Automated clustering reveals CD4 T cell subset imbalances in rheumatoid arthritis.

Background: Despite the report of an imbalance between CD4 T helper (Th) cell subsets in rheumatoid arthritis (RA), patient stratification for precision medicine has been hindered by the discovery of ever more Th cell subsets, as well as contradictory association results.

Objectives: To capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results.

Methods: Unstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intra-nuclear transcription factors).

Maximizing statistical power to detect differentially abundant cell states with scPOST.

To estimate a study design's power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observed in single-cell data.

PD-1hiCXCR5- T peripheral helper cells promote B cell responses in lupus via MAF and IL-21.

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by pathologic T cell-B cell interactions and autoantibody production. Defining the T cell populations that drive B cell responses in SLE may enable design of therapies that specifically target pathologic cell subsets. Here, we evaluated the phenotypes of CD4+ T cells in the circulation of 52 SLE patients drawn from multiple cohorts and identified a highly expanded PD-1hiCXCR5-CD4+ T cell population.

Multimodal single-cell approaches shed light on T cell heterogeneity.

Single-cell methods have revolutionized the study of T cell biology by enabling the identification and characterization of individual cells. This has led to a deeper understanding of T cell heterogeneity by generating functionally relevant measurements - like gene expression, surface markers, chromatin accessibility, T cell receptor sequences - in individual cells. While these methods are independently valuable, they can be augmented when applied jointly, either on separate cells from the same sample or on the same cells.

Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry.

To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing (RNA-seq) and flow cytometry to T cells, B cells, monocytes, and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis (OA). Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics revealed cell states expanded in RA synovia: THY1(CD90)HLA-DRA sublining fibroblasts, IL1B pro-inflammatory monocytes, ITGAXTBX21 autoimmune-associated B cells and PDCD1 peripheral helper T (T) cells and follicular helper T (T) cells.

Mixed-effects association of single cells identifies an expanded effector CD4 T cell subset in rheumatoid arthritis.

High-dimensional single-cell analyses have improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging because of technical and interindividual variation. Here, we present mixed-effects modeling of associations of single cells (MASC), a reverse single-cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounders and biological variation. Applying MASC to mass cytometry analyses of CD4 T cells from the blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4 T cells, identified as CD27 HLA-DR effector memory cells, in RA patients (odds ratio, 1.

Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis.

CD4+ T cells have been long known to play an important role in the pathogenesis of rheumatoid arthritis (RA), but the specific cell populations and states that drive the disease have been challenging to identify with low dimensional single cell data and bulk assays. The advent of high dimensional single cell technologies-like single cell RNA-seq or mass cytometry-has offered promise to defining key populations, but brings new methodological and statistical challenges. Recent single cell profiling studies have revealed a broad diversity of cell types among CD4+ T cells, identifying novel populations that are expanded or altered in RA.

Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis.

CD4 T cells are central mediators of autoimmune pathology; however, defining their key effector functions in specific autoimmune diseases remains challenging. Pathogenic CD4 T cells within affected tissues may be identified by expression of markers of recent activation. Here we use mass cytometry to analyse activated T cells in joint tissue from patients with rheumatoid arthritis, a chronic immune-mediated arthritis that affects up to 1% of the population.

Single-molecule super-resolution imaging of chromosomes and in situ haplotype visualization using Oligopaint FISH probes.

Fluorescence in situ hybridization (FISH) is a powerful single-cell technique for studying nuclear structure and organization. Here we report two advances in FISH-based imaging. We first describe the in situ visualization of single-copy regions of the genome using two single-molecule super-resolution methodologies.

Abnormal dosage of ultraconserved elements is highly disfavored in healthy cells but not cancer cells.

Ultraconserved elements (UCEs) are strongly depleted from segmental duplications and copy number variations (CNVs) in the human genome, suggesting that deletion or duplication of a UCE can be deleterious to the mammalian cell. Here we address the process by which CNVs become depleted of UCEs. We begin by showing that depletion for UCEs characterizes the most recent large-scale human CNV datasets and then find that even newly formed de novo CNVs, which have passed through meiosis at most once, are significantly depleted for UCEs.

Versatile design and synthesis platform for visualizing genomes with Oligopaint FISH probes.

A host of observations demonstrating the relationship between nuclear architecture and processes such as gene expression have led to a number of new technologies for interrogating chromosome positioning. Whereas some of these technologies reconstruct intermolecular interactions, others have enhanced our ability to visualize chromosomes in situ. Here, we describe an oligonucleotide- and PCR-based strategy for fluorescence in situ hybridization (FISH) and a bioinformatic platform that enables this technology to be extended to any organism whose genome has been sequenced.