Valentina Proserpio

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.

Accounting for technical noise in single-cell RNA-seq experiments

Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.

T cell fate and clonality inference from single-cell transcriptomes

We developed TraCeR, a computational method to reconstruct full-length, paired T cell receptor (TCR) sequences from T lymphocyte single-cell RNA sequence data. TraCeR links T cell specificity with functional response by revealing clonal relationships between cells alongside their transcriptional profiles. We found that T cell clonotypes in a mouse Salmonella infection model span early activated CD4+ T cells as well as mature effector and memory cells.

Single-Cell RNA Sequencing Reveals T Helper Cells Synthesizing Steroids De Novo to Contribute to Immune Homeostasis

Summary T helper 2 (Th2) cells regulate helminth infections, allergic disorders, tumor immunity, and pregnancy by secreting various cytokines. It is likely that there are undiscovered Th2 signaling molecules. Although steroids are known to be immunoregulators, de novo steroid production from immune cells has not been previously characterized. Here, we demonstrate production of the steroid pregnenolone by Th2 cells in vitro and in vivo in a helminth infection model. Single-cell RNA sequencing and quantitative PCR analysis suggest that pregnenolone synthesis in Th2 cells is related to immunosuppression. In support of this, we show that pregnenolone inhibits Th cell proliferation and B cell immunoglobulin class switching. We also show that steroidogenic Th2 cells inhibit Th cell proliferation in a Cyp11a1 enzyme-dependent manner. We propose pregnenolone as a “lymphosteroid,” a steroid produced by lymphocytes. We speculate that this de novo steroid production may be an intrinsic phenomenon of Th2-mediated immune responses to actively restore immune homeostasis.

Computational assignment of cell-cycle stage from single-cell transcriptome data.

The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering dividing populations of cells, is to understand the cell-cycle stage of each captured cell. Here we describe and compare five established supervised machine learning methods and a custom-built predictor for allocating cells to their cell-cycle stage on the basis of their transcriptome. In particular, we assess the impact of different normalisation strategies and the usage of prior knowledge on the predictive power of the classifiers. We tested the methods on previously published datasets and found that a PCA-based approach and the custom predictor performed best. Moreover, our analysis shows that the performance depends strongly on normalisation and the usage of prior knowledge. Only by leveraging prior knowledge in form of cell-cycle annotated genes and by preprocessing the data using a rank-based normalisation, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols.

Epidermal Wnt/β-catenin signaling regulates adipocyte differentiation via secretion of adipogenic factors

Significance The synchronized patterns of hair follicle growth and expansion of the dermal adipocyte layer have long been recognized. Although factors secreted by adipocytes are known to regulate the hair growth cycle, it is unclear whether, conversely, the epidermis can regulate adipogenesis. Our study now demonstrates that activation of epidermal Wnt/β-catenin signaling stimulates adipocyte differentiation in vivo and in vitro. The effect can be mediated by secreted factors, including insulin-like growth factor 2 and bone morphogenetic proteins 2 and 6. It has long been recognized that the hair follicle growth cycle and oscillation in the thickness of the underlying adipocyte layer are synchronized. Although factors secreted by adipocytes are known to regulate the hair growth cycle, it is unclear whether the epidermis can regulate adipogenesis. We show that inhibition of epidermal Wnt/β-catenin signaling reduced adipocyte differentiation in developing and adult mouse dermis. Conversely, ectopic activation of epidermal Wnt signaling promoted adipocyte differentiation and hair growth. When the Wnt pathway was activated in the embryonic epidermis, there was a dramatic and premature increase in adipocytes in the absence of hair follicle formation, demonstrating that Wnt activation, rather than mature hair follicles, is required for adipocyte generation. Epidermal and dermal gene expression profiling identified keratinocyte-derived adipogenic factors that are induced by β-catenin activation. Wnt/β-catenin signaling-dependent secreted factors from keratinocytes promoted adipocyte differentiation in vitro, and we identified ligands for the bone morphogenetic protein and insulin pathways as proadipogenic factors. Our results indicate epidermal Wnt/β-catenin as a critical initiator of a signaling cascade that induces adipogenesis and highlight the role of epidermal Wnt signaling in synchronizing adipocyte differentiation with the hair growth cycle.

Single-cell technologies are revolutionizing the approach to rare cells

In the last lustrum single‐cell techniques such as single‐cell quantitative PCR, RNA and DNA sequencing, and the state‐of‐the‐art cytometry by time of flight (CyTOF) mass cytometer have allowed a detailed analysis of the sub‐composition of different organs from the bone marrow hematopoietic compartment to the brain. These fine‐grained analyses have highlighted the great heterogeneity within each cell compartment revealing previously unknown subpopulations of cells. In this review, we analyze how this fast technological evolution has improved our understanding of the biological processes with a particular focus on rare cells of the immune system.

Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation

Differentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells. We perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing. The link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.BackgroundDifferentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells.ResultsWe perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing.ConclusionThe link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.

Simultaneously inferring T cell fate and clonality from single cell transcriptomes

The heterodimeric T cell receptor (TCR) comprises two protein chains that pair to determine the antigen specificity of each T lymphocyte. The enormous sequence diversity within TCR repertoires allows specific TCR sequences to be used as lineage markers for T cells that derive from a common progenitor. We have developed a computational method, called TraCeR, to reconstruct full-length, paired TCR sequences from T lymphocyte single-cell RNA-seq by combining existing assembly and alignment programs with a “synthetic genome” library comprising all possible TCR sequences. We validate this method with PCR to quantify its accuracy and sensitivity, and compare to other TCR sequencing methods. Our inferred TCR sequences reveal clonal relationships between T cells, which we put into the context of each cell’s functional state from the complete transcriptional landscape quantified from the remaining RNA-seq data. This provides a powerful tool to link T cell specificity with functional response in a variety of normal and pathological conditions. We demonstrate this by determining the distribution of members of expanded T cell clonotypes in response to Salmonella infection in the mouse. We show that members of the same clonotype span early activated CD4+ T cells, as well as mature effector and memory cells.

A Balance Between Regulatory Constraints And Pathogen Pressure Shapes The Evolution Of Innate Immunity

The immune system is under constant pressure from pathogens to evolve and mount a sufficiently strong response. At the same time, an overresponsive immune system can lead to autoimmunity and tissue damage. How these conflicting demands have shaped human immunity is not well understood. Here, we characterize transcriptional divergence in the innate immune response across primates and rodents using bulk and single-cell transcriptomics, combined with chromatin analysis. We discover that genes that diverge in transcriptional response across species vary in cell-to-cell expression within each species. These divergent genes are evolutionarily younger, experience rapid coding sequence evolution and display a distinct promoter architecture. They have exclusive immune functions, such as cellular defence and inflammation, while pleiotropic genes involved in immunity and other pathways are more conserved. Analysis of viral interactions and mimicry shows that viruses target conserved elements of the innate immune response, suggesting that regulatory constraints imposed on the host are exploited by viruses. Importantly, innate immune genes implicated in autoimmune diseases show high levels of transcriptional divergence but also more interactions with viruses. This reveals a conflict between pathogens and regulatory constraints, which has likely contributed to genetic architectures driving the pathogenesis of immune disorders.As the first line of defence against pathogens, cells mount an innate immune response, which is highly variable from cell to cell. The response must be potent yet carefully controlled to avoid self-damage. How these constraints have shaped the evolution of innate immunity remains poorly understood. Here, we characterise this programme’s transcriptional divergence between species and expression variability across cells. Using bulk and single-cell transcriptomics in primate and rodent fibroblasts challenged with an immune stimulus, we reveal a striking architecture of the innate immune response. Rapidly diverging genes, including cytokines and chemokines, also vary across cells and have distinct promoter structures. Conversely, genes involved in response regulation, such as transcription factors and kinases, are conserved between species and display low cell-to-cell variability. We suggest that this unique expression pattern, observed across species and conditions, has evolved as a mechanism for fine-tuned regulation, to achieve an effective but balanced response.