Construction of a Prognostic Model for Mitochondria and Macrophage Polarization Correlation in Glioma Based on Single-Cell and Transcriptome Sequencing.

BACKGROUND: Numerous diseases are associated with the interplay of mitochondrial and macrophage polarization. However, the correlation of mitochondria-related genes (MRGs) and macrophage polarization-related genes (MPRGs) with the prognosis of glioma remains unclear. This study aimed to examine this relationship based on bioinformatic analysis. METHODS: Glioma-related datasets (TCGA-GBMLGG, mRNA-seq-325, mRNA-seq-693, GSE16011, GSE4290, and GSE138794) were included in this study. The intersection genes were obtained by overlapping differentially expressed genes (DEGs) from differential expression analysis in GSE16011, key module genes from WGCNA, and MRGs. Subsequently, the intersection genes were further screened to obtain prognostic genes. Following this, a risk model was developed and verified. After that, independent prognostic factors were identified, followed by the construction of a nomogram and subsequent evaluation of its predictive ability. Furthermore, immune microenvironment analysis and expression validation were implemented. The GSE138794 dataset was utilized to evaluate the expression of prognostic genes at a cellular level, followed by conducting an analysis on cell-to-cell communication. Finally, the results were validated in different datasets and tissue samples from patients. RESULTS: ECI2, MCCC2, OXCT1, SUCLG2, and CPT2 were identified as prognostic genes for glioma. The risk model constructed based on these genes in TCGA-GBMLGG demonstrated certain accuracy in predicting the occurrence of glioma. Additionally, the nomogram constructed based on risk score and grade exhibited strong performance in predicting patient survival. Significant differences were observed in the proportion of 27 immune cell types (e.g., activated B cells and macrophages) and the expression of 32 immune checkpoints (e.g., CD70, CD200, and CD48) between the two risk groups. Single-cell RNA sequencing showed that CPT2, ECI2, and SUCLG2 were highly expressed in oligodendrocytes, neural progenitor cells, and BMDMs, respectively. The results of cell-cell communication analysis revealed that both oligodendrocytes and BMDMs exhibited a substantial number of interactions with high strength. CONCLUSION: This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis.

Integrated temporal transcriptional and epigenetic single-cell analysis reveals the intrarenal immune characteristics in an early-stage model of IgA nephropathy during its acute injury.

Rationale: Kidney inflammation plays a crucial role in the pathogenesis of IgA nephropathy (IgAN), yet the specific phenotypes of immune cells involved in disease progression remain incompletely understood. Utilizing joint profiling through longitudinal single-cell RNA-sequencing (scRNAseq) and single-cell assay for transposase-accessible chromatin sequencing (scATACseq) can provide a comprehensive framework for elucidating the development of cell subset diversity and how chromatin accessibility regulates transcription. Objective: We aimed to characterize the dynamic immune cellular landscape at a high resolution in an early IgAN mouse model with acute kidney injury (AKI). Methods and results: A murine model was utilized to mimic 3 immunological states -"immune stability (IS), immune activation (IA) and immune remission (IR)" in early human IgAN-associated glomerulopathy during AKI, achieved through lipopolysaccharide (LPS) injection. Urinary albumin to creatinine ratio (UACR) was measured to further validate the exacerbation and resolution of kidney inflammation during this course. Paired scRNAseq and scATACseq analysis was performed on CD45+ immune cells isolated from kidney tissues obtained from CTRL (healthy vehicle), IS, IA and IR (4 or 5 mice each). The analyses revealed 7 major cell types and 24 clusters based on 72304 single-cell transcriptomes, allowing for the identification and characterization of various immune cell types within each cluster. Our data offer an impartial depiction of the immunological characteristics, as the proportions of immune cell types fluctuated throughout different stages of the disease. Specifically, these analyses also revealed novel subpopulations, such as a macrophage subset (Nlrp1b Mac) with distinct epigenetic features and a unique transcription factor motif profile, potentially exerting immunoregulatory effects, as well as an early subset of Tex distinguished by their effector and cytolytic potential (CX3CR1-transTeff). Furthermore, in order to investigate the potential interaction between immune cells and renal resident cells, we conducted single-cell RNA sequencing on kidney cells obtained from a separate cohort of IS and IA mice without isolating immune cells. These findings underscored the diverse roles played by macrophages and CD8+ T cells in maintaining homeostasis of endothelial cells (ECs) under stress. Conclusions: This study presents a comprehensive analysis of the dynamic changes in immune cell profiles in a model of IgAN, identifying key cell types and their roles and interactions. These findings significantly contribute to the understanding of the pathogenesis of IgAN and may provide potential targets for therapeutic intervention.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues.

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington's disease using single-cell RNA sequencing data.

BACKGROUND: Huntington's disease (HD) is a hereditary neurological disorder caused by mutations in HTT, leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data. RESULTS: We used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology. CONCLUSION: We developed the model capable of analyzing HD at single-cell transcriptomic level.

Identification of functional heterogeneity of immune cells and tubular-immune cellular interplay action in diabetic kidney disease.

Background: Renal inflammation plays key roles in the pathogenesis of diabetic kidney disease (DKD). Immune cell infiltration is the main pathological feature in the progression of DKD. Sodium glucose cotransporter 2 inhibitor (SGLT2i) were reported to have antiinflammatory effects on DKD. While the heterogeneity and molecular basis of the pathogenesis and treatment with SGLT2i in DKD remains poorly understood. Methods: To address this question, we performed a single-cell transcriptomics data analysis and cell cross-talk analysis based on the database (GSE181382). The single-cell transcriptome analysis findings were validated using multiplex immunostaining. Results: A total of 58760 cells are categorized into 25 distinct cell types. A subset of macrophages with anti-inflammatory potential was identified. We found that Ccl3+ (S100a8/a9 high) macrophages with anti-inflammatory and antimicrobial in the pathogenesis of DKD decreased and reversed the dapagliflozin treatment. Besides, dapagliflozin treatment enhanced the accumulation of Pck1+ macrophage, characterized by gluconeogenesis signaling pathway. Cell-cross talk analysis showed the GRN/SORT1 pair and CD74 related signaling pathways were enriched in the interactions between tubular epithelial cells and immune cells. Conclusions: Our study depicts the heterogeneity of macrophages and clarifies a new possible explanation of dapagliflozin treatment, showing the metabolism shifts toward gluconeogenesis in macrophages, fueling the anti-inflammatory function of M2 macrophages, highlighting the new molecular features and signaling pathways and potential therapeutic targets, which has provided an important reference for the study of immune-related mechanisms in the progression of the disease.

Integrated Single-cell RNA-seq and Bulk RNA-seq Identify Diagnostic Biomarkers for Postmenopausal Osteoporosis.

AIM: We aimed to explore diagnostic biomarkers of postmenopausal osteoporosis (PMOP). BACKGROUND: PMOP brings enormous physical and economic burden to elderly women. OBJECTIVES: This study aims to screen new biomarkers for osteoporosis, providing insights for early diagnosis and therapeutic targets of osteoporosis. METHODS: Weighted gene co-expression network analysis (WGCNA) was applied to identify osteoporosis-related hub genes. Single-cell transcriptomic atlas of osteoporosis was depicted and the heterogeneity of monocytes was analyzed, based on which the biomarkers for osteoporosis were screened. Gene set enrichment analysis (GSEA) was conducted on the biomarkers. The diagnostic model (nomogram) was established and evaluated based on the expression levels of biomarkers. Additionally, the transcription factor (TF) regulatory network was constructed to predict the potential TF and targeted miRNA of biomarkers. The drugs with significant correlation with biomarkers were identified by Spearman correlation analysis. RESULTS: We obtained 30 osteoporosis-associated hub genes. 9 cell types were identified, and the monocytes were subdivided to 4 subtypes. Three biomarkers, DHX29, LSM5, and UBE2V2, were screened. DHX29 and UBE2V2 were highly expressed in non-classical monocytes, while LSM5 exhibited the highest expression in other monocytes, followed by non-classical monocytes. GSEA indicated that osteoporosis may be correlated with vascular calcification and the biomarkers may be involved in the formation of immune cells. Then, nomogram was constructed and exhibited good robustness. In addition, MYC and SETDB1 were the shared IF in three biomarkers, which may play critical regulatory roles in the progression of osteoporosis. Moreover, 41, 49, and 68 drugs appeared significant correlations with DHX29, LSM5, and UBE2V2, respectively. CONCLUSION: This study provided a basis for early diagnosis and targeted treatment of osteoporosis.

scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq.

Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.

Identification and validation of SHC1 and FGFR1 as novel immune-related oxidative stress biomarkers of non-obstructive azoospermia.

Background: Non-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility. Methods: NOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein-protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA-microRNA (miRNA)-transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression. Results: Two immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in toll-like receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data. Conclusion: It appears that SHC1 and FGFR1 could be significant immune-related oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility.

RAG-seq: A NSR Primed and Transposase Tagmentation Mediated Strand-specific Total RNA Sequencing in Single Cell.

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular diversity with unprecedented resolution. However, many current methods are limited in capturing full-length transcripts and discerning strand orientation. We present RAG-seq, an innovative strand-specific total RNA sequencing technique that combines not-so-random (NSR) primers with Tn5 transposase-mediated tagmentation. RAG-seq overcomes previous limitations by delivering comprehensive transcript coverage and maintaining strand orientation, which is essential for accurate quantification of overlapping genes and detection of antisense transcripts. Through optimized reverse transcription with oligo dT primers, rRNA depletion via Depletion of Abundant Sequences by Hybridization (DASH), and linear amplification, RAG-seq enhances sensitivity and reproducibility, especially for low-input samples and single cells. Application to mouse oocytes and early embryos highlights RAG-seq's superior performance in identifying stage-specific antisense transcripts, shedding light on their regulatory roles during early development. This advancement represents a significant leap in transcriptome analysis within complex biological contexts.

A prospective study of neoadjuvant pembrolizumab plus chemotherapy for resectable esophageal squamous cell carcinoma: The Keystone-001 trial.

In this phase II study, 47 patients with locally advanced, resectable esophageal squamous cell carcinoma (ESCC) received three cycles of pembrolizumab plus chemotherapy, followed by Da Vinci robot-assisted surgery. The primary endpoints were safety and major pathological response (MPR). Key secondary endpoints included complete pathological response (pCR) and survival. No grade ≥3 adverse events or surgical delays occurred during neoadjuvant therapy. Among 46 patients studied for efficacy, the MPR and pCR rates were 72% and 41%, respectively. After a median follow-up of 27.2 months, the 2-year overall survival (OS) and disease-free survival (DFS) rates were 91% and 89%, respectively. Expansion of TRGC2+ NKT cells in peripheral blood correlated with neoadjuvant treatment effectiveness, which was validated by in vitro organoid experiments and external cancer datasets, and its functional classification and mechanism of action were further explored. These findings show preoperative pembrolizumab plus chemotherapy is a promising therapeutic strategy for resectable ESCC.

Targeted Drug Screening Leveraging Senescence-Induced T-Cell Exhaustion Signatures in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the sixth most prevalent cancer and a leading cause of cancer-related mortality globally, with most patients diagnosed at advanced stages and facing limited early treatment options. This study aimed to identify characteristic genes associated with T-cell exhaustion due to senescence in hepatocellular carcinoma patients, elucidating the interplay between senescence and T-cell exhaustion. We constructed prognostic models based on five signature genes (ENO1, STMN1, PRDX1, RAN, and RANBP1) linked to T-cell exhaustion, utilizing elastic net regression. The findings indicate that increased expression of ENO1 in T cells may contribute to T-cell exhaustion and Treg infiltration in hepatocellular carcinoma. Furthermore, molecular docking was employed to screen small molecule compounds that target the anti-tumor effects of these exhaustion-related genes. This study provides crucial insights into the diagnosis and treatment of hepatocellular carcinoma, establishing a strong foundation for the development of predictive biomarkers and therapeutic targets for affected patients.

Research progress on single-cell expression quantitative trait loci.

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.

scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for studying cellular heterogeneity. However, dropouts are inherent to the sequencing process, known as dropout events, posing challenges in downstream analysis and interpretation. Imputing dropout data becomes a critical concern in scRNA-seq data analysis. Present imputation methods predominantly rely on statistical or machine learning approaches, often overlooking inter-sample correlations. RESULTS: To address this limitation, We introduced SAE-Impute, a new computational method for imputing single-cell data by combining subspace regression and auto-encoders for enhancing the accuracy and reliability of the imputation process. Specifically, SAE-Impute assesses sample correlations via subspace regression, predicts potential dropout values, and then leverages these predictions within an autoencoder framework for interpolation. To validate the performance of SAE-Impute, we systematically conducted experiments on both simulated and real scRNA-seq datasets. These results highlight that SAE-Impute effectively reduces false negative signals in single-cell data and enhances the retrieval of dropout values, gene-gene and cell-cell correlations. Finally, We also conducted several downstream analyses on the imputed single-cell RNA sequencing (scRNA-seq) data, including the identification of differential gene expression, cell clustering and visualization, and cell trajectory construction. CONCLUSIONS: These results once again demonstrate that SAE-Impute is able to effectively reduce the droupouts in single-cell dataset, thereby improving the functional interpretability of the data.

A tumor cornification and immune-infiltration-based scheme for anti-PD-1 plus chemotherapy response in advanced squamous cell lung carcinoma.

BACKGROUND: Anti-PD-1 immunotherapy plus chemotherapy (combo) exhibits significantly prolonged survival for squamous cell lung cancer (LUSC). An exploration of predictive biomarkers is still needed. METHODS: High-throughput RNA sequencing (RNA-seq) of 349 LUSC samples from the randomized, multi-center, phase 3 trial ORIENT-12 (ClinicalTrials.gov: NCT03629925) was conducted for biomarker discovery, followed by flow cytometry and multiplex immunohistochemistry (mIHC) in additional clinical cohorts, and in vitro experiments were performed for verification. RESULTS: A high abundance of activated CD8+ T and CD56bright natural killer (NK) cells benefited patients' outcomes (progression-free survival [PFS]; overall survival [OS]) with combo treatment. Tumor cornification level remarkably affected the infiltration of the two crucial immune cells. Thus, a novel scheme of LUSC immune infiltration and cornification characterization-based classification (LICC) was established for combo efficacy prediction. Patients who received combo treatment achieved significant PFS improvements in LICC1 (hazard ratio [HR] = 0.43, 95% confidence interval [CI]: 0.25-0.75, p = 0.0029) and LICC2 (HR = 0.32, 95% CI: 0.17-0.58, p = 0.0002) subtypes but not in the LICC3 subtype (HR = 0.86, 95% CI: 0.60-1.23, p = 0.4053). Via single-cell RNA-seq analysis, the tumor cornification signal was mainly mapped to SPRR3+ tumor cells, whose relationships with activated CD8+ T or CD56bright NK cells were verified using flow cytometry and mIHC. Our data suggest that SPRR3+ tumor cells might evade immune surveillance via the CD24-SIGLEC10 (M2 macrophage) axis to maintain a suppressive tumor microenvironment. CONCLUSIONS: Tumor cornification greatly impacts immune infiltration, and the LICC scheme may guide clinical medication of anti-PD-1+chemo treatment in patients with LUSC. FUNDING: The study was funded by the National Key R&D Program of China, the National Natural Science Foundation of China, Shanghia Multidisplinary Cooperation Building Project for Diagnosis and Treatment of Major Disease, and Innovent Biologics, Inc.

Elucidation of the pluripotent potential of bovine embryonic lineages facilitates the establishment of formative stem cell lines.

The establishment of epiblast-derived pluripotent stem cells (PSCs) from cattle, which are important domestic animals that provide humans with milk and meat while also serving as bioreactors for producing valuable proteins, poses a challenge due to the unclear molecular signaling required for embryonic epiblast development and maintenance of PSC self-renewal. Here, we selected six key stages of bovine embryo development (E5, E6, E7, E10, E12, and E14) to track changes in pluripotency and the dependence on signaling pathways via modified single-cell transcription sequencing technology. The remarkable similarity of the gene expression patterns between cattle and pigs during embryonic lineage development contributed to the successful establishment of bovine epiblast stem cells (bEpiSCs) using 3i/LAF (WNTi, GSK3ßi, SRCi, LIF, Activin A, and FGF2) culture system. The generated bEpiSCs exhibited consistent expression patterns of formative epiblast pluripotency genes and maintained clonal morphology, normal karyotypes, and proliferative capacity for more than 112 passages. Moreover, these cells exhibited high-efficiency teratoma formation as well as the ability to differentiate into various cell lineages. The potential of bEpiSCs for myogenic differentiation, primordial germ cell like cells (PGCLCs) induction, and as donor cells for cell nuclear transfer was also assessed, indicating their promise in advancing cell-cultured meat production, gene editing, and animal breeding.

The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium.

Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissue-specific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.

Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data.

BACKGROUND: Gene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protein-protein interaction, or metabolic pathways. To analyse gene interaction networks at a large scale, gene co-expression network analysis is often applied on high-throughput gene expression data such as RNA sequencing data. With the advance in sequencing technology, expression of genes can be measured in individual cells. Single-cell RNA sequencing (scRNAseq) provides insights of cellular development, differentiation and characteristics at the transcriptomic level. High sparsity and high-dimensional data structures pose challenges in scRNAseq data analysis. RESULTS: In this study, a sparse inverse covariance matrix estimation framework for scRNAseq data is developed to capture direct functional interactions between genes. Comparative analyses highlight high performance and fast computation of Stein-type shrinkage in high-dimensional data using simulated scRNAseq data. Data transformation approaches also show improvement in performance of shrinkage methods in non-Gaussian distributed data. Zero-inflated modelling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in zero-inflated data without interference on non zero-inflated count data. CONCLUSION: The proposed framework broadens application of graphical model in scRNAseq analysis with flexibility in sparsity of count data resulting from dropout events, high performance, and fast computational time. Implementation of the framework is in a reproducible Snakemake workflow https://github.com/calathea24/ZINBGraphicalModel and R package ZINBStein https://github.com/calathea24/ZINBStein .

Identification and Targeting of Regulators of SARS-CoV-2-Host interactions in the Airway Epithelium.

Although the impact of SARS-CoV-2 in the lung has been extensively studied, the molecular regulators and targets of the host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. This is in part ascribed to the use of non-primary cell systems, overreliance on single-cell gene expression profiling that not ultimately reflect protein activity and bias toward the downstream effects rather than their mechanistic determinants. Here we address these issues by network-based analysis of single cell transcriptomic profiles of pathophysiologically relevant human adult basal, ciliated and secretory cells to identify master regulator (MR) protein modules controlling their SARS-CoV-2-mediated reprogramming. This uncovered chromatin remodeling, endosomal sorting, ubiquitin pathway as well as proviral factors identified by CRISPR analyses as components of the host response collectively or selectively activated in these cells. Large-scale perturbation assays, using a clinically-relevant drug library, identified 11 drugs able to invert the entire MR signature activated by SARS-CoV-2 in these cell types. Leveraging MR analysis and perturbational profiles of human primary cells, represents a novel mechanism-based approach and resource that can be directly generalized to interrogate signatures of other airway conditions for drug prioritization.

Single-cell transcriptional analysis of murine norovirus infection in a human intestinal cell line.

Noroviruses are a major agent of acute gastroenteritis in humans, but host cell requirements for efficient replication in vitro have not been established. We engineered a human intestinal cell line (designated mCD300lf-hCaco2) expressing the murine norovirus (MNV) receptor, mouse CD300lf to become fully permissive for MNV replication. To explore the replicative machinery and host response of these cells, we performed a single-cell RNA sequencing (scRNA-seq) transcriptomics analysis of an MNV infection over time. Marked similarities were observed between certain global features of MNV infection in human cells compared to those previously reported in mouse cells by whole population transcriptomics such as downregulation of ribosome biogenesis, mitochondrial dysfunction, and cell cycle preference for G1. Our scRNA-seq analysis allowed further resolution of an infected cell population into distinct clusters with varying levels of viral RNA and interferon-stimulated gene ISG15 transcripts. Cells with high viral replication displayed downregulated ribosomal protein small (RPS) and large (RPL) genes and mitochondrial complexes I, III, IV, and V genes during exponential viral propagation. Ferritin subunit genes FTL and FTH1 were also downregulated during active MNV replication, suggesting that inhibition of iron metabolism may increase replication efficiency. Consistent with this, transcriptional activation of these genes with ferric ammonium citrate and overexpression of FTL lowered virus yields. Comparative studies of cells that support varying levels of norovirus replication efficiency, as determined by scRNA-seq may lead to improved human cell-based culture systems and effective viral interventions.IMPORTANCEHuman noroviruses cause acute gastroenteritis in all age groups. Vaccines and antiviral drugs are not yet available, in part, because it is difficult to propagate the viruses causing human disease in standard laboratory cell culture systems. In contrast, a norovirus found in mice [murine norovirus (MNV)] replicates efficiently in murine-based cell culture and has served as a model system. In this study, we established a new human intestinal cell line that was genetically modified to express the murine norovirus receptor so that the human cells became permissive to murine norovirus infection. We then defined the host response to MNV infection in the engineered human cell line at a single-cell resolution and identified cellular genes associated with the highest levels of MNV replication. This study may lead to the improvement of the current human norovirus cell culture systems and help to identify norovirus-host interactions that could be targeted for antiviral drugs.