Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.09.09.459582

Characterization of the secretome, transcriptome and proteome of human β cell line EndoC-βH1

Ryaboshapkina, M.·Saitoski, K.·Hamza, G. M.·Jarnuczak, A. F.·Berthault, C.·Sengupta, K.·Underwood, C. R.·Andersson, S.·Scharfmann, R.
View original source ↗Export metadata ↓

Abstract

Early diabetes research is hampered by limited availability, variable quality and instability of human pancreatic islets in culture. Little is known about the human {beta} cell secretome, and recent studies question translatability of rodent {beta} cell secretory profiles. Here, we verify representativeness of EndoC-{beta}H1, one of the most widely used human {beta} cell lines, as a translational human {beta} cell model based on omics and characterize the EndoC-{beta}H1 secretome. We profiled EndoC-{beta}H1 cells using RNA-seq, Data Independent Acquisition (DIA) and Tandem Mass Tag proteomics of cell lysate. Omics profiles of EndoC-{beta}H1 cells were compared to human {beta} cells and insulinomas. Secretome composition was assessed by DIA proteomics. Agreement between EndoC-{beta}H1 cells and primary adult human {beta} cells was ~90% for global omics profiles as well as for {beta} cell markers, transcription factors and enzymes. Discrepancies in expression were due to elevated proliferation rate of EndoC-{beta}H1 cells compared to adult {beta} cells. Consistently, similarity was slightly higher with benign non-metastatic insulinomas. EndoC-{beta}H1 secreted 671 proteins in untreated baseline state and 3,278 proteins when stressed with non-targeting control siRNA, including known {beta} cell hormones INS, IAPP, and IGF2. Further, EndoC-{beta}H1 secreted proteins known to generate bioactive peptides such as granins and enzymes required for production of bioactive peptides. Unexpectedly, exosomes appeared to be a major mode of secretion in EndoC-{beta}H1 cells. We believe that secretion of exosomes and bioactive peptides warrant further investigation with specialized proteomics workflows in future studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/459582v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1976bc0org.highwire.dtl.DTLVardef@233c4eorg.highwire.dtl.DTLVardef@14c3350org.highwire.dtl.DTLVardef@1bcf0d5_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWe validate EndoC-{beta}H1 as a translational human {beta} cell model using omics. C_LIO_LIWe present the first unbiased proteomics composition of human {beta} cell line secretome. C_LIO_LIThe secretome of human {beta} cells is more extensive than previously thought. C_LIO_LIUntreated cells secreted 671 proteins and stressed cells secreted 3,278 proteins. C_LIO_LISecretion of exosomes and bioactive peptides constitute directions of future research. C_LI

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ryaboshapkina, M., Saitoski, K., Hamza, G. M., Jarnuczak, A. F., Berthault, C., Sengupta, K., Underwood, C. R., Andersson, S., Scharfmann, R.. 2021-09-11. Characterization of the secretome, transcriptome and proteome of human β cell line EndoC-βH1. https://doi.org/10.1101/2021.09.09.459582

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗