Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics; however, researchers still encounter challenges in their analysis due to uncertainty with respect to selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods' performances are highly dataset-specific. To address these challenges, we developed Escort, a novel framework for evaluating a dataset's suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort evaluates the suitability of trajectory analysis and the combined effects of processing choices using trajectory-specific metrics. Escort navigates single-cell trajectory analysis through these data-driven assessments, reducing uncertainty and much of the decision burden inherent to trajectory inference analyses. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.

The immunology of type 1 diabetes.

Following the seminal discovery of insulin a century ago, treatment of individuals with type 1 diabetes (T1D) has been largely restricted to efforts to monitor and treat metabolic glucose dysregulation. The recent regulatory approval of the first immunotherapy that targets T cells as a means to delay the autoimmune destruction of pancreatic ß-cells highlights the critical role of the immune system in disease pathogenesis and tends to pave the way for other immune-targeted interventions for T1D. Improving the efficacy of such interventions across the natural history of the disease will probably require a more detailed understanding of the immunobiology of T1D, as well as technologies to monitor residual ß-cell mass and function. Here we provide an overview of the immune mechanisms that underpin the pathogenesis of T1D, with a particular emphasis on T cells.

A global view of T cell metabolism in systemic lupus erythematosus.

Impaired metabolism is recognized as an important contributor to pathogenicity of T cells in Systemic Lupus Erythematosus (SLE). Over the last two decades, we have acquired significant knowledge about the signaling and transcriptomic programs related to metabolic rewiring in healthy and SLE T cells. However, our understanding of metabolic network activity derives largely from studying metabolic pathways in isolation. Here, we argue that enzymatic activities are necessarily coupled through mass and energy balance constraints with in-built network-wide dependencies and compensation mechanisms. Therefore, metabolic rewiring of T cells in SLE must be understood in the context of the entire network, including changes in metabolic demands such as shifts in biomass composition and cytokine secretion rates as well as changes in uptake/excretion rates of multiple nutrients and waste products. As a way forward, we suggest cell physiology experiments and integration of orthogonal metabolic measurements through computational modeling towards a comprehensive understanding of T cell metabolism in lupus.

Low-Dose Antithymocyte Globulin: A Pragmatic Approach to Treating Stage 2 Type 1 Diabetes.

OBJECTIVE: Low-dose antithymocyte globulin (ATG) (2.5 mg/kg) preserves C-peptide and reduces HbA1c in new-onset stage 3 type 1 diabetes, yet efficacy in delaying progression from stage 2 to stage 3 has not been evaluated. RESEARCH DESIGN AND METHODS: Children (n = 6) aged 5-14 years with stage 2 type 1 diabetes received off-label, low-dose ATG. HbA1c, C-peptide, continuous glucose monitoring, insulin requirements, and side effects were followed for 18-48 months. RESULTS: Three subjects (50%) remained diabetes free after 1.5, 3, and 4 years of follow-up, while three developed stage 3 within 1-2 months after therapy. Eighteen months posttreatment, even disease progressors demonstrated near-normal HbA1c (5.1% [32 mmol/mol], 5.6% [38 mmol/mol], and 5.3% [34 mmol/mol]), time in range (93%, 88%, and 98%), low insulin requirements (0.17, 0.18, and 0.34 units/kg/day), and robust C-peptide 90 min after mixed meal (1.3 ng/dL, 2.3 ng/dL, and 1.4 ng/dL). CONCLUSIONS: These observations support additional prospective studies evaluating ATG in stage 2 type 1 diabetes.

Inhibition of CD226 Co-Stimulation Suppresses Diabetes Development in the NOD Mouse by Augmenting Tregs and Diminishing Effector T Cell Function.

Aims/hypothesis: Immunotherapeutics targeting T cells are crucial for inhibiting autoimmune disease progression proximal to disease onset in type 1 diabetes. A growing number of T cell-directed therapeutics have demonstrated partial therapeutic efficacy, with anti-CD3 (α-CD3) representing the only regulatory agency-approved drug capable of slowing disease progression through a mechanism involving the induction of partial T cell exhaustion. There is an outstanding need to augment the durability and effectiveness of T cell targeting by directly restraining proinflammatory T helper type 1 (Th1) and type 1 cytotoxic CD8+ T cell (Tc1) subsets, while simultaneously augmenting regulatory T cell (Treg) activity. Here, we present a novel strategy for reducing diabetes incidence in the NOD mouse model using a blocking monoclonal antibody targeting the type 1 diabetes-risk associated T cell co-stimulatory receptor, CD226. Methods: Female NOD mice were treated with anti-CD226 between 7-8 weeks of age and then monitored for diabetes incidence and therapeutic mechanism of action. Results: Compared to isotype-treated controls, anti-CD226 treated NOD mice showed reduced insulitis severity at 12 weeks and decreased disease incidence at 30 weeks. Flow cytometric analysis performed five weeks post-treatment demonstrated reduced proliferation of CD4+ and CD8+ effector memory T cells in spleens of anti-CD226 treated mice. Phenotyping of pancreatic Tregs revealed increased CD25 expression and STAT5 phosphorylation following anti-CD226, with splenic Tregs displaying augmented suppression of CD4+ T cell responders in vitro. Anti-CD226 treated mice exhibited reduced frequencies of islet-specific glucose-6-phosphatase catalytic subunit related protein (IGRP)-reactive CD8+ T cells in the pancreas, using both ex vivo tetramer staining and single-cell T cell receptor sequencing (scTCR-seq) approaches. 51Cr-release assays demonstrated reduced cell-mediated lysis of beta-cells by anti-CD226-treated autoreactive cytotoxic T lymphocytes. Conclusions/interpretation: CD226 blockade reduces T cell cytotoxicity and improves Treg function, representing a targeted and rational approach for restoring immune regulation in type 1 diabetes.

Human γδ T cells in diverse tissues exhibit site-specific maturation dynamics across the life span.

During ontogeny, γδ T cells emerge from the thymus and directly seed peripheral tissues for in situ immunity. However, their functional role in humans has largely been defined from blood. Here, we analyzed the phenotype, transcriptome, function, and repertoire of human γδ T cells in blood and mucosal and lymphoid tissues from 176 donors across the life span, revealing distinct profiles in children compared with adults. In early life, clonally diverse Vδ1 subsets predominate across blood and tissues, comprising naïve and differentiated effector and tissue repair functions, whereas cytolytic Vδ2 subsets populate blood, spleen, and lungs. With age, Vδ1 and Vδ2 subsets exhibit clonal expansions and elevated cytolytic signatures, which are disseminated across sites. In adults, Vδ2 cells predominate in blood, whereas Vδ1 cells are enriched across tissues and express residency profiles. Thus, antigenic exposures over childhood drive the functional evolution and tissue compartmentalization of γδ T cells, leading to age-dependent roles in immunity.

The post-septic peripheral myeloid compartment reveals unexpected diversity in myeloid-derived suppressor cells.

Introduction: Sepsis engenders distinct host immunologic changes that include the expansion of myeloid-derived suppressor cells (MDSCs). These cells play a physiologic role in tempering acute inflammatory responses but can persist in patients who develop chronic critical illness. Methods: Cellular Indexing of Transcriptomes and Epitopes by Sequencing and transcriptomic analysis are used to describe MDSC subpopulations based on differential gene expression, RNA velocities, and biologic process clustering. Results: We identify a unique lineage and differentiation pathway for MDSCs after sepsis and describe a novel MDSC subpopulation. Additionally, we report that the heterogeneous response of the myeloid compartment of blood to sepsis is dependent on clinical outcome. Discussion: The origins and lineage of these MDSC subpopulations were previously assumed to be discrete and unidirectional; however, these cells exhibit a dynamic phenotype with considerable plasticity.

Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics, however researchers still encounter challenges in their analysis due to uncertainties in selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods' performances are highly dataset-specific. To address these challenges, we developed Escort, a framework for evaluating a dataset's suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort navigates single-cell trajectory analysis through data-driven assessments, reducing uncertainty and much of the decision burden associated with trajectory inference. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.