There is a growing need for scaffold material with tissue-specific bioactivity for use in regenerative medicine, tissue engineering, and for surgical repair of structural defects. We developed a novel composite biomaterial by processing human cardiac extracellular matrix (ECM) into a hydrogel and combining it with cell-free amniotic membrane via a dry-coating procedure. Cardiac biocompatibility and immunogenicity were tested in vitro using human cardiac fibroblasts, epicardial progenitor cells, murine HL-1 cells, and human immune cells derived from buffy coat. Processing of the ECM preserved important matrix proteins as demonstrated by mass spectrometry. ECM coating did not alter the mechanical characteristics of decellularized amniotic membrane but did cause a clear increase in adhesion capacity, cell proliferation and viability. Activated monocytes secreted less pro-inflammatory cytokines, and both macrophage polarization towards the pro-inflammatory M1 type and T cell proliferation were prevented. We conclude that the incorporation of human cardiac ECM hydrogel shifts and enhances the bioactivity of decellularized amniotic membrane, facilitating its use in future cardiac applications.

Multipotent stromal cells are considered attractive sources for cell therapy and tissue engineering. Despite numerous experimental and clinical studies, broad application of stromal cell therapeutics is not yet emerging. A major challenge is the functional diversity of available cell sources. Here, we investigated the regenerative potential of clinically relevant human stromal cells from bone marrow (BMSCs), white adipose tissue, and umbilical cord compared with mature chondrocytes and skin fibroblasts in vitro and in vivo. Although all stromal cell types could express transcription factors related to endochondral ossification, only BMSCs formed cartilage discs in vitro that fully regenerated critical-size femoral defects after transplantation into mice. We identified cell type–specific epigenetic landscapes as the underlying molecular mechanism controlling transcriptional stromal differentiation networks. Binding sites of commonly expressed transcription factors in the enhancer and promoter regions of ossification-related genes, including Runt and bZIP families, were accessible only in BMSCs but not in extraskeletal stromal cells. This suggests an epigenetically predetermined differentiation potential depending on cell origin that allows common transcription factors to trigger distinct organ-specific transcriptional programs, facilitating forward selection of regeneration-competent cell sources. Last, we demonstrate that viable human BMSCs initiated defect healing through the secretion of osteopontin and contributed to transient mineralized bone hard callus formation after transplantation into immunodeficient mice, which was eventually replaced by murine recipient bone during final tissue remodeling.

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.