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

Temporal and spatial analysis of astrocytes following stroke identifies novel drivers of reactivity

Astrocytes undergo robust gene expression changes in response to a variety of perturbations, including ischemic injury. How these transitions are affected by time, and how heterogeneous and spatially distinct various reactive astrocyte populations are, remain unclear. To address these questions, we performed spatial transcriptomics as well as single nucleus RNAseq of ∼138,000 mouse forebrain astrocytes at 1, 3, and 14 days after ischemic injury. We observed a widespread and temporally diverse response across many astrocyte subtypes. We identified astrocyte clusters unique in injury, including a transiently proliferative substate that may be BRCA1-dependent. We also found an interferon-responsive population that rapidly expands to the perilesion cortex at 1 day and persists up to 14 days post stroke. These lowly abundant, spatially restricted populations are likely functionally important in post-injury stabilization and resolution. These datasets offer valuable insights into injury-induced reactive astrocyte heterogeneity and can be used to guide functional interrogation of biologically meaningful reactive astrocyte substates to understand their pro- and anti-reparative functions following acute injuries such as stroke.

Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data

Background

Single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed in recent years. The droplet-based single cell platforms enable the profiling of gene expression in tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Additionally, trajectory analysis can be used to infer the developmental or differentiation trajectories of cells.

Methods

This article demonstrates a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single-cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudo-bulk time course analysis. Sample integration and cell clustering follows the Seurat pipeline while the trajectory inference is conducted using the monocle3 package. The pseudo-bulk time course analysis uses the quasi-likelihood framework of edgeR.

Results

Cells are ordered and positioned along a pseudotime trajectory that represented a biological process of cell differentiation and development. The study successfully identified genes that were significantly associated with pseudotime in the mouse mammary gland.

Conclusions

The demonstrated workflow provides a valuable resource for researchers conducting scRNA-seq analysis using open-source software packages. The study successfully demonstrated the usefulness of trajectory analysis for understanding the developmental or differentiation trajectories of cells. This analysis can be applied to various biological processes such as cell development or disease progression, and can help identify potential biomarkers or therapeutic targets.

Human MCTS1-dependent translation of JAK2 is essential for IFN-γ immunity to mycobacteria

Human inherited disorders of interferon-gamma (IFN-γ) immunity underlie severe mycobacterial diseases. We report X-linked recessive MCTS1 deficiency in men with mycobacterial disease from kindreds of different ancestries (from China, Finland, Iran, and Saudi Arabia). Complete deficiency of this translation re-initiation factor impairs the translation of a subset of proteins, including the kinase JAK2 in all cell types tested, including T lymphocytes and phagocytes. JAK2 expression is sufficiently low to impair cellular responses to interleukin-23 (IL-23) and partially IL-12, but not other JAK2-dependent cytokines. Defective responses to IL-23 preferentially impair the production of IFN-γ by innate-like adaptive mucosal-associated invariant T cells (MAIT) and γδ T lymphocytes upon mycobacterial challenge. Surprisingly, the lack of MCTS1-dependent translation re-initiation and ribosome recycling seems to be otherwise physiologically redundant in these patients. These findings suggest that X-linked recessive human MCTS1 deficiency underlies isolated mycobacterial disease by impairing JAK2 translation in innate-like adaptive T lymphocytes, thereby impairing the IL-23-dependent induction of IFN-γ.

The multi-omics single-cell landscape of sinus mucosa in uncontrolled severe chronic rhinosinusitis with nasal polyps

Uncontrolled severe chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with elevated levels of type 2 inflammatory cytokines and raised immunoglobulin concentrations in nasal polyp tissue. By using single-cell RNA sequencing, transcriptomics, surface proteomics, and T cell and B cell receptor sequencing, we found the predominant cell types in nasal polyps were shifted from epithelial and mesenchymal cells to inflammatory cells compared to nasal mucosa from healthy controls. Broad expansions of CD4 T effector memory cells, CD4 tissue-resident memory T cells, CD8 T effector memory cells and all subtypes of B cells in nasal polyp tissues. The T and B cell receptor repertoires were skewed in NP. This study highlights the deviated immune response and remodeling mechanisms that contribute to the pathogenesis of uncontrolled severe CRSwNP. CLINICAL IMPLICATIONS: We identified differences in the cellular compositions, transcriptomes, proteomes, and deviations in the immune profiles of T cell and B cell receptors as well as alterations in the intercellular communications in uncontrolled severe CRSwNP patients versus healthy controls, which might help to define potential therapeutic targets in the future.

Exploring the heterogeneous transcriptional response of the CNS to systemic LPS and Poly(I:C)

Peripheral contact to pathogen-associated molecular patterns (PAMPs) evokes a systemic innate immune response which is rapidly relayed to the central nervous system (CNS). The remarkable cellular heterogeneity of the CNS poses a significant challenge to the study of cell type and stimulus dependent responses of neural cells during acute inflammation. Here we utilized single nuclei RNA sequencing (snRNAseq), serum proteome profiling and primary cell culture methods to systematically compare the acute response of the mammalian brain to the bacterial PAMP lipopolysaccharide (LPS) and the viral PAMP polyinosinic:polycytidylic acid (Poly(I:C)), at single cell resolution. Our study unveiled convergent transcriptional cytokine and cellular stress responses in brain vascular and ependymal cells and a downregulation of several key mediators of directed blood brain barrier (BBB) transport. In contrast the neuronal response to PAMPs was limited in acute neuroinflammation. Moreover, our study highlighted the dominant role of IFN signalling upon Poly(I:C) challenge, particularly in cells of the oligodendrocyte lineage. Collectively our study unveils heterogeneous, shared and distinct cell type and stimulus dependent acute responses of the CNS to bacterial and viral PAMP challenges. Our findings highlight inflammation induced dysregulations of BBB-transporter gene expression, suggesting potential translational implications on drug pharmacokinetics variability during acute neuroinflammation. The pronounced dependency of oligodendrocytes on IFN stimulation during viral PAMP challenges, emphasizes their limited molecular viral response repertoire.

Widespread sex dimorphism across single-cell transcriptomes of adult African turquoise killifish tissues

The African turquoise killifish (Nothobranchius furzeri), the shortest-lived vertebrate that can be bred in captivity, is an emerging model organism for aging research. Here, we describe a multitissue, single-cell gene expression atlas of female and male blood, kidney, liver, and spleen. We annotate 22 cell types, define marker genes, and infer differentiation trajectories. We find pervasive sex-dimorphic gene expression across cell types. Sex-dimorphic genes tend to be linked to lipid metabolism, consistent with clear differences in lipid storage in female vs. male turquoise killifish livers. We use machine learning to predict sex using single-cell gene expression and identify potential markers for molecular sex identity. As a proof of principle, we show that our atlas can be used to deconvolute existing bulk RNA sequencing (RNA-seq) data to obtain accurate estimates of cell type proportions. This atlas can be a resource to the community that could be leveraged to develop cell-type-specific expression in transgenic animals.

Microfluidics-free single-cell genomics reveals complex central-peripheral immune crosstalk in the mouse brain during peripheral inflammation

Inflammation is a realized detriment to brain health in a growing number of neurological diseases, but querying neuroinflammation in its cellular complexity remains a challenge. This manuscript aims to provide a reliable and accessible strategy for examining the brain's immune system. We compare the efficacy of cell isolation methods in producing ample and pure immune samples from mouse brains. Then, with the high-input single-cell genomics platform PIPseq, we generate a rich neuroimmune dataset containing microglia and many peripheral immune populations. To demonstrate this strategy's utility, we interrogate the well-established model of LPS-induced neuroinflammation with single-cell resolution. We demonstrate the activation of crosstalk between microglia and peripheral phagocytes and highlight the unique contributions of microglia and peripheral immune cells to neuroinflammation. Our approach enables the high-depth evaluation of inflammation in longstanding rodent models of neurological disease to reveal novel insight into the contributions of the immune system to brain health.

Single-cell chromatin accessibility and transcriptomic characterization of Behcet's disease

Behect's disease is a chronic vasculitis characterized by complex multi-organ immune aberrations. However, a comprehensive understanding of the gene-regulatory profile of peripheral autoimmunity and the diverse immune responses across distinct cell types in Behcet's disease (BD) is still lacking. Here, we present a multi-omic single-cell study of 424,817 cells in BD patients and non-BD individuals. This study maps chromatin accessibility and gene expression in the same biological samples, unraveling vast cellular heterogeneity. We identify widespread cell-type-specific, disease-associated active and pro-inflammatory immunity in both transcript and epigenomic aspects. Notably, integrative multi-omic analysis reveals putative TF regulators that might contribute to chromatin accessibility and gene expression in BD. Moreover, we predicted gene-regulatory networks within nominated TF activators, including AP-1, NF-kB, and ETS transcript factor families, which may regulate cellular interaction and govern inflammation. Our study illustrates the epigenetic and transcriptional landscape in BD peripheral blood and expands understanding of potential epigenomic immunopathology in this disease.

Tumor-infiltrating CCR2+ inflammatory monocytes counteract specific immunotherapy

Tumor development and progression is shaped by the tumor microenvironment (TME), a heterogeneous assembly of infiltrating and resident host cells, their secreted mediators and intercellular matrix. In this context, tumors are infiltrated by various immune cells with either pro-tumoral or anti-tumoral functions. Recently, we published our non-invasive immunization platform DIVA suitable as a therapeutic vaccination method, further optimized by repeated application (DIVA2). In our present work, we revealed the therapeutic effect of DIVA2 in an MC38 tumor model and specifically focused on the mechanisms induced in the TME after immunization. DIVA2 resulted in transient tumor control followed by an immune evasion phase within three weeks after the initial tumor inoculation. High-dimensional flow cytometry analysis and single-cell mRNA-sequencing of tumor-infiltrating leukocytes revealed cytotoxic CD8+ T cells as key players in the immune control phase. In the immune evasion phase, inflammatory CCR2+ PDL-1+ monocytes with immunosuppressive properties were recruited into the tumor leading to suppression of DIVA2-induced tumor-reactive T cells. Depletion of CCR2+ cells with specific antibodies resulted in prolonged survival revealing CCR2+ monocytes as important for tumor immune escape in the TME. In summary, the present work provides a platform for generating a strong antigen-specific primary and memory T cell immune response using the optimized transcutaneous immunization method DIVA2. This enables protection against tumors by therapeutic immune control of solid tumors and highlights the immunosuppressive influence of tumor infiltrating CCR2+ monocytes that need to be inactivated in addition for successful cancer immunotherapy.

Longitudinal scRNA-seq analysis in mouse and human informs optimization of rapid mouse astrocyte differentiation protocols

Macroglia (astrocytes and oligodendrocytes) are required for normal development and function of the central nervous system, yet many questions remain about their emergence during the development of the brain and spinal cord. Here we used single-cell/single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) to analyze over 298,000 cells and nuclei during macroglia differentiation from mouse embryonic and human-induced pluripotent stem cells. We computationally identify candidate genes involved in the fate specification of glia in both species and report heterogeneous expression of astrocyte surface markers across differentiating cells. We then used our transcriptomic data to optimize a previous mouse astrocyte differentiation protocol, decreasing the overall protocol length and complexity. Finally, we used multi-omic, dual single-nuclei (sn)RNA-seq/snATAC-seq analysis to uncover potential genomic regulatory sites mediating glial differentiation. These datasets will enable future optimization of glial differentiation protocols and provide insight into human glial differentiation.

Principles and challenges of modeling temporal and spatial omics data

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.

Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration

Persistent DNA double-strand breaks (DSBs) in neurons are an early pathological hallmark of neurodegenerative diseases including Alzheimer's disease (AD), with the potential to disrupt genome integrity. We used single-nucleus RNA-seq in human postmortem prefrontal cortex samples and found that excitatory neurons in AD were enriched for somatic mosaic gene fusions. Gene fusions were particularly enriched in excitatory neurons with DNA damage repair and senescence gene signatures. In addition, somatic genome structural variations and gene fusions were enriched in neurons burdened with DSBs in the CK-p25 mouse model of neurodegeneration. Neurons enriched for DSBs also had elevated levels of cohesin along with progressive multiscale disruption of the 3D genome organization aligned with transcriptional changes in synaptic, neuronal development, and histone genes. Overall, this study demonstrates the disruption of genome stability and the 3D genome organization by DSBs in neurons as pathological steps in the progression of neurodegenerative diseases.

Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer's disease pathology

Alzheimer's disease (AD) is the most common cause of dementia worldwide, but the molecular and cellular mechanisms underlying cognitive impairment remain poorly understood. To address this, we generated a single-cell transcriptomic atlas of the aged human prefrontal cortex covering 2.3 million cells from postmortem human brain samples of 427 individuals with varying degrees of AD pathology and cognitive impairment. Our analyses identified AD-pathology-associated alterations shared between excitatory neuron subtypes, revealed a coordinated increase of the cohesin complex and DNA damage response factors in excitatory neurons and in oligodendrocytes, and uncovered genes and pathways associated with high cognitive function, dementia, and resilience to AD pathology. Furthermore, we identified selectively vulnerable somatostatin inhibitory neuron subtypes depleted in AD, discovered two distinct groups of inhibitory neurons that were more abundant in individuals with preserved high cognitive function late in life, and uncovered a link between inhibitory neurons and resilience to AD pathology.

scMultiSim: simulation of single cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, hile also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data, many of them were not benchmarked before due to the lack of proper tools. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.

A complement atlas identifies interleukin-6-dependent alternative pathway dysregulation as a key druggable feature of COVID-19

Improvements in COVID-19 treatments, especially for the critically ill, require deeper understanding of the mechanisms driving disease pathology. The complement system is not only a crucial component of innate host defense but can also contribute to tissue injury. Although all complement pathways have been implicated in COVID-19 pathogenesis, the upstream drivers and downstream effects on tissue injury remain poorly defined. We demonstrate that complement activation is primarily mediated by the alternative pathway, and we provide a comprehensive atlas of the complement alterations around the time of respiratory deterioration. Proteomic and single-cell sequencing mapping across cell types and tissues reveals a division of labor between lung epithelial, stromal, and myeloid cells in complement production, in addition to liver-derived factors. We identify IL-6 and STAT1/3 signaling as an upstream driver of complement responses, linking complement dysregulation to approved COVID-19 therapies. Furthermore, an exploratory proteomic study indicates that inhibition of complement C5 decreases epithelial damage and markers of disease severity. Collectively, these results support complement dysregulation as a key druggable feature of COVID-19.

PSF toolkit: an R package for pathway curation and topology-aware analysis

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis.

RESULTS

We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance.

CONCLUSIONS

iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers

Tumor microenvironments (TMEs) influence cancer progression but are complex and often differ between patients. Considering that microenvironment variations may reveal rules governing intratumoral cellular programs and disease outcome, we focused on tumor-to-tumor variation to examine 52 head and neck squamous cell carcinomas. We found that macrophage polarity-defined by CXCL9 and SPP1 (CS) expression but not by conventional M1 and M2 markers-had a noticeably strong prognostic association. CS macrophage polarity also identified a highly coordinated network of either pro- or antitumor variables, which involved each tumor-associated cell type and was spatially organized. We extended these findings to other cancer indications. Overall, these results suggest that, despite their complexity, TMEs coordinate coherent responses that control human cancers and for which CS macrophage polarity is a relevant yet simple variable.

The vascular gene Apold1 is dispensable for normal development but controls angiogenesis under pathological conditions

The molecular mechanisms of angiogenesis have been intensely studied, but many genes that control endothelial behavior and fate still need to be described. Here, we characterize the role of Apold1 (Apolipoprotein L domain containing 1) in angiogenesis in vivo and in vitro. Single-cell analyses reveal that - across tissues - the expression of Apold1 is restricted to the vasculature and that Apold1 expression in endothelial cells (ECs) is highly sensitive to environmental factors. Using Apold1-/- mice, we find that Apold1 is dispensable for development and does not affect postnatal retinal angiogenesis nor alters the vascular network in adult brain and muscle. However, when exposed to ischemic conditions following photothrombotic stroke as well as femoral artery ligation, Apold1-/- mice display dramatic impairments in recovery and revascularization. We also find that human tumor endothelial cells express strikingly higher levels of Apold1 and that Apold1 deletion in mice stunts the growth of subcutaneous B16 melanoma tumors, which have smaller and poorly perfused vessels. Mechanistically, Apold1 is activated in ECs upon growth factor stimulation as well as in hypoxia, and Apold1 intrinsically controls EC proliferation but not migration. Our data demonstrate that Apold1 is a key regulator of angiogenesis in pathological settings, whereas it does not affect developmental angiogenesis, thus making it a promising candidate for clinical investigation.

Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture

Background

Reproducibility of human cortical organoid (hCO) phenotypes remains a concern for modeling neurodevelopmental disorders. While guided hCO protocols reproducibly generate cortical cell types in multiple cell lines at one site, variability across sites using a harmonized protocol has not yet been evaluated. We present an hCO cross-site reproducibility study examining multiple phenotypes.

Methods

Three independent research groups generated hCOs from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol. scRNA-seq, 3D fluorescent imaging, phase contrast imaging, qPCR, and flow cytometry were used to characterize the 3 month differentiations across sites.

Results

In all sites, hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions with moderate to high fidelity to the in vivo brain that were consistently organized in cortical wall-like buds. Cross-site differences were detected in hCO size and morphology. Differential gene expression showed differences in metabolism and cellular stress across sites. Although iPSC culture conditions were consistent and iPSCs remained undifferentiated, primed stem cell marker expression prior to differentiation correlated with cell type proportions in hCOs.

Conclusions

We identified hCO phenotypes that are reproducible across sites using a harmonized differentiation protocol. Previously described limitations of hCO models were also reproduced including off-target differentiations, necrotic cores, and cellular stress. Improving our understanding of how stem cell states influence early hCO cell types may increase reliability of hCO differentiations. Cross-site reproducibility of hCO cell type proportions and organization lays the foundation for future collaborative prospective meta-analytic studies modeling neurodevelopmental disorders in hCOs.

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Patient-Derived iPSCs Faithfully Represent the Genetic Diversity and Cellular Architecture of Human Acute Myeloid Leukemia

The reprogramming of human acute myeloid leukemia (AML) cells into induced pluripotent stem cell (iPSC) lines could provide new faithful genetic models of AML, but is currently hindered by low success rates and uncertainty about whether iPSC-derived cells resemble their primary counterparts. Here we developed a reprogramming method tailored to cancer cells, with which we generated iPSCs from 15 patients representing all major genetic groups of AML. These AML-iPSCs retain genetic fidelity and produce transplantable hematopoietic cells with hallmark phenotypic leukemic features. Critically, single-cell transcriptomics reveal that, upon xenotransplantation, iPSC-derived leukemias faithfully mimic the primary patient-matched xenografts. Transplantation of iPSC-derived leukemias capturing a clone and subclone from the same patient allowed us to isolate the contribution of a FLT3-ITD mutation to the AML phenotype. The results and resources reported here can transform basic and preclinical cancer research of AML and other human cancers.

SIGNIFICANCE

We report the generation of patient-derived iPSC models of all major genetic groups of human AML. These exhibit phenotypic hallmarks of AML in vitro and in vivo, inform the clonal hierarchy and clonal dynamics of human AML, and exhibit striking similarity to patient-matched primary leukemias upon xenotransplantation. See related commentary by Doulatov, p. 252. This article is highlighted in the In This Issue feature, p. 247.

Robustness of single-cell RNA-seq for identifying differentially expressed genes

BACKGROUND

A common feature of single-cell RNA-seq (scRNA-seq) data is that the number of cells in a cell cluster may vary widely, ranging from a few dozen to several thousand. It is not clear whether scRNA-seq data from a small number of cells allow robust identification of differentially expressed genes (DEGs) with various characteristics.

RESULTS

We addressed this question by performing scRNA-seq and poly(A)-dependent bulk RNA-seq in comparable aliquots of human induced pluripotent stem cells-derived, purified vascular endothelial and smooth muscle cells. We found that scRNA-seq data needed to have 2,000 or more cells in a cluster to identify the majority of DEGs that would show modest differences in a bulk RNA-seq analysis. On the other hand, clusters with as few as 50-100 cells may be sufficient for identifying the majority of DEGs that would have extremely small p values or transcript abundance greater than a few hundred transcripts per million in a bulk RNA-seq analysis.

CONCLUSION

Findings of the current study provide a quantitative reference for designing studies that aim for identifying DEGs for specific cell clusters using scRNA-seq data and for interpreting results of such studies.

Conserved stromal-immune cell circuits secure B cell homeostasis and function

B cell zone reticular cells (BRCs) form stable microenvironments that direct efficient humoral immunity with B cell priming and memory maintenance being orchestrated across lymphoid organs. However, a comprehensive understanding of systemic humoral immunity is hampered by the lack of knowledge of global BRC sustenance, function and major pathways controlling BRC-immune cell interactions. Here we dissected the BRC landscape and immune cell interactome in human and murine lymphoid organs. In addition to the major BRC subsets underpinning the follicle, including follicular dendritic cells, PI16+ RCs were present across organs and species. As well as BRC-produced niche factors, immune cell-driven BRC differentiation and activation programs governed the convergence of shared BRC subsets, overwriting tissue-specific gene signatures. Our data reveal that a canonical set of immune cell-provided cues enforce bidirectional signaling programs that sustain functional BRC niches across lymphoid organs and species, thereby securing efficient humoral immunity.

Ambient RNAs removal of cortex-specific snRNA-seq reveals Apoe+ microglia/macrophage after deeper cerebral hypoperfusion in mice

BACKGROUND

Ambient RNAs contamination in single-nuclei RNA sequencing (snRNA-seq) is a challenging problem, but the consequences of ambient RNAs contamination of damaged and/or diseased tissues are poorly understood. Cognitive impairments and white/gray matter injuries are characteristic of deeper cerebral hypoperfusion mouse models induced by bilateral carotid artery stenosis (BCAS), but the molecular mechanisms still need to be further explored. More importantly, the BCAS mice can also offer an excellent model to examine the signatures of ambient RNAs contamination in damaged tissues when performing snRNA-seq.

METHODS

After the sham and BCAS mice were established, cortex-specific single-nuclei libraries were constructed. Single-nuclei transcriptomes were described informatically by the R package Seurat, and ambient RNA markers of were identified in each library. Then, after removing ambient RNAs in each sample using the in silico approaches, the combination of CellBender and subcluster cleaning, single-nuclei transcriptomes were reconstructed. Next, the comparison of ambient RNA contamination was performed using irGSEA analysis before and after the in silico approaches. Finally, further bioinformatic analyses were performed.

RESULTS

The ambient RNAs are more predominant in the BCAS group than the sham group. The contamination mainly originated from damaged neuronal nuclei, but could be reduced largely using the in silico approaches. The integrative analysis of cortex-specific snRNA-seq data and the published bulk transcriptome revealed that microglia and other immune cells were the primary effectors. In the sequential microglia/immune subgroups analysis, the subgroup of Apoe⁺ MG/Mac (microglia/macrophages) was identified. Interestingly, this subgroup mainly participated in the pathways of lipid metabolism, associated with the phagocytosis of cell debris.

CONCLUSIONS

Taken together, our current study unravels the features of ambient RNAs in snRNA-seq datasets under diseased conditions, and the in silico approaches can effectively eliminate the incorrected cell annotation and following misleading analysis. In the future, snRNA-seq data analysis should be carefully revisited, and ambient RNAs removal needs to be taken into consideration, especially for those diseased tissues. To our best knowledge, our study also offers the first cortex-specific snRNA-seq data of deeper cerebral hypoperfusion, which provides with novel therapeutic targets.

Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis

Metastasis is the major cause of cancer-related deaths. Neuroblastoma (NB), a childhood tumor has been molecularly defined at the primary cancer site, however, the bone marrow (BM) as the metastatic niche of NB is poorly characterized. Here we perform single-cell transcriptomic and epigenomic profiling of BM aspirates from 11 subjects spanning three major NB subtypes and compare these to five age-matched and metastasis-free BM, followed by in-depth single cell analyses of tissue diversity and cell-cell interactions, as well as functional validation. We show that cellular plasticity of NB tumor cells is conserved upon metastasis and tumor cell type composition is NB subtype-dependent. NB cells signal to the BM microenvironment, rewiring via macrophage mgration inhibitory factor and midkine signaling specifically monocytes, which exhibit M1 and M2 features, are marked by activation of pro- and anti-inflammatory programs, and express tumor-promoting factors, reminiscent of tumor-associated macrophages. The interactions and pathways characterized in our study provide the basis for therapeutic approaches that target tumor-to-microenvironment interactions.

Characterization and Optimization of Multiomic Single-Cell Epigenomic Profiling

The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n = 18) and a solid tumor type, ovarian cancer (OC, n = 18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell type identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n = 6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Microglial INPP5D limits plaque formation and glial reactivity in the PSAPP mouse model of Alzheimer's disease

INTRODUCTION

The inositol polyphosphate-5-phosphatase D (INPP5D) gene encodes a dual-specificity phosphatase that can dephosphorylate both phospholipids and phosphoproteins. Single nucleotide polymorphisms in INPP5D impact risk for developing late onset sporadic Alzheimer's disease (LOAD).

METHODS

To assess the consequences of inducible Inpp5d knockdown in microglia of APPKM670/671NL /PSEN1Δexon9 (PSAPP) mice, we injected 3-month-old Inpp5dfl/fl /Cx3cr1CreER/+ and PSAPP/Inpp5dfl/fl /Cx3cr1CreER/+ mice with either tamoxifen (TAM) or corn oil (CO) to induce recombination.

RESULTS

At age 6 months, we found that the percent area of 6E10+ deposits and plaque-associated microglia in Inpp5d knockdown mice were increased compared to controls. Spatial transcriptomics identified a plaque-specific expression profile that was extensively altered by Inpp5d knockdown.

DISCUSSION

These results demonstrate that conditional Inpp5d downregulation in the PSAPP mouse increases plaque burden and recruitment of microglia to plaques. Spatial transcriptomics highlighted an extended gene expression signature associated with plaques and identified CST7 (cystatin F) as a novel marker of plaques.

HIGHLIGHTS

Inpp5d knockdown increases plaque burden and plaque-associated microglia number. Spatial transcriptomics identifies an expanded plaque-specific gene expression profile. Plaque-induced gene expression is altered by Inpp5d knockdown in microglia. Our plaque-associated gene signature overlaps with human Alzheimer's disease gene networks.

scViewer: An Interactive Single-Cell Gene Expression Visualization Tool

Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer's disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.

An empirical Bayes method for differential expression analysis of single cells with deep generative models

Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. Deep generative models have been extensively applied to scRNA-seq data, with a special focus on embedding cells into a low-dimensional latent space and correcting for batch effects. However, little attention has been paid to the problem of utilizing the uncertainty from the deep generative model for differential expression (DE). Furthermore, the existing approaches do not allow for controlling for effect size or the false discovery rate (FDR). Here, we present lvm-DE, a generic Bayesian approach for performing DE predictions from a fitted deep generative model, while controlling the FDR. We apply the lvm-DE framework to scVI and scSphere, two deep generative models. The resulting approaches outperform state-of-the-art methods at estimating the log fold change in gene expression levels as well as detecting differentially expressed genes between subpopulations of cells.

Cell type specific transcriptomic differences in depression show similar patterns between males and females but implicate distinct cell types and genes

Major depressive disorder (MDD) is a common, heterogenous, and potentially serious psychiatric illness. Diverse brain cell types have been implicated in MDD etiology. Significant sexual differences exist in MDD clinical presentation and outcome, and recent evidence suggests different molecular bases for male and female MDD. We evaluated over 160,000 nuclei from 71 female and male donors, leveraging new and pre-existing single-nucleus RNA-sequencing data from the dorsolateral prefrontal cortex. Cell type specific transcriptome-wide threshold-free MDD-associated gene expression patterns were similar between the sexes, but significant differentially expressed genes (DEGs) diverged. Among 7 broad cell types and 41 clusters evaluated, microglia and parvalbumin interneurons contributed the most DEGs in females, while deep layer excitatory neurons, astrocytes, and oligodendrocyte precursors were the major contributors in males. Further, the Mic1 cluster with 38% of female DEGs and the ExN10_L46 cluster with 53% of male DEGs, stood out in the meta-analysis of both sexes.

A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection

Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of screening thousands of single cells in parallel. Thus, contrary to the traditional bulk measurements that only paint a macroscopic picture, gene measurements at the cell level aid researchers in studying different tissues and organs at various stages. However, accurate clustering methods for such high-dimensional data remain exiguous and a persistent challenge in this domain. Of late, several methods and techniques have been promulgated to address this issue. In this article, we propose a novel framework for clustering large-scale single-cell data and subsequently identifying the rare-cell sub-populations. To handle such sparse, high-dimensional data, we leverage PaCMAP (Pairwise Controlled Manifold Approximation), a feature extraction algorithm that preserves both the local and the global structures of the data and Gaussian Mixture Model to cluster single-cell data. Subsequently, we exploit Edited Nearest Neighbours sampling and Isolation Forest/One-class Support Vector Machine to identify rare-cell sub-populations. The performance of the proposed method is validated using the publicly available datasets with varying degrees of cell types and rare-cell sub-populations. On several benchmark datasets, the proposed method outperforms the existing state-of-the-art methods. The proposed method successfully identifies cell types that constitute populations ranging from 0.1 to 8% with F1-scores of 0.91 0.09. The source code is available at https://github.com/scrab017/RarPG.

Widespread sex-dimorphism across single-cell transcriptomes of adult African turquoise killifish tissues

The African turquoise killifish (Nothobranchius furzeri), the shortest-lived vertebrate that can be bred in captivity, is an emerging model organism to study vertebrate aging. Here we describe the first multi-tissue, single-cell gene expression atlas of female and male turquoise killifish tissues comprising immune and metabolic cells from the blood, kidney, liver, and spleen. We were able to annotate 22 distinct cell types, define associated marker genes, and infer differentiation trajectories. Using this dataset, we found pervasive sex-dimorphic gene expression across cell types, especially in the liver. Sex-dimorphic genes tended to be involved in processes related to lipid metabolism, and indeed, we observed clear differences in lipid storage in female vs. male turquoise killifish livers. Importantly, we use machine-learning to predict sex using single-cell gene expression in our atlas and identify potential transcriptional markers for molecular sex identity in this species. As proof-of-principle, we show that our atlas can be used to deconvolute existing liver bulk RNA-seq data in this species to obtain accurate estimates of cell type proportions across biological conditions. We believe that this single-cell atlas can be a resource to the community that could notably be leveraged to identify cell type-specific genes for cell type-specific expression in transgenic animals.

Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data

Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially expressed genes. Since most bulk RNA-seq methods assume equal group variances, we introduce two new approaches that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold-standard methods that do not account for group heteroscedasticity, we show results from simulations and various experiments that demonstrate the superior performance of voomByGroup and voomQWB in terms of error control and power when group variances in pseudo-bulk single-cell RNA-seq data are unequal.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Comparison of transformations for single-cell RNA-seq data

The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe four transformation approaches based on the delta method, model residuals, inferred latent expression state and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties; however, in benchmarks using simulated and real-world data, it turns out that a rather simple approach, namely, the logarithm with a pseudo-count followed by principal-component analysis, performs as well or better than the more sophisticated alternatives. This result highlights limitations of current theoretical analysis as assessed by bottom-line performance benchmarks.

Consequences and opportunities arising due to sparser single-cell RNA-seq datasets

With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.

Single-cell transcriptomic atlas of lung microvascular regeneration after targeted endothelial cell ablation

We sought to define the mechanism underlying lung microvascular regeneration in a model of severe acute lung injury (ALI) induced by selective lung endothelial cell ablation. Intratracheal instillation of DT in transgenic mice expressing human diphtheria toxin (DT) receptor targeted to ECs resulted in ablation of >70% of lung ECs, producing severe ALI with near complete resolution by 7 days. Using single-cell RNA sequencing, eight distinct endothelial clusters were resolved, including alveolar aerocytes (aCap) ECs expressing apelin at baseline and general capillary (gCap) ECs expressing the apelin receptor. At 3 days post-injury, a novel gCap EC population emerged characterized by de novo expression of apelin, together with the stem cell marker, protein C receptor. These stem-like cells transitioned at 5 days to proliferative endothelial progenitor-like cells, expressing apelin receptor together with the pro-proliferative transcription factor, Foxm1, and were responsible for the rapid replenishment of all depleted EC populations by 7 days post-injury. Treatment with an apelin receptor antagonist prevented ALI resolution and resulted in excessive mortality, consistent with a central role for apelin signaling in EC regeneration and microvascular repair. The lung has a remarkable capacity for microvasculature EC regeneration which is orchestrated by newly emergent apelin-expressing gCap endothelial stem-like cells that give rise to highly proliferative, apelin receptor-positive endothelial progenitors responsible for the regeneration of the lung microvasculature.

The structure of a hibernating ribosome in a Lyme disease pathogen

The spirochete bacterial pathogen Borrelia ( Borreliella) burgdorferi ( Bbu ) affects more than 10% of the world population and causes Lyme disease in about half a million people in the US annually. Therapy for Lyme disease includes antibiotics that target the Bbu ribosome. We determined the structure of the Bbu 70S ribosome by single particle cryo-electron microscopy (cryo-EM) at a resolution of 2.9 Å, revealing its distinctive features. In contrast to a previous study suggesting that the single hibernation promoting factor protein present in Bbu (bbHPF) may not bind to its ribosome, our structure reveals a clear density for bbHPF bound to the decoding center of the small ribosomal 30S subunit. The 30S subunit has a non-annotated ribosomal protein, bS22, that has been found only in mycobacteria and Bacteroidetes so far. The protein bL38, recently discovered in Bacteroidetes, is also present in the Bbu large 50S ribosomal subunit. The protein bL37, previously seen only in mycobacterial ribosomes, is replaced by an N-terminal α-helical extension of uL30, suggesting that the two bacterial ribosomal proteins uL30 and bL37 may have evolved from one longer uL30 protein. The longer uL30 protein interacts with both the 23S rRNA and the 5S rRNA, is near the peptidyl transferase center (PTC), and could impart greater stability to this region. Its analogy to proteins uL30m and mL63 in mammalian mitochondrial ribosomes also suggests a plausible evolutionary pathway for expansion of protein content in mammalian mitochondrial ribosomes. Computational binding free energies are predicted for antibiotics, bound to the decoding center or PTC and are in clinical use for Lyme disease, that account for subtle distinctions in antibiotic-binding regions in the Bbu ribosome structure. Besides revealing unanticipated structural and compositional features for the Bbu ribosome, our study thus provides groundwork to enable ribosome-targeted antibiotic design for more effective treatment of Lyme disease.

Defining the molecular identity and morphology of glia limitans superficialis astrocytes in mouse and human

Astrocytes are a highly abundant glial cell type that perform critical homeostatic functions in the central nervous system. Like neurons, astrocytes have many discrete heterogenous subtypes. The subtype identity and functions are, at least in part, associated with their anatomical location and can be highly restricted to strategically important anatomical domains. Here, we report that astrocytes forming the glia limitans superficialis, the outermost border of brain and spinal cord, are a highly specialized astrocyte subtype and can be identified by a single marker: Myocilin (Myoc). We show that Myoc+ astrocytes cover the entire brain and spinal cord surface, exhibit an atypical morphology, and are evolutionarily conserved from rodents to humans. Identification of this highly specialized astrocyte subtype will advance our understanding of CNS homeostasis and potentially be targeted for therapeutic intervention to combat peripheral inflammatory effects on the CNS.

Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root

Brassinosteroids are plant steroid hormones that regulate diverse processes, such as cell division and cell elongation, through gene regulatory networks that vary in space and time. By using time series single-cell RNA sequencing to profile brassinosteroid-responsive gene expression specific to different cell types and developmental stages of the Arabidopsis root, we identified the elongating cortex as a site where brassinosteroids trigger a shift from proliferation to elongation associated with increased expression of cell wall-related genes. Our analysis revealed HOMEOBOX FROM ARABIDOPSIS THALIANA 7 (HAT7) and GT-2-LIKE 1 (GTL1) as brassinosteroid-responsive transcription factors that regulate cortex cell elongation. These results establish the cortex as a site of brassinosteroid-mediated growth and unveil a brassinosteroid signaling network regulating the transition from proliferation to elongation, which illuminates aspects of spatiotemporal hormone responses.

The shaky foundations of simulating single-cell RNA sequencing data

BACKGROUND

With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyze aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant-on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task and often use simulated data that provide a ground truth for evaluations, thus demanding a high quality standard results credible and transferable to real data.

RESULTS

Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity.

CONCLUSIONS

Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects, they yield over-optimistic performance of integration and potentially unreliable ranking of clustering methods, and it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.

Molecular and metabolic heterogeneity of astrocytes and microglia

Astrocytes and microglia are central players in a myriad of processes in the healthy and diseased brain, ranging from metabolism to immunity. The crosstalk between these two cell types contributes to pathology in many if not all neuroinflammatory and neurodegenerative diseases. Recent advancements in integrative multimodal sequencing techniques have begun to highlight how heterogeneous both cell types are and the importance of metabolism to their regulation. We discuss here the transcriptomic, metabolic, and functional heterogeneity of astrocytes and microglia and highlight their interaction in health and disease.

scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, while also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including cell clustering and trajectory inference, multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

Neuronal identity defines α-synuclein and tau toxicity

Pathogenic α-synuclein and tau are critical drivers of neurodegeneration, and their mutations cause neuronal loss in patients. Whether the underlying preferential neuronal vulnerability is a cell-type-intrinsic property or a consequence of increased expression levels remains elusive. Here, we explore cell-type-specific α-synuclein and tau expression in human brain datasets and use deep phenotyping as well as brain-wide single-cell RNA sequencing of >200 live neuron types in fruit flies to determine which cellular environments react most to α-synuclein or tau toxicity. We detect phenotypic and transcriptomic evidence of differential neuronal vulnerability independent of α-synuclein or tau expression levels. Comparing vulnerable with resilient neurons in Drosophila enabled us to predict numerous human neuron subtypes with increased intrinsic susceptibility to pathogenic α-synuclein or tau. By uncovering synapse- and Ca²⁺ homeostasis-related genes as tau toxicity modifiers, our work paves the way to leverage neuronal identity to uncover modifiers of neurodegeneration-associated toxic proteins.

Sensitive cluster-free differential expression testing

Comparing molecular features, including the identification of genes with differential expression (DE) between conditions, is a powerful approach for characterising disease-specific phenotypes. When testing for DE in single-cell RNA sequencing data, current pipelines first assign cells into discrete clusters (or cell types), followed by testing for differences within each cluster. Consequently, the sensitivity and specificity of DE testing are limited and ultimately dictated by the granularity of the cell type annotation, with discrete clustering being especially suboptimal for continuous trajectories. To overcome these limitations, we present miloDE - a cluster-free framework for differential expression testing. We build on the Milo approach, introduced for differential cell abundance testing, which leverages the graph representation of single-cell data to assign relatively homogenous, 'neighbouring' cells into overlapping neighbourhoods. We address key differences between differential abundance and expression testing at the level of neighbourhood assignment, statistical testing, and multiple testing correction. To illustrate the performance of miloDE we use both simulations and real data, in the latter case identifying a transient haemogenic endothelia-like state in chimeric mouse embryos lacking Tal1 as well as uncovering distinct transcriptional programs that characterise changes in macrophages in patients with Idiopathic Pulmonary Fibrosis. miloDE is available as an open-source R package at https://github.com/MarioniLab/miloDE.

Prolidase Deficiency Causes Spontaneous T Cell Activation and Lupus-like Autoimmunity

Prolidase deficiency (PD) is a multisystem disorder caused by mutations in the PEPD gene, which encodes a ubiquitously expressed metallopeptidase essential for the hydrolysis of dipeptides containing C-terminal proline or hydroxyproline. PD typically presents in childhood with developmental delay, skin ulcers, recurrent infections, and, in some patients, autoimmune features that can mimic systemic lupus erythematosus. The basis for the autoimmune association is uncertain, but might be due to self-antigen exposure with tissue damage, or indirectly driven by chronic infection and microbial burden. In this study, we address the question of causation and show that Pepd-null mice have increased antinuclear autoantibodies and raised serum IgA, accompanied by kidney immune complex deposition, consistent with a systemic lupus erythematosus-like disease. These features are associated with an accumulation of CD4 and CD8 effector T cells in the spleen and liver. Pepd deficiency leads to spontaneous T cell activation and proliferation into the effector subset, which is cell intrinsic and independent of Ag receptor specificity or antigenic stimulation. However, an increase in KLRG1+ effector CD8 cells is not observed in mixed chimeras, in which the autoimmune phenotype is also absent. Our findings link autoimmune susceptibility in PD to spontaneous T cell dysfunction, likely to be acting in combination with immune activators that lie outside the hemopoietic system but result from the abnormal metabolism or loss of nonenzymatic prolidase function. This knowledge provides insight into the role of prolidase in the maintenance of self-tolerance and highlights the importance of treatment to control T cell activation.

Capturing the multifaceted function of adipose tissue macrophages

Adipose tissue macrophages (ATMs) bolster obesity-induced metabolic dysfunction and represent a targetable population to lessen obesity-associated health risks. However, ATMs also facilitate adipose tissue function through multiple actions, including adipocyte clearance, lipid scavenging and metabolism, extracellular remodeling, and supporting angiogenesis and adipogenesis. Thus, high-resolution methods are needed to capture macrophages' dynamic and multifaceted functions in adipose tissue. Herein, we review current knowledge on regulatory networks critical to macrophage plasticity and their multifaceted response in the complex adipose tissue microenvironment.