Single-cell Transcriptomic Studies Research Articles

Integrated single-cell transcriptomic and epigenetic study of cell state transition and lineage commitment in embryonic mouse cerebellum.

Recent studies using single-cell RNA-sequencing have revealed cellular heterogeneity in the developing mammalian cerebellum, yet the regulatory logic underlying this cellular diversity remains to be elucidated. Using integrated single-cell RNA and ATAC analyses, we resolved developmental trajectories of cerebellar progenitors and identified putative trans- and cis-elements that control cell state transition. We reverse engineered gene regulatory networks (GRNs) of each cerebellar cell type. Through in silico simulations and in vivo experiments, we validated the efficacy of GRN analyses and uncovered the molecular control of a posterior transitory zone (PTZ), a distinct progenitor zone residing immediately anterior to the morphologically defined rhombic lip (RL). We showed that perturbing cell fate specification in the PTZ and RL causes posterior cerebellar vermis hypoplasia, the most common cerebellar birth defect in humans. Our study provides a foundation for comprehensive studies of developmental programs of the mammalian cerebellum.

Glucose Metabolism and Aging of Hematopoietic Stem and Progenitor Cells.

Comprehensive proteomics studies of human hematopoietic stem and progenitor cells (HSPC) have revealed that aging of the HSPC compartment is characterized by elevated glycolysis. This is in addition to deregulations found in murine transcriptomics studies, such as an increased differentiation bias towards the myeloid lineage, alterations in DNA repair, and a decrease in lymphoid development. The increase in glycolytic enzyme activity is caused by the expansion of a more glycolytic HSPC subset. We therefore developed a method to isolate HSPC into three distinct categories according to their glucose uptake (GU) levels, namely the GUhigh, GUinter and GUlow subsets. Single-cell transcriptomics studies showed that the GUhigh subset is highly enriched for HSPC with a differentiation bias towards myeloid lineages. Gene set enrichment analysis (GSEA) demonstrated that the gene sets for cell cycle arrest, senescence-associated secretory phenotype, and the anti-apoptosis and P53 pathways are significantly upregulated in the GUhigh population. With this series of studies, we have produced a comprehensive proteomics and single-cell transcriptomics atlas of molecular changes in human HSPC upon aging. Although many of the molecular deregulations are similar to those found in mice, there are significant differences. The most unique finding is the association of elevated central carbon metabolism with senescence. Due to the lack of specific markers, the isolation and collection of senescent cells have yet to be developed, especially for human HSPC. The GUhigh subset from the human HSPC compartment possesses all the transcriptome characteristics of senescence. This property may be exploited to accurately enrich, visualize, and trace senescence development in human bone marrow.

Transcriptomically-Guided Pharmacological Experiments in Neocortical and Hippocampal NPY-Positive GABAergic Interneurons.

Cortical GABAergic interneurons have been shown to fulfil important roles by inhibiting excitatory principal neurons. Recent transcriptomic studies have confirmed seminal discoveries that used anatomic and electrophysiological methods highlighting the existence of multiple different classes of GABAergic interneurons. Although some of these studies have emphasized that inter-regional differences may exist for a given class, the extent of such differences remains unknown. To address this problem, we used single-cell Patch-RNAseq to characterize neuropeptide Y (NPY)-positive GABAergic interneurons in superficial layers of the primary auditory cortex (AC) and in distal layers of area CA3 in mice. We found that more than 300 genes are differentially expressed in NPY-positive neurons between these two brain regions. For example, the AMPA receptor (AMPAR) auxiliary subunit Shisa9/CKAMP44 and the 5HT2a receptor (5HT2aR) are significantly higher expressed in auditory NPY-positive neurons. These findings guided us to perform pharmacological experiments that revealed a role for 5HT2aRs in auditory NPY-positive neurons. Specifically, although the application of 5HT led to a depolarization of both auditory and CA3 NPY-positive neurons, the 5HT2aR antagonist ketanserin only reversed membrane potential changes in auditory NPY-positive neurons. Our study demonstrates the potential of single-cell transcriptomic studies in guiding directed pharmacological experiments.

Nervous system-wide analysis of Hox regulation of terminal neuronal fate specification in Caenorhabditis elegans.

Hox genes encode evolutionarily conserved transcription factors that specify regional identities along the anterior-posterior (A-P) axis. Although some Hox genes are known to regulate the differentiation of certain neurons, to what extent Hox genes are involved in the terminal specification of the entire nervous system is unclear. Here, we systematically mapped the expression of all six Hox genes in C. elegans nervous system and found Hox expression in 97 (32%) of the 302 neurons in adult hermaphrodites. Our results are generally consistent with previous high-throughput expression mapping and single-cell transcriptomic studies. Detailed analysis of the fate markers for these neurons revealed that Hox genes regulate the differentiation of 29 (25%) of the 118 classes of C. elegans neurons. Hox genes not only regulate the specification of terminal neuronal fates through multiple mechanisms but also control subtype diversification along the A-P axis. The widespread involvement of Hox genes in neuronal differentiation indicates their roles in establishing complex nervous systems.

Morpho-Electric Properties and Diversity of Oxytocin Neurons in Paraventricular Nucleus of Hypothalamus in Female and Male Mice.

Oxytocin (OXT) neurons in paraventricular nucleus of hypothalamus (PVN) are involved in modulating multiple functions, including social, maternal, feeding, and emotional related behaviors. PVN OXT neurons are canonically classified into magnocellular (Magno) and parvocellular (Parvo) subtypes. However, morpho-electric properties and the diversity of PVN OXT neurons are not well investigated. In this study, we profiled the morpho-electric properties of PVN OXT neurons by combining transgenic mice, electrophysiological recording, morphologic reconstruction, and unsupervised clustering analyses. Total 224 PVN OXT neurons from 23 mice were recorded and used for analyses in this study, and 29 morpho-electric parameters were measured. Magno and Parvo OXT neurons have prominent differences in their morpho-electric features, and PVN OXT neurons in male and female mice share similar neuronal properties. Some morpho-electric features of PVN OXT neurons, especially Magno neurons, exhibit significant diverse changes along the rostral-caudal axis. Furthermore, we find that PVN OXT neurons are classified into at least six subtypes based on their morpho-electric properties via unsupervised clustering. Only one Magno-Parvo mixed subtype in posterior PVN subregion, but not the other five subtypes, showed significant neuronal activity change in different feeding conditions. Our study supports the diversity of PVN OXT neurons and subtle neuron classification will promote excavating the functions of oxytocinergic system.SIGNIFICANCE STATEMENT Oxytocin (OXT) is well known for its function in labor induction, but it also plays multiple roles in social, feeding, and emotional behaviors via modulating different brain regions. Paraventricular nucleus of hypothalamus (PVN) OXT neurons are traditionally classified into magnocellular and parvocellular. However, functional and single-cell transcriptomic studies indicate that OXT neurons should be further classified. Here, we thoroughly investigated the morpho-electric properties and spatial distribution of PVN OXT neurons, and find that OXT neurons have at least six subtypes based on their morpho-electric features. Among these six subtypes, only one magnocellular-parvocellular mixed subtype, which are distributed in the posterior PVN subregion, change their activities with different feeding states. Our study uncovers the diversity of PVN OXT neurons and suggests the necessary of subtle neuronal classification.

Survival Genie, a web platform for survival analysis across pediatric and adult cancers

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan–Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/.

Human CD206+ macrophages associate with diabetes and adipose tissue lymphoid clusters.

Increased adipose tissue macrophages (ATMs) correlate with metabolic dysfunction in humans and are causal in development of insulin resistance in mice. Recent bulk and single-cell transcriptomics studies reveal a wide spectrum of gene expression signatures possible for macrophages that depends on context, but the signatures of human ATM subtypes are not well defined in obesity and diabetes. We profiled 3 prominent ATM subtypes from human adipose tissue in obesity and determined their relationship to type 2 diabetes. Visceral adipose tissue (VAT) and s.c. adipose tissue (SAT) samples were collected from diabetic and nondiabetic obese participants to evaluate cellular content and gene expression. VAT CD206+CD11c− ATMs were increased in diabetic participants, were scavenger receptor–rich with low intracellular lipids, secreted proinflammatory cytokines, and diverged significantly from 2 CD11c+ ATM subtypes, which were lipid-laden, were lipid antigen presenting, and overlapped with monocyte signatures. Furthermore, diabetic VAT was enriched for CD206+CD11c− ATM and inflammatory signatures, scavenger receptors, and MHC II antigen presentation genes. VAT immunostaining found CD206+CD11c– ATMs concentrated in vascularized lymphoid clusters adjacent to CD206–CD11c+ ATMs, while CD206+CD11c+ were distributed between adipocytes. Our results show ATM subtype–specific profiles that uniquely contribute to the phenotypic variation in obesity.

Diversity of human NK cell developmental pathways defined by single-cell analyses.

Human natural killer (NK) and innate lymphoid cells (ILCs) include diverse specialized phenotypic and functional subsets that reflect their roles as innate immune effector cells present in tissue and circulation. In recent years, significant advances have been made in better defining their tissue resident phenotypes, developmental pathways, and phenotypic plasticity. Here we offer a brief review of new insights into human NK cell diversity specifically defined by next generation sequencing and single-cell transcriptomic studies and integrate these into our current models of human NK cell developmental trajectories and mature subsets. These studies highlight both a deeper understanding of innate lymphoid cell differentiation and homeostasis and underscore critical questions that remain outstanding in the field.

Cryopreservation and post-thaw characterization of dissociated human islet cells.

The objective of this study is to optimize the cryopreservation of dissociated islet cells and obtain functional cells that can be used in single-cell transcriptome studies on the pathology and treatment of diabetes. Using an iterative graded freezing approach we obtained viable cells after cooling in 10% dimethyl sulfoxide and 6% hydroxyethyl starch at 1°C/min to –40°C, storage in liquid nitrogen, rapid thaw, and removal of cryoprotectants by serial dilution. The expression of epithelial cell adhesion molecule declined immediately after thaw, but recovered after overnight incubation, while that of an endocrine cell marker (HPi2) remained high after cryopreservation. Patch-clamp electrophysiology revealed differences in channel activities and exocytosis of various islet cell types; however, exocytotic responses, and the biophysical properties of voltage-gated Na+ and Ca2+ channels, are sustained after cryopreservation. Single-cell RNA sequencing indicates that overall transcriptome and crucial exocytosis genes are comparable between fresh and cryopreserved dispersed human islet cells. Thus, we report an optimized procedure for cryopreserving dispersed islet cells that maintained their membrane integrity, along with their molecular and functional phenotypes. Our findings will not only provide a ready source of cells for investigating cellular mechanisms in diabetes but also for bio-engineering pseudo-islets and islet sheets for modeling studies and potential transplant applications.

Epithelial-Mesenchymal Transition Signaling and Prostate Cancer Stem Cells: Emerging Biomarkers and Opportunities for Precision Therapeutics.

Prostate cancers may reactivate a latent embryonic program called the epithelial–mesenchymal transition (EMT) during the development of metastatic disease. Through EMT, tumors can develop a mesenchymal phenotype similar to cancer stem cell traits that contributes to metastasis and variation in therapeutic responses. Some of the recurrent somatic mutations of prostate cancer affect EMT driver genes and effector transcription factors that induce the chromatin- and androgen-dependent epigenetic alterations that characterize castrate-resistant prostate cancer (CRPC). EMT regulators in prostate cancer comprise transcription factors (SNAI1/2, ZEB1, TWIST1, and ETS), tumor suppressor genes (RB1, PTEN, and TP53), and post-transcriptional regulators (miRNAs) that under the selective pressures of antiandrogen therapy can develop an androgen-independent metastatic phenotype. In prostate cancer mouse models of EMT, Slug expression, as well as WNT/β-Catenin and notch signaling pathways, have been shown to increase stemness potential. Recent single-cell transcriptomic studies also suggest that the stemness phenotype of advanced prostate cancer may be related to EMT. Other evidence correlates EMT and stemness with immune evasion, for example, activation of the polycomb repressor complex I, promoting EMT and stemness and cytokine secretion through RB1, TP53, and PRC1. These findings are helping clinical trials in CRPC that seek to understand how drugs and biomarkers related to the acquisition of EMT can improve drug response.

ScPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

Integrated single-cell transcriptomic and epigenetic study of cell-state transition and lineage commitment in embryonic mouse cerebellum

Integrated single-cell transcriptomic and epigenetic study of cell-state transition and lineage commitment in embryonic mouse cerebellum

Integrated single-cell transcriptomic and epigenetic study of cell-state transition and lineage commitment in embryonic mouse cerebellum

Integrated single-cell transcriptomic and epigenetic study of cell-state transition and lineage commitment in embryonic mouse cerebellum

ATRX regulates glial identity and the tumor microenvironment in IDH-mutant glioma

BackgroundRecent single-cell transcriptomic studies report that IDH-mutant gliomas share a common hierarchy of cellular phenotypes, independent of genetic subtype. However, the genetic differences between IDH-mutant glioma subtypes are prognostic, predictive of response to chemotherapy, and correlate with distinct tumor microenvironments.ResultsTo reconcile these findings, we profile 22 human IDH-mutant gliomas using scATAC-seq and scRNA-seq. We determine the cell-type-specific differences in transcription factor expression and associated regulatory grammars between IDH-mutant glioma subtypes. We find that while IDH-mutant gliomas do share a common distribution of cell types, there are significant differences in the expression and targeting of transcription factors that regulate glial identity and cytokine elaboration. We knock out the chromatin remodeler ATRX, which suffers loss-of-function alterations in most IDH-mutant astrocytomas, in an IDH-mutant immunocompetent intracranial murine model. We find that both human ATRX-mutant gliomas and murine ATRX-knockout gliomas are more heavily infiltrated by immunosuppressive monocytic-lineage cells derived from circulation than ATRX-intact gliomas, in an IDH-mutant background. ATRX knockout in murine glioma recapitulates gene expression and open chromatin signatures that are specific to human ATRX-mutant astrocytomas, including drivers of astrocytic lineage and immune-cell chemotaxis. Through single-cell cleavage under targets and tagmentation assays and meta-analysis of public data, we show that ATRX loss leads to a global depletion in CCCTC-binding factor association with DNA, gene dysregulation along associated chromatin loops, and protection from therapy-induced senescence.ConclusionsThese studies explain how IDH-mutant gliomas from different subtypes maintain distinct phenotypes and tumor microenvironments despite a common lineage hierarchy.

Survival Genie: A Web Portal for Single-Cell Data, Gene-Ratio, and Cell Composition-Based Survival Analyses

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. Many packages and tools have been developed to correlate single-gene features to clinical outcomes, but lack in performing such analysis based on multiple-genes, gene sets, and genes ratio. Furthermore, cluster marker genes associated with cell types, states and function from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates associations between the enrichment of known cell types and survival outcome across cancers.We have developed Survival Genie (https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/, a web tool to perform survival analysis on single-cell RNA-seq data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden (Fig. 1). For comprehensive survival evaluation, the Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs for both adult and pediatric cancers including different types of leukemia. Users can upload single-cell data or gene sets and select partitioning methods (i.e., mean, median, quartile, cutp) to determine the effect of their levels on patient survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile (Fig. 1). The Survival Genie source code is written in the R programming language and the interactive web application with the R Shiny framework.We demonstrate the application of the Survival Genie tool by exploring the prognostic utility of blast cell and immune cell markers generated from single cell RNA-seq analysis of paired pediatric AML bone marrow samples taken at the time of diagnosis and end of induction (Thomas, Perumalla et al. 2020) . We identified AML blast specific signature consisting of 7 genes (CLEC11A, PRAME, AZU1, NREP, ARMH1, C1QBP, TRH) that depicted significant association with poor survival (HR=2.3 and Log Rank P-value=.007). Further analysis of AML relapse-associated single cell clusters showed increased levels of individual markers, including CRIP1, FLNA, and RFLNB/FAM101B and their significant association with poor survival in TARGET AML dataset. Additionally, expression of combined RFLNB/FAM101B and WDFY4 genes was associated with poor overall survival (HR=1.8 Log Rank P-value=0.01) and shorter event-free survival (HR=1.9, Log Rank P-value<0.0001). This clearly shows the usefulness of Survival Genie tool in exploring the prognostic association of genes as well as gene sets.Survival Genie is a one-stop web-portal for single-cell phenotype clusters, list of genes, and cell composition-based survival analyses across multiple cancer datasets including hematological malignancies. The analytical options and harmonized collection of multiple cancer types makes Survival Genie a comprehensive resource to correlate gene sets, pathways, cellular enrichment, and single cell clusters to clinical outcome to assist in developing next generation prognostic and therapeutic biomarkers. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

OAB-007: Single-cell multiomic analysis identifies regulatory programs in relapsed/refractory multiple myeloma

<h3>Introduction</h3> Despite new treatment approaches, in most patients with multiple myeloma (MM) resistant subclones expand, finally resulting in the development of relapsed/refractory disease (RRMM). Recent single-cell transcriptomic (scRNA-seq) studies have improved our understanding of tumor heterogeneity in MM, but our knowledge about the regulatory processes and gene expression signatures in resistant subclones is still limited. Thus, we performed single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) as well as scRNA-seq from the same MM patient samples and developed a new approach for the analysis of the transcriptome and epigenome of competing subclones. <h3>Methods</h3> CD138+ bone marrow cells of 11 RRMM patients were longitudinally collected and profiled using 10X Genomics scRNA- and scATAC-seq platform. Pre-processing was performed with CellRanger and downstream analysis using Seurat and ArchR. To determine clones based on copy number variations (CNVs), we leveraged inferCNV for scRNA-seq and developed a sliding window approach adapted from a previously published method by Satpathy et al. for scATAC-seq data. Whole genome sequencing was available to confirm the identified CNV clones. <h3>Results</h3> We obtained chromatin accessibility profiles of >43000 cells after quality control, establishing the first epigenetic map of RRMM with clearly separated clusters for each patient. Samples from distinct MM subgroups, particularly t(4;14) and t(11;14), clustered together in a pseudo-bulk analysis based on transcription factor activity scores from scATAC. To investigate treatment-induced clonal dynamics we aligned sc chromatin accessibility profiles to clones inferred in scRNA-seq based on CNVs enabling us to track individual tumor subclones over time. An example for clonal adaptation is a patient with two subclones that both responded differently to pomalidomide plus anti-CD38 treatment: while clone 1 showed a higher activity for members of IRF family, ZEB1 and KLF family members were more active in clone 2. Branching evolution was e.g. observed in a patient treated with a MEK/BRAF inhibitor. This patient showed two subclones before treatment, achieved a complete remission and relapsed with one dominant clone. One of the initial subclones could be identified as the precursor of this dominant clone with both showing high activity of NFKB family members, while higher activity of SOX10/15 was only observed upon MEK/BRAF inhibition. An example for combined clonal adaptation and selection was observed in a patient with bi-allelic inactivation of TP53 in one of the two subclones before MCL1 inhibition. Interestingly, this clone was depleted at relapse. <h3>Conclusions</h3> In summary, our new approach to track individual tumor subclones over time indicates clonal adaptation or selection in virtually all RRMM patients and reveals new insights into regulatory mechanisms underlying treatment-resistance, opening potential avenues for new therapies.

The Cellular Origins of Cancer-Associated Fibroblasts and Their Opposing Contributions to Pancreatic Cancer Growth.

Pancreatic tumors are known to harbor an abundant and highly desmoplastic stroma. Among the various cell types that reside within tumor stroma, cancer-associated fibroblasts (CAFs) have gained a lot of attention in the cancer field due to their contributions to carcinogenesis and tumor architecture. These cells are not a homogeneous population, but have been shown to have different origins, phenotypes, and contributions. In pancreatic tumors, CAFs generally emerge through the activation and/or recruitment of various cell types, most notably resident fibroblasts, pancreatic stellate cells (PSCs), and tumor-infiltrating mesenchymal stem cells (MSCs). In recent years, single cell transcriptomic studies allowed the identification of distinct CAF populations in pancreatic tumors. Nonetheless, the exact sources and functions of those different CAF phenotypes remain to be fully understood. Considering the importance of stromal cells in pancreatic cancer, many novel approaches have aimed at targeting the stroma but current stroma-targeting therapies have yielded subpar results, which may be attributed to heterogeneity in the fibroblast population. Thus, fully understanding the roles of different subsets of CAFs within the stroma, and the cellular dynamics at play that contribute to heterogeneity in CAF subsets may be essential for the design of novel therapies and improving clinical outcomes. Fortunately, recent advances in technologies such as microfluidics and bio-printing have made it possible to establish more advanced ex vivo models that will likely prove useful. In this review, we will present the different roles of stromal cells in pancreatic cancer, focusing on CAF origin as a source of heterogeneity, and the role this may play in therapy failure. We will discuss preclinical models that could be of benefit to the field and that may contribute to further clinical development.

High-throughput RNA sequencing of paraformaldehyde-fixed single cells

Single-cell transcriptomic studies that require intracellular protein staining, rare cell sorting, or inactivation of infectious pathogens are severely limited. This is because current high-throughput single-cell RNA sequencing methods are either incompatible with or necessitate laborious sample preprocessing for paraformaldehyde treatment, a common tissue and cell fixation and preservation technique. Here we present FD-seq (Fixed Droplet RNA sequencing), a high-throughput method for droplet-based RNA sequencing of paraformaldehyde-fixed, permeabilized and sorted single cells. We show that FD-seq preserves the RNA integrity and relative gene expression levels after fixation and permeabilization. Furthermore, FD-seq can detect a higher number of genes and transcripts than methanol fixation. We first apply FD-seq to analyze a rare subpopulation of cells supporting lytic reactivation of the human tumor virus KSHV, and identify TMEM119 as a potential host factor that mediates viral reactivation. Second, we find that infection with the human betacoronavirus OC43 leads to upregulation of pro-inflammatory pathways in cells that are exposed to the virus but fail to express high levels of viral genes. FD-seq thus enables integrating phenotypic with transcriptomic information in rare cell subpopulations, and preserving and inactivating pathogenic samples.

Who am I? (re-)Defining fibroblast identity and immunological function in the age of bioinformatics.

Who am I? (re-)Defining fibroblast identity and immunological function in the age of bioinformatics.

Computational Drug Repurposing for Alzheimer's Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies.

Background: Traditional therapeutics targeting Alzheimer’s disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.