Significance analysis for clustering with single-cell RNA-sequencing data.

Unsupervised clustering of single-cell RNA-sequencing data enables the identification of distinct cell populations. However, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty. We find that not addressing known sources of variability in a statistically rigorous manner can lead to overconfidence in the discovery of novel cell types. Here we extend a previous method, significance of hierarchical clustering, to propose a model-based hypothesis testing approach that incorporates significance analysis into the clustering algorithm and permits statistical evaluation of clusters as distinct cell populations. We also adapt this approach to permit statistical assessment on the clusters reported by any algorithm. Finally, we extend these approaches to account for batch structure. We benchmarked our approach against popular clustering workflows, demonstrating improved performance. To show practical utility, we applied our approach to the Human Lung Cell Atlas and an atlas of the mouse cerebellar cortex, identifying several cases of over-clustering and recapitulating experimentally validated cell type definitions.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

BACKGROUND: Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. RESULTS: We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. CONCLUSIONS: Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.

Bacterial microbiome of breast milk and child saliva from low-income Mexican-American women and children.

BACKGROUND: The childhood salivary microbiome, which plays an important role in healthy development, may be influenced by breast milk consumption. The composition of the milk microbiome and the role it plays in the establishment of the infant microbiome are not well understood. METHODS: Here, we sequenced the bacterial 16S rRNA gene to characterize microbial communities in breast milk and 5-year-old child saliva from 10 low-income, Mexican-American mother-child pairs with a high prevalence of obesity. RESULTS: Members of the genus Streptococcus dominated both milk and salivary microbial communities in most subjects. Staphylococcus was observed predominately in milk samples while Prevotella was more prevalent in child saliva. No statistically significant relationships were observed between maternal and child microbiomes or between child microbiome and BMI. However, prepregnancy BMI was correlated with both lower Streptococcus abundance (r = -0.67) and higher microbial diversity (r = 0.77) in breast milk (P < 0.05 for both). Diversity estimates were notably similar to data from other low-income cohorts or children. CONCLUSION: These findings contribute to the currently limited state of knowledge regarding the breast milk and salivary microbiomes in mother-child pairs and may inform future studies seeking to elucidate the relationship between early-life microbial exposures and pediatric health.

Trajectory inference across multiple conditions with condiments.

In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell's conditions along the trajectory's path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

A review of single-cell transcriptomics and epigenomics studies in maternal and child health.

Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.

Single-cell sequencing technologies offer new biological insights on pregnancy at the cellular level. We reviewed these technologies and their applications in maternal and child health studies, including 16 placenta cell databases and 31 studies on health challenges during pregnancy such as COVID infection. New cell types and biological pathways among specific groups of cells were found.

Spatially resolved immune exhaustion within the alloreactive microenvironment predicts liver transplant rejection.

Allograft rejection is common following clinical organ transplantation, but defining specific immune subsets mediating alloimmunity has been elusive. Calcineurin inhibitor dose escalation, corticosteroids, and/or lymphocyte depleting antibodies have remained the primary options for treatment of clinical rejection episodes. Here, we developed a highly multiplexed imaging mass cytometry panel to study the immune response in archival biopsies from 79 liver transplant (LT) recipients with either no rejection (NR), acute T cell-mediated rejection (TCMR), or chronic rejection (CR). This approach generated a spatially resolved proteomic atlas of 461,816 cells (42 phenotypes) derived from 96 pathologist-selected regions of interest. Our analysis revealed that regulatory (HLADR+ Treg) and PD1+ T cell phenotypes (CD4+ and CD8+ subsets), combined with variations in M2 macrophage polarization, were a unique signature of active TCMR. These data provide insights into the alloimmune microenvironment in clinical LT, including identification of potential targets for focused immunotherapy during rejection episodes and suggestion of a substantial role for immune exhaustion in TCMR.

Attenuated kidney oxidative metabolism in young adults with type 1 diabetes.

BACKGROUND: In type 1 diabetes (T1D), impaired insulin sensitivity may contribute to the development of diabetic kidney disease (DKD) through alterations in kidney oxidative metabolism. METHODS: Young adults with T1D (n = 30) and healthy controls (HC, n = 20) underwent hyperinsulinemic-euglycemic clamp studies, MRI, 11C-acetate PET, kidney biopsies, single-cell RNA sequencing, and spatial metabolomics to assess this relationship. RESULTS: Participants with T1D had significantly higher glomerular basement membrane thickness compared to HC. T1D participants exhibited lower insulin sensitivity and cortical oxidative metabolism, correlating with higher insulin sensitivity. Proximal tubular transcripts of TCA cycle and oxidative phosphorylation enzymes were lower in T1D. Spatial metabolomics showed reductions in tubular TCA cycle intermediates, indicating mitochondrial dysfunction. The Slingshot algorithm identified a lineage of proximal tubular cells progressing from stable to adaptive/maladaptive subtypes, using pseudotime trajectory analysis, which computationally orders cells along a continuum of states. This analysis revealed distinct distribution patterns between T1D and HC, with attenuated oxidative metabolism in T1D attributed to a greater proportion of adaptive/maladaptive subtypes with low expression of TCA cycle and oxidative phosphorylation transcripts. Pseudotime progression associated with higher HbA1c, BMI, GBM, and lower insulin sensitivity and cortical oxidative metabolism. CONCLUSION: These early structural and metabolic changes in T1D kidneys may precede clinical DKD. CLINICALTRIALS: gov NCT04074668.

A Guide to Trajectory Inference and RNA Velocity.

Technological developments have led to an explosion of high-throughput single-cell data, which are revealing unprecedented perspectives on cell identity. Recently, significant attention has focused on investigating, from single-cell RNA-sequencing (scRNA-seq) data, cellular dynamic processes, such as cell differentiation, cell cycle and cell (de)activation. In particular, trajectory inference methods, by ordering cells along a trajectory, allow estimating a differentiation tree of cells. While trajectory inference tools typically work with gene expression levels, common scRNA-seq protocols allow the identification and quantification of unspliced pre-mRNAs and mature spliced mRNAs for each gene. By exploiting the abundance of unspliced and spliced mRNA, one can infer the RNA velocity of individual cells, i.e., the time derivative of the gene expression state of cells. Whereas traditional trajectory inference methods reconstruct cellular dynamics given a population of cells of varying maturity, RNA velocity relies on a dynamical model describing splicing dynamics. Here, we initially discuss conceptual and theoretical aspects of both approaches, then illustrate how they can be combined together, and finally present an example use case on real data.

Spatially resolved immune exhaustion within the alloreactive microenvironment predicts liver transplant rejection.

Allograft rejection is a frequent complication following solid organ transplantation, but defining specific immune subsets mediating alloimmunity has been elusive due to the scarcity of tissue in clinical biopsy specimens. Single cell techniques have emerged as valuable tools for studying mechanisms of disease in complex tissue microenvironments. Here, we developed a highly multiplexed imaging mass cytometry panel, single cell analysis pipeline, and semi-supervised immune cell clustering algorithm to study archival biopsy specimens from 79 liver transplant (LT) recipients with histopathological diagnoses of either no rejection (NR), acute T-cell mediated rejection (TCMR), or chronic rejection (CR). This approach generated a spatially resolved proteomic atlas of 461,816 cells derived from 98 pathologist-selected regions of interest relevant to clinical diagnosis of rejection. We identified 41 distinct cell populations (32 immune and 9 parenchymal cell phenotypes) that defined key elements of the alloimmune microenvironment (AME), identified significant cell-cell interactions, and established higher order cellular neighborhoods. Our analysis revealed that both regulatory (HLA-DR+ Treg) and exhausted T-cell phenotypes (PD1+CD4+ and PD1+CD8+ T-cells), combined with variations in M2 macrophage polarization, were a unique signature of TCMR. TCMR was further characterized by alterations in cell-to-cell interactions among both exhausted immune subsets and inflammatory populations, with expansion of a CD8 enriched cellular neighborhood comprised of Treg, exhausted T-cell subsets, proliferating CD8+ T-cells, and cytotoxic T-cells. These data enabled creation of a predictive model of clinical outcomes using a subset of cell types to differentiate TCMR from NR (AUC = 0.96 ± 0.04) and TCMR from CR (AUC = 0.96 ± 0.06) with high sensitivity and specificity. Collectively, these data provide mechanistic insights into the AME in clinical LT, including a substantial role for immune exhaustion in TCMR with identification of novel targets for more focused immunotherapy in allograft rejection. Our study also offers a conceptual framework for applying spatial proteomics to study immunological diseases in archival clinical specimens.

A Latent Activated Olfactory Stem Cell State Revealed by Single Cell Transcriptomic and Epigenomic Profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration after injury-induced cell death. In this study, we used single cell sequencing to identify major regulatory players in determining olfactory epithelial stem cell fate after acute injury. We combined gene expression and accessible chromatin profiles of individual lineage traced olfactory stem cells to predict transcription factor activity specific to different lineages and stages of recovery. We further identified a discrete stem cell state that appears poised for activation, characterized by accessible chromatin around wound response and lineage specific genes prior to their later expression in response to injury. Together these results provide evidence that a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

MUC1-C Dictates JUN and BAF-Mediated Chromatin Remodeling at Enhancer Signatures in Cancer Stem Cells.

The oncogenic MUC1-C protein promotes dedifferentiation of castrate-resistant prostate cancer (CRPC) and triple-negative breast cancer (TNBC) cells. Chromatin remodeling is critical for the cancer stem cell (CSC) state; however, there is no definitive evidence that MUC1-C regulates chromatin accessibility and thereby expression of stemness-associated genes. We demonstrate that MUC1-C drives global changes in chromatin architecture in the dedifferentiation of CRPC and TNBC cells. Our results show that MUC1-C induces differentially accessible regions (DAR) across their genomes, which are significantly associated with differentially expressed genes (DEG). Motif and cistrome analysis further demonstrated MUC1-C-induced DARs align with genes regulated by the JUN/AP-1 family of transcription factors. MUC1-C activates the BAF chromatin remodeling complex, which is recruited by JUN in enhancer selection. In studies of the NOTCH1 gene, which is required for CRPC and TNBC cell self-renewal, we demonstrate that MUC1-C is necessary for (i) occupancy of JUN and ARID1A/BAF, (ii) increases in H3K27ac and H3K4me3 signals, and (iii) opening of chromatin accessibility on a proximal enhancer-like signature. Studies of the EGR1 and LY6E stemness-associated genes further demonstrate that MUC1-C-induced JUN/ARID1A complexes regulate chromatin accessibility on proximal and distal enhancer-like signatures. These findings uncover a role for MUC1-C in chromatin remodeling that is mediated at least in part by JUN/AP-1 and ARID1A/BAF in association with driving the CSC state. IMPLICATIONS: These findings show that MUC1-C, which is necessary for the CRPC and TNBC CSC state, activates a novel pathway involving JUN/AP-1 and ARID1A/BAF that regulates chromatin accessibility of stemness-associated gene enhancers.

Progressive immune dysfunction with advancing disease stage in renal cell carcinoma.

The tumor immune microenvironment plays a critical role in cancer progression and response to immunotherapy in clear cell renal cell carcinoma (ccRCC), yet the composition and phenotypic states of immune cells in this tumor are incompletely characterized. We performed single-cell RNA and T cell receptor sequencing on 164,722 individual cells from tumor and adjacent non-tumor tissue in patients with ccRCC across disease stages: early, locally advanced, and advanced/metastatic. Terminally exhausted CD8+ T cells were enriched in metastatic disease and were restricted in T cell receptor diversity. Within the myeloid compartment, pro-inflammatory macrophages were decreased, and suppressive M2-like macrophages were increased in advanced disease. Terminally exhausted CD8+ T cells and M2-like macrophages co-occurred in advanced disease and expressed ligands and receptors that support T cell dysfunction and M2-like polarization. This immune dysfunction circuit is associated with a worse prognosis in external cohorts and identifies potentially targetable immune inhibitory pathways in ccRCC.

Trajectory-based differential expression analysis for single-cell sequencing data.

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia.

Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) - the causal agent in COVID-19 - affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.

Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference.

Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery and characterization of rare cell types. However, such assays raise challenging statistical and computational questions and require the development of novel methodology and software. Using stem cell differentiation in the mouse olfactory epithelium as a case study, this integrated workflow provides a step-by-step tutorial to the methodology and associated software for the following four main tasks: (1) dimensionality reduction accounting for zero inflation and over dispersion and adjusting for gene and cell-level covariates; (2) cell clustering using resampling-based sequential ensemble clustering; (3) inference of cell lineages and pseudotimes; and (4) differential expression analysis along lineages.

Deconstructing Olfactory Stem Cell Trajectories at Single-Cell Resolution.

A detailed understanding of the paths that stem cells traverse to generate mature progeny is vital for elucidating the mechanisms governing cell fate decisions and tissue homeostasis. Adult stem cells maintain and regenerate multiple mature cell lineages in the olfactory epithelium. Here we integrate single-cell RNA sequencing and robust statistical analyses with in vivo lineage tracing to define a detailed map of the postnatal olfactory epithelium, revealing cell fate potentials and branchpoints in olfactory stem cell lineage trajectories. Olfactory stem cells produce support cells via direct fate conversion in the absence of cell division, and their multipotency at the population level reflects collective unipotent cell fate decisions by single stem cells. We further demonstrate that Wnt signaling regulates stem cell fate by promoting neuronal fate choices. This integrated approach reveals the mechanisms guiding olfactory lineage trajectories and provides a model for deconstructing similar hierarchies in other stem cell niches.

Injury Activates Transient Olfactory Stem Cell States with Diverse Lineage Capacities.

Tissue homeostasis and regeneration are mediated by programs of adult stem cell renewal and differentiation. However, the mechanisms that regulate stem cell fates under such widely varying conditions are not fully understood. Using single-cell techniques, we assessed the transcriptional changes associated with stem cell self-renewal and differentiation and followed the maturation of stem cell-derived clones using sparse lineage tracing in the regenerating mouse olfactory epithelium. Following injury, quiescent olfactory stem cells rapidly shift to activated, transient states unique to regeneration and tailored to meet the demands of injury-induced repair, including barrier formation and proliferation. Multiple cell fates, including renewed stem cells and committed differentiating progenitors, are specified during this early window of activation. We further show that Sox2 is essential for cells to transition from the activated to neuronal progenitor states. Our study highlights strategies for stem cell-mediated regeneration that may be conserved in other adult stem cell niches.

Urinary Phthalate Metabolites and Biomarkers of Oxidative Stress in a Mexican-American Cohort: Variability in Early and Late Pregnancy.

People are exposed to phthalates through their wide use as plasticizers and in personal care products. Many phthalates are endocrine disruptors and have been associated with adverse health outcomes. However, knowledge gaps exist in understanding the molecular mechanisms associated with the effects of exposure in early and late pregnancy. In this study, we examined the relationship of eleven urinary phthalate metabolites with isoprostane, an established marker of oxidative stress, among pregnant Mexican-American women from an agricultural cohort. Isoprostane levels were on average 20% higher at 26 weeks than at 13 weeks of pregnancy. Urinary phthalate metabolite concentrations suggested relatively consistent phthalate exposures over pregnancy. The relationship between phthalate metabolite concentrations and isoprostane levels was significant for the sum of di-2-ethylhexyl phthalate and the sum of high molecular weight metabolites with the exception of monobenzyl phthalate, which was not associated with oxidative stress at either time point. In contrast, low molecular weight metabolite concentrations were not associated with isoprostane at 13 weeks, but this relationship became stronger later in pregnancy (p-value = 0.009 for the sum of low molecular weight metabolites). Our findings suggest that prenatal exposure to phthalates may influence oxidative stress, which is consistent with their relationship with obesity and other adverse health outcomes.