Common approaches to gene signature discovery in single-cell RNA-sequencing (scRNA-seq) depend upon predefined structures like clusters or pseudo-temporal order, require prior normalization, or do not account for the sparsity of single-cell data. We present single-cell hierarchical Poisson factorization (scHPF), a Bayesian factorization method that adapts hierarchical Poisson factorization (Gopalan et al, 2015, Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, 326) for de novo discovery of both continuous and discrete expression patterns from scRNA-seq. scHPF does not require prior normalization and captures statistical properties of single-cell data better than other methods in benchmark datasets. Applied to scRNA-seq of the core and margin of a high-grade glioma, scHPF uncovers marked differences in the abundance of glioma subpopulations across tumor regions and regionally associated expression biases within glioma subpopulations. scHFP revealed an expression signature that was spatially biased toward the glioma-infiltrated margins and associated with inferior survival in glioblastoma.

The concept that solid tumors are maintained by a productive interplay between neoplastic and non-neoplastic elements has gained traction with the demonstration that stromal fibroblasts and immune system cells dictate cancer development and progression. While less studied, brain tumor (glioma) biology is likewise influenced by non-neoplastic immune system cells (macrophages and microglia) which interact with neoplastic glioma cells to create a unique physiological state (glioma ecosystem) distinct from that found in the normal tissue. To explore this neoplastic ground state, we leveraged several preclinical mouse models of neurofibromatosis type 1 (NF1) optic glioma, a low-grade astrocytoma whose formation and maintenance requires productive interactions between non-neoplastic and neoplastic cells, and employed whole tumor RNA-sequencing and mathematical deconvolution strategies to characterize this low-grade glioma ecosystem as an aggregate of cellular and acellular elements. Using this approach, we demonstrate that optic gliomas generated by altering the germline Nf1 gene mutation, the glioma cell of origin, or the presence of co-existing genetic alterations represent molecularly-distinct tumors. However, these optic glioma tumors share a 25-gene core signature, not found in normal optic nerve, that is normalized by microglia inhibition (minocycline), but not conventional (carboplatin) or molecularly-targeted (rapamycin) chemotherapy. Lastly, we identify a genetic signature conferred by Pten reduction and corrected by PI3K inhibition. This signature predicts progression-free survival in patients with either low-grade or high-grade glioma. Collectively, these findings support the concept that gliomas are composite ecological systems whose biology and response to therapy may be best defined by examining the tumor as a whole.

Dopaminergic neurons modulate neural circuits and behaviors via dopamine (DA) release from expansive, long range axonal projections. The elaborate cytoarchitecture of these neurons is embedded within complex brain tissue, making it difficult to access the neuronal proteome using conventional methods. Here, we demonstrate APEX2 proximity labeling within genetically targeted neurons in the mouse brain, enabling subcellular proteomics with cell-type specificity. By combining APEX2 biotinylation with mass spectrometry, we mapped the somatodendritic and axonal proteomes of midbrain dopaminergic neurons. Our dataset reveals the proteomic architecture underlying proteostasis, axonal metabolism, and neurotransmission in these neurons. We find that most proteins encoded by DA neuron-enriched genes are localized within striatal dopaminergic axons, including ion channels with previously undescribed axonal localization. These proteomic datasets provide a resource for neuronal cell biology, and this approach can be readily adapted for study of other neural cell types.

The persistence of anti-viral immunity is essential for protection and exhibits profound heterogeneity across individuals. Here, we elucidate the factors that shape maintenance and function of anti-viral T cell immunity in the body by comprehensive profiling of virus-specific T cells across blood, lymphoid organs, and mucosal tissues of organ donors. We use flow cytometry, T cell receptor sequencing, single-cell transcriptomics, and cytokine analysis to profile virus-specific CD8+ T cells recognizing the ubiquitous pathogens influenza and cytomegalovirus. Our results reveal that virus specificity determines overall magnitude, tissue distribution, differentiation, and clonal repertoire of virus-specific T cells. Age and sex influence T cell differentiation and dissemination in tissues, while T cell tissue residence and functionality are highly correlated with the site. Together, our results demonstrate how the covariates of virus, tissue, age, and sex impact the anti-viral immune response, which is important for targeting, monitoring, and predicting immune responses to existing and emerging viruses.

α-Synuclein is expressed at high levels at presynaptic terminals, but defining its role in the regulation of neurotransmission under physiologically relevant conditions has proven elusive. We report that, in vivo, α-synuclein is responsible for the facilitation of dopamine release triggered by action potential bursts separated by short intervals (seconds) and a depression of release with longer intervals between bursts (minutes). These forms of presynaptic plasticity appear to be independent of the presence of β- and γ-synucleins or effects on presynaptic calcium and are consistent with a role for synucleins in the enhancement of synaptic vesicle fusion and turnover. These results indicate that the presynaptic effects of α-synuclein depend on specific patterns of neuronal activity.

Hair greying is a hallmark of aging generally believed to be irreversible and linked to psychological stress.

Flow cytometry and fluorescence-activated sorting are powerful techniques that hold great promise for studying heterogeneous populations of submicron particles such as synaptosomes, but many technical challenges arise in these experiments. To date, most flow cytometry studies of synaptosomes have relied on particle detection using forward scatter (FSC) measurements and size estimation with polystyrene (PS) bead standards. However, these practices have serious limitations, and special care must be taken to overcome the poor sensitivity of conventional flow cytometers in the analysis of submicron particles. Technical artifacts can confound these experiments, especially the detection of multiple particles as a single event. Here, we compared analysis of P2 crude synaptosomal preparations from murine forebrain on multiple flow cytometers using both FSC-triggered and fluorescence-triggered detection. We implemented multicolor fluorescent dye-based assays to quantify coincident particle detection and aggregation, and we assessed the false colocalization of antigens in immunostaining analyses. Our results demonstrate that fluorescence triggering and proper dilution can control for coincident particle detection, but not particle aggregation. We confirmed previous studies showing that FSC-based size estimation with PS beads underestimates biological particle size, and we identified pervasive aggregation in the FSC range analyzed in most synaptosome flow cytometry studies. We found that analyzing P2 samples in sucrose/EDTA/tris (SET) buffer reduces aggregation compared to PBS, but does not completely eliminate the presence of aggregates, especially in immunostaining experiments. Our study highlights challenges and pitfalls in synaptosome flow cytometry and provides a methodological framework for future studies.

Human T cells coordinate adaptive immunity in diverse anatomic compartments through production of cytokines and effector molecules, but it is unclear how tissue site influences T cell persistence and function. Here, we use single cell RNA-sequencing (scRNA-seq) to define the heterogeneity of human T cells isolated from lungs, lymph nodes, bone marrow and blood, and their functional responses following stimulation. Through analysis of >50,000 resting and activated T cells, we reveal tissue T cell signatures in mucosal and lymphoid sites, and lineage-specific activation states across all sites including distinct effector states for CD8+ T cells and an interferon-response state for CD4+ T cells. Comparing scRNA-seq profiles of tumor-associated T cells to our dataset reveals predominant activated CD8+ compared to CD4+ T cell states within multiple tumor types. Our results therefore establish a high dimensional reference map of human T cell activation in health for analyzing T cells in disease.

Puromycin is an amino-acyl transfer RNA analog widely employed in studies of protein synthesis. Since puromycin is covalently incorporated into nascent polypeptide chains, anti-puromycin immunofluorescence enables visualization of nascent protein synthesis. A common assumption in studies of local messenger RNA translation is that the anti-puromycin staining of puromycylated nascent polypeptides in fixed cells accurately reports on their original site of translation, particularly when ribosomes are stalled with elongation inhibitors prior to puromycin treatment. However, when we attempted to implement a proximity ligation assay to detect ribosome-puromycin complexes, we found no evidence to support this assumption. We further demonstrated, using biochemical assays and live cell imaging of nascent polypeptides in mammalian cells, that puromycylated nascent polypeptides rapidly dissociate from ribosomes even in the presence of elongation inhibitors. Our results suggest that attempts to define precise subcellular translation sites using anti-puromycin immunostaining may be confounded by release of puromycylated nascent polypeptide chains prior to fixation.

Preclinical studies require models that recapitulate the cellular diversity of human tumors and provide insight into the drug sensitivities of specific cellular populations. The ideal platform would enable rapid screening of cell type-specific drug sensitivities directly in patient tumor tissue and reveal strategies to overcome intratumoral heterogeneity.

Single-cell transcriptomic analyses now frequently involve elaborate study designs including samples from multiple individuals, experimental conditions, perturbations, and batches from complex tissues. Dimensionality reduction is required to facilitate integration, interpretation, and statistical analysis. However, these datasets often include subtly different cellular subpopulations or state transitions, which are poorly described by clustering. We previously reported a Bayesian matrix factorization algorithm called single-cell hierarchical Poisson factorization (scHPF) that identifies gene co-expression patterns directly from single-cell RNA-seq (scRNA-seq) count matrices while accounting for transcript drop-out and noise. Here, we describe consensus scHPF, which analyzes scHPF models from multiple random initializations to identify the most robust gene signatures and automatically determine the number of factors for a given dataset. Consensus scHPF facilitates integration of complex datasets with highly multi-modal posterior distributions, resulting in factors that can be uniformly analyzed across individuals and conditions. To demonstrate the utility of consensus scHPF, we performed a meta-analysis of a large-scale scRNA-seq dataset from drug-treated, human glioma slice cultures generated from surgical specimens across three major cell types, 19 patients, 10 drug treatment conditions, and 52 samples. In addition to recapitulating previously reported cell type-specific drug responses from smaller studies, consensus scHPF identified disparate effects of the topoisomerase poisons etoposide and topotecan that are highly consistent with the distinct roles and expression patterns of their respective protein targets.

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm's value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

Despite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs).

Using a cell fusion assay, we show here that in addition to complete fusion SNAREs also promote hemifusion as an alternative outcome. Approximately 65% of events resulted in full fusion, and the remaining 35% in hemifusion; of those, approximately two thirds were permanent and approximately one third were reversible. We predict that this relatively close balance among outcomes could be tipped by binding of regulatory proteins to the SNAREs, allowing for dynamic physiological regulation between full fusion and reversible kiss-and-run-like events.

To control sprouting angiogenesis, endothelial Notch signaling suppresses tip cell formation, migration, and proliferation while promoting barrier formation. Each of these responses may be regulated by distinct Notch-regulated effectors. Notch activity is highly dynamic in sprouting endothelial cells, while constitutive Notch signaling drives homeostatic endothelial polarization, indicating the need for both rapid and constitutive Notch targets. In contrast to previous screens that focus on genes regulated by constitutively active Notch, we characterized the dynamic response to Notch. We examined transcriptional changes from 1.5 to 6 h after Notch signal activation via ligand-specific or EGTA induction in cultured primary human endothelial cells and neonatal mouse brain. In each combination of endothelial type and Notch manipulation, transcriptomic analysis identified distinct but overlapping sets of rapidly regulated genes and revealed many novel Notch target genes. Among the novel Notch-regulated signaling pathways identified were effectors in GPCR signaling, notably, the constitutively active GTPase RND1. In endothelial cells, RND1 was shown to be a novel direct Notch transcriptional target and required for Notch control of sprouting angiogenesis, endothelial migration, and Ras activity. We conclude that RND1 is directly regulated by endothelial Notch signaling in a rapid fashion in order to suppress endothelial migration.

Protein synthesis is dysregulated in many diseases, but we lack a systems-level picture of how signaling molecules and RNA binding proteins interact with the translational machinery, largely due to technological limitations. Here we present riboPLATE-seq, a scalable method for generating paired libraries of ribosome-associated and total mRNA. As an extension of the PLATE-seq protocol, riboPLATE-seq utilizes barcoded primers for pooled library preparation, but additionally leverages anti-rRNA ribosome immunoprecipitation on whole polysomes to measure ribosome association (RA). We compare RA to its analogue in ribosome profiling and RNA sequencing, translation efficiency, and demonstrate both the performance of riboPLATE-seq and its utility in detecting translational alterations induced by specific inhibitors of protein kinases.

Immune response dynamics in coronavirus disease 2019 (COVID-19) and their severe manifestations have largely been studied in circulation. Here, we examined the relationship between immune processes in the respiratory tract and circulation through longitudinal phenotypic, transcriptomic, and cytokine profiling of paired airway and blood samples from patients with severe COVID-19 relative to heathy controls. In COVID-19 airways, T cells exhibited activated, tissue-resident, and protective profiles; higher T cell frequencies correlated with survival and younger age. Myeloid cells in COVID-19 airways featured hyperinflammatory signatures, and higher frequencies of these cells correlated with mortality and older age. In COVID-19 blood, aberrant CD163+ monocytes predominated over conventional monocytes, and were found in corresponding airway samples and in damaged alveoli. High levels of myeloid chemoattractants in airways suggest recruitment of these cells through a CCL2-CCR2 chemokine axis. Our findings provide insights into immune processes driving COVID-19 lung pathology with therapeutic implications for targeting inflammation in the respiratory tract.

Plasmacytoid dendritic cells (pDCs) can rapidly produce interferons and other soluble factors in response to extracellular viruses or virus mimics such as CpG-containing DNA. pDCs can also recognize live cells infected with certain RNA viruses, but the relevance and functional consequences of such recognition remain unclear. We studied the response of primary DCs to the prototypical persistent DNA virus, human cytomegalovirus (CMV). Human pDCs produced high amounts of type I interferon (IFN-I) when incubated with live CMV-infected fibroblasts but not with free CMV; the response involved integrin-mediated adhesion, transfer of DNA-containing virions to pDCs, and the recognition of DNA through TLR9. Compared with transient polyfunctional responses to CpG or free influenza virus, pDC response to CMV-infected cells was long-lasting, dominated by the production of IFN-I and IFN-III, and lacked diversification into functionally distinct populations. Similarly, pDC activation by influenza-infected lung epithelial cells was highly efficient, prolonged, and dominated by interferon production. Prolonged pDC activation by CMV-infected cells facilitated the activation of natural killer cells critical for CMV control. Last, patients with CMV viremia harbored phenotypically activated pDCs and increased circulating IFN-I and IFN-III. Thus, recognition of live infected cells is a mechanism of virus detection by pDCs that elicits a unique antiviral immune response.

Acute cellular rejection is common after lung transplantation and is associated with an increased risk of early chronic rejection. We present combined single-cell RNA and TCR sequencing on recipient-derived T cells obtained from the bronchoalveolar lavage of three lung transplant recipients with rejection and compare them with T cells obtained from the same patients after treatment of rejection with high-dose systemic glucocorticoids. At the time of rejection, we found an oligoclonal expansion of cytotoxic CD8+ T cells that all persisted as tissue resident memory T cells after successful treatment. Persisting CD8+ allograft-resident T cells have reduced gene expression for cytotoxic mediators after therapy with glucocorticoids but accumulate around airways. This clonal expansion is discordant with circulating T cell clonal expansion at the time of rejection, suggesting in situ expansion. We thus highlight the accumulation of cytotoxic, recipient-derived tissue resident memory T cells within the lung allograft that persist despite the administration of high-dose systemic glucocorticoids. The long-term clinical consequences of this persistence have yet to be characterized.

Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets.