Gene Signature of Proliferating Human Pancreatic α Cells

Gutiérrez, Giselle Domínguez; Xin, Yurong; Okamoto, Haruka; Kim, Jinrang; Lee, Ann–Hwee; Ni, Min; Adler, Christina; Yancopouloš, George D.; Murphy, Andrew; Gromada, Jesper

doi:10.1210/en.2018-00469

Cited by 14 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While we presented a novel glucagon interactome within enriched secretory granules of α-TC1-6 cells and its alterations due to nutritional or paracrine effectors, direct comparisons to primary alpha cells may be limited. When we compared our described glucagon interactome with the transcriptomic profile of mouse alpha cells (56), and human α-cells (57), there were some differences in the protein profiles. Additionally, Lawlor et al (58) compared gene expression profiles of α-TC1 cells with their primary mouse and human counterparts and showed a high level of discrepancy between them.…”

Section: Discussionmentioning

confidence: 99%

Plasticity in the Glucagon Interactome Reveals Novel Proteins That Regulate Glucagon Secretion in α-TC1-6 Cells

Asadi

Dhanvantari

2019

Front. Endocrinol.

View full text Add to dashboard Cite

Glucagon is stored within the secretory granules of pancreatic alpha cells until stimuli trigger its release. The alpha cell secretory responses to the stimuli vary widely, possibly due to differences in experimental models or microenvironmental conditions. We hypothesized that the response of the alpha cell to various stimuli could be due to plasticity in the network of proteins that interact with glucagon within alpha cell secretory granules. We used tagged glucagon with Fc to pull out glucagon from the enriched preparation of secretory granules in α-TC1-6 cells. Isolation of secretory granules was validated by immunoisolation with Fc-glucagon and immunoblotting for organelle-specific proteins. Isolated enriched secretory granules were then used for affinity purification with Fc-glucagon followed by liquid chromatography/tandem mass spectrometry to identify secretory granule proteins that interact with glucagon. Proteomic analyses revealed a network of proteins containing glucose regulated protein 78 KDa (GRP78) and histone H4. The interaction between glucagon and the ER stress protein GRP78 and histone H4 was confirmed through co-immunoprecipitation of secretory granule lysates, and colocalization immunofluorescence confocal microscopy. Composition of the protein networks was altered at different glucose levels (25 vs. 5.5 mM) and in response to the paracrine inhibitors of glucagon secretion, GABA and insulin. siRNA-mediated silencing of a subset of these proteins revealed their involvement in glucagon secretion in α-TC1-6 cells. Therefore, our results show a novel and dynamic glucagon interactome within α-TC1-6 cell secretory granules. We suggest that variations in the alpha cell secretory response to stimuli may be governed by plasticity in the glucagon “interactome.”

show abstract

Section: Discussionmentioning

confidence: 99%

Plasticity in the Glucagon Interactome Reveals Novel Proteins That Regulate Glucagon Secretion in α-TC1-6 Cells

Asadi

Dhanvantari

2019

Front. Endocrinol.

View full text Add to dashboard Cite

show abstract

“…The capability of a method to deconvolute real bulk samples shall be addressed to ensure it is useful in the real-world applications. We acquired 70 real human pancreatic islets bulk samples from published studies 38,40,41 (Supplementary Table 10) and used single cell data of the same tissue 38 as reference to infer the percentages of 4 endocrine cell types (i.e., Alpha, Beta, Delta, PP). The 70 bulk samples were collected from 39 distinct donors, including 26 healthy donors, and 13 donors with type 2 diabetes (T2D).…”

Section: Resultsmentioning

confidence: 99%

“…The capability of a method to deconvolute real bulk samples shall be addressed to ensure it is useful in the real-world applications. We acquired 70 real human pancreatic islets bulk samples from published studies 38,40,41 (Supplementary Table 10 Replicates from the same donor are expected to have similar compositions and thus were used to assess the reproducibility of the estimates from AdRoit. For all cell types, AdRoit had highly consistent estimates for the same donors (Fig.…”

Section: Application To Real Bulk Rna-seq Data Of Human Pancreatic Ismentioning

confidence: 99%

“…The RNA-FISH data was also from this study 38 . For the real bulk human pancreatic islets data 38,40,41 , the read counts table were deconvoluted. Only data from donors with HbA1C level available were included in the regression of Beta cell proportion on HbA1C level (Fig.…”

Section: Human Isletsmentioning

confidence: 99%

“…At the end, the original UMI data from 17271 genes and 3352 cells that passed Data availability DRG single cell data are deposited at NCBI GEO (accession number: GSE163252) . The bulk RNAseq and RNA-FISH data for human pancreatic islets were initially published as aggregated data where the data processing and experimental procedure were described therein 38,40,41 . We acquired the individual sample data from the authors and released them along with the current study (Supplementary Table 10 and Supplementary Table 12).…”

Section: Data Preprocessingmentioning

confidence: 99%

See 2 more Smart Citations

AdRoit: an accurate and robust method to infer complex transcriptome composition

Yang

Alessandri‐Haber

Fury

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

RNA sequencing technology promises an unprecedented opportunity in learning disease mechanisms and discovering new treatment targets. Recent spatial transcriptomics methods further enable the transcriptome profiling at spatially resolved spots in a tissue section. In controlled experiments, it is often of immense importance to know the cell composition in different samples. Understanding the cell type content in each tissue spot is also crucial to the spatial transcriptome data interpretation. Though single cell RNA-seq has the power to reveal cell type composition and expression heterogeneity in different cells, it remains costly and sometimes infeasible when live cells cannot be obtained or sufficiently dissociated. To computationally resolve the cell composition in RNA-seq data of mixed cells, we present AdRoit, an accurate androbust method to infer transcriptome composition. The method estimates the proportions of each cell type in the compound RNA-seq data using known single cell data of relevant cell types. It uniquely uses an adaptive learning approach to correct the bias gene-wise due to the difference in sequencing techniques. AdRoit also utilizes cell type specific genes while control their cross-sample variability. Our systematic benchmarking, spanning from simple to complex tissues, shows that AdRoit has superior sensitivity and specificity compared to other existing methods. Its performance holds for multiple single cell and compound RNA-seq platforms. In addition, AdRoit is computationally efficient and runs one to two orders of magnitude faster than some of the state-of-the-art methods.

show abstract

Lessons from single-cell RNA sequencing of human islets

Ngara

Wierup

2022

Diabetologia

View full text Add to dashboard Cite

Islet dysfunction is central in type 2 diabetes and full-blown type 2 diabetes develops first when the beta cells lose their ability to secrete adequate amounts of insulin in response to raised plasma glucose. Several mechanisms behind beta cell dysfunction have been put forward but many important questions still remain. Furthermore, our understanding of the contribution of each islet cell type in type 2 diabetes pathophysiology has been limited by technical boundaries. Closing this knowledge gap will lead to a leap forward in our understanding of the islet as an organ and potentially lead to improved treatments. The development of single-cell RNA sequencing (scRNAseq) has led to a breakthrough for characterising the transcriptome of each islet cell type and several important observations on the regulation of cell-type-specific gene expression have been made. When it comes to identifying type 2 diabetes disease mechanisms, the outcome is still limited. Several studies have identified differentially expressed genes, although there is very limited consensus between the studies. As with all new techniques, scRNAseq has limitations; in addition to being extremely expensive, genes expressed at low levels may not be detected, noise may not be appropriately filtered and selection biases for certain cell types are at hand. Furthermore, recent advances suggest that commonly used computational tools may be suboptimal for analysis of scRNAseq data in small-scale studies. Fortunately, development of new computational tools holds promise for harnessing the full potential of scRNAseq data. Here we summarise how scRNAseq has contributed to increasing the understanding of various aspects of islet biology as well as type 2 diabetes disease mechanisms. We also focus on challenges that remain and propose steps to promote the utilisation of the full potential of scRNAseq in this area. Graphical abstract

show abstract

Gene Signature of Proliferating Human Pancreatic α Cells

Cited by 14 publications

References 36 publications

Plasticity in the Glucagon Interactome Reveals Novel Proteins That Regulate Glucagon Secretion in α-TC1-6 Cells

Plasticity in the Glucagon Interactome Reveals Novel Proteins That Regulate Glucagon Secretion in α-TC1-6 Cells

AdRoit: an accurate and robust method to infer complex transcriptome composition

Lessons from single-cell RNA sequencing of human islets

Contact Info

Product

Resources

About