Scaling up single-cell RNA-seq data analysis with CellBridge workflow.

SUMMARY: Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, unraveling unprecedented insights into cellular heterogeneity. However, the analysis of scRNA-seq data remains a challenging and time-consuming task, often demanding advanced computational expertise, rendering it impractical for high-volume environments and applications. We present CellBridge, an automated workflow designed to simplify the standard procedures entailed in scRNA-seq data analysis, eliminating the need for specialized computational expertise. CellBridge utilizes state-of-the-art computational methods, integrating a range of advanced functionalities, covering the entire process from raw unaligned sequencing reads to cell type annotation. Hence, CellBridge accelerates the pace of discovery by seamlessly enabling insights into vast volumes of scRNA-seq data, without compromising workflow control and reproducibility. AVAILABILITY AND IMPLEMENTATION: The source code, detailed documentation, and materials required to reproduce the results are available on GitHub and archived in Zenodo. For the CellBridge pre-processing step (v1.0.0), access the GitHub repository at https://github.com/Sanofi-Public/PMCB-ToBridge and the Zenodo archive at https://zenodo.org/records/10246161. For the CellBridge processing step (v1.0.0), visit the GitHub repository at https://github.com/Sanofi-Public/PMCB-CellBridge and the Zenodo archive at https://zenodo.org/records/10246046.

Retinoic acid is essential for Th1 cell lineage stability and prevents transition to a Th17 cell program.

CD4(+) T cells differentiate into phenotypically distinct T helper cells upon antigenic stimulation. Regulation of plasticity between these CD4(+) T-cell lineages is critical for immune homeostasis and prevention of autoimmune disease. However, the factors that regulate lineage stability are largely unknown. Here we investigate a role for retinoic acid (RA) in the regulation of lineage stability using T helper 1 (Th1) cells, traditionally considered the most phenotypically stable Th subset. We found that RA, through its receptor RARα, sustains stable expression of Th1 lineage specifying genes, as well as repressing genes that instruct Th17-cell fate. RA signaling is essential for limiting Th1-cell conversion into Th17 effectors and for preventing pathogenic Th17 responses in vivo. Our study identifies RA-RARα as a key component of the regulatory network governing maintenance and plasticity of Th1-cell fate and defines an additional pathway for the development of Th17 cells.

Novel biomarkers of preterm brain injury from blood transcriptome in sheep model of intrauterine asphyxia.

BACKGROUND: Infants born preterm have a higher incidence of neurological deficits. A key step in finding effective treatments is to identify biomarkers that reliably predict outcome. METHODS: Following umbilical cord occlusion (UCO) in pregnant sheep, whole fetal blood RNA was sequenced pre- and post-UCO, brain injury outcome was determined by battery of neuropathology scoring and the transcriptome signature correlated to the degree of brain injury. Additionally, we developed a novel analytical procedure to deduce cell blood composition over time. RESULTS: Sixty-one genes were identified with significant altered expression after UCO. In pre-UCO blood, the level of three mRNAs (Trex2, Znf280b, novel miRNA) and in post-UCO, four mRNAs (Fam184a, Angptl2, novel lincRNA and an unknown protein-coding gene) were associated to brain injury (FDR < 0.01). Several of these mRNAs are related to inflammation and angiogenesis. Pathway analysis highlighted genes playing a role in perinatal death and growth failure. Results also indicate that several leukocyte populations undergo significant changes after UCO. CONCLUSION: We have used a whole transcriptomic approach to uncover novel biomarkers in fetal blood that correlate to neuropathology in the preterm sheep brain. The current data forms a basis for future studies to investigate mechanisms of these mRNAs in the injury progression. IMPACT: Trend analysis of genes following asphyxia reveal a group of genes associated with perinatal death and growth failure. Several pre-asphyxia transcripts were associated to brain injury severity suggesting genomic susceptibility to injury. Several post-asphyxia transcripts were correlated to brain injury severity, thus, serve as potential novel biomarkers of injury outcome. Successfully adaptation of cell profiling algorithms suggests significant changes in blood cell composition following asphyxia.

IL-33 Expression Is Lower in Current Smokers at both Transcriptomic and Protein Levels.

Rationale: IL-33 is a proinflammatory cytokine thought to play a role in the pathogenesis of asthma and chronic obstructive pulmonary disease (COPD). A recent clinical trial using an anti-IL-33 antibody showed a reduction in exacerbation and improved lung function in ex-smokers but not current smokers with COPD. Objectives: This study aimed to understand the effects of smoking status on IL-33. Methods: We investigated the association of smoking status with the level of gene expression of IL-33 in the airways in eight independent transcriptomic studies of lung airways. Additionally, we performed Western blot analysis and immunohistochemistry for IL-33 in lung tissue to assess protein levels. Measurements and Main Results: Across the bulk RNA-sequencing datasets, IL-33 gene expression and its signaling pathway were significantly lower in current versus former or never-smokers and increased upon smoking cessation (P < 0.05). Single-cell sequencing showed that IL-33 is predominantly expressed in resting basal epithelial cells and decreases during the differentiation process triggered by smoke exposure. We also found a higher transitioning of this cellular subpopulation into a more differentiated cell type during chronic smoking, potentially driving the reduction of IL-33. Protein analysis demonstrated lower IL-33 levels in lung tissue from current versus former smokers with COPD and a lower proportion of IL-33-positive basal cells in current versus ex-smoking controls. Conclusions: We provide strong evidence that cigarette smoke leads to an overall reduction in IL-33 expression in transcriptomic and protein level, and this may be due to the decrease in resting basal cells. Together, these findings may explain the clinical observation that a recent antibody-based anti-IL-33 treatment is more effective in former than current smokers with COPD.

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis.

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

De novo mutations implicate novel genes in systemic lupus erythematosus.

The omnigenic model of complex disease stipulates that the majority of the heritability will be explained by the effects of common variation on genes in the periphery of core disease pathways. Rare variant associations, expected to explain far less of the heritability, may be enriched in core disease genes and thus will be instrumental in the understanding of complex disease pathogenesis and their potential therapeutic targets. Here, using complementary whole-exome sequencing, high-density imputation, and in vitro cellular assays, we identify candidate core genes in the pathogenesis of systemic lupus erythematosus (SLE). Using extreme-phenotype sampling, we sequenced the exomes of 30 SLE parent-affected-offspring trios and identified 14 genes with missense de novo mutations (DNM), none of which are within the >80 SLE susceptibility loci implicated through genome-wide association studies. In a follow-up cohort of 10, 995 individuals of matched European ancestry, we imputed genotype data to the density of the combined UK10K-1000 genomes Phase III reference panel across the 14 candidate genes. Gene-level analyses indicate three functional candidates: DNMT3A, PRKCD, and C1QTNF4. We identify a burden of rare variants across PRKCD associated with SLE risk (P = 0.0028), and across DNMT3A associated with two severe disease prognosis sub-phenotypes (P = 0.0005 and P = 0.0033). We further characterise the TNF-dependent functions of the third candidate gene C1QTNF4 on NF-κB activation and apoptosis, which are inhibited by the p.His198Gln DNM. Our results identify three novel genes in SLE susceptibility and support extreme-phenotype sampling and DNM gene discovery to aid the search for core disease genes implicated through rare variation.

Phase I/II open-label trial of intravenous allogeneic mesenchymal stromal cell therapy in adults with recessive dystrophic epidermolysis bullosa.

BACKGROUND: Recessive dystrophic epidermolysis bullosa (RDEB) is a hereditary blistering disorder due to a lack of type VII collagen. At present, treatment is mainly supportive. OBJECTIVES: To determine whether intravenous allogeneic bone marrow-derived mesenchymal stromal/stem cells (BM-MSCs) are safe in RDEB adults and if the cells improve wound healing and quality of life. METHODS: We conducted a prospective, phase I/II, open-label study recruiting 10 RDEB adults to receive 2 intravenous infusions of BM-MSCs (on day 0 and day 14; each dose 2-4 × 106 cells/kg). RESULTS: BM-MSCs were well tolerated with no serious adverse events to 12 months. Regarding efficacy, there was a transient reduction in disease activity scores (8/10 subjects) and a significant reduction in itch. One individual showed a transient increase in type VII collagen. LIMITATIONS: Open-label trial with no placebo. CONCLUSIONS: MSC infusion is safe in RDEB adults and can have clinical benefits for at least 2 months.

Deep phenotyping of Tregs identifies an immune signature for idiopathic aplastic anemia and predicts response to treatment.

Idiopathic aplastic anemia (AA) is an immune-mediated and serious form of bone marrow failure. Akin to other autoimmune diseases, we have previously shown that in AA regulatory T cells (Tregs) are reduced in number and function. The aim of this study was to further characterize Treg subpopulations in AA and investigate the potential correlation between specific Treg subsets and response to immunosuppressive therapy (IST) as well as their in vitro expandability for potential clinical use. Using mass cytometry and an unbiased multidimensional analytical approach, we identified 2 specific human Treg subpopulations (Treg A and Treg B) with distinct phenotypes, gene expression, expandability, and function. Treg B predominates in IST responder patients, has a memory/activated phenotype (with higher expression of CD95, CCR4, and CD45RO within FOXP3(hi), CD127(lo) Tregs), expresses the interleukin-2 (IL-2)/STAT5 pathway and cell-cycle commitment genes. Furthermore, in vitro-expanded Tregs become functional and take on the characteristics of Treg B. Collectively, this study identifies human Treg subpopulations that can be used as predictive biomarkers for response to IST in AA and potentially other autoimmune diseases. We also show that Tregs from AA patients are IL-2-sensitive and expandable in vitro, suggesting novel therapeutic approaches such as low-dose IL-2 therapy and/or expanded autologous Tregs and meriting further exploration.

Candidate driver genes involved in genome maintenance and DNA repair in Sézary syndrome.

Sézary syndrome (SS) is a leukemic variant of cutaneous T-cell lymphoma (CTCL) and represents an ideal model for study of T-cell transformation. We describe whole-exome and single-nucleotide polymorphism array-based copy number analyses of CD4(+) tumor cells from untreated patients at diagnosis and targeted resequencing of 101 SS cases. A total of 824 somatic nonsynonymous gene variants were identified including indels, stop-gain/loss, splice variants, and recurrent gene variants indicative of considerable molecular heterogeneity. Driver genes identified using MutSigCV include POT1, which has not been previously reported in CTCL; and TP53 and DNMT3A, which were also identified consistent with previous reports. Mutations in PLCG1 were detected in 11% of tumors including novel variants not previously described in SS. This study is also the first to show BRCA2 defects in a significant proportion (14%) of SS tumors. Aberrations in PRKCQ were found to occur in 20% of tumors highlighting selection for activation of T-cell receptor/NF-κB signaling. A complex but consistent pattern of copy number variants (CNVs) was detected and many CNVs involved genes identified as putative drivers. Frequent defects involving the POT1 and ATM genes responsible for telomere maintenance were detected and may contribute to genomic instability in SS. Genomic aberrations identified were enriched for genes implicated in cell survival and fate, specifically PDGFR, ERK, JAK STAT, MAPK, and TCR/NF-κB signaling; epigenetic regulation (DNMT3A, ASLX3, TET1-3); and homologous recombination (RAD51C, BRCA2, POLD1). This study now provides the basis for a detailed functional analysis of malignant transformation of mature T cells and improved patient stratification and treatment.

Association of MicroRNAs and YRNAs With Platelet Function.

RATIONALE: Platelets shed microRNAs (miRNAs). Plasma miRNAs change on platelet inhibition. It is unclear whether plasma miRNA levels correlate with platelet function. OBJECTIVE: To link small RNAs to platelet reactivity. METHODS AND RESULTS: Next-generation sequencing of small RNAs in plasma revealed 2 peaks at 22 to 23 and 32 to 33 nucleotides corresponding to miRNAs and YRNAs, respectively. Among YRNAs, predominantly, fragments of RNY4 and RNY5 were detected. Plasma miRNAs and YRNAs were measured in 125 patients with a history of acute coronary syndrome who had undergone detailed assessment of platelet function 30 days after the acute event. Using quantitative real-time polymerase chain reactions, 92 miRNAs were assessed in patients with acute coronary syndrome on different antiplatelet therapies. Key platelet-related miRNAs and YRNAs were correlated with platelet function tests. MiR-223 (rp=0.28; n=121; P=0.002), miR-126 (rp=0.22; n=121; P=0.016), and other abundant platelet miRNAs and YRNAs showed significant positive correlations with the vasodilator-stimulated phosphoprotein phosphorylation assay. YRNAs, miR-126, and miR-223 were also among the small RNAs showing the greatest dependency on platelets and strongly correlated with plasma levels of P-selectin, platelet factor 4, and platelet basic protein in the population-based Bruneck study (n=669). A single-nucleotide polymorphism that facilitates processing of pri-miR-126 to mature miR-126 accounted for a rise in circulating platelet activation markers. Inhibition of miR-126 in mice reduced platelet aggregation. MiR-126 directly and indirectly affects ADAM9 and P2Y12 receptor expression. CONCLUSIONS: Levels of platelet-related plasma miRNAs and YRNAs correlate with platelet function tests in patients with acute coronary syndrome and platelet activation markers in the general population. Alterations in miR-126 affect platelet reactivity.

Cluster stability in the analysis of mass cytometry data.

Manual gating has been traditionally applied to cytometry data sets to identify cells based on protein expression. The advent of mass cytometry allows for a higher number of proteins to be simultaneously measured on cells, therefore providing a means to define cell clusters in a high dimensional expression space. This enhancement, whilst opening unprecedented opportunities for single cell-level analyses, makes the incremental replacement of manual gating with automated clustering a compelling need. To this aim many methods have been implemented and their successful applications demonstrated in different settings. However, the reproducibility of automatically generated clusters is proving challenging and an analytical framework to distinguish spurious clusters from more stable entities, and presumably more biologically relevant ones, is still missing. One way to estimate cell clusters' stability is the evaluation of their consistent re-occurrence within- and between-algorithms, a metric that is commonly used to evaluate results from gene expression. Herein we report the usage and importance of cluster stability evaluations, when applied to results generated from three popular clustering algorithms - SPADE, FLOCK and PhenoGraph - run on four different data sets. These algorithms were shown to generate clusters with various degrees of statistical stability, many of them being unstable. By comparing the results of automated clustering with manually gated populations, we illustrate how information on cluster stability can assist towards a more rigorous and informed interpretation of clustering results. We also explore the relationships between statistical stability and other properties such as clusters' compactness and isolation, demonstrating that whilst cluster stability is linked to other properties it cannot be reliably predicted by any of them. Our study proposes the introduction of cluster stability as a necessary checkpoint for cluster interpretation and contributes to the construction of a more systematic and standardized analytical framework for the assessment of cytometry clustering results. © 2016 International Society for Advancement of Cytometry.

Phenotypic Complexity of the Human Regulatory T Cell Compartment Revealed by Mass Cytometry.

Regulatory T cells (Tregs) are an essential component of the cellular immune response, occupying a key role in maintaining immunological tolerance and present an attractive therapeutic target in a range of immunopathologies. Comprehensive analysis of the human Treg compartment has been restricted due to technical limitations. The advent of mass cytometry enables simultaneous assessment of vastly increased phenotypic parameters at single-cell resolution. In this study, we used mass cytometry to examine the complexity of human Tregs using an extensive panel of surface markers associated with Treg function and phenotype. We applied unsupervised clustering analysis, revealing 22 distinct subpopulations of Tregs, representing previously identified and novel subpopulations. Our data represent the most in-depth phenotypic description of the human Treg compartment at single-cell resolution and show a hitherto unrecognized degree of phenotypic complexity among cells of the regulatory lineage.

Role of retinoic acid in the stability of the T-helper-type 1 lineage and implications for autoimmunity.

BACKGROUND: CD4 T cells with features of both T-helper-type 1 (Th1) and 17 (Th17) cells have been implicated in several autoimmune diseases suggesting that plasticity among CD4 T-cell lineages is potentially pathogenic. However, the factors that regulate T-cell lineage stability are largely unknown. Retinoic acid (RA) is synthesised at sites of inflammation. We hypothesised that retinoic acid, a profound epigenetic modifier, could regulate T-cell lineage stability. METHODS: We used a mouse model in which retinoic acid signalling is specifically ablated within the T-cell compartment through overexpression of a dominant negative retinoic acid receptor α (RARα) (dnRARα mice) to investigate its role in the regulation of Th1 lineage stability. Genome-wide ChIP-seq analysis was done to identify RARα targets. In parallel, we performed global mapping of regulatory regions, termed enhancers, to gain mechanistic insight into retinoic acid regulation of T-cell fate. The in-vivo relevance of our findings was determined in a model of oral antigen-induced intestinal inflammation. FINDINGS: We found that retinoic acid is crucial for maintenance of the Th1 lineage. Abrogation of retinoic acid signalling in Th1 cells resulted in loss of T-bet expression and STAT4 activity. Th1 cells from dnRARα mice showed enhanced plasticity with the emergence of hybrid Th1-Th17 and Th17 effector cells. Global analysis of RARα binding and enhancer mapping revealed that RA-RARα directly regulated enhancer activity at Th1 lineage defining genes while repressing genes that regulate Th17 cell fate. Retinoic acid inhibition of Th1 plasticity was essential for maintaining appropriate Th cell responses in vivo and preventing autoimmune intestinal inflammation. INTERPRETATION: Our study has identified RA-RARα as a key component of the regulatory network governing maintenance and plasticity of Th1 cells and defines a new pathway for the development of pathogenic Th17 cells. Retinoids might be novel therapeutic agents for Th17-associated autoimmune diseases. FUNDING: Wellcome Trust.

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

Genetic analyses of inflammatory polyneuropathy and chronic inflammatory demyelinating polyradiculoneuropathy identified candidate genes.

Chronic inflammatory demyelinating polyneuropathy (CIDP) is a rare, immune-mediated disorder in which an aberrant immune response causes demyelination and axonal damage of the peripheral nerves. Genetic contribution to CIDP is unclear and no genome-wide association study (GWAS) has been reported so far. In this study, we aimed to identify CIDP-related risk loci, genes, and pathways. We first focused on CIDP, and 516 CIDP cases and 403,545 controls were included in the GWAS analysis. We also investigated genetic risk for inflammatory polyneuropathy (IP), in which we performed a GWAS study using FinnGen data and combined the results with GWAS from the UK Biobank using a fixed-effect meta-analysis. A total of 1,261 IP cases and 823,730 controls were included in the analysis. Stratified analyses by gender were performed. Mendelian randomization (MR), colocalization, and transcriptome-wide association study (TWAS) analyses were performed to identify associated genes. Gene-set analyses were conducted to identify associated pathways. We identified one genome-wide significant locus at 20q13.33 for CIDP risk among women, the top variant located at the intron region of gene CDH4. Sex-combined MR, colocalization, and TWAS analyses identified three candidate pathogenic genes for CIDP and five genes for IP. MAGMA gene-set analyses identified a total of 18 pathways related to IP or CIDP. Sex-stratified analyses identified three genes for IP among males and two genes for IP among females. Our study identified suggestive risk genes and pathways for CIDP and IP. Functional analyses should be conducted to further confirm these associations.

Molecular subtypes explain lupus epigenomic heterogeneity unveiling new regulatory genetic risk variants.

The heterogeneity of systemic lupus erythematosus (SLE) can be explained by epigenetic alterations that disrupt transcriptional programs mediating environmental and genetic risk. This study evaluated the epigenetic contribution to SLE heterogeneity considering molecular and serological subtypes, genetics and transcriptional status, followed by drug target discovery. We performed a stratified epigenome-wide association studies of whole blood DNA methylation from 213 SLE patients and 221 controls. Methylation quantitative trait loci analyses, cytokine and transcription factor activity - epigenetic associations and methylation-expression correlations were conducted. New drug targets were searched for based on differentially methylated genes. In a stratified approach, a total of 974 differential methylation CpG sites with dependency on molecular subtypes and autoantibody profiles were found. Mediation analyses suggested that SLE-associated SNPs in the HLA region exert their risk through DNA methylation changes. Novel genetic variants regulating DNAm in disease or in specific molecular contexts were identified. The epigenetic landscapes showed strong association with transcription factor activity and cytokine levels, conditioned by the molecular context. Epigenetic signals were enriched in known and novel drug targets for SLE. This study reveals possible genetic drivers and consequences of epigenetic variability on SLE heterogeneity and disentangles the DNAm mediation role on SLE genetic risk and novel disease-specific meQTLs. Finally, novel targets for drug development were discovered.

Integrated genomic analysis of triple-negative breast cancers reveals novel microRNAs associated with clinical and molecular phenotypes and sheds light on the pathways they control.

BACKGROUND: This study focuses on the analysis of miRNAs expression data in a cohort of 181 well characterised breast cancer samples composed primarily of triple-negative (ER/PR/HER2-negative) tumours with associated genome-wide DNA and mRNA data, extensive patient follow-up and pathological information. RESULTS: We identified 7 miRNAs associated with prognosis in the triple-negative tumours and an additional 7 when the analysis was extended to the set of all ER-negative cases. miRNAs linked to an unfavourable prognosis were associated with a broad spectrum of motility mechanisms involved in the invasion of stromal tissues, such as cell-adhesion, growth factor-mediated signalling pathways, interaction with the extracellular matrix and cytoskeleton remodelling. When we compared different intrinsic molecular subtypes we found 46 miRNAs that were specifically expressed in one or more intrinsic subtypes. Integrated genomic analyses indicated these miRNAs to be influenced by DNA genomic aberrations and to have an overall influence on the expression levels of their predicted targets. Among others, our analyses highlighted the role of miR-17-92 and miR-106b-25, two polycistronic miRNA clusters with known oncogenic functions. We showed that their basal-like subtype specific up-regulation is influenced by increased DNA copy number and contributes to the transcriptional phenotype as well as the activation of oncogenic pathways in basal-like tumours. CONCLUSIONS: This study analyses previously unreported miRNA, mRNA and DNA data and integrates these with pathological and clinical information, from a well-annotated cohort of breast cancers enriched for triple-negative subtypes. It provides a conceptual framework, as well as integrative methods and system-level results and contributes to elucidate the role of miRNAs as biomarkers and modulators of oncogenic processes in these types of tumours.

A marker gene-based method for identifying the cell-type of origin from single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) experiments provide opportunities to peer into complex tissues at single-cell resolution. However, insightful biological interpretation of scRNA-seq data relies upon precise identification of cell types. The ability to identify the origin of a cell quickly and accurately will greatly improve downstream analyses. We present Sargent, a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers. We demonstrate Sargent's high accuracy by annotating simulated datasets. Further, we compare Sargent performance against expert-annotated scRNA-seq data from human organs including PBMC, heart, kidney, and lung. We demonstrate that Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation. Additionally, the automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.•Sargent is a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers.•Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation.•Automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.

DiSiR: fast and robust method to identify ligand-receptor interactions at subunit level from single-cell RNA-sequencing data.

Most cell-cell interactions and crosstalks are mediated by ligand-receptor interactions. The advent of single-cell RNA-sequencing (scRNA-seq) techniques has enabled characterizing tissue heterogeneity at single-cell level. In the past few years, several methods have been developed to study ligand-receptor interactions at cell type level using scRNA-seq data. However, there is still no easy way to query the activity of a specific user-defined signaling pathway in a targeted way or to map the interactions of the same subunit with different ligands as part of different receptor complexes. Here, we present DiSiR, a fast and easy-to-use permutation-based software framework to investigate how individual cells are interacting with each other by analyzing signaling pathways of multi-subunit ligand-activated receptors from scRNA-seq data, not only for available curated databases of ligand-receptor interactions, but also for interactions that are not listed in these databases. We show that, when utilized to infer ligand-receptor interactions from both simulated and real datasets, DiSiR outperforms other well-known permutation-based methods, e.g. CellPhoneDB and ICELLNET. Finally, to demonstrate DiSiR's utility in exploring data and generating biologically relevant hypotheses, we apply it to COVID lung and rheumatoid arthritis (RA) synovium scRNA-seq datasets and highlight potential differences between inflammatory pathways at cell type level for control versus disease samples.