Cutting Edge: Dynamic Expression of Id3 Defines the Stepwise Differentiation of Tissue-Resident Regulatory T Cells.

Foxp3+ regulatory T (TR) cells are phenotypically and functionally diverse and broadly distributed in lymphoid and nonlymphoid tissues. However, the pathways guiding the differentiation of tissue-resident TR cell populations have not been well defined. By regulating E-protein function, Id3 controls the differentiation of CD8+ effector T cells and is essential for TR cell maintenance and function. We show that dynamic expression of Id3 helps define three distinct mouse TR cell populations: Id3+CD62LhiCD44lo central TR cells, Id3+CD62LloCD44hi effector TR (eTR) cells, and Id3- eTR cells. Adoptive transfer experiments and transcriptome analyses support a stepwise model of differentiation from Id3+ central TR to Id3+ eTR to Id3- eTR cells. Furthermore, Id3- eTR cells have high expression of functional inhibitory markers and a transcriptional signature of tissue-resident TR cells. Accordingly, Id3- eTR cells are highly enriched in nonlymphoid organs but virtually absent from blood and lymph. Thus, we propose that tissue-resident TR cells develop in a multistep process associated with Id3 downregulation.

Abatacept Targets T Follicular Helper and Regulatory T Cells, Disrupting Molecular Pathways That Regulate Their Proliferation and Maintenance.

Abatacept is a CTLA-4-Ig fusion protein that binds to the costimulatory ligands CD80 and CD86 and blocks their interaction with the CD28 and CTLA-4 receptors expressed by T cells, therefore inhibiting T cell activation and function. Abatacept has shown clinical efficacy in treating some autoimmune diseases but has failed to show clinical benefit in other autoimmune conditions. The reasons for these disparate results are not clear and warrant further investigation of abatacept's mode of action. Longitudinal specimens from the Immune Tolerance Network's A Cooperative Clinical Study of Abatacept in Multiple Sclerosis trial were used to examine the effects of abatacept treatment on the frequency and transcriptional profile of specific T cell populations in peripheral blood. We found that the relative abundance of CD4+ T follicular helper (Tfh) cells and regulatory T cells was selectively decreased in participants following abatacept treatment. Within both cell types, abatacept reduced the proportion of activated cells expressing CD38 and ICOS and was associated with decreased expression of genes that regulate cell-cycle and chromatin dynamics during cell proliferation, thereby linking changes in costimulatory signaling to impaired activation, proliferation, and decreased abundance. All cellular and molecular changes were reversed following termination of abatacept treatment. These data expand upon the mechanism of action of abatacept reported in other autoimmune diseases and identify new transcriptional targets of CD28-mediated costimulatory signaling in human regulatory T and Tfh cells, further informing on its potential use in diseases associated with dysregulated Tfh activity.

Identification of Proteases and Protease Inhibitors in Allergenic and Non-Allergenic Pollen.

Pollen is one of the most common causes of allergy worldwide, making the study of their molecular composition crucial for the advancement of allergy research. Despite substantial efforts in this field, it is not yet clear why some plant pollens strongly provoke allergies while others do not. However, proteases and protease inhibitors from allergen sources are known to play an important role in the development of pollen allergies. In this study, we aim to uncover differences in the transcriptional pattern of proteases and protease inhibitors in Betula verrucosa and Pinus sylvestris pollen as models for high and low allergenic potential, respectively. We applied RNA sequencing to Betula verrucosa and Pinus sylvestris pollen. After de-novo assembly we derived general functional profiles of the protein coding transcripts. By utilization of domain based functional annotation we identified potential proteases and protease inhibitors and compared their expression in the two types of pollen. Functional profiles are highly similar between Betula verrucosa and Pinus sylvestris pollen. Both pollen contain proteases and inhibitors from 53 and 7 Pfam families, respectively. Some of the members comprised within those families are implicated in facilitating allergen entry, while others are known allergens themselves. Our work revealed several candidate proteins which, with further investigation, represent exciting new leads in elucidating the process behind allergic sensitization.

Machine learning reveals STAT motifs as predictors for GR-mediated gene repression.

Glucocorticoids are potent immunosuppressive drugs, but long-term treatment leads to severe side-effects. While there is a commonly accepted model for GR-mediated gene activation, the mechanism behind repression remains elusive. Understanding the molecular action of the glucocorticoid receptor (GR) mediated gene repression is the first step towards developing novel therapies. We devised an approach that combines multiple epigenetic assays with 3D chromatin data to find sequence patterns predicting gene expression change. We systematically tested> 100 models to evaluate the best way to integrate the data types and found that GR-bound regions hold most of the information needed to predict the polarity of Dex-induced transcriptional changes. We confirmed NF-κB motif family members as predictors for gene repression and identified STAT motifs as additional negative predictors.

Profiling of Tregs across tissues reveals plasticity in ST2 expression and hierarchies in tissue-specific phenotypes.

Foxp3+ regulatory T cells (Tregs) are critical mediators of peripheral tolerance and immune homeostasis and exert tissue-specific functions. In many nonlymphoid tissues, Tregs show enriched expression of the IL-33 receptor ST2. Through comprehensive profiling of murine ST2+ and ST2- Tregs, we found that Treg transcriptomes and phenotypes formed a hierarchical relationship across tissues. Only a small core signature distinguished ST2+ Tregs from ST2- Tregs across all tissues, and differences in transcriptional profiles were predominantly tissue-specific. We also identified unique, highly proliferative, circulating ST2+ Tregs with high migratory potential. In adoptive transfers, both ST2+ and ST2- Tregs seeded various host tissues and demonstrated plasticity in ST2 expression. Furthermore, Tregs from donor lungs were differentially recovered from host nonlymphoid tissues in an IL-33-dependent manner. In summary, our work identified tissue residency rather than ST2 expression as a primary driver of tissue Treg identity and highlights the unique, tissue-specific adaption of ST2+ Tregs.

scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies.

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation.

Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.

Transcriptomic Profiling of Human Effector and Regulatory T Cell Subsets Identifies Predictive Population Signatures.

After activation, CD4+ Th cells differentiate into functionally specialized populations that coordinate distinct immune responses and protect against different types of pathogens. In humans, these effector and memory Th cell subsets can be readily identified in peripheral blood based on their differential expression of chemokine receptors that govern their homeostatic and inflammatory trafficking. Foxp3+ regulatory T (Treg) cells can also be divided into subsets that phenotypically mirror each of these effector populations and share expression of key transcription factors and effector cytokines. In this study, we performed comprehensive transcriptional profiling of 11 phenotypically distinct Th and Treg cell subsets sorted from peripheral blood of healthy individuals. Despite their shared phenotypes, we found that mirror Th and Treg subsets were transcriptionally dissimilar and that Treg cell populations showed limited transcriptional diversity compared with Th cells. We identified core transcriptional signatures shared across all Th and Treg cell populations and unique signatures that define each of the Th or Treg populations. Finally, we applied these signatures to bulk Th and Treg RNA-sequencing data and found enrichment of specific Th and Treg cell populations in different human tissues. These results further define the molecular basis for the functional specialization and differentiation of Th and Treg cell populations and provide a new resource for examining Th and Treg specialization in RNA-sequencing data.

Human CD4+CD103+ cutaneous resident memory T cells are found in the circulation of healthy individuals.

Tissue-resident memory T cells (TRM) persist locally in nonlymphoid tissues where they provide frontline defense against recurring insults. TRM at barrier surfaces express the markers CD103 and/or CD69, which function to retain them in epithelial tissues. In humans, neither the long-term migratory behavior of TRM nor their ability to reenter the circulation and potentially migrate to distant tissue sites has been investigated. Using tissue explant cultures, we found that CD4+CD69+CD103+ TRM in human skin can down-regulate CD69 and exit the tissue. In addition, we identified a skin-tropic CD4+CD69-CD103+ population in human lymph and blood that is transcriptionally, functionally, and clonally related to the CD4+CD69+CD103+ TRM population in the skin. Using a skin xenograft model, we confirmed that a fraction of the human cutaneous CD4+CD103+ TRM population can reenter circulation and migrate to secondary human skin sites where they reassume a TRM phenotype. Thus, our data challenge current concepts regarding the strict tissue compartmentalization of CD4+ T cell memory in humans.