffold to Provide a Molecular Readout for Tissue Engineering

The use of extracellular matrix (ECM) 1 1The abbreviations used are:ECMextracellular matrixCHAPS3-(3-cholamidopropyl) dimethylammonio-1-propanesulfonateQconCATquantification concatamerLC-MS/MSliquid chromatography-tandem mass spectrometrySRMselected reaction monitoringSILstable isotope labeledNTAnitrilotriacetic acidLODlimit of detectionLOQlimit of quantificationCANacetonitrileTPCKl-1-tosylamido-2-phenylethyl chloromethyl ketone or tosylphenylalanyl chloromethyl ketoneTCEP(tris(2-carboxyethyl)phosphine)TEMEDtetramethylethylenediamine. scaffolds, derived from decellularized tissues for engineered organ generation, holds enormous potential in the field of regenerative medicine. To support organ engineering efforts, we developed a targeted proteomics method to extract and quantify extracellular matrix components from tissues. Our method provides more complete and accurate protein characterization than traditional approaches. This is accomplished through the analysis of both the chaotrope-soluble and -insoluble protein fractions and using recombinantly generated stable isotope labeled peptides for endogenous protein quantification. Using this approach, we have generated 74 peptides, representing 56 proteins to quantify protein in native (nondecellularized) and decellularized lung matrices. We have focused on proteins of the ECM and additional intracellular proteins that are challenging to remove during the decellularization procedure. Results indicate that the acellular lung scaffold is predominantly composed of structural collagens, with the majority of these proteins found in the insoluble ECM, a fraction that is often discarded using widely accepted proteomic methods. The decellularization procedure removes over 98% of intracellular proteins evaluated and retains, to varying degrees, proteoglycans and glycoproteins of the ECM. Accurate characterization of ECM proteins from tissue samples will help advance organ engineering efforts by generating a molecular readout that can be correlated with functional outcome to drive the next generation of engineered organs.