Multimodal Profiling of 500,000 Memory T Cells from a Tuberculosis Cohort Identifies Cell State Associations with Demographics, Environment, and Disease

Abstract

T cells occupy a complex landscape of functional states characterized by combinations of mRNA, surface proteins, transcription factors and cytokines. These modalities individually lend limited insight into cellular function, but jointly they reca-pitulate the breadth of T cell states. However, profiling strategies frequently assay a single modality, average across heterogeneous states (bulk RNA sequencing (RNA-seq)), have limited detection efficiency for key markers (single-cell RNA-seq 1) or only target pre-defined phenotypes of interest (flow cytometry and mass cytom-etry 2). Recent technologies incorporate oligonucleotide-labeled antibodies into droplet-based single-cell sequencing to simultaneously measure surface markers alongside intracellular mRNA transcripts 3,4 , enabling comprehensive characterization of T cell states. Interindividual differences in T cell state abundance and function can correlate with demographics (for example, age and sex) or environment 5. Moreover, they may underlie divergent disease outcomes 6 , such as response to pathogens like M.tb-a leading infectious cause of death. Nearly a quarter of the world's population is estimated to be infected with M.tb, but only 5-15% of infected individuals develop TB disease, causing an estimated 1.5 million annual deaths 7,8. Given the prevalence of infection and mortality upon progression to active disease, there is an urgent need to understand the immune correlates of progression risk. For example, people who previously progressed to active TB tend to have higher progression risk than others 9 , which may indicate that persistent baseline immune differences reduce the capacity to control M.tb infection. Previous studies implicate key memory T cell states in TB disease progression 10-17. However, T cell immunoprofiling studies often have key limitations: (1) insufficient clinical and demographic data to mitigate confounding factors and (2) profiling donors during disease , when disease-induced inflammation cannot be disentangled from inherent immune differences. Similarly, studies limited to antigen-specific cells may miss broader immune context. Here, we use cellular indexing of transcriptomes and epitopes by sequenc-ing (CITE-seq) to profile >500,000 memory T cells from a TB progression cohort at post-disease immune steady state (that is, after treatment and TB disease resolution) and multimodally define cell Multimodal T cell profiling can enable more precise characterization of elusive cell states underlying disease. Here, we integrated single-cell RNA and surface protein data from 500,089 memory T cells to define 31 cell states from 259 individuals in a Peruvian tuberculosis (TB) progression cohort. At immune steady state >4 years after infection and disease resolution, we found that, after accounting for significant effects of age, sex, season and genetic ancestry on T cell composition, a polyfunc-tional type 17 helper T (T H 17) cell-like effector state was reduced in abundance and function in individuals who previously progressed from Mycobacterium tuberculosis (M.tb) infection to active TB disease. These cells are capable of responding to M.tb peptides. Deconvoluting this state-uniquely identifiable with multimodal analysis-from public data demonstrated that its depletion may precede and persist beyond active disease. Our study demonstrates the power of integrative multimodal single-cell profiling to define cell states relevant to disease and other traits. NATuRE IMMuNoLogY | www.nature.com/natureimmunology