Biology subjects

Groenborg, M.

Publications and source records attributed to Groenborg, M..

3 recordsLinked to original sources

bioRxiv2026

No One-Size-Fits-All: An Evidence-Based Framework to Select Plasma EV Isolation Methods

Extracellular vesicles (EVs) hold significant promise as biomarkers, but their clinical translation is constrained by variability in pre-analytical handling and isolation. EV isolation methods directly shape which EV populations are captured and characterized, yet systematic method comparisons across multiple analytical dimensions are limited. We comprehensively evaluated eleven EV isolation methods to define their performance and applications. EVs were quantified by NanoFCM, profiled for tetraspanins (CD9, CD63, CD81) via MSD assays, and further characterized by LC-MS/MS proteomics. We show that different EV isolation methods recover different EV populations. Our data provide guidance on method selection based on downstream application needs and serve as a look-up tool if a protein of interest is detected. EV isolation methods broadened proteome coverage but showed divergent performance and recover different EV populations. While all methods captured EVs in the 50-150nm range, centrifugation and ultracentrifugation identified the broadest proteomes (up to 1093 proteins) driven by higher plasma protein carryover. Conversely, ExoEasy and qEV 70 isolated larger EVs and achieved stronger depletion of abundant plasma proteins but showed lower proteome coverage. A total of 117 proteins were detected across all isolation methods. Pre-clearing samples removed contaminants but at the cost of protein identifications. We demonstrate that method selection must align with the specific analytical goal: centrifugation for comprehensive proteome profiling, affinity/size-exclusion methods for contaminant-sensitive assays, and precipitation for high-throughput applications. This systematic characterization provides an evidence-based framework and look-up resource for matching isolation strategies to downstream applications and research questions. Graphical Abstract for Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/710675v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@12ad967org.highwire.dtl.DTLVardef@270e4eorg.highwire.dtl.DTLVardef@1c41bcorg.highwire.dtl.DTLVardef@11fb236_HPS_FORMAT_FIGEXP M_FIG C_FIG This study evaluated 11 extracellular vesicle (EV) isolation methods which enriched distinct EV subpopulations with varying degrees of contaminants. No single approach optimized purity or proteome coverage; in this paper we present an Evidence-Based Framework to select plasma EV isolation methods based on downstream application needs.

molecular biology↗

bioRxiv2026

Early Identification of At-Risk Patients: ProteomicSignature Predicts Progression to DecompensatedCirrhosis

Background & AimsEarly identification of decompensation in patients with cirrhosis is important to enable timely detection, management of complications and for effective treatment. This study investigates the biology of decompensation and aim to identify protein biomarkers for identification of high-risk patients. MethodsThe primary analysis included plasma samples from 46 patients with metabolic dysfunction associated steatotic liver disease (MASLD) related cirrhosis. Plasma samples were depleted for the top 14 most abundant proteins and the proteome was measured by liquid chromatography tandem mass spectrometry. The dataset was divided into a training (14 compensated, 10 decompensated) and a test cohort of compensated patients (11 progressing to decompensation, 11 remaining compensated). Changes in protein levels were determined by ANCOVA and a prognostic model was developed using logistic regression. External validation was performed in an independent cohort of 120 patients with alcohol-related cirrhosis. Time-to-event analyses were conducted in this cohort using Cox regression. Results52 proteins involved in impaired hepatic function, fibrogenesis, immune activation, and metabolic changes were significantly different between compensated and decompensated patients. A prognostic model with four proteins (NBL1, LTBP4, APOC4, GHR), demonstrated predictive ability for future decompensation (AUC=0.93, 73% sensitivity, 100% specificity). In the external validation cohort, the model demonstrated generalizability (AUC=0.78, 72% sensitivity, 82% specificity). Validation cohort time-to-event analyses showed that higher baseline scores were associated with shorter time to liver-related events (HR 1.32; log-rank p = 0.027), underscoring the panels prognostic value. ConclusionOur study indicates that patients with decompensated cirrhosis are characterized by proteomic signatures of fibrogenesis and metabolic dysfunction. Capturing these signatures could help identify patients at risk of complications and potentially those eligible for aetiology directed treatment. Impact and ImplicationsAddressing a critical unmet need for early detection of cirrhosis decompensation, our proteomic study identifies a four-protein panel with predictive ability for decompensation. These findings hold significant implications for hepatologists, clinical researchers, and healthcare systems, offering a novel tool to enhance prognostication and refine treatment strategies, potentially facilitating targeted patient monitoring. However, considering the small discovery sample size and the distinct aetiology of the external validation cohort, further validation is essential before broad clinical integration. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/709475v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@6620e2org.highwire.dtl.DTLVardef@f8dfe4org.highwire.dtl.DTLVardef@1331101org.highwire.dtl.DTLVardef@1a195ca_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Multi-layered proteomics identifies insulin-induced upregulation of the EphA2 receptor via the ERK pathway and dependent on low IGF1R level

Insulin resistance impairs the cellular insulin response and frequently precedes metabolic disorders, like type 2 diabetes, which are affecting an increasing number of people globally. Given the critical role of the liver in glucose and lipid metabolism, understanding the molecular mechanisms in hepatic insulin resistance is essential for early preventive treatments. To elucidate changes in insulin signal transduction associated with hepatocellular resistance, we employed a multi-layered mass spectrometry-based proteomics approach focusing on insulin receptor (IR) signaling at the interactome, phosphoproteome, and proteome levels.in a long-term hyperinsulinemia-induced insulin-resistant HepG2 cell line with a knockout of the insulin-like growth factor 1 receptor (IGF1R KO). Analysis of the dynamic insulin-induced IR interactome revealed recruitment of the PI3K complex in both insulin-sensitive and -resistant cells. From the phosphoproteomics dataset, a change in insulin-stimulated signaling responses in insulin resistance was observed and showed attenuated signaling via the metabolic PI3K-AKT pathway but sustained extracellular signal-regulated kinase (ERK) activity. At the proteome level, the ephrin type-A receptor 2 (EphA2) showed an insulin-induced increase in expression. This receptor belongs to the Eph receptor family and participates in various cellular processes, such as cell adhesion, migration, and tissue development. The protein abundance regulation of EphA2 occurred through the ERK signaling pathway and was concordantly independent of insulin resistance. Induction of EphA2 by insulin was confirmed in other cell lines and observed uniquely in cells with high levels of IR compared to IGF1R. The multi-layered proteomics dataset provided insights into insulin signaling in general and in the context of insulin resistance, and it can going forward serve as a resource to generate and test hypotheses, leading to an improved understanding of insulin resistance.

cell biology↗