A common drawback of metabolic analyses of complex biological samples is the inability to consider cell-to-cell heterogeneity in the context of an organ or tissue. To overcome this limitation, we present an advanced high-spatial-resolution metabolomics approach using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) combined with isotope tracing. This method allows mapping of cell-type-specific dynamic changes in central carbon metabolism in the context of a complex heterogeneous tissue architecture, such as the kidney. Combined with multiplexed immunofluorescence staining, this method can detect metabolic changes and nutrient partitioning in targeted cell types, as demonstrated in a bilateral renal ischemia–reperfusion injury (bIRI) experimental model. Our approach enables us to identify region-specific metabolic perturbations associated with the lesion and throughout recovery, including unexpected metabolic anomalies in cells with an apparently normal phenotype in the recovery phase. These findings may be relevant to an understanding of the homeostatic capacity of the kidney microenvironment. In sum, this method allows us to achieve resolution at the single-cell level in situ and hence to interpret cell-type-specific metabolic dynamics in the context of structure and metabolism of neighboring cells.

Previously, we identified plasma microRNA (miR) profiles that associate with markers of microvascular injury in patients with diabetic nephropathy (DN). However, miRs circulate in extracellular vesicles (EVs) or in association with HDL or the RNA-binding protein argonaute-2 (Ago-2). Given that the EV- and HDL-mediated miR transfer toward endothelial cells (ECs) regulates cellular quiescence and inflammation, we hypothesized that the distribution of miRs among carriers affects microvascular homeostasis in DN. Therefore, we determined the miR expression in EV, HDL, and Ago-2 fractions isolated from EDTA plasma of healthy control subjects, patients with diabetes mellitus (DM) with or without early DN (estimated glomerular filtration rate [eGFR] >30 mL/min/1.73 m²), and patients with DN (eGFR <30 mL/min/1.73 m²). Consistent with our hypothesis, we observed alterations in miR carrier distribution in plasma of patients with DM and DN compared with healthy control subjects. Both miR-21 and miR-126 increased in EVs of patients with DN, whereas miR-660 increased in the Ago-2 fraction and miR-132 decreased in the HDL fraction. Moreover, in vitro, differentially expressed miRs improved EC barrier formation (EV-miR-21) and rescued the angiogenic potential (HDL-miR-132) of ECs cultured in serum from patients with DM and DN. In conclusion, miR measurement in EVs, HDL, and Ago-2 may improve the biomarker sensitivity of these miRs for microvascular injury in DN, while carrier-specific miRs can improve endothelial barrier formation (EV-miR-21/126) or exert a proangiogenic response (HDL-miR-132).

There is an increasing prevalence of chronic kidney disease (CKD), which associates with the development of interstitial fibrosis. Pericytes (perivascular fibroblasts) provide a major source of α-SMA-positive myofibroblasts that are responsible for the excessive deposition of extracellular matrix. In order to identify pericyte long non-coding RNAs (lncRNAs) that could serve as a target to decrease myofibroblast formation and counteract the progression of kidney fibrosis we employed two models of experimental kidney injury, one focused on kidney fibrosis (unilateral ureteral obstruction; UUO), and one focused on acute kidney injury that yields kidney fibrosis in the longer term (unilateral ischemia-reperfusion injury; IRI). This was performed in FoxD1-GC;tdTomato stromal cell reporter mice that allowed pericyte fate tracing. Tomato red-positive FoxD1-derivative cells of control and injured kidneys were FACS-sorted and used for lncRNA and mRNA profiling yielding a distinctive transcriptional signature of pericytes and myofibroblasts with 244 and 586 differentially expressed lncRNAs (>twofold, P < 0.05), in the UUO and IRI models, respectively. Next, we selected two differentially expressed and conserved lncRNAs, Rian (RNA imprinted and accumulated in nucleus) and Miat (Myocardial infarction associated transcript), and explored their potential regulatory role in myofibroblast formation through knockdown of their function with gapmers. While Miat was upregulated in myofibroblasts of UUO and IRI in mice, gapmer silencing of Miat attenuated myofibroblast formation as evidenced by decreased expression of α-SMA, col1α1, SMAD2, and SMAD3, as well as decreased α-SMA and pro-collagen-1α1 protein levels. In contrast, silencing Rian, which was found to be downregulated in kidney myofibroblast after IRI and UUO, resulted in increased myofibroblast formation. In addition, we found microRNAs that were previously linked to Miat (miR-150) and Rian (14q32 miRNA cluster), to be dysregulated in the FoxD1-derivative cells, suggesting a possible interaction between miRNAs and these lncRNAs in myofibroblast formation. Taken together, lncRNAs play a regulatory role in myofibroblast formation, possibly through interacting with miRNA regulation, implicating that understanding their biology and their modulation may have the potential to counteract the development of renal fibrosis.

Aims/hypothesis: The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes. Methods: Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA_1c, C-peptide and HDL-cholesterol. Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset. To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data. Results: Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10^–15), without evidence for reverse causality. Conclusions/interpretation: Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits. Graphical