Deciphering the Kidney Matrisome: Identification and Quantification of Renal Extracellular Matrix Proteins in Healthy Mice

Benefits and limits of decellularization on mass-spectrometry-based extracellular matrix proteome analysis of mouse kidney

The extracellular matrix (ECM) is composed of collagens, ECM glycoproteins, and proteoglycans (also named core matrisome proteins) that are critical for tissue structure and function, and matrisome-associated proteins that balance the production and degradation of the ECM proteins. The identification and quantification of core matrisome proteins using mass spectrometry is often hindered by their low abundance and their propensity to form macromolecular insoluble structures. In this study, we aimed to investigate the added value of decellularization in identifying and quantifying core matrisome proteins in mouse kidney. The decellularization strategy combined freeze-thaw cycles and sodium dodecyl sulphate treatment. We found that decellularization preserved 95% of the core matrisome proteins detected in non-decellularized kidney and revealed few additional ones. Decellularization also led to an average of 59 times enrichment of 96% of the core matrisome proteins as the result of the successful removal of cellular and matrisome-associated proteins. However, the enrichment varied greatly among core matrisome proteins, resulting in a misrepresentation of the native ECM composition in decellularized kidney. This should be brought to the attention of the matrisome research community, as it highlights the need for caution when interpreting proteomic data obtained from a decellularized organ.

Proteomic analysis of decellularized mice liver and kidney extracellular matrices

BACKGROUND

The extracellular matrix (ECM) is a three-dimensional network of proteins that encases and supports cells within a tissue and promotes physiological and pathological cellular differentiation and functionality. Understanding the complex composition of the ECM is essential to decrypt physiological processes as well as pathogenesis. In this context, the method of decellularization is a useful technique to eliminate cellular components from tissues while preserving the majority of the structural and functional integrity of the ECM.

RESULTS

In this study, we employed a bottom-up proteomic approach to elucidate the intricate network of proteins in the decellularized extracellular matrices of murine liver and kidney tissues. This approach involved the use of a novel, perfusion-based decellularization protocol to generate acellular whole organ scaffolds. Proteomic analysis of decellularized mice liver and kidney ECM scaffolds revealed tissue-specific differences in matrisome composition, while we found a predominantly stable composition of the core matrisome, consisting of collagens, glycoproteins, and proteoglycans. Liver matrisome analysis revealed unique proteins such as collagen type VI alpha-6, fibrillin-2 or biglycan. In the kidney, specific ECM-regulators such as cathepsin z were detected.

CONCLUSION

The identification of distinct proteomic signatures provides insights into how different matrisome compositions might influence the biological properties of distinct tissues. This experimental workflow will help to further elucidate the proteomic landscape of decellularized extracellular matrix scaffolds of mice in order to decipher complex cell-matrix interactions and their contribution to a tissue-specific microenvironment.

Comprehensive Matrisome Profiling of Human Adipose Tissue for Soft Tissue Reconstruction

For effective translation of research from tissue engineering and regenerative medicine domains, the cell-instructive extracellular matrix (ECM) of specific tissues must be accurately realized. As adipose tissue is gaining traction as a biomaterial for soft tissue reconstruction, with highly variable clinical outcomes obtained, a quantitative investigation of the adipose tissue matrisome is overdue. In this study, the human adipose tissue matrisome is profiled using quantitative sequential windowed acquisition of all theoretical fragment ion spectra - mass spectrometry (SWATH-MS) proteomics across a cohort of 13 fat-grafting patients, to provide characterization of ECM proteins within the tissue, and to understand human population variation. There are considerable differences in the expression of matrisome proteins across the patient cohort, with age and lipoaspirate collection technique contributing to the greatest variation across the core matrisome. A high abundance of basement membrane proteins (collagen IV and heparan sulfate proteoglycan) is detected, as well as fibrillar collagens I and II, reflecting the hierarchical structure of the tissue. This study provides a comprehensive proteomic evaluation of the adipose tissue matrisome and contributes to an enhanced understanding of the influence of the matrisome in adipose-related pathologies by providing a healthy reference cohort and details an experimental pipeline that can be further exploited for future biomaterial development.

Value of GCET1, HGAL (GCET2), and LMO2 in the Determination of Germinal Center Phenotype in Diffuse Large B-cell Lymphoma

Objective

Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous disease that is classified into germinal center B-cell (GCB) and non-GCB subtypes, which are prognostically different. The Hans algorithm is the most widely used tool based on CD10, BCL6, and MUM1 expression, but some cases with the non-GCB phenotype are still known to be misclassified. In this study, we investigate the extent to which GCET1, HGAL, and LMO2 protein expressions reflect GCB phenotype together with their roles in determining the GCB phenotype of DLBCL and their contributions to the performance of the Hans algorithm.

Materials and Methods

Sixty-five cases of DLBCL-not otherwise specified, 40 cases of follicular lymphoma (FL), and 19 non-GC-derived lymphoma cases were included in this study. The DLBCL cases were grouped as CD10+ (Group A) or only MUM1+ (Group B), and the remaining cases constituted the intermediate group (Group C). GCET1, HGAL, and LMO2 expressions were evaluated.

Results

In the FL group, GCET1, HGAL, and LMO2 were positive in 85%, 77.5%, and 100% of the cases, respectively. Among the non-GC-derived lymphoma cases, all three markers were negative in cases of small lymphocytic lymphoma, plasmablastic lymphoma, peripheral T-cell lymphoma, and anaplastic large cell lymphoma. GCET1 and HGAL were negative in cases of marginal zone lymphoma (MZL) and mantle cell lymphoma (MCL). Two of the 3 MZL and 2 of the 4 MCL cases were positive for LMO2. In the DLBCL group, the number of cases with GCET1, HGAL, and LMO2 positivity was 18 (90%), 17 (85%), and 20 (100%), respectively, in Group A and 0 (0%), 2 (13.3%), and 2 (13.3%), respectively, in Group B. Considering these rates, when the cases in the intermediate group were evaluated, it was concluded that 13 cases typed as non-GCB according to the Hans algorithm may have the GCB phenotype.

Conclusion

GCET1, HGAL, and LMO2 are highly sensitive markers for determining the germinal center cell phenotype and can increase the accuracy of the subclassification of DLBCL cases, especially for cases that are negative for CD10.