|10:40 AM - 11:10 AM||Do differences make a difference: from single cells to humans||John Tsang, National Institutes of Health, United States|
|11:10 AM - 11:40 AM||The impact of cancer associated CTCF mutations on chromatin architecture and gene regulation||Jane Skok, New York University School of Medicine, United States|
|12:00 PM - 12:40 PM||Probing the Immune Response to Vaccination with Systems Biology||Bali Pulendran , United States|
|2:00 PM - 2:30 PM||Chemistry between genes: a proposition for ImmunoMetabolomics||Shuzhao Li, The Jackson Laboratory for Genomic Medicine, United States|
|2:30 PM - 3:00 PM||The role of cis-regulatory elements in cell fate and immune disorders||David Hawkins , United States|
|3:20 PM - 3:30 PM||Uncovering B-ALL TF-gene regulatory interactions associated with CRLF2-overexpression||Sana Badri, Department of Pathology, New York University School of Medicine, New York, NY, USA., United States|
|3:30 PM - 4:00 PM||Examining waning immunity of B. pertussis vaccination||Bjoern Peters, La Jolla Institute for Allergy & Immunology, United States|
|4:00 PM - 4:20 PM||Sexual dimorphism in human immune system aging ||Duygu Ucar, The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA., United States|
|4:20 PM - 4:30 PM||Integrated transcriptomic analysis of SLE reveals IFN-driven cross-talk between immune cells||Bharat Panwar, La Jolla Institute for Immunology, United States|
|4:30 PM - 4:40 PM||Semi-connected multilayer perceptron for single-cell profiling||James Anibal, National Cancer Institute, United States|
Grégoire Altan-Bonnet, National Cancer Institute, United States
|5:00 PM - 5:20 PM||Interpreting genetic variants through chromatin interaction maps in primary human immune cells||Ferhat Ay, La Jolla Institute for Immunology, United States|
|5:20 PM - 5:50 PM||Exploring the functional consequences of macrophage heterogeneity||Kathryn Miller-Jensen , United States|
|5:50 PM - 6:00 PM||Dissecting the heterogeneity of protein and transcriptional responses in human blood derived immune cells after T- and monocyte-specific activation||Nathan Lawlor, The Jackson Laboratory, United States|
|6:00 PM - 6:10 PM||Single-cell transcriptomic analysis of SARS-CoV-2 reactive CD4+ T cells||Benjamin Meckiff, La Jolla Institute for Immunology, United States|
|6:10 PM - 6:20 PM||Single-cell transcriptomic analysis of allergen-specific T cells in allergy and asthma||Gregory Seumois, La Jolla Institute for Immunology, United States|
Although genetic alterations are initial drivers of disease, aberrantly activated transcriptional regulatory programs are often responsible for maintenance and progression in cancer. Alterations leading to CRLF2-overexpression in B-ALL patients are associated with poor outcome and activate JAK-STAT, PI3K and ERK/MAPK signaling pathways. Although inhibitors of these pathways are available, there still remains the issue of treatment-associated toxicities and poorly studied regulatory structures controlling leukemogenesis. Comparing RNA-seq from CRLF2-High and other B-ALL patients (CRLF2-Low), we defined a CRLF2-High gene signature. Patient-specific chromatin accessibility was interrogated to identify altered putative regulatory elements that could be linked to transcriptional changes. To delineate these regulatory interactions, a B-ALL cancer-specific regulatory network was inferred using 868 B-ALL patient samples from the NCI TARGET database coupled with priors generated from ATAC-seq peak TF-motif analysis. Analysis of CRISPRi, siRNA knockdown and ChIP-seq of nine TFs involved in the inferred network were analyzed to validate a cohort of predicted TF-gene regulatory interactions. Inferred interactions were used to identify differential patient-specific transcription factor activities predicted to control CRLF2-High deregulated genes, thereby enabling identification of robust gene targets.
Single cell profiling of activated immune cells is a powerful way to study immune function disruptions in diverse diseases and conditions. However, generating and analyzing single cell data from activated cells harbor unique challenges. To address these challenges and derive reference response genes to frequently studied immune cell activation conditions, we generated CITE-seq data from 10 healthy adults (5 men, 5 women) before and after stimulating their peripheral blood mononuclear cells (PBMCs) via i) anti-CD3/CD28 and ii) LPS. Using a comprehensive antibody panel (n=39) including cell type (e.g., CD16, CD14) and cell state (e.g., CD69, CD25) markers, we uncovered how each immune cell type responds to these conditions. Two levels of multiplexing (cell hashing and individuals’ genotypes) were instrumental to avoid batch effects and to eliminate multiplet cells that increased in number upon activation. All PBMCs responded to stimulation via anti-CD3/CD28, whereas LPS specifically activated monocytes and induced pro-inflammatory genes and pathways. Pseudo-temporal ordering of single cells before and after activation revealed cell- and condition-specific heterogeneity in cellular responses independent of genetic variation. Together, these data are shared within an interactive web application (https://czi-pbmc-cite-seq.jax.org/) and will serve as a resource to guide future studies of immune cell responses.
The contribution of CD4+ T cells to protective or pathogenic immune responses to SARS-CoV-2 infection remains unknown. Here, we present large-scale single-cell transcriptomic analysis of viral antigen-reactive CD4+ T cells from 32 COVID-19 patients. In patients with severe disease compared to mild disease, we found increased proportions of cytotoxic follicular helper (TFH) cells and cytotoxic T helper cells (CD4-CTLs) responding to SARS-CoV-2, and reduced proportion of SARS-CoV-2 reactive regulatory T cells. Importantly, the CD4-CTLs were highly enriched for the expression of transcripts encoding chemokines that are involved in the recruitment of myeloid cells and dendritic cells to the sites of viral infection. Polyfunctional T helper (TH)1 cells and TH17 cell subsets were underrepresented in the repertoire of SARS-CoV-2-reactive CD4+ T cells compared to influenza-reactive CD4+ T cells. Together, our analyses provide so far unprecedented insights into the gene expression patterns of SARS-CoV-2 reactive CD4+ T cells in distinct disease severities.
CD4+ helper and regulatory T cells that respond to common allergens play an important role in driving and dampening airway inflammation in patients with asthma. Until recently, direct, unbiased molecular analysis of allergen-reactive T-cells has not been possible. To better understand the diversity of these T-cells in allergy and asthma, we analyzed the single-cell transcriptome of ~50,000 house dust mite (HDM) allergen-reactive T cells from asthmatics with HDM allergy and from three control groups: asthmatics without HDM allergy and non-asthmatics with and without HDM allergy. Our analyses show that HDM allergen-reactive T cells are highly heterogeneous, and certain subsets are quantitatively and qualitatively different in subjects with HDM-reactive asthma. The number of interleukin-9 expressing HDM-reactive TH cells is greater in asthmatics compared with non-asthmatics with HDM allergy and display enhanced pathogenic properties. More HDM-reactive TH and Treg cells expressing the interferon-response signature are present in asthmatics without HDM allergy. In cells from these subsets, expression of TNFSF10 was enriched; its product, TRAIL, dampens activation of TH cells. These findings suggest that these subsets may dampen allergic responses, which may help explain why only some people develop TH2 responses to nearly ubiquitous allergens.