CD81 marks immature and dedifferentiated pancreatic β-cells

Salinno, Ciro; Büttner, Maren; Cota, Perla; Tritschler, Sophie; Tarquis-Medina, Marta; Bastidas-Ponce, Aimée; Scheibner, Katharina; Burtscher, Ingo; Böttcher, Anika; Theis, Fabian J.; Bakhti, Mostafa; Lickert, Heiko

doi:10.1016/j.molmet.2021.101188

Cited by 37 publications

(46 citation statements)

References 63 publications

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“…Several genes implicated in β-cell proliferation and differentiation were also dysregulated ( E2f3, Rfx3, Nfatc1, E2f1 ) ( Figure 3C ) (Ait-Lounis et al, 2010; Fajas et al, 2004; Rady et al, 2013; Simonett et al, 2021). Intriguingly, markers of immature or dedifferentiated β-cells, including CD81 (Salinno et al, 2021) and Aldh1a3 (Kim-Muller et al, 2016), were upregulated in the Zmiz1 βKO islet transcriptome ( Figure 3C ). Immunoblotting confirmed upregulation of CD81 and Aldh1a3 protein in Zmiz1 βKO islets ( Figure 3D-E ), highlighting a potential role for Zmiz1 in maintaining β-cell maturation.…”

Section: Resultsmentioning

confidence: 99%

“…The enzyme aldehyde dehydrogenase 1 isoform A3 (ALDH1A3) has been shown to mark dysfunctional β-cells that have progenitor like features including the expression of Neurgo3 (Kim-Muller et al, 2016), which itself was upregulated in Zmiz1 βKO islets. CD81 has been recently identified as a novel surface marker for immature and de-differentiated β-cells in the adult mouse and human islets (Salinno et al, 2021, p. 81). Salinno et al showed that subpopulation of high CD81 expressing mouse β-cells have low expression levels of the mature β-cell marker Ucn3 , which we also find to be downregulated in the Zmiz1 βKO islets.…”

Section: Discussionmentioning

confidence: 99%

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Zmiz1 is required for mature β-cell function and mass expansion upon high fat feeding

Alghamdi

Krentz

Smith

et al. 2022

Preprint

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Genome-wide association studies have identified hundreds of signals for type 2 diabetes (T2D), most of which confer risk through effects on gene expression. We previously identified the transcription factor ZMIZ1 as a probable effector transcript in human islets, but how altered ZMIZ1 expression impacts T2D risk is unknown. We now show that islets from carriers of the T2D-risk alleles have reduced islet insulin content and glucose-stimulated insulin secretion. To elucidate the mechanism for islet-cell dysfunction, we generated β-cell-specific Zmiz1 knockout (Zmiz1βKO) mice. Male and female Zmiz1βKO mice were glucose intolerant with impaired insulin secretion, compared with control littermates. Transcriptomic profiling of Zmiz1βKO islets identified over 500 differentially expressed genes including those involved in β-cell function and maturity which we confirmed at the protein level. After high fat feeding, Zmiz1βKO mice fail to expand β-cell mass and become severely diabetic. Thus, Zmiz1 is required for normal glucose homeostasis and may contribute to T2D risk by maintaining a mature β-cell state and allowing islet mass expansion upon metabolic stress.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Zmiz1 is required for mature β-cell function and mass expansion upon high fat feeding

Alghamdi

Krentz

Smith

et al. 2022

Preprint

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“…As a final use case we asked if expiMap could assist with interpretable cell type annotation and the analysis of cell state heterogeneity. As example we used multiple scRNA-seq data sets from mouse pancreatic islets to capture beta cell heterogeneity, including data from young healthy mice on postnatal day 16 (dataset name: Fltp_P16) 66 , healthy young mice from a non-obese diabetic model before the onset of type 1 diabetes (T1D) at 5 weeks of age (dataset name: NOD) 67 , healthy adult mice and those exposed to chemically-induced stress (dataset name: spikein_drug) 68 , and healthy adult mice and those exposed to chemically-induced (streptozotocin-induced) type 2 diabetes (T2D) with and without therapy with different combinations of insulin, GLP1, and estrogen (dataset name: STZ) 69 .…”

Section: Resultsmentioning

confidence: 99%

Biologically informed deep learning to infer gene program activity in single cells

Lotfollahi

Rybakov

Hrovatin

et al. 2022

Preprint

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The increasing availability of large-scale single-cell datasets has enabled the detailed description of cell states across multiple biological conditions and perturbations. In parallel, recent advances in unsupervised machine learning, particularly in transfer learning, have enabled fast and scalable mapping of these new single-cell datasets onto reference atlases. The resulting large-scale machine learning models however often have millions of parameters, rendering interpretation of the newly mapped datasets challenging. Here, we propose expiMap, a deep learning model that enables interpretable reference mapping using biologically understandable entities, such as curated sets of genes and gene programs. The key concept is the substitution of the uninterpretable nodes in an autoencoder's bottleneck by labeled nodes mapping to interpretable lists of genes, such as gene ontologies, biological pathways, or curated gene sets, for which activities are learned as constraints during reconstruction. This is enabled by the incorporation of predefined gene programs into the reference model, and at the same time allowing the model to learn de novo new programs and refine existing programs durin reference mapping. We show that the model retains similar integration performance as existing methods while providing a biologically interpretable framework for understanding cellular behavior. We demonstrate the capabilities of expiMap by applying it to 15 datasets encompassing five different tissues and species. The interpretable nature of the mapping revealed unreported associations between interferon signaling via the RIG-I/MDA5 and GPCRs pathways, with differential behavior in CD8+ T cells and CD14+ monocytes in severe COVID-19, as well as the role of annexins in the cellular communications between lymphoid and myeloid compartments for explaining patient response to the applied drugs. Finally, expiMap enabled the direct comparison of a diverse set of pancreatic beta cells from multiple studies where we observed a strong, previously unreported correlation between the unfolded protein response and asparagine N-linked glycosylation. Altogether, expiMap enables the interpretable mapping of single cell transcriptome data sets across cohorts, disease states and other perturbations.

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“…To isolate islets and exocrine tissues, we applied adult pancreas digestion as described previously [28,29]. Briefly, we perfused the pancreas by injecting collagenase P (Sigma-Aldrich, Schnelldorf, Germany) dissolved in Hanks Balanced Salt Solution (HBSS) with Ca 2+ /Mg 2+ into the bile duct.…”

Section: Pancreatic Islet and Exocrine Tissue Isolation And Facs Sortingmentioning

confidence: 99%

Synaptotagmin-13 Is a Neuroendocrine Marker in Brain, Intestine and Pancreas

Tarquis-Medina

Scheibner

González-García

et al. 2021

IJMS

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Synaptotagmin-13 (Syt13) is an atypical member of the vesicle trafficking synaptotagmin protein family. The expression pattern and the biological function of this Ca2+-independent protein are not well resolved. Here, we have generated a novel Syt13-Venus fusion (Syt13-VF) fluorescence reporter allele to track and isolate tissues and cells expressing Syt13 protein. The reporter allele is regulated by endogenous cis-regulatory elements of Syt13 and the fusion protein follows an identical expression pattern of the endogenous Syt13 protein. The homozygous reporter mice are viable and fertile. We identify the expression of the Syt13-VF reporter in different regions of the brain with high expression in tyrosine hydroxylase (TH)-expressing and oxytocin-producing neuroendocrine cells. Moreover, Syt13-VF is highly restricted to all enteroendocrine cells in the adult intestine that can be traced in live imaging. Finally, Syt13-VF protein is expressed in the pancreatic endocrine lineage, allowing their specific isolation by flow sorting. These findings demonstrate high expression levels of Syt13 in the endocrine lineages in three major organs harboring these secretory cells. Collectively, the Syt13-VF reporter mouse line provides a unique and reliable tool to dissect the spatio-temporal expression pattern of Syt13 and enables isolation of Syt13-expressing cells that will aid in deciphering the molecular functions of this protein in the neuroendocrine system.

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CD81 marks immature and dedifferentiated pancreatic β-cells

Cited by 37 publications

References 63 publications

Zmiz1 is required for mature β-cell function and mass expansion upon high fat feeding

Zmiz1 is required for mature β-cell function and mass expansion upon high fat feeding

Biologically informed deep learning to infer gene program activity in single cells

Synaptotagmin-13 Is a Neuroendocrine Marker in Brain, Intestine and Pancreas

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