Deciphering the TET3 interactome in primary thymic developing T cells.

Ten-eleven translocation (TET) proteins are DNA dioxygenases that mediate active DNA demethylation. TET3 is the most highly expressed TET protein in thymic developing T cells. TET3, either independently or in cooperation with TET1 or TET2, has been implicated in T cell lineage specification by regulating DNA demethylation. However, TET-deficient mice exhibit complex phenotypes, suggesting that TET3 exerts multifaceted roles, potentially by interacting with other proteins. We performed liquid chromatography with tandem mass spectrometry in primary developing T cells to identify TET3 interacting partners in endogenous, in vivo conditions. We discover TET3 interacting partners. Our data establish that TET3 participates in a plethora of fundamental biological processes, such as transcriptional regulation, RNA polymerase elongation, splicing, DNA repair, and DNA replication. This resource brings in the spotlight emerging functions of TET3 and sets the stage for systematic studies to dissect the precise mechanistic contributions of TET3 in shaping T cell biology.

Tissue and cellular spatiotemporal dynamics in colon aging.

Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.

Improving the Reliability of Peer Review Without a Gold Standard.

Peer review plays a crucial role in accreditation and credentialing processes as it can identify outliers and foster a peer learning approach, facilitating error analysis and knowledge sharing. However, traditional peer review methods may fall short in effectively addressing the interpretive variability among reviewing and primary reading radiologists, hindering scalability and effectiveness. Reducing this variability is key to enhancing the reliability of results and instilling confidence in the review process. In this paper, we propose a novel statistical approach called "Bayesian Inter-Reviewer Agreement Rate" (BIRAR) that integrates radiologist variability. By doing so, BIRAR aims to enhance the accuracy and consistency of peer review assessments, providing physicians involved in quality improvement and peer learning programs with valuable and reliable insights. A computer simulation was designed to assign predefined interpretive error rates to hypothetical interpreting and peer-reviewing radiologists. The Monte Carlo simulation then sampled (100 samples per experiment) the data that would be generated by peer reviews. The performances of BIRAR and four other peer review methods for measuring interpretive error rates were then evaluated, including a method that uses a gold standard diagnosis. Application of the BIRAR method resulted in 93% and 79% higher relative accuracy and 43% and 66% lower relative variability, compared to "Single/Standard" and "Majority Panel" peer review methods, respectively. Accuracy was defined by the median difference of Monte Carlo simulations between measured and pre-defined "actual" interpretive error rates. Variability was defined by the 95% CI around the median difference of Monte Carlo simulations between measured and pre-defined "actual" interpretive error rates. BIRAR is a practical and scalable peer review method that produces more accurate and less variable assessments of interpretive quality by accounting for variability within the group's radiologists, implicitly applying a standard derived from the level of consensus within the group across various types of interpretive findings.

Protocol to isolate mature thymic T cell subsets using fluorescence-activated cell sorting for assessing gene expression by RNA-seq and transcription factor binding across the genome by CUT&RUN.

Here, we describe steps to isolate mature thymic T cell subsets, namely CD4 single positive (SP), CD8 SP, and invariant natural killer T (iNKT) cells starting from murine total thymocytes using fluorescence-activated cell sorting. We detail protocols to study gene expression by RNA-seq and assess binding of transcription factors across the genome using CUT&RUN. This approach deciphers the molecular principles that govern T cell lineage specification and function. This protocol works well with limited starting material. For complete details on the use and execution of this protocol, please refer to Äijö et al. (2022).1.

TET proteins regulate T cell and iNKT cell lineage specification in a TET2 catalytic dependent manner.

TET proteins mediate DNA demethylation by oxidizing 5-methylcytosine to 5-hydroxymethylcytosine (5hmC) and other oxidative derivatives. We have previously demonstrated a dynamic enrichment of 5hmC during T and invariant natural killer T cell lineage specification. Here, we investigate shared signatures in gene expression of Tet2/3 DKO CD4 single positive (SP) and iNKT cells in the thymus. We discover that TET proteins exert a fundamental role in regulating the expression of the lineage specifying factor Th-POK, which is encoded by Zbtb7b. We demonstrate that TET proteins mediate DNA demethylation - surrounding a proximal enhancer, critical for the intensity of Th-POK expression. In addition, TET proteins drive the DNA demethylation of site A at the Zbtb7b locus to facilitate GATA3 binding. GATA3 induces Th-POK expression in CD4 SP cells. Finally, by introducing a novel mouse model that lacks TET3 and expresses full length, catalytically inactive TET2, we establish a causal link between TET2 catalytic activity and lineage specification of both conventional and unconventional T cells.

Migratory DCs activate TGF-ß to precondition naïve CD8+ T cells for tissue-resident memory fate.

Epithelial resident memory T (eTRM) cells serve as sentinels in barrier tissues to guard against previously encountered pathogens. How eTRM cells are generated has important implications for efforts to elicit their formation through vaccination or prevent it in autoimmune disease. Here, we show that during immune homeostasis, the cytokine transforming growth factor ß (TGF-ß) epigenetically conditions resting naïve CD8+ T cells and prepares them for the formation of eTRM cells in a mouse model of skin vaccination. Naïve T cell conditioning occurs in lymph nodes (LNs), but not in the spleen, through major histocompatibility complex class I-dependent interactions with peripheral tissue-derived migratory dendritic cells (DCs) and depends on DC expression of TGF-ß-activating αV integrins. Thus, the preimmune T cell repertoire is actively conditioned for a specialized memory differentiation fate through signals restricted to LNs.

High-definition spatial transcriptomics for in situ tissue profiling.

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-µm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis.

Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use spatial transcriptomics to obtain gene expression measurements of mouse spinal cords over the course of disease, as well as of postmortem tissue from ALS patients, to characterize the underlying molecular mechanisms in ALS. We identify pathway dynamics, distinguish regional differences between microglia and astrocyte populations at early time points, and discern perturbations in several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords.

Generative Models for Quantification of DNA Modifications.

There are multiple chemical modifications of cytosine that are important to the regulation and ultimately the functional expression of the genome. To date no single experiment can capture these separate modifications, and integrative experimental designs are needed to fully characterize cytosine methylation and chemical modification. This chapter describes a generative probabilistic model, Lux, for integrative analysis of cytosine methylation and its oxidized variants. Lux simultaneously analyzes partially orthogonal bisulfite sequencing data sets to estimate proportions of different cytosine methylation modifications and estimate multiple cytosine modifications for a single sample by integrating across experimental designs composed of multiple parallel destructive genomic measurements. Lux also considers the variation in measurements introduced by different imperfect experimental steps; the experimental variation can be quantified by using appropriate spike-in controls, allowing Lux to deconvolve the measurements and recover accurately the underlying signal.

Tumor Tolerance-Promoting Function of Regulatory T Cells Is Optimized by CD28, but Strictly Dependent on Calcineurin.

Regulatory T cells (Treg) restrain immune responses against malignant tumors, but their global depletion in cancer patients will likely be limited by systemic autoimmune toxicity. Instead, approaches to "tune" their activities may allow for preferential targeting of tumor-reactive Treg. Although Ag recognition regulates Treg function, the roles of individual TCR-dependent signaling pathways in enabling Treg to promote tumor tolerance are not well characterized. In this study, we examined in mouse tumor models the role of calcineurin, a key mediator of TCR signaling, and the role of the costimulatory receptor CD28 in the differentiation of resting central Treg into effector Treg endowed with tumor tropism. We find that calcineurin, although largely dispensable for suppressive activity in vitro, is essential for upregulation of ICOS and CTLA-4 in Treg, as well as for expression of chemokine receptors driving their accumulation in tumors. In contrast, CD28 is not critical, but optimizes the formation of tumor-homing Treg and their fitness in tumor tissue. Accordingly, although deletion of either CnB or CD28 strongly impairs Treg-mediated tumor tolerance, lack of CnB has an even more pronounced impact than lack of CD28. Hence, our studies reveal distinct roles for what has classically been defined as signal 1 and signal 2 of conventional T cell activation in the context of Treg-mediated tumor tolerance.

Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing.

Motivation: The number of microbial and metagenomic studies has increased drastically due to advancements in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis. Results: To address these challenges in human microbiome research, we introduce a novel probabilistic approach to explicitly model overdispersion and sampling zeros by considering the temporal correlation between nearby time points using Gaussian Processes. The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial and non-parametric regression models on real and synthetic data. We demonstrate that the nonreplicative nature of human gut microbiota studies can be partially overcome by our method with proper experimental design of dense temporal sampling. We also show that different modeling approaches have a strong impact on ecological interpretation of the data, such as stationarity, persistence and environmental noise models. Availability and implementation: A Stan implementation of the proposed method is available under MIT license at https://github.com/tare/GPMicrobiome. Contact: taijo@flatironinstitute.org or rb113@nyu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide Analysis of STAT3-Mediated Transcription during Early Human Th17 Cell Differentiation.

The development of therapeutic strategies to combat immune-associated diseases requires the molecular mechanisms of human Th17 cell differentiation to be fully identified and understood. To investigate transcriptional control of Th17 cell differentiation, we used primary human CD4+ T cells in small interfering RNA (siRNA)-mediated gene silencing and chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) to identify both the early direct and indirect targets of STAT3. The integrated dataset presented in this study confirms that STAT3 is critical for transcriptional regulation of early human Th17 cell differentiation. Additionally, we found that a number of SNPs from loci associated with immune-mediated disorders were located at sites where STAT3 binds to induce Th17 cell specification. Importantly, introduction of such SNPs alters STAT3 binding in DNA affinity precipitation assays. Overall, our study provides important insights for modulating Th17-mediated pathogenic immune responses in humans.

The microRNA miR-31 inhibits CD8+ T cell function in chronic viral infection.

During infection, antigen-specific T cells undergo tightly regulated developmental transitions controlled by transcriptional and post-transcriptional regulation of gene expression. We found that the microRNA miR-31 was strongly induced by activation of the T cell antigen receptor (TCR) in a pathway involving calcium and activation of the transcription factor NFAT. During chronic infection with lymphocytic choriomeningitis virus (LCMV) clone 13, miR-31-deficent mice recovered from clinical disease, while wild-type mice continued to show signs of disease. This disease phenotype was explained by the presence of larger numbers of cytokine-secreting LCMV-specific CD8+ T cells in miR-31-deficent mice than in wild-type mice. Mechanistically, miR-31 increased the sensitivity of T cells to type I interferons, which interfered with effector T cell function and increased the expression of several proteins related to T cell dysfunction during chronic infection. These studies identify miR-31 as an important regulator of T cell exhaustion in chronic infection.

Approaches to Detect microRNA Expression in T Cell Subsets and T Cell Differentiation.

MicroRNAs (miRNAs) are crucial components of the molecular networks regulating differentiation and responses of T lymphocytes in health and disease. It is therefore essential to rely on robust methods of qualitative and quantitative investigation of miRNA expression in T cell subsets, and during T cell activation and differentiation. Here, we focus on different methods for miRNA analysis, including Northern blots, quantitative RT-PCR, and next-generation sequencing, and we discuss advantages and disadvantages of each method. While we mainly focus on the study of miRNA expression in human T lymphocytes, these methods can also be applied to other species and/or cell types.

LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs.

MOTIVATION: 5-methylcytosine (5mC) is a widely studied epigenetic modification of DNA. The ten-eleven translocation (TET) dioxygenases oxidize 5mC into oxidized methylcytosines (oxi-mCs): 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). DNA methylation modifications have multiple functions. For example, 5mC is shown to be associated with diseases and oxi-mC species are reported to have a role in active DNA demethylation through 5mC oxidation and DNA repair, among others, but the detailed mechanisms are poorly understood. Bisulphite sequencing and its various derivatives can be used to gain information about all methylation modifications at single nucleotide resolution. Analysis of bisulphite based sequencing data is complicated due to the convoluted read-outs and experiment-specific variation in biochemistry. Moreover, statistical analysis is often complicated by various confounding effects. How to analyse 5mC and oxi-mC data sets with arbitrary and complex experimental designs is an open and important problem. RESULTS: We propose the first method to quantify oxi-mC species with arbitrary covariate structures from bisulphite based sequencing data. Our probabilistic modeling framework combines a previously proposed hierarchical generative model for oxi-mC-seq data and a general linear model component to account for confounding effects. We show that our method provides accurate methylation level estimates and accurate detection of differential methylation when compared with existing methods. Analysis of novel and published data gave insights into to the demethylation of the forkhead box P3 (Foxp3) locus during the induced T regulatory cell differentiation. We also demonstrate how our covariate model accurately predicts methylation levels of the Foxp3 locus. Collectively, LuxGLM method improves the analysis of DNA methylation modifications, particularly for oxi-mC species. AVAILABILITY AND IMPLEMENTATION: An implementation of the proposed method is available under MIT license at https://github.org/tare/LuxGLM/ CONTACT: taijo@simonsfoundation.org or harri.lahdesmaki@aalto.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative analysis of human and mouse transcriptomes of Th17 cell priming.

Uncontrolled Th17 cell activity is associated with cancer and autoimmune and inflammatory diseases. To validate the potential relevance of mouse models of targeting the Th17 pathway in human diseases we used RNA sequencing to compare the expression of coding and non-coding transcripts during the priming of Th17 cell differentiation in both human and mouse. In addition to already known targets, several transcripts not previously linked to Th17 cell polarization were found in both species. Moreover, a considerable number of human-specific long non-coding RNAs were identified that responded to cytokines stimulating Th17 cell differentiation. We integrated our transcriptomics data with known disease-associated polymorphisms and show that conserved regulation pinpoints genes that are relevant to Th17 cell-mediated human diseases and that can be modelled in mouse. Substantial differences observed in non-coding transcriptomes between the two species as well as increased overlap between Th17 cell-specific gene expression and disease-associated polymorphisms underline the need of parallel analysis of human and mouse models. Comprehensive analysis of genes regulated during Th17 cell priming and their classification to conserved and non-conserved between human and mouse facilitates translational research, pointing out which candidate targets identified in human are worth studying by using in vivo mouse models.

A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways.

We present a generative model, Lux, to quantify DNA methylation modifications from any combination of bisulfite sequencing approaches, including reduced, oxidative, TET-assisted, chemical-modification assisted, and methylase-assisted bisulfite sequencing data. Lux models all cytosine modifications (C, 5mC, 5hmC, 5fC, and 5caC) simultaneously together with experimental parameters, including bisulfite conversion and oxidation efficiencies, as well as various chemical labeling and protection steps. We show that Lux improves the quantification and comparison of cytosine modification levels and that Lux can process any oxidized methylcytosine sequencing data sets to quantify all cytosine modifications. Analysis of targeted data from Tet2-knockdown embryonic stem cells and T cells during development demonstrates DNA modification quantification at unprecedented detail, quantifies active demethylation pathways and reveals 5hmC localization in putative regulatory regions.

Control of Foxp3 stability through modulation of TET activity.

Ten-eleven translocation (TET) enzymes oxidize 5-methylcytosine (5mC) to 5-hydroxymethylcytosine and other oxidized methylcytosines, intermediates in DNA demethylation. In this study, we examine the role of TET proteins in regulating Foxp3, a transcription factor essential for the development and function of regulatory T cells (T reg cells), a distinct lineage of CD4(+) T cells that prevent autoimmunity and maintain immune homeostasis. We show that during T reg cell development in the thymus, TET proteins mediate the loss of 5mC in T reg cell-specific hypomethylated regions, including CNS1 and CNS2, intronic cis-regulatory elements in the Foxp3 locus. Similar to CNS2-deficient T reg cells, the stability of Foxp3 expression is markedly compromised in T reg cells from Tet2/Tet3 double-deficient mice. Vitamin C potentiates TET activity and acts through Tet2/Tet3 to increase the stability of Foxp3 expression in TGF-ß-induced T reg cells. Our data suggest that targeting TET enzymes with small molecule activators such as vitamin C might increase induced T reg cell efficacy.

Biophysically Motivated Regulatory Network Inference: Progress and Prospects.

Thanks to the confluence of genomic technology and computational developments, the possibility of network inference methods that automatically learn large comprehensive models of cellular regulation is closer than ever. This perspective focuses on enumerating the elements of computational strategies that, when coupled to appropriate experimental designs, can lead to accurate large-scale models of chromatin state and transcriptional regulatory structure and dynamics. We highlight 4 research questions that require further investigation in order to make progress in network inference: (1) using overall constraints on network structure such as sparsity, (2) use of informative priors and data integration to constrain individual model parameters, (3) estimation of latent regulatory factor activity under varying cell conditions, and (4) new methods for learning and modeling regulatory factor interactions. We conclude that methods combining advances in these 4 categories of required effort with new genomic technologies will result in biophysically motivated dynamic genome-wide regulatory network models for several of the best-studied organisms and cell types.