Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin accessibility in single nuclei. These two data ...types provide complementary information for deciphering cell types and states. However, when analyzed individually, they sometimes produce conflicting results regarding cell type/state assignment. The power is compromised since the two modalities reflect the same underlying biology. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data enable the direct modeling of the relationships between the two modalities. Given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality datasets to gain a comprehensive view of the cellular complexity.
We benchmark nine existing single-cell multi-omic data integration methods. Specifically, we evaluate to what extent the multiome data provide additional guidance for analyzing the existing single-modality data, and whether these methods uncover peak-gene associations from single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data. However, we emphasize that the availability of an adequate number of nuclei in the multiome dataset is crucial for achieving accurate cell type annotation. Insufficient representation of nuclei may compromise the reliability of the annotations. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation.
Seurat v4 is the best currently available platform for integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.
Aims/hypothesis
While pancreatic beta cells have been shown to originate from endocrine progenitors in ductal regions, it remains unclear precisely where beta cells emerge from and which transcripts ...define newborn beta cells. We therefore investigated characteristics of newborn beta cells extracted by a time-resolved reporter system.
Methods
We established a mouse model, ‘
Ins1-GFP; Timer
’, which provides spatial information during beta cell neogenesis with high temporal resolution. Single-cell RNA-sequencing (scRNA-seq) was performed on mouse beta cells sorted by fluorescent reporter to uncover transcriptomic profiles of newborn beta cells. scRNA-seq of human embryonic stem cell (hESC)-derived beta-like cells was also performed to compare newborn beta cell features between mouse and human.
Results
Fluorescence imaging of
Ins1-GFP; Timer
mouse pancreas successfully dissected newly generated beta cells as green fluorescence-dominant cells. This reporter system revealed that, as expected, some newborn beta cells arise close to the ducts (β
duct
); unexpectedly, the others arise away from the ducts and adjacent to blood vessels (β
vessel
). Single-cell transcriptomic analyses demonstrated five distinct populations among newborn beta cells, confirming spatial heterogeneity of beta cell neogenesis such as high probability of glucagon-positive β
duct
, musculoaponeurotic fibrosarcoma oncogene family B (MafB)-positive β
duct
and musculoaponeurotic fibrosarcoma oncogene family A (MafA)-positive β
vessel
cells. Comparative analysis with scRNA-seq data of mouse newborn beta cells and hESC-derived beta-like cells uncovered transcriptional similarity between mouse and human beta cell neogenesis including microsomal glutathione S-transferase 1 (MGST1)- and synaptotagmin 13 (SYT13)-highly-expressing state.
Conclusions/interpretation
The combination of time-resolved histological imaging with single-cell transcriptional mapping demonstrated novel features of spatial and transcriptional heterogeneity in beta cell neogenesis, which will lead to a better understanding of beta cell differentiation for future cell therapy.
Data availability
Raw and processed single-cell RNA-sequencing data for this study has been deposited in the Gene Expression Omnibus under accession number GSE155742.
Graphical abstract
Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence ...(AI) model using deep learning in a population-based diabetic retinopathy screening programme in Zambia, a lower-middle-income country.
We adopted an ensemble AI model consisting of a combination of two convolutional neural networks (an adapted VGGNet architecture and a residual neural network architecture) for classifying retinal colour fundus images. We trained our model on 76 370 retinal fundus images from 13 099 patients with diabetes who had participated in the Singapore Integrated Diabetic Retinopathy Program, between 2010 and 2013, which has been published previously. In this clinical validation study, we included all patients with a diagnosis of diabetes that attended a mobile screening unit in five urban centres in the Copperbelt province of Zambia from Feb 1 to June 31, 2012. In our model, referable diabetic retinopathy was defined as moderate non-proliferative diabetic retinopathy or worse, diabetic macular oedema, and ungradable images. Vision-threatening diabetic retinopathy comprised severe non-proliferative and proliferative diabetic retinopathy. We calculated the area under the curve (AUC), sensitivity, and specificity for referable diabetic retinopathy, and sensitivities of vision-threatening diabetic retinopathy and diabetic macular oedema compared with the grading by retinal specialists. We did a multivariate analysis for systemic risk factors and referable diabetic retinopathy between AI and human graders.
A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable diabetic retinopathy was found in 697 (22·5%) eyes, vision-threatening diabetic retinopathy in 171 (5·5%) eyes, and diabetic macular oedema in 249 (8·1%) eyes. The AUC of the AI system for referable diabetic retinopathy was 0·973 (95% CI 0·969-0·978), with corresponding sensitivity of 92·25% (90·10-94·12) and specificity of 89·04% (87·85-90·28). Vision-threatening diabetic retinopathy sensitivity was 99·42% (99·15-99·68) and diabetic macular oedema sensitivity was 97·19% (96·61-97·77). The AI model and human graders showed similar outcomes in referable diabetic retinopathy prevalence detection and systemic risk factors associations. Both the AI model and human graders identified longer duration of diabetes, higher level of glycated haemoglobin, and increased systolic blood pressure as risk factors associated with referable diabetic retinopathy.
An AI system shows clinically acceptable performance in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, and diabetic macular oedema in population-based diabetic retinopathy screening. This shows the potential application and adoption of such AI technology in an under-resourced African population to reduce the incidence of preventable blindness, even when the model is trained in a different population.
National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.
The immunopathological mechanisms driving the development of severe COVID-19 remain poorly defined. Here, we utilize a rhesus macaque model of acute SARS-CoV-2 infection to delineate perturbations in ...the innate immune system. SARS-CoV-2 initiates a rapid infiltration of plasmacytoid dendritic cells into the lower airway, commensurate with IFNA production, natural killer cell activation, and a significant increase of blood CD14
CD16
monocytes. To dissect the contribution of lung myeloid subsets to airway inflammation, we generate a longitudinal scRNA-Seq dataset of airway cells, and map these subsets to corresponding populations in the human lung. SARS-CoV-2 infection elicits a rapid recruitment of two macrophage subsets: CD163
MRC1
, and TREM2
populations that are the predominant source of inflammatory cytokines. Treatment with baricitinib (Olumiant®), a JAK1/2 inhibitor is effective in eliminating the influx of non-alveolar macrophages, with a reduction of inflammatory cytokines. This study delineates the major lung macrophage subsets driving airway inflammation during SARS-CoV-2 infection.
Significance Stroke is the leading cause of disability in the United States and has very limited treatment options. Brain stimulation techniques that promote recovery after stroke are a promising ...area of research; however, current stimulation techniques nonspecifically activate/inhibit the target area, which not only leads to undesired side effects but also makes it difficult to understand which cell types and mechanisms drive recovery. We used the optogenetic technique to specifically stimulate only neurons after stroke and demonstrate that selective neuronal stimulations can activate beneficial mechanisms and promote recovery. Understanding the cell type and mechanisms driving recovery may identify potential drug targets for stroke treatment, as well as ultimately help develop precise brain stimulation techniques for stroke therapy.
Significant progress has been made since the first report of inflammatory bowel disease (IBD) in 1859, after decades of research that have contributed to the understanding of the genetic and ...environmental factors involved in IBD pathogenesis. Today, a range of treatments is available for directed therapy, mostly targeting the overactive immune response. However, the mechanisms by which the immune system contributes to disease pathogenesis and progression are not fully understood. One challenge hindering IBD research is the heterogeneous nature of the disease and the lack of understanding of how immune cells interact with one another in the gut mucosa. Introduction of a technology that enables expansive characterization of the inflammatory environment of human IBD tissues may address this gap in knowledge.
We used the imaging mass cytometry platform to perform highly multiplex image analysis of IBD and healthy deidentified intestine sections (6 Crohn’s disease compared to 6 control ileum; 6 ulcerative colitis compared to 6 control colon). The acquired images were graded for inflammation severity by analysis of adjacent H&E tissue sections. We assigned more than 300,000 cells to unique cell types and performed analyses of tissue integrity, epithelial activity, and immune cell composition.
The intestinal epithelia of patients with IBD exhibited increased proliferation rates and expression of HLA-DR compared to control tissues, and both features were positively correlated with the severity of inflammation. The neighborhood analysis determined enrichment of regulatory T cell interactions with CD68+ macrophages, CD4+ T cells, and plasma cells in both forms of IBD, whereas activated lysozyme C+ macrophages were preferred regulatory T cell neighbors in Crohn’s disease but not ulcerative colitis.
Altogether, our study shows the power of imaging mass cytometry and its ability to both quantify immune cell types and characterize their spatial interactions within the inflammatory environment by a single analysis platform.
Inflammatory bowel disease—with its 2 forms, Crohn’s disease and ulcerative colitis—affects millions of patients worldwide and has a major impact on quality of life and life expectancy, because it dramatically increases the risk of colon cancer. Here, a novel methodology was developed to better understand the interplay of various cell types that contribute to disease onset and severity. This methodology promises to answer major outstanding questions in inflammatory bowel disease and to become a critical new tool for the rapid assessment of new drug efficacy.
The ability to control matter at the atomic scale and build devices with atomic precision is central to nanotechnology. The scanning tunnelling microscope can manipulate individual atoms and ...molecules on surfaces, but the manipulation of silicon to make atomic-scale logic circuits has been hampered by the covalent nature of its bonds. Resist-based strategies have allowed the formation of atomic-scale structures on silicon surfaces, but the fabrication of working devices-such as transistors with extremely short gate lengths, spin-based quantum computers and solitary dopant optoelectronic devices-requires the ability to position individual atoms in a silicon crystal with atomic precision. Here, we use a combination of scanning tunnelling microscopy and hydrogen-resist lithography to demonstrate a single-atom transistor in which an individual phosphorus dopant atom has been deterministically placed within an epitaxial silicon device architecture with a spatial accuracy of one lattice site. The transistor operates at liquid helium temperatures, and millikelvin electron transport measurements confirm the presence of discrete quantum levels in the energy spectrum of the phosphorus atom. We find a charging energy that is close to the bulk value, previously only observed by optical spectroscopy.
Pancreatic islets depend on cytosolic calcium (Ca
) to trigger the secretion of glucoregulatory hormones and trigger transcriptional regulation of genes important for islet response to stimuli. To ...date, there has not been an attempt to profile Ca
-regulated gene expression in all islet cell types. Our aim was to construct a large single-cell transcriptomic dataset from human islets exposed to conditions that would acutely induce or inhibit intracellular Ca
signalling, while preserving biological heterogeneity.
We exposed intact human islets from three donors to the following conditions: (1) 2.8 mmol/l glucose; (2) 16 mmol/l glucose and 40 mmol/l KCl to maximally stimulate Ca
signalling; and (3) 16 mmol/l glucose, 40 mmol/l KCl and 5 mmol/l EGTA (Ca
chelator) to inhibit Ca
signalling, for 1 h. We sequenced 68,650 cells from all islet cell types, and further subsetted the cells to form an endocrine cell-specific dataset of 59,373 cells expressing INS, GCG, SST or PPY. We compared transcriptomes across conditions to determine the differentially expressed Ca
-regulated genes in each endocrine cell type, and in each endocrine cell subcluster of alpha and beta cells.
Based on the number of Ca
-regulated genes, we found that each alpha and beta cell cluster had a different magnitude of Ca
response. We also showed that polyhormonal clusters expressing both INS and GCG, or both INS and SST, are defined by Ca
-regulated genes specific to each cluster. Finally, we identified the gene PCDH7 from the beta cell clusters that had the highest number of Ca
-regulated genes, and showed that cells expressing cell surface PCDH7 protein have enhanced glucose-stimulated insulin secretory function.
Here we use our large-scale, multi-condition, single-cell dataset to show that human islets have cell-type-specific Ca
-regulated gene expression profiles, some of them specific to subpopulations. In our dataset, we identify PCDH7 as a novel marker of beta cells having an increased number of Ca
-regulated genes and enhanced insulin secretory function.
A searchable and user-friendly format of the data in this study, specifically designed for rapid mining of single-cell RNA sequencing data, is available at https://lynnlab.shinyapps.io/Human_Islet_Atlas/ . The raw data files are available at NCBI Gene Expression Omnibus (GSE196715).
Effective screening is a desirable method for the early detection and successful treatment for diabetic retinopathy, and fundus photography is currently the dominant medium for retinal imaging due to ...its convenience and accessibility. Manual screening using fundus photographs has however involved considerable costs for patients, clinicians and national health systems, which has limited its application particularly in less-developed countries. The advent of artificial intelligence, and in particular deep learning techniques, has however raised the possibility of widespread automated screening.
In this review, we first briefly survey major published advances in retinal analysis using artificial intelligence. We take care to separately describe standard multiple-field fundus photography, and the newer modalities of ultra-wide field photography and smartphone-based photography. Finally, we consider several machine learning concepts that have been particularly relevant to the domain and illustrate their usage with extant works.
In the ophthalmology field, it was demonstrated that deep learning tools for diabetic retinopathy show clinically acceptable diagnostic performance when using colour retinal fundus images. Artificial intelligence models are among the most promising solutions to tackle the burden of diabetic retinopathy management in a comprehensive manner. However, future research is crucial to assess the potential clinical deployment, evaluate the cost-effectiveness of different DL systems in clinical practice and improve clinical acceptance.
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