Biology subjects

Baglaenko, Y.

Publications and source records attributed to Baglaenko, Y..

7 recordsLinked to original sources

bioRxiv2024

Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions

T-cells recognize antigens and induce specialized gene expression programs (GEPs) enabling functions including proliferation, cytotoxicity, and cytokine production. Traditionally, different classes of helper T-cells express mutually exclusive responses - for example, Th1, Th2, and Th17 programs. However, new single-cell RNA sequencing (scRNA-Seq) experiments have revealed a continuum of T-cell states without discrete clusters corresponding to these subsets, implying the need for new analytical frameworks. Here, we advance the characterization of T-cells with T-CellAnnoTator (TCAT), a pipeline that simultaneously quantifies pre-defined GEPs capturing activation states and cellular subsets. From 1,700,000 T-cells from 700 individuals across 38 tissues and five diverse disease contexts, we discover 46 reproducible GEPs reflecting the known core functions of T-cells including proliferation, cytotoxicity, exhaustion, and T helper effector states. We experimentally characterize several novel activation programs and apply TCAT to describe T-cell activation and exhaustion in Covid-19 and cancer, providing insight into T-cell function in these diseases.

genomics↗

bioRxiv2024

Defining the function of disease variants with CRISPR editing and multimodal single cell sequencing

Abstract / Summary ParagraphGenetic studies have identified thousands of individual disease-associated non-coding alleles, but identification of the causal alleles and their functions remain critical bottlenecks. Even though CRISPR-Cas editing has enabled targeted modification of DNA, inefficient editing leads to heterogeneous outcomes across individual cells, limiting the ability to detect functional consequences of disease alleles. To overcome these challenges, we present a multi-omic single cell sequencing approach that directly identifies genomic DNA edits, assays the transcriptome, and measures cell surface protein expression. We apply this approach to investigate the effects of gene disruption, deletions in regulatory regions, and non-coding single nucleotide polymorphisms. We identify the specific effects of individual SNPs, including the state-specific effects of an IL2RA autoimmune variant in primary human T cells. Multimodal functional genomic single cell assays including DNA sequencing bridge a crucial gap in our understanding of complex human diseases by directly identifying causal variation in primary human cells.

genomics↗

bioRxiv2022

Non-coding autoimmune risk variant accelerates T peripheral helper cell development via ICOS

Fine-mapping and functional studies implicate rs117701653, a common non-coding variant in the CD28/CTLA4/ICOS locus, as a contributor to risk for rheumatoid arthritis and type 1 diabetes. Using DNA pulldown, mass spectrometry, genome editing and eQTL analysis, we establish that the disease-associated allele reduces affinity for the inhibitory chromosomal regulator SMCHD1 to drive expression of inducible T-cell costimulator (ICOS), enhancing memory CD4+ T cell ICOS expression in individuals bearing the risk allele. Higher ICOS expression is paralleled by an increase in circulating T peripheral helper (Tph) cells, and in rheumatoid arthritis patients, of blood and joint fluid Tph cells and circulating plasmablasts, suggesting a causal link. Indeed, ICOS ligation accelerates T cell differentiation into CXCR5-PD-1high Tph cells producing IL-21 and CXCL13, as does carriage of the rs117701653 risk allele. Thus, mechanistic dissection of a causal non-coding variant in human autoimmunity discloses a new pathway through which ICOS regulates Tph abundance.

genetics↗

bioRxiv2021

Modeling memory T cell states at single-cell resolution identifies in vivo state-dependence of eQTLs influencing disease

Many non-coding genetic variants cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by gene-regulation differences between cell states. T cells, for example, have fluid, multifaceted functional states in vivo that cannot be modeled in eQTL studies that aggregate cells. Here, we modeled T cell states and eQTLs at single-cell resolution. Using >500,000 resting memory T cells from 259 Peruvians, we found over one-third of the 6,511 cis-eQTLs had state-dependent effects. By integrating single-cell RNA and surface protein measurements, we defined continuous cell states that explained more eQTL variation than discrete states like CD4+ or CD8+ T cells and could have opposing effects on independent eQTL variants in a locus. Autoimmune variants were enriched in cell-state-dependent eQTLs, such as a rheumatoid-arthritis variant near ORMDL3 strongest in cytotoxic CD8+ T cells. These results argue that fine-grained cell state context is crucial to understanding disease-associated eQTLs.

genetics↗

bioRxiv2020

Multimodal memory T cell profiling identifies a reduction in a polyfunctional Th17 state associated with tuberculosis progression

Mycobacterium tuberculosis (M.tb) results in 10 million active tuberculosis (TB) cases and 1.5 million deaths each year1, making it the worlds leading infectious cause of death2. Infection leads to either an asymptomatic latent state or TB disease. Memory T cells have been implicated in TB disease progression, but the specific cell states involved have not yet been delineated because of the limited scope of traditional profiling strategies. Furthermore, immune activation during infection confounds underlying differences in T cell state distributions that influence risk of progression. Here, we used a multimodal single-cell approach to integrate measurements of transcripts and 30 functionally relevant surface proteins to comprehensively define the memory T cell landscape at steady state (i.e., outside of active infection). We profiled 500,000 memory T cells from 259 Peruvians > 4.7 years after they had either latent M.tb infection or active disease and defined 31 distinct memory T cell states, including a CD4+CD26+CD161+CCR6+ effector memory state that was significantly reduced in patients who had developed active TB (OR = 0.80, 95% CI: 0.73-0.87, p = 1.21 x 10-6). This state was also polyfunctional; in ex vivo stimulation, it was enriched for IL-17 and IL-22 production, consistent with a Th17-skewed phenotype, but also had more capacity to produce IFN{gamma} than other CD161+CCR6+ Th17 cells. Additionally, in progressors, IL-17 and IL-22 production in this cell state was significantly lower than in non-progressors. Reduced abundance and function of this state may be an important factor in failure to control M.tb infection.

immunology↗

bioRxiv2019

Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci

Understanding how genetic regulatory variation affects gene expression in different T cell states is essential to deciphering autoimmunity. We conducted a high-resolution RNA-seq time course analysis of stimulated memory CD4+ T cells from 24 healthy individuals. We identified 186 genes with dynamic allele-specific expression, where the balance of alleles changes over time. These genes were four fold enriched in autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes, particularly for a major autoimmune risk gene, HLA-DQB1. Each HLA-DQB1 allele had one of three distinct transcriptional regulatory programs. Using CRISPR/Cas9 genomic editing we demonstrated that a single nucleotide variant at the promoter is causal for T cell-specific control of HLA-DQB1 expression. Our study in CD4+ T cells shows that genetic variation in cis regulatory elements may affect gene expression in a lymphocyte activation status-dependent manner contributing to the inter-individual complexity of immune responses.

genomics↗

bioRxiv2018

Fast, sensitive, and flexible integration of single cell data with Harmony

The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of[~] 106 cells feasible on a personal computer. We demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting integrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a variety of experimental and biological conditions.

bioinformatics↗