Deep immune profiling by mass cytometry links human T and NK cell differentiation and cytotoxic molecule expression patterns

The elimination of infected or tumor cells by direct lysis is a key T and NK cell effector function. T and NK cells can kill target cells by coordinated secretion of cytotoxic granules containing one or both pore-forming proteins, perforin and granulysin and combinations of granzyme (Gzm) family effector proteases (in humans: Gzm A, B, K, M and H). Understanding the pattern of expression of cytotoxic molecules and the relationship to different states of T and NK cells may have direct relevance for immune responses in autoimmunity, infectious disease and cancer. Approaches capable of simultaneously evaluating expression of multiple cytotoxic molecules with detailed information on T and NK differentiation state, however, remain limited. Here, we established a high dimensional mass cytometry approach to comprehensively interrogate single cell proteomic expression of cytotoxic programs and lymphocyte differentiation. This assay identified a coordinated expression pattern of cytotoxic molecules linked to CD8 T cell differentiation stages. Coordinated high expression of perforin, granulysin, Gzm A, Gzm B and Gzm M was associated with markers of late effector memory differentiation and expression of chemokine receptor CX3CR1. However, classical gating and dimensionality reduction approaches also identified other discordant patterns of cytotoxic molecule expression in CD8 T cells, including reduced perforin, but high Gzm A, Gzm K and Gzm M expression. When applied to non-CD8 T cells, this assay identified different patterns of cytotoxic molecule co-expression by CD56hi versus CD56dim defined NK cell developmental stages; in CD4 T cells, low expression of cytotoxic molecules was found mainly in TH1 phenotype cells, but not in Tregs or T follicular helper cells (TFH). Thus, this comprehensive, single cell, proteomic assessment of cytotoxic protein co-expression patterns demonstrates specialized cytotoxic programs in T cells and NK cells linked to their differentiation stages. Such comprehensive cytotoxic profiling may identify distinct patterns of cytotoxic potential relevant for specific infections, autoimmunity or tumor settings

Radiographic Evaluation of Bones and Joints in Mucopolysaccharidosis I and VII Dogs after Neonatal Gene Therapy

Mucopolysaccharidosis I (MPS I) and MPS VII are due to deficient activity of the glycosaminoglycan-degrading lysosomal enzymes alpha-L-iduronidase and beta-glucuronidase, respectively, and result in abnormal bones and joints. Here, the severity of skeletal disease in MPS I and MPS VII dogs and the effects of neonatal gene therapy were evaluated. For untreated MPS VII dogs, the lengths of the second cervical vertebrae (C2) and the femur were only 56% and 84% of normal, respectively, and bone dysplasia and articular erosions, and joint subluxation were severe. Previously, we reported that neonatal intravenous injection of a retroviral vector (RV) with the appropriate gene resulted in expression in liver and blood cells, and high serum enzyme activity. In this study, we demonstrate that C2 and femurs of RV-treated MPS VII dogs were longer at 82% and 101% of normal, respectively, and there were partial improvements of qualitative abnormalities. For untreated MPS I dogs, the lengths of C2 and femurs (91% and 96% of normal, respectively) were not significantly different from normal dogs. Qualitative changes in MPS I bones and joints were generally modest and were partially improved with RV treatment, although cervical spine disease was severe and was difficult to correct with gene therapy in both models. The greater severity of skeletal disease in MPS VII than in MPS I dogs may reflect accumulation of chondroitin sulfate in cartilage in MPS VII, or could relate to the specific mutations. Neonatal RV-mediated gene therapy ameliorates, but does not prevent, skeletal disease in MPS I and MPS VII dogs

COVID-19 vaccination single cell datasets

The datasets presented here comprise the sequencing data featured in the research paper titled: "Multimodal single-cell datasets characterize antigen-specific CD8+ T cells across SARS-CoV-2 vaccination and infection": https://www.nature.com/articles/s41590-023-01608-9 Peripheral Blood Mononuclear Cell (PBMC) samples utilized for both CITE-seq and ASAP-seq were systematically collected at four distinct time intervals: Pre-vaccination (Day 0) Post-primary vaccination (Day 2 and Day 10. Seven days post-boost vaccination (Day 28). The count matrix folder contains count matrices for each experimental type, specifically CITE-seq, ASAP-seq, and ECCITE-seq. In addition, we have included the fully integrated, processed Seurat objects for downstream analysis. Details of the content within the count matrix folder are as follows: The RNA, ATAC, and TCR modality outputs were generated using the 10x Cellranger pipeline. HTO and ADT modalities were mapped with Alevin. Outlined below are the three processed single-cell datasets: PBMC_vaccine_CITE.rds: 3' RNA and surface proteins (173 TotalSeq-A antibodies) PBMC_vaccine_ASAP.rds: Chromatin accessibility and surface proteins (173 TotalSeq-A antibodies) PBMC_vaccine_ECCITE_TCR.rds: 5' RNA, surface proteins (137 TotalSeq-C antibodies), TCR and dextramer loaded with peptides of SARS-CoV-2 spike protein. antigen_module_genes.rds: This file contains the vaccine-induced gene sets. antigen_module_peaks.rds: This file contains the DE peaks specific for vaccine-induced cells. To map the scRNA-seq query dataset onto our CITE-seq reference: library(Seurat) PBMC_CITE <- readRDS("/zenedo/PBMC_vaccine_CITE.rds") query_scRNA <- readRDS("/home/xx/your_own_data.rds") anchors <- FindTransferAnchors( reference = PBMC_CITE, query = query_scRNA, normalization.method = "SCT", k.anchor = 5, reference.reduction = "spca", dims = 1:50) query_scRNA <- MapQuery( anchorset = anchors, query = query_scRNA, reference = PBMC_CITE, refdata = list( l1 = "celltypel1", l2 = "celltypel2", l3 = "celltypel3"), reference.reduction = "spca", reduction.model = "wnn.umap") To use the scATAC-seq data, please run the commands below to update the path of the fragment file for the object. Vaccine_ASAP <- readRDS("PBMC_vaccine_ASAP.rds") # remove fragment file information Fragments(Vaccine_ASAP) <- NULL # Update the path of the fragment file Fragments(Vaccine_ASAP) <- CreateFragmentObject(path = "download/PBMC_vaccine_ASAP_fragments.tsv.gz", cells = Cells(Vaccine_ASAP)

Epigenomic-Guided Mass Cytometry Profiling Reveals Disease-Specific Features of Exhausted CD8 T Cells

Exhausted CD8 T (Tex) cells are immunotherapy targets in chronic infection and cancer, but a comprehensive assessment of Tex cell diversity in human disease is lacking. Here, we developed a transcriptomic- and epigenetic-guided mass cytometry approach to define core exhaustion-specific genes and disease-induced changes in Tex cells in HIV and human cancer. Single-cell proteomic profiling identified 9 distinct Tex cell clusters using phenotypic, functional, transcription factor, and inhibitory receptor co-expression patterns. An exhaustion severity metric was developed and integrated with high-dimensional phenotypes to define Tex cell clusters that were present in healthy subjects, common across chronic infection and cancer or enriched in either disease, linked to disease severity, and changed with HIV therapy. Combinatorial patterns of immunotherapy targets on different Tex cell clusters were also defined. This approach and associated datasets present a resource for investigating human Tex cell biology, with implications for immune monitoring and immunomodulation in chronic infections, autoimmunity, and cancer. Exhausted T (Tex) cells have poor function in chronic infections and cancer but can be therapeutically re-invigorated. Bengsch et al. use genes modified epigenetically during exhaustion and high-dimensional CyTOF profiling to define Tex cell heterogeneity in humans with HIV or lung cancer and link Tex cell features to disease progression and response to immunotherapy

Hepatitis C Virus Testing in Adults Living with HIV: A Need for Improved Screening Efforts

<div><p>Objectives</p><p>Guidelines recommend hepatitis C virus (HCV) screening for all people living with HIV (PLWH). Understanding HCV testing practices may improve compliance with guidelines and can help identify areas for future intervention.</p><p>Methods</p><p>We evaluated HCV screening and unnecessary repeat HCV testing in 8,590 PLWH initiating care at 12 U.S. HIV clinics between 2006 and 2010, with follow-up through 2011. Multivariable logistic regression examined the association between patient factors and the outcomes: HCV screening (≥1 HCV antibody tests during the study period) and unnecessary repeat HCV testing (≥1 HCV antibody tests in patients with a prior positive test result).</p><p>Results</p><p>Overall, 82% of patients were screened for HCV, 18% of those screened were HCV antibody-positive, and 40% of HCV antibody-positive patients had unnecessary repeat HCV testing. The likelihood of being screened for HCV increased as the number of outpatient visits rose (adjusted odds ratio 1.02, 95% confidence interval 1.01–1.03). Compared to men who have sex with men (MSM), patients with injection drug use (IDU) were less likely to be screened for HCV (0.63, 0.52–0.78); while individuals with Medicaid were more likely to be screened than those with private insurance (1.30, 1.04–1.62). Patients with heterosexual (1.78, 1.20–2.65) and IDU (1.58, 1.06–2.34) risk compared to MSM, and those with higher numbers of outpatient (1.03, 1.01–1.04) and inpatient (1.09, 1.01–1.19) visits were at greatest risk of unnecessary HCV testing.</p><p>Conclusions</p><p>Additional efforts to improve compliance with HCV testing guidelines are needed. Leveraging health information technology may increase HCV screening and reduce unnecessary testing.</p></div

Proportion of Patients Screened for HCV Infection, HCV Antibody Positive, and Unnecessarily Tested for HCV Infection by HIV Outpatient Utilization. Note:

<p>The mean value divided the number of outpatient HIV visits during the observation period (13.92) into two groups – low and high.</p