Confronting false discoveries in single-cell differential expression

Single-cell 5' RNA sequencing of camelid peripheral B cells provides insights into cellular basis of heavy-chain antibody production

Camelids produce both conventional tetrameric antibodies (Abs) and dimeric heavy-chain antibodies (HCAbs). Although B cells that generate these two types of Abs exhibit distinct B cell receptors (BCRs), whether these two B cell populations differ in their phenotypes and developmental processes remains unclear. Here, we performed single-cell 5' RNA profiling of peripheral blood mononuclear cell samples from Bactrian camels before and after immunization. We characterized the functional subtypes and differentiation trajectories of circulating B cells in camels, and reconstructed single-cell BCR sequences. We found that in contrast to humans, the proportion of T-bet+ B cells was high among camelid peripheral B cells. Several marker genes of human B cell subtypes, including CD27 and IGHD, were expressed at low levels in the corresponding camel B cell subtypes. Camelid B cells expressing variable genes of HACbs (VHH) were widely present in various functional subtypes and showed highly overlapping differentiation trajectories with B cells expressing variable genes of conventional Abs (VH). After immunization, the transcriptional changes in VHH+ and VH+ B cells were largely consistent. Through structure modeling, we identified a variety of scaffold types among the reconstructed VHH sequences. Our study provides insights into the cellular context of HCAb production in camels and lays the foundation for developing single-B cell-based camelid single-domain Ab screening.

Antagonistic effects of the cytotoxic molecules granzyme B and TRAIL in the immunopathogenesis of sclerosing cholangitis

BACKGROUND AND AIMS

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by biliary inflammation and fibrosis. We showed an elevated interferon γ response in patients with primary sclerosing cholangitis and in multidrug resistance protein 2-deficient ( Mdr2-/- ) mice developing sclerosing cholangitis. Interferon γ induced expression of the cytotoxic molecules granzyme B (GzmB) and TRAIL in hepatic lymphocytes and mediated liver fibrosis in sclerosing cholangitis.

APPROACH AND RESULTS

In patient samples and Mdr2-/- mice, we identified lymphocyte clusters with a cytotoxic gene expression profile using single-cell RNA-seq and cellular indexing of transcriptomes and epitopes by sequencing analyses combined with multi-parameter flow cytometry. CD8 + T cells and NK cells showed increased expression of GzmB and TRAIL in sclerosing cholangitis. Depletion of CD8 + T cells ameliorated disease severity in Mdr2-/- mice. By using Mdr2-/- × Gzmb-/- and Mdr2-/- × Tnfsf10-/- mice, we investigated the significance of GzmB and TRAIL for disease progression in sclerosing cholangitis. Interestingly, the lack of GzmB resulted in reduced cholangiocyte apoptosis, liver injury, and fibrosis. In contrast, sclerosing cholangitis was aggravated in the absence of TRAIL. This correlated with elevated GzmB and interferon γ expression by CD8 + T cells and NK cells enhanced T-cell survival, and increased apoptosis and expansion of cholangiocytes.

CONCLUSIONS

GzmB induces apoptosis and fibrosis in sclerosing cholangitis, whereas TRAIL regulates inflammatory and cytotoxic immune responses, subsequently leading to reduced liver injury and fibrosis.

ATR inhibition activates cancer cell cGAS/STING-interferon signaling and promotes antitumor immunity in small-cell lung cancer

Patients with small-cell lung cancer (SCLC) have poor prognosis and typically experience only transient benefits from combined immune checkpoint blockade (ICB) and chemotherapy. Here, we show that inhibition of ataxia telangiectasia and rad3 related (ATR), the primary replication stress response activator, induces DNA damage-mediated micronuclei formation in SCLC models. ATR inhibition in SCLC activates the stimulator of interferon genes (STING)-mediated interferon signaling, recruits T cells, and augments the antitumor immune response of programmed death-ligand 1 (PD-L1) blockade in mouse models. We demonstrate that combined ATR and PD-L1 inhibition causes improved antitumor response than PD-L1 alone as the second-line treatment in SCLC. This study shows that targeting ATR up-regulates major histocompatibility class I expression in preclinical models and SCLC clinical samples collected from a first-in-class clinical trial of ATR inhibitor, berzosertib, with topotecan in patients with relapsed SCLC. Targeting ATR represents a transformative vulnerability of SCLC and is a complementary strategy to induce STING-interferon signaling-mediated immunogenicity in SCLC.

Rewiring movements: A single neuronal population in the spinal cord is crucial to restore walking after paralysis

A single neuronal population in the spinal cord is crucial to restore walking after paralysis.

Single-cell multi-omics map of human fetal blood in Down syndrome

Down syndrome predisposes individuals to haematological abnormalities, such as increased number of erythrocytes and leukaemia in a process that is initiated before birth and is not entirely understood1-3. Here, to understand dysregulated haematopoiesis in Down syndrome, we integrated single-cell transcriptomics of over 1.1 million cells with chromatin accessibility and spatial transcriptomics datasets using human fetal liver and bone marrow samples from 3 fetuses with disomy and 15 fetuses with trisomy. We found that differences in gene expression in Down syndrome were dependent on both cell type and environment. Furthermore, we found multiple lines of evidence that haematopoietic stem cells (HSCs) in Down syndrome are 'primed' to differentiate. We subsequently established a Down syndrome-specific map linking non-coding elements to genes in disomic and trisomic HSCs using 10X multiome data. By integrating this map with genetic variants associated with blood cell counts, we discovered that trisomy restructured regulatory interactions to dysregulate enhancer activity and gene expression critical to erythroid lineage differentiation. Furthermore, as mutations in Down syndrome display a signature of oxidative stress4,5, we validated both increased mitochondrial mass and oxidative stress in Down syndrome, and observed that these mutations preferentially fell into regulatory regions of expressed genes in HSCs. Together, our single-cell, multi-omic resource provides a high-resolution molecular map of fetal haematopoiesis in Down syndrome and indicates significant regulatory restructuring giving rise to co-occurring haematological conditions.

Patient-derived xenografts and single-cell sequencing identifies three subtypes of tumor-reactive lymphocytes in uveal melanoma metastases

Uveal melanoma (UM) is a rare melanoma originating in the eye's uvea, with 50% of patients experiencing metastasis predominantly in the liver. In contrast to cutaneous melanoma, there is only a limited effectiveness of combined immune checkpoint therapies, and half of patients with uveal melanoma metastases succumb to disease within 2 years. This study aimed to provide a path toward enhancing immunotherapy efficacy by identifying and functionally validating tumor-reactive T cells in liver metastases of patients with UM. We employed single-cell RNA-seq of biopsies and tumor-infiltrating lymphocytes (TILs) to identify potential tumor-reactive T cells. Patient-derived xenograft (PDX) models of UM metastases were created from patients, and tumor sphere cultures were generated from these models for co-culture with autologous or MART1-specific HLA-matched allogenic TILs. Activated T cells were subjected to TCR-seq, and the TCRs were matched to those found in single-cell sequencing data from biopsies, expanded TILs, and in livers or spleens of PDX models injected with TILs. Our findings revealed that tumor-reactive T cells resided not only among activated and exhausted subsets of T cells, but also in a subset of cytotoxic effector cells. In conclusion, combining single-cell sequencing and functional analysis provides valuable insights into which T cells in UM may be useful for cell therapy amplification and marker selection.

Leveraging gene correlations in single cell transcriptomic data

BACKGROUND

Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually).

RESULTS

We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.

CONCLUSIONS

New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

Multi-condition and multi-modal temporal profile inference during mouse embryonic development

The emergence of single-cell time-series datasets enables modeling of changes in various types of cellular profiles over time. However, due to the disruptive nature of single-cell measurements, it is impossible to capture the full temporal trajectory of a particular cell. Furthermore, single-cell profiles can be collected at mismatched time points across different conditions (e.g., sex, batch, disease) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here we propose a joint modeling framework, Sunbear, for integrating multi-condition and multi-modal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi-dataset and multi-modal profiles across time, and extrapolate single-cell profiles in a missing modality. We applied Sunbear to reveal sex-biased transcription during mouse embryonic development and predict dynamic relationships between epigenetic priming and transcription for cells in which multi-modal profiles are unavailable. Sunbear thus enables the projection of single-cell time-series snapshots to multi-modal and multi-condition views of cellular trajectories.

The opposite aging effect to single cell transcriptome profile among cell subsets

Comparing transcriptome profiling between younger and older samples reveals genes related to aging and provides insight into the biological functions affected by aging. Recent research has identified sex, tissue, and cell type-specific age-related changes in gene expression. This study reports the overall picture of the opposite aging effect, in which aging increases gene expression in one cell subset and decreases it in another cell subset. Using the Tabula Muris Senis dataset, a large public single-cell RNA sequencing dataset from mice, we compared the effects of aging in different cell subsets. As a result, the opposite aging effect was observed widely in the genes, particularly enriched in genes related to ribosomal function and translation. The opposite aging effect was observed in the known aging-related genes. Furthermore, the opposite aging effect was observed in the transcriptome diversity quantified by the number of expressed genes and the Shannon entropy. This study highlights the importance of considering the cell subset when intervening with aging-related genes.

VEGF-A-mediated venous endothelial cell proliferation results in neoangiogenesis during neuroinflammation

Newly formed leaky vessels and blood-brain barrier (BBB) damage are present in demyelinating acute and chronic lesions in multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). However, the endothelial cell subtypes and signaling pathways contributing to these leaky neovessels are unclear. Here, using single-cell transcriptional profiling and in vivo validation studies, we show that venous endothelial cells express neoangiogenesis gene signatures and show increased proliferation resulting in enlarged veins and higher venous coverage in acute and chronic EAE lesions in female adult mice. These changes correlate with the upregulation of vascular endothelial growth factor A (VEGF-A) signaling. We also confirmed increased expression of neoangiogenic markers in acute and chronic human MS lesions. Treatment with a VEGF-A blocking antibody diminishes the neoangiogenic transcriptomic signatures and vascular proliferation in female adult mice with EAE, but it does not restore BBB function or ameliorate EAE pathology. Our data demonstrate that venous endothelial cells contribute to neoangiogenesis in demyelinating neuroinflammatory conditions.

Platinum-based chemotherapy promotes antigen presenting potential in monocytes of patients with high-grade serous ovarian carcinoma

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide. The major clinical challenge includes the asymptomatic state of the disease, making diagnosis possible only at advanced stages. Another OC complication is the high relapse rate and poor prognosis following the standard first-line treatment with platinum-based chemotherapy. At present, numerous clinical trials are being conducted focusing on immunotherapy in OC; nevertheless, there are still no FDA-approved indications. Personalized decision regarding the immunotherapy, including immune checkpoint blockade and immune cell-based immunotherapies, can depend on the effective antigen presentation required for the cytotoxic immune response. The major aim of our study was to uncover tumor-specific transcriptional and epigenetic changes in peripheral blood monocytes in patients with high-grade serous ovarian cancer (HGSOC). Another key point was to elucidate how chemotherapy can reprogram monocytes and how that relates to changes in other immune subpopulations in the blood. To this end, we performed single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from patients with HGSOC who underwent neoadjuvant chemotherapeutic treatment (NACT) and in treatment-naïve patients. Monocyte cluster was significantly affected by tumor-derived factors as well as by chemotherapeutic treatment. Bioinformatical analysis revealed three distinct monocyte subpopulations within PBMCs based on feature gene expression - CD14.Mn.S100A8.9hi, CD14.Mn.MHC2hi and CD16.Mn subsets. The intriguing result was that NACT induced antigen presentation in monocytes by the transcriptional upregulation of MHC class II molecules, but not by epigenetic changes. Increased MHC class II gene expression was a feature observed across all three monocyte subpopulations after chemotherapy. Our data also demonstrated that chemotherapy inhibited interferon-dependent signaling pathways, but activated some TGFb-related genes. Our results can enable personalized decision regarding the necessity to systemically re-educate immune cells to prime ovarian cancer to respond to anti-cancer therapy or to improve personalized prescription of existing immunotherapy in either combination with chemotherapy or a monotherapy regimen.

A primer on single-cell RNA-seq analysis using dendritic cells as a case study

Recent advances in single-cell (sc) transcriptomics have revolutionized our understanding of dendritic cells (DCs), pivotal players of the immune system. ScRNA-sequencing (scRNA-seq) has unraveled a previously unrecognized complexity and heterogeneity of DC subsets, shedding light on their ontogeny and specialized roles. However, navigating the rapid technological progress and computational methods can be daunting for researchers unfamiliar with the field. This review aims to provide immunologists with a comprehensive introduction to sc transcriptomic analysis, offering insights into recent developments in DC biology. Addressing common analytical queries, we guide readers through popular tools and methodologies, supplemented with references to benchmarks and tutorials for in-depth understanding. By examining findings from pioneering studies, we illustrate how computational techniques have expanded our knowledge of DC biology. Through this synthesis, we aim to equip researchers with the necessary tools and knowledge to navigate and leverage scRNA-seq for unraveling the intricacies of DC biology and advancing immunological research.

Neoadjuvant PARPi or chemotherapy in ovarian cancer informs targeting effector Treg cells for homologous-recombination-deficient tumors

Homologous recombination deficiency (HRD) is prevalent in cancer, sensitizing tumor cells to poly (ADP-ribose) polymerase (PARP) inhibition. However, the impact of HRD and related therapies on the tumor microenvironment (TME) remains elusive. Our study generates single-cell gene expression and T cell receptor profiles, along with validatory multimodal datasets from >100 high-grade serous ovarian cancer (HGSOC) samples, primarily from a phase II clinical trial (NCT04507841). Neoadjuvant monotherapy with the PARP inhibitor (PARPi) niraparib achieves impressive 62.5% and 73.6% response rates per RECIST v.1.1 and GCIG CA125, respectively. We identify effector regulatory T cells (eTregs) as key responders to HRD and neoadjuvant therapies, co-occurring with other tumor-reactive T cells, particularly terminally exhausted CD8+ T cells (Tex). TME-wide interferon signaling correlates with cancer cells upregulating MHC class II and co-inhibitory ligands, potentially driving Treg and Tex fates. Depleting eTregs in HRD mouse models, with or without PARP inhibition, significantly suppresses tumor growth without observable toxicities, underscoring the potential of eTreg-focused therapeutics for HGSOC and other HRD-related tumors.

Transcriptome meta-analysis and validation to discovery of hub genes and pathways in focal and segmental glomerulosclerosis

BACKGROUND

Focal segmental glomerulosclerosis (FSGS), a histologic pattern of injury in the glomerulus, is one of the leading glomerular causes of end-stage renal disease (ESRD) worldwide. Despite extensive research, the underlying biological alterations causing FSGS remain poorly understood. Studying variations in gene expression profiles offers a promising approach to gaining a comprehensive understanding of FSGS molecular pathogenicity and identifying key elements as potential therapeutic targets. This work is a meta-analysis of gene expression profiles from glomerular samples of FSGS patients. The main aims of this study are to establish a consensus list of differentially expressed genes in FSGS, validate these findings, understand the disease's pathogenicity, and identify novel therapeutic targets.

METHODS

After a thorough search in the GEO database and subsequent quality control assessments, seven gene expression datasets were selected for the meta-analysis: GSE47183 (GPL14663), GSE47183 (GPL11670), GSE99340, GSE108109, GSE121233, GSE129973, and GSE104948. The random effect size method was applied to identify differentially expressed genes (meta-DEGs), which were then used to construct a regulatory network (STRING, MiRTarBase, and TRRUST) and perform various pathway enrichment analyses. The expression levels of several meta-DEGs, specifically ADAMTS1, PF4, EGR1, and EGF, known as angiogenesis regulators, were analyzed using quantitative reverse transcription polymerase chain reaction (RT-qPCR).

RESULTS

The identified 2,898 meta-DEGs, including 665 downregulated and 669 upregulated genes, were subjected to various analyses. A co-regulatory network comprising 2,859 DEGs, 2,688 microRNAs (miRNAs), and 374 transcription factors (TFs) was constructed, and the top molecules in the network were identified based on degree centrality. Part of the pathway enrichment analysis revealed significant disruption in the angiogenesis regulatory pathways in the FSGS kidney. The RT-qPCR results confirmed an imbalance in angiogenesis pathways by demonstrating the differential expression levels of ADAMTS1 and EGR1, two key angiogenesis regulators, in the FSGS condition.

CONCLUSION

In addition to presenting a consensus list of differentially expressed genes in FSGS, this meta-analysis identified significant distortions in angiogenesis-related pathways and factors in the FSGS kidney. Targeting these factors may offer a viable strategy to impede the progression of FSGS.

Hepatic BMAL1 and HIF1α regulate a time-dependent hypoxic response and prevent hepatopulmonary-like syndrome

The transcriptional response to hypoxia is temporally regulated, yet the molecular underpinnings and physiological implications are unknown. We examined the roles of hepatic Bmal1 and Hif1α in the circadian response to hypoxia in mice. We found that the majority of the transcriptional response to hypoxia is dependent on either Bmal1 or Hif1α, through shared and distinct roles that are daytime determined. We further show that hypoxia-inducible factor (HIF)1α accumulation upon hypoxia is temporally regulated and Bmal1 dependent. Unexpectedly, mice lacking both hepatic Bmal1 and Hif1α are hypoxemic and exhibit increased mortality upon hypoxic exposure in a daytime-dependent manner. These mice display mild liver dysfunction with pulmonary vasodilation likely due to extracellular signaling regulated kinase (ERK) activation, endothelial nitric oxide synthase, and nitric oxide accumulation in lungs, suggestive of hepatopulmonary syndrome. Our findings indicate that hepatic BMAL1 and HIF1α are key time-dependent regulators of the hypoxic response and can provide molecular insights into the pathophysiology of hepatopulmonary syndrome.

Revealing gene function with statistical inference at single-cell resolution

Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.

GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data

Single-cell data is increasingly used for gene regulatory network (GRN) inference, and benchmarks for this have been developed based on simulated data. However, existing single-cell simulators cannot model the effects of gene perturbations. A further challenge lies in generating large-scale GRNs that often struggle with computational and stability issues. We present GeneSPIDER2, an update of the GeneSPIDER MATLAB toolbox for GRN benchmarking, inference, and analysis. Several software modules have improved capabilities and performance, and new functionalities have been added. A major improvement is the ability to generate large GRNs with biologically realistic topological properties in terms of scale-free degree distribution and modularity. Another major addition is a simulation of single-cell data, which is becoming increasingly popular as input for GRN inference. Specifically, we introduced the unique feature to generate single-cell data based on genetic perturbations. Finally, the simulated single-cell data was compared to real single-cell Perturb-seq data from two cell lines, showing that the synthetic and real data exhibit similar properties.

A brain cell atlas integrating single-cell transcriptomes across human brain regions

While single-cell technologies have greatly advanced our comprehension of human brain cell types and functions, studies including large numbers of donors and multiple brain regions are needed to extend our understanding of brain cell heterogeneity. Integrating atlas-level single-cell data presents a chance to reveal rare cell types and cellular heterogeneity across brain regions. Here we present the Brain Cell Atlas, a comprehensive reference atlas of brain cells, by assembling single-cell data from 70 human and 103 mouse studies of the brain throughout major developmental stages across brain regions, covering over 26.3 million cells or nuclei from both healthy and diseased tissues. Using machine-learning based algorithms, the Brain Cell Atlas provides a consensus cell type annotation, and it showcases the identification of putative neural progenitor cells and a cell subpopulation of PCDH9high microglia in the human brain. We demonstrate the gene regulatory difference of PCDH9high microglia between hippocampus and prefrontal cortex and elucidate the cell-cell communication network. The Brain Cell Atlas presents an atlas-level integrative resource for comparing brain cells in different environments and conditions within the Human Cell Atlas.

Pathophysiological insights into HFpEF from studies of human cardiac tissue

Heart failure with preserved ejection fraction (HFpEF) is a major, worldwide health-care problem. Few therapies for HFpEF exist because the pathophysiology of this condition is poorly defined and, increasingly, postulated to be diverse. Although perturbations in other organs contribute to the clinical profile in HFpEF, altered cardiac structure, function or both are the primary causes of this heart failure syndrome. Therefore, studying myocardial tissue is fundamental to improve pathophysiological insights and therapeutic discovery in HFpEF. Most studies of myocardial changes in HFpEF have relied on cardiac tissue from animal models without (or with limited) confirmatory studies in human cardiac tissue. Animal models of HFpEF have evolved based on theoretical HFpEF aetiologies, but these models might not reflect the complex pathophysiology of human HFpEF. The focus of this Review is the pathophysiological insights gained from studies of human HFpEF myocardium. We outline the rationale for these studies, the challenges and opportunities in obtaining myocardial tissue from patients with HFpEF and relevant comparator groups, the analytical approaches, the pathophysiological insights gained to date and the remaining knowledge gaps. Our objective is to provide a roadmap for future studies of cardiac tissue from diverse cohorts of patients with HFpEF, coupling discovery biology with measures to account for pathophysiological diversity.

Cell-specific AHR-driven differential gene expression in the mouse liver cell following acute TCDD exposure

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental contaminant that disrupts hepatic function leading to steatotic liver disease (SLD)-like pathologies, such as steatosis, steatohepatitis, and fibrosis. These effects are mediated by the aryl hydrocarbon receptor following changes in gene expression. Although diverse cell types are involved, initial cell-specific changes in gene expression have not been reported. In this study, differential gene expression in hepatic cell types was examined in male C57BL/6 mice gavaged with 30 µg/kg of TCDD using single-nuclei RNA-sequencing. Ten liver cell types were identified with the proportions of most cell types remaining unchanged, except for neutrophils which increased at 72 h. Gene expression suggests TCDD induced genes related to oxidative stress in hepatocytes as early as 2 h. Lipid homeostasis was disrupted in hepatocytes, macrophages, B cells, and T cells, characterized by the induction of genes associated with lipid transport, steroid hormone biosynthesis, and the suppression of β-oxidation, while linoleic acid metabolism was altered in hepatic stellate cells (HSCs), B cells, portal fibroblasts, and plasmacytoid dendritic cells. Pro-fibrogenic processes were also enriched, including the induction retinol metabolism genes in HSCs and the early induction of anti-fibrolysis genes in hepatocytes, endothelial cells, HSCs, and macrophages. Hepatocytes also had gene expression changes consistent with hepatocellular carcinoma. Collectively, these findings underscore the effects of TCDD in initiating SLD-like phenotypes and identified cell-specific gene expression changes related to oxidative stress, steatosis, fibrosis, cell proliferation and the development of HCC.

Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart

Sex differences and age-related changes in the human heart at the tissue, cell, and molecular level have been well-documented and many may be relevant for cardiovascular disease. However, how molecular programs within individual cell types vary across individuals by age and sex remains poorly characterized. To better understand this variation, we performed single-nucleus combinatorial indexing (sci) ATAC- and RNA-Seq in human heart samples from nine donors. We identify hundreds of differentially expressed genes by age and sex and find epigenetic signatures of variation in ATAC-Seq data in this discovery cohort. We then scale up our single-cell RNA-Seq analysis by combining our data with five recently published single nucleus RNA-Seq datasets of healthy adult hearts. We find variation such as metabolic alterations by sex and immune changes by age in differential expression tests, as well as alterations in abundance of cardiomyocytes by sex and neurons with age. In addition, we compare our adult-derived ATAC-Seq profiles to analogous fetal cell types to identify putative developmental-stage-specific regulatory factors. Finally, we train predictive models of cell-type-specific RNA expression levels utilizing ATAC-Seq profiles to link distal regulatory sequences to promoters, quantifying the predictive value of a simple TF-to-expression regulatory grammar and identifying cell-type-specific TFs. Our analysis represents the largest single-cell analysis of cardiac variation by age and sex to date and provides a resource for further study of healthy cardiac variation and transcriptional regulation at single-cell resolution.

Integrative genomics approach identifies glial transcriptomic dysregulation and risk in the cortex of individuals with Alcohol Use Disorder

Alcohol use disorder (AUD) is a prevalent neuropsychiatric disorder that is a major global health concern, affecting millions of people worldwide. Past molecular studies of AUD used underpowered single cell analysis or bulk homogenates of postmortem brain tissue, which obscures gene expression changes in specific cell types. Here we performed single nuclei RNA-sequencing analysis of 73 post-mortem samples from individuals with AUD (N=36, N nuclei = 248,873) and neurotypical controls (N=37, N nuclei = 210,573) in both sexes across two institutional sites. We identified 32 clusters and found widespread cell type-specific transcriptomic changes across the cortex in AUD, particularly affecting glia. We found the greatest dysregulation in novel microglial and astrocytic subtypes that accounted for the majority of differential gene expression and co-expression modules linked to AUD. Analysis for cell type-specific enrichment of aggregate genetic risk for AUD identified subtypes of microglia and astrocytes as potential key players not only affected by but causally linked to the progression of AUD. These results highlight the importance of cell-type specific molecular changes in AUD and offer opportunities to identify novel targets for treatment.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

Detecting Rhythmic Gene Expression in Single Cell Transcriptomics

An autonomous, environmentally-synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription--translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue--dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNAseq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single cell RNAseq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain, and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient, reliable strategy to detect circadian genes in the single--cell setting.

MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

mTOR activation induces endolysosomal remodeling and nonclassical secretion of IL-32 via exosomes in inflammatory reactive astrocytes

Astrocytes respond and contribute to neuroinflammation by adopting inflammatory reactive states. Although recent efforts have characterized the gene expression signatures associated with these reactive states, the cell biology underlying inflammatory reactive astrocyte phenotypes remains under-explored. Here, we used CRISPR-based screening in human iPSC-derived astrocytes to identify mTOR activation a driver of cytokine-induced endolysosomal system remodeling, manifesting as alkalinization of endolysosomal compartments, decreased autophagic flux, and increased exocytosis of certain endolysosomal cargos. Through endolysosomal proteomics, we identified and focused on one such cargo-IL-32, a disease-associated pro-inflammatory cytokine not present in rodents, whose secretion mechanism is not well understood. We found that IL-32 was partially secreted in extracellular vesicles likely to be exosomes. Furthermore, we found that IL-32 was involved in the polarization of inflammatory reactive astrocyte states and was upregulated in astrocytes in multiple sclerosis lesions. We believe that our results advance our understanding of cell biological pathways underlying inflammatory reactive astrocyte phenotypes and identify potential therapeutic targets.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

BACKGROUND

Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.

RESULTS

We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl .

CONCLUSIONS

The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Fasting induces metabolic switches and spatial redistributions of lipid processing and neuronal interactions in tanycytes

The ependyma lining the third ventricle (3V) in the mediobasal hypothalamus plays a crucial role in energy balance and glucose homeostasis. It is characterized by a high functional heterogeneity and plasticity, but the underlying molecular mechanisms governing its features are not fully understood. Here, 5481 hypothalamic ependymocytes were cataloged using FACS-assisted scRNAseq from fed, 12h-fasted, and 24h-fasted adult male mice. With standard clustering analysis, typical ependymal cells and β2-tanycytes appear sharply defined, but other subpopulations, β1- and α-tanycytes, display fuzzy boundaries with few or no specific markers. Pseudospatial approaches, based on the 3V neuroanatomical distribution, enable the identification of specific versus shared tanycyte markers and subgroup-specific versus general tanycyte functions. We show that fasting dynamically shifts gene expression patterns along the 3V, leading to a spatial redistribution of cell type-specific responses. Altogether, we show that changes in energy status induce metabolic and functional switches in tanycyte subpopulations, providing insights into molecular and functional diversity and plasticity within the tanycyte population.

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data

Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells.

Pseudobulk with proper offsets has the same statistical properties as generalized linear mixed models in single-cell case-control studies

MOTIVATION

Generalized linear mixed models (GLMMs), such as the negative-binomial or Poisson linear mixed model, are widely applied to single-cell RNA sequencing data to compare transcript expression between different conditions determined at the subject level. However, the model is computationally intensive, and its relative statistical performance to pseudobulk approaches is poorly understood.

RESULTS

We propose offset-pseudobulk as a lightweight alternative to GLMMs. We prove that a count-based pseudobulk equipped with a proper offset variable has the same statistical properties as GLMMs in terms of both point estimates and standard errors. We confirm our findings using simulations based on real data. Offset-pseudobulk is substantially faster (>×10) and numerically more stable than GLMMs.

AVAILABILITY AND IMPLEMENTATION

Offset pseudobulk can be easily implemented in any generalized linear model software by tweaking a few options. The codes can be found at https://github.com/hanbin973/pseudobulk_is_mm.

Proteome-wide analysis identifies plasma immune regulators of amyloid-beta progression

While immune function is known to play a mechanistic role in Alzheimer's disease (AD), whether immune proteins in peripheral circulation influence the rate of amyloid-β (Aβ) progression - a central feature of AD - remains unknown. In the Baltimore Longitudinal Study of Aging, we quantified 942 immunological proteins in plasma and identified 32 (including CAT [catalase], CD36 [CD36 antigen], and KRT19 [keratin 19]) associated with rates of cortical Aβ accumulation measured with positron emission tomography (PET). Longitudinal changes in a subset of candidate proteins also predicted Aβ progression, and the mid- to late-life (20-year) trajectory of one protein, CAT, was associated with late-life Aβ-positive status in the Atherosclerosis Risk in Communities (ARIC) study. Genetic variation that influenced plasma levels of CAT, CD36 and KRT19 predicted rates of Aβ accumulation, including causal relationships with Aβ PET levels identified with two-sample Mendelian randomization. In addition to associations with tau PET and plasma AD biomarker changes, as well as expression patterns in human microglia subtypes and neurovascular cells in AD brain tissue, we showed that 31 % of candidate proteins were related to mid-life (20-year) or late-life (8-year) dementia risk in ARIC. Our findings reveal plasma proteins associated with longitudinal Aβ accumulation, and identify specific peripheral immune mediators that may contribute to the progression of AD pathophysiology.

Selective advantage of mutant stem cells in human clonal hematopoiesis is associated with attenuated response to inflammation and aging

Clonal hematopoiesis (CH) arises when hematopoietic stem cells (HSCs) acquire mutations, most frequently in the DNMT3A and TET2 genes, conferring a competitive advantage through mechanisms that remain unclear. To gain insight into how CH mutations enable gradual clonal expansion, we used single-cell multi-omics with high-fidelity genotyping on human CH bone marrow (BM) samples. Most of the selective advantage of mutant cells occurs within HSCs. DNMT3A- and TET2-mutant clones expand further in early progenitors, while TET2 mutations accelerate myeloid maturation in a dose-dependent manner. Unexpectedly, both mutant and non-mutant HSCs from CH samples are enriched for inflammatory and aging transcriptomic signatures, compared with HSCs from non-CH samples, revealing a non-cell-autonomous effect. However, DNMT3A- and TET2-mutant HSCs have an attenuated inflammatory response relative to wild-type HSCs within the same sample. Our data support a model whereby CH clones are gradually selected because they are resistant to the deleterious impact of inflammation and aging.

Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis-regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.

Critical role of Slc22a8 in maintaining blood-brain barrier integrity after experimental cerebral ischemia-reperfusion

Blood-brain barrier (BBB) damage significantly affects the prognosis of ischemic stroke patients. This project employed multi-omics analysis to identify key factors regulating BBB disruption during cerebral ischemia-reperfusion. An integrated analysis of three transcriptome sequencing datasets from mouse middle cerebral artery occlusion/reperfusion (MCAO/R) models identified eight downregulated genes in endothelial cells. Additionally, transcriptome analysis of BBB (cortex) and non-BBB (lung) endothelium of E13.5 mice revealed 2,102 upregulated genes potentially associated with BBB integrity. The eight downregulated genes were intersected with the 2,102 BBB-related genes and mapped using single-cell RNA sequencing data, revealing that solute carrier family 22 member 8 (Slc22a8) is specifically expressed in endothelial cells and pericytes and significantly decreases after MCAO/R. This finding was validated in the mouse MCAO/R model at both protein and mRNA levels in this study. External overexpression of Slc22a8 using a lentivirus carrying Tie2 improved Slc22a8 and tight junction protein levels and reduced BBB leakage after MCAO/R, accompanied by Wnt/β-catenin signaling activation. In conclusion, this study suggested that MCAO/R-induced downregulation of Slc22a8 expression may be a crucial mechanism underlying BBB disruption. Interventions that promote Slc22a8 expression or enhance its function hold promise for improving the prognosis of patients with cerebral ischemia.

Transcriptional analysis of primary ciliary dyskinesia airway cells reveals a dedicated cilia glutathione pathway

Primary ciliary dyskinesia (PCD) is a genetic condition that results in dysmotile cilia. The repercussions of cilia dysmotility and gene variants on the multiciliated cell remain poorly understood. We used single-cell RNA-Seq, proteomics, and advanced microscopy to compare primary culture epithelial cells from patients with PCD, their heterozygous mothers, and healthy individuals, and we induced pluripotent stem cells (iPScs) generated from a patient with PCD. Transcriptomic analysis revealed unique signatures in PCD airway cells compared with their mothers' cells and the cells of healthy individuals. Gene expression in heterozygous mothers' cells diverged from both control and PCD cells, marked by increased inflammatory and cellular stress signatures. Primary and iPS-derived PCD multiciliated cells had increased expression of glutathione-S-transferases GSTA2 and GSTA1, as well as NRF2 target genes, accompanied by elevated levels of reactive oxygen species (ROS). Immunogold labeling in human cilia and proteomic analysis of the ciliated organism Chlamydomonas reinhardtii demonstrated that GSTA2 localizes to motile cilia. Loss of human GSTA2 and C. reinhardtii GSTA resulted in slowed cilia motility, pointing to local cilia regulatory roles. Our findings identify cellular responses unique to PCD variants and independent of environmental stress and uncover a dedicated ciliary GSTA2 pathway essential for normal motility that may be a therapeutic target.

Systemic and skin-limited delayed-type drug hypersensitivity reactions associate with distinct resident and recruited T cell subsets

Delayed-type drug hypersensitivity reactions are major causes of morbidity and mortality. The origin, phenotype, and function of pathogenic T cells across the spectrum of severity require investigation. We leveraged recent technical advancements to study skin-resident memory T cells (TRMs) versus recruited T cell subsets in the pathogenesis of severe systemic forms of disease, Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) and drug reaction with eosinophilia and systemic symptoms (DRESS), and skin-limited disease, morbilliform drug eruption (MDE). Microscopy, bulk transcriptional profiling, and single-cell RNA-sequencing (scRNA-Seq) plus cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) plus T cell receptor sequencing (TCR-Seq) supported clonal expansion and recruitment of cytotoxic CD8+ T cells from circulation into skin along with expanded and nonexpanded cytotoxic CD8+ skin TRM in SJS/TEN. Comparatively, MDE displayed a cytotoxic T cell profile in skin without appreciable expansion and recruitment of cytotoxic CD8+ T cells from circulation, implicating TRMs as potential protagonists in skin-limited disease. Mechanistic interrogation in patients unable to recruit T cells from circulation into skin and in a parallel mouse model supported that skin TRMs were sufficient to mediate MDE. Concomitantly, SJS/TEN displayed a reduced Treg signature compared with MDE. DRESS demonstrated recruitment of cytotoxic CD8+ T cells into skin as in SJS/TEN, yet a pro-Treg signature as in MDE. These findings have important implications for fundamental skin immunology and clinical care.

Interneuron diversity in the human dorsal striatum

Deciphering the striatal interneuron diversity is key to understanding the basal ganglia circuit and to untangling the complex neurological and psychiatric diseases affecting this brain structure. We performed snRNA-seq and spatial transcriptomics of postmortem human caudate nucleus and putamen samples to elucidate the diversity and abundance of interneuron populations and their inherent transcriptional structure in the human dorsal striatum. We propose a comprehensive taxonomy of striatal interneurons with eight main classes and fourteen subclasses, providing their full transcriptomic identity and spatial expression profile as well as additional quantitative FISH validation for specific populations. We have also delineated the correspondence of our taxonomy with previous standardized classifications and shown the main transcriptomic and class abundance differences between caudate nucleus and putamen. Notably, based on key functional genes such as ion channels and synaptic receptors, we found matching known mouse interneuron populations for the most abundant populations, the recently described PTHLH and TAC3 interneurons. Finally, we were able to integrate other published datasets with ours, supporting the generalizability of this harmonized taxonomy.

Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE

Single-cell RNA-sequencing enables testing for differential expression (DE) between conditions at a cell type level. While powerful, one of the limitations of such approaches is that the sensitivity of DE testing is dictated by the sensitivity of clustering, which is often suboptimal. To overcome this, we present miloDE-a cluster-free framework for DE testing (available as an open-source R package). We illustrate the performance of miloDE on both simulated and real data. Using miloDE, we identify a transient hemogenic endothelia-like state in mouse embryos lacking Tal1 and detect distinct programs during macrophage activation in idiopathic pulmonary fibrosis.

Engrailed transcription factors direct excitatory cerebellar neuron diversity and survival

The neurons of the three cerebellar nuclei (CN) are the primary output neurons of the cerebellum. The excitatory neurons (e) of the medial (m) CN (eCNm) were recently divided into molecularly defined subdomains in the adult; however, how they are established during development is not known. We define molecular subdomains of the mouse embryonic eCNm using single-cell RNA-sequencing and spatial expression analysis, showing that they evolve during embryogenesis to prefigure the adult. Furthermore, eCNm are transcriptionally divergent from cells in the other nuclei by embryonic day 14.5. We previously showed that loss of the homeobox genes En1 and En2 leads to loss of approximately half of the embryonic eCNm. We demonstrate that mutation of En1/2 in the embryonic eCNm results in death of specific posterior eCNm molecular subdomains and downregulation of TBR2 (EOMES) in an anterior embryonic subdomain, as well as reduced synaptic gene expression. We further reveal a similar function for EN1/2 in mediating TBR2 expression, neuron differentiation and survival in the other excitatory neurons (granule and unipolar brush cells). Thus, our work defines embryonic eCNm molecular diversity and reveals conserved roles for EN1/2 in the cerebellar excitatory neuron lineage.

Msh3 and Pms1 Set Neuronal CAG-repeat Migration Rate to Drive Selective Striatal and Cortical Pathogenesis in HD Mice

Modifiers of Huntington's disease (HD) include mismatch repair (MMR) genes; however, their underlying disease-altering mechanisms remain unresolved. Knockout (KO) alleles for 9 HD GWAS modifiers/MMR genes were crossed to the Q140 Huntingtin (mHtt) knock-in mice to probe such mechanisms. Four KO mice strongly ( Msh3 and Pms1 ) or moderately ( Msh2 and Mlh1 ) rescue a triad of adult-onset, striatal medium-spiny-neuron (MSN)-selective phenotypes: somatic Htt DNA CAG-repeat expansion, transcriptionopathy, and mHtt protein aggregation. Comparatively, Q140 cortex also exhibits an analogous, but later-onset, pathogenic triad that is Msh3 -dependent. Remarkably, Q140/homozygous Msh3-KO lacks visible mHtt aggregates in the brain, even at advanced ages (20-months). Moreover, Msh3 -deficiency prevents striatal synaptic marker loss, astrogliosis, and locomotor impairment in HD mice. Purified Q140 MSN nuclei exhibit highly linear age-dependent mHtt DNA repeat expansion (i.e. repeat migration), with modal-CAG increasing at +8.8 repeats/month (R 2 =0.98). This linear rate is reduced to 2.3 and 0.3 repeats/month in Q140 with Msh3 heterozygous and homozygous alleles, respectively. Our study defines somatic Htt CAG-repeat thresholds below which there are no detectable mHtt nuclear or neuropil aggregates. Mild transcriptionopathy can still occur in Q140 mice with stabilized Htt 140-CAG repeats, but the majority of transcriptomic changes are due to somatic repeat expansion. Our analysis reveals 479 genes with expression levels highly correlated with modal-CAG length in MSNs. Thus, our study mechanistically connects HD GWAS genes to selective neuronal vulnerability in HD, in which Msh3 and Pms1 set the linear rate of neuronal mHtt CAG-repeat migration to drive repeat-length dependent pathogenesis; and provides a preclinical platform for targeting these genes for HD suppression across brain regions.

One Sentence Summary

Msh3 and Pms1 are genetic drivers of sequential striatal and cortical pathogenesis in Q140 mice by mediating selective CAG-repeat migration in HD vulnerable neurons.

Disease-specific transcriptional programs govern airway goblet cell metaplasia

Hypersecretion of airway mucus caused by goblet cell metaplasia is a characteristic of chronic pulmonary inflammatory diseases including asthma, cystic fibrosis (CF), and chronic obstructive pulmonary disease (COPD). Goblet cells originate from airway progenitor club cells. However, the molecular mechanisms and features of goblet cell metaplasia in lung disease are poorly understood. Herein, public single-cell RNA sequencing datasets of human lungs were reanalyzed to explore the transitional phase as club cells differentiate into goblet cells in asthma, CF, and COPD. We found that changes in club and goblet cells during pathogenesis and cellular transition were associated with signalling pathways related to immune response, oxidative stress, and apoptosis. Moreover, other key drivers of goblet cell specification appeared to be pathologically specific, with interleukin (IL)-13 and hypoxia inducible factor 1 (HIF-1)-induced genetic changes in asthma, cystic fibrosis transmembrane conductance regulator (CFTR) mutation being present in CF, and interactions with CD8+ T cells, mitophagy, and mitochondria-induced apoptosis in COPD. In conclusion, this study revealed the similarities and differences in goblet cell metaplasia in asthma, CF, and COPD at the transcriptome level, thereby providing insights into possible novel therapeutic approaches for these diseases.

Acute rapamycin treatment reveals novel mechanisms of behavioral, physiological, and functional dysfunction in a maternal inflammation mouse model of autism and sensory over-responsivity

Maternal inflammatory response (MIR) during early gestation in mice induces a cascade of physiological and behavioral changes that have been associated with autism spectrum disorder (ASD). In a prior study and the current one, we find that mild MIR results in chronic systemic and neuro-inflammation, mTOR pathway activation, mild brain overgrowth followed by regionally specific volumetric changes, sensory processing dysregulation, and social and repetitive behavior abnormalities. Prior studies of rapamycin treatment in autism models have focused on chronic treatments that might be expected to alter or prevent physical brain changes. Here, we have focused on the acute effects of rapamycin to uncover novel mechanisms of dysfunction and related to mTOR pathway signaling. We find that within 2 hours, rapamycin treatment could rapidly rescue neuronal hyper-excitability, seizure susceptibility, functional network connectivity and brain community structure, and repetitive behaviors and sensory over-responsivity in adult offspring with persistent brain overgrowth. These CNS-mediated effects are also associated with alteration of the expression of several ASD-,ion channel-, and epilepsy-associated genes, in the same time frame. Our findings suggest that mTOR dysregulation in MIR offspring is a key contributor to various levels of brain dysfunction, including neuronal excitability, altered gene expression in multiple cell types, sensory functional network connectivity, and modulation of information flow. However, we demonstrate that the adult MIR brain is also amenable to rapid normalization of these functional changes which results in the rescue of both core and comorbid ASD behaviors in adult animals without requiring long-term physical alterations to the brain. Thus, restoring excitatory/inhibitory imbalance and sensory functional network modularity may be important targets for therapeutically addressing both primary sensory and social behavior phenotypes, and compensatory repetitive behavior phenotypes.

Control of innate olfactory valence by segregated cortical amygdala circuits

Animals perform innate behaviors that are stereotyped responses to specific evolutionarily relevant stimuli in the absence of prior learning or experience. These behaviors can be reduced to an axis of valence, whereby specific odors evoke approach or avoidance. The cortical amygdala (plCoA) mediates innate attraction and aversion to odor. However, little is known about how this brain area gives rise to behaviors of opposing motivational valence. Here, we sought to define the circuit features of plCoA that give rise to innate olfactory behaviors of valence. We characterized the physiology, gene expression, and projections of this structure, identifying a divergent, topographic organization that selectively controls innate attraction and avoidance to odor. First, we examined odor-evoked responses in these areas and found sparse encoding of odor identity, but not valence. We next considered a topographic organization and found that optogenetic stimulation of the anterior and posterior domains of plCoA elicits attraction and avoidance, respectively, suggesting a functional axis for valence. Using single cell and spatial RNA sequencing, we identified the molecular cell types in plCoA, revealing an anteroposterior gradient in cell types, whereby anterior glutamatergic neurons preferentially express Slc17a6 and posterior neurons express Slc17a7. Activation of these respective cell types recapitulates appetitive and aversive valence behaviors, and chemogenetic inhibition reveals partial necessity for valence responses to innate appetitive or aversive odors. Finally, we identified topographically organized circuits defined by projections, whereby anterior neurons preferentially project to medial amygdala, and posterior neurons preferentially project to nucleus accumbens, which are respectively sufficient and necessary for innate negative and positive olfactory valence. Together, these data advance our understanding of how the olfactory system generates stereotypic, hardwired attraction and avoidance, and supports a model whereby distinct, topographically distributed plCoA populations direct innate olfactory valence responses by signaling to divergent valence-specific targets, linking upstream olfactory identity to downstream valence behaviors, through a population code. This represents a novel circuit motif in which valence encoding is represented not by the firing properties of individual neurons, but by population level identity encoding that is routed through divergent targets to mediate distinct valence.

Single cell regulatory architecture of human pancreatic islets suggests sex differences in β cell function and the pathogenesis of type 2 diabetes

Type 2 and type 1 diabetes (T2D, T1D) exhibit sex differences in insulin secretion, the mechanisms of which are unknown. We examined sex differences in human pancreatic islets from 52 donors with and without T2D combining single cell RNA-seq (scRNA-seq), single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), hormone secretion, and bioenergetics. In nondiabetic (ND) donors, sex differences in islet cells gene accessibility and expression predominantly involved sex chromosomes. Islets from T2D donors exhibited similar sex differences in sex chromosomes differentially expressed genes (DEGs), but also exhibited sex differences in autosomal genes. Comparing β cells from T2D vs. ND donors, gene enrichment of female β cells showed suppression in mitochondrial respiration, while male β cells exhibited suppressed insulin secretion. Thus, although sex differences in gene accessibility and expression of ND β cells predominantly affect sex chromosomes, the transition to T2D reveals sex differences in autosomes highlighting mitochondrial failure in females.

SLAM/SAP signaling regulates discrete γδ T cell developmental checkpoints and shapes the innate-like γδ TCR repertoire

During thymic development, most γδ T cells acquire innate-like characteristics that are critical for their function in tumor surveillance, infectious disease, and tissue repair. The mechanisms, however, that regulate γδ T cell developmental programming remain unclear. Recently, we demonstrated that the SLAM-SAP signaling pathway regulates the development and function of multiple innate-like γδ T cell subsets. Here, we used a single-cell proteogenomics approach to identify SAP-dependent developmental checkpoints and to define the SAP-dependent γδ TCR repertoire. SAP deficiency resulted in both a significant loss of an immature Gzma + Blk + Etv5 + Tox2 + γδT17 precursor population, and a significant increase in Cd4 + Cd8+ Rorc + Ptcra + Rag1 + thymic γδ T cells. SAP-dependent diversion of embryonic day 17 thymic γδ T cell clonotypes into the αβ T cell developmental pathway was associated with a decreased frequency of mature clonotypes in neonatal thymus, and an altered γδ TCR repertoire in the periphery. Finally, we identify TRGV4/TRAV13-4(DV7)-expressing T cells as a novel, SAP-dependent Vγ4 γδT1 subset. Together, the data suggest that SAP-dependent γδ/αβ T cell lineage commitment regulates γδ T cell developmental programming and shapes the γδ TCR repertoire.

Eomes expression identifies the early bone marrow precursor to classical NK cells

Natural killer (NK) cells traffic through the blood and mount cytolytic and interferon-γ (IFNγ)-focused responses to intracellular pathogens and tumors. Type 1 innate lymphoid cells (ILC1s) also produce type 1 cytokines but reside in tissues and are not cytotoxic. Whether these differences reflect discrete lineages or distinct states of a common cell type is not understood. Using single-cell RNA sequencing and flow cytometry, we focused on populations of TCF7+ cells that contained precursors for NK cells and ILC1s and identified a subset of bone marrow lineage-negative NK receptor-negative cells that expressed the transcription factor Eomes, termed EomeshiNKneg cells. Transfer of EomeshiNKneg cells into Rag2-/-Il2rg-/- recipients generated functional NK cells capable of preventing metastatic disease. By contrast, transfer of PLZF+ ILC precursors generated a mixture of ILC1s, ILC2s and ILC3s that lacked cytotoxic potential. These findings identified EomeshiNKneg cells as the bone marrow precursor to classical NK cells and demonstrated that the NK and ILC1 lineages diverged early during development.

Single-cell and spatial atlases of spinal cord injury in the Tabulae Paralytica

Here, we introduce the Tabulae Paralytica-a compilation of four atlases of spinal cord injury (SCI) comprising a single-nucleus transcriptome atlas of half a million cells, a multiome atlas pairing transcriptomic and epigenomic measurements within the same nuclei, and two spatial transcriptomic atlases of the injured spinal cord spanning four spatial and temporal dimensions. We integrated these atlases into a common framework to dissect the molecular logic that governs the responses to injury within the spinal cord1. The Tabulae Paralytica uncovered new biological principles that dictate the consequences of SCI, including conserved and divergent neuronal responses to injury; the priming of specific neuronal subpopulations to upregulate circuit-reorganizing programs after injury; an inverse relationship between neuronal stress responses and the activation of circuit reorganization programs; the necessity of re-establishing a tripartite neuroprotective barrier between immune-privileged and extra-neural environments after SCI and a failure to form this barrier in old mice. We leveraged the Tabulae Paralytica to develop a rejuvenative gene therapy that re-established this tripartite barrier, and restored the natural recovery of walking after paralysis in old mice. The Tabulae Paralytica provides a window into the pathobiology of SCI, while establishing a framework for integrating multimodal, genome-scale measurements in four dimensions to study biology and medicine.

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.

Autocrine VEGF drives neural stem cell proximity to the adult hippocampus vascular niche

The vasculature is a key component of adult brain neural stem cell (NSC) niches. In the adult mammalian hippocampus, NSCs reside in close contact with a dense capillary network. How this niche is maintained is unclear. We recently found that adult hippocampal NSCs express VEGF, a soluble factor with chemoattractive properties for vascular endothelia. Here, we show that global and NSC-specific VEGF loss led to dissociation of NSCs and their intermediate progenitor daughter cells from local vasculature. Surprisingly, though, we found no changes in local vascular density. Instead, we found that NSC-derived VEGF supports maintenance of gene expression programs in NSCs and their progeny related to cell migration and adhesion. In vitro assays revealed that blockade of VEGF receptor 2 impaired NSC motility and adhesion. Our findings suggest that NSCs maintain their own proximity to vasculature via self-stimulated VEGF signaling that supports their motility towards and/or adhesion to local blood vessels.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

BACKGROUND

Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions.

RESULTS

In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers.

CONCLUSIONS

Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.