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Fowlds, K.

Publications and source records attributed to Fowlds, K..

2 recordsLinked to original sources

Expansion and Differentiation of Adult Human Pancreas-Derived Progenitor Cells into Functional Islet-Like Organoids

Background and AimsAdult pancreas-derived islet progenitor cells (IPCs) have recently been shown to expand in culture and differentiate into endocrine-like organoids. However, translation of this approach to a clinically compatible workflow requires cell enrichment strategies and validation using tissue obtained during real-world clinical procedures. Here, we adapted our previously described IPC platform to non-endocrine pancreatic tissue fractions generated during clinical islet isolation procedures and evaluated their capacity to generate functional islet organoids. MethodsNon-endocrine pancreatic tissue fractions obtained during clinical islet isolation were expanded ex vivo and enriched using fluorescence-activated cell sorting (FACS) for CD81 and CD9, surface markers previously identified in IPC populations. Sorted cells were expanded, induced to form IPC clusters, and differentiated with ISX9 to generate islet organoids. Differentiation was assessed by gene expression analysis, flow cytometry, immunofluorescence, calcium flux assays, glucose-stimulated insulin and glucagon secretion, and single-cell RNA sequencing. ResultsClinically derived non-endocrine cell fractions yielded expandable IPC populations expressing progenitor-associated markers. FACS-purified and expanded CD81+/CD9+ IPCs were enriched with BMPR1A and P2RY1. Sorted cells generated three-dimensional BMPR1A+ and RGS16+ IPC clusters. IPC clusters differentiated into islet organoids with upregulated expression of canonical beta-and alpha-cell transcription factors. Single-cell transcriptomic profiling revealed activation of coordinated endocrine gene programs and alignment with reference human islet endocrine signatures, while the undifferentiated IPC compartment was marked by enrichment of PTX3, FST, CEMIP, and GREM1. Terminally differentiated cells exhibited depolarization-induced calcium influx and glucose-regulated insulin and glucagon secretion. ConclusionsThese findings establish an adaptable workflow for expansion and production of functional islet organoids recovered from clinically derived pancreatic tissue. This strategy may provide an unlimited autologous source of adult progenitor-derived islets for future islet cell replacement therapies in diabetes.

cell biology↗

BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells

Insulin secretion from pancreatic {beta}-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of {beta}-cells, involve multiple software packages, and necessitate multiple researchers - all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in {beta}-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions in a cluster of {beta}-cells.

bioinformatics↗