Biology subjects

Keutler, K.

Publications and source records attributed to Keutler, K..

5 recordsLinked to original sources

bioRxiv2025

Glucagon and GLP-1 Accelerate Pseudo-Islet Assembly and Unmask Sex-Specific Islet Fragmentation Dynamics

Pancreatic hormones are best known for their role in regulating blood sugar levels as well as islet cell function and proliferation. However, their impact on maintaining and inducing cell aggregation in culture remains under-explored. In this study, we investigated the effects of glucagon (GCG) and glucagon-like-peptide 1(GLP-1) on the formation and integrity of human islet clusters. Native human islets were dissociated and sorted into pure -, {beta}-, and {delta}-cell populations using antibody-based fluorescence-activated cell sorting (FACS). The sorted cells were then co-cultured with mouse endothelial MS1 cells in suspension to generate pseudo-islets of varying cell composition. Hormonal supplementation with GCG or GLP-1 versus blank was administered during the tissue culture phase. Hormone-treated pseudo-islets formed faster, dependent on the cellular composition and the sex of the donor. In parallel, we also exposed native islets, maintained in suspension without prior dissociation or sorting, to hormone supplementation. These islets exhibited accelerated fragmentation under hormone treatment compared to controls, again dependent on donor sex with islets from female donors fragmenting faster than from male donors. These findings suggest that GCG and GLP-1 enhance pseudo-islet formation and affect the structural integrity of native islets in a sex-specific manner, offering insights into islet biology and implications for diabetes research and therapy. Article HighlightsWe established a manipulatable, expandable human pseudo-islet platform to investigate islet morphogenesis, architecture, and intercellular signaling. We examined the contribution of individual -, {beta}-, and {delta}-cell populations and assessed how glucagon (GCG) and glucagon-like peptide-1 (GLP-1) modulate islet integrity in culture. In native islets, hormonal supplementation attenuated fragmentation in male donors but accelerated it in females. In pseudo-islets, cellular composition was the predominant determinant of maturation versus fragmentation, with donor sex exerting a secondary influence. We present methodological guidelines for generating and maintaining human pseudo-islets, thereby providing a framework to optimize donor selection, culture conditions, and experimental design in diabetes research.

biochemistry↗

bioRxiv2025

Aggregating multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal

AI based embeddings offer the possibilities of encoding complex biological data into low dimensional spaces, called embedding spaces, that maintain the relationships between entities. There is an open question about the compatibility of embedding spaces that are created without any coordination. It has been assumed that signals in these unaligned embedding spaces would be destroyed if vectors were aggregated into summed values. We trained embedding models across different data modalities and tested aggregating the values together to test this assumption. Our research shows that signal from unaligned embedded values is conserved and able to still be used for learning tasks, such as data modality and tumor of origin recognition.

bioinformatics↗

bioRxiv2024

Trace Amines are Essential Metabolites for the Autocrine Regulation of β-Cell Signaling and Insulin Secretion

Secretion of insulin in response to extracellular stimuli, such as elevated glucose levels and small molecules that act on G-protein coupled receptors (GPCRs), is the hallmark of {beta}-cell physiology. Trace amines (TAs) are small aromatic metabolites that were identified as low-abundant ligands of the trace amine-associated receptor 1 (TAAR1) in the central nervous system (CNS), a GPCR that is also expressed by pancreatic {beta}-cells. In the present work, we identify TAs as essential autocrine signaling factors for {beta}-cell activity and insulin secretion. We find that {beta}-cells are producing TAs in significant amounts and that the modulation of endogenous TA levels by the selective inhibition of TA biosynthetic pathways directly translated into changes of oscillations of the intracellular Ca2+ concentration ([Ca2+]i oscillations) and insulin secretion. Selective TAAR1 agonists or inhibitors of monoamine oxidases increased [Ca2+]i oscillations and insulin secretion. Opposite effects were mediated by selective TAAR1 antagonists, by recombinant monoamine oxidase action and by the inhibition of amino acid decarboxylase. As the modulation of TA biochemical pathways immediately translated into changes of [Ca2+]i oscillations, we inferred high metabolic turnover rates of TAs and autocrine feedback. We found that psychotropic drugs modulate [Ca2+]i oscillations and insulin secretion, either directly acting on TAAR1 or by altering endogenous TA levels. Our combined data support the hypothesis of TAs as essential autocrine signaling factors for {beta}-cell activity and insulin secretion as well as TAAR1 as an important mediator of amine-modulated insulin secretion.

biochemistry↗

bioRxiv2023

Imputing Single-Cell Protein Abundance in Multiplex Tissue Imaging

Multiplex tissue imaging are a collection of increasingly popular single-cell spatial proteomics and transcriptomics assays for characterizing biological tissues both compositionally and spatially. However, several technical issues limit the utility of multiplex tissue imaging, including the limited number of molecules (proteins and RNAs) that can be assayed, tissue loss, and protein probe failure. In this work, we demonstrate how machine learning methods can address these limitations by imputing protein abundance at the single-cell level using multiplex tissue imaging datasets from a breast cancer cohort. We first compared machine learning methods strengths and weaknesses for imputing single-cell protein abundance. Machine learning methods used in this work include regularized linear regression, gradient-boosted regression trees, and deep learning autoencoders. We also incorporated cellular spatial information to improve imputation performance. Using machine learning, single-cell protein expression can be imputed with mean absolute error ranging between 0.05-0.3 on a [0,1] scale. Finally, we used imputed data to predict whether single cells were more likely to come from pre-treatment or post-treatment biopsies. Our results demonstrate (1) the feasibility of imputing single-cell abundance levels for many proteins using machine learning; (2) how including cellular spatial information can substantially enhance imputation results; and (3) the use of single-cell protein abundance levels in a use case to demonstrate biological relevance.

cancer biology↗

bioRxiv2023

Thermogenetic control of Ca2+ levels in cells and tissues

Virtually all major processes in cells and tissues are regulated by calcium ions (Ca2+). Understanding the influence of Ca2+ on cell function requires technologies that allow for non-invasive manipulation of intracellular calcium levels including the formation of calcium patterns, ideally in a way that is expandable to intact organisms. The currently existing tools for optical and optogenetic Ca2+ manipulation are limited with respect to response time, and tissue penetration depth. Here we present Genetically Encoded Calcium Controller (GECCO), a system for thermogenetic Ca2+ manipulation based on snake TRP channels optically controlled by infrared illumination. GECCO is functional in animal and plant cells and allows studying how cells decode different profiles of Ca2+ signals. GECCO enabled the shaping of insulin release from {beta}-cells, the identification of drugs that potentiate Ca2+-induced insulin release, and the generation of synthetic Ca2+ signatures in plants.

synthetic biology↗