muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data

Quality control (QC) is a critical component of single-cell RNA-seq (scRNA-seq) processing pipelines. Current approaches to QC implicitly assume that datasets are comprised of one cell type, potentially resulting in biased exclusion of rare cell types. We introduce SampleQC, which robustly fits a Gaussian mixture model across multiple samples, improves sensitivity, and reduces bias compared to current approaches. We show via simulations that SampleQC is less susceptible to exclusion of rarer cell types. We also demonstrate SampleQC on a complex real dataset (867k cells over 172 samples). SampleQC is general, is implemented in R, and could be applied to other data types.

SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNA-seq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single-cell research.

Breast cancer cells survive chemotherapy by activating targetable immune-modulatory programs characterized by PD-L1 or CD80

Breast cancer cells must avoid intrinsic and extrinsic cell death to relapse following chemotherapy. Entering senescence enables survival from mitotic catastrophe, apoptosis and nutrient deprivation, but mechanisms of immune evasion are poorly understood. Here we show that breast tumors surviving chemotherapy activate complex programs of immune modulation. Characterization of residual disease revealed distinct tumor cell populations. The first population was characterized by interferon response genes, typified by Cd274, whose expression required chemotherapy to enhance chromatin accessibility, enabling recruitment of IRF1 transcription factor. A second population was characterized by p53 signaling, typified by CD80 expression. Treating mammary tumors with chemotherapy followed by targeting the PD-L1 and/or CD80 axes resulted in marked accumulation of T cells and improved response; however, even combination strategies failed to fully eradicate tumors in the majority of cases. Our findings reveal the challenge of eliminating residual disease populated by senescent cells expressing redundant immune inhibitory pathways and highlight the need for rational immune targeting strategies.

A novel strategy to dissect multifaceted macrophage function in human diseases

Macrophages are widely distributed immune cells that play central roles in a variety of physiologic and pathologic processes, including obesity and cardiovascular disease (CVD). They are highly plastic cells that execute diverse functions according to a combination of signaling and environmental cues. While macrophages have traditionally been understood to polarize to either proinflammatory M1-like or anti-inflammatory M2-like states, evidence has shown that they exist in a spectrum of states between those 2 phenotypic extremes. In obesity-related disease, M1-like macrophages exacerbate inflammation and promote insulin resistance, while M2-like macrophages reduce inflammation, promoting insulin sensitivity. However, polarization markers are expressed inconsistently in adipose tissue macrophages, and they additionally exhibit phenotypes differing from the M1/M2 paradigm. In atherosclerotic CVD, activated plaque macrophages can also exist in a range of proinflammatory or anti-inflammatory states. Some of these macrophages scavenge lipids, developing into heterogeneous foam cell populations. To better characterize the many actions of macrophages in human disease, we have designed a novel set of computational tools: MacSpectrum and AtheroSpectrum. These tools provide information on the inflammatory polarization status, differentiation, and foaming of macrophages in both human and mouse samples, allowing for better characterization of macrophage subpopulations based on their function. Using these tools, we identified disease-relevant cell states in obesity and CVD, including the novel concept that macrophage-derived foam cell formation can follow homeostatic noninflammatory or pathogenic inflammatory foaming programs.

EBF1 primes B-lymphoid enhancers and limits the myeloid bias in murine multipotent progenitors

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) generate all cells of the blood system. Despite their multipotency, MPPs display poorly understood lineage bias. Here, we examine whether lineage-specifying transcription factors, such as the B-lineage determinant EBF1, regulate lineage preference in early progenitors. We detect low-level EBF1 expression in myeloid-biased MPP3 and lymphoid-biased MPP4 cells, coinciding with expression of the myeloid determinant C/EBPα. Hematopoietic deletion of Ebf1 results in enhanced myelopoiesis and reduced HSC repopulation capacity. Ebf1-deficient MPP3 and MPP4 cells exhibit an augmented myeloid differentiation potential and a transcriptome with an enriched C/EBPα signature. Correspondingly, EBF1 binds the Cebpa enhancer, and the deficiency and overexpression of Ebf1 in MPP3 and MPP4 cells lead to an up- and downregulation of Cebpa expression, respectively. In addition, EBF1 primes the chromatin of B-lymphoid enhancers specifically in MPP3 cells. Thus, our study implicates EBF1 in regulating myeloid/lymphoid fate bias in MPPs by constraining C/EBPα-driven myelopoiesis and priming the B-lymphoid fate.

TREM2 drives microglia response to amyloid-β via SYK-dependent and -independent pathways

Genetic studies have highlighted microglia as pivotal in orchestrating Alzheimer's disease (AD). Microglia that adhere to Aβ plaques acquire a transcriptional signature, "disease-associated microglia" (DAM), which largely emanates from the TREM2-DAP12 receptor complex that transmits intracellular signals through the protein tyrosine kinase SYK. The human TREM2R47H variant associated with high AD risk fails to activate microglia via SYK. We found that SYK-deficient microglia cannot encase Aβ plaques, accelerating brain pathology and behavioral deficits. SYK deficiency impaired the PI3K-AKT-GSK-3β-mTOR pathway, incapacitating anabolic support required for attaining the DAM profile. However, SYK-deficient microglia proliferated and advanced to an Apoe-expressing prodromal stage of DAM; this pathway relied on the adapter DAP10, which also binds TREM2. Thus, microglial responses to Aβ involve non-redundant SYK- and DAP10-pathways. Systemic administration of an antibody against CLEC7A, a receptor that directly activates SYK, rescued microglia activation in mice expressing the TREM2R47H allele, unveiling new options for AD immunotherapy.

Impaired IL-23-dependent induction of IFN-γ underlies mycobacterial disease in patients with inherited TYK2 deficiency

Human cells homozygous for rare loss-of-expression (LOE) TYK2 alleles have impaired, but not abolished, cellular responses to IFN-α/β (underlying viral diseases in the patients) and to IL-12 and IL-23 (underlying mycobacterial diseases). Cells homozygous for the common P1104A TYK2 allele have selectively impaired responses to IL-23 (underlying isolated mycobacterial disease). We report three new forms of TYK2 deficiency in six patients from five families homozygous for rare TYK2 alleles (R864C, G996R, G634E, or G1010D) or compound heterozygous for P1104A and a rare allele (A928V). All these missense alleles encode detectable proteins. The R864C and G1010D alleles are hypomorphic and loss-of-function (LOF), respectively, across signaling pathways. By contrast, hypomorphic G996R, G634E, and A928V mutations selectively impair responses to IL-23, like P1104A. Impairment of the IL-23-dependent induction of IFN-γ is the only mechanism of mycobacterial disease common to patients with complete TYK2 deficiency with or without TYK2 expression, partial TYK2 deficiency across signaling pathways, or rare or common partial TYK2 deficiency specific for IL-23 signaling.

CellDrift: inferring perturbation responses in temporally sampled single-cell data

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.

Propeller: testing for differences in cell type proportions in single cell data

Motivation

Single cell RNA-Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. This technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments, which has been difficult to directly address with bulk RNA-seq data, is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportion estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions.

Results

We have developed propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. Using simulated cell type proportions data, we show that propeller performs well under a variety of scenarios. We applied propeller to test for significant changes in cell type proportions related to human heart development, ageing and COVID-19 disease severity.

Availability and implementation

The propeller method is publicly available in the open source speckle R package (https://github.com/phipsonlab/speckle). All the analysis code for the article is available at the associated analysis website: https://phipsonlab.github.io/propeller-paper-analysis/. The speckle package, analysis scripts and datasets have been deposited at https://doi.org/10.5281/zenodo.7009042.

Supplementary information

Supplementary data are available at Bioinformatics online.

A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity

The homeostatic mechanisms that fail to restrain chronic tissue inflammation in diseases, such as psoriasis vulgaris, remain incompletely understood. We profiled transcriptomes and epitopes of single psoriatic and normal skin-resident T cells, revealing a gradated transcriptional program of coordinately regulated inflammation-suppressive genes. This program, which is sharply suppressed in lesional skin, strikingly restricts Th17/Tc17 cytokine and other inflammatory mediators on the single-cell level. CRISPR-based deactivation of two core components of this inflammation-suppressive program, ZFP36L2 and ZFP36, replicates the interleukin-17A (IL-17A), granulocyte macrophage-colony-stimulating factor (GM-CSF), and interferon gamma (IFNγ) elevation in psoriatic memory T cells deficient in these transcripts, functionally validating their influence. Combinatoric expression analysis indicates the suppression of specific inflammatory mediators by individual program members. Finally, we find that therapeutic IL-23 blockade reduces Th17/Tc17 cell frequency in lesional skin but fails to normalize this inflammatory-suppressive program, suggesting how treated lesions may be primed for recurrence after withdrawal of treatment.

COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels.

Defining Patient-Level Molecular Heterogeneity in Psoriasis Vulgaris Based on Single-Cell Transcriptomics

Identifying genetic variation underlying human diseases establishes targets for therapeutic development and helps tailor treatments to individual patients. Large-scale transcriptomic profiling has extended the study of such molecular heterogeneity between patients to somatic tissues. However, the lower resolution of bulk RNA profiling, especially in a complex, composite tissue such as the skin, has limited its success. Here we demonstrate approaches to interrogate patient-level molecular variance in a chronic skin inflammatory disease, psoriasis vulgaris, leveraging single-cell RNA-sequencing of CD45⁺ cells isolated from active lesions. Highly psoriasis-specific transcriptional abnormalities display greater than average inter-individual variance, nominating them as potential sources of clinical heterogeneity. We find that one of these chemokines, CXCL13, demonstrates significant correlation with severity of lesions within our patient series. Our analyses also establish that genes elevated in psoriatic skin-resident memory T cells are enriched for programs orchestrating chromatin and CDC42-dependent cytoskeleton remodeling, specific components of which are distinctly correlated with and against Th17 identity on a single-cell level. Collectively, these analyses describe systematic means to dissect cell type- and patient-level differences in cutaneous psoriasis using high-resolution transcriptional profiles of human inflammatory disease.

Single-cell RNA-seq of primary bone marrow neutrophils from female and male adult mice

Widespread sex-dimorphism is observed in the mammalian immune system. Consistently, studies have reported sex differences in the transcriptome of immune cells at the bulk level, including neutrophils. Neutrophils are the most abundant cell type in human blood, and they are key components of the innate immune system as they form a first line of defense against pathogens. Neutrophils are produced in the bone marrow, and differentiation and maturation produce distinct neutrophil subpopulations. Thus, single-cell resolution studies are crucial to decipher the biological significance of neutrophil heterogeneity. However, since neutrophils are very RNA-poor, single-cell profiling of these cells has been technically challenging. Here, we generated a single-cell RNA-seq dataset of primary neutrophils from adult female and male mouse bone marrow. After stringent quality control, we found that previously characterized neutrophil subpopulations can be detected in both sexes. Additionally, we confirmed that canonical sex-linked markers are differentially expressed between female and male cells across neutrophil subpopulations. This dataset provides a groundwork for comparative studies on the lifelong transcriptional sexual dimorphism of neutrophils.

Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data

Dimension reduction and (spatial) clustering is usually performed sequentially; however, the low-dimensional embeddings estimated in the dimension-reduction step may not be relevant to the class labels inferred in the clustering step. We therefore developed a computation method, Dimension-Reduction Spatial-Clustering (DR-SC), that can simultaneously perform dimension reduction and (spatial) clustering within a unified framework. Joint analysis by DR-SC produces accurate (spatial) clustering results and ensures the effective extraction of biologically informative low-dimensional features. DR-SC is applicable to spatial clustering in spatial transcriptomics that characterizes the spatial organization of the tissue by segregating it into multiple tissue structures. Here, DR-SC relies on a latent hidden Markov random field model to encourage the spatial smoothness of the detected spatial cluster boundaries. Underlying DR-SC is an efficient expectation-maximization algorithm based on an iterative conditional mode. As such, DR-SC is scalable to large sample sizes and can optimize the spatial smoothness parameter in a data-driven manner. With comprehensive simulations and real data applications, we show that DR-SC outperforms existing clustering and spatial clustering methods: it extracts more biologically relevant features than conventional dimension reduction methods, improves clustering performance, and offers improved trajectory inference and visualization for downstream trajectory inference analyses.

IL-13-programmed airway tuft cells produce PGE2, which promotes CFTR-dependent mucociliary function

Chronic type 2 (T2) inflammatory diseases of the respiratory tract are characterized by mucus overproduction and disordered mucociliary function, which are largely attributed to the effects of IL-13 on common epithelial cell types (mucus secretory and ciliated cells). The role of rare cells in airway T2 inflammation is less clear, though tuft cells have been shown to be critical in the initiation of T2 immunity in the intestine. Using bulk and single-cell RNA sequencing of airway epithelium and mouse modeling, we found that IL-13 expanded and programmed airway tuft cells toward eicosanoid metabolism and that tuft cell deficiency led to a reduction in airway prostaglandin E2 (PGE2) concentration. Allergic airway epithelia bore a signature of PGE2 activation, and PGE2 activation led to cystic fibrosis transmembrane receptor-dependent ion and fluid secretion and accelerated mucociliary transport. These data reveal a role for tuft cells in regulating epithelial mucociliary function in the allergic airway.

Spatially Resolved Transcriptomes of Mammalian Kidneys Illustrate the Molecular Complexity and Interactions of Functional Nephron Segments

Available transcriptomes of the mammalian kidney provide limited information on the spatial interplay between different functional nephron structures due to the required dissociation of tissue with traditional transcriptome-based methodologies. A deeper understanding of the complexity of functional nephron structures requires a non-dissociative transcriptomics approach, such as spatial transcriptomics sequencing (ST-seq). We hypothesize that the application of ST-seq in normal mammalian kidneys will give transcriptomic insights within and across species of physiology at the functional structure level and cellular communication at the cell level. Here, we applied ST-seq in six mice and four human kidneys that were histologically absent of any overt pathology. We defined the location of specific nephron structures in the captured ST-seq datasets using three lines of evidence: pathologist's annotation, marker gene expression, and integration with public single-cell and/or single-nucleus RNA-sequencing datasets. We compared the mouse and human cortical kidney regions. In the human ST-seq datasets, we further investigated the cellular communication within glomeruli and regions of proximal tubules-peritubular capillaries by screening for co-expression of ligand-receptor gene pairs. Gene expression signatures of distinct nephron structures and microvascular regions were spatially resolved within the mouse and human ST-seq datasets. We identified 7,370 differentially expressed genes (p adj < 0.05) distinguishing species, suggesting changes in energy production and metabolism in mouse cortical regions relative to human kidneys. Hundreds of potential ligand-receptor interactions were identified within glomeruli and regions of proximal tubules-peritubular capillaries, including known and novel interactions relevant to kidney physiology. Our application of ST-seq to normal human and murine kidneys confirms current knowledge and localization of transcripts within the kidney. Furthermore, the generated ST-seq datasets provide a valuable resource for the kidney community that can be used to inform future research into this complex organ.

Selective activation and expansion of regulatory T cells using lipid encapsulated mRNA encoding a long-acting IL-2 mutein

Interleukin-2 (IL-2) is critical for regulatory T cell (Treg) function and homeostasis. At low doses, IL-2 can suppress immune pathologies by expanding Tregs that constitutively express the high affinity IL-2Rα subunit. However, even low dose IL-2, signaling through the IL2-Rβ/γ complex, may lead to the activation of proinflammatory, non-Treg T cells, so improving specificity toward Tregs may be desirable. Here we use messenger RNAs (mRNA) to encode a half-life-extended human IL-2 mutein (HSA-IL2m) with mutations promoting reliance on IL-2Rα. Our data show that IL-2 mutein subcutaneous delivery as lipid-encapsulated mRNA nanoparticles selectively activates and expands Tregs in mice and non-human primates, and also reduces disease severity in mouse models of acute graft versus host disease and experimental autoimmune encephalomyelitis. Single cell RNA-sequencing of mouse splenic CD4+ T cells identifies multiple Treg states with distinct response dynamics following IL-2 mutein treatment. Our results thus demonstrate the potential of mRNA-encoded HSA-IL2m immunotherapy to treat autoimmune diseases.

Proinflammatory signaling in islet β cells propagates invasion of pathogenic immune cells in autoimmune diabetes

Type 1 diabetes is a disorder of immune tolerance that leads to death of insulin-producing islet β cells. We hypothesize that inflammatory signaling within β cells promotes progression of autoimmunity within the islet microenvironment. To test this hypothesis, we deleted the proinflammatory gene encoding 12/15-lipoxygenase (Alox15) in β cells of non-obese diabetic mice at a pre-diabetic time point when islet inflammation is a feature. Deletion of Alox15 leads to preservation of β cell mass, reduces populations of infiltrating T cells, and protects against spontaneous autoimmune diabetes in both sexes. Mice lacking Alox15 in β cells exhibit an increase in a population of β cells expressing the gene encoding the protein programmed death ligand 1 (PD-L1), which engages receptors on immune cells to suppress autoimmunity. Delivery of a monoclonal antibody against PD-L1 recovers the diabetes phenotype in knockout animals. Our results support the contention that inflammatory signaling in β cells promotes autoimmunity during type 1 diabetes progression.

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.

Comparative transcriptional analysis of satellite glial cell injury response

Background: Satellite glial cells (SGCs) tightly surround and support primary sensory neurons in the peripheral nervous system and are increasingly recognized for their involvement in the development of neuropathic pain following nerve injury. SGCs are difficult to investigate due to their flattened shape and tight physical connection to neurons in vivo and their rapid changes in phenotype and protein expression when cultured in vitro. Consequently, several aspects of SGC function under normal conditions as well as after a nerve injury remain to be explored. The recent advance in single cell RNA sequencing (scRNAseq) technologies has enabled a new approach to investigate SGCs. Methods: In this study we used scRNAseq to investigate SGCs from mice subjected to sciatic nerve injury. We used a meta-analysis approach to compare the injury response with that found in other published datasets.  Furthermore, we also used scRNAseq to investigate how cells from the dorsal root ganglion (DRG) change after 3 days in culture. Results: From our meta-analysis of the injured conditions, we find that SGCs share a common signature of 18 regulated genes following sciatic nerve crush or sciatic nerve ligation, involving transcriptional regulation of cholesterol biosynthesis. We also observed a considerable transcriptional change when culturing SGCs, suggesting that some differentiate into a specialised in vitro state while others start resembling Schwann cell-like precursors. Conclusion: By using integrated analyses of new and previously published scRNAseq datasets, this study provides a consensus view of which genes are most robustly changed in SGCs after injury. Our results are available via the Broad Institute Single Cell Portal, so that readers can explore and search for genes of interest.

BSDE: barycenter single-cell differential expression for case-control studies

MOTIVATION

Single-cell sequencing brings about a revolutionarily high resolution for finding differentially expressed genes (DEGs) by disentangling highly heterogeneous cell tissues. Yet, such analysis is so far mostly focused on comparing between different cell types from the same individual. As single-cell sequencing becomes cheaper and easier to use, an increasing number of datasets from case-control studies are becoming available, which call for new methods for identifying differential expressions between case and control individuals.

RESULTS

To bridge this gap, we propose barycenter single-cell differential expression (BSDE), a nonparametric method for finding DEGs for case-control studies. Through the use of optimal transportation for aggregating distributions and computing their distances, our method overcomes the restrictive parametric assumptions imposed by standard mixed-effect-modeling approaches. Through simulations, we show that BSDE can accurately detect a variety of differential expressions while maintaining the type-I error at a prescribed level. Further, 1345 and 1568 cell type-specific DEGs are identified by BSDE from datasets on pulmonary fibrosis and multiple sclerosis, among which the top findings are supported by previous results from the literature.

AVAILABILITY AND IMPLEMENTATION

R package BSDE is freely available from doi.org/10.5281/zenodo.6332254. For real data analysis with the R package, see doi.org/10.5281/zenodo.6332566. These can also be accessed thorough GitHub at github.com/mqzhanglab/BSDE and github.com/mqzhanglab/BSDE_pipeline. The two single-cell sequencing datasets can be download with UCSC cell browser from cells.ucsc.edu/?ds=ms and cells.ucsc.edu/?ds=lung-pf-control.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data

The development of single-cell RNA-sequencing (scRNA-seq) technologies has offered insights into complex biological systems at the single-cell resolution. In particular, these techniques facilitate the identifications of genes showing cell-type-specific differential expressions (DE). In this paper, we introduce MARBLES, a novel statistical model for cross-condition DE gene detection from scRNA-seq data. MARBLES employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability. Our simulation results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate. Applications of MARBLES to real data identified novel disease-related DE genes and biological pathways from both a single-cell lipopolysaccharide mouse dataset with 24 381 cells and 11 076 genes and a Parkinson's disease human data set with 76 212 cells and 15 891 genes. Overall, MARBLES is a powerful tool to identify cell-type-specific DE genes across conditions from scRNA-seq data.

Integrated analysis of transcriptomic data reveals the platelet response in COVID-19 disease

COVID-19 is associated with an increased risk of thrombotic events. However, the pathogenesis of these complications is unclear and reports on platelet infection and activation by the virus are conflicting. Here, we integrated single-cell transcriptomic data to elucidate whether platelet activation is a specific response to SARS-CoV-2 infection or a consequence of a generalized inflammatory state. Although platelets from patients infected with SARS-CoV-2 over expressed genes involved in activation and aggregation when compared to healthy controls; those differences disappeared when the comparison was made with patients with generalized inflammatory conditions of other etiology than COVID-19. The membrane receptor for the virus, ACE-2, was not expressed by infected or control platelets. Our results suggest that platelet activation in patients with severe COVID-19 is mainly a consequence of a systemic inflammatory state than direct invasion and activation.

Cellular and transcriptional diversity over the course of human lactation

Human breast milk (hBM) is a dynamic fluid that contains millions of cells, but their identities and phenotypic properties are poorly understood. We generated and analyzed single-cell RNA-sequencing (scRNA-seq) data to characterize the transcriptomes of cells from hBM across lactational time from 3 to 632 d postpartum in 15 donors. We found that the majority of cells in hBM are lactocytes, a specialized epithelial subset, and that cell-type frequencies shift over the course of lactation, yielding greater epithelial diversity at later points. Analysis of lactocytes reveals a continuum of cell states characterized by transcriptional changes in hormone-, growth factor-, and milk production-related pathways. Generalized additive models suggest that one subcluster, LC1 epithelial cells, increases as a function of time postpartum, daycare attendance, and the use of hormonal birth control. We identify several subclusters of macrophages in hBM that are enriched for tolerogenic functions, possibly playing a role in protecting the mammary gland during lactation. Our description of the cellular components of breast milk, their association with maternal–infant dyad metadata, and our quantification of alterations at the gene and pathway levels provide a detailed longitudinal picture of hBM cells across lactational time. This work paves the way for future investigations of how a potential division of cellular labor and differential hormone regulation might be leveraged therapeutically to support healthy lactation and potentially aid in milk production.

Multiomic profiling of the acute stress response in the mouse hippocampus

The acute stress response mobilizes energy to meet situational demands and re-establish homeostasis. However, the underlying molecular cascades are unclear. Here, we use a brief swim exposure to trigger an acute stress response in mice, which transiently increases anxiety, without leading to lasting maladaptive changes. Using multiomic profiling, such as proteomics, phospho-proteomics, bulk mRNA-, single-nuclei mRNA-, small RNA-, and TRAP-sequencing, we characterize the acute stress-induced molecular events in the mouse hippocampus over time. Our results show the complexity and specificity of the response to acute stress, highlighting both the widespread changes in protein phosphorylation and gene transcription, and tightly regulated protein translation. The observed molecular events resolve efficiently within four hours after initiation of stress. We include an interactive app to explore the data, providing a molecular resource that can help us understand how acute stress impacts brain function in response to stress.

Acute stress can help individuals to respond to challenging events, although chronic stress leads to maladaptive changes. Here, the authors present a multi omic analysis profiling acute stress-induced changes in the mouse hippocampus, providing a resource for the scientific community.

Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references

Characterizing the molecular identity of a cell is an essential step in single-cell RNA sequencing (scRNA-seq) data analysis. Numerous tools exist for predicting cell identity using single-cell reference atlases. However, many challenges remain, including correcting for inherent batch effects between reference and query data andinsufficient phenotype data from the reference. One solution is to project single-cell data onto established bulk reference atlases to leverage their rich phenotype information. Sincast is a computational framework to query scRNA-seq data by projection onto bulk reference atlases. Prior to projection, single-cell data are transformed to be directly comparable to bulk data, either with pseudo-bulk aggregation or graph-based imputation to address sparse single-cell expression profiles. Sincast avoids batch effect correction, and cell identity is predicted along a continuum to highlight new cell states not found in the reference atlas. In several case study scenarios, we show that Sincast projects single cells into the correct biological niches in the expression space of the bulk reference atlas. We demonstrate the effectiveness of our imputation approach that was specifically developed for querying scRNA-seq data based on bulk reference atlases. We show that Sincast is an efficient and powerful tool for single-cell profiling that will facilitate downstream analysis of scRNA-seq data.

microRNA-138 controls hippocampal interneuron function and short-term memory in mice

The proper development and function of neuronal circuits rely on a tightly regulated balance between excitatory and inhibitory (E/I) synaptic transmission, and disrupting this balance can cause neurodevelopmental disorders, for example, schizophrenia. MicroRNA-dependent gene regulation in pyramidal neurons is important for excitatory synaptic function and cognition, but its role in inhibitory interneurons is poorly understood. Here, we identify miR138-5p as a regulator of short-term memory and inhibitory synaptic transmission in the mouse hippocampus. Sponge-mediated miR138-5p inactivation specifically in mouse parvalbumin (PV)-expressing interneurons impairs spatial recognition memory and enhances GABAergic synaptic input onto pyramidal neurons. Cellular and behavioral phenotypes associated with miR138-5p inactivation are paralleled by an upregulation of the schizophrenia (SCZ)-associated Erbb4, which we validated as a direct miR138-5p target gene. Our findings suggest that miR138-5p is a critical regulator of PV interneuron function in mice, with implications for cognition and SCZ. More generally, they provide evidence that microRNAs orchestrate neural circuit development by fine-tuning both excitatory and inhibitory synaptic transmission.

Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain

A key aspect of nearly all single-cell sequencing experiments is dissociation of intact tissues into single-cell suspensions. While many protocols have been optimized for optimal cell yield, they have often overlooked the effects that dissociation can have on ex vivo gene expression. Here, we demonstrate that use of enzymatic dissociation on brain tissue induces an aberrant ex vivo gene expression signature, most prominently in microglia, which is prevalent in published literature and can substantially confound downstream analyses. To address this issue, we present a rigorously validated protocol that preserves both in vivo transcriptional profiles and cell-type diversity and yield across tissue types and species. We also identify a similar signature in postmortem human brain single-nucleus RNA-sequencing datasets, and show that this signature is induced in freshly isolated human tissue by exposure to elevated temperatures ex vivo. Together, our results provide a methodological solution for preventing artifactual gene expression changes during fresh tissue digestion and a reference for future deeper analysis of the potential confounding states present in postmortem human samples.

Toward modeling metabolic state from single-cell transcriptomics

BACKGROUND

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

SCOPE OF REVIEW

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

MAJOR CONCLUSIONS

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

LKB1 drives stasis and C/EBP-mediated reprogramming to an alveolar type II fate in lung cancer

LKB1 is among the most frequently altered tumor suppressors in lung adenocarcinoma. Inactivation of Lkb1 accelerates the growth and progression of oncogenic KRAS-driven lung tumors in mouse models. However, the molecular mechanisms by which LKB1 constrains lung tumorigenesis and whether the cancer state that stems from Lkb1 deficiency can be reverted remains unknown. To identify the processes governed by LKB1 in vivo, we generated an allele which enables Lkb1 inactivation at tumor initiation and subsequent Lkb1 restoration in established tumors. Restoration of Lkb1 in oncogenic KRAS-driven lung tumors suppressed proliferation and led to tumor stasis. Lkb1 restoration activated targets of C/EBP transcription factors and drove neoplastic cells from a progenitor-like state to a less proliferative alveolar type II cell-like state. We show that C/EBP transcription factors govern a subset of genes that are induced by LKB1 and depend upon NKX2-1. We also demonstrate that a defining factor of the alveolar type II lineage, C/EBPα, constrains oncogenic KRAS-driven lung tumor growth in vivo. Thus, this key tumor suppressor regulates lineage-specific transcription factors, thereby constraining lung tumor development through enforced differentiation.

Neuronal-glial communication perturbations in murine SOD1G93A spinal cord

Amyotrophic lateral sclerosis (ALS) is an incurable disease characterized by proteinaceous aggregate accumulation and neuroinflammation culminating in rapidly progressive lower and upper motor neuron death. To interrogate cell-intrinsic and inter-cell type perturbations in ALS, single-nucleus RNA sequencing was performed on the lumbar spinal cord in the murine ALS model SOD1^G93A transgenic and littermate control mice at peri-symptomatic onset stage of disease, age 90 days. This work uncovered perturbed tripartite synapse functions, complement activation and metabolic stress in the affected spinal cord; processes evidenced by cell death and proteolytic stress-associated gene sets. Concomitantly, these pro-damage events in the spinal cord co-existed with dysregulated reparative mechanisms. This work provides a resource of cell-specific niches in the ALS spinal cord and asserts that interwoven dysfunctional neuronal-glial communications mediating neurodegeneration are underway prior to overt disease manifestation and are recapitulated, in part, in the human post-mortem ALS spinal cord.

In this paper, single-nucleus RNA sequencing was performed to provide a resource of cell-specific niches in the murine ALS model spinal cord at peri-symptomatic onset stage of disease. The data suggest that dysfunctional neuronal-glial communication occurs prior to disease onset, which is partially recapitulated in human post-mortem ALS spinal cord tissue.

Deciphering the Retinal Epigenome during Development, Disease and Reprogramming: Advancements, Challenges and Perspectives

Retinal neurogenesis is driven by concerted actions of transcription factors, some of which are expressed in a continuum and across several cell subtypes throughout development. While seemingly redundant, many factors diversify their regulatory outcome on gene expression, by coordinating variations in chromatin landscapes to drive divergent retinal specification programs. Recent studies have furthered the understanding of the epigenetic contribution to the progression of age-related macular degeneration, a leading cause of blindness in the elderly. The knowledge of the epigenomic mechanisms that control the acquisition and stabilization of retinal cell fates and are evoked upon damage, holds the potential for the treatment of retinal degeneration. Herein, this review presents the state-of-the-art approaches to investigate the retinal epigenome during development, disease, and reprogramming. A pipeline is then reviewed to functionally interrogate the epigenetic and transcriptional networks underlying cell fate specification, relying on a truly unbiased screening of open chromatin states. The related work proposes an inferential model to identify gene regulatory networks, features the first footprinting analysis and the first tentative, systematic query of candidate pioneer factors in the retina ever conducted in any model organism, leading to the identification of previously uncharacterized master regulators of retinal cell identity, such as the nuclear factor I, NFI. This pipeline is virtually applicable to the study of genetic programs and candidate pioneer factors in any developmental context. Finally, challenges and limitations intrinsic to the current next-generation sequencing techniques are discussed, as well as recent advances in super-resolution imaging, enabling spatio-temporal resolution of the genome.

The histone demethylase Kdm6b regulates subtype diversification of mouse spinal motor neurons during development

How a single neuronal population diversifies into subtypes with distinct synaptic targets is a fundamental topic in neuroscience whose underlying mechanisms are unclear. Here, we show that the histone H3-lysine 27 demethylase Kdm6b regulates the diversification of motor neurons to distinct subtypes innervating different muscle targets during spinal cord development. In mouse embryonic motor neurons, Kdm6b promotes the medial motor column (MMC) and hypaxial motor column (HMC) fates while inhibiting the lateral motor column (LMC) and preganglionic motor column (PGC) identities. Our single-cell RNA-sequencing analyses reveal the heterogeneity of PGC, LMC, and MMC motor neurons. Further, our single-cell RNA-sequencing data, combined with mouse model studies, demonstrates that Kdm6b acquires cell fate specificity together with the transcription factor complex Isl1-Lhx3. Our study provides mechanistic insight into the gene regulatory network regulating neuronal cell-type diversification and defines a regulatory role of Kdm6b in the generation of motor neuron subtypes in the mouse spinal cord.

TIM3+ TRBV11-2 T cells and IFNγ signature in patrolling monocytes and CD16+ NK cells delineate MIS-C

In rare instances, pediatric SARS-CoV-2 infection results in a novel immunodysregulation syndrome termed multisystem inflammatory syndrome in children (MIS-C). We compared MIS-C immunopathology with severe COVID-19 in adults. MIS-C does not result in pneumocyte damage but is associated with vascular endotheliitis and gastrointestinal epithelial injury. In MIS-C, the cytokine release syndrome is characterized by IFNγ and not type I interferon. Persistence of patrolling monocytes differentiates MIS-C from severe COVID-19, which is dominated by HLA-DRlo classical monocytes. IFNγ levels correlate with granzyme B production in CD16+ NK cells and TIM3 expression on CD38+/HLA-DR+ T cells. Single-cell TCR profiling reveals a skewed TCRβ repertoire enriched for TRBV11-2 and a superantigenic signature in TIM3+/CD38+/HLA-DR+ T cells. Using NicheNet, we confirm IFNγ as a central cytokine in the communication between TIM3+/CD38+/HLA-DR+ T cells, CD16+ NK cells, and patrolling monocytes. Normalization of IFNγ, loss of TIM3, quiescence of CD16+ NK cells, and contraction of patrolling monocytes upon clinical resolution highlight their potential role in MIS-C immunopathogenesis.

Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreatic function.

Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

Comparison and evaluation of statistical error models for scRNA-seq

BACKGROUND

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate.

RESULTS

Here, we analyze 59 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation.

CONCLUSIONS

Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

Data-driven comparison of multiple high-dimensional single-cell expression profiles

Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.

splatPop: simulating population scale single-cell RNA sequencing data

Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmarking methods but current scRNA-seq simulation frameworks do not simulate population-scale data with genetic effects. Here, we present splatPop, a model for flexible, reproducible, and well-documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci. splatPop can also simulate complex batch, cell group, and conditional effects between individuals from different cohorts as well as genetically-driven co-expression.

Cancer-specific type-I interferon receptor signaling promotes cancer stemness and effector CD8+ T-cell exhaustion

Type-I interferon (IFN-I) signaling is critical to maintaining antigen-presenting cell function for anti-tumor immunity. However, recent studies have suggested that IFN-I signaling may also contribute to more aggressive phenotypes, raising the possibility that IFN-I downstream signaling in cancer and myeloid cells may exert dichotomous functions.We analyzed the clinicopathologic correlation of cancer-specific IFN-I activation in 195 head and neck squamous cell carcinoma patients. We also characterized the immune impact of IFN-I receptor (IFNAR1)-deficiency in syngeneic tumor models using biochemistry, flow cytometry, and single-cell RNA-Seq. We stained HNSCC tissue microarrays with a sensitive IFN-I downstream signaling activation marker, MX1, and quantitated cancer cell-specific MX1 staining. Kaplan-Meier analysis revealed that MX1-high tumors exhibited worse survival, a phenotype that depends on the number of CD8⁺ intratumoral T-cells. We found that cancer-specific IFNAR1 engagement promotes cancer stemness and higher expression levels of suppressive immune checkpoint receptor ligands in cancer-derived exosomes. Notably, mice bearing Ifnar1-deficient tumors exhibited lower tumor burden, increased T-cell infiltration, reduced exhausted CD4⁺PD1^high T-cells, and increased effector population CD8⁺IFN-γ⁺ T-cells. Then, we performed single-cell RNA-sequencing and discovered that cancer-specific IFN-I signaling not only restricts effector cells expansion but also dampens their functional fitness.The beneficial role of IFN-I activation is largely dependent on the myeloid compartment. Cancer-specific IFN-I receptor engagement promotes cancer stemness and the release of cancer-derived exosomes with high expression levels of immune checkpoint receptor ligands. Cancer-specific IFN-I activation is associated with poor immunogenicity and worse clinical outcomes in HNSCC.

Evaluating microglial phenotypes using single-cell technologies

Recent single-cell technologies have enabled researchers to simultaneously assess the transcriptomes and other modalities of thousands of cells within their spatial context. Here, we have summarized available single-cell methods for dissociated tissues and tissue slides with respect to the specifics of microglial biology. We have focused on next-generation-based technologies. We review the potential of these single-cell sequencing methods and newer multiomics approaches to extend the understanding of microglia function beyond the status quo.

Differential analysis of binarized single-cell RNA sequencing data captures biological variation

Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts.

Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across Clytia medusa cell types

We present an organism-wide, transcriptomic cell atlas of the hydrozoan medusa Clytia hemisphaerica and describe how its component cell types respond to perturbation. Using multiplexed single-cell RNA sequencing, in which individual animals were indexed and pooled from control and perturbation conditions into a single sequencing run, we avoid artifacts from batch effects and are able to discern shifts in cell state in response to organismal perturbations. This work serves as a foundation for future studies of development, function, and regeneration in a genetically tractable jellyfish species. Moreover, we introduce a powerful workflow for high-resolution, whole-animal, multiplexed single-cell genomics that is readily adaptable to other traditional or nontraditional model organisms.

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

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

Human prostate cancer bone metastases have an actionable immunosuppressive microenvironment

Bone metastases are devastating complications of cancer. They are particularly common in prostate cancer (PCa), represent incurable disease, and are refractory to immunotherapy. We seek to define distinct features of the bone marrow (BM) microenvironment by analyzing single cells from bone metastatic prostate tumors, involved BM, uninvolved BM, and BM from cancer-free, orthopedic patients, and healthy individuals. Metastatic PCa is associated with multifaceted immune distortion, specifically exhaustion of distinct T cell subsets, appearance of macrophages with states specific to PCa bone metastases. The chemokine CCL20 is notably overexpressed by myeloid cells, as is its cognate CCR6 receptor on T cells. Disruption of the CCL20-CCR6 axis in mice with syngeneic PCa bone metastases restores T cell reactivity and significantly prolongs animal survival. Comparative high-resolution analysis of PCa bone metastases shows a targeted approach for relieving local immunosuppression for therapeutic effect.

A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data

BACKGROUND

Recent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. In this article, we propose to borrow information through known biological networks to increase statistical power to identify differentially expressed genes (DEGs).

RESULTS

We develop MRFscRNAseq, which is based on a Markov random field (MRF) model to appropriately accommodate gene network information as well as dependencies among cell types to identify cell-type specific DEGs. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method has better power to detect cell-type specific DEGs than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls.

CONCLUSIONS

The proposed MRF model is implemented in the R package MRFscRNAseq available on GitHub. By utilizing gene-gene and cell-cell networks, our method increases statistical power to detect differentially expressed genes from scRNA-seq data.

Comprehensive investigations revealed consistent pathophysiological alterations after vaccination with COVID-19 vaccines

Large-scale COVID-19 vaccinations are currently underway in many countries in response to the COVID-19 pandemic. Here, we report, besides generation of neutralizing antibodies, consistent alterations in hemoglobin A1c, serum sodium and potassium levels, coagulation profiles, and renal functions in healthy volunteers after vaccination with an inactivated SARS-CoV-2 vaccine. Similar changes had also been reported in COVID-19 patients, suggesting that vaccination mimicked an infection. Single-cell mRNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) before and 28 days after the first inoculation also revealed consistent alterations in gene expression of many different immune cell types. Reduction of CD8+ T cells and increase in classic monocyte contents were exemplary. Moreover, scRNA-seq revealed increased NF-κB signaling and reduced type I interferon responses, which were confirmed by biological assays and also had been reported to occur after SARS-CoV-2 infection with aggravating symptoms. Altogether, our study recommends additional caution when vaccinating people with pre-existing clinical conditions, including diabetes, electrolyte imbalances, renal dysfunction, and coagulation disorders.