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. We then introduce a robust and reliable system that harnesses single-nucleotide polymorphisms (SNPs) to visually distinguish the maternal and paternal homologous chromosomes in mammalian and insect systems. Both of these new technologies are enabled by renewable, bioinformatically designed, oligonucleotide-based Oligopaint probes, which we augment with a strategy that uses secondary oligonucleotides (oligos) to produce and enhance fluorescent signals. These advances should substantially expand the capability to query parent-of-origin-specific chromosome positioning and gene expression on a cell-by-cell basis.
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. The oligonucleotide probes are renewable, highly efficient, and able to robustly label chromosomes in cell culture, fixed tissues, and metaphase spreads. Our method gives researchers precise control over the sequences they target and allows for single and multicolor imaging of regions ranging from tens of kilobases to megabases with the same basic protocol. We anticipate this technology will lead to an enhanced ability to visualize interphase and metaphase chromosomes.
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. This approach revealed a markedly expanded population of PD-1
CXCR5
CD4
T cells in synovium of patients with rheumatoid arthritis. However, these cells are not exhausted, despite high PD-1 expression. Rather, using multidimensional cytometry, transcriptomics, and functional assays, we define a population of PD-1
CXCR5
'peripheral helper' T (T
) cells that express factors enabling B-cell help, including IL-21, CXCL13, ICOS, and MAF. Like PD-1
CXCR5
T follicular helper cells, T
cells induce plasma cell differentiation in vitro through IL-21 secretion and SLAMF5 interaction (refs 3, 4). However, global transcriptomics highlight differences between T
cells and T follicular helper cells, including altered expression of BCL6 and BLIMP1 and unique expression of chemokine receptors that direct migration to inflamed sites, such as CCR2, CX3CR1, and CCR5, in T
cells. T
cells appear to be uniquely poised to promote B-cell responses and antibody production within pathologically inflamed non-lymphoid tissues.
•Combining different types of single-cell markers captures complex T cell states.•Single-cell sequencing technologies have been expanded to collect 2+ data types.•Multimodal methods have been ...successful in characterizing T cell heterogeneity.•Multimodal single-cell data integration poses new statistical challenges.
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. Multimodal approaches are already being deployed to characterize T cells in diverse disease contexts and demonstrate the value of having multiple insights into a cell’s function. But, these data sets pose new statistical challenges for integration and joint analysis.
•Single cell immunoprofiling reveals extensive heterogeneity among CD4+ T cells.•Multidimensional analyses identify novel CD4+ T cell populations associated with rheumatoid arthritis.•Single cell ...disease association studies require careful attention to study design to avoid confounding technical effects.•New analysis methods are emerging to take full advantage of complex single cell datasets.
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. Here, we will review recent findings on CD4+ T cell heterogeneity and RA that have come from single cell profiling studies and discuss the best practices for conducting these studies.
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, 1.7;
= 1.1 × 10
). The frequency of CD27
HLA-DR
cells was similarly elevated in blood samples from a second RA patient cohort, and CD27
HLA-DR
cell frequency decreased in RA patients who responded to immunosuppressive therapy. Mass cytometry and flow cytometry analyses indicated that CD27
HLA-DR
cells were associated with RA (meta-analysis
= 2.3 × 10
). Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained about fivefold higher frequencies of CD27
HLA-DR
cells, which comprised ~10% of synovial CD4
T cells. CD27
HLA-DR
cells expressed a distinctive effector memory transcriptomic program with T helper 1 (T
1)- and cytotoxicity-associated features and produced abundant interferon-γ (IFN-γ) and granzyme A protein upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single-cell data.
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. In striking contrast, CNVs arising specifically in cancer cells are, as a rule, not depleted for UCEs and can even become significantly enriched. This observation raises the possibility that CNVs that arise somatically and are relatively newly formed are less likely to have established a CNV profile that is depleted for UCEs. Alternatively, lack of depletion for UCEs from cancer CNVs may reflect the diseased state. In support of this latter explanation, somatic CNVs that are not associated with disease are depleted for UCEs. Finally, we show that it is possible to observe the CNVs of induced pluripotent stem (iPS) cells become depleted of UCEs over time, suggesting that depletion may be established through selection against UCE-disrupting CNVs without the requirement for meiotic divisions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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. Cytometric, transcriptomic, and functional assays demonstrated that PD-1hiCXCR5-CD4+ T cells from SLE patients are T peripheral helper (Tph) cells, a CXCR5- T cell population that stimulates B cell responses via IL-21. The frequency of Tph cells, but not T follicular helper (Tfh) cells, correlated with both clinical disease activity and the frequency of CD11c+ B cells in SLE patients. PD-1hiCD4+ T cells were found within lupus nephritis kidneys and correlated with B cell numbers in the kidney. Both IL-21 neutralization and CRISPR-mediated deletion of MAF abrogated the ability of Tph cells to induce memory B cell differentiation into plasmablasts in vitro. These findings identify Tph cells as a highly expanded T cell population in SLE and suggest a key role for Tph cells in stimulating pathologic B cell responses.
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. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.
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•single-cell POwer Simulation Tool (scPOST) is a fast and flexible simulation framework•scPOST allows users to compare study designs to assess how they affect detection power•Increasing sample size improves power more than increasing cells per sample
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. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.
Millard et al. present single-cell POwer Simulation Tool (scPOST), a tool that allows investigators to simulate single-cell datasets in order to perform power analyses for study design optimization, as well as compare and assess algorithm performance.
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, ITGAX
TBX21
autoimmune-associated B cells and PDCD1
peripheral helper T (T
) cells and follicular helper T (T
) cells. We defined distinct subsets of CD8
T cells characterized by GZMK
, GZMB
, and GNLY
phenotypes. We mapped inflammatory mediators to their source cell populations; for example, we attributed IL6 expression to THY1
HLA-DRA
fibroblasts and IL1B production to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.
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