Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis

Here we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4's automated cell type annotation.

Identification of Regulatory RNA-Binding Proteins Associated with Immune Infiltration in Laryngeal Squamous Cell Carcinoma

We analyzed bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to identify alternative splicing (AS) events and regulatory RNA-binding proteins (RBPs) associated with immune infiltration in human laryngeal squamous cell carcinoma (LSCC). Whole-transcriptome sequencing data of 20 human laryngeal cancer and paracancerous tissues were downloaded from the Gene Expression Omnibus public database, using newly published splicing-site usage variation analysis software to obtain highly conserved regulated AS (RAS) events, and scientific reverse convolution algorithm analysis was used to identify significantly different immune cells and perform a correlation analysis between the two. The software package edgeR was used to identify differentially expressed RBPs and the immune infiltration-related LSCC-RAS they may regulate. Finally, we present the expression profiles and survival curves of 117 human laryngeal cancer samples from The Cancer Genome Atlas dataset for the identified RBPs and LSCC-RAS. We also downloaded the gene set enrichment 150321 scRNA-seq data for two human LSCC tissue samples. The RBP expression pattern and the expression of prophase RBP genes were analyzed in different LSCC cell populations. RNA-binding motif protein 47 (RBM47) and filamin A, as well as the RBP-RAS events that were screened in both the fibulin 2 and fibronectin 1 genes, were all significantly associated with the prognosis, and the RBM47 gene was upregulated in myeloid cells. Because the prognosis was significantly associated with two RBP regulators and two LSCC-RAS events, they may be critical regulators of immune cell survival during laryngeal cancer progression, and RBM47 may regulate macrophage-associated AS and affect immunity.

Thymic stromal lymphopoietin induces IL-4/IL-13 from T cells to promote sebum secretion and adipose loss

BACKGROUND

The cytokine TSLP promotes type 2 immune responses and can induce adipose loss by stimulating lipid loss from the skin through sebum secretion by sebaceous glands, which enhances the skin barrier. However, the mechanism by which TSLP upregulates sebaceous gland function is unknown.

OBJECTIVES

This study investigated the mechanism by which TSLP stimulates sebum secretion and adipose loss.

METHODS

RNA-sequencing analysis was performed on sebaceous glands isolated by laser capture microdissection and single-cell RNA-sequencing analysis was performed on sorted skin T cells. Sebocyte function was analyzed by histological analysis and sebum secretion in vivo and by measuring lipogenesis and proliferation in vitro.

RESULTS

This study found that TSLP sequentially stimulated the expression of lipogenesis genes followed by cell death genes in sebaceous glands to induce holocrine secretion of sebum. TSLP did not affect sebaceous gland activity directly. Rather, single-cell RNA-sequencing revealed that TSLP recruited distinct T-cell clusters that produce IL-4 and IL-13, which were necessary for TSLP-induced adipose loss and sebum secretion. Moreover, IL-13 was sufficient to cause sebum secretion and adipose loss in vivo and to induce lipogenesis and proliferation of a human sebocyte cell line in vitro.

CONCLUSIONS

This study proposes that TSLP stimulates T cells to deliver IL-4 and IL-13 to sebaceous glands, which enhances sebaceous gland function, turnover, and subsequent adipose loss.

Single-cell profiling identifies a CD8bright CD244bright Natural Killer cell subset that reflects disease activity in HLA-A29-positive birdshot chorioretinopathy

Birdshot chorioretinopathy is an inflammatory eye condition strongly associated with MHC-I allele HLA-A29. The striking association with MHC-I suggests involvement of T cells, whereas natural killer (NK) cell involvement remains largely unstudied. Here we show that HLA-A29-positive birdshot chorioretinopathy patients have a skewed NK cell pool containing expanded CD16 positive NK cells which produce more proinflammatory cytokines. These NK cells contain populations that express CD8A which is involved in MHC-I recognition on target cells, display gene signatures indicative of high cytotoxic activity (GZMB, PRF1 and ISG15), and signaling through NK cell receptor CD244 (SH2D1B). Long-term monitoring of a cohort of birdshot chorioretinopathy patients with active disease identifies a population of CD8bright CD244bright NK cells, which rapidly declines to normal levels upon clinical remission following successful treatment. Collectively, these studies implicate CD8bright CD244bright NK cells in birdshot chorioretinopathy.

Tropism for ciliated cells is the dominant driver of influenza viral burst size in the human airway

Influenza viruses pose a significant burden on global human health. Influenza has a broad cellular tropism in the airway, but how infection of different epithelial cell types impacts replication kinetics and burden in the airways is not fully understood. Using primary human airway cultures, which recapitulate the diverse epithelial cell landscape of the human airways, we investigated the impact of cell type composition on virus tropism and replication kinetics. Cultures were highly diverse across multiple donors and 30 independent differentiation conditions and supported a range of influenza replication. Although many cell types were susceptible to influenza, ciliated and secretory cells were predominantly infected. Despite the strong tropism preference for secretory and ciliated cells, which consistently make up 75% or more of infected cells, only ciliated cells were associated with increased virus production. Surprisingly, infected secretory cells were associated with overall reduced virus output. The disparate response and contribution to influenza virus production could be due to different pro- and antiviral interferon-stimulated gene signatures between ciliated and secretory populations, which were interrogated with single-cell RNA sequencing. These data highlight the heterogeneous outcomes of influenza virus infections in the complex cellular environment of the human airway and the disparate impacts of infected cell identity on multiround burst size, even among preferentially infected cell types.

Directed differentiation of pancreatic δ cells from human pluripotent stem cells

Dysfunction of pancreatic δ cells contributes to the etiology of diabetes. Despite their important role, human δ cells are scarce, limiting physiological studies and drug discovery targeting δ cells. To date, no directed δ-cell differentiation method has been established. Here, we demonstrate that fibroblast growth factor (FGF) 7 promotes pancreatic endoderm/progenitor differentiation, whereas FGF2 biases cells towards the pancreatic δ-cell lineage via FGF receptor 1. We develop a differentiation method to generate δ cells from human stem cells by combining FGF2 with FGF7, which synergistically directs pancreatic lineage differentiation and modulates the expression of transcription factors and SST activators during endoderm/endocrine precursor induction. These δ cells display mature RNA profiles and fine secretory granules, secrete somatostatin in response to various stimuli, and suppress insulin secretion from in vitro co-cultured β cells and mouse β cells upon transplantation. The generation of human pancreatic δ cells from stem cells in vitro would provide an unprecedented cell source for drug discovery and cell transplantation studies in diabetes.

Hepatocyte-macrophage crosstalk via the PGRN-EGFR axis modulates ADAR1-mediated immunity in the liver

ADAR1-mediated RNA editing establishes immune tolerance to endogenous double-stranded RNA (dsRNA) by preventing its sensing, primarily by MDA5. Although deleting Ifih1 (encoding MDA5) rescues embryonic lethality in ADAR1-deficient mice, they still experience early postnatal death, and removing other MDA5 signaling proteins does not yield the same rescue. Here, we show that ablation of MDA5 in a liver-specific Adar knockout (KO) murine model fails to rescue hepatic abnormalities caused by ADAR1 loss. Ifih1;Adar double KO (dKO) hepatocytes accumulate endogenous dsRNAs, leading to aberrant transition to a highly inflammatory state and recruitment of macrophages into dKO livers. Mechanistically, progranulin (PGRN) appears to mediate ADAR1 deficiency-induced liver pathology, promoting interferon signaling and attracting epidermal growth factor receptor (EGFR)+ macrophages into dKO liver, exacerbating hepatic inflammation. Notably, the PGRN-EGFR crosstalk communication and consequent immune responses are significantly repressed in ADAR1high tumors, revealing that pre-neoplastic or neoplastic cells can exploit ADAR1-dependent immune tolerance to facilitate immune evasion.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still low. In this work, we proposed a novel Gaussian noise augmentation-based scRNA-seq contrastive learning method (GsRCL) to learn a type of discriminative feature representations for cell type identification tasks. A large-scale computational evaluation suggests that GsRCL successfully outperformed other state-of-the-art predictive methods on difficult cell type identification tasks, while the conventional random genes masking augmentation-based contrastive learning method also improved the accuracy of easy cell type identification tasks in general.

Timing of whole genome duplication is associated with tumor-specific MHC-II depletion in serous ovarian cancer

Whole genome duplication is frequently observed in cancer, and its prevalence in our prior analysis of end-stage, homologous recombination deficient high grade serous ovarian cancer (almost 80% of samples) supports the notion that whole genome duplication provides a fitness advantage under the selection pressure of therapy. Here, we therefore aim to identify potential therapeutic vulnerabilities in primary high grade serous ovarian cancer with whole genome duplication by assessing differentially expressed genes and pathways in 79 samples. We observe that MHC-II expression is lowest in tumors which have acquired whole genome duplication early in tumor evolution, and further demonstrate that reduced MHC-II expression occurs in subsets of tumor cells rather than in canonical antigen-presenting cells. Early whole genome duplication is also associated with worse patient survival outcomes. Our results suggest an association between the timing of whole genome duplication, MHC-II expression and clinical outcome in high grade serous ovarian cancer that warrants further investigation for therapeutic targeting.

Eed controls craniofacial osteoblast differentiation and mesenchymal proliferation from the neural crest

The histone methyltransferase Polycomb repressive complex 2 (PRC2) is required for specification of the neural crest, and mis-regulation of neural crest development can cause severe congenital malformations. PRC2 is necessary for neural crest induction, but the embryonic, cellular, and molecular consequences of PRC2 activity after neural crest induction are incompletely understood. Here we show that Eed, a core subunit of PRC2, is required for craniofacial osteoblast differentiation and mesenchymal proliferation after induction of the neural crest. Integrating mouse genetics with single-cell RNA sequencing, our results reveal that conditional knockout of Eed after neural crest cell induction causes severe craniofacial hypoplasia, impaired craniofacial osteogenesis, and attenuated craniofacial mesenchymal cell proliferation that is first evident in post-migratory neural crest cell populations. We show that Eed drives mesenchymal differentiation and proliferation in vivo and in primary craniofacial cell cultures by regulating diverse transcription factor programs that are required for specification of post-migratory neural crest cells. These data enhance understanding of epigenetic mechanisms that underlie craniofacial development, and shed light on the embryonic, cellular, and molecular drivers of rare congenital syndromes in humans.

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

mosaicMPI: a framework for modular data integration across cohorts and -omics modalities

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

ScType enables fast and accurate cell type identification from spatial transcriptomics data

SUMMARY

The limited resolution of spatial transcriptomics (ST) assays in the past has led to the development of cell type annotation methods that separate the convolved signal based on available external atlas data. In light of the rapidly increasing resolution of the ST assay technologies, we made available and investigated the performance of a deconvolution-free marker-based cell annotation method called scType. In contrast to existing methods, the spatial application of scType does not require computationally strenuous deconvolution, nor large single-cell reference atlases. We show that scType enables ultra-fast and accurate identification of abundant cell types from ST data, especially when a large enough panel of genes is detected. Examples of such assays are Visium and Slide-seq, which currently offer the best trade-off between high resolution and number of genes detected by the assay for cell type annotation.

AVAILABILITY AND IMPLEMENTATION

scType source R and python codes for spatial data are openly available in GitHub (https://github.com/kris-nader/sp-type or https://github.com/kris-nader/sc-type-py). Step-by-step tutorials for R and python spatial data analysis can be found in https://github.com/kris-nader/sp-type and https://github.com/kris-nader/sc-type-py/blob/main/spatial_tutorial.md, respectively.

Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing

BACKGROUND

It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for identifying Neo T cells and their corresponding T cell receptors (TCRs).

METHODS

By mapping neoantigen-reactive T cells from the single-cell transcriptomes of thousands of tumour-infiltrating lymphocytes, we developed a 26-gene machine learning model for the identification of neoantigen-reactive T cells.

RESULTS

In both training and validation sets, the model performed admirably. We discovered that the majority of Neo T cells exhibited notable differences in the biological processes of amide-related signal pathways. The analysis of potential cell-to-cell interactions, in conjunction with spatial transcriptomic and multiplex immunohistochemistry data, has revealed that Neo T cells possess potent signalling molecules, including LTA, which can potentially engage with tumour cells within the tumour microenvironment, thereby exerting anti-tumour effects. By sequencing CD8 + T cells in tumour samples of patients undergoing neoadjuvant immunotherapy, we determined that the fraction of Neo T cells was significantly and positively linked with the clinical benefit and overall survival rate of patients.

CONCLUSION

This method expedites the identification of neoantigen-reactive TCRs and the engineering of neoantigen-reactive T cells for therapy.

CTCF mutation at R567 causes developmental disorders via 3D genome rearrangement and abnormal neurodevelopment

The three-dimensional genome structure organized by CTCF is required for development. Clinically identified mutations in CTCF have been linked to adverse developmental outcomes. Nevertheless, the underlying mechanism remains elusive. In this investigation, we explore the regulatory roles of a clinically relevant R567W point mutation, located within the 11th zinc finger of CTCF, by introducing this mutation into both murine models and human embryonic stem cell-derived cortical organoid models. Mice with homozygous CTCFR567W mutation exhibit growth impediments, resulting in postnatal mortality, and deviations in brain, heart, and lung development at the pathological and single-cell transcriptome levels. This mutation induces premature stem-like cell exhaustion, accelerates the maturation of GABAergic neurons, and disrupts neurodevelopmental and synaptic pathways. Additionally, it specifically hinders CTCF binding to peripheral motifs upstream to the core consensus site, causing alterations in local chromatin structure and gene expression, particularly at the clustered protocadherin locus. Comparative analysis using human cortical organoids mirrors the consequences induced by this mutation. In summary, this study elucidates the influence of the CTCFR567W mutation on human neurodevelopmental disorders, paving the way for potential therapeutic interventions.

B cell-mediated antigen presentation promotes adverse cardiac remodeling in chronic heart failure

Cardiovascular disease remains the leading cause of death worldwide. A primary driver of cardiovascular mortality is ischemic heart failure, a form of cardiac dysfunction that can develop in patients who survive myocardial infarction. Acute cardiac damage triggers robust changes in the spleen with rapid migration of immune cells from the spleen to the heart. Activating this "cardio-splenic" axis contributes to progressive cardiac dysfunction. The cardio-splenic axis has, therefore, been identified as a promising therapeutic target to prevent or treat heart failure. However, our understanding of the precise mechanisms by which specific immune cells contribute to adverse cardiac remodeling within the cardio-splenic axis remains limited. Here, we show that splenic B cells contribute to the development of heart failure via MHC II-mediated antigen presentation. We found that the adoptive transfer of splenic B cells from mice with ischemic heart failure promoted adverse cardiac remodeling and splenic inflammatory changes in naïve recipient mice. Based on single-cell RNA sequencing analysis of splenic B cells from mice with ischemic heart failure, we hypothesized that B cells contributed to adverse cardiac remodeling through antigen presentation by MHC II molecules. This mechanism was confirmed using transgenic mice with B cell-specific MHC II deletion, and by analyzing circulating B cells from humans who experienced myocardial infarction. Our results broaden our understanding of B lymphocyte biology, reshape current models of immune activation in response to myocardial injury, and point towards MHC II-mediated signaling in B cells as a novel and specific therapeutic target in chronic heart failure.

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

Gliovascular transcriptional perturbations in Alzheimer's disease reveal molecular mechanisms of blood brain barrier dysfunction

To uncover molecular changes underlying blood-brain-barrier dysfunction in Alzheimer's disease, we performed single nucleus RNA sequencing in 24 Alzheimer's disease and control brains and focused on vascular and astrocyte clusters as main cell types of blood-brain-barrier gliovascular-unit. The majority of the vascular transcriptional changes were in pericytes. Of the vascular molecular targets predicted to interact with astrocytic ligands, SMAD3, upregulated in Alzheimer's disease pericytes, has the highest number of ligands including VEGFA, downregulated in Alzheimer's disease astrocytes. We validated these findings with external datasets comprising 4,730 pericyte and 150,664 astrocyte nuclei. Blood SMAD3 levels are associated with Alzheimer's disease-related neuroimaging outcomes. We determined inverse relationships between pericytic SMAD3 and astrocytic VEGFA in human iPSC and zebrafish models. Here, we detect vast transcriptome changes in Alzheimer's disease at the gliovascular-unit, prioritize perturbed pericytic SMAD3-astrocytic VEGFA interactions, and validate these in cross-species models to provide a molecular mechanism of blood-brain-barrier disintegrity in Alzheimer's disease.

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis

Background

Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis.

Methods

CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay.

Results

Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls.

Conclusion

This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

Single-nucleus multiomic analysis of Beckwith-Wiedemann syndrome liver reveals PPARA signaling enrichment and metabolic dysfunction

Beckwith-Wiedemann Syndrome (BWS) is an epigenetic overgrowth syndrome caused by methylation changes in the human 11p15 chromosomal locus. Patients with BWS exhibit tissue overgrowth, as well as an increased risk of childhood neoplasms in the liver and kidney. To understand the impact of these 11p15 changes, specifically in the liver, we performed single-nucleus RNA sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) to generate paired, cell-type-specific transcriptional and chromatin accessibility profiles of both BWS-liver and nonBWS-liver nontumorous tissue. Our integrated RNA+ATACseq multiomic approach uncovered hepatocyte-specific enrichment and activation of the peroxisome proliferator-activated receptor α (PPARA) - a liver metabolic regulator. To confirm our findings, we utilized a BWS-induced pluripotent stem cell (iPSC) model, where cells were differentiated into hepatocytes. Our data demonstrates the dysregulation of lipid metabolism in BWS-liver, which coincided with observed upregulation of PPARA during hepatocyte differentiation. BWS liver cells exhibited decreased neutral lipids and increased fatty acid β-oxidation, relative to controls. We also observed increased reactive oxygen species (ROS) byproducts in the form of peroxidated lipids in BWS hepatocytes, which coincided with increased oxidative DNA damage. This study proposes a putative mechanism for overgrowth and cancer predisposition in BWS liver due to perturbed metabolism.

Metabolic inflexibility promotes mitochondrial health during liver regeneration

Mitochondria are critical for proper organ function and mechanisms to promote mitochondrial health during regeneration would benefit tissue homeostasis. We report that during liver regeneration, proliferation is suppressed in electron transport chain (ETC)-dysfunctional hepatocytes due to an inability to generate acetyl-CoA from peripheral fatty acids through mitochondrial β-oxidation. Alternative modes for acetyl-CoA production from pyruvate or acetate are suppressed in the setting of ETC dysfunction. This metabolic inflexibility forces a dependence on ETC-functional mitochondria and restoring acetyl-CoA production from pyruvate is sufficient to allow ETC-dysfunctional hepatocytes to proliferate. We propose that metabolic inflexibility within hepatocytes can be advantageous by limiting the expansion of ETC-dysfunctional cells.

Gene regulatory landscape of cerebral cortex folding

Folding of the cerebral cortex is a key aspect of mammalian brain development and evolution, and defects are linked to severe neurological disorders. Primary folding occurs in highly stereotyped patterns that are predefined in the cortical germinal zones by a transcriptomic protomap. The gene regulatory landscape governing the emergence of this folding protomap remains unknown. We characterized the spatiotemporal dynamics of gene expression and active epigenetic landscape (H3K27ac) across prospective folds and fissures in ferret. Our results show that the transcriptomic protomap begins to emerge at early embryonic stages, and it involves cell-fate signaling pathways. The H3K27ac landscape reveals developmental cell-fate restriction and engages known developmental regulators, including the transcription factor Cux2. Manipulating Cux2 expression in cortical progenitors changed their proliferation and the folding pattern in ferret, caused by selective transcriptional changes as revealed by single-cell RNA sequencing analyses. Our findings highlight the key relevance of epigenetic mechanisms in defining the patterns of cerebral cortex folding.

A dynamic in vitro model of Down syndrome neurogenesis with trisomy 21 gene dosage correction

Excess gene dosage from chromosome 21 (chr21) causes Down syndrome (DS), spanning developmental and acute phenotypes in terminal cell types. Which phenotypes remain amenable to intervention after development is unknown. To address this question in a model of DS neurogenesis, we derived trisomy 21 (T21) human induced pluripotent stem cells (iPSCs) alongside, otherwise, isogenic euploid controls from mosaic DS fibroblasts and equipped one chr21 copy with an inducible XIST transgene. Monoallelic chr21 silencing by XIST is near-complete and irreversible in iPSCs. Differential expression reveals that T21 neural lineages and iPSCs share suppressed translation and mitochondrial pathways and activate cellular stress responses. When XIST is induced before the neural progenitor stage, T21 dosage correction suppresses a pronounced skew toward astrogenesis in neural differentiation. Because our transgene remains inducible in postmitotic T21 neurons and astrocytes, we demonstrate that XIST efficiently represses genes even after terminal differentiation, which will empower exploration of cell type-specific T21 phenotypes that remain responsive to chr21 dosage.

CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes

Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe ( https://github.com/yao-laboratory/cellMarkerPipe ), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.

B cell-mediated antigen presentation promotes adverse cardiac remodeling in chronic heart failure

Cardiovascular disease remains the leading cause of death worldwide. A primary driver of cardiovascular mortality is ischemic heart failure, a form of cardiac dysfunction that can develop in patients who survive myocardial infarction. Acute cardiac damage triggers robust changes in the spleen with rapid migration of immune cells from the spleen to the heart. Activating this "cardio-splenic" axis contributes to progressive cardiac dysfunction. The cardio-splenic axis has, therefore, been identified as a promising therapeutic target to prevent or treat heart failure. However, our understanding of the precise mechanisms by which specific immune cells contribute to adverse cardiac remodeling within the cardio-splenic axis remains limited. Here, we show that splenic B cells contribute to the development of heart failure via MHC II-mediated antigen presentation. We found that the adoptive transfer of splenic B cells from mice with ischemic heart failure promoted adverse cardiac remodeling and splenic inflammatory changes in naïve recipient mice. Based on single-cell RNA sequencing analysis of splenic B cells from mice with ischemic heart failure, we hypothesized that B cells contributed to adverse cardiac remodeling through antigen presentation by MHC II molecules. This mechanism was confirmed using transgenic mice with B cell-specific MHC II deletion, and by analyzing circulating B cells from humans who experienced myocardial infarction. Our results broaden our understanding of B lymphocyte biology, reshape current models of immune activation in response to myocardial injury, and point towards MHC II-mediated signaling in B cells as a novel and specific therapeutic target in chronic heart failure.

ScSNViz: a user-friendly toolset for visualization and analysis of Cell-Specific Expressed SNVs

Motivation

Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there is a scarcity of tools for visualizing and analyzing cell-level genetic variants.

Results

We introduce scSNViz, a comprehensive R-based toolset for visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs) within cell-barcoded single-cell RNA-sequencing (scRNA-seq) data. ScSNViz offers 3D sceSNV visualization capabilities for dimensionally reduced scRNA-seq gene expression data, compatibility with popular scRNA-seq processing tools like Seurat, cell-type classification tools such as SingleR and scType, and trajectory inference computation using Slingshot. Furthermore, scSNViz conducts estimation, summary, and graphical representation of statistical metrics pertaining to sceSNVs distribution and expression across individual cells. It also provides support for the analysis of individual sceSNVs as well as sets comprising multiple expressed sceSNVs of interest.

Availability

ScSNViz is implemented as user-friendly R-scripts, freely available on https://horvathlab.github.io/NGS/scSNViz , supported by help utilities, and requiring no specialized bioinformatics skills for use.

Myelin plasticity in the ventral tegmental area is required for opioid reward

All drugs of abuse induce long-lasting changes in synaptic transmission and neural circuit function that underlie substance-use disorders1,2. Another recently appreciated mechanism of neural circuit plasticity is mediated through activity-regulated changes in myelin that can tune circuit function and influence cognitive behaviour3-7. Here we explore the role of myelin plasticity in dopaminergic circuitry and reward learning. We demonstrate that dopaminergic neuronal activity-regulated myelin plasticity is a key modulator of dopaminergic circuit function and opioid reward. Oligodendroglial lineage cells respond to dopaminergic neuronal activity evoked by optogenetic stimulation of dopaminergic neurons, optogenetic inhibition of GABAergic neurons, or administration of morphine. These oligodendroglial changes are evident selectively within the ventral tegmental area but not along the axonal projections in the medial forebrain bundle nor within the target nucleus accumbens. Genetic blockade of oligodendrogenesis dampens dopamine release dynamics in nucleus accumbens and impairs behavioural conditioning to morphine. Taken together, these findings underscore a critical role for oligodendrogenesis in reward learning and identify dopaminergic neuronal activity-regulated myelin plasticity as an important circuit modification that is required for opioid reward.

A guide to single-cell RNA sequencing analysis using web-based tools for non-bioinformatician

Single-cell RNA sequencing (scRNA-seq) is a technique that has proven to be a powerful tool for a wide range of fields and research studies. However, scRNA-seq data analysis has been dominated by scientists highly trained in bioinformatics or those with extensive computational experience and understanding. Recently, this trend has begun to shift as more user-friendly web-based scRNA-seq analysis tools have been developed that require little computational experience to use. However, barriers persist for nonbioinformaticians in using this technique. Complex, unfamiliar language and scarce comprehensive literature guidance to provide a framework for understanding scRNA-seq analysis outputs are among the obstacles. This work introduces many popular web-based tools for scRNA-seq and provides a general overview of their user interfaces and features. Then, a comprehensive start-to-finish introductory scRNA-seq analysis pipeline is described in detail, which aims to enable researchers to carry out scRNA-seq analysis, regardless of computational experience. Companion video tutorials can be found at "EasyScRNAseqTutorials" on YouTube (https://www.youtube.com/@scrnaseqtutorials). However, as scRNA-seq continues to penetrate new fields and expand in importance, there remains a need for more literature to help overcome barriers to its use by explaining further the highly complex and advanced analyses that are introduced within this paper.

Single-cell investigation of lead toxicity from neurodevelopment to neurodegeneration: Current review and future opportunities

Human exposure to the metal lead (Pb) is prevalent and associated with adverse neurodevelopmental and neurodegenerative outcomes. Pb disrupts normal brain function by inducing oxidative stress and neuroinflammation, altering cellular metabolism, and displacing essential metals. Prior studies on the molecular impacts of Pb have examined bulk tissues, which collapse information across all cell types, or in targeted cells, which are limited to cell autonomous effects. These approaches are unable to represent the complete biological implications of Pb exposure because the brain is a cooperative network of highly heterogeneous cells, with cellular diversity and proportions shifting throughout development, by brain region, and with disease. New technologies are necessary to investigate whether Pb and other environmental exposures alter cell composition in the brain and whether they cause molecular changes in a cell-type-specific manner. Cutting-edge, single-cell approaches now enable research resolving cell-type-specific effects from bulk tissues. This article reviews existing Pb neurotoxicology studies with genome-wide molecular signatures and provides a path forward for the field to implement single-cell approaches with practical recommendations.

Analyses of the autism-associated neuroligin-3 R451C mutation in human neurons reveal a gain-of-function synaptic mechanism

Mutations in many synaptic genes are associated with autism spectrum disorders (ASD), suggesting that synaptic dysfunction is a key driver of ASD pathogenesis. Among these mutations, the R451C substitution in the NLGN3 gene that encodes the postsynaptic adhesion molecule Neuroligin-3 is noteworthy because it was the first specific mutation linked to ASDs. In mice, the corresponding Nlgn3 R451C-knockin mutation recapitulates social interaction deficits of ASD patients and produces synaptic abnormalities, but the impact of the NLGN3 R451C mutation on human neurons has not been investigated. Here, we generated human knockin neurons with the NLGN3 R451C and NLGN3 null mutations. Strikingly, analyses of NLGN3 R451C-mutant neurons revealed that the R451C mutation decreased NLGN3 protein levels but enhanced the strength of excitatory synapses without affecting inhibitory synapses; meanwhile NLGN3 knockout neurons showed reduction in excitatory synaptic strengths. Moreover, overexpression of NLGN3 R451C recapitulated the synaptic enhancement in human neurons. Notably, the augmentation of excitatory transmission was confirmed in vivo with human neurons transplanted into mouse forebrain. Using single-cell RNA-seq experiments with co-cultured excitatory and inhibitory NLGN3 R451C-mutant neurons, we identified differentially expressed genes in relatively mature human neurons corresponding to synaptic gene expression networks. Moreover, gene ontology and enrichment analyses revealed convergent gene networks associated with ASDs and other mental disorders. Our findings suggest that the NLGN3 R451C mutation induces a gain-of-function enhancement in excitatory synaptic transmission that may contribute to the pathophysiology of ASD.

Deciphering bat influenza H18N11 infection dynamics in male Jamaican fruit bats on a single-cell level

Jamaican fruit bats (Artibeus jamaicensis) naturally harbor a wide range of viruses of human relevance. These infections are typically mild in bats, suggesting unique features of their immune system. To better understand the immune response to viral infections in bats, we infected male Jamaican fruit bats with the bat-derived influenza A virus (IAV) H18N11. Using comparative single-cell RNA sequencing, we generated single-cell atlases of the Jamaican fruit bat intestine and mesentery. Gene expression profiling showed that H18N11 infection resulted in a moderate induction of interferon-stimulated genes and transcriptional activation of immune cells. H18N11 infection was predominant in various leukocytes, including macrophages, B cells, and NK/T cells. Confirming these findings, human leukocytes, particularly macrophages, were also susceptible to H18N11, highlighting the zoonotic potential of this bat-derived IAV. Our study provides insight into a natural virus-host relationship and thus serves as a fundamental resource for future in-depth characterization of bat immunology.

Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities

Advancements in single-cell sequencing have transformed the genomics field by allowing researchers to delve into the intricate cellular heterogeneity within tissues at greater resolution. While single-cell omics are more widely applied in model organisms and humans, their use in livestock species is just beginning. Studies in cattle, sheep, and goats have already leveraged single-cell and single-nuclei RNA-seq as well as single-cell and single-nuclei ATAC-seq to delineate cellular diversity in tissues, track changes in cell populations and gene expression over developmental stages, and characterize immune cell populations important for disease resistance and resilience. Although challenges exist for the use of this technology in ruminant livestock, such as the precise annotation of unique cell populations and spatial resolution of cells within a tissue, there is vast potential to enhance our understanding of the cellular and molecular mechanisms underpinning traits essential for healthy and productive livestock. This review intends to highlight the insights gained from published single-cell omics studies in cattle, sheep, and goats, particularly those with publicly accessible data. Further, this manuscript will discuss the challenges and opportunities of this technology in ruminant livestock and how it may contribute to enhanced profitability and sustainability of animal agriculture in the future.

Decoding the complexity of delayed wound healing following Enterococcus faecalis infection

Wound infections are highly prevalent and can lead to delayed or failed healing, causing significant morbidity and adverse economic impacts. These infections occur in various contexts, including diabetic foot ulcers, burns, and surgical sites. Enterococcus faecalis is often found in persistent non-healing wounds, but its contribution to chronic wounds remains understudied. To address this, we employed single-cell RNA sequencing (scRNA-seq) on infected wounds in comparison to uninfected wounds in a mouse model. Examining over 23,000 cells, we created a comprehensive single-cell atlas that captures the cellular and transcriptomic landscape of these wounds. Our analysis revealed unique transcriptional and metabolic alterations in infected wounds, elucidating the distinct molecular changes associated with bacterial infection compared to the normal wound healing process. We identified dysregulated keratinocyte and fibroblast transcriptomes in response to infection, jointly contributing to an anti-inflammatory environment. Notably, E. faecalis infection prompted a premature, incomplete epithelial-mesenchymal transition in keratinocytes. Additionally, E. faecalis infection modulated M2-like macrophage polarization by inhibiting pro-inflammatory resolution in vitro, in vivo, and in our scRNA-seq atlas. Furthermore, we discovered macrophage crosstalk with neutrophils, which regulates chemokine signaling pathways, while promoting anti-inflammatory interactions with endothelial cells. Overall, our findings offer new insights into the immunosuppressive role of E. faecalis in wound infections.

Transcriptomic signature, bioactivity and safety of a non-hepatotoxic analgesic generating AM404 in the midbrain PAG region

Safe and effective pain management is a critical healthcare and societal need. The potential for acute liver injury from paracetamol (ApAP) overdose; nephrotoxicity and gastrointestinal damage from chronic non-steroidal anti-inflammatory drug (NSAID) use; and opioids' addiction are unresolved challenges. We developed SRP-001, a non-opioid and non-hepatotoxic small molecule that, unlike ApAP, does not produce the hepatotoxic metabolite N-acetyl-p-benzoquinone-imine (NAPQI) and preserves hepatic tight junction integrity at high doses. CD-1 mice exposed to SRP-001 showed no mortality, unlike a 70% mortality observed with increasing equimolar doses of ApAP within 72 h. SRP-001 and ApAP have comparable antinociceptive effects, including the complete Freund's adjuvant-induced inflammatory von Frey model. Both induce analgesia via N-arachidonoylphenolamine (AM404) formation in the midbrain periaqueductal grey (PAG) nociception region, with SRP-001 generating higher amounts of AM404 than ApAP. Single-cell transcriptomics of PAG uncovered that SRP-001 and ApAP also share modulation of pain-related gene expression and cell signaling pathways/networks, including endocannabinoid signaling, genes pertaining to mechanical nociception, and fatty acid amide hydrolase (FAAH). Both regulate the expression of key genes encoding FAAH, 2-arachidonoylglycerol (2-AG), cannabinoid receptor 1 (CNR1), CNR2, transient receptor potential vanilloid type 4 (TRPV4), and voltage-gated Ca2+ channel. Phase 1 trial (NCT05484414) (02/08/2022) demonstrates SRP-001's safety, tolerability, and favorable pharmacokinetics, including a half-life from 4.9 to 9.8 h. Given its non-hepatotoxicity and clinically validated analgesic mechanisms, SRP-001 offers a promising alternative to ApAP, NSAIDs, and opioids for safer pain treatment.

Impact of benzo[a]pyrene, PCB153 and sex hormones on human ESC-Derived thyroid follicles using single cell transcriptomics

INTRODUCTION

Endocrine disruptors are compounds of manmade origin able to interfere with the endocrine system and constitute an important environmental concern. Indeed, detrimental effects on thyroid physiology and functioning have been described. Differences exist in the susceptibility of human sexes to the incidence of thyroid disorders, like autoimmune diseases or cancer.

METHODS

To study how different hormonal environments impact the thyroid response to endocrine disruptors, we exposed human embryonic stem cell-derived thyroid organoids to physiological concentrations of sex hormones resembling the serum levels of human females post-ovulation or males of reproductive age for three days. Afterwards, we added 10 µM benzo[a]pyrene or PCB153 for 24 h and analyzed the transcriptome changes via single-cell RNA sequencing with differential gene expression and gene ontology analysis.

RESULTS

The sex hormones receptors genes AR, ESR1, ESR2 and PGR were expressed at low levels. Among the thyroid markers, only TG resulted downregulated by benzo[a]pyrene or benzo[a]pyrene with the "male" hormones mix. Both hormone mixtures and benzo[a]pyrene alone upregulated ribosomal genes and genes involved in oxidative phosphorylation, while their combination decreased the expression compared to benzo[a]pyrene alone. The "male" mix and benzo[a]pyrene, alone or in combination, upregulated genes involved in lipid transport and metabolism (APOA1, APOC3, APOA4, FABP1, FABP2, FABP6). The combination of "male" hormones and benzo[a]pyrene induced also genes involved in inflammation and NFkB targets. Benzo[a]pyrene upregulated CYP1A1, CYP1B1 and NQO1 irrespective of the hormonal context. The induction was stronger in the "female" mix. Benzo[a]pyrene alone upregulated genes involved in cell cycle regulation, response to reactive oxygen species and apoptosis. PCB153 had a modest effect in presence of "male" hormones, while we did not observe any changes with the "female" mix.

CONCLUSION

This work shows how single cell transcriptomics can be applied to selectively study the in vitro effects of endocrine disrupters and their interaction with different hormonal contexts.

Single-cell transcriptome revealed dysregulated RNA-binding protein expression patterns and functions in human ankylosing spondylitis

Objective

To explore the expression characteristics and regulatory patterns of RBPs in different immune cell types of AS, and to clarify the potential key role of RBPs in the occurrence and development of AS disease.

Methods

PBMC sample data from scRNA-seq (HC*29, AS*10) and bulk RNA-seq (NC*3, AS*5) were selected for correlation analysis.

Results

(1) Compared with the HC group, the numbers of B, DC (dendritic cells), CD14+ Mono and CD8+ T cells were increased in AS group, while the numbers of platelet (platelets), CD8+ NKT, CD16+ Mono (non-classical monocytes), Native CD4+ T and NK were decreased. (2) Through the analysis of RBP genes in B cells, some RBPs were found to play an important role in B cell differentiation and function, such as DDX3X, SFPQ, SRRM1, UPF2. (3) It may be related to B-cell receptor, IgA immunity, NOD-like receptor and other signaling pathways; Through the analysis of RBP genes in CD8+ T cells, some RBPs that play an important role in the immune regulation of CD8+ T were found, such as EIF2S3, EIF4B, HSPA5, MSL3, PABPC1 and SRSF7; It may be related to T cell receptor, TNF, IL17 and other signaling pathways. (4) Based on bulk RNA-seq, it was found that compared with HC and AS patients, differentially expressed variable splicing genes (RASGs) may play an important role in the occurrence and development of AS by participating in transcriptional regulation, protein phosphorylation and ubiquitination, DNA replication, angiogenesis, intracellular signal transduction and other related pathways.

Conclusion

RBPs has specific expression characteristics in different immune cell types of AS patients, and has important regulatory functions. Its abnormal expression and regulation may be closely related to the occurrence and development of AS.

Single-nucleus and spatial landscape of the sub-ventricular zone in human glioblastoma

The sub-ventricular zone (SVZ) is the most well-characterized neurogenic area in the mammalian brain. We previously showed that in 65% of patients with glioblastoma (GBM), the SVZ is a reservoir of cancer stem-like cells that contribute to treatment resistance and emergence of recurrence. Here, we built a single-nucleus RNA-sequencing-based microenvironment landscape of the tumor mass (T_Mass) and the SVZ (T_SVZ) of 15 GBM patients and 2 histologically normal SVZ (N_SVZ) samples as controls. We identified a mesenchymal signature in the T_SVZ of GBM patients: tumor cells from the T_SVZ relied on the ZEB1 regulatory network, whereas tumor cells in the T_Mass relied on the TEAD1 regulatory network. Moreover, the T_SVZ microenvironment was predominantly characterized by tumor-supportive microglia, which spatially co-exist and establish heterotypic interactions with tumor cells. Lastly, differential gene expression analyses, predictions of ligand-receptor and incoming/outgoing interactions, and functional assays revealed that the IL-1β/IL-1RAcP and Wnt-5a/Frizzled-3 pathways are therapeutic targets in the T_SVZ microenvironment. Our data provide insights into the biology of the SVZ in GBM patients and identify specific targets of this microenvironment.

Single-nucleus transcriptomics of epicardial adipose tissue from female pigs reveals effects of exercise training on resident innate and adaptive immune cells

BACKGROUND

Coronary artery disease (CAD) is a leading cause of death in women. Epicardial adipose tissue (EAT) secretes cytokines to modulate coronary artery function, and the release of fatty acids from EAT serves as a readily available energy source for cardiomyocytes. However, despite having beneficial functions, excessive amounts of EAT can cause the secretion of proinflammatory molecules that increase the instability of atherosclerotic plaques and contribute to CAD progression. Although exercise mitigates CAD, the mechanisms by which exercise impacts EAT are unknown. The Yucatan pig is an excellent translational model for the effects of exercise on cardiac function. Therefore, we sought to determine if chronic aerobic exercise promotes an anti-inflammatory microenvironment in EAT from female Yucatan pigs.

METHODS

Sexually mature, female Yucatan pigs (n = 7 total) were assigned to sedentary (Sed, n = 3) or exercise (Ex, n = 4) treatments, and coronary arteries were occluded (O) with an ameroid to mimic CAD or remained non-occluded (N). EAT was collected for bulk (n = 7 total) and single nucleus transcriptomic sequencing (n = 2 total, 1 per exercise treatment).

RESULTS

Based on the bulk transcriptomic analysis, exercise upregulated S100 family, G-protein coupled receptor, and CREB signaling in neurons canonical pathways in EAT. The top networks in EAT affected by exercise as measured by bulk RNA sequencing were SRC kinase family, fibroblast growth factor receptor, Jak-Stat, and vascular endothelial growth factor. Single nucleus transcriptomic analysis revealed that exercise increased the interaction between immune, endothelial, and mesenchymal cells in the insulin-like growth factor pathway and between endothelial and other cell types in the platelet endothelial cell adhesion molecule 1 pathway. Sub-clustering revealed nine cell types in EAT, with fibroblast and macrophage populations predominant in O-Ex EAT and T cell populations predominant in N-Ex EAT. Unlike the findings for exercise alone as a treatment, there were not increased interactions between endothelial and mesenchymal cells in O-Ex EAT. Coronary artery occlusion impacted the most genes in T cells and endothelial cells. Genes related to fatty acid metabolism were the most highly upregulated in non-immune cells from O-Ex EAT. Sub-clustering of endothelial cells revealed that N-Ex EAT separated from other treatments.

CONCLUSIONS

According to bulk transcriptomics, exercise upregulated pathways and networks related to growth factors and immune cell communication. Based on single nucleus transcriptomics, aerobic exercise increased cell-to-cell interaction amongst immune, mesenchymal, and endothelial cells in female EAT. Yet, exercise was minimally effective at reversing alterations in gene expression in endothelial and mesenchymal cells in EAT surrounding occluded arteries. These findings lay the foundation for future work focused on the impact of exercise on cell types in EAT.

Myocardial B cells have specific gene expression and predicted interactions in dilated cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy

Introduction

Growing evidence from animal models indicates that the myocardium hosts a population of B cells that play a role in the development of cardiomyopathy. However, there is minimal data on human myocardial B cells in the context of cardiomyopathy.

Methods

We integrated single-cell and single-nuclei datasets from 45 healthy human hearts, 70 hearts with dilated cardiomyopathy (DCM), and 8 hearts with arrhythmogenic right ventricular cardiomyopathy (ARVC). Interactions between B cells and other cell types were investigated using the CellChat Package. Differential gene expression analysis comparing B cells across conditions was performed using DESeq2. Pathway analysis was performed using Ingenuity, KEGG, and GO pathways analysis.

Results

We identified 1,100 B cells, including naive B cells and plasma cells. Cells showed an extensive network of interactions within the healthy myocardium that included outgoing signaling to macrophages, T cells, endothelial cells, and pericytes, and incoming signaling from endothelial cells, pericytes, and fibroblasts. This niche relied on ECM-receptor, contact, and paracrine interactions; and changed significantly in the context of cardiomyopathy, displaying disease-specific features. Differential gene expression analysis showed that in the context of DCM both naive and plasma B cells upregulated several pathways related to immune activation, including upregulation of oxidative phosphorylation, upregulation of leukocyte extravasation, and, in naive B cells, antigen presentation.

Discussion

The human myocardium contains naive B cells and plasma cells, integrated into a diverse and dynamic niche that has distinctive features in healthy, DCM, and ARVC. Naive myocardial-associated B cells likely contribute to the pathogenesis of human DCM.

Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data

BACKGROUND

In recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis.

RESULTS

We proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise.

CONCLUSION

Shaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud , and its source code is publicly available at https://github.com/WiedenWei/shaoxia .

Short-Term Dietary Restriction Potentiates an Anti-Inflammatory Circulating Mucosal-Associated Invariant T-Cell Response

Short-term protein-calorie dietary restriction (StDR) is a promising preoperative strategy for modulating postoperative inflammation. We have previously shown marked gut microbial activity during StDR, but relationships between StDR, the gut microbiome, and systemic immunity remain poorly understood. Mucosal-associated invariant T-cells (MAITs) are enriched on mucosal surfaces and in circulation, bridge innate and adaptive immunity, are sensitive to gut microbial changes, and may mediate systemic responses to StDR. Herein, we characterized the MAIT transcriptomic response to StDR using single-cell RNA sequencing of human PBMCs and evaluated gut microbial species-level changes through sequencing of stool samples. Healthy volunteers underwent 4 days of DR during which blood and stool samples were collected before, during, and after DR. MAITs composed 2.4% of PBMCs. More MAIT genes were differentially downregulated during DR, particularly genes associated with MAIT activation (CD69), regulation of pro-inflammatory signaling (IL1, IL6, IL10, TNFα), and T-cell co-stimulation (CD40/CD40L, CD28), whereas genes associated with anti-inflammatory IL10 signaling were upregulated. Stool analysis showed a decreased abundance of multiple MAIT-stimulating Bacteroides species during DR. The analyses suggest that StDR potentiates an anti-inflammatory MAIT immunophenotype through modulation of TCR-dependent signaling, potentially secondary to gut microbial species-level changes.

The Extracellular Niche and Tumor Microenvironment Enhance KRAS Inhibitor Efficacy in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease that lacks effective treatment options, highlighting the need for developing new therapeutic interventions. Here, we assessed the response to pharmacologic inhibition of KRAS, the central oncogenic driver of PDAC. In a panel of PDAC cell lines, inhibition of KRASG12D with MRTX1133 yielded variable efficacy in suppressing cell growth and downstream gene expression programs in 2D cultures. On the basis of CRISPR-Cas9 loss-of-function screens, ITGB1 was identified as a target to enhance the therapeutic response to MRTX1133 by regulating mechanotransduction signaling and YAP/TAZ expression, which was confirmed by gene-specific knockdown and combinatorial drug synergy. Interestingly, MRTX1133 was considerably more efficacious in 3D cell cultures. Moreover, MRTX1133 elicited a pronounced cytostatic effect in vivo and controlled tumor growth in PDAC patient-derived xenografts. In syngeneic models, KRASG12D inhibition led to tumor regression that did not occur in immune-deficient hosts. Digital spatial profiling on tumor tissues indicated that MRTX1133-mediated KRAS inhibition enhanced IFNγ signaling and induced antigen presentation that modulated the tumor microenvironment. Further investigation of the immunologic response using single-cell sequencing and multispectral imaging revealed that tumor regression was associated with suppression of neutrophils and influx of effector CD8+ T cells. Together, these findings demonstrate that both tumor cell-intrinsic and -extrinsic events contribute to response to MRTX1133 and credential KRASG12D inhibition as a promising therapeutic strategy for a large percentage of patients with PDAC.

SIGNIFICANCE

Pharmacologic inhibition of KRAS elicits varied responses in pancreatic cancer 2D cell lines, 3D organoid cultures, and xenografts, underscoring the importance of mechanotransduction and the tumor microenvironment in regulating therapeutic responses.

Tumor-infiltrating γδ T cells as targets of immune checkpoint blockade in melanoma

Melanoma is one of the most sensitive tumors to immune modulation, and the major challenge for melanoma patients' survival is immune checkpoint inhibitor (ICI) therapy. γδ T lymphocytes play an antitumoral role in a broad variety of tumors including melanoma and they are optimal candidates for cellular immunotherapy. Thus, a comprehensive analysis of the correlation between γδ T cells and immune checkpoint receptors in the context of melanoma was conducted, with the aim of devising an innovative combined immunotherapeutic strategy. In this study, using the GEPIA2.0 database, a significant positive correlation was observed between the expression of γδ T cell-related genes (TRGC1, TRGC2, TCRD) and immune checkpoint genes (PDCD1, HAVCR2, LAG3), highlighting the potential role of γδ T cells in the immune response within melanoma. Moreover, flow cytometry analysis unveiled a significant augmentation in the population of γδ T cells within melanoma lesions, which exhibited the expression of immune checkpoint receptors including LAG3, TIM3, and PD1. Analysis of single-cell RNA sequencing data revealed a significant enrichment and functional reprogramming of γδ T cell clusters in response to ICIs. Interestingly, the effects of ICI therapy varied between Vδ1 and Vδ2 γδ T cell subsets, with distinct changes in gene expression patterns. Last, a correlation analysis between γδ T cell abundance, immune checkpoint gene expression, and clinical outcomes in melanoma patients showed that low expression of immune checkpoint genes, including LAG3, HAVCR2, and PDCD1, was associated with improved 1-year overall survival, emphasizing the significance of these genes in predicting patient outcomes, potentially outweighing the impact of γδ T cell abundance. This study offers critical insights into the dynamic interaction between γδ T cells, immune checkpoint receptors, and melanoma, providing valuable perspectives for potential therapeutic avenues and predictive markers in this intricate interplay.

scAnnoX: an R package integrating multiple public tools for single-cell annotation

Background

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking.

Methods

This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms.

Results

The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

Single-cell RNA sequencing data imputation using bi-level feature propagation

Single-cell RNA sequencing (scRNA-seq) enables the exploration of cellular heterogeneity by analyzing gene expression profiles in complex tissues. However, scRNA-seq data often suffer from technical noise, dropout events and sparsity, hindering downstream analyses. Although existing works attempt to mitigate these issues by utilizing graph structures for data denoising, they involve the risk of propagating noise and fall short of fully leveraging the inherent data relationships, relying mainly on one of cell-cell or gene-gene associations and graphs constructed by initial noisy data. To this end, this study presents single-cell bilevel feature propagation (scBFP), two-step graph-based feature propagation method. It initially imputes zero values using non-zero values, ensuring that the imputation process does not affect the non-zero values due to dropout. Subsequently, it denoises the entire dataset by leveraging gene-gene and cell-cell relationships in the respective steps. Extensive experimental results on scRNA-seq data demonstrate the effectiveness of scBFP in various downstream tasks, uncovering valuable biological insights.

Integrated analysis of disulfidptosis-related immune genes signature to boost the efficacy of prognostic prediction in gastric cancer

BACKGROUND

Gastric cancer (GC) remains a malignant tumor with high morbidity and mortality, accounting for approximately 1,080,000 diagnosed cases and 770,000 deaths worldwide annually. Disulfidptosis, characterized by the stress-induced abnormal accumulation of disulfide, is a recently identified form of programmed cell death. Substantial studies have demonstrated the significant influence of immune clearance on tumor progression. Therefore, we aimed to explore the intrinsic correlations between disulfidptosis and immune-related genes (IRGs) in GC, as well as the potential value of disulfidptosis-related immune genes (DRIGs) as biomarkers.

METHODS

This study incorporated the single-cell RNA sequencing (scRNA-seq) dataset GSE183904 and transcriptome RNA sequencing of GC from the TCGA database. Disulfidptosis-related genes (DRGs) and IRGs were derived from the representative literature on both cell disulfidptosis and immunity. The expression and distribution of DRGs were investigated at the single-cell level in different GC cell types. Pearson correlation analysis was used to identify the IRGs closely related to disulfidptosis. The prognostic signature of DRIGs was established using Cox and LASSO analyses. We then analyzed and evaluated the differences in long-term prognosis, Gene Set Enrichment Analysis (GSEA), immune infiltration, mutation profile, CD274 expression, and response to chemotherapeutic drugs between the two groups. A tissue array containing 63 paired GC specimens was used to verify the expression of 4 DRIGs and disulfidptosis regulator SLC7A11 through immunohistochemistry staining.

RESULTS

The scRNA-seq analysis found that SLC7A11, SLC3A2, RPN1 and NCKAP1 were enriched in specific cell types and closely related to immune infiltration. Four DIRGs (GLA, HIF-1α, VPS35 and CDC37) were successfully identified to establish a signature to potently predict the survival time of GC patients. Patients with high risk scores generally experienced worse prognoses and exhibited greater resistant to classical chemotherapy drugs. Furthermore, the expression of GLA, HIF-1α, VPS35, CDC37 and SLC7A11 were elevated in GC tissues. A high expression of GLA, HIF-1α, VPS35 or CDC37 was associated with more advanced clinical stage of GC and increased SLC7A11 expression.

CONCLUSION

Current study first highlights the potential value of DRIGs as biomarkers in GC. We successfully constructed a robust model incorporating four DRIGs to accurately predict the survival time and clinicopathological characteristics of GC patients.

Engineering Tumor Stroma Morphogenesis Using Dynamic Cell-Matrix Spheroid Assembly

The tumor microenvironment consists of resident tumor cells organized within a compositionally diverse, three-dimensional (3D) extracellular matrix (ECM) network that cannot be replicated in vitro using bottom-up synthesis. We report a new self-assembly system to engineer ECM-rich 3D MatriSpheres wherein tumor cells actively organize and concentrate microgram quantities of decellularized ECM dispersions which modulate cell phenotype. 3D colorectal cancer (CRC) MatriSpheres were created using decellularized small intestine submucosa (SIS) as an orthotopic ECM source that had greater proteomic homology to CRC tumor ECM than traditional ECM formulations such as Matrigel. SIS ECM was rapidly concentrated from its environment and assembled into ECM-rich 3D stroma-like regions by mouse and human CRC cell lines within 4-5 days via a mechanism that was rheologically distinct from bulk hydrogel formation. Both ECM organization and transcriptional regulation by 3D ECM cues affected programs of malignancy, lipid metabolism, and immunoregulation that corresponded with an in vivo MC38 tumor cell subpopulation identified via single cell RNA sequencing. This 3D modeling approach stimulates tumor specific tissue morphogenesis that incorporates the complexities of both cancer cell and ECM compartments in a scalable, spontaneous assembly process that may further facilitate precision medicine.

Revealing the role of SPP1+ macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas

BACKGROUND

Glioma is a highly heterogeneous brain tumor categorized into World Health Organization (WHO) grades 1-4 based on its malignancy. The suppressive immune microenvironment of glioma contributes significantly to unfavourable patient outcomes. However, the cellular composition and their complex interplays within the glioma environment remain poorly understood, and reliable prognostic markers remain elusive. Therefore, in-depth exploration of the tumor microenvironment (TME) and identification of predictive markers are crucial for improving the clinical management of glioma patients.

RESULTS

Our analysis of single-cell RNA-sequencing data from glioma samples unveiled the immunosuppressive role of tumor-associated macrophages (TAMs), mediated through intricate interactions with tumor cells and lymphocytes. We also discovered the heterogeneity within TAMs, among which a group of suppressive TAMs named TAM-SPP1 demonstrated a significant association with Epidermal Growth Factor Receptor (EGFR) amplification, impaired T cell response and unfavourable patient survival outcomes. Furthermore, by leveraging genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) dataset, two distinct molecular subtypes with a different constitution of TAMs, EGFR status and clinical outcomes were identified. Exploiting the molecular differences between these two subtypes, we developed a four-gene-based prognostic model. This model displayed strong associations with an elevated level of suppressive TAMs and could be used to predict anti-tumor immune response and prognosis in glioma patients.

CONCLUSION

Our findings illuminated the molecular and cellular mechanisms that shape the immunosuppressive microenvironment in gliomas, providing novel insights into potential therapeutic targets. Furthermore, the developed prognostic model holds promise for predicting immunotherapy response and assisting in more precise risk stratification for glioma patients.