IL-6-Driven pSTAT1 Response Is Linked to T Cell Features Implicated in Early Immune Dysregulation.

Elevated levels and enhanced sensing of the pro-inflammatory cytokine interleukin-6 (IL-6) are key features of many autoimmune and inflammatory diseases. To better understand how IL-6 signaling may influence human T cell fate, we investigated the relationships between levels of components of the IL-6R complex, pSTAT responses, and transcriptomic and translational changes in CD4+ and CD8+ T cell subsets from healthy individuals after exposure to IL-6. Our findings highlight the striking heterogeneity in mbIL-6R and gp130 expression and IL-6-driven pSTAT1/3 responses across T cell subsets. Increased mbIL-6R expression correlated with enhanced signaling via pSTAT1 with less impact on pSTAT3, most strikingly in CD4+ naïve T cells. Additionally, IL-6 rapidly induced expression of transcription factors and surface receptors expressed by T follicular helper cells and altered expression of markers of apoptosis. Importantly, many of the features associated with the level of mbIL-6R expression on T cells were recapitulated both in the setting of tocilizumab therapy and when comparing donor CD4+ T cells harboring the genetic variant, IL6R Asp358Ala (rs2228145), known to alter mbIL-6R expression on T cells. Collectively, these findings should be taken into account as we consider the role of IL-6 in disease pathogenesis and translating IL-6 biology into effective therapies for T cell-mediated autoimmune disease.

Exhausted-like CD8+ T cell phenotypes linked to C-peptide preservation in alefacept-treated T1D subjects.

Clinical trials of biologic therapies in type 1 diabetes (T1D) aim to mitigate autoimmune destruction of pancreatic ß cells through immune perturbation and serve as resources to elucidate immunological mechanisms in health and disease. In the T1DAL trial of alefacept (LFA3-Ig) in recent-onset T1D, endogenous insulin production was preserved in 30% of subjects for 2 years after therapy. Given our previous findings linking exhausted-like CD8+ T cells to beneficial response in T1D trials, we applied unbiased analyses to sorted CD8+ T cells to evaluate their potential role in T1DAL. Using RNA sequencing, we found that greater insulin C-peptide preservation was associated with a module of activation- and exhaustion-associated genes. This signature was dissected into 2 CD8 memory phenotypes through correlation with cytometry data. These cells were hypoproliferative, shared expanded rearranged TCR junctions, and expressed exhaustion-associated markers including TIGIT and KLRG1. The 2 phenotypes could be distinguished by reciprocal expression of CD8+ T and NK cell markers (GZMB, CD57, and inhibitory killer cell immunoglobulin-like receptor [iKIR] genes), versus T cell activation and differentiation markers (PD-1 and CD28). These findings support previous evidence linking exhausted-like CD8+ T cells to successful immune interventions for T1D, while suggesting that multiple inhibitory mechanisms can promote this beneficial cell state.

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival. Statistical validation within the workflow included repeated runs of stochastic steps and cell subsampling. Biological validation used an orthogonal platform, immunohistochemistry, and a larger cohort of 73 glioblastoma patients to confirm the findings from the pilot cohort. RAPID was also validated to find known risk stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically and biologically significant cells using cytometry data from patient samples.

Human Germinal Center B Cells Differ from Naïve and Memory B Cells in CD40 Expression and CD40L-Induced Signaling Response.

CD40 expression is required for germinal center (GC) formation and function, but the kinetics and magnitude of signaling following CD40 engagement remain poorly characterized in human B cells undergoing GC reactions. Here, differences in CD40 expression and signaling responses were compared across differentiation stages of mature human tonsillar B cells. A combination of mass cytometry and phospho-specific flow cytometry was used to quantify protein expression and CD40L-induced signaling in primary human naïve, GC, and memory B cells. Protein expression signatures of cell subsets were quantified using viSNE and Marker Enrichment Modeling (MEM). This approach revealed enriched expression of CD40 protein in GC B cells, compared to naïve and memory B cells. Despite this, GC B cells responded to CD40L engagement with lower phosphorylation of NFκB p65 during the first 30 min following CD40L activation. Before CD40L stimulation, GC B cells expressed higher levels of suppressor protein IκBα than naïve and memory B cells. Following CD40 activation, IκBα was rapidly degraded and reached equivalently low levels in naïve, GC, and memory B cells at 30 min following CD40L. Quantifying CD40 signaling responses as a function of bound ligand revealed a correlation between bound CD40L and degree of induced NFκB p65 phosphorylation, whereas comparable IκBα degradation occurred at all measured levels of CD40L binding. These results characterize cell-intrinsic signaling differences that exist in mature human B cells undergoing GC reactions. © 2019 International Society for Advancement of Cytometry.

Machine learning reveals chronic graft-versus-host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies.

The application of machine learning in medicine has been productive in multiple fields, but has not previously been applied to analyze the complexity of organ involvement by chronic graft-versus-host disease. Chronic graft-versus-host disease is classified by an overall composite score as mild, moderate or severe, which may overlook clinically relevant patterns in organ involvement. Here we applied a novel computational approach to chronic graft-versus-host disease with the goal of identifying phenotypic groups based on the subcomponents of the National Institutes of Health Consensus Criteria. Computational analysis revealed seven distinct groups of patients with contrasting clinical risks. The high-risk group had an inferior overall survival compared to the low-risk group (hazard ratio 2.24; 95% confidence interval: 1.36-3.68), an effect that was independent of graft-versus-host disease severity as measured by the National Institutes of Health criteria. To test clinical applicability, knowledge was translated into a simplified clinical prognostic decision tree. Groups identified by the decision tree also stratified outcomes and closely matched those from the original analysis. Patients in the high- and intermediate-risk decision-tree groups had significantly shorter overall survival than those in the low-risk group (hazard ratio 2.79; 95% confidence interval: 1.58-4.91 and hazard ratio 1.78; 95% confidence interval: 1.06-3.01, respectively). Machine learning and other computational analyses may better reveal biomarkers and stratify risk than the current approach based on cumulative severity. This approach could now be explored in other disease models with complex clinical phenotypes. External validation must be completed prior to clinical application. Ultimately, this approach has the potential to reveal distinct pathophysiological mechanisms that may underlie clusters. Clinicaltrials.gov identifier: NCT00637689.

Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types.

Advances in single-cell biology have enabled measurements of >40 protein features on millions of immune cells within clinical samples. However, the data analysis steps following cell population identification are susceptible to bias, time-consuming, and challenging to compare across studies. Here, an ensemble of unsupervised tools was developed to evaluate four essential types of immune cell information, incorporate changes over time, and address diverse immune monitoring challenges. The four complementary properties characterized were (i) systemic plasticity, (ii) change in population abundance, (iii) change in signature population features, and (iv) novelty of cellular phenotype. Three systems immune monitoring studies were selected to challenge this ensemble approach. In serial biopsies of melanoma tumors undergoing targeted therapy, the ensemble approach revealed enrichment of double-negative (DN) T cells. Melanoma tumor-resident DN T cells were abnormal and phenotypically distinct from those found in nonmalignant lymphoid tissues, but similar to those found in glioblastoma and renal cell carcinoma. Overall, ensemble systems immune monitoring provided a robust, quantitative view of changes in both the system and cell subsets, allowed for transparent review by human experts, and revealed abnormal immune cells present across multiple human tumor types.

Frequency and phenotype consequence of APOC3 rare variants in patients with very low triglyceride levels.

BACKGROUND: High levels of triglycerides (TG ≥200 mg/dL) are an emerging risk factor for cardiovascular disease. Conversely, very low levels of TG are associated with decreased risk for cardiovascular disease. Precision medicine aims to capitalize on recent findings that rare variants such as APOC3 R19X (rs76353203) are associated with risk of disease, but it is unclear how population-based associations can be best translated in clinical settings at the individual-patient level. METHODS: To explore the potential usefulness of screening for genetic predictors of cardiovascular disease, we surveyed BioVU, the Vanderbilt University Medical Center's biorepository linked to de-identified electronic health records (EHRs), for APOC3 19X mutations among adult European American patients (> 45 and > 55 years of age for men and women, respectively) with the lowest percentile of TG levels. The initial search identified 262 patients with the lowest TG levels in the biorepository; among these, 184 patients with sufficient DNA and the lowest TG levels were chosen for Illumina ExomeChip genotyping. RESULTS: A total of two patients were identified as heterozygotes of APOC3 R19X for a minor allele frequency (MAF) of 0.55% in this patient population. Both heterozygous patients had only a single mention of TG in the EHR (31 and 35 mg/dL, respectively), and one patient had evidence of previous cardiovascular disease. CONCLUSIONS: In this patient population, we identified two patients who were carriers of the APOC3 19X null variant, but only one lacked evidence of disease in the EHR highlighting the challenges of inclusion of functional or previously associated genetic variation in clinical risk assessment.

BRAF and MEK inhibitor therapy eliminates Nestin-expressing melanoma cells in human tumors.

Little is known about the in vivo impacts of targeted therapy on melanoma cell abundance and protein expression. Here, 21 antibodies were added to an established melanoma mass cytometry panel to measure 32 cellular features, distinguish malignant cells, and characterize dabrafenib and trametinib responses in BRAFV600mut melanoma. Tumor cells were biopsied before neoadjuvant therapy and compared to cells surgically resected from the same site after 4 weeks of therapy. Approximately 50,000 cells per tumor were characterized by mass cytometry and computational tools t-SNE/viSNE, FlowSOM, and MEM. The resulting single-cell view of melanoma treatment response revealed initially heterogeneous melanoma tumors were consistently cleared of Nestin-expressing melanoma cells. Melanoma cell subsets that persisted to week 4 were heterogeneous but expressed SOX2 or SOX10 proteins and specifically lacked surface expression of MHC I proteins by MEM analysis. Traditional histology imaging of tissue microarrays from the same tumors confirmed mass cytometry results, including persistence of NES- SOX10+ S100ß+ melanoma cells. This quantitative single-cell view of melanoma treatment response revealed protein features of malignant cells that are not eliminated by targeted therapy.

Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).

Multiplexed single-cell experimental techniques like mass cytometry measure 40 or more features and enable deep characterization of well-known and novel cell populations. However, traditional data analysis techniques rely extensively on human experts or prior knowledge, and novel machine learning algorithms may generate unexpected population groupings. Marker enrichment modeling (MEM) creates quantitative identity labels based on features enriched in a population relative to a reference. While developed for cell type analysis, MEM labels can be generated for a wide range of multidimensional data types, and MEM works effectively with output from expert analysis and diverse machine learning algorithms. MEM is implemented as an R package and includes three steps: (1) calculation of MEM values that quantify each feature's relative enrichment in the population, (2) reporting of MEM labels as a heatmap or as a text label, and (3) quantification of MEM label similarity between populations. The protocols here show MEM analysis using datasets from immunology and oncology. These MEM implementations provide a way to characterize population identity and novelty in the context of computational and expert analyses. © 2018 by John Wiley & Sons, Inc.

Mass cytometry deep phenotyping of human mononuclear phagocytes and myeloid-derived suppressor cells from human blood and bone marrow.

The monocyte phagocyte system (MPS) includes numerous monocyte, macrophage, and dendritic cell (DC) populations that are heterogeneous, both phenotypically and functionally. In this study, we sought to characterize those diverse MPS phenotypes with mass cytometry (CyTOF). To identify a deep phenotype of monocytes, macrophages, and DCs, a panel was designed to measure 38 identity, activation, and polarization markers, including CD14, CD16, HLA-DR, CD163, CD206, CD33, CD36, CD32, CD64, CD13, CD11b, CD11c, CD86, and CD274. MPS diversity was characterized for 1) circulating monocytes from healthy donors, 2) monocyte-derived macrophages further polarized in vitro (i.e., M-CSF, GM-CSF, IL-4, IL-10, IFN-γ, or LPS long-term stimulations), 3) monocyte-derived DCs, and 4) myeloid-derived suppressor cells (MDSCs), generated in vitro from bone marrow and/or peripheral blood. Known monocyte subsets were detected in peripheral blood to validate the panel and analysis pipeline. Then, using various culture conditions and stimuli before CyTOF analysis, we constructed a multidimensional framework for the MPS compartment, which was registered against historical M1 or M2 macrophages, monocyte subsets, and DCs. Notably, MDSCs generated in vitro from bone marrow expressed more S100A9 than when generated from peripheral blood. Finally, to test the approach in vivo, peripheral blood from patients with melanoma (n = 5) was characterized and observed to be enriched for MDSCs with a phenotype of CD14+HLA-DRlowS100A9high (3% of PBMCs in healthy donors, 15.5% in patients with melanoma, P < 0.02). In summary, mass cytometry comprehensively characterized phenotypes of human monocyte, MDSC, macrophage, and DC subpopulations in both in vitro models and patients.

Characterizing cell subsets using marker enrichment modeling.

Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.

Deep phenotyping of Tregs identifies an immune signature for idiopathic aplastic anemia and predicts response to treatment.

Idiopathic aplastic anemia (AA) is an immune-mediated and serious form of bone marrow failure. Akin to other autoimmune diseases, we have previously shown that in AA regulatory T cells (Tregs) are reduced in number and function. The aim of this study was to further characterize Treg subpopulations in AA and investigate the potential correlation between specific Treg subsets and response to immunosuppressive therapy (IST) as well as their in vitro expandability for potential clinical use. Using mass cytometry and an unbiased multidimensional analytical approach, we identified 2 specific human Treg subpopulations (Treg A and Treg B) with distinct phenotypes, gene expression, expandability, and function. Treg B predominates in IST responder patients, has a memory/activated phenotype (with higher expression of CD95, CCR4, and CD45RO within FOXP3(hi), CD127(lo) Tregs), expresses the interleukin-2 (IL-2)/STAT5 pathway and cell-cycle commitment genes. Furthermore, in vitro-expanded Tregs become functional and take on the characteristics of Treg B. Collectively, this study identifies human Treg subpopulations that can be used as predictive biomarkers for response to IST in AA and potentially other autoimmune diseases. We also show that Tregs from AA patients are IL-2-sensitive and expandable in vitro, suggesting novel therapeutic approaches such as low-dose IL-2 therapy and/or expanded autologous Tregs and meriting further exploration.

TESTING POPULATION-SPECIFIC QUANTITATIVE TRAIT ASSOCIATIONS FOR CLINICAL OUTCOME RELEVANCE IN A BIOREPOSITORY LINKED TO ELECTRONIC HEALTH RECORDS: LPA AND MYOCARDIAL INFARCTION IN AFRICAN AMERICANS.

Previous candidate gene and genome-wide association studies have identified common genetic variants in LPA associated with the quantitative trait Lp(a), an emerging risk factor for cardiovascular disease. These associations are population-specific and many have not yet been tested for association with the clinical outcome of interest. To fill this gap in knowledge, we accessed the epidemiologic Third National Health and Nutrition Examination Surveys (NHANES III) and BioVU, the Vanderbilt University Medical Center biorepository linked to de-identified electronic health records (EHRs), including billing codes (ICD-9-CM) and clinical notes, to test population-specific Lp(a)-associated variants for an association with myocardial infarction (MI) among African Americans. We performed electronic phenotyping among African Americans in BioVU≥40 years of age using billing codes. At total of 93 cases and 522 controls were identified in NHANES III and 265 cases and 363 controls were identified in BioVU. We tested five known Lp(a)-associated genetic variants (rs1367211, rs41271028, rs6907156, rs10945682, and rs1652507) in both NHANES III and BioVU for association with myocardial infarction. We also tested LPA rs3798220 (I4399M), previously associated with increased levels of Lp(a), MI, and coronary artery disease in European Americans, in BioVU. After meta-analysis, tests of association using logistic regression assuming an additive genetic model revealed no significant associations (p<0.05) for any of the five LPA variants previously associated with Lp(a) levels in African Americans. Also, I4399M rs3798220 was not associated with MI in African Americans (odds ratio = 0.51; 95% confidence interval: 0.16 - 1.65; p=0.26) despite strong, replicated associations with MI and coronary artery disease in European American genome-wide association studies. These data highlight the challenges in translating quantitative trait associations to clinical outcomes in diverse populations using large epidemiologic and clinic-based collections as envisioned for the Precision Medicine Initiative.

In Vivo Autofluorescence Imaging of Tumor Heterogeneity in Response to Treatment.

Subpopulations of cells that escape anti-cancer treatment can cause relapse in cancer patients. Therefore, measurements of cellular-level tumor heterogeneity could enable improved anti-cancer treatment regimens. Cancer exhibits altered cellular metabolism, which affects the autofluorescence of metabolic cofactors NAD(P)H and FAD. The optical redox ratio (fluorescence intensity of NAD(P)H divided by FAD) reflects global cellular metabolism. The fluorescence lifetime (amount of time a fluorophore is in the excited state) is sensitive to microenvironment, particularly protein-binding. High-resolution imaging of the optical redox ratio and fluorescence lifetimes of NAD(P)H and FAD (optical metabolic imaging) enables single-cell analyses. In this study, mice with FaDu tumors were treated with the antibody therapy cetuximab or the chemotherapy cisplatin and imaged in vivo two days after treatment. Results indicate that fluorescence lifetimes of NAD(P)H and FAD are sensitive to early response (two days post-treatment, P<.05), compared with decreases in tumor size (nine days post-treatment, P<.05). Frequency histogram analysis of individual optical metabolic imaging parameters identifies subpopulations of cells, and a new heterogeneity index enables quantitative comparisons of cellular heterogeneity across treatment groups for individual variables. Additionally, a dimensionality reduction technique (viSNE) enables holistic visualization of multivariate optical measures of cellular heterogeneity. These analyses indicate increased heterogeneity in the cetuximab and cisplatin treatment groups compared with the control group. Overall, the combination of optical metabolic imaging and cellular-level analyses provide novel, quantitative insights into tumor heterogeneity.

Characterizing Phenotypes and Signaling Networks of Single Human Cells by Mass Cytometry.

Single cell mass cytometry is revolutionizing our ability to quantitatively characterize cellular biomarkers and signaling networks. Mass cytometry experiments routinely measure 25-35 features of each cell in primary human tissue samples. The relative ease with which a novice user can generate a large amount of high quality data and the novelty of the approach have created a need for example protocols, analysis strategies, and datasets. In this chapter, we present detailed protocols for two mass cytometry experiments designed as training tools. The first protocol describes detection of 26 features on the surface of human peripheral blood mononuclear cells. In the second protocol, a mass cytometry signaling network profile measures 25 node states comprised of five key signaling effectors (AKT, ERK1/2, STAT1, STAT5, and p38) quantified under five conditions (Basal, FLT3L, SCF, IL-3, and IFNγ). This chapter compares manual and unsupervised data analysis approaches, including bivariate plots, heatmaps, histogram overlays, SPADE, and viSNE. Data files in this chapter have been shared online using Cytobank ( http://www.cytobank.org/irishlab/ ).

Cutting Edge: Redox Signaling Hypersensitivity Distinguishes Human Germinal Center B Cells.

Differences in the quality of BCR signaling control key steps of B cell maturation and differentiation. Endogenously produced H2O2 is thought to fine tune the level of BCR signaling by reversibly inhibiting phosphatases. However, relatively little is known about how B cells at different stages sense and respond to such redox cues. In this study, we used phospho-specific flow cytometry and high-dimensional mass cytometry (CyTOF) to compare BCR signaling responses in mature human tonsillar B cells undergoing germinal center (GC) reactions. GC B cells, in contrast to mature naive B cells, memory B cells, and plasmablasts, were hypersensitive to a range of H2O2 concentrations and responded by phosphorylating SYK and other membrane-proximal BCR effectors in the absence of BCR engagement. These findings reveal that stage-specific redox responses distinguish human GC B cells.

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data.

The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: (1) distinguishing initial populations, (2) revealing cell subsets, and (3) characterizing subset features. In the implementation described here, viSNE, SPADE, and heatmaps were used sequentially to comprehensively characterize and compare healthy and malignant human tissue samples. The use of multiple methods helps provide a comprehensive view of results, and the largely unsupervised workflow facilitates automation and helps researchers avoid missing cell populations with unusual or unexpected phenotypes. Together, these methods develop a framework for future machine learning of cell identity.