CD4+ T regulatory (Treg) cells are central to immune homeostasis, their phenotypic heterogeneity reflecting the diverse environments and target cells they regulate. To understand this heterogeneity, we combined single-cell RNAseq, activation reporter and TCR analysis to profile thousands of Tregs or Tconvs from mouse lymphoid organs or human blood. Treg and Tconv pools showed areas of overlap, as resting “furtive” Tregs with overall similarity to Tconv, or as a convergence of activated states. All Tregs express a small core of FoxP3-dependent transcripts, onto which additional programs are added less uniformly. Among suppressive functions, Il2ra and Ctla4 were quasi-constant, inhibitory cytokines being more sparsely distributed. TCR signal intensity didn’t affect resting/activated Treg proportions, but molded activated Treg programs. The main lines of Treg heterogeneity in mice were strikingly conserved in human blood. These results reveal unexpected TCR-shaped states of activation, providing a framework to synthesize previous observations about Treg heterogeneity.

FoxP3+T regulatory (Treg) cells are central elements of immunologic tolerance. They are abundant in many tumors, where they restrict potentially favorable antitumor responses. We used a three-pronged strategy to identify genes related to the presence and function of Tregs in the tumor microenvironment. Gene expression profiles were generated from tumor-infiltrating Tregs (TITRs) of both human and mouse tumors and were compared with those of Tregs of lymphoid organs or normal tissues from the same individuals. A computational deconvolution of whole-tumor datasets from the Cancer Genome Atlas (TCGA) was performed to identify transcripts specifically associated with Tregs across thousands of tumors from different stages and locations. We identified a set of TITR-differential transcripts with striking reproducibility between tumor types in mice, between mice and humans, and between different human patients spanning tumor stages. Many of the TITR-preferential transcripts were shared with “tissue Tregs” residing in nonlymphoid tissues, but a tumor-preferential segment could be identified. Many of these TITR signature transcripts were confirmed by mining of TCGA datasets, which also brought forth transcript modules likely representing the parenchymal attraction of, or response to, tumor Tregs. Importantly, the TITR signature included several genes encoding effective targets of tumor immunotherapy. A number of other targets were validated by CRISPR-based gene inactivation in mouse Tregs. These results confirm the validity of the signature, generating a wealth of leads for understanding the role of Tregs in tumor progression and identifying potential targets for cancer immunotherapy.

CD4 + effector lymphocytes (Teff) are traditionally classified by the cytokines they produce. To determine the states that Teff actually adopt in frontline tissues in vivo , we applied single-cell transcriptome and chromatin analysis on colonic Teff cells, in germ-free or conventional mice, or after challenge with a range of phenotypically biasing microbes. Subsets were marked by expression of interferon-signature or myeloid-specific transcripts, but transcriptome or chromatin structure could not resolve discrete clusters fitting classic T H subsets. At baseline or at different times of infection, transcripts encoding cytokines or proteins commonly used as T H markers distributed in a polarized continuum, which was also functionally validated. Clones derived from single progenitors gave rise to both IFN-γ and IL17-producing cells. Most transcriptional variance was tied to the infecting agent, independent of the cytokines produced, and chromatin variance primarily reflected activity of AP1 and IRF transcription factor families, not the canonical subset master regulators T-bet, GATA3, RORγ.

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Immune checkpoint blockers (ICBs) have failed in all phase III glioblastoma (GBM) trials. Here, we show that regulatory T (Treg) cells play a key role in GBM resistance to ICBs in experimental gliomas. Targeting glucocorticoid-induced TNFR-related receptor (GITR) in Treg cells using an agonistic antibody (αGITR) promotes CD4 Treg cell differentiation into CD4 effector T cells, alleviates Treg cell-mediated suppression of anti-tumor immune response, and induces potent anti-tumor effector cells in GBM. The reprogrammed GBM-infiltrating Treg cells express genes associated with a Th1 response signature, produce IFNγ, and acquire cytotoxic activity against GBM tumor cells while losing their suppressive function. αGITR and αPD1 antibodies increase survival benefit in three experimental GBM models, with a fraction of cohorts exhibiting complete tumor eradication and immune memory upon tumor re-challenge. Moreover, αGITR and αPD1 synergize with the standard of care treatment for newly-diagnosed GBM, enhancing the cure rates in these GBM models.

Synthetic small interfering RNAs (siRNAs) are an indispensable tool to investigate gene function in eukaryotic cells1,2 and may be used for therapeutic purposes to knockdown genes implicated in disease3. Thus far, most synthetic siRNAs have been produced by chemical synthesis. Here we present a method to produce highly potent siRNAs in E. coli. This method relies on ectopic expression of p19, a siRNA-binding protein found in a plant RNA virus4, 5. When expressed in E. coli, p19 stabilizes ~21 nt siRNA-like species produced by bacterial RNase III. Transfection of mammalian cells with siRNAs, generated in bacteria expressing p19 and a hairpin RNA encoding 200 or more nucleotides of a target gene, at low nanomolar concentrations reproducibly knocks down gene expression by ~90% without immunogenicity or off-target effects. Because bacterially produced siRNAs contain multiple sequences against a target gene, they may be especially useful for suppressing polymorphic cellular or viral genes.

The interactions between antibodies, SARS-CoV-2 and immune cells contribute to the pathogenesis of COVID-19 and protective immunity. To understand the differences between antibody responses in mild versus severe cases of COVID-19, we analyzed the B cell responses in patients 1.5 months post SARS-CoV-2 infection. Severe, and not mild, infection correlated with high titers of IgG against Spike receptor binding domain (RBD) that were capable of ACE2:RBD inhibition. B cell receptor (BCR) sequencing revealed that VH3-53 was enriched during severe infection. Of the 22 antibodies cloned from two severe donors, six exhibited potent neutralization against authentic SARS-CoV-2, and inhibited syncytia formation. Using peptide libraries, competition ELISA and mutagenesis of RBD, we mapped the epitopes of the neutralizing antibodies (nAbs) to three different sites on the Spike. Finally, we used combinations of nAbs targeting different immune-sites to efficiently block SARS-CoV-2 infection. Analysis of 49 healthy BCR repertoires revealed that the nAbs germline VHJH precursors comprise up to 2.7% of all VHJHs. We demonstrate that severe COVID-19 is associated with unique BCR signatures and multi-clonal neutralizing responses that are relatively frequent in the population. Moreover, our data support the use of combination antibody therapy to prevent and treat COVID-19.

The GenomeRNAi database (http://www.genomernai.org/) contains phenotypes from published cell-based RNA interference (RNAi) screens in Drosophila and Homo sapiens. The database connects observed phenotypes with annotations of targeted genes and information about the RNAi reagent used for the perturbation experiment. The availability of phenotypes from Drosophila and human screens also allows for phenotype searches across species. Besides reporting quantitative data from genome-scale screens, the new release of GenomeRNAi also enables reporting of data from microscopy experiments and curated phenotypes from published screens. In addition, the database provides an updated resource of RNAi reagents and their predicted quality that are available for the Drosophila and the human genome. The new version also facilitates the integration with other genomic data sets and contains expression profiling (RNA-Seq) data for several cell lines commonly used in RNAi experiments.