Matrix density regulates adipocyte phenotype.

Alterations of the extracellular matrix contribute to adipose tissue dysfunction in metabolic disease. We studied the role of matrix density in regulating human adipocyte phenotype in a tunable hydrogel culture system. Lipid accumulation was maximal in intermediate hydrogel density of 5 weight %, relative to 3% and 10%. Adipogenesis and lipid and oxidative metabolic gene pathways were enriched in adipocytes in 5% relative to 3% hydrogels, while fibrotic gene pathways were enriched in 3% hydrogels. These data demonstrate that the intermediate density matrix promotes a more adipogenic, less fibrotic adipocyte phenotype geared towards increased lipid and aerobic metabolism. These observations contribute to a growing literature describing the role of matrix density in regulating adipose tissue function.

A combination of matrix stiffness and degradability dictate microvascular network assembly and remodeling in cell-laden poly(ethylene glycol) hydrogels.

The formation of functional capillary blood vessels that can sustain the metabolic demands of transplanted parenchymal cells remains one of the biggest challenges to the clinical realization of engineered tissues for regenerative medicine. As such, there remains a need to better understand the fundamental influences of the microenvironment on vascularization. Poly(ethylene glycol) (PEG) hydrogels have been widely adopted to interrogate the influence of matrix physicochemical properties on cellular phenotypes and morphogenetic programs, including the formation of microvascular networks, in part due to the ease with which their properties can be controlled. In this study, we co-encapsulated endothelial cells and fibroblasts in PEG-norbornene (PEGNB) hydrogels in which stiffness and degradability were tuned to assess their independent and synergistic effects on vessel network formation and cell-mediated matrix remodeling longitudinally. Specifically, we achieved a range of stiffnesses and differing rates of degradation by varying the crosslinking ratio of norbornenes to thiols and incorporating either one (sVPMS) or two (dVPMS) cleavage sites within the matrix metalloproteinase- (MMP-) sensitive crosslinker, respectively. In less degradable sVPMS gels, decreasing the crosslinking ratio (thereby decreasing the initial stiffness) supported enhanced vascularization. When degradability was increased in dVPMS gels, all crosslinking ratios supported robust vascularization regardless of initial mechanical properties. The vascularization in both conditions was coincident with the deposition of extracellular matrix proteins and cell-mediated stiffening, which was greater in dVPMS conditions after a week of culture. Collectively, these results indicate that enhanced cell-mediated remodeling of a PEG hydrogel, achieved either by reduced crosslinking or increased degradability, leads to more rapid vessel formation and higher degrees of cell-mediated stiffening.

Defining the carrier proteome limit for single-cell proteomics.

Single-cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single-cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry (MS)) have technical limitations with the potential to affect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500× the amount of a single-cell proteome, is used to enable peptide identifications. Here we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy, as it relates to mass analyzer dynamic range, multiplexing level and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single-Cell Proteomics Companion (SCPCompanion), a software tool that enables rapid evaluation of single-cell proteomic data and recommends instrument and data analysis parameters for improved data quality.