Biology subjects

Lagattuta, K. A.

Publications and source records attributed to Lagattuta, K. A..

4 recordsLinked to original sources

bioRxiv2023

Identifying genetic variants that influence the abundance of cell states in single-cell data

Introductory ParagraphTo understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1 GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96x10-11) associates with increased abundance of NK cells expressing TNF- response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

genetics↗

bioRxiv2023

The T cell receptor sequence influences the likelihood of T cell memory formation

T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired {beta}TCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2, that promotes positive selection in the thymus as well as transition from naive to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.

immunology↗

bioRxiv2021

Repertoire analyses reveal TCR sequence features that influence T cell fate

T cells acquire a regulatory phenotype when their T cell receptors (TCRs) experience an intermediate-high affinity interaction with a self-peptide presented on MHC. Using TCR sequences from FACS-sorted human cells, we identified TCR features that shape affinity to these self-peptide-MHC complexes, finding that 1) CDR3{beta} hydrophobicity and 2) certain TRBV genes promote Treg fate. We developed a scoring system for TCR-intrinsic regulatory potential (TiRP) and found that within the tumor microenvironment clones exhibiting Treg-Tconv plasticity had higher TiRP than expanded clones maintaining the Tconv phenotype. To elucidate drivers of these predictive TCR features, we examined the two elements of the Treg TCR ligand separately: the self-peptide via murine Tregs, and the human MHC II molecule via human memory Tconvs. These analyses revealed that CDR3{beta} hydrophobicity promotes reactivity to self-peptides, while TRBV gene usage shapes the TCRs general propensity for MHC II-restricted activation.

immunology↗

bioRxiv2020

Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment

The ability to monitor anti-tumor CD8+ T cell responses in the blood has tremendous therapeutic potential. Here, we used paired single-cell RNA sequencing and T cell receptor (TCR) sequencing to detect and characterize "tumor matching" (TM) CD8+ T cells in the blood of mice with MC38 tumors and melanoma patients using the TCR as a molecular barcode. TM cells showed increased activation compared to non-matching T cells in blood, and appeared less exhausted than matching counterparts in tumor. Importantly, PD-1, which has been used to identify putative circulating anti-tumor CD8+ T cells, showed poor sensitivity for identifying TM cells. By leveraging the transcriptome we identified candidate cell surface marker panels for TM cells in mice and melanoma patients, and validated NKG2D, CD39, and CX3CR1 in mice. These data demonstrate that the TCR can be used to identify tumor-relevant populations for comprehensive characterization, reveal unique transcriptional properties of TM cells, and develop marker panels for tracking and analysis of these cells. SummaryUsing single-cell RNA-sequencing coupled with TCR sequencing, we detected CD8+ T cell clones shared between blood and tumor in mice and melanoma patients, characterized these matching clones in blood and tumor, and identified potential biomarkers for their isolation in blood.

immunology↗