Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

Red2Flpe-SCON: a versatile, multicolor strategy for generating mosaic conditional knockout mice

Image-based lineage tracing enables tissue turnover kinetics and lineage potentials of different adult cell populations to be investigated. Previously, we reported a genetic mouse model system, Red2Onco, which ectopically expressed mutated oncogenes together with red fluorescent proteins (RFP). This system enabled the expansion kinetics and neighboring effects of oncogenic clones to be dissected. We now report Red2Flpe-SCON: a mosaic knockout system that uses multicolor reporters to label both mutant and wild-type cells. We develop the Red2Flpe mouse line for red clone-specific Flpe expression, as well as the FRT-based SCON (Short Conditional IntrON) method to facilitate tunable conditional mosaic knockouts in mice. We use the Red2Flpe-SCON method to study Sox2 mutant clonal analysis in the esophageal epithelium of adult mice which reveal that the stem cell gene, Sox2, is less essential for adult stem cell maintenance itself, but rather for stem cell proliferation and differentiation.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes

Motivation

Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions.

Results

Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data.

Availability and implementation

DifferentialRegulation is distributed as a Bioconductor R package.

Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression

Motivation

Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.

Results

We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of six isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve interindividual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min. 0.94, Mean 0.98, Max. 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analysing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to nonimmune cells.

Availability and implementation

EAD workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for single-cell RNA-sequencing data demultiplexing using interindividual variations).

Remodeling ceramide homeostasis promotes functional maturation of human pluripotent stem cell-derived β cells

Human pluripotent stem cell-derived β cells (hPSC-β cells) show the potential to restore euglycemia. However, the immature functionality of hPSC-β cells has limited their efficacy in application. Here, by deciphering the continuous maturation process of hPSC-β cells post transplantation via single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), we show that functional maturation of hPSC-β cells is an orderly multistep process during which cells sequentially undergo metabolic adaption, removal of negative regulators of cell function, and establishment of a more specialized transcriptome and epigenome. Importantly, remodeling lipid metabolism, especially downregulating the metabolic activity of ceramides, the central hub of sphingolipid metabolism, is critical for β cell maturation. Limiting intracellular accumulation of ceramides in hPSC-β cells remarkably enhanced their function, as indicated by improvements in insulin processing and glucose-stimulated insulin secretion. In summary, our findings provide insights into the maturation of human pancreatic β cells and highlight the importance of ceramide homeostasis in function acquisition.

spillR: spillover compensation in mass cytometry data

MOTIVATION

Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. Chevrier et al. introduce an experimental and computational procedure to estimate and compensate for spillover implemented in their R package CATALYST. They assume spillover can be described by a spillover matrix that encodes the ratio between the signal in the unstained spillover receiving and stained spillover emitting channel. They estimate the spillover matrix from experiments with beads. We propose to skip the matrix estimation step and work directly with the full bead distributions. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. Spillover correction is often a pre-processing step followed by downstream analyses, and choosing a flexible model reduces the chance of introducing biases that can propagate downstream.

RESULTS

We implement our method in an R package spillR using expectation-maximization to fit the mixture model. We test our method on simulated, semi-simulated, and real data from CATALYST. We find that our method compensates low counts accurately, does not introduce negative counts, avoids overcompensating high counts, and preserves correlations between markers that may be biologically meaningful.

AVAILABILITY AND IMPLEMENTATION

Our new R package spillR is on bioconductor at bioconductor.org/packages/spillR. All experiments and plots can be reproduced by compiling the R markdown file spillR_paper.Rmd at github.com/ChristofSeiler/spillR_paper.

Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry

Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer's disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk.

Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis

PURPOSE

Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets.

METHODS

ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors.

RESULTS

Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001).

CONCLUSIONS

This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

Trans-omic profiling uncovers molecular controls of early human cerebral organoid formation

Defining the molecular networks orchestrating human brain formation is crucial for understanding neurodevelopment and neurological disorders. Challenges in acquiring early brain tissue have incentivized the use of three-dimensional human pluripotent stem cell (hPSC)-derived neural organoids to recapitulate neurodevelopment. To elucidate the molecular programs that drive this highly dynamic process, here, we generate a comprehensive trans-omic map of the phosphoproteome, proteome, and transcriptome of the exit of pluripotency and neural differentiation toward human cerebral organoids (hCOs). These data reveal key phospho-signaling events and their convergence on transcriptional factors to regulate hCO formation. Comparative analysis with developing human and mouse embryos demonstrates the fidelity of our hCOs in modeling embryonic brain development. Finally, we demonstrate that biochemical modulation of AKT signaling can control hCO differentiation. Together, our data provide a comprehensive resource to study molecular controls in human embryonic brain development and provide a guide for the future development of hCO differentiation protocols.

A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex

The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, there lacks specific evaluation of correction efficacy for varying contamination levels. Here, we show that DecontX and CellBender under-correct highly contaminating genes, while SoupX and scAR over-correct lowly/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly contaminating genes while avoiding over-correction of other genes.

A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma

Multiple Myeloma (MM) remains incurable despite advances in treatment options. Although tumor subtypes and specific DNA abnormalities are linked to worse prognosis, the impact of immune dysfunction on disease emergence and/or treatment sensitivity remains unclear. We established a harmonized consortium to generate an Immune Atlas of MM aimed at informing disease etiology, risk stratification, and potential therapeutic strategies. We generated a transcriptome profile of 1,149,344 single cells from the bone marrow of 263 newly diagnosed patients enrolled in the CoMMpass study and characterized immune and hematopoietic cell populations. Associating cell abundances and gene expression with disease progression revealed the presence of a proinflammatory immune senescence-associated secretory phenotype in rapidly progressing patients. Furthermore, signaling analyses suggested active intercellular communication involving APRIL-BCMA, potentially promoting tumor growth and survival. Finally, we demonstrate that integrating immune cell levels with genetic information can significantly improve patient stratification.

Promotion of an Antitumor Immune Program by a Tumor-specific, Complement-activating Antibody

Tumor-targeting Abs can be used to initiate an antitumor immune program, which appears essential to achieve a long-term durable clinical response to cancer. We previously identified an anti-complement factor H (CFH) autoantibody associated with patients with early-stage non-small cell lung cancer. We cloned from their peripheral B cells an mAb, GT103, that specifically recognizes CFH on tumor cells. Although the underlying mechanisms are not well defined, GT103 targets a conformationally distinct CFH epitope that is created when CFH is associated with tumor cells, kills tumor cells in vitro, and has potent antitumor activity in vivo. In the effort to better understand how an Ab targeting a tumor epitope can promote an effective antitumor immune response, we used the syngeneic CMT167 lung tumor C57BL/6 mouse model, and we found that murinized GT103 (mGT103) activates complement and enhances antitumor immunity through multiple pathways. It creates a favorable tumor microenvironment by decreasing immunosuppressive regulatory T cells and myeloid-derived suppressor cells, enhances Ag-specific effector T cells, and has an additive antitumor effect with anti-PD-L1 mAb. Furthermore, the immune landscape of tumors from early-stage patients expressing the anti-CFH autoantibody is associated with an immunologically active tumor microenvironment. More broadly, our results using an mAb cloned from autoantibody-expressing B cells provides novel, to our knowledge, mechanistic insights into how a tumor-specific, complement-activating Ab can generate an immune program to kill tumor cells and inhibit tumor growth.

Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint

Multimodal single-cell profiling methods can capture immune cell variations unfolding over time at the molecular, cellular, and population levels. Transforming these data into biological insights remains challenging. Here, we introduce a framework to integrate variations at the human population and single-cell levels in vaccination responses. Comparing responses following AS03-adjuvanted versus unadjuvanted influenza vaccines with CITE-seq revealed AS03-specific early (day 1) response phenotypes, including a B cell signature of elevated germinal center competition. A correlated network of cell-type-specific transcriptional states defined the baseline immune status associated with high antibody responders to the unadjuvanted vaccine. Certain innate subsets in the network appeared "naturally adjuvanted," with transcriptional states resembling those induced uniquely by AS03-adjuvanted vaccination. Consistently, CD14+ monocytes from high responders at baseline had elevated phospho-signaling responses to lipopolysaccharide stimulation. Our findings link baseline immune setpoints to early vaccine responses, with positive implications for adjuvant development and immune response engineering.

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

Spatiotemporal analysis of gene expression in the human dentate gyrus reveals age-associated changes in cellular maturation and neuroinflammation

The dentate gyrus of the hippocampus is important for many cognitive functions, including learning, memory, and mood. Here, we investigated age-associated changes in transcriptome-wide spatial gene expression in the human dentate gyrus across the lifespan. Genes associated with neurogenesis and the extracellular matrix were enriched in infants, while gene markers of inhibitory neurons and cell proliferation showed increases and decreases in post-infancy, respectively. While we did not find evidence for neural proliferation post-infancy, we did identify molecular signatures supporting protracted maturation of granule cells. We also identified a wide-spread hippocampal aging signature and an age-associated increase in genes related to neuroinflammation. Our findings suggest major changes to the putative neurogenic niche after infancy and identify molecular foci of brain aging in glial and neuropil enriched tissue.

scRNA-seq data from the larval Drosophila ventral cord provides a resource for studying motor systems function and development

The Drosophila larval ventral nerve cord (VNC) shares many similarities with the spinal cord of vertebrates and has emerged as a major model for understanding the development and function of motor systems. Here, we use high-quality scRNA-seq, validated by anatomical identification, to create a comprehensive census of larval VNC cell types. We show that the neural lineages that comprise the adult VNC are already defined, but quiescent, at the larval stage. Using fluorescence-activated cell sorting (FACS)-enriched populations, we separate all motor neuron bundles and link individual neuron clusters to morphologically characterized known subtypes. We discovered a glutamate receptor subunit required for basal neurotransmission and homeostasis at the larval neuromuscular junction. We describe larval glia and endorse the general view that glia perform consistent activities throughout development. This census represents an extensive resource and a powerful platform for future discoveries of cellular and molecular mechanisms in repair, regeneration, plasticity, homeostasis, and behavioral coordination.

Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures

Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.

Characterizing cell type specific transcriptional differences between the living and postmortem human brain

Single-nucleus RNA sequencing (snRNA-seq) is often used to define gene expression patterns characteristic of brain cell types as well as to identify cell type specific gene expression signatures of neurological and mental illnesses in postmortem human brains. As methods to obtain brain tissue from living individuals emerge, it is essential to characterize gene expression differences associated with tissue originating from either living or postmortem subjects using snRNA-seq, and to assess whether and how such differences may impact snRNA-seq studies of brain tissue. To address this, human prefrontal cortex single nuclei gene expression was generated and compared between 31 samples from living individuals and 21 postmortem samples. The same cell types were consistently identified in living and postmortem nuclei, though for each cell type, a large proportion of genes were differentially expressed between samples from postmortem and living individuals. Notably, estimation of cell type proportions by cell type deconvolution of pseudo-bulk data was found to be more accurate in samples from living individuals. To allow for future integration of living and postmortem brain gene expression, a model was developed that quantifies from gene expression data the probability a human brain tissue sample was obtained postmortem. These probabilities are established as a means to statistically account for the gene expression differences between samples from living and postmortem individuals. Together, the results presented here provide a deep characterization of both differences between snRNA-seq derived from samples from living and postmortem individuals, as well as qualify and account for their effect on common analyses performed on this type of data.

Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provide both common and discordant biological insights

Whether single-cell RNA-sequencing (scRNA-seq) captures the same biological information as single-nucleus RNA-sequencing (snRNA-seq) remains uncertain and likely to be context-dependent. Herein, a head-to-head comparison was performed in matched normal-adenocarcinoma human lung samples to assess biological insights derived from scRNA-seq versus snRNA-seq and better understand the cellular transition that occurs from normal to tumoral tissue. Here, the transcriptome of 160,621 cells/nuclei was obtained. In non-tumor lung, cell type proportions varied widely between scRNA-seq and snRNA-seq with a predominance of immune cells in the former (81.5%) and epithelial cells (69.9%) in the later. Similar results were observed in adenocarcinomas, in addition to an overall increase in cell type heterogeneity and a greater prevalence of copy number variants in cells of epithelial origin, which suggests malignant assignment. The cell type transition that occurs from normal lung tissue to adenocarcinoma was not always concordant whether cells or nuclei were examined. As expected, large differential expression of the whole-cell and nuclear transcriptome was observed, but cell-type specific changes of paired normal and tumor lung samples revealed a set of common genes in the cells and nuclei involved in cancer-related pathways. In addition, we showed that the ligand-receptor interactome landscape of lung adenocarcinoma was largely different whether cells or nuclei were evaluated. Immune cell depletion in fresh specimens partly mitigated the difference in cell type composition observed between cells and nuclei. However, the extra manipulations affected cell viability and amplified the transcriptional signatures associated with stress responses. In conclusion, research applications focussing on mapping the immune landscape of lung adenocarcinoma benefit from scRNA-seq in fresh samples, whereas snRNA-seq of frozen samples provide a low-cost alternative to profile more epithelial and cancer cells, and yield cell type proportions that more closely match tissue content.

A dietary commensal microbe enhances antitumor immunity by activating tumor macrophages to sequester iron

Innate immune cells generate a multifaceted antitumor immune response, including the conservation of essential nutrients such as iron. These cells can be modulated by commensal bacteria; however, identifying and understanding how this occurs is a challenge. Here we show that the food commensal Lactiplantibacillus plantarum IMB19 augments antitumor immunity in syngeneic and xenograft mouse tumor models. Its capsular heteropolysaccharide is the major effector molecule, functioning as a ligand for TLR2. In a two-pronged manner, it skews tumor-associated macrophages to a classically active phenotype, leading to generation of a sustained CD8+ T cell response, and triggers macrophage 'nutritional immunity' to deploy the high-affinity iron transporter lipocalin-2 for capturing and sequestering iron in the tumor microenvironment. This process induces a cycle of tumor cell death, epitope expansion and subsequent tumor clearance. Together these data indicate that food commensals might be identified and developed into 'oncobiotics' for a multi-layered approach to cancer therapy.

Brain endothelial GSDMD activation mediates inflammatory BBB breakdown

The blood-brain barrier (BBB) protects the central nervous system from infections or harmful substances1; its impairment can lead to or exacerbate various diseases of the central nervous system2-4. However, the mechanisms of BBB disruption during infection and inflammatory conditions5,6 remain poorly defined. Here we find that activation of the pore-forming protein GSDMD by the cytosolic lipopolysaccharide (LPS) sensor caspase-11 (refs. 7-9), but not by TLR4-induced cytokines, mediates BBB breakdown in response to circulating LPS or during LPS-induced sepsis. Mice deficient in the LBP-CD14 LPS transfer and internalization pathway10-12 resist BBB disruption. Single-cell RNA-sequencing analysis reveals that brain endothelial cells (bECs), which express high levels of GSDMD, have a prominent response to circulating LPS. LPS acting on bECs primes Casp11 and Cd14 expression and induces GSDMD-mediated plasma membrane permeabilization and pyroptosis in vitro and in mice. Electron microscopy shows that this features ultrastructural changes in the disrupted BBB, including pyroptotic endothelia, abnormal appearance of tight junctions and vasculature detachment from the basement membrane. Comprehensive mouse genetic analyses, combined with a bEC-targeting adeno-associated virus system, establish that GSDMD activation in bECs underlies BBB disruption by LPS. Delivery of active GSDMD into bECs bypasses LPS stimulation and opens the BBB. In CASP4-humanized mice, Gram-negative Klebsiella pneumoniae infection disrupts the BBB; this is blocked by expression of a GSDMD-neutralizing nanobody in bECs. Our findings outline a mechanism for inflammatory BBB breakdown, and suggest potential therapies for diseases of the central nervous system associated with BBB impairment.

Tomato root specialized metabolites evolved through gene duplication and regulatory divergence within a biosynthetic gene cluster

Tremendous plant metabolic diversity arises from phylogenetically restricted specialized metabolic pathways. Specialized metabolites are synthesized in dedicated cells or tissues, with pathway genes sometimes colocalizing in biosynthetic gene clusters (BGCs). However, the mechanisms by which spatial expression patterns arise and the role of BGCs in pathway evolution remain underappreciated. In this study, we investigated the mechanisms driving acylsugar evolution in the Solanaceae. Previously thought to be restricted to glandular trichomes, acylsugars were recently found in cultivated tomato roots. We demonstrated that acylsugars in cultivated tomato roots and trichomes have different sugar cores, identified root-enriched paralogs of trichome acylsugar pathway genes, and characterized a key paralog required for root acylsugar biosynthesis, SlASAT1-LIKE (SlASAT1-L), which is nested within a previously reported trichome acylsugar BGC. Last, we provided evidence that ASAT1-L arose through duplication of its paralog, ASAT1, and was trichome-expressed before acquiring root-specific expression in the Solanum genus. Our results illuminate the genomic context and molecular mechanisms underpinning metabolic diversity in plants.

Generation of circulating autoreactive pre-plasma cells fueled by naive B cells in celiac disease

Autoantibodies against the enzyme transglutaminase 2 (TG2) are characteristic of celiac disease (CeD), and TG2-specific immunoglobulin (Ig) A plasma cells are abundant in gut biopsies of patients. Here, we describe the corresponding population of autoreactive B cells in blood. Circulating TG2-specific IgA cells are present in untreated patients on a gluten-containing diet but not in controls. They are clonally related to TG2-specific small intestinal plasma cells, and they express gut-homing molecules, indicating that they are plasma cell precursors. Unlike other IgA-switched cells, the TG2-specific cells are negative for CD27, placing them in the double-negative (IgD-CD27-) category. They have a plasmablast or activated memory B cell phenotype, and they harbor fewer variable region mutations than other IgA cells. Based on their similarity to naive B cells, we propose that autoreactive IgA cells in CeD are generated mainly through chronic recruitment of naive B cells via an extrafollicular response involving gluten-specific CD4+ T cells.

Transcriptome and single-cell analysis reveal disulfidptosis-related modification patterns of tumor microenvironment and prognosis in osteosarcoma

Osteosarcoma (OS) is the most common malignant bone tumor with high pathological heterogeneity. Our study aimed to investigate disulfidptosis-related modification patterns in OS and their relationship with survival outcomes in patients with OS. We analyzed the single-cell-level expression profiles of disulfidptosis-related genes (DSRGs) in both OS microenvironment and OS subclusters, and HMGB1 was found to be crucial for intercellular regulation of OS disulfidptosis. Next, we explored the molecular clusters of OS based on DSRGs and related immune cell infiltration using transcriptome data. Subsequently, the hub genes of disulfidptosis in OS were screened by applying multiple machine models. In vitro and patient experiments validated our results. Three main disulfidptosis-related molecular clusters were defined in OS, and immune infiltration analysis suggested high immune heterogeneity between distinct clusters. The in vitro experiment confirmed decreased cell viability of OS after ACTB silencing and higher expression of ACTB in patients with lower immune scores. Our study systematically revealed the underlying relationship between disulfidptosis and OS at the single-cell level, identified disulfidptosis-related subtypes, and revealed the potential role of ACTB expression in OS disulfidptosis.

Transposable elements regulate thymus development and function

Transposable elements (TEs) are repetitive sequences representing ~45% of the human and mouse genomes and are highly expressed by medullary thymic epithelial cells (mTECs). In this study, we investigated the role of TEs on T-cell development in the thymus. We performed multiomic analyses of TEs in human and mouse thymic cells to elucidate their role in T-cell development. We report that TE expression in the human thymus is high and shows extensive age- and cell lineage-related variations. TE expression correlates with multiple transcription factors in all cell types of the human thymus. Two cell types express particularly broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDCs). In mTECs, transcriptomic data suggest that TEs interact with transcription factors essential for mTEC development and function (e.g., PAX1 and REL), and immunopeptidomic data showed that TEs generate MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 regulate small yet non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large numbers of TEs that likely form dsRNA, which can activate innate immune receptors, potentially explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study highlights the diversity of interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. Therefore, we propose that orchestrating TE expression in thymic cells is critical to prevent autoimmunity in vertebrates.

Enhancer-promoter interactions are reconfigured through the formation of long-range multiway hubs as mouse ES cells exit pluripotency

Enhancers bind transcription factors, chromatin regulators, and non-coding transcripts to modulate the expression of target genes. Here, we report 3D genome structures of single mouse ES cells as they are induced to exit pluripotency and transition through a formative stage prior to undergoing neuroectodermal differentiation. We find that there is a remarkable reorganization of 3D genome structure where inter-chromosomal intermingling increases dramatically in the formative state. This intermingling is associated with the formation of a large number of multiway hubs that bring together enhancers and promoters with similar chromatin states from typically 5-8 distant chromosomal sites that are often separated by many Mb from each other. In the formative state, genes important for pluripotency exit establish contacts with emerging enhancers within these multiway hubs, suggesting that the structural changes we have observed may play an important role in modulating transcription and establishing new cell identities.

Expression characteristics and potential function of non-coding RNA in mouse cortical cells

Non-coding RNAs (ncRNAs) play essential regulatory functions in various physiological and pathological processes in the brain. To systematically characterize the ncRNA profile in cortical cells, we downloaded single-cell SMART-Seq v4 data of mouse cerebral cortex. Our results revealed that the ncRNAs alone are sufficient to define the identity of most cortical cell types. We identified 1,600 ncRNAs that exhibited cell type specificity, even yielding to distinguish microglia from perivascular macrophages with ncRNA. Moreover, we characterized cortical layer and region specific ncRNAs, in line with the results by spatial transcriptome (ST) data. By constructing a co-expression network of ncRNAs and protein-coding genes, we predicted the function of ncRNAs. By integrating with genome-wide association studies data, we established associations between cell type-specific ncRNAs and traits related to neurological disorders. Collectively, our study identified differentially expressed ncRNAs at multiple levels and provided the valuable resource to explore the functions and dysfunctions of ncRNAs in cortical cells.

lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression

Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. Current computational tools estimate relative RNA abundances rather than cell type proportions in tissues with varying cell sizes, leading to biased estimates. We present lute, a computational tool to accurately deconvolute cell types with varying sizes. Our software wraps existing deconvolution algorithms in a standardized framework. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition. Software is available from https://bioconductor.org/packages/lute.

Landscape of brain myeloid cell transcriptome along the spatiotemporal progression of Alzheimer's disease reveals distinct sequential responses to Aβ and tau

Human microglia are critically involved in Alzheimer's disease (AD) progression, as shown by genetic and molecular studies. However, their role in tau pathology progression in human brain has not been well described. Here, we characterized 32 human donors along progression of AD pathology, both in time-from early to late pathology-and in space-from entorhinal cortex (EC), inferior temporal gyrus (ITG), prefrontal cortex (PFC) to visual cortex (V2 and V1)-with biochemistry, immunohistochemistry, and single nuclei-RNA-sequencing, profiling a total of 337,512 brain myeloid cells, including microglia. While the majority of microglia are similar across brain regions, we identified a specific subset unique to EC which may contribute to the early tau pathology present in this region. We calculated conversion of microglia subtypes to diseased states and compared conversion patterns to those from AD animal models. Targeting genes implicated in this conversion, or their upstream/downstream pathways, could halt gene programs initiated by early tau progression. We used expression patterns of early tau progression to identify genes whose expression is reversed along spreading of spatial tau pathology (EC > ITG > PFC > V2 > V1) and identified their potential involvement in microglia subtype conversion to a diseased state. This study provides a data resource that builds on our knowledge of myeloid cell contribution to AD by defining the heterogeneity of microglia and brain macrophages during both temporal and regional pathology aspects of AD progression at an unprecedented resolution.

Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC-seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene-peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

Human osteoclasts in vitro are dose dependently both inhibited and stimulated by cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC)

Legalized use of cannabis for medical or recreational use is becoming more and more common. With respect to potential side-effects on bone health only few clinical trials have been conducted - and with opposing results. Therefore, it seems that there is a need for more knowledge on the potential effects of cannabinoids on human bone cells. We studied the effect of cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC) (dose range from 0.3 to 30 μM) on human osteoclasts in mono- as well as in co-cultures with human osteoblast lineage cells. We have used CD14+ monocytes from anonymous blood donors to differentiate into osteoclasts, and human osteoblast lineage cells from outgrowths of human trabecular bone. Our results show that THC and CBD have dose-dependent effects on both human osteoclast fusion and bone resorption. In the lower dose ranges of THC and CBD, osteoclast fusion was unaffected while bone resorption was increased. At higher doses, both osteoclast fusion and bone resorption were inhibited. In co-cultures, both osteoclastic bone resorption and alkaline phosphatase activity of the osteoblast lineage cells were inhibited. Finally, we observed that the cannabinoid receptor CNR2 is more highly expressed than CNR1 in CD14+ monocytes and pre-osteoclasts, but also that differentiation to osteoclasts was coupled to a reduced expression of CNR2, in particular. Interestingly, under co-culture conditions, we only detected the expression of CNR2 but not CNR1 for both osteoclast as well as osteoblast lineage nuclei. In line with the existing literature on the effect of cannabinoids on bone cells, our current study shows both stimulatory and inhibitory effects. This highlights that potential unfavorable effects of cannabinoids on bone cells and bone health is a complex matter. The contradictory and lacking documentation for such potential unfavorable effects on bone health as well as other potential effects, should be taken into consideration when considering the use of cannabinoids for both medical and recreational use.

Transcriptional signature of durable effector T cells elicited by a replication defective HCMV vaccine

Human cytomegalovirus (HCMV) is a leading infectious cause of birth defects and the most common opportunistic infection that causes life-threatening diseases post-transplantation; however, an effective vaccine remains elusive. V160 is a live-attenuated replication defective HCMV vaccine that showed a 42.4% efficacy against primary HCMV infection among seronegative women in a phase 2b clinical trial. Here, we integrated the multicolor flow cytometry, longitudinal T cell receptor (TCR) sequencing, and single-cell RNA/TCR sequencing approaches to characterize the magnitude, phenotype, and functional quality of human T cell responses to V160. We demonstrated that V160 de novo induces IE-1 and pp65 specific durable polyfunctional effector CD8 T cells that are comparable to those induced by natural HCMV infection. We identified a variety of V160-responsive T cell clones which exhibit distinctive "transient" and "durable" expansion kinetics, and revealed a transcriptional signature that marks durable CD8 T cells post-vaccination. Our study enhances the understanding of human T-cell immune responses to V160 vaccination.

TM4SF19-mediated control of lysosomal activity in macrophages contributes to obesity-induced inflammation and metabolic dysfunction

Adipose tissue (AT) adapts to overnutrition in a complex process, wherein specialized immune cells remove and replace dysfunctional and stressed adipocytes with new fat cells. Among immune cells recruited to AT, lipid-associated macrophages (LAMs) have emerged as key players in obesity and in diseases involving lipid stress and inflammation. Here, we show that LAMs selectively express transmembrane 4 L six family member 19 (TM4SF19), a lysosomal protein that represses acidification through its interaction with Vacuolar-ATPase. Inactivation of TM4SF19 elevates lysosomal acidification and accelerates the clearance of dying/dead adipocytes in vitro and in vivo. TM4SF19 deletion reduces the LAM accumulation and increases the proportion of restorative macrophages in AT of male mice fed a high-fat diet. Importantly, male mice lacking TM4SF19 adapt to high-fat feeding through adipocyte hyperplasia, rather than hypertrophy. This adaptation significantly improves local and systemic insulin sensitivity, and energy expenditure, offering a potential avenue to combat obesity-related metabolic dysfunction.

A molecular atlas of adult C. elegans motor neurons reveals ancient diversity delineated by conserved transcription factor codes

Motor neurons (MNs) constitute an ancient cell type targeted by multiple adult-onset diseases. It is therefore important to define the molecular makeup of adult MNs in animal models and extract organizing principles. Here, we generate a comprehensive molecular atlas of adult Caenorhabditis elegans MNs and a searchable database. Single-cell RNA sequencing of 13,200 cells reveals that ventral nerve cord MNs cluster into 29 molecularly distinct subclasses. Extending C. elegans Neuronal Gene Expression Map and Network (CeNGEN) findings, all MN subclasses are delineated by distinct expression codes of either neuropeptide or transcription factor gene families. Strikingly, combinatorial codes of homeodomain transcription factor genes succinctly delineate adult MN diversity in both C. elegans and mice. Further, molecularly defined MN subclasses in C. elegans display distinct patterns of connectivity. Hence, our study couples the connectivity map of the C. elegans motor circuit with a molecular atlas of its constituent MNs and uncovers organizing principles and conserved molecular codes of adult MN diversity.

Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.

CDK12 Loss Promotes Prostate Cancer Development While Exposing Vulnerabilities to Paralog-Based Synthetic Lethality

Biallelic loss of cyclin-dependent kinase 12 (CDK12) defines a unique molecular subtype of metastatic castration-resistant prostate cancer (mCRPC). It remains unclear, however, whether CDK12 loss per se is sufficient to drive prostate cancer development-either alone, or in the context of other genetic alterations-and whether CDK12-mutant tumors exhibit sensitivity to specific pharmacotherapies. Here, we demonstrate that tissue-specific Cdk12 ablation is sufficient to induce preneoplastic lesions and robust T cell infiltration in the mouse prostate. Allograft-based CRISPR screening demonstrated that Cdk12 loss is positively associated with Trp53 inactivation but negatively associated with Pten inactivation-akin to what is observed in human mCRPC. Consistent with this, ablation of Cdk12 in prostate organoids with concurrent Trp53 loss promotes their proliferation and ability to form tumors in mice, while Cdk12 knockout in the Pten-null prostate cancer mouse model abrogates tumor growth. Bigenic Cdk12 and Trp53 loss allografts represent a new syngeneic model for the study of androgen receptor (AR)-positive, luminal prostate cancer. Notably, Cdk12/Trp53 loss prostate tumors are sensitive to immune checkpoint blockade. Cdk12-null organoids (either with or without Trp53 co-ablation) and patient-derived xenografts from tumors with CDK12 inactivation are highly sensitive to inhibition or degradation of its paralog kinase, CDK13. Together, these data identify CDK12 as a bona fide tumor suppressor gene with impact on tumor progression and lends support to paralog-based synthetic lethality as a promising strategy for treating CDK12-mutant mCRPC.

Trading acyls and swapping sugars: metabolic innovations in Solanum trichomes

Solanaceae (nightshade family) species synthesize a remarkable array of clade- and tissue-specific specialized metabolites. Protective acylsugars, one such class of structurally diverse metabolites, are produced by AcylSugar AcylTransferases from sugars and acyl-coenzyme A esters. Published research revealed trichome acylsugars composed of glucose and sucrose cores in species across the family. In addition, acylsugars were analyzed across a small fraction of the >1200 species in the phenotypically megadiverse Solanum genus, with a handful containing inositol and glycosylated inositol cores. The current study sampled several dozen species across subclades of the Solanum to get a more detailed view of acylsugar chemodiversity. In depth characterization of acylsugars from the Clade II species Solanum melongena (brinjal eggplant) led to the identification of eight unusual structures with inositol or inositol glycoside cores, and hydroxyacyl chains. Liquid chromatography-mass spectrometry analysis of 31 additional species in the Solanum genus revealed striking acylsugar diversity with some traits restricted to specific clades and species. Acylinositols and inositol-based acyldisaccharides were detected throughout much of the genus. In contrast, acylglucoses and acylsucroses were more restricted in distribution. Analysis of tissue-specific transcriptomes and interspecific acylsugar acetylation differences led to the identification of the S. melongena AcylSugar AcylTransferase 3-Like 1 (SmASAT3-L1; SMEL4.1_12g015780) enzyme. This enzyme is distinct from previously characterized acylsugar acetyltransferases, which are in the ASAT4 clade, and appears to be a functionally divergent ASAT3. This study provides a foundation for investigating the evolution and function of diverse Solanum acylsugar structures and harnessing this diversity in breeding and synthetic biology.

Intratumoral T-cell receptor repertoire composition predicts overall survival in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and "True entropy." Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.

Dabrafenib Alters MDSC Differentiation and Function by Activation of GCN2

The effect of targeted therapeutics on anticancer immune responses is poorly understood. The BRAF inhibitor dabrafenib has been reported to activate the integrated stress response (ISR) kinase GCN2, and the therapeutic effect has been partially attributed to GCN2 activation. Because ISR signaling is a key component of myeloid-derived suppressor cell (MDSC) development and function, we measured the effect of dabrafenib on MDSC differentiation and suppressive activity. Our data showed that dabrafenib attenuated MDSC ability to suppress T-cell activity, which was associated with a GCN2-dependent block of the transition from monocytic progenitor to polymorphonuclear (PMN)-MDSCs and proliferative arrest resulting in PMN-MDSC loss. Transcriptional profiling revealed that dabrafenib-driven GCN2 activation altered metabolic features in MDSCs enhancing oxidative respiration, and attenuated transcriptional programs required for PMN development. Moreover, we observed a broad downregulation of transcriptional networks associated with PMN developmental pathways, and increased activity of transcriptional regulons driven by Atf5, Mafg, and Zbtb7a. This transcriptional program alteration underlies the basis for PMN-MDSC developmental arrest, skewing immature MDSC development toward monocytic lineage cells. In vivo, we observed a pronounced reduction in PMN-MDSCs in dabrafenib-treated tumor-bearing mice suggesting that dabrafenib impacts MDSC populations systemically and locally, in the tumor immune infiltrate. Thus, our data reveal transcriptional networks that govern MDSC developmental programs, and the impact of GCN2 stress signaling on the innate immune landscape in tumors, providing novel insight into potentially beneficial off-target effects of dabrafenib.

SIGNIFICANCE

An important, but poorly understood, aspect of targeted therapeutics for cancer is the effect on antitumor immune responses. This article shows that off-target effects of dabrafenib activating the kinase GCN2 impact MDSC development and function reducing PMN-MDSCs in vitro and in vivo. This has important implications for our understanding of how this BRAF inhibitor impacts tumor growth and provides novel therapeutic target and combination possibilities.

scX: A user-friendly tool for scRNA-seq exploration

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. In this paper, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and Single-CellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis.

ScLinear predicts protein abundance at single-cell resolution

Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches.

Redox regulation of m6A methyltransferase METTL3 in β-cells controls the innate immune response in type 1 diabetes

Type 1 diabetes (T1D) is characterized by the destruction of pancreatic β-cells. Several observations have renewed the interest in β-cell RNA sensors and editors. Here, we report that N 6-methyladenosine (m6A) is an adaptive β-cell safeguard mechanism that controls the amplitude and duration of the antiviral innate immune response at T1D onset. m6A writer methyltransferase 3 (METTL3) levels increase drastically in β-cells at T1D onset but rapidly decline with disease progression. m6A sequencing revealed the m6A hyper methylation of several key innate immune mediators, including OAS1, OAS2, OAS3 and ADAR1 in human islets and EndoC-βH1 cells at T1D onset. METTL3 silencing enhanced 2'-5'-oligoadenylate synthetase levels by increasing its mRNA stability. Consistently, in vivo gene therapy to prolong Mettl3 overexpression specifically in β-cells delayed diabetes progression in the non-obese diabetic mouse model of T1 D. Mechanistically, the accumulation of reactive oxygen species blocked upregulation of METTL3 in response to cytokines, while physiological levels of nitric oxide enhanced METTL3 levels and activity. Furthermore, we report that the cysteines in position C276 and C326 in the zinc finger domains of the METTL3 protein are sensitive to S-nitrosylation and are important to the METTL3-mediated regulation of oligoadenylate synthase mRNA stability in human β-cells. Collectively, we report that m6A regulates the innate immune response at the β-cell level during the onset of T1D in humans.

A risk-reward examination of sample multiplexing reagents for single cell RNA-Seq

Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for understanding cellular heterogeneity and function. However the choice of sample multiplexing reagents can impact data quality and experimental outcomes. In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, across diverse sample types such as human peripheral blood mononuclear cells (PBMCs), mouse embryonic brain and patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We compared multiple demultiplexing algorithms which differed in performance depending on data quality. We find that minor improvements to laboratory workflows such as titration and rapid processing are critical to optimal performance. We also compared the performance of fixed scRNA-Seq kits and highlight the advantages of the Parse Biosciences kit for fragile samples. Highly multiplexed scRNA-Seq experiments require more sequencing resources, therefore we evaluated CRISPR-based destruction of non-informative genes to enhance sequencing value. Our comprehensive analysis provides insights into the selection of appropriate sample multiplexing reagents and protocols for scRNA-Seq experiments, facilitating more accurate and cost-effective studies.

Comparative analysis of cell-cell communication at single-cell resolution

Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.

CAR-engineered lymphocyte persistence is governed by a FAS ligand/FAS auto-regulatory circuit

Chimeric antigen receptor (CAR)-engineered T and NK cells can cause durable remission of B-cell malignancies; however, limited persistence restrains the full potential of these therapies in many patients. The FAS ligand (FAS-L)/FAS pathway governs naturally-occurring lymphocyte homeostasis, yet knowledge of which cells express FAS-L in patients and whether these sources compromise CAR persistence remains incomplete. Here, we constructed a single-cell atlas of diverse cancer types to identify cellular subsets expressing FASLG, the gene encoding FAS-L. We discovered that FASLG is limited primarily to endogenous T cells, NK cells, and CAR-T cells while tumor and stromal cells express minimal FASLG. To establish whether CAR-T/NK cell survival is regulated through FAS-L, we performed competitive fitness assays using lymphocytes modified with or without a FAS dominant negative receptor (ΔFAS). Following adoptive transfer, ΔFAS-expressing CAR-T and CAR-NK cells became enriched across multiple tissues, a phenomenon that mechanistically was reverted through FASLG knockout. By contrast, FASLG was dispensable for CAR-mediated tumor killing. In multiple models, ΔFAS co-expression by CAR-T and CAR-NK enhanced antitumor efficacy compared with CAR cells alone. Together, these findings reveal that CAR-engineered lymphocyte persistence is governed by a FAS-L/FAS auto-regulatory circuit.

Identification of direct connections between the dura and the brain

The arachnoid barrier delineates the border between the central nervous system and dura mater. Although the arachnoid barrier creates a partition, communication between the central nervous system and the dura mater is crucial for waste clearance and immune surveillance1,2. How the arachnoid barrier balances separation and communication is poorly understood. Here, using transcriptomic data, we developed transgenic mice to examine specific anatomical structures that function as routes across the arachnoid barrier. Bridging veins create discontinuities where they cross the arachnoid barrier, forming structures that we termed arachnoid cuff exit (ACE) points. The openings that ACE points create allow the exchange of fluids and molecules between the subarachnoid space and the dura, enabling the drainage of cerebrospinal fluid and limited entry of molecules from the dura to the subarachnoid space. In healthy human volunteers, magnetic resonance imaging tracers transit along bridging veins in a similar manner to access the subarachnoid space. Notably, in neuroinflammatory conditions such as experimental autoimmune encephalomyelitis, ACE points also enable cellular trafficking, representing a route for immune cells to directly enter the subarachnoid space from the dura mater. Collectively, our results indicate that ACE points are a critical part of the anatomy of neuroimmune communication in both mice and humans that link the central nervous system with the dura and its immunological diversity and waste clearance systems.

Bone morphogenic protein-4 availability in the cardiac microenvironment controls inflammation and fibrosis in autoimmune myocarditis

Myocarditis is an inflammatory heart disease that leads to loss of cardiomyocytes and frequently precipitates fibrotic remodeling of the myocardium, culminating in heart failure. However, the molecular mechanisms underlying immune cell control and maintenance of tissue integrity in the inflamed cardiac microenvironment remain elusive. In this study, we found that bone morphogenic protein-4 (BMP4) gradients maintain cardiac tissue homeostasis by single-cell transcriptomics analyses of inflamed murine and human myocardial tissues. Cardiac BMP pathway dysregulation was reflected by reduced BMP4 serum concentration in patients with myocarditis. Restoration of BMP signaling by antibody-mediated neutralization of the BMP inhibitors gremlin-1 and gremlin-2 ameliorated T cell-induced myocardial inflammation in mice. Moreover, progression to inflammatory cardiomyopathy was blocked through the reduction of fibrotic remodeling and preservation of cardiomyocyte integrity. These results unveil the BMP4-gremlin axis as a druggable pathway for the treatment of myocardial inflammation, limiting the severe sequelae of cardiac fibrosis and heart failure.

Single-cell RNA sequencing reveals peripheral blood leukocyte responses to spinal cord injury in mice with humanised immune systems

Next-generation humanised mouse models and single-cell RNA sequencing (scRNAseq) approaches enable in-depth studies into human immune cell biology. Here we used NSG-SGM3 mice engrafted with human umbilical cord haematopoietic stem cells to investigate how human immune cells respond to and/or are changed by traumatic spinal cord injury (SCI). We hypothesised that the use of such mice could help advance our understanding of spinal cord injury-induced immune depression syndrome (SCI-IDS), and also how human leukocytes change as they migrate from the circulation into the lesion site. Our scRNAseq experiments, supplemented by flow cytometry, demonstrate the existence of up to 11 human immune cell (sub-) types and/or states across the blood and injured spinal cord (7 days post-SCI) of humanised NSG-SGM3 mice. Further comparisons of human immune cell transcriptomes between naïve, sham-operated and SCI mice identified a total of 579 differentially expressed genes, 190 of which were 'SCI-specific' (that is, genes regulated only in response to SCI but not sham surgery). Gene ontology analysis showed a prominent downregulation of immune cell function under SCI conditions, including for T cell receptor signalling and antigen presentation, confirming the presence of SCI-IDS and the transcriptional signature of human leukocytes in association with this phenomenon. We also highlight the activating influence of the local spinal cord lesion microenvironment by comparing the transcriptomes of circulating versus infiltrated human immune cells; those isolated from the lesion site were enriched for genes relating to both immune cell activity and function (e.g., oxidative phosphorylation, T cell proliferation and antigen presentation). We lastly applied an integrated bioinformatics approach to determine where immune responses in humanised NSG-SGM3 mice appear congruent to the native responses of human SCI patients, and where they diverge. Collectively, our study provides a valuable resource and methodological framework for the use of these mice in translational research.

spatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images

Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.

Molecular profiling of human blastocysts reveals primitive endoderm defects among embryos of decreased implantation potential

Human embryo implantation is remarkably inefficient, and implantation failure remains among the greatest obstacles in treating infertility. Gene expression data from human embryos have accumulated rapidly in recent years; however, identification of the subset of genes that determine successful implantation remains a challenge. We leverage clinical morphologic grading-known for decades to correlate with implantation potential-and transcriptome analyses of matched embryonic and abembryonic samples to identify factors and pathways enriched and depleted in human blastocysts of good and poor morphology. Unexpectedly, we discovered that the greatest difference was in the state of extraembryonic primitive endoderm (PrE) development, with relative deficiencies in poor morphology blastocysts. Our results suggest that implantation success is most strongly influenced by the embryonic compartment and that deficient PrE development is common among embryos with decreased implantation potential. Our study provides a valuable resource for those investigating the markers and mechanisms of human embryo implantation.