Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes

Mapping Endothelial-Macrophage Interactions in Diabetic Vasculature: Role of TREM2 in Vascular Inflammation and Ischemic Response

Diabetes mellitus (DM) significantly accelerates vascular diseases like peripheral arterial disease (PAD). Endothelial cells (ECs) and macrophages (MΦs) singularly and synergistically are important contributors to DM-associated vascular dysfunction. Single-cell (sc) profiling technologies are revealing the true heterogeneity of ECs and MΦs, but how this cellular diversity translates to cell-cell interactions, and consequentially vascular function, remains unknown. We leveraged scRNA sequencing and spatial transcriptome (ST) profiling to analyze human mesenteric arteries from non-diabetic (ND) and type 2 diabetic (T2D) donors. We generated a transcriptome and interactome map encompassing the major arterial cells and highlighted Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) as a top T2D-induced gene in mononuclear phagocytes (MPs), with concomitant increases of TREM2 ligands in ECs. We verified DM-associated TREM2 induction in cell and mouse models, and found that TREM2 inhibition decreases pro-inflammatory responses in MPs and ECs, as well as increases EC migration in vitro. Furthermore, TREM2 inhibition using a neutralizing antibody enhanced ischemic recovery and flow reperfusion in DM mice subjected to hindlimb ischemia, suggesting that TREM2 promotes ischemic injury in DM. Finally, in human PAD, co-existing DM was associated with greater expression of TREM2 and its interaction with ECs, with a further increase in ischemic tissue compared to patient-matched non-ischemic tissue. Collectively, our study presents the first atlas of human diabetic vessels with single cell and spatial resolution, and identifies TREM2-EC interaction as a key driver of diabetic vasculopathies, the targeting of which may offer an opportunity to ameliorate vascular dysfunction associated with DM-PAD.

The role of endoplasmic reticulum stress in type 2 diabetes mellitus mechanisms and impact on islet function

Type 2 diabetes mellitus (T2DM) is a globally prevalent metabolic disorder characterized by insulin resistance and dysfunction of islet cells. Endoplasmic reticulum (ER) stress plays a crucial role in the pathogenesis and progression of T2DM, especially in the function and survival of β-cells. β-cells are particularly sensitive to ER stress because they require substantial insulin synthesis and secretion energy. In the early stages of T2DM, the increased demand for insulin exacerbates β-cell ER stress. Although the unfolded protein response (UPR) can temporarily alleviate this stress, prolonged or excessive stress leads to pancreatic cell dysfunction and apoptosis, resulting in insufficient insulin secretion. This review explores the mechanisms of ER stress in T2DM, particularly its impact on islet cells. We discuss how ER stress activates UPR signaling pathways to regulate protein folding and degradation, but when stress becomes excessive, these pathways may contribute to β-cell death. A deeper understanding of how ER stress impacts islet cells could lead to the development of novel T2DM treatment strategies aimed at improving islet function and slowing disease progression.

Islet single-cell transcriptomic profiling during obesity-induced beta cell expansion in female mice

Targeting beta cell proliferation is an appealing approach to restore glucose control in type 1 diabetes. However, the underlying mechanisms of beta cell proliferation remain incompletely understood, limiting identification of new therapeutic targets. Obesity is a naturally occurring process that potently induces human and rodent beta cell replication, representing an ideal model to study mechanisms of beta cell proliferation. We showed previously acute whole-body Lepr gene deletion in adult mice induces obesity and massive beta cell expansion. Here, using single-cell transcriptomics with female Lepr KO islets, we identified distinct populations of beta cells undergoing unfolded protein response (UPR), stress resolution, and cell cycle progression. Lepr KO beta cells undergoing UPR markedly increased chaperone protein, ribosomal biogenesis, and cell cycle transcriptional programs that were enriched for Xbp1 and Myc target genes. Our findings suggest a coordinated transcriptional mechanism involving Xbp1 and Myc to alleviate UPR and stimulate beta cell proliferation in obese female mice.

Application and Progression of Single-Cell RNA Sequencing in Diabetes Mellitus and Diabetes Complications

Diabetes is a systemic metabolic disorder primarily caused by insulin deficiency and insulin resistance, leading to chronic hyperglycemia. Prolonged diabetes can result in metabolic damage to multiple organs, including the heart, brain, liver, muscles, and adipose tissue, thereby causing various chronic fatal complications such as diabetic retinopathy, diabetic cardiomyopathy, and diabetic nephropathy. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for investigating the cell diversity and pathogenesis of diabetes and identifying potential therapeutic targets in diabetes or diabetes complications. This review provides a comprehensive overview of recent applications of scRNA-seq in diabetes-related researches and highlights novel biomarkers and immunotherapy targets with cell-type information for diabetes and its associated complications.

Role of Protein Lysine Acetylation in the Pathogenesis and Treatment of Obesity and Metabolic Syndrome

PURPOSE OF REVIEW

This review aimed to highlight the known role of histone deacetylases (HDACs) and lysine acetyltransferases (KATs) in individuals with obesity, better understand the role of HDACs and KATs enzymes in obesity and related metabolic disorders.

RECENT FINDINGS

Numerous cellular activities, including DNA replication, DNA repair, cell cycle regulation, RNA splicing, signal transmission, metabolic function, protein stability, transportation, and transcriptional regulation, are influenced by lysine acetylation. Protein lysine acetylation serves several purposes, which not only contribute to the development of metabolic disorders linked to obesity but also hold promise for therapeutic approaches. The current study demonstrates that HDACs and KATs control lysine acetylation. This review details the advancements made in the study of obesity, related metabolic diseases, and protein lysine acetylation. It contributes to our understanding of the function and mechanism of protein lysine acetylation in obesity and MS and offers a fresh method for treating these diseases.

Single-cell decoding of human islet cell type-specific alterations in type 2 diabetes reveals converging genetic- and state-driven β -cell gene expression defects

Pancreatic islets maintain glucose homeostasis through coordinated action of their constituent endocrine and affiliate cell types and are central to type 2 diabetes (T2D) genetics and pathophysiology. Our understanding of robust human islet cell type-specific alterations in T2D remains limited. Here, we report comprehensive single cell transcriptome profiling of 245,878 human islet cells from a 48-donor cohort spanning non-diabetic (ND), pre-diabetic (PD), and T2D states, identifying 14 distinct cell types detected in every donor from each glycemic state. Cohort analysis reveals ~25-30% loss of functional beta cell mass in T2D vs. ND or PD donors resulting from (1) reduced total beta cell numbers/proportions and (2) reciprocal loss of 'high function' and gain of senescent β -cell subpopulations. We identify in T2D β -cells 511 differentially expressed genes (DEGs), including new (66.5%) and validated genes (e.g., FXYD2, SLC2A2, SYT1), and significant neuronal transmission and vitamin A metabolism pathway alterations. Importantly, we demonstrate newly identified DEG roles in human β -cell viability and/or insulin secretion and link 47 DEGs to diabetes-relevant phenotypes in knockout mice, implicating them as potential causal islet dysfunction genes. Additionally, we nominate as candidate T2D causal genes and therapeutic targets 27 DEGs for which T2D genetic risk variants (GWAS SNPs) and pathophysiology (T2D vs. ND) exert concordant expression effects. We provide this freely accessible atlas for data exploration, analysis, and hypothesis testing. Together, this study provides new genomic resources for and insights into T2D pathophysiology and human islet dysfunction.

Identification of a pancreatic stellate cell gene signature and lncRNA interactions associated with type 2 diabetes progression

Background

Type 2 diabetes (T2D) has become a significant global health threat, yet its precise causes and mechanisms remain unclear. This study aims to identify gene expression patterns specific to T2D pancreatic islet cells and to explore the potential role of pancreatic stellate cells (PSCs) in T2D progression through regulatory networks involving lncRNA-mRNA interactions.

Methods

In this study, we screened for upregulated genes in T2D pancreatic islet samples using bulk sequencing (bulkseq) datasets and mapped these gene expression profiles onto three T2D single-cell RNA sequencing (scRNAseq) datasets. The identified T2D-specific gene features were further validated in an additional T2D scRNAseq dataset, a T1D scRNAseq dataset, and a T2D bulkseq dataset. To investigate regulatory networks, we analyzed the potential lncRNA-mRNA interactions within T2D peripheral blood mononuclear cell (PBMC) bulkseq data.

Results

Our analysis identified a specific gene panel-COL1A2, VCAN, and SULF1-that was consistently upregulated in T2D pancreatic islet samples. Expression of this gene panel was strongly associated with the activation of pancreatic stellate cells (PSCs), suggesting a unique T2D-specific signature characterized by COL1A2hi/VCANhi/SULF1hi PSCs. This signature was exclusive to T2D and was not observed in Type 1 diabetes (T1D) samples, indicating a distinct role for activated PSCs in T2D progression. Furthermore, we identified six long non-coding RNAs (lncRNAs) that potentially interact with the COL1A2hi/VCANhi/SULF1hi PSCs. These lncRNAs were mapped to a lncRNA-mRNA network, suggesting they may modulate immune responses and potentially reshape the immune microenvironment in T2D.

Discussion

Our findings highlight the potential immune-regulatory role of PSCs in T2D and suggest that PSC-related lncRNA-mRNA networks could serve as novel therapeutic targets for T2D treatment. This research provides insights into PSCs as a modulator in T2D progression, paving the way for innovative treatment strategies.

Heterogeneous enhancer states orchestrate β cell responses to metabolic stress

Obesity-induced β cell dysfunction contributes to the onset of type 2 diabetes. Nevertheless, elucidating epigenetic mechanisms underlying islet dysfunction at single cell level remains challenging. Here we profile single-nuclei RNA along with enhancer marks H3K4me1 or H3K27ac in islets from lean or obese mice. Our study identifies distinct gene signatures and enhancer states correlating with β cell dysfunction trajectory. Intriguingly, while many metabolic stress-induced genes exhibit concordant changes in both H3K4me1 and H3K27ac at their enhancers, expression changes of specific subsets are solely attributable to either H3K4me1 or H3K27ac dynamics. Remarkably, a subset of H3K4me1+H3K27ac- primed enhancers prevalent in lean β cells and occupied by FoxA2 are largely absent after metabolic stress. Lastly, cell-cell communication analysis identified the nerve growth factor (NGF) as protective paracrine signaling for β cells through repressing ER stress. In summary, our findings define the heterogeneous enhancer responses to metabolic challenges in individual β cells.

Current approaches in CRISPR-Cas systems for diabetes

In the face of advancements in health care and a shift towards healthy lifestyle, diabetes mellitus (DM) still presents as a global health challenge. This chapter explores recent advancements in the areas of genetic and molecular underpinnings of DM, addressing the revolutionary potential of CRISPR-based genome editing technologies. We delve into the multifaceted relationship between genes and molecular pathways contributing to both type1 and type 2 diabetes. We highlight the importance of how improved genetic screening and the identification of susceptibility genes are aiding in early diagnosis and risk stratification. The spotlight then shifts to CRISPR-Cas9, a robust genome editing tool capable of various applications including correcting mutations in type 1 diabetes, enhancing insulin production in T2D, modulating genes associated with metabolism of glucose and insulin sensitivity. Delivery methods for CRISPR to targeted tissues and cells are explored, including viral and non-viral vectors, alongside the exciting possibilities offered by nanocarriers. We conclude by discussing the challenges and ethical considerations surrounding CRISPR-based therapies for DM. These include potential off-target effects, ensuring long-term efficacy and safety, and navigating the ethical implications of human genome modification. This chapter offers a comprehensive perspective on how genetic and molecular insights, coupled with the transformative power of CRISPR, are paving the way for potential cures and novel therapeutic approaches for DM.

scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.

Coxsackievirus B infection invokes unique cell-type specific responses in primary human pancreatic islets

Coxsackievirus B (CVB) infection has long been considered an environmental factor precipitating Type 1 diabetes (T1D), an autoimmune disease marked by loss of insulin-producing β cells within pancreatic islets. Previous studies have shown CVB infection negatively impacts islet function and viability but do not report on how virus infection individually affects the multiple cell types present in human primary islets. Therefore, we hypothesized that the various islet cell populations have unique transcriptional responses to CVB infection. Here, we performed single-cell RNA sequencing on human cadaveric islets treated with either CVB or poly(I:C), a viral mimic, for 24 and 48 hours. Our global analysis reveals CVB differentially induces dynamic transcriptional changes associated with multiple cell processes and functions over time whereas poly(I:C) promotes an immune response that progressively increases with treatment duration. At the single-cell resolution, we find CVB infects all islet cell types at similar rates yet induces unique cell-type specific transcriptional responses with β, α, and ductal cells having the strongest response. Sequencing and functional data suggest that CVB negatively impacts mitochondrial respiration and morphology in distinct ways in β and α cells, while also promoting the generation of reactive oxygen species. We also observe an increase in the expression of the long-noncoding RNA MIR7-3HG in β cells with high viral titers and reveal its knockdown reduces gene expression of viral proteins as well as apoptosis in stem cell-derived islets. Together, these findings demonstrate a cell-specific transcriptional, temporal, and functional response to CVB infection and provide new insights into the relationship between CVB infection and T1D.

Chromosome 20p11.2 deletions cause congenital hyperinsulinism via the loss of FOXA2 or its regulatory elements

Persistent congenital hyperinsulinism (HI) is a rare genetically heterogeneous condition characterised by dysregulated insulin secretion leading to life-threatening hypoglycaemia. For up to 50% of affected individuals screening of the known HI genes does not identify a disease-causing variant. Large deletions have previously been used to identify novel regulatory regions causing HI. Here, we used genome sequencing to search for novel large (>1 Mb) deletions in 180 probands with HI of unknown cause and replicated our findings in a large cohort of 883 genetically unsolved individuals with HI using off-target copy number variant calling from targeted gene panels. We identified overlapping heterozygous deletions in five individuals (range 3-8 Mb) spanning chromosome 20p11.2. The pancreatic beta-cell transcription factor gene, FOXA2, a known cause of HI was deleted in two of the five individuals. In the remaining three, we found a minimal deleted region of 2.4 Mb adjacent to FOXA2 that encompasses multiple non-coding regulatory elements that are in conformational contact with FOXA2. Our data suggests that the deletions in these three children may cause disease through the dysregulation of FOXA2 expression. These findings provide new insights into the regulation of FOXA2 in the beta-cell and confirm an aetiological role for chromosome 20p11.2 deletions in syndromic HI.

Regulation of endocrine cell alternative splicing revealed by single-cell RNA sequencing in type 2 diabetes pathogenesis

The prevalent RNA alternative splicing (AS) contributes to molecular diversity, which has been demonstrated in cellular function regulation and disease pathogenesis. However, the contribution of AS in pancreatic islets during diabetes progression remains unclear. Here, we reanalyze the full-length single-cell RNA sequencing data from the deposited database to investigate AS regulation across human pancreatic endocrine cell types in non-diabetic (ND) and type 2 diabetic (T2D) individuals. Our analysis demonstrates the significant association between transcriptomic AS profiles and cell-type-specificity, which could be applied to distinguish the clustering of major endocrine cell types. Moreover, AS profiles are enabled to clearly define the mature subset of β-cells in healthy controls, which is completely lost in T2D. Further analysis reveals that RNA-binding proteins (RBPs), heterogeneous nuclear ribonucleoproteins (hnRNPs) and FXR1 family proteins are predicted to induce the functional impairment of β-cells through regulating AS profiles. Finally, trajectory analysis of endocrine cells suggests the β-cell identity shift through dedifferentiation and transdifferentiation of β-cells during the progression of T2D. Together, our study provides a mechanism for regulating β-cell functions and suggests the significant contribution of AS program during diabetes pathogenesis.

Integrating machine learning and genome editing for crop improvement

Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.

The Importance of Intra-Islet Communication in the Function and Plasticity of the Islets of Langerhans during Health and Diabetes

Islets of Langerhans are anatomically dispersed within the pancreas and exhibit regulatory coordination between islets in response to nutritional and inflammatory stimuli. However, within individual islets, there is also multi-faceted coordination of function between individual beta-cells, and between beta-cells and other endocrine and vascular cell types. This is mediated partly through circulatory feedback of the major secreted hormones, insulin and glucagon, but also by autocrine and paracrine actions within the islet by a range of other secreted products, including somatostatin, urocortin 3, serotonin, glucagon-like peptide-1, acetylcholine, and ghrelin. Their availability can be modulated within the islet by pericyte-mediated regulation of microvascular blood flow. Within the islet, both endocrine progenitor cells and the ability of endocrine cells to trans-differentiate between phenotypes can alter endocrine cell mass to adapt to changed metabolic circumstances, regulated by the within-islet trophic environment. Optimal islet function is precariously balanced due to the high metabolic rate required by beta-cells to synthesize and secrete insulin, and they are susceptible to oxidative and endoplasmic reticular stress in the face of high metabolic demand. Resulting changes in paracrine dynamics within the islets can contribute to the emergence of Types 1, 2 and gestational diabetes.

Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes

Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.

All-potassium channel CRISPR screening reveals a lysine-specific pathway of insulin secretion

OBJECTIVE

Genome-scale CRISPR-Cas9 knockout coupled with single-cell RNA sequencing (scRNA-seq) has been used to identify function-related genes. However, this method may knock out too many genes, leading to low efficiency in finding genes of interest. Insulin secretion is controlled by several electrophysiological events, including fluxes of KATP depolarization and K+ repolarization. It is well known that glucose stimulates insulin secretion from pancreatic β-cells, mainly via the KATP depolarization channel, but whether other nutrients directly regulate the repolarization K+ channel to promote insulin secretion is unknown.

METHODS

We used a system involving CRISPR-Cas9-mediated knockout of all 83 K+ channels and scRNA-seq in a pancreatic β cell line to identify genes associated with insulin secretion.

RESULTS

The expression levels of insulin genes were significantly increased after all-K+ channel knockout. Furthermore, Kcnb1 and Kcnh6 were the two most important repolarization K+ channels for the increase in high-glucose-dependent insulin secretion that occurred upon application of specific inhibitors of the channels. Kcnh6 currents, but not Kcnb1 currents, were reduced by one of the amino acids, lysine, in both transfected cells, primary cells and mice with β-cell-specific deletion of Kcnh6.

CONCLUSIONS

Our function-related CRISPR screen with scRNA-seq identifies Kcnh6 as a lysine-specific channel.

Comparative and integrative single cell analysis reveals new insights into the transcriptional immaturity of stem cell-derived β cells

Diabetes cell replacement therapy has the potential to be transformed by human pluripotent stem cell-derived β cells (SC-β cells). However, the precise identity of SC-β cells in relationship to primary fetal and adult β-cells remains unclear. Here, we used single-cell sequencing datasets to characterize the transcriptional identity of islets from in vitro differentiation, fetal islets, and adult islets. Our analysis revealed that SC-β cells share a core β-cell transcriptional identity with human adult and fetal β-cells, however SC-β cells possess a unique transcriptional profile characterized by the persistent expression and activation of progenitor and neural-biased gene networks. These networks are present in SC-β cells, irrespective of the derivation protocol used. Notably, fetal β-cells also exhibit this neural signature at the transcriptional level. Our findings offer insights into the transcriptional identity of SC-β cells and underscore the need for further investigation of the role of neural transcriptional networks in their development.

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet β-cells are the sole source of endogenous insulin and our understanding of β-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in β-cell function and survival. However, it is difficult to identify which β-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related β-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct β-cell subpopulations. A singular cluster of T2D-associated β-cells was identified; however, β-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. DLK1 was heterogeneously expressed among β-cells and appeared depleted from T2D islets. In conclusion, DEGAS has the potential to advance our holistic understanding of the β-cell transcriptomic phenotypes, including features that distinguish β-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas

BACKGROUND

The human pancreas is composed of specialized cell types producing hormones and enzymes critical to human health. These specialized functions are the result of cell type-specific transcriptional programs which manifest in cell-specific gene expression. Understanding these programs is essential to developing therapies for pancreatic disorders. Transcription in the human pancreas has been widely studied by single-cell RNA technologies, however the diversity of protocols and analysis methods hinders their interpretability in the aggregate.

RESULTS

In this work, we perform a meta-analysis of pancreatic single-cell RNA sequencing data. We present a database for reference transcriptome abundances and cell-type specificity metrics. This database facilitates the identification and definition of marker genes within the pancreas. Additionally, we introduce a versatile tool which is freely available as an R package, and should permit integration into existing workflows. Our tool accepts count data files generated by widely-used single-cell gene expression platforms in their original format, eliminating an additional pre-formatting step. Although we designed it to calculate expression specificity of pancreas cell types, our tool is agnostic to the biological source of count data, extending its applicability to other biological systems.

CONCLUSIONS

Our findings enhance the current understanding of expression specificity within the pancreas, surpassing previous work in terms of scope and detail. Furthermore, our database and tool enable researchers to perform similar calculations in diverse biological systems, expanding the applicability of marker gene identification and facilitating comparative analyses.

Pathway analysis through mutual information

MOTIVATION

In pathway analysis, we aim to establish a connection between the activity of a particular biological pathway and a difference in phenotype. There are many available methods to perform pathway analysis, many of them rely on an upstream differential expression analysis, and many model the relations between the abundances of the analytes in a pathway as linear relationships.

RESULTS

Here, we propose a new method for pathway analysis, MIPath, that relies on information theoretical principles and, therefore, does not model the association between pathway activity and phenotype, resulting in relatively few assumptions. For this, we construct a graph of the data points for each pathway using a nearest-neighbor approach and score the association between the structure of this graph and the phenotype of these same samples using Mutual Information while adjusting for the effects of random chance in each score. The initial nearest neighbor approach evades individual gene-level comparisons, hence making the method scalable and less vulnerable to missing values. These properties make our method particularly useful for single-cell data. We benchmarked our method on several single-cell datasets, comparing it to established and new methods, and found that it produces robust, reproducible, and meaningful scores.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/statisticalbiotechnology/mipath, or through Python Package Index as "mipathway."

Independent activation of CREB3L2 by glucose fills a regulatory gap in mouse β-cells by co-ordinating insulin biosynthesis with secretory granule formation

OBJECTIVE

Although individual steps have been characterized, there is little understanding of the overall process whereby glucose co-ordinates the biosynthesis of insulin with its export out of the endoplasmic reticulum (ER) and incorporation into insulin secretory granules (ISGs). Here we investigate a role for the transcription factor CREB3L2 in this context.

METHODS

MIN6 cells and mouse islets were analysed by immunoblotting after treatment with glucose, fatty acids, thapsigargin and various inhibitors. Knockdown of CREB3L2 was achieved using si or sh constructs by transfection, or viral delivery. In vivo metabolic phenotyping was conducted after deletion of CREB3L2 in β-cells of adult mice using Ins1-CreER+. Islets were isolated for RNAseq and assays of glucose-stimulated insulin secretion (GSIS). Trafficking was monitored in islet monolayers using a GFP-tagged proinsulin construct that allows for synchronised release from the ER.

RESULTS

With a Km ≈3.5 mM, glucose rapidly (T1/2 0.9 h) increased full length (FL) CREB3L2 followed by a slower rise (T1/2 2.5 h) in its transcriptionally-active cleavage product, P60 CREB3L2. Glucose stimulation repressed the ER stress marker, CHOP, and this was partially reverted by knockdown of CREB3L2. Activation of CREB3L2 by glucose was not due to ER stress, however, but a combination of O-GlcNAcylation, which impaired proteasomal degradation of FL-CREB3L2, and mTORC1 stimulation, which enhanced its conversion to P60. cAMP generation also activated CREB3L2, but independently of glucose. Deletion of CREB3L2 inhibited GSIS ex vivo and, following a high-fat diet (HFD), impaired glucose tolerance and insulin secretion in vivo. RNAseq revealed that CREB3L2 regulated genes controlling trafficking to-and-from the Golgi, as well as a broader cohort associated with β-cell compensation during a HFD. Although post-Golgi trafficking appeared intact, knockdown of CREB3L2 impaired the generation of both nascent ISGs and proinsulin condensates in the Golgi, implying a defect in ER export of proinsulin and/or its processing in the Golgi.

CONCLUSION

The stimulation of CREB3L2 by glucose defines a novel, rapid and direct mechanism for co-ordinating the synthesis, packaging and storage of insulin, thereby minimizing ER overload and optimizing β-cell function under conditions of high secretory demand. Upregulation of CREB3L2 also potentially contributes to the benefits of GLP1 agonism and might in itself constitute a novel means of treating β-cell failure.

Deep neural network learning biological condition information refines gene-expression-based cell subtypes

With the recent advent of single-cell level biological understanding, a growing interest is in identifying cell states or subtypes that are homogeneous in terms of gene expression and are also enriched in certain biological conditions, including disease samples versus normal samples (condition-specific cell subtype). Despite the importance of identifying condition-specific cell subtypes, existing methods have the following limitations: since they train models separately between gene expression and the biological condition information, (1) they do not consider potential interactions between them, and (2) the weights from both types of information are not properly controlled. Also, (3) they do not consider non-linear relationships in the gene expression and the biological condition. To address the limitations and accurately identify such condition-specific cell subtypes, we develop scDeepJointClust, the first method that jointly trains both types of information via a deep neural network. scDeepJointClust incorporates results from the power of state-of-the-art gene-expression-based clustering methods as an input, incorporating their sophistication and accuracy. We evaluated scDeepJointClust on both simulation data in diverse scenarios and biological data of different diseases (melanoma and non-small-cell lung cancer) and showed that scDeepJointClust outperforms existing methods in terms of sensitivity and specificity. scDeepJointClust exhibits significant promise in advancing our understanding of cellular states and their implications in complex biological systems.

Dynamic scRNA-seq of live human pancreatic slices reveals functional endocrine cell neogenesis through an intermediate ducto-acinar stage

Human pancreatic plasticity is implied from multiple single-cell RNA sequencing (scRNA-seq) studies. However, these have been invariably based on static datasets from which fate trajectories can only be inferred using pseudotemporal estimations. Furthermore, the analysis of isolated islets has resulted in a drastic underrepresentation of other cell types, hindering our ability to interrogate exocrine-endocrine interactions. The long-term culture of human pancreatic slices (HPSs) has presented the field with an opportunity to dynamically track tissue plasticity at the single-cell level. Combining datasets from same-donor HPSs at different time points, with or without a known regenerative stimulus (BMP signaling), led to integrated single-cell datasets storing true temporal or treatment-dependent information. This integration revealed population shifts consistent with ductal progenitor activation, blurring of ductal/acinar boundaries, formation of ducto-acinar-endocrine differentiation axes, and detection of transitional insulin-producing cells. This study provides the first longitudinal scRNA-seq analysis of whole human pancreatic tissue, confirming its plasticity in a dynamic fashion.

Single cell multiomic analysis reveals diabetes-associated β-cell heterogeneity driven by HNF1A

Broad heterogeneity in pancreatic β-cell function and morphology has been widely reported. However, determining which components of this cellular heterogeneity serve a diabetes-relevant function remains challenging. Here, we integrate single-cell transcriptome, single-nuclei chromatin accessibility, and cell-type specific 3D genome profiles from human islets and identify Type II Diabetes (T2D)-associated β-cell heterogeneity at both transcriptomic and epigenomic levels. We develop a computational method to explicitly dissect the intra-donor and inter-donor heterogeneity between single β-cells, which reflect distinct mechanisms of T2D pathogenesis. Integrative transcriptomic and epigenomic analysis identifies HNF1A as a principal driver of intra-donor heterogeneity between β-cells from the same donors; HNF1A expression is also reduced in β-cells from T2D donors. Interestingly, HNF1A activity in single β-cells is significantly associated with lower Na+ currents and we nominate a HNF1A target, FXYD2, as the primary mitigator. Our study demonstrates the value of investigating disease-associated single-cell heterogeneity and provides new insights into the pathogenesis of T2D.

Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas

Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin β-cell ablation model. The β-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that β-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD β-cells. We also report pathways that are shared between β-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation and demise.

Pancreatic islet α cell function and proliferation requires the arginine transporter SLC7A2

Interrupting glucagon signaling decreases gluconeogenesis and the fractional extraction of amino acids by liver from blood resulting in lower glycemia. The resulting hyperaminoacidemia stimulates α cell proliferation and glucagon secretion via a liver-α cell axis. We hypothesized that α cells detect and respond to circulating amino acids levels via a unique amino acid transporter repertoire. We found that Slc7a2ISLC7A2 is the most highly expressed cationic amino acid transporter in α cells with its expression being three-fold greater in α than β cells in both mouse and human. Employing cell culture, zebrafish, and knockout mouse models, we found that the cationic amino acid arginine and SLC7A2 are required for α cell proliferation in response to interrupted glucagon signaling. Ex vivo and in vivo assessment of islet function in Slc7a2-/- mice showed decreased arginine-stimulated glucagon and insulin secretion. We found that arginine activation of mTOR signaling and induction of the glutamine transporter SLC38A5 was dependent on SLC7A2, showing that both's role in α cell proliferation is dependent on arginine transport and SLC7A2. Finally, we identified single nucleotide polymorphisms in SLC7A2 associated with HbA1c. Together, these data indicate a central role for SLC7A2 in amino acid-stimulated α cell proliferation and islet hormone secretion.

Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts

Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.

The human α cell in health and disease

In commemoration of 100 years since the discovery of glucagon, we review current knowledge about the human α cell. Alpha cells make up 30-40% of human islet endocrine cells and play a major role in regulating whole-body glucose homeostasis, largely through the direct actions of their main secretory product - glucagon - on peripheral organs. Additionally, glucagon and other secretory products of α cells, namely acetylcholine, glutamate, and glucagon-like peptide-1, have been shown to play an indirect role in the modulation of glucose homeostasis through autocrine and paracrine interactions within the islet. Studies of glucagon's role as a counterregulatory hormone have revealed additional important functions of the α cell, including the regulation of multiple aspects of energy metabolism outside that of glucose. At the molecular level, human α cells are defined by the expression of conserved islet-enriched transcription factors and various enriched signature genes, many of which have currently unknown cellular functions. Despite these common threads, notable heterogeneity exists amongst human α cell gene expression and function. Even greater differences are noted at the inter-species level, underscoring the importance of further study of α cell physiology in the human context. Finally, studies on α cell morphology and function in type 1 and type 2 diabetes, as well as other forms of metabolic stress, reveal a key contribution of α cell dysfunction to dysregulated glucose homeostasis in disease pathogenesis, making targeting the α cell an important focus for improving treatment.

Monitoring autophagic flux in vivo revealed its physiological response and significance of heterogeneity in pancreatic beta cells

Autophagy plays an essential role in preserving cellular homeostasis in pancreatic beta cells. However, the extent of autophagic flux in pancreatic islets induced in various physiological settings remains unclear. In this study, we generate transgenic mice expressing pHluorin-LC3-mCherry reporter for monitoring systemic autophagic flux by measuring the pHluorin/mCherry ratio, validating them in the starvation and insulin-deficient model. Our findings reveal that autophagic flux in pancreatic islets enhances after starvation, and suppression of the flux after short-term refeeding needs more prolonged re-starvation in islets than in the other insulin-targeted organs. Furthermore, heterogeneity of autophagic flux in pancreatic beta cells manifests under insulin resistance, and intracellular calcium influx by glucose stimulation increases more in high- than low-autophagic flux beta cells, with differential gene expression, including lipoprotein lipase. Our pHluorin-LC3-mCherry mice enable us to reveal biological insight into heterogeneity in autophagic flux in pancreatic beta cells.

Transfer learning enables predictions in network biology

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

Microfluidic systems for particle capture and release: A review

Microfluidic technology has emerged as a promising tool in various applications, including biosensing, disease diagnosis, and environmental monitoring. One of the notable features of microfluidic devices is their ability to selectively capture and release specific cells, biomolecules, bacteria, and particles. Compared to traditional bulk analysis instruments, microfluidic capture-and-release platforms offer several advantages, such as contactless operation, label-free detection, high accuracy, good sensitivity, and minimal reagent requirements. However, despite significant efforts dedicated to developing innovative capture mechanisms in the past, the release and recovery efficiency of trapped particles have often been overlooked. Many previous studies have focused primarily on particle capture techniques and their efficiency, disregarding the crucial role of successful particle release for subsequent analysis. In reality, the ability to effectively release trapped particles is particularly essential to ensure ongoing, high-throughput analysis. To address this gap, this review aims to highlight the importance of both capture and release mechanisms in microfluidic systems and assess their effectiveness. The methods are classified into two categories: those based on physical principles and those using biochemical approaches. Furthermore, the review offers a comprehensive summary of recent applications of microfluidic platforms specifically designed for particle capture and release. It outlines the designs and performance of these devices, highlighting their advantages and limitations in various target applications and purposes. Finally, the review concludes with discussions on the current challenges faced in the field and presents potential future directions.

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Cellular exposure to free fatty acids (FFAs) is implicated in the pathogenesis of obesity-associated diseases. However, there are no scalable approaches to comprehensively assess the diverse FFAs circulating in human plasma. Furthermore, assessing how FFA-mediated processes interact with genetic risk for disease remains elusive. Here, we report the design and implementation of fatty acid library for comprehensive ontologies (FALCON), an unbiased, scalable, and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids associated with decreased membrane fluidity. Furthermore, we prioritized genes that reflect the combined effects of harmful FFA exposure and genetic risk for type 2 diabetes (T2D). We found that c-MAF-inducing protein (CMIP) protects cells from FFA exposure by modulating Akt signaling. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.

Pancreatic islet cell type-specific transcriptomic changes during pregnancy and postpartum

Pancreatic β-cell mass expands during pregnancy and regresses in the postpartum period in conjunction with dynamic metabolic demands on maternal glucose homeostasis. To understand transcriptional changes driving these adaptations in β-cells and other islet cell types, we performed single-cell RNA sequencing on islets from virgin, late gestation, and early postpartum mice. We identified transcriptional signatures unique to gestation and the postpartum in β-cells, including induction of the AP-1 transcription factor subunits and other genes involved in the immediate-early response (IEGs). In addition, we found pregnancy and postpartum-induced changes differed within each endocrine cell type, and in endothelial cells and antigen-presenting cells within islets. Together, our data reveal insights into cell type-specific transcriptional changes responsible for adaptations by islet cells to pregnancy and their resolution postpartum.

A beta cell subset with enhanced insulin secretion and glucose metabolism is reduced in type 2 diabetes

The pancreatic islets are composed of discrete hormone-producing cells that orchestrate systemic glucose homeostasis. Here we identify subsets of beta cells using a single-cell transcriptomic approach. One subset of beta cells marked by high CD63 expression is enriched for the expression of mitochondrial metabolism genes and exhibits higher mitochondrial respiration compared with CD63lo beta cells. Human and murine pseudo-islets derived from CD63hi beta cells demonstrate enhanced glucose-stimulated insulin secretion compared with pseudo-islets from CD63lo beta cells. We show that CD63hi beta cells are diminished in mouse models of and in humans with type 2 diabetes. Finally, transplantation of pseudo-islets generated from CD63hi but not CD63lo beta cells into diabetic mice restores glucose homeostasis. These findings suggest that loss of a specific subset of beta cells may lead to diabetes. Strategies to reconstitute or maintain CD63hi beta cells may represent a potential anti-diabetic therapy.

Deep into the niche: Deciphering local endoderm-microenvironment interactions in development, homeostasis, and disease of pancreas and intestine

Unraveling molecular and functional heterogeneity of niche cells within the developing endoderm could resolve mechanisms of tissue formation and maturation. Here, we discuss current unknowns in molecular mechanisms underlying key developmental events in pancreatic islet and intestinal epithelial formation. Recent breakthroughs in single-cell and spatial transcriptomics, paralleled with functional studies in vitro, reveal that specialized mesenchymal subtypes drive the formation and maturation of pancreatic endocrine cells and islets via local interactions with epithelium, neurons, and microvessels. Analogous to this, distinct intestinal niche cells regulate both epithelial development and homeostasis throughout life. We propose how this knowledge can be used to progress research in the human context using pluripotent stem cell-derived multilineage organoids. Overall, understanding the interactions between the multitude of microenvironmental cells and how they drive tissue development and function could help us make more therapeutically relevant in vitro models.

Integrated transcriptomics contrasts fatty acid metabolism with hypoxia response in β-cell subpopulations associated with glycemic control

BACKGROUND

Understanding how heterogeneous β-cell function impacts diabetes is imperative for therapy development. Standard single-cell RNA sequencing analysis illuminates some factors driving heterogeneity, but new strategies are required to enhance information capture.

RESULTS

We integrate pancreatic islet single-cell and bulk RNA sequencing data to identify β-cell subpopulations based on gene expression and characterize genetic networks associated with β-cell function in obese SM/J mice. We identify β-cell subpopulations associated with basal insulin secretion, hypoxia response, cell polarity, and stress response. Network analysis associates fatty acid metabolism and basal insulin secretion with hyperglycemic-obesity, while expression of Pdyn and hypoxia response is associated with normoglycemic-obesity.

CONCLUSIONS

By integrating single-cell and bulk islet transcriptomes, our study explores β-cell heterogeneity and identifies novel subpopulations and genetic pathways associated with β-cell function in obesity.

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Cellular exposure to free fatty acids (FFA) is implicated in the pathogenesis of obesity-associated diseases. However, studies to date have assumed that a few select FFAs are representative of broad structural categories, and there are no scalable approaches to comprehensively assess the biological processes induced by exposure to diverse FFAs circulating in human plasma. Furthermore, assessing how these FFA- mediated processes interact with genetic risk for disease remains elusive. Here we report the design and implementation of FALCON (Fatty Acid Library for Comprehensive ONtologies) as an unbiased, scalable and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids (MUFAs) with a distinct lipidomic profile associated with decreased membrane fluidity. Furthermore, we developed a new approach to prioritize genes that reflect the combined effects of exposure to harmful FFAs and genetic risk for type 2 diabetes (T2D). Importantly, we found that c-MAF inducing protein (CMIP) protects cells from exposure to FFAs by modulating Akt signaling and we validated the role of CMIP in human pancreatic beta cells. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.

Highlights

FALCON (Fatty Acid Library for Comprehensive ONtologies) enables multimodal profiling of 61 free fatty acids (FFAs) to reveal 5 FFA clusters with distinct biological effectsFALCON is applicable to many and diverse cell typesA subset of monounsaturated FAs (MUFAs) equally or more toxic than canonical lipotoxic saturated FAs (SFAs) leads to decreased membrane fluidityNew approach prioritizes genes that represent the combined effects of environmental (FFA) exposure and genetic risk for diseaseC-Maf inducing protein (CMIP) is identified as a suppressor of FFA-induced lipotoxicity via Akt-mediated signaling.

Pancreatic microexons regulate islet function and glucose homeostasis

Pancreatic islets control glucose homeostasis by the balanced secretion of insulin and other hormones, and their abnormal function causes diabetes or hypoglycaemia. Here we uncover a conserved programme of alternative microexons included in mRNAs of islet cells, particularly in genes involved in vesicle transport and exocytosis. Islet microexons (IsletMICs) are regulated by the RNA binding protein SRRM3 and represent a subset of the larger neural programme that are particularly sensitive to SRRM3 levels. Both SRRM3 and IsletMICs are induced by elevated glucose levels, and depletion of SRRM3 in human and rat beta cell lines and mouse islets, or repression of particular IsletMICs using antisense oligonucleotides, leads to inappropriate insulin secretion. Consistently, mice harbouring mutations in Srrm3 display defects in islet cell identity and function, leading to hyperinsulinaemic hypoglycaemia. Importantly, human genetic variants that influence SRRM3 expression and IsletMIC inclusion in islets are associated with fasting glucose variation and type 2 diabetes risk. Taken together, our data identify a conserved microexon programme that regulates glucose homeostasis.

Integration of single-cell multiomic measurements across disease states with genetics identifies mechanisms of beta cell dysfunction in type 2 diabetes

Altered function and gene regulation of pancreatic islet beta cells is a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of mechanisms driving T2D is still missing. Here we integrate information from measurements of chromatin activity, gene expression and function in single beta cells with genetic association data to identify disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 non-diabetic, pre-T2D and T2D donors, we robustly identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift in T2D. Subtype-defining active chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is likely induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for identifying mechanisms of complex diseases.

JAZF1: A metabolic actor subunit of the NuA4/TIP60 chromatin modifying complex

The multisubunit NuA4/TIP60 complex is a lysine acetyltransferase, chromatin modifying factor and gene co-activator involved in diverse biological processes. The past decade has seen a growing appreciation for its role as a metabolic effector and modulator. However, molecular insights are scarce and often contradictory, underscoring the need for further mechanistic investigation. A particularly exciting route emerged with the recent identification of a novel subunit, JAZF1, which has been extensively linked to metabolic homeostasis. This review summarizes the major findings implicating NuA4/TIP60 in metabolism, especially in light of JAZF1 as part of the complex.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Non-coding variants disrupting a tissue-specific regulatory element in HK1 cause congenital hyperinsulinism

Gene expression is tightly regulated, with many genes exhibiting cell-specific silencing when their protein product would disrupt normal cellular function1. This silencing is largely controlled by non-coding elements, and their disruption might cause human disease2. We performed gene-agnostic screening of the non-coding regions to discover new molecular causes of congenital hyperinsulinism. This identified 14 non-coding de novo variants affecting a 42-bp conserved region encompassed by a regulatory element in intron 2 of the hexokinase 1 gene (HK1). HK1 is widely expressed across all tissues except in the liver and pancreatic beta cells and is thus termed a 'disallowed gene' in these specific tissues. We demonstrated that the variants result in a loss of repression of HK1 in pancreatic beta cells, thereby causing insulin secretion and congenital hyperinsulinism. Using epigenomic data accessed from public repositories, we demonstrated that these variants reside within a regulatory region that we determine to be critical for cell-specific silencing. Importantly, this has revealed a disease mechanism for non-coding variants that cause inappropriate expression of a disallowed gene.

Transcriptome reveals the dysfunction of pancreatic islets after wound healing in severely burned mice

BACKGROUND

Severely burned patients have a higher risk of diabetes mellitus after healing, but its mechanism remains unclear. Therefore, the purpose of the study was to explore the influence of burns on pancreatic islets of mice after wound healing.

METHODS

Forty-two male C57BL/6 mice were randomized into a sham group and a burn group and subjected to sham treatment or a third-degree burn model of 30% total body surface area. Fasting blood glucose was detected weekly for 8 weeks after severe burns. Glucose-stimulated insulin secretion was measured 8 weeks post severe burns. Islets of the two groups were isolated and mRNA libraries were sequenced by the Illumina sequencing platform. The expressions of differentially expressed genes (DEGs) related to the cell cycle and the amounts of mitochondrial DNA were detected by quantitative real-time polymerase chain reaction after gene ontology, gene set enrichment analysis, and protein-protein network analysis. Hematoxylin-eosin staining of pancreatic tail tissue and adenosine triphosphate (ATP) assay of islets were performed.

RESULTS

The levels of fasting blood glucose were significantly higher within 8 weeks post severe burns. Glucose-stimulated insulin secretion was impaired at the eighth week post severe burns. Totally 128 DEGs were selected. Gene ontology and gene set enrichment analysis indicated that the pathways related to the cell cycle, protein processing, and oxidative phosphorylation were downregulated. The expressions of DEGs related to the cell cycle showed a consistent trend with mRNA sequencing data, and most of them were downregulated post severe burns. The cell mass of the burn group was less than that of the sham group. Also, the concentration of ATP and the amount of mitochondrial DNA were lower in the burn group.

CONCLUSION

In the model of severe-burned mice, disorders in glucose metabolism persist for 8 weeks after burns, which may be related to low islet cell proliferation, downregulation of protein processing, and less ATP production.

A transcriptional cross species map of pancreatic islet cells

OBJECTIVE

Pancreatic islets of Langerhans secrete hormones to regulate systemic glucose levels. Emerging evidence suggests that islet cells are functionally heterogeneous to allow a fine-tuned and efficient endocrine response to physiological changes. A precise description of the molecular basis of this heterogeneity, in particular linking animal models to human islets, is an important step towards identifying the factors critical for endocrine cell function in physiological and pathophysiological conditions.

METHODS

In this study, we used single-cell RNA sequencing to profile more than 50'000 endocrine cells isolated from healthy human, pig and mouse pancreatic islets and characterize transcriptional heterogeneity and evolutionary conservation of those cells across the three species. We systematically delineated endocrine cell types and α- and β-cell heterogeneity through prior knowledge- and data-driven gene sets shared across species, which altogether capture common and differential cellular properties, transcriptional dynamics and putative driving factors of state transitions.

RESULTS

We showed that global endocrine expression profiles correlate, and that critical identity and functional markers are shared between species, while only approximately 20% of cell type enriched expression is conserved. We resolved distinct human α- and β-cell states that form continuous transcriptional landscapes. These states differentially activate maturation and hormone secretion programs, which are related to regulatory hormone receptor expression, signaling pathways and different types of cellular stress responses. Finally, we mapped mouse and pig cells to the human reference and observed that the spectrum of human α- and β-cell heterogeneity and aspects of such functional gene expression are better recapitulated in the pig than mouse data.

CONCLUSIONS

Here, we provide a high-resolution transcriptional map of healthy human islet cells and their murine and porcine counterparts, which is easily queryable via an online interface. This comprehensive resource informs future efforts that focus on pancreatic endocrine function, failure and regeneration, and enables to assess molecular conservation in islet biology across species for translational purposes.

Characterization of stem-cell-derived islets during differentiation and after implantation

Recapitulation of embryonic pancreatic development has enabled development of methods for in vitro islet cell differentiation using human pluripotent stem cells (hPSCs), which have the potential to cure diabetes. Advanced methods for optimal generation of stem-cell-derived islets (SC-islets) has enabled successful diabetes reversal in rodents and shown promising early clinical trial outcomes. The main impediment for use of SC-islets is concern about safety because of off-target growth resulting from contaminated residual cells. In this review, we summarize the different endocrine and non-endocrine cell populations that have been described to emerge throughout β cell differentiation and after transplantation. We discuss the most recent approaches to enrich endocrine populations and remove off-target cells. Finally, we discuss the critical quality control and release criteria testing that we anticipate will be required prior to transplantation to ensure product safety.

Lessons from single-cell RNA sequencing of human islets

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.