Metacells untangle large and complex single-cell transcriptome networks

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization.

RESULTS

We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

CONCLUSIONS

SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data

As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf’s memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf.

As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Here the authors present Scarf, a modularly designed Python package that makes the analysis workflow highly memory efficient such that even the largest existing datasets can be analyzed on an average modern laptop.

Synaptotagmin-13 orchestrates pancreatic endocrine cell egression and islet morphogenesis

During pancreas development endocrine cells leave the ductal epithelium to form the islets of Langerhans, but the morphogenetic mechanisms are incompletely understood. Here, we identify the Ca2+-independent atypical Synaptotagmin-13 (Syt13) as a key regulator of endocrine cell egression and islet formation. We detect specific upregulation of the Syt13 gene and encoded protein in endocrine precursors and the respective lineage during islet formation. The Syt13 protein is localized to the apical membrane of endocrine precursors and to the front domain of egressing endocrine cells, marking a previously unidentified apical-basal to front-rear repolarization during endocrine precursor cell egression. Knockout of Syt13 impairs endocrine cell egression and skews the α-to-β-cell ratio. Mechanistically, Syt13 is a vesicle trafficking protein, transported via the microtubule cytoskeleton, and interacts with phosphatidylinositol phospholipids for polarized localization. By internalizing a subset of plasma membrane proteins at the front domain, including α6β4 integrins, Syt13 modulates cell-matrix adhesion and allows efficient endocrine cell egression. Altogether, these findings uncover an unexpected role for Syt13 as a morphogenetic driver of endocrinogenesis and islet formation.

A PDX1 cistrome and single-cell transcriptome resource of the developing pancreas

Pancreatic and duodenal homeobox 1 (PDX1) is crucial for pancreas organogenesis, yet the dynamic changes in PDX1 binding in human or mouse developing pancreas have not been examined. To address this knowledge gap, we performed PDX1 ChIP-seq and single-cell RNA-seq using fetal human pancreata. We integrated our datasets with published datasets and revealed the dynamics of PDX1 binding and potential cell lineage-specific PDX1-bound genes in the pancreas from fetal to adult stages. We identified a core set of developmentally conserved PDX1-bound genes that reveal the broad multifaceted role of PDX1 in pancreas development. Despite the well-known dramatic changes in PDX1 function and expression, we found that PDX1-bound genes are largely conserved from embryonic to adult stages. This points towards a dual role of PDX1 in regulating the expression of its targets at different ages, dependent on other functionally congruent or directly interacting partners. We also showed that PDX1 binding is largely conserved in mouse pancreas. Together, our study reveals PDX1 targets in the developing pancreas in vivo and provides an essential resource for future studies on pancreas development.

Similarities in Calcium Oscillations Between Neonatal Mouse Islets and Mature Islets Exposed to Chronic Hyperglycemia

Pulsatility is important to islet function. As islets mature into fully developed insulin-secreting micro-organs, their ability to produce oscillatory intracellular calcium ([Ca2+]i) patterns in response to glucose also matures. In this study, we measured [Ca2+]i using fluorescence imaging to characterize oscillations from neonatal mice on postnatal (PN) days 0, 4, and 12 in comparison to adult islets. Under substimulatory (3-mM) glucose levels, [Ca2+]i was low and quiescent for adult islets as expected, as well as for PN day 12 islets. In contrast, one-third of islets on PN day 0 and 4 displayed robust [Ca2+]i oscillations in low glucose. In stimulatory glucose (11 mM) conditions, oscillations were present on all neonatal days but differed from patterns in adults. By PN day 12, [Ca2+]i oscillations were approaching characteristics of fully developed islets. The immature response pattern of neonatal islets was due, at least in part, to differences in adenosine 5'-triphosphate (ATP)-sensitive K+-channel activity estimated by [Ca2+]i responses to KATP channel agents diazoxide and tolbutamide. Neonatal [Ca2+]i patterns were also strikingly similar to patterns observed in mature islets exposed to hyperglycemic conditions (20 mM glucose for 48 hours): elevated [Ca2+]i and oscillations in low glucose along with reduced pulse mass in high glucose. Since a hallmark of diabetic islets is dedifferentiation, we propose that diabetic islets display features of "reverse maturation," demonstrating similar [Ca2+]i dynamics as neonatal islets. Pulsatility is thus an important emergent feature of neonatal islets. Our findings may provide insight into reversing β-cell dedifferentiation and to producing better functioning β cells from pluripotent stem cells.

Single-cell transcriptome and accessible chromatin dynamics during endocrine pancreas development

Delineating gene regulatory networks that orchestrate cell-type specification is a continuing challenge for developmental biologists. Single-cell analyses offer opportunities to address these challenges and accelerate discovery of rare cell lineage relationships and mechanisms underlying hierarchical lineage decisions. Here, we describe the molecular analysis of mouse pancreatic endocrine cell differentiation using single-cell transcriptomics, chromatin accessibility assays coupled to genetic labeling, and cytometry-based cell purification. We uncover transcription factor networks that delineate β-, α-, and δ-cell lineages. Through genomic footprint analysis, we identify transcription factor-regulatory DNA interactions governing pancreatic cell development at unprecedented resolution. Our analysis suggests that the transcription factor Neurog3 may act as a pioneer transcription factor to specify the pancreatic endocrine lineage. These findings could improve protocols to generate replacement endocrine cells from renewable sources, like stem cells, for diabetes therapy.

Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder

Motivation

Single-cell RNA sequencing (scRNA-seq) allows studying the development of cells in unprecedented detail. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data are expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree structure in two dimensions is highly desirable for biological interpretation and exploratory analysis.

Results

Our two contributions are an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data, and a visualization method respecting the tree structure. We extract the tree structure by means of a density-based maximum spanning tree on a vector quantization of the data and show that it captures biological information well. We then introduce density-tree biased autoencoder (DTAE), a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space. We compare to other dimension reduction methods and demonstrate the success of our method both qualitatively and quantitatively on real and toy data.

Availability and implementation

Our implementation relying on PyTorch and Higra is available at github.com/hci-unihd/DTAE.

Supplementary information

Supplementary data are available at Bioinformatics online.

Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to these dynamics without externally perturbing the cell. Here, by generating scRNA-seq libraries in different cell systems, we observe cycling patterns in the unspliced-spliced RNA space of cell cycle-related genes. Since existing methods to analyze scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach (DeepCycle) to fit these patterns and build a high-resolution map of the entire cell cycle transcriptome. Characterizing the cell cycle in embryonic and somatic cells, we identify major waves of transcription during the G1 phase and systematically study the stages of the cell cycle. Our work will facilitate the study of the cell cycle in multiple cellular models and different biological contexts.

Single-cell RNA-sequencing technology gives access to cell cycle dynamics without externally perturbing the cell. Here the authors present DeepCycle,a robust deep learning method to infer the cell cycle state in single cells from scRNA-seq data.

Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data

The rapid growth of high-throughput single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) technologies has produced a wealth of data over the past few years. The size, volume, and distinctive characteristics of these data necessitate the development of new computational methods to accurately and efficiently quantify sc/snRNA-seq data into count matrices that constitute the input to downstream analyses. We introduce the alevin-fry framework for quantifying sc/snRNA-seq data. In addition to being faster and more memory frugal than other accurate quantification approaches, alevin-fry ameliorates the memory scalability and false-positive expression issues that are exhibited by other lightweight tools. We demonstrate how alevin-fry can be effectively used to quantify sc/snRNA-seq data, and also how the spliced and unspliced molecule quantification required as input for RNA velocity analyses can be seamlessly extracted from the same preprocessed data used to generate regular gene expression count matrices.

Single-Cell Transcriptomics Reveals a Conserved Metaplasia Program in Pancreatic Injury

BACKGROUND & AIMS

Acinar to ductal metaplasia (ADM) occurs in the pancreas in response to tissue injury and is a potential precursor for adenocarcinoma. The goal of these studies was to define the populations arising from ADM, the associated transcriptional changes, and markers of disease progression.

METHODS

Acinar cells were lineage-traced with enhanced yellow fluorescent protein (EYFP) to follow their fate post-injury. Transcripts of more than 13,000 EYFP+ cells were determined using single-cell RNA sequencing (scRNA-seq). Developmental trajectories were generated. Data were compared with gastric metaplasia, KrasG12D-induced neoplasia, and human pancreatitis. Results were confirmed by immunostaining and electron microscopy. KrasG12D was expressed in injury-induced ADM using several inducible Cre drivers. Surgical specimens of chronic pancreatitis from 15 patients were evaluated by immunostaining.

RESULTS

scRNA-seq of ADM revealed emergence of a mucin/ductal population resembling gastric pyloric metaplasia. Lineage trajectories suggest that some pyloric metaplasia cells can generate tuft and enteroendocrine cells (EECs). Comparison with KrasG12D-induced ADM identifies populations associated with disease progression. Activation of KrasG12D expression in HNF1B+ or POU2F3+ ADM populations leads to neoplastic transformation and formation of MUC5AC+ gastric-pit-like cells. Human pancreatitis samples also harbor pyloric metaplasia with a similar transcriptional phenotype.

CONCLUSIONS

Under conditions of chronic injury, acinar cells undergo a pyloric-type metaplasia to mucinous progenitor-like populations, which seed disparate tuft cell and EEC lineages. ADM-derived EEC subtypes are diverse. KrasG12D expression is sufficient to drive neoplasia when targeted to injury-induced ADM populations and offers an alternative origin for tumorigenesis. This program is conserved in human pancreatitis, providing insight into early events in pancreas diseases.

Universal prediction of cell-cycle position using transfer learning

BACKGROUND

The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data.

RESULTS

Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays.

CONCLUSIONS

Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data.

CellRank for directed single-cell fate mapping

Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

CellRank infers directed cell state transitions and cell fates incorporating RNA velocity information into a graph based Markov process.

Mapping single-cell data to reference atlases by transfer learning

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

Mapping single-cell data to reference atlases by transfer learning

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

Nested Stochastic Block Models applied to the analysis of single cell data

Single cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, schist, that is compatible with the popular scanpy framework.

Synaptotagmin-13 Is a Neuroendocrine Marker in Brain, Intestine and Pancreas

Synaptotagmin-13 (Syt13) is an atypical member of the vesicle trafficking synaptotagmin protein family. The expression pattern and the biological function of this Ca²⁺-independent protein are not well resolved. Here, we have generated a novel Syt13-Venus fusion (Syt13-VF) fluorescence reporter allele to track and isolate tissues and cells expressing Syt13 protein. The reporter allele is regulated by endogenous cis-regulatory elements of Syt13 and the fusion protein follows an identical expression pattern of the endogenous Syt13 protein. The homozygous reporter mice are viable and fertile. We identify the expression of the Syt13-VF reporter in different regions of the brain with high expression in tyrosine hydroxylase (TH)-expressing and oxytocin-producing neuroendocrine cells. Moreover, Syt13-VF is highly restricted to all enteroendocrine cells in the adult intestine that can be traced in live imaging. Finally, Syt13-VF protein is expressed in the pancreatic endocrine lineage, allowing their specific isolation by flow sorting. These findings demonstrate high expression levels of Syt13 in the endocrine lineages in three major organs harboring these secretory cells. Collectively, the Syt13-VF reporter mouse line provides a unique and reliable tool to dissect the spatio-temporal expression pattern of Syt13 and enables isolation of Syt13-expressing cells that will aid in deciphering the molecular functions of this protein in the neuroendocrine system.

singlecellVR: Interactive Visualization of Single-Cell Data in Virtual Reality

Single-cell assays have transformed our ability to model heterogeneity within cell populations. As these assays have advanced in their ability to measure various aspects of molecular processes in cells, computational methods to analyze and meaningfully visualize such data have required matched innovation. Independently, Virtual Reality (VR) has recently emerged as a powerful technology to dynamically explore complex data and shows promise for adaptation to challenges in single-cell data visualization. However, adopting VR for single-cell data visualization has thus far been hindered by expensive prerequisite hardware or advanced data preprocessing skills. To address current shortcomings, we present singlecellVR, a user-friendly web application for visualizing single-cell data, designed for cheap and easily available virtual reality hardware (e.g., Google Cardboard, ∼$8). singlecellVR can visualize data from a variety of sequencing-based technologies including transcriptomic, epigenomic, and proteomic data as well as combinations thereof. Analysis modalities supported include approaches to clustering as well as trajectory inference and visualization of dynamical changes discovered through modelling RNA velocity. We provide a companion software package, scvr to streamline data conversion from the most widely-adopted single-cell analysis tools as well as a growing database of pre-analyzed datasets to which users can contribute.

VeTra: a tool for trajectory inference based on RNA velocity

MOTIVATION

Trajectory inference (TI) for single cell RNA sequencing (scRNAseq) data is a powerful approach to interpret dynamic cellular processes such as cell cycle and development. Still, however, accurate inference of trajectory is challenging. Recent development of RNA velocity provides an approach to visualize cell state transition without relying on prior knowledge.

RESULTS

To perform TI and group cells based on RNA velocity we developed VeTra. By applying cosine similarity and merging weakly connected components, VeTra identifies cell groups from the direction of cell transition. Besides, VeTra suggests key regulators from the inferred trajectory. VeTra is a useful tool for TI and subsequent analysis.

AVAILABILITY AND IMPLEMENTATION

The Vetra is available at https://github.com/wgzgithub/VeTra.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity

Trajectory inference (TI) methods infer cell developmental trajectory from single-cell RNA sequencing data. Current TI methods can be categorized into those using RNA velocity information and those using only single-cell gene expression data. The latter type of methods are restricted to certain trajectory structures, and cannot determine cell developmental direction. Recently proposed TI methods using RNA velocity information have limited accuracy. We present CellPath, a method that infers cell trajectories by integrating single-cell gene expression and RNA velocity information. CellPath overcomes the restrictions of TI methods that do not use RNA velocity information: it can find multiple high-resolution trajectories without constraints on the trajectory structure, and can automatically detect the direction of each trajectory path. We evaluate CellPath on both real and simulated datasets and show that CellPath finds more accurate and detailed trajectories than the state-of-the-art TI methods using or not using RNA velocity information.

Generalized and scalable trajectory inference in single-cell omics data with VIA

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories

MOTIVATION

Single-cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single-cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity analysis as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding and other 2D embeddings derived from the observed single-cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes.

RESULTS

We developed VeloViz to create RNA velocity-informed 2D and 3D embeddings from single-cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By considering the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories.

AVAILABILITY AND IMPLEMENTATION

Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bioconductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Datasets used can be found on Zenodo (https://doi.org/10.5281/zenodo.4632471).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Velo-Predictor: an ensemble learning pipeline for RNA velocity prediction

Background

RNA velocity is a novel and powerful concept which enables the inference of dynamical cell state changes from seemingly static single-cell RNA sequencing (scRNA-seq) data. However, accurate estimation of RNA velocity is still a challenging problem, and the underlying kinetic mechanisms of transcriptional and splicing regulations are not fully clear. Moreover, scRNA-seq data tend to be sparse compared with possible cell states, and a given dataset of estimated RNA velocities needs imputation for some cell states not yet covered.

Results

We formulate RNA velocity prediction as a supervised learning problem of classification for the first time, where a cell state space is divided into equal-sized segments by directions as classes, and the estimated RNA velocity vectors are considered as ground truth. We propose Velo-Predictor, an ensemble learning pipeline for predicting RNA velocities from scRNA-seq data. We test different models on two real datasets, Velo-Predictor exhibits good performance, especially when XGBoost was used as the base predictor. Parameter analysis and visualization also show that the method is robust and able to make biologically meaningful predictions.

Conclusion

The accurate result shows that Velo-Predictor can effectively simplify the procedure by learning a predictive model from gene expression data, which could help to construct a continous landscape and give biologists an intuitive picture about the trend of cellular dynamics.

Single-cell-resolved differentiation of human induced pluripotent stem cells into pancreatic duct-like organoids on a microwell chip

Creating in vitro models of diseases of the pancreatic ductal compartment requires a comprehensive understanding of the developmental trajectories of pancreas-specific cell types. Here we report the single-cell characterization of the differentiation of pancreatic duct-like organoids (PDLOs) from human induced pluripotent stem cells (hiPSCs) on a microwell chip that facilitates the uniform aggregation and chemical induction of hiPSC-derived pancreatic progenitors. Using time-resolved single-cell transcriptional profiling and immunofluorescence imaging of the forming PDLOs, we identified differentiation routes from pancreatic progenitors through ductal intermediates to two types of mature duct-like cells and a few non-ductal cell types. PDLO subpopulations expressed either mucins or the cystic fibrosis transmembrane conductance regulator, and resembled human adult duct cells. We also used the chip to uncover ductal markers relevant to pancreatic carcinogenesis, and to establish PDLO co-cultures with stellate cells, which allowed for the study of epithelial-mesenchymal signalling. The PDLO microsystem could be used to establish patient-specific pancreatic duct models.

Monogenic Diabetes Modeling: In Vitro Pancreatic Differentiation From Human Pluripotent Stem Cells Gains Momentum

The occurrence of diabetes mellitus is characterized by pancreatic β cell loss and chronic hyperglycemia. While Type 1 and Type 2 diabetes are the most common types, rarer forms involve mutations affecting a single gene. This characteristic has made monogenic diabetes an interesting disease group to model in vitro using human pluripotent stem cells (hPSCs). By altering the genotype of the original hPSCs or by deriving human induced pluripotent stem cells (hiPSCs) from patients with monogenic diabetes, changes in the outcome of the in vitro differentiation protocol can be analyzed in detail to infer the regulatory mechanisms affected by the disease-associated genes. This approach has been so far applied to a diversity of genes/diseases and uncovered new mechanisms. The focus of the present review is to discuss the latest findings obtained by modeling monogenic diabetes using hPSC-derived pancreatic cells generated in vitro. We will specifically focus on the interpretation of these studies, the advantages and limitations of the models used, and the future perspectives for improvement.

Temporal single-cell regeneration studies: the greatest thing since sliced pancreas?

The application of single-cell analytic techniques to the study of stem/progenitor cell niches supports the emerging view that pancreatic cell lineages are in a state of flux between differentiation stages. For all their value, however, such analyses merely offer a snapshot of the cellular palette of the tissue at any given time point. Conclusions about potential developmental/regeneration paths are solely based on bioinformatics inferences. In this context, the advent of new techniques for the long-term culture and lineage tracing of human pancreatic slices offers a virtual window into the native organ and presents the field with a unique opportunity to serially resolve pancreatic regeneration dynamics at the single-cell level.

CD81 marks immature and dedifferentiated pancreatic β-cells

OBJECTIVE

Islets of Langerhans contain heterogeneous populations of insulin-producing β-cells. Surface markers and respective antibodies for isolation, tracking, and analysis are urgently needed to study β-cell heterogeneity and explore the mechanisms to harness the regenerative potential of immature β-cells.

METHODS

We performed single-cell mRNA profiling of early postnatal mouse islets and re-analyzed several single-cell mRNA sequencing datasets from mouse and human pancreas and islets. We used mouse primary islets, iPSC-derived endocrine cells, Min6 insulinoma, and human EndoC-βH1 β-cell lines and performed FAC sorting, Western blotting, and imaging to support and complement the findings from the data analyses.

RESULTS

We found that all endocrine cell types expressed the cluster of differentiation 81 (CD81) during pancreas development, but the expression levels of this protein were gradually reduced in β-cells during postnatal maturation. Single-cell gene expression profiling and high-resolution imaging revealed an immature signature of β-cells expressing high levels of CD81 (CD81high) compared to a more mature population expressing no or low levels of this protein (CD81low/-). Analysis of β-cells from different diabetic mouse models and in vitro β-cell stress assays indicated an upregulation of CD81 expression levels in stressed and dedifferentiated β-cells. Similarly, CD81 was upregulated and marked stressed human β-cells in vitro.

CONCLUSIONS

We identified CD81 as a novel surface marker that labels immature, stressed, and dedifferentiated β-cells in the adult mouse and human islets. This novel surface marker will allow us to better study β-cell heterogeneity in healthy subjects and diabetes progression.

NEUROD1 Is Required for the Early α and β Endocrine Differentiation in the Pancreas

Diabetes is a metabolic disease that involves the death or dysfunction of the insulin-secreting β cells in the pancreas. Consequently, most diabetes research is aimed at understanding the molecular and cellular bases of pancreatic development, islet formation, β-cell survival, and insulin secretion. Complex interactions of signaling pathways and transcription factor networks regulate the specification, growth, and differentiation of cell types in the developing pancreas. Many of the same regulators continue to modulate gene expression and cell fate of the adult pancreas. The transcription factor NEUROD1 is essential for the maturation of β cells and the expansion of the pancreatic islet cell mass. Mutations of the Neurod1 gene cause diabetes in humans and mice. However, the different aspects of the requirement of NEUROD1 for pancreas development are not fully understood. In this study, we investigated the role of NEUROD1 during the primary and secondary transitions of mouse pancreas development. We determined that the elimination of Neurod1 impairs the expression of key transcription factors for α- and β-cell differentiation, β-cell proliferation, insulin production, and islets of Langerhans formation. These findings demonstrate that the Neurod1 deletion altered the properties of α and β endocrine cells, resulting in severe neonatal diabetes, and thus, NEUROD1 is required for proper activation of the transcriptional network and differentiation of functional α and β cells.

TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data

Single-cell RNA-seq technology provides an unprecedented opportunity to allow researchers to study the biological heterogeneity during cell differentiation and development with higher resolution. Although many computational methods have been proposed to infer cell lineages from single-cell RNA-seq data, constructing accurate cell trajectories remains a challenge. We develop a novel trajectory inference method-based probability distribution (TIPD) to describe the heterogeneity of cell population. TIPD combines signalling entropy and clustering results of the gene expression profile to describe the probability distributions of heterogeneous states in a cell population. It does not require external knowledge to determine the direction of the differentiation trajectories, so its application is not limited by the annotations of the data set. We also propose a new distance metric to measure the distance of the probability distributions of the identified heterogeneous states. On this distance matrix, a minimum spanning tree (MST) is built to reorganize the order of cell clusters. The constructed MST is calculated based on systems-level information, so it is consistent with the real biological process. We validated our method on four previously published single-cell RNA-seq data sets including the linear structure and branch structure. The results showed that TIPD successfully reconstructed the differentiation trajectories that are highly consistent with the known differentiation trajectories and outperformed the other four state-of-the-art methods under different assessment criteria.

A 3D system to model human pancreas development and its reference single-cell transcriptome atlas identify signaling pathways required for progenitor expansion

Human organogenesis remains relatively unexplored for ethical and practical reasons. Here, we report the establishment of a single-cell transcriptome atlas of the human fetal pancreas between 7 and 10 post-conceptional weeks of development. To interrogate cell–cell interactions, we describe InterCom, an R-Package we developed for identifying receptor–ligand pairs and their downstream effects. We further report the establishment of a human pancreas culture system starting from fetal tissue or human pluripotent stem cells, enabling the long-term maintenance of pancreas progenitors in a minimal, defined medium in three-dimensions. Benchmarking the cells produced in 2-dimensions and those expanded in 3-dimensions to fetal tissue identifies that progenitors expanded in 3-dimensions are transcriptionally closer to the fetal pancreas. We further demonstrate the potential of this system as a screening platform and identify the importance of the EGF and FGF pathways controlling human pancreas progenitor expansion.

From single-cell transcriptome analyses to defining culture media for spheroids, the authors provide a census of information to understand the development of human pancreatic progenitors. This approach identifies signalling pathways (EGF and FGF) regulating progenitor proliferation.

MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of three large single-cell RNA-seq datasets and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.

Endocrine Pancreas Development and Dysfunction Through the Lens of Single-Cell RNA-Sequencing

A chronic inability to maintain blood glucose homeostasis leads to diabetes, which can damage multiple organs. The pancreatic islets regulate blood glucose levels through the coordinated action of islet cell-secreted hormones, with the insulin released by β-cells playing a crucial role in this process. Diabetes is caused by insufficient insulin secretion due to β-cell loss, or a pancreatic dysfunction. The restoration of a functional β-cell mass might, therefore, offer a cure. To this end, major efforts are underway to generate human β-cells de novo, in vitro, or in vivo. The efficient generation of functional β-cells requires a comprehensive knowledge of pancreas development, including the mechanisms driving cell fate decisions or endocrine cell maturation. Rapid progress in single-cell RNA sequencing (scRNA-Seq) technologies has brought a new dimension to pancreas development research. These methods can capture the transcriptomes of thousands of individual cells, including rare cell types, subtypes, and transient states. With such massive datasets, it is possible to infer the developmental trajectories of cell transitions and gene regulatory pathways. Here, we summarize recent advances in our understanding of endocrine pancreas development and function from scRNA-Seq studies on developing and adult pancreas and human endocrine differentiation models. We also discuss recent scRNA-Seq findings for the pathological pancreas in diabetes, and their implications for better treatment.

The glucose-dependent insulinotropic polypeptide (GIP) regulates body weight and food intake via CNS-GIPR signaling

Uncertainty exists as to whether the glucose-dependent insulinotropic polypeptide receptor (GIPR) should be activated or inhibited for the treatment of obesity. Gipr was recently demonstrated in hypothalamic feeding centers, but the physiological relevance of CNS Gipr remains unknown. Here we show that HFD-fed CNS-Gipr KO mice and humanized (h)GIPR knockin mice with CNS-hGIPR deletion show decreased body weight and improved glucose metabolism. In DIO mice, acute central and peripheral administration of acyl-GIP increases cFos neuronal activity in hypothalamic feeding centers, and this coincides with decreased body weight and food intake and improved glucose handling. Chronic central and peripheral administration of acyl-GIP lowers body weight and food intake in wild-type mice, but shows blunted/absent efficacy in CNS-Gipr KO mice. Also, the superior metabolic effect of GLP-1/GIP co-agonism relative to GLP-1 is extinguished in CNS-Gipr KO mice. Our data hence establish a key role of CNS Gipr for control of energy metabolism.

Pancreatic β-cell heterogeneity in health and diabetes: classes, sources, and subtypes

Pancreatic β-cells perform glucose-stimulated insulin secretion, a process at the center of type 2 diabetes etiology. Efforts to understand how β-cells behave in healthy and stressful conditions have revealed a wide degree of morphological, functional, and transcriptional heterogeneity. Sources of heterogeneity include β-cell topography, developmental origin, maturation state, and stress response. Advances in sequencing and imaging technologies have led to the identification of β-cell subtypes, which play distinct roles in the islet niche. This review examines β-cell heterogeneity from morphological, functional, and transcriptional perspectives, and considers the relevance of topography, maturation, development, and stress response. It also discusses how these factors have been used to identify β-cell subtypes, and how heterogeneity is impacted by diabetes. We examine open questions in the field and discuss recent technological innovations that could advance understanding of β-cell heterogeneity in health and disease.

Generation of a Novel Nkx6-1 Venus Fusion Reporter Mouse Line

Nkx6-1 is a member of the Nkx family of homeodomain transcription factors (TFs) that regulates motor neuron development, neuron specification and pancreatic endocrine and β-cell differentiation. To facilitate the isolation and tracking of Nkx6-1-expressing cells, we have generated a novel Nkx6-1 Venus fusion (Nkx6-1-VF) reporter allele. The Nkx6-1-VF knock-in reporter is regulated by endogenous cis-regulatory elements of Nkx6-1 and the fluorescent protein fusion does not interfere with the TF function, as homozygous mice are viable and fertile. The nuclear localization of Nkx6-1-VF protein reflects the endogenous Nkx6-1 protein distribution. During embryonic pancreas development, the reporter protein marks the pancreatic ductal progenitors and the endocrine lineage, but is absent in the exocrine compartment. As expected, the levels of Nkx6-1-VF reporter are upregulated upon β-cell differentiation during the major wave of endocrinogenesis. In the adult islets of Langerhans, the reporter protein is exclusively found in insulin-secreting β-cells. Importantly, the Venus reporter activities allow successful tracking of β-cells in live-cell imaging and their specific isolation by flow sorting. In summary, the generation of the Nkx6-1-VF reporter line reflects the expression pattern and dynamics of the endogenous protein and thus provides a unique tool to study the spatio-temporal expression pattern of this TF during organ development and enables isolation and tracking of Nkx6-1-expressing cells such as pancreatic β-cells, but also neurons and motor neurons in health and disease.

Sequential progenitor states mark the generation of pancreatic endocrine lineages in mice and humans

The pancreatic islet contains multiple hormone⁺ endocrine lineages (α, β, δ, PP and ε cells), but the developmental processes that underlie endocrinogenesis are poorly understood. Here, we generated novel mouse lines and combined them with various genetic tools to enrich all types of hormone⁺ cells for well-based deep single-cell RNA sequencing (scRNA-seq), and gene coexpression networks were extracted from the generated data for the optimization of high-throughput droplet-based scRNA-seq analyses. These analyses defined an entire endocrinogenesis pathway in which different states of endocrine progenitor (EP) cells sequentially differentiate into specific endocrine lineages in mice. Subpopulations of the EP cells at the final stage (EP4^early and EP4^late) show different potentials for distinct endocrine lineages. ε cells and an intermediate cell population were identified as distinct progenitors that independently generate both α and PP cells. Single-cell analyses were also performed to delineate the human pancreatic endocrinogenesis process. Although the developmental trajectory of pancreatic lineages is generally conserved between humans and mice, clear interspecies differences, including differences in the proportions of cell types and the regulatory networks associated with the differentiation of specific lineages, have been detected. Our findings support a model in which sequential transient progenitor cell states determine the differentiation of multiple cell lineages and provide a blueprint for directing the generation of pancreatic islets in vitro.

Non-canonical Wnt/PCP signalling regulates intestinal stem cell lineage priming towards enteroendocrine and Paneth cell fates

A detailed understanding of intestinal stem cell (ISC) self-renewal and differentiation is required to treat chronic intestinal diseases. However, the different models of ISC lineage hierarchy^1-6 and segregation^7-12 are subject to debate. Here, we have discovered non-canonical Wnt/planar cell polarity (PCP)-activated ISCs that are primed towards the enteroendocrine or Paneth cell lineage. Strikingly, integration of time-resolved lineage labelling with single-cell gene expression analysis revealed that both lineages are directly recruited from ISCs via unipotent transition states, challenging the existence of formerly predicted bi- or multipotent secretory progenitors^7-12. Transitory cells that mature into Paneth cells are quiescent and express both stem cell and secretory lineage genes, indicating that these cells are the previously described Lgr5⁺ label-retaining cells⁷. Finally, Wnt/PCP-activated Lgr5⁺ ISCs are molecularly indistinguishable from Wnt/β-catenin-activated Lgr5⁺ ISCs, suggesting that lineage priming and cell-cycle exit is triggered at the post-transcriptional level by polarity cues and a switch from canonical to non-canonical Wnt/PCP signalling. Taken together, we redefine the mechanisms underlying ISC lineage hierarchy and identify the Wnt/PCP pathway as a new niche signal preceding lateral inhibition in ISC lineage priming and segregation.

Preprocessing choices affect RNA velocity results for droplet scRNA-seq data

Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a 'direction of change' and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare five widely used quantification tools, in total yielding thirteen different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in five experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration.

Tracing the cellular basis of islet specification in mouse pancreas

Pancreatic islets play an essential role in regulating blood glucose level. Although the molecular pathways underlying islet cell differentiation are beginning to be resolved, the cellular basis of islet morphogenesis and fate allocation remain unclear. By combining unbiased and targeted lineage tracing, we address the events leading to islet formation in the mouse. From the statistical analysis of clones induced at multiple embryonic timepoints, here we show that, during the secondary transition, islet formation involves the aggregation of multiple equipotent endocrine progenitors that transition from a phase of stochastic amplification by cell division into a phase of sublineage restriction and limited islet fission. Together, these results explain quantitatively the heterogeneous size distribution and degree of polyclonality of maturing islets, as well as dispersion of progenitors within and between islets. Further, our results show that, during the secondary transition, α- and β-cells are generated in a contemporary manner. Together, these findings provide insight into the cellular basis of islet development.

The cellular basis of islet morphogenesis and fate allocation remain unclear. Here, the authors use a R26-CreER-R26R-Confetti mouse line to follow quantitatively the clonal dynamics of islet formation showing how, during the secondary transition, islet progenitors amplify through rounds of stochastic cell division before becoming restricted to α and β cell sublineages.

Single-Cell RNA Sequencing of the Cynomolgus Macaque Testis Reveals Conserved Transcriptional Profiles during Mammalian Spermatogenesis

Spermatogenesis is highly orchestrated and involves the differentiation of diploid spermatogonia into haploid sperm. The process is driven by spermatogonial stem cells (SSCs). SSCs undergo mitotic self-renewal, whereas sub-populations undergo differentiation and later gain competence to initiate meiosis. Here, we describe a high-resolution single-cell RNA-seq atlas of cells derived from Cynomolgus macaque testis. We identify gene signatures that define spermatogonial populations and explore self-renewal versus differentiation dynamics. We detail transcriptional changes occurring over the entire process of spermatogenesis and highlight the concerted activity of DNA damage response (DDR) pathway genes, which have dual roles in maintaining genomic integrity and effecting meiotic sex chromosome inactivation (MSCI). We show remarkable similarities and differences in gene expression during spermatogenesis with two other eutherian mammals, i.e., mouse and humans. Sex chromosome expression in the male germline in all three species demonstrates conserved features of MSCI but divergent multicopy and ampliconic gene content.

Generalizing RNA velocity to transient cell states through dynamical modeling

RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell's position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.

Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference

Motivation

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulatory network (GRNs) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology.

Results

In this work, we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual cells, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data.

Availability and implementation

The MATLAB code of GRISLI is available at: https://github.com/PCAubin/GRISLI.

Supplementary information

Supplementary data are available at Bioinformatics online.

A single-cell transcriptomic atlas of primate pancreatic islet aging

Aging-related degeneration of pancreatic islet cells contributes to impaired glucose tolerance and diabetes. Endocrine cells age heterogeneously, complicating the efforts to unravel the molecular drivers underlying endocrine aging. To overcome these obstacles, we undertook single-cell RNA sequencing of pancreatic islet cells obtained from young and aged non-diabetic cynomolgus monkeys. Despite sex differences and increased transcriptional variations, aged β-cells showed increased unfolded protein response (UPR) along with the accumulation of protein aggregates. We observed transcriptomic dysregulation of UPR components linked to canonical ATF6 and IRE1 signaling pathways, comprising adaptive UPR during pancreatic aging. Notably, we found aging-related β-cell-specific upregulation of HSP90B1, an endoplasmic reticulum-located chaperone, impeded high glucose-induced insulin secretion. Our work decodes aging-associated transcriptomic changes that underlie pancreatic islet functional decay at single-cell resolution and indicates that targeting UPR components may prevent loss of proteostasis, suggesting an avenue to delaying β-cell aging and preventing aging-related diabetes.

Understanding generation and regeneration of pancreatic β cells from a single-cell perspective

Understanding the mechanisms that underlie the generation and regeneration of β cells is crucial for developing treatments for diabetes. However, traditional research methods, which are based on populations of cells, have limitations for defining the precise processes of β-cell differentiation and trans-differentiation, and the associated regulatory mechanisms. The recent development of single-cell technologies has enabled re-examination of these processes at a single-cell resolution to uncover intermediate cell states, cellular heterogeneity and molecular trajectories of cell fate specification. Here, we review recent advances in understanding β-cell generation and regeneration, in vivo and in vitro, from single-cell technologies, which could provide insights for optimization of diabetes therapy strategies.

Establishment of a high-resolution 3D modeling system for studying pancreatic epithelial cell biology in vitro

OBJECTIVE

Translation of basic research from bench-to-bedside relies on a better understanding of similarities and differences between mouse and human cell biology, tissue formation, and organogenesis. Thus, establishing ex vivo modeling systems of mouse and human pancreas development will help not only to understand evolutionary conserved mechanisms of differentiation and morphogenesis but also to understand pathomechanisms of disease and design strategies for tissue engineering.

METHODS

Here, we established a simple and reproducible Matrigel-based three-dimensional (3D) cyst culture model system of mouse and human pancreatic progenitors (PPs) to study pancreatic epithelialization and endocrinogenesis ex vivo. In addition, we reanalyzed previously reported single-cell RNA sequencing (scRNA-seq) of mouse and human pancreatic lineages to obtain a comprehensive picture of differential expression of key transcription factors (TFs), cell-cell adhesion molecules and cell polarity components in PPs during endocrinogenesis.

RESULTS

We generated mouse and human polarized pancreatic epithelial cysts derived from PPs. This system allowed to monitor establishment of pancreatic epithelial polarity and lumen formation in cellular and sub-cellular resolution in a dynamic time-resolved fashion. Furthermore, both mouse and human pancreatic cysts were able to differentiate towards the endocrine fate. This differentiation system together with scRNA-seq analysis revealed how apical-basal polarity and tight and adherens junctions change during endocrine differentiation.

CONCLUSIONS

We have established a simple 3D pancreatic cyst culture system that allows to tempo-spatial resolve cellular and subcellular processes on the mechanistical level, which is otherwise not possible in vivo.

Human stem cell models: lessons for pancreatic development and disease

A comprehensive understanding of mechanisms that underlie the development and function of human cells requires human cell models. For the pancreatic lineage, protocols have been developed to differentiate human pluripotent stem cells (hPSCs) into pancreatic endocrine and exocrine cells through intermediates resembling in vivo development. In recent years, this differentiation system has been employed to decipher mechanisms of pancreatic development, congenital defects of the pancreas, as well as genetic forms of diabetes and exocrine diseases. In this review, we summarize recent insights gained from studies of pancreatic hPSC models. We discuss how genome-scale analyses of the differentiation system have helped elucidate roles of chromatin state, transcription factors, and noncoding RNAs in pancreatic development and how the analysis of cells with disease-relevant mutations has provided insight into the molecular underpinnings of genetically determined diseases of the pancreas.

β-Cell Maturation and Identity in Health and Disease

The exponential increase of patients with diabetes mellitus urges for novel therapeutic strategies to reduce the socioeconomic burden of this disease. The loss or dysfunction of insulin-producing β-cells, in patients with type 1 and type 2 diabetes respectively, put these cells at the center of the disease initiation and progression. Therefore, major efforts have been taken to restore the β-cell mass by cell-replacement or regeneration approaches. Implementing novel therapies requires deciphering the developmental mechanisms that generate β-cells and determine the acquisition of their physiological phenotype. In this review, we summarize the current understanding of the mechanisms that coordinate the postnatal maturation of β-cells and define their functional identity. Furthermore, we discuss different routes by which β-cells lose their features and functionality in type 1 and 2 diabetic conditions. We then focus on potential mechanisms to restore the functionality of those β-cell populations that have lost their functional phenotype. Finally, we discuss the recent progress and remaining challenges facing the generation of functional mature β-cells from stem cells for cell-replacement therapy for diabetes treatment.