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)

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

Cutting Edge: IL-4, IL-21, and IFN-γ Interact To Govern T-bet and CD11c Expression in TLR-Activated B Cells

T-bet and CD11c expression in B cells is linked with IgG2c isotype switching, virus-specific immune responses, and humoral autoimmunity. However, the activation requisites and regulatory cues governing T-bet and CD11c expression in B cells remain poorly defined. In this article, we reveal a relationship among TLR engagement, IL-4, IL-21, and IFN-γ that regulates T-bet expression in B cells. We find that IL-21 or IFN-γ directly promote T-bet expression in the context of TLR engagement. Further, IL-4 antagonizes T-bet induction. Finally, IL-21, but not IFN-γ, promotes CD11c expression independent of T-bet. Using influenza virus and Heligmosomoides polygyrus infections, we show that these interactions function in vivo to determine whether T-bet(+) and CD11c(+) B cells are formed. These findings suggest that T-bet(+) B cells seen in health and disease share the common initiating features of TLR-driven activation within this circumscribed cytokine milieu

Cutting Edge: IL-4, IL-21, and IFN-γ Interact To Govern T-bet and CD11c Expression in TLR-Activated B Cells

TBET and CD11c expression in B cells is linked with IgG(2c) isotype switching, virus-specific immune responses, and humoral autoimmunity. However, the activation requisites and regulatory cues governing TBET and CD11c expression remain poorly defined. Herein we reveal a relationship between TLR engagement, IL4, IL21, and IFNγ that regulates TBET expression in B cells. We find that IL21 or IFNγ directly promote TBET(+) expression in the context of TLR engagement. Further, IL4 antagonizes TBET induction. Finally, IL21, but not IFNγ, promotes CD11c expression independent of TBET. Using influenza virus and H. polygyrus infections, we show that these interactions function in vivo to determine whether TBET(+) and CD11c(+) B cells are formed. These findings suggest that TBET(+) B cells seen in health and disease share the common initiating features of TLR driven activation within this circumscribed cytokine milieu

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

Proportion of Patients Screened for HCV Infection, HCV Antibody Positive, and Unnecessarily Tested for HCV Infection (2006–2011).

<p><b>Abbreviations:</b> HET, heterosexual transmission; IDU, injection drug use; MSM, men who have sex with men.</p><p>*Continuous variables were dichotomized to facilitate calculation of proportions. The mean value divided the number of outpatient HIV visits during the observation period into two groups; whereas the number of inpatient and emergency department visits during the observation period differentiated between 0 and 1 or more visits.</p>†<p><i>P</i><0.05 when comparing differences in the proportion screened for HCV using the χ² test.</p>‡<p><i>P</i><0.05 when comparing differences in the proportion HCV antibody positive using the χ² test.</p>§<p><i>P</i><0.05 when comparing differences in the proportion unnecessary tested for HCV using the χ² test.</p

Factors Associated with HCV Screening and Unnecessary Repeat HCV Testing.

<p><b>Abbreviations:</b> CI, confidence interval; HET, heterosexual transmission; IDU, injection drug use; MSM, men who have sex with men.</p

Demographic and Clinical Characteristics of HIV-infected Patients.

<p><b>Abbreviations:</b> HET, heterosexual transmission; IDU, injection drug use; MSM, men who have sex with men.</p><p>*Mean number of outpatient, inpatient, and emergency department visits over the observation period were: 13.92 (standard deviation12.03), 0.75 (1.86), and 1.41 (3.70), respectively.</p