primary human pancreatic islets  (AcceGen Biotechnology)

Journal: eLife

Article Title: Fluorescein-based sensors to purify human α-cells for functional and transcriptomic analyses

doi: 10.7554/eLife.85056

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Article Snippet: Biological sample ( Homo sapiens ) , Primary human pancreatic islets , Integrated Islet Distribution Program (IIDP), Prodo Laboratories Inc, ADI Islet Core , http://iidp.coh.org ; RRID: SCR_014387 , https://prodolabs.com/ , Freshly isolated.

Techniques: Isolation, Cell Culture, Enzyme-linked Immunosorbent Assay, Cell Viability Assay, In Situ, Software

Journal: Advanced Science

Article Title: Bioengineered Pancreas–Liver Crosstalk in a Microfluidic Coculture Chip Identifies Human Metabolic Response Signatures in Prediabetic Hyperglycemia

doi: 10.1002/advs.202203368

Figure Lengend Snippet: Pneumatic actuation achieves rapid and stable mixing across the entire range of physiologically relevant flow rates. A) Quantification of mixing kinetics. The indicated mixing times refer to the complete distribution of a colored marker from the central chamber to both satellite compartments. B) Side view of pneumatic actuation during equilibration, pumping, and withdrawal stages. Filling levels are indicated for the central (orange), left (blue), and right (green) chambers. The channel connecting the central chamber to the right compartment (L 2 ) is twice as long as the connector with the left compartment (L 1 ). During pumping, media is pressed from the central chamber to the side chambers with medium flowing back to the central chamber during the withdrawal stage. Note that the meandered chamber (green), is less filled and less emptied at the end of the pumping and withdrawal stages, respectively. C) Fluid displacement for the different compartments is shown for different channel ratios (left 1:1; middle 1:2; right 1:3). D) Mixing kinetics are shown for the different compartments during different pumping cycle (light red = pumping phase; light blue = withdrawal phase). Arrows indicate the cycle after which complete mixing was achieved. Note that increasing channel ratios result in reduced fluid displacement and increased mixing times in the meandered channel. E) Pumping efficiency remains stable over at least 13 days ( p = 0.54 for slope deviation from zero; F‐test). Pumping efficiency is indicated as fluid displacement at the indicated days relative to fluid displacement at the start of the experiment. F) Snapshots of the simulation results of solute distribution in the chip at different cycle phases. Note that local solute concentrations are almost equilibrated after 3 cycles. G) The maximum wall shear stress on the cell surface of liver spheroids (left) and pancreatic islets (right) as a function of distance from the connecting channels is shown. Shear stress increases with flow rate and proximity to the connector. Flow rates ≤100 µL min −1 maintain physiological shear stress ≤30 mPa. Error bars indicate SEM. *, **, ***, and **** indicate p < 0.05, p < 0.01, p < 0.001, and p < 0.0001 based on heteroscedastic two‐tailed t ‐tests, respectively.

Article Snippet: Primary human pancreatic islets were commercially acquired from Tissue Solutions (Glasgow, UK).

Techniques: Marker, Shear, Two Tailed Test

Journal: Advanced Science

Article Title: Bioengineered Pancreas–Liver Crosstalk in a Microfluidic Coculture Chip Identifies Human Metabolic Response Signatures in Prediabetic Hyperglycemia

doi: 10.1002/advs.202203368

Figure Lengend Snippet: Schematic depiction of the experimental workflow. Pancreatic islets and liver spheroids are generated and recovered in ultralow attachment (ULA) plates. After conditioning for two days to low (0.1 n m ) insulin, the microtissues are washed and transferred into the pneumatically actuated coculture device in media without insulin. In the integrated device, islets and liver spheroids are cultured in either low (3.5 m m ) or high (11 m m ) glucose concentrations and molecular and functional responses are evaluated using RNA‐Sequencing and regulatory network decomposition.

Article Snippet: Primary human pancreatic islets were commercially acquired from Tissue Solutions (Glasgow, UK).

Techniques: Generated, Cell Culture, Functional Assay, RNA Sequencing

Journal: Advanced Science

Article Title: Bioengineered Pancreas–Liver Crosstalk in a Microfluidic Coculture Chip Identifies Human Metabolic Response Signatures in Prediabetic Hyperglycemia

doi: 10.1002/advs.202203368

Figure Lengend Snippet: On‐chip exposure of primary human islets to hyperglycemia results in β ‐cell stress, dedifferentiation and islet exhaustion. A) Immunofluorescent staining of primary human islets for insulin ( β ‐cells), glucagon ( α ‐cells), and somatostatin ( δ ‐cells). Dashed line indicates island circumference. Scale bar = 100 µm. B) Expression levels of endocrine hormones secreted by α ‐cells (GCG), β ‐cells (INS), δ ‐cells (SST), γ ‐cells (PPY), and ε ‐cells (GHRL). Expression is shown as log 10 (FPKM) and sorted in descending order. N = 3. C) Quantification of insulin secretion after exposure of islets to hypoglycemic (3.5 m m glucose) or hyperglycemic (11 m m glucose) conditions. For each measurement, 10–20 islets were exposed to the indicated glucose concentrations for 4 h in 120 µL. N = 3. D) Insulin secretion dynamics show that insulin is secreted very rapidly with the bulk of insulin release being detected within the first few minutes after exposure to an increase in glucose concentrations from 3.5 to 11 m m . N = 4. E) Principal component analysis of RNA‐Sequencing data show that isogenic islets exposed to low glucose (LG; 3.5 m m ) and high glucose (HG; 11 m m ) conditions rapidly alter their transcriptomic signatures. F) Left: heatmap visualization of differentially expressed genes. Right: dot plot visualization of Reactome pathway analysis of the indicated clusters. Dot size indicates the number of pathway‐associated genes in the respective cluster; dot color indicates the statistical strength of the association. G) Volcano plot showing the changes in gene expression expressed as fold‐change (FC) of high glucose compared to low glucose. H) The most highly up‐ and downregulated genes include various metabolic modulators, such as the glucose transporter SLC5A9 or the lipid regulator PCSK4 , as well as the proinflammatory chemokine CCL15 . I) The on‐chip glucose response closely resembles response signatures of primary human islets based on established candidate markers. J) Comparison of differentially expressed genes identified in chronic hyperglycemia [ ⁴⁹ ] with our chip data indicates a clear correlation. K) Scatter plot showing the correlation between the motif activities of 503 transcription factors in high glucose (HG) and low glucose (LG) conditions. L) Transcription factors with the largest differences in motif activities between glucose levels are indicated. M) Activity of candidate transcription factors with known importance in islet function. Note that activity signatures of transcription factors involved in β ‐cell stress are induced while motifs associated with pancreatic differentiation are overall decreased. Sequence logos show the binding motif of the respective transcription factor. RNA‐Seq data was analyzed from N = 4 biological replicates. Error bars indicate SEM. *, **, and *** indicate p < 0.05, p < 0.01, and p < 0.001 based on heteroscedastic two‐tailed t ‐tests, respectively.

Article Snippet: Primary human pancreatic islets were commercially acquired from Tissue Solutions (Glasgow, UK).

Techniques: Staining, Expressing, RNA Sequencing, Gene Expression, Comparison, Activity Assay, Sequencing, Binding Assay, Two Tailed Test

Journal: Advanced Science

Article Title: Bioengineered Pancreas–Liver Crosstalk in a Microfluidic Coculture Chip Identifies Human Metabolic Response Signatures in Prediabetic Hyperglycemia

doi: 10.1002/advs.202203368

Figure Lengend Snippet: Profiling of 3D primary human liver cultures reveal the counter‐regulation of transcriptional programs in liver and pancreas. All panels show analyses of liver spheroids cocultured on‐chip together with pancreatic islets. A) Principal component analysis of RNA‐Sequencing data shows that liver spheroids in the integrated coculture chip undergo rapid molecular changes upon glucose challenge. B) Left: heatmap visualization of differentially expressed genes. Right: dot plot visualization of Reactome pathway analysis of the indicated clusters. Dot size indicates the number of pathway‐associated genes in the respective cluster; dot color indicates the statistical strength of the association. Note that pathways downstream of insulin signaling (AKT phosphorylation, activation of SREBP) are activated in high glucose, demonstrating functional interaction between the tissues. C) The canonical insulin response markers PCK1 , G6PC , IGFBP1 , and PPARGC1A (PGC1 α ) are repressed on‐chip after exposure to prediabetic (11 m m ) glucose levels. D) Volcano plot showing the changes in gene expression expressed as fold‐change (FC) of high glucose compared to low glucose. E) Scatter plot showing the correlation between the motif activities of 503 transcription factors in liver spheroids in high glucose (HG) and low glucose (LG) conditions. F) Transcription factors with the largest differences in motif activities between glucose levels are indicated. G) Activity of candidate transcription factors is shown. Note that transcription factors implicated in the hepatic insulin resistance are significantly increased in HG. Sequence logos show the binding motif of the respective transcription factor. H) Venn diagram showing the overlap of differentially expressed genes in liver and islet after high glucose exposure. Interestingly, the overlap is relatively small and in significant anticorrelation ( p = 0.003; F‐test), indicative of the tissue‐specificity of the human metabolic response signatures. Experiments were done with N = 4 biological replicates. Error bars indicate SEM. * indicate p < 0.05 based on heteroscedastic two‐tailed t ‐tests.

Article Snippet: Primary human pancreatic islets were commercially acquired from Tissue Solutions (Glasgow, UK).

Techniques: RNA Sequencing, Phospho-proteomics, Activation Assay, Functional Assay, Gene Expression, Activity Assay, Sequencing, Binding Assay, Two Tailed Test