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The development of CD4 and CD8 T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-MHCI/II by the T cell antigen receptor (TCR) determines the lineage choice, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. We applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and lineage-restricted mice to generate a comprehensive timeline of cell state for each lineage. These analyses revealed a sequential process whereby all thymocytes initiate CD4 lineage differentiation during an initial wave of TCR signaling, followed by a second TCR signaling wave that coincides with CD8 lineage specification. CITE-seq and pharmaceutical inhibition experiments implicate a TCR/calcineurin/NFAT/GATA3 axis in driving the CD4 fate. Overall, our data suggest that multiple redundant mechanisms contribute to the accuracy and efficiency of the lineage choice.

The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.org ), a framework for end-to-end joint analysis of CITE-seq data that probabilistically represents the data as a composite of biological and technical factors, including protein background and batch effects. To evaluate totalVI's performance, we profiled immune cells from murine spleen and lymph nodes with CITE-seq, measuring over 100 surface proteins. We demonstrate that totalVI provides a cohesive solution for common analysis tasks such as dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules and differential expression testing.

Functional tuning of T cells based on their degree of self-reactivity is established during positive selection in the thymus, although how positive selection differs for thymocytes with relatively low versus high self-reactivity is unclear. In addition, preselection thymocytes are highly sensitive to low-affinity ligands, but the mechanism underlying their enhanced TCR sensitivity is not fully understood. Here we show that murine thymocytes with low self-reactivity experience briefer TCR signals and complete positive selection more slowly than those with high self-reactivity. Additionally, we provide evidence that cells with low self-reactivity retain a preselection gene expression signature as they mature, including genes previously implicated in modulating TCR sensitivity and a novel group of ion channel genes. Our results imply that thymocytes with low self-reactivity down-regulate TCR sensitivity more slowly during positive selection, and suggest that modulation of membrane ion channel function may play a role in regulating TCR tuning throughout development.Impact StatementDeveloping T cells whose TCRs have relatively low reactivity experience very brief TCR signaling events, delayed positive selection, and do not fully down regulate their TCR sensitivity as they mature.

The paired measurement of RNA and surface protein abundance in single cells with CITE-seq is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, each data modality exhibits unique technical biases, making it challenging to conduct a joint analysis and combine these two views into a unified representation of cell state. Here we present Total Variational Inference (totalVI), a framework for the joint probabilistic analysis of paired RNA and protein data from single cells. totalVI probabilistically represents the data as a composite of biological and technical factors such as limited sensitivity of the RNA data, background in the protein data, and batch effects. To evaluate totalVI, we performed CITE-seq on immune cells from murine spleen and lymph nodes with biological replicates and with different antibody panels measuring over 100 surface proteins. With this dataset, we demonstrate that totalVI provides a cohesive solution for common analysis tasks like the integration of datasets with matched or unmatched protein panels, dimensionality reduction, clustering, evaluation of correlations between molecules, and differential expression testing. totalVI enables scalable, end-to-end analysis of paired RNA and protein data from single cells and is available as open-source software.

Production of effector CD8+ Tcells during persistent infection requires a stable pool of stem-like cells that can give rise to effector cells via a proliferative intermediate population. In infection models marked by Tcell exhaustion, this process can be transiently induced by checkpoint blockade but occurs spontaneously in mice chronically infected with the protozoan intracellular parasite Toxoplasma gondii. We observe distinct locations for parasite-specific Tcell subsets, implying a link between differentiation and anatomical niches in the spleen. Loss of the chemokine receptor CXCR3 on Tcells does not prevent white pulp-to-red pulp migration but reduces interactions with CXCR3 ligand-producing dendritic cells (DCs) and impairs memory-to-intermediate transition, leading to a buildup of memory Tcells in the red pulp. Thus, CXCR3 increases Tcell exposure to differentiation-inducing signals during red pulp migration, providing a dynamic mechanism for modulating effector differentiation in response to environmental signals.

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) combines unbiased single-cell transcriptome measurements with surface protein quantification comparable to flow cytometry, the gold standard for cell type identification. However, current analysis pipelines cannot address the two primary challenges of CITE-seq data: combining both modalities in a shared latent space that harnesses the power of the paired measurements, and handling the technical artifacts of the protein measurement, which is obscured by non-negligible background noise. Here we present Total Variational Inference (totalVI), a fully probabilistic end-to-end framework for normalizing and analyzing CITE-seq data, based on a hierarchical Bayesian model. In totalVI, the mRNA and protein measurements for each cell are generated from a low-dimensional latent random variable unique to that cell, representing its cellular state. totalVI uses deep neural networks to specify conditional distributions. By leveraging advances in stochastic variational inference, it scales easily to millions of cells. Explicit modeling of nuisance factors enables totalVI to produce denoised data in both domains, as well as a batch-corrected latent representation of cells for downstream analysis tasks.

Infections with the opportunistic yeast Candida glabrata have increased dramatically in recent years. Antifungal therapy of yeast infections commonly employs azoles, such as fluconazole (FLC), but C. glabrata frequently develops resistance to these inhibitors of ergosterol biosynthesis. The pyrimidine analog flucytosine (5-fluorocytosine [5FC]) is highly active versus C. glabrata but is now rarely used clinically due to similar concerns over resistance and, a related concern, the toxicity associated with high doses used to counter resistance. Azole-5FC combination therapy would potentially address these concerns; however, previous studies suggest that 5FC may antagonize azole activity versus C. glabrata . Here, we report that 5FC at subinhibitory concentrations antagonized the activity of FLC 4- to 16-fold versus 8 of 8 C. glabrata isolates tested. 5FC antagonized the activity of other azoles similarly but had only indifferent effects in combination with unrelated antifungals. Since azole resistance in C. glabrata results from transcription factor Pdr1-dependent upregulation of the multidrug transporter gene CDR1 , we reasoned that 5FC antagonism might be similarly mediated. Indeed, 5FC-FLC antagonism was abrogated in pdr1 Δ and cdr1 Δ strains. In further support of this hypothesis, 5FC exposure induced CDR1 expression 6-fold, and this upregulation was Pdr1 dependent. In contrast to azoles, 5FC is not a Cdr1 substrate and so its activation of Pdr1 was unexpected. We observed, however, that 5FC exposure readily induced petite mutants, which exhibit Pdr1-dependent CDR1 upregulation. Thus, mitochondrial dysfunction resulting in Pdr1 activation is the likely basis for 5FC antagonism of azole activity versus C. glabrata .