Systematic benchmark evaluation of distance metrics for scRNA-seq data

When attempting to generate statistical inference, the notion of distance or (dis)similarity among observations is a crucial for understanding the data's structure. When the data are sparse, as in Single-cell RNA-seq (scRNA-seq), some notions of distance can give false signals regarding observation structure. Motivated by a multinomial model for \scRNA-seq data, we test sought to test the performance of several dissimilarities using experimental and simulated scRNA-seq data. Methods and results for the permutations of these analyses are provided and summarized herein. We leveraged \tool as an efficient and accurate means to compute fifteen notions of dissimilarity for experimental and simulated scRNA-seq data. Calculations were performed in experimental scRNA-seq data that had cluster and lineage structure using multiple levels of variable genes for robustness. The simulated scRNA-seq data sought to test robustness in response to experimental factors, so simulated cluster and lineage structure data was tested with multiple varying simulation settings. We provide five fitness metrics for each dissimilarity, kAcc(nearest-neighbor accurarcy), TrajCor(lineage structure accuracy), ARI(truth label concordance with simple clustering algorithm), 1-G+ (tightness of truth cluster labels), and GapStat(evidence for k>1 clusters). While no single distance vastly outperforms all others, geometric (non-normalized) distances are consistently out-performed by statistical (normalized). We reiterate the suggestions of \tool and recommend JSD as a distance which demonstrates strong overall performance in almost all test scenarios

CD11c+ Cells Are Gatekeepers for Lymphocyte Trafficking to Infiltrated Islets During Type 1 Diabetes.

Type 1 diabetes (T1D) is a T cell mediated autoimmune disease that affects more than 19 million people with incidence increasing rapidly worldwide. For T cells to effectively drive T1D, they must first traffic to the islets and extravasate through the islet vasculature. Understanding the cues that lead to T cell entry into inflamed islets is important because diagnosed T1D patients already have established immune infiltration of their islets. Here we show that CD11

CD11c+ Cells Are Gatekeepers for Lymphocyte Trafficking to Infiltrated Islets During Type 1 Diabetes

Type 1 diabetes (T1D) is a T cell mediated autoimmune disease that affects more than 19 million people with incidence increasing rapidly worldwide. For T cells to effectively drive T1D, they must first traffic to the islets and extravasate through the islet vasculature. Understanding the cues that lead to T cell entry into inflamed islets is important because diagnosed T1D patients already have established immune infiltration of their islets. Here we show that CD11c+ cells are a key mediator of T cell trafficking to infiltrated islets in non-obese diabetic (NOD) mice. Using intravital 2-photon islet imaging we show that T cell extravasation into the islets is an extended process, with T cells arresting in the islet vasculature in close proximity to perivascular CD11c+ cells. Antigen is not required for T cell trafficking to infiltrated islets, but T cell chemokine receptor signaling is necessary. Using RNAseq, we show that islet CD11c+ cells express over 20 different chemokines that bind chemokine receptors expressed on islet T cells. One highly expressed chemokine-receptor pair is CXCL16-CXCR6. However, NOD. CXCR6−/− mice progressed normally to T1D and CXCR6 deficient T cells trafficked normally to the islets. Even with CXCR3 and CXCR6 dual deficiency, T cells trafficked to infiltrated islets. These data reinforce that chemokine receptor signaling is highly redundant for T cell trafficking to inflamed islets. Importantly, depletion of CD11c+ cells strongly inhibited T cell trafficking to infiltrated islets of NOD mice. We suggest that targeted depletion of CD11c+ cells associated with the islet vasculature may yield a therapeutic target to inhibit T cell trafficking to inflamed islets to prevent progression of T1D

MATHEMATICAL MODELING OF MAPK DYNAMICS AND SIGNAL ADAPTATION

(Statement of Responsibility) by Nathan DyjackThesis (B.A.) -- New College of Florida, 2016RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE(Bibliography) Includes bibliographical references.This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.Faculty Sponsor: Yildirim, Necmetti

Olfactory Receptors Expression in the Skin of Atopic Dermatitis Patients

10.1016/j.jaci.2017.12.432Journal of Allergy and Clinical Immunology1412, SupplementAB13

A Transcriptomic Method to Determine Airway Immune Dysfunction in T2-High and T2-Low Asthma

BackgroundType 2 (T2) inflammation drives airway dysfunction in many patients with asthma; yet, we lack a comprehensive understanding of the airway immune cell types and networks that sustain this inflammation. Moreover, defects in the airway immune system in patients with asthma without T2 inflammation are not established.ObjectivesTo determine the gene networks that sustain T2 airway inflammation in T2-high asthma and to explore the gene networks that characterize T2-low asthma.MethodsNetwork analysis of sputum cell transcriptome expression data from 84 subjects with asthma and 27 healthy control subjects was used to identify immune cell type-enriched networks that underlie asthma subgroups.ResultsSputum T2 gene expression was characterized by an immune cell network derived from multiple innate immune cells, including eosinophils, mast cells/basophils, and inflammatory dendritic cells. Clustering of subjects within this network stratified subjects into T2-high and T2-low groups, but it also revealed a subgroup of T2-high subjects with uniformly higher expression of the T2 network. These "T2-ultrahigh subjects" were characterized clinically by older age and more severe airflow obstruction and pathologically by a second T2 network derived from T2-skewed, CD11b+/CD103-/IRF4+ classical dendritic cells. Subjects with T2-low asthma were differentiated from healthy control subjects by lower expression of a cytotoxic CD8+ T-cell network, which was negatively correlated with body mass index and plasma IL-6 concentrations.ConclusionsPersistent airway T2 inflammation is a complex construct of innate and adaptive immunity gene expression networks that are variable across individuals with asthma and persist despite steroid treatment. Individuals with T2-low asthma exhibit an airway deficiency in cytotoxic T cells associated with obesity-driven inflammation

Single cell analysis of host response to helminth infection reveals the clonal breadth, heterogeneity, and tissue-specific programming of the responding CD4\u3csup\u3e+\u3c/sup\u3e T cell repertoire

The CD4+ T cell response is critical to host protection against helminth infection. How this response varies across different hosts and tissues remains an important gap in our understanding. Using IL-4-reporter mice to identify responding CD4+ T cells to Nippostrongylus brasiliensis infection, T cell receptor sequencing paired with novel clustering algorithms revealed a broadly reactive and clonally diverse CD4+ T cell response. While the most prevalent clones and clonotypes exhibited some tissue selectivity, most were observed to reside in both the lung and lung-draining lymph nodes. Antigen-reactivity of the broader repertoires was predicted to be shared across both tissues and individual mice. Transcriptome, trajectory, and chromatin accessibility analysis of lung and lymph-node repertoires revealed three unique but related populations of responding IL-4+ CD4+ T cells consistent with T follicular helper, T helper 2, and a transitional population sharing similarity with both populations. The shared antigen reactivity of lymph node and lung repertoires combined with the adoption of tissue-specific gene programs allows for the pairing of cellular and humoral responses critical to the orchestration of anti-helminth immunity