Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.

Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data.

Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) enables the quantification of chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types/states. However, when analyzed individually, scRNA-seq and snATAC-seq data often produce conflicting results regarding cell type/state assignment. In addition, there is a loss of power as the two modalities reflect the same underlying cell types/states. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data make it possible to directly model the relationships between the two modalities. However, given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality data to gain a comprehensive view of the cellular complexity. Here, we benchmarked the performance of seven existing single-cell multi-omic data integration methods. Specifically, we evaluated whether these methods are able to uncover peak-gene associations from single-modality data, and to what extent the multiome data can provide additional guidance for the analysis of the existing single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data, but the number of cells in the multiome data is critical to ensure a good cell type annotation. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation. Lastly, Seurat v4 is the best at integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.

Calcium-dependent transcriptional changes in human pancreatic islet cells reveal functional diversity in islet cell subtypes.

AIMS/HYPOTHESIS: Pancreatic islets depend on cytosolic calcium (Ca2+) 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 Ca2+-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 Ca2+ signalling, while preserving biological heterogeneity. METHODS: 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 Ca2+ signalling; and (3) 16 mmol/l glucose, 40 mmol/l KCl and 5 mmol/l EGTA (Ca2+ chelator) to inhibit Ca2+ 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 Ca2+-regulated genes in each endocrine cell type, and in each endocrine cell subcluster of alpha and beta cells. RESULTS: Based on the number of Ca2+-regulated genes, we found that each alpha and beta cell cluster had a different magnitude of Ca2+ response. We also showed that polyhormonal clusters expressing both INS and GCG, or both INS and SST, are defined by Ca2+-regulated genes specific to each cluster. Finally, we identified the gene PCDH7 from the beta cell clusters that had the highest number of Ca2+-regulated genes, and showed that cells expressing cell surface PCDH7 protein have enhanced glucose-stimulated insulin secretory function. CONCLUSIONS/INTERPRETATION: Here we use our large-scale, multi-condition, single-cell dataset to show that human islets have cell-type-specific Ca2+-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 Ca2+-regulated genes and enhanced insulin secretory function. DATA AVAILABILITY: 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).

Spatial and transcriptional heterogeneity of pancreatic beta cell neogenesis revealed by a time-resolved reporter system.

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 (ßduct); unexpectedly, the others arise away from the ducts and adjacent to blood vessels (ßvessel). 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 ßduct, musculoaponeurotic fibrosarcoma oncogene family B (MafB)-positive ßduct and musculoaponeurotic fibrosarcoma oncogene family A (MafA)-positive ßvessel 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.

A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation.

CITE-seq, a single-cell multi-omics technology that measures RNA and protein expression simultaneously in single cells, has been widely applied in biomedical research, especially in immune related disorders and other diseases such as influenza and COVID-19. Despite the proliferation of CITE-seq, it is still costly to generate such data. Although data integration can increase information content, this raises computational challenges. First, combining multiple datasets is prone to batch effects that need to be addressed. Secondly, it is difficult to combine multiple CITE-seq datasets because the protein panels in different datasets may only partially overlap. Integrating multiple CITE-seq and single-cell RNA-seq (scRNA-seq) datasets is important because this allows the utilization of as many data as possible to uncover cell population heterogeneity. To overcome these challenges, we present sciPENN, a multi-use deep learning approach that supports CITE-seq and scRNA-seq data integration, protein expression prediction for scRNA-seq, protein expression imputation for CITE-seq, quantification of prediction and imputation uncertainty, and cell type label transfer from CITE-seq to scRNA-seq. Comprehensive evaluations spanning multiple datasets demonstrate that sciPENN outperforms other current state-of-the-art methods.

Single-cell analysis of the human pancreas in type 2 diabetes using multi-spectral imaging mass cytometry.

Type 2 diabetes mellitus (T2D) is a chronic age-related disorder characterized by hyperglycemia due to the failure of pancreatic beta cells to compensate for increased insulin demand. Despite decades of research, the pathogenic mechanisms underlying T2D remain poorly defined. Here, we use imaging mass cytometry (IMC) with a panel of 34 antibodies to simultaneously quantify markers of pancreatic exocrine, islet, and immune cells and stromal components. We analyze over 2 million cells from 16 pancreata obtained from donors with T2D and 13 pancreata from age-similar non-diabetic controls. In the T2D pancreata, we observe significant alterations in islet architecture, endocrine cell composition, and immune cell constituents. Thus, both HLA-DR-positive CD8 T cells and macrophages are enriched intra-islet in the T2D pancreas. These efforts demonstrate the utility of IMC for investigating complex events at the cellular level in order to provide insights into the pathophysiology of T2D.

Highly Multiplexed Image Analysis of Intestinal Tissue Sections in Patients With Inflammatory Bowel Disease.

BACKGROUND & AIMS: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Single-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors.

Human embryonic stem cells (hESCs) are a potential unlimited source of insulin-producing ß cells for diabetes treatment. A greater understanding of how ß cells form during embryonic development will improve current hESC differentiation protocols. All pancreatic endocrine cells, including ß cells, are derived from Neurog3-expressing endocrine progenitors. This study characterizes the single-cell transcriptomes of 6,905 mouse embryonic day (E) 15.5 and 6,626 E18.5 pancreatic cells isolated from Neurog3-Cre; Rosa26mT/mG embryos, allowing for enrichment of endocrine progenitors (yellow; tdTomato + EGFP) and endocrine cells (green; EGFP). Using a NEUROG3-2A-eGFP CyT49 hESC reporter line (N5-5), 4,462 hESC-derived GFP+ cells were sequenced. Differential expression analysis revealed enrichment of markers that are consistent with progenitor, endocrine, or previously undescribed cell-state populations. This study characterizes the single-cell transcriptomes of mouse and hESC-derived endocrine progenitors and serves as a resource (https://lynnlab.shinyapps.io/embryonic_pancreas) for improving the formation of functional ß-like cells from hESCs.

Anti-tumor and anti-osteolysis effects of the metronomic use of zoledronic acid in primary and metastatic breast cancer mouse models.

This study aims to determine the effect of metronomic (0.0125 mg/kg twice a week for 4 weeks) zoledronic acid (ZOL) on cancer propagation and osteolysis against both metastatic and primary breast cancer in mice model. From our results, metronomic ZOL resulted in a significant reduction of tumor burden and did not promote lung or liver metastasis. The metronomic ZOL appeared to be more effective than the conventional regimen (0.1 mg/kg once in 4 weeks) in reducing breast cancer tumor burden, and regulating its movement to lung and liver. This dosing schedule of ZOL showed great potential against metastatic breast cancer.

Long-term follow-up using a higher target range for lamotrigine monitoring.

The aims of the study were (1) to review the clinical application of the higher target plasma lamotrigine (LTG) concentration of 3-14 mg/L previously proposed by our therapeutic drug monitoring (TDM) laboratory following our initial study 7 years earlier, and (2) to survey clinical application of LTG assays by experienced neurologists (n = 11) who frequently use LTG. There was a 2.9-fold increase in LTG assay requests received by our laboratory from 1996 to 2003. By comparison, data for the number of LTG prescriptions filled throughout Australia were limited to the 4 years from 1997 to 2000, where a 1.7-fold increase was seen. LTG assay requests increased 1.5-fold in this same 4-year period (r2 = 0.97), indicating that the growth in assay requests paralleled the growth in prescriptions. The distribution of LTG concentrations measured in 2003 was compared with those for 1996 and 1997. This indicated there was a significantly increased (P < 0.01) clinical usage of the higher LTG target range. This result was reinforced by questionnaire responses. Respondents (100% of those surveyed), (1) considered the target LTG concentration (3-14 mg/L) to be one of the primary parameters applied in individualizing LTG dosage regimens, (2) were using target concentrations above 7 mg/L in 75% of patients, and (3) reported dose-limiting toxicities in some (but not all) patients typically at concentrations above, or well above, 13 mg/L. In conclusion, the growth in LTG assay requests received by our laboratory paralleled prescribing of this drug. The clinical use of the higher LTG target concentration range was increased during the 7 years since its introduction, indicating clinical acceptance and therapeutic benefit as well as the absence of long-term adverse effects associated with higher plasma LTG concentrations.