Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA).

Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.

Fragment-sequencing unveils local tissue microenvironments at single-cell resolution.

Cells collectively determine biological functions by communicating with each other-both through direct physical contact and secreted factors. Consequently, the local microenvironment of a cell influences its behavior, gene expression, and cellular crosstalk. Disruption of this microenvironment causes reciprocal changes in those features, which can lead to the development and progression of diseases. Hence, assessing the cellular transcriptome while simultaneously capturing the spatial relationships of cells within a tissue provides highly valuable insights into how cells communicate in health and disease. Yet, methods to probe the transcriptome often fail to preserve native spatial relationships, lack single-cell resolution, or are highly limited in throughput, i.e. lack the capacity to assess multiple environments simultaneously. Here, we introduce fragment-sequencing (fragment-seq), a method that enables the characterization of single-cell transcriptomes within multiple spatially distinct tissue microenvironments. We apply fragment-seq to a murine model of the metastatic liver to study liver zonation and the metastatic niche. This analysis reveals zonated genes and ligand-receptor interactions enriched in specific hepatic microenvironments. Finally, we apply fragment-seq to other tissues and species, demonstrating the adaptability of our method.

Active eosinophils regulate host defence and immune responses in colitis.

In the past decade, single-cell transcriptomics has helped to uncover new cell types and states and led to the construction of a cellular compendium of health and disease. Despite this progress, some difficult-to-sequence cells remain absent from tissue atlases. Eosinophils-elusive granulocytes that are implicated in a plethora of human pathologies1-5-are among these uncharted cell types. The heterogeneity of eosinophils and the gene programs that underpin their pleiotropic functions remain poorly understood. Here we provide a comprehensive single-cell transcriptomic profiling of mouse eosinophils. We identify an active and a basal population of intestinal eosinophils, which differ in their transcriptome, surface proteome and spatial localization. By means of a genome-wide CRISPR inhibition screen and functional assays, we reveal a mechanism by which interleukin-33 (IL-33) and interferon-γ (IFNγ) induce the accumulation of active eosinophils in the inflamed colon. Active eosinophils are endowed with bactericidal and T cell regulatory activity, and express the co-stimulatory molecules CD80 and PD-L1. Notably, active eosinophils are enriched in the lamina propria of a small cohort of patients with inflammatory bowel disease, and are closely associated with CD4+ T cells. Our findings provide insights into the biology of eosinophils and highlight the crucial contribution of this cell type to intestinal homeostasis, immune regulation and host defence. Furthermore, we lay a framework for the characterization of eosinophils in human gastrointestinal diseases.

Coagulation factor V is a T-cell inhibitor expressed by leukocytes in COVID-19.

Clotting Factor V (FV) is primarily synthesized in the liver and when cleaved by thrombin forms pro-coagulant Factor Va (FVa). Using whole blood RNAseq and scRNAseq of peripheral blood mononuclear cells, we find that FV mRNA is expressed in leukocytes, and identify neutrophils, monocytes, and T regulatory cells as sources of increased FV in hospitalized patients with COVID-19. Proteomic analysis confirms increased FV in circulating neutrophils in severe COVID-19, and immunofluorescence microscopy identifies FV in lung-infiltrating leukocytes in COVID-19 lung disease. Increased leukocyte FV expression in severe disease correlates with T-cell lymphopenia. Both plasma-derived and a cleavage resistant recombinant FV, but not thrombin cleaved FVa, suppress T-cell proliferation in vitro. Anticoagulants that reduce FV conversion to FVa, including heparin, may have the unintended consequence of suppressing the adaptive immune system.

Transcriptional changes in the mammary gland during lactation revealed by single cell sequencing of cells from human milk.

Under normal conditions, the most significant expansion and differentiation of the adult mammary gland occurs in response to systemic reproductive hormones during pregnancy and lactation to enable milk synthesis and secretion to sustain the offspring. However, human mammary tissue remodelling that takes place during pregnancy and lactation remains poorly understood due to the challenge of acquiring samples. We report here single-cell transcriptomic analysis of 110,744 viable breast cells isolated from human milk or non-lactating breast tissue, isolated from nine and seven donors, respectively. We found that human milk largely contains epithelial cells belonging to the luminal lineage and a repertoire of immune cells. Further transcriptomic analysis of the milk cells identified two distinct secretory cell types that shared similarities with luminal progenitors, but no populations comparable to hormone-responsive cells. Taken together, our data offers a reference map and a window into the cellular dynamics that occur during human lactation and may provide further insights on the interplay between pregnancy, lactation and breast cancer.

Single-cell multi-omics analysis of the immune response in COVID-19.

Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

Time-resolved single-cell analysis of Brca1 associated mammary tumourigenesis reveals aberrant differentiation of luminal progenitors.

It is unclear how genetic aberrations impact the state of nascent tumour cells and their microenvironment. BRCA1 driven triple negative breast cancer (TNBC) has been shown to arise from luminal progenitors yet little is known about how BRCA1 loss-of-function (LOF) and concomitant mutations affect the luminal progenitor cell state. Here we demonstrate how time-resolved single-cell profiling of genetically engineered mouse models before tumour formation can address this challenge. We found that perturbing Brca1/p53 in luminal progenitors induces aberrant alveolar differentiation pre-malignancy accompanied by pro-tumourigenic changes in the immune compartment. Unlike alveolar differentiation during gestation, this process is cell autonomous and characterised by the dysregulation of transcription factors driving alveologenesis. Based on our data we propose a model where Brca1/p53 LOF inadvertently promotes a differentiation program hardwired in luminal progenitors, highlighting the deterministic role of the cell-of-origin and offering a potential explanation for the tissue specificity of BRCA1 tumours.

Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins.

Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present nonparametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP data sets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. An open source software implementation of NPARC is provided.

Single-cell RNAseq reveals seven classes of colonic sensory neuron.

OBJECTIVE: Integration of nutritional, microbial and inflammatory events along the gut-brain axis can alter bowel physiology and organism behaviour. Colonic sensory neurons activate reflex pathways and give rise to conscious sensation, but the diversity and division of function within these neurons is poorly understood. The identification of signalling pathways contributing to visceral sensation is constrained by a paucity of molecular markers. Here we address this by comprehensive transcriptomic profiling and unsupervised clustering of individual mouse colonic sensory neurons. DESIGN: Unbiased single-cell RNA-sequencing was performed on retrogradely traced mouse colonic sensory neurons isolated from both thoracolumbar (TL) and lumbosacral (LS) dorsal root ganglia associated with lumbar splanchnic and pelvic spinal pathways, respectively. Identified neuronal subtypes were validated by single-cell qRT-PCR, immunohistochemistry (IHC) and Ca2+-imaging. RESULTS: Transcriptomic profiling and unsupervised clustering of 314 colonic sensory neurons revealed seven neuronal subtypes. Of these, five neuronal subtypes accounted for 99% of TL neurons, with LS neurons almost exclusively populating the remaining two subtypes. We identify and classify neurons based on novel subtype-specific marker genes using single-cell qRT-PCR and IHC to validate subtypes derived from RNA-sequencing. Lastly, functional Ca2+-imaging was conducted on colonic sensory neurons to demonstrate subtype-selective differential agonist activation. CONCLUSIONS: We identify seven subtypes of colonic sensory neurons using unbiased single-cell RNA-sequencing and confirm translation of patterning to protein expression, describing sensory diversity encompassing all modalities of colonic neuronal sensitivity. These results provide a pathway to molecular interrogation of colonic sensory innervation in health and disease, together with identifying novel targets for drug development.

European Network of Breast Development and Cancer turned 10 years: a growing family of mammary gland researchers.

The European Network for Breast Development and Cancer (ENBDC), a worldwide network ( http://www.enbdc.org/ ), celebrated its tenth anniversary with a fantastic meeting last March 15-17, 2018 in Weggis with 76 attendees.

BCL11A interacts with SOX2 to control the expression of epigenetic regulators in lung squamous carcinoma.

Patients diagnosed with lung squamous cell carcinoma (LUSC) have limited targeted therapies. We report here the identification and characterisation of BCL11A, as a LUSC oncogene. Analysis of cancer genomics datasets revealed BCL11A to be upregulated in LUSC but not in lung adenocarcinoma (LUAD). Experimentally we demonstrate that non-physiological levels of BCL11A in vitro and in vivo promote squamous-like phenotypes, while its knockdown abolishes xenograft tumour formation. At the molecular level we found that BCL11A is transcriptionally regulated by SOX2 and is required for its oncogenic functions. Furthermore, we show that BCL11A and SOX2 regulate the expression of several transcription factors, including SETD8. We demonstrate that shRNA-mediated or pharmacological inhibition of SETD8 selectively inhibits LUSC growth. Collectively, our study indicates that BCL11A is integral to LUSC pathology and highlights the disruption of the BCL11A-SOX2 transcriptional programme as a novel candidate for drug development.

Cannabinoid 1 receptor signaling on GABAergic neurons influences astrocytes in the ageing brain.

Astrocytes, key regulators of brain homeostasis, interact with neighboring glial cells, neurons and the vasculature through complex processes involving different signaling pathways. It is not entirely clear how these interactions change in the ageing brain and which factors influence astrocyte ageing. Here, we investigate the role of endocannabinoid signaling, because it is an important modulator of neuron and astrocyte functions, as well as brain ageing. We demonstrate that mice with a specific deletion of CB1 receptors on GABAergic neurons (GABA-Cnr1-/- mice), which show a phenotype of accelerated brain ageing, affects age-related changes in the morphology of astrocytes in the hippocampus. Thus, GABA-Cnr1-/- mice showed a much more pronounced age-related and layer-specific increase in GFAP-positive areas in the hippocampus compared to wild-type animals. The number of astrocytes, in contrast, was similar between the two genotypes. Astrocytes in the hippocampus of old GABA-Cnr1-/- mice also showed a different morphology with enhanced GFAP-positive process branching and a less polarized intrahippocampal distribution. Furthermore, astrocytic TNFα levels were higher in GABA-Cnr1-/- mice, indicating that these morphological changes were accompanied by a more pro-inflammatory function. These findings demonstrate that the disruption of endocannabinoid signaling on GABAergic neurons is accompanied by functional changes in astrocyte activity, which are relevant to brain ageing.

Detection and removal of barcode swapping in single-cell RNA-seq data.

Barcode swapping results in the mislabelling of sequencing reads between multiplexed samples on patterned flow-cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays; however, the severity and consequences of barcode swapping remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in two plate-based single-cell RNA-sequencing datasets. We found that approximately 2.5% of reads were mislabelled between samples on the HiSeq 4000, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA-sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10x Genomics experiments, allowing the continued use of cutting-edge sequencing machines for these assays.

Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing.

Characterising the hierarchy of mammary epithelial cells (MECs) and how they are regulated during adult development is important for understanding how breast cancer arises. Here we report the use of single-cell RNA sequencing to determine the gene expression profile of MECs across four developmental stages; nulliparous, mid gestation, lactation and post involution. Our analysis of 23,184 cells identifies 15 clusters, few of which could be fully characterised by a single marker gene. We argue instead that the epithelial cells-especially in the luminal compartment-should rather be conceptualised as being part of a continuous spectrum of differentiation. Furthermore, our data support the existence of a common luminal progenitor cell giving rise to intermediate, restricted alveolar and hormone-sensing progenitors. This luminal progenitor compartment undergoes transcriptional changes in response to a full pregnancy, lactation and involution. In summary, our results provide a global, unbiased view of adult mammary gland development.

Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.

Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses.

Anxiety, Stress, and Fear Response in Mice With Reduced Endocannabinoid Levels.

BACKGROUND: Disruption of the endocannabinoid system through pharmacological or genetic invalidation of cannabinoid CB1 receptors has been linked to depression in humans and depression-like behaviors in mice. The two main endogenous cannabinoids, anandamide and 2-arachidonoyl glycerol (2-AG), are produced on demand from phospholipids. The pathways and enzymes involved in endocannabinoid biosynthesis thus play a major role in regulating the activity of this system. This study investigates the role of the main 2-AG producing enzyme diacylglycerol lipase α (DAGL-α). METHODS: We generated and used knockout mice lacking DAGL-α (Dagla(-/-)) to assess the behavioral consequences of reduced endocannabinoid levels in the brain. We performed different behavior tests to determine anxiety- and depression-related behavioral changes in Dagla(-/-) mice. We also analyzed expression of genes related to the endocannabinoid system via real-time polymerase chain reaction and used the mitotic marker 5-bromo-2'-deoxyuridine to analyze adult neurogenesis. RESULTS: Dagla(-/-) animals show an 80% reduction of brain 2-AG levels but also a reduction in cortical and amygdalar anandamide. The behavioral changes induced by Dagla deletion include a reduced exploration of the central area of the open field, a maternal neglect behavior, a fear extinction deficit, increased behavioral despair, increased anxiety-related behaviors in the light/dark box, and reduced hippocampal neurogenesis. Some of these behavioral changes resemble those observed in animals lacking the CB1 receptor. CONCLUSIONS: Our findings demonstrate that the deletion of Dagla adversely affects the emotional state of animals and results in enhanced anxiety, stress, and fear responses.