Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions

Gutiérrez‐Arcelus, María; Teslovich, Nikola C.; Mola, Alex R.; Polidoro, Rafael B.; Nathan, Aparna; Kim, Hyun; Hannes, Susan; Slowikowski, Kamil; Watts, Gerald F.; Korsunsky, Ilya; Brenner, Michael B.; Raychaudhuri, Soumya; Brennan, Patrick J.

doi:10.1038/s41467-019-08604-4

Cited by 139 publications

(158 citation statements)

References 71 publications

(75 reference statements)

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“…Our results are in line with a similar trajectory, which was reported using simultaneous targeted quantification of mRNA and protein expression in single T cells 45 . Interestingly, a similar gradient is also present in innate T cells, as shown by a previous study where higher expression levels of effector molecules were negatively associated with ribosome synthesis and proliferative capacity 46 .…”

Section: Discussionsupporting

confidence: 79%

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Cano-Gamez

Soskic

Roumeliotis

et al. 2019

Preprint

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AbstractNaïve CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. We used quantitative proteomics, bulk RNA-seq and single-cell RNA-seq of over 40,000 human naïve and memory CD4+ T cells to generate a detailed map of cytokine-regulated gene expression programs. We demonstrated that cytokine response differs substantially between naïve and memory T cells and showed that memory cells are unable to differentiate into the Th2 phenotype. Moreover, memory T cells acquire a Th17-like phenotype in response to iTreg polarization. At the single-cell level, we demonstrated that T cells form a continuum which progresses from naïve to effector memory T cells. This continuum is accompanied by a gradual increase in the expression levels of chemokines and cytokines and thus represents an effectorness gradient. Finally, we found that T cell cytokine responses are determined by where the cells lie in the effectorness gradient and identified genes whose expression is controlled by cytokines in an effectorness-dependent manner. Our results shed light on the heterogeneity of T cells and their responses to cytokines, provide insight into immune disease inflammation and could inform drug development.

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Section: Discussionsupporting

confidence: 79%

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Cano-Gamez

Soskic

Roumeliotis

et al. 2019

Preprint

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“…S1A,B). We annotated identified clusters by comparing differentially expressed (DE) genes that defined each to known lineage markers and previously published scRNA-Seq datasets 19–21 (Fig. S1C, see Table S2 for list of DE markers).…”

Section: Resultsmentioning

confidence: 99%

“…5C and table S7): traditional CD8+ T cells (1-red), hyper-proliferative CD8+ T cells (2-green), naïve CD4+ T cells (3-cyan), and a subset of cells that is CD8– but TRDC+ and FCGR3A + (CD16) (4-lilac). A recent scRNA-Seq study on cytotoxic innate-ness looked at cytotoxic γδ T and NK cells in healthy humans, noting basal levels of TRDC in both cell-types 21 . To determine whether these TRDC + CD16 + cells were γδ T or NK cells, we scored them, as well as non-proliferating CTLs and NK cells, against gene signatures described in that study (Fig.…”

Section: Resultsmentioning

confidence: 99%

Integrated Single-Cell Analysis of Multicellular Immune Dynamics during Hyper-Acute HIV-1 Infection

Kazer

Aicher

Muema

et al. 2019

Preprint

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30Cellular immunity is critical for controlling intracellular pathogens, but the dynamics and 31 cooperativity of the evolving host response to infection are not well defined. Here, we apply single-32 cell RNA-sequencing to longitudinally profile pre-and immediately post-HIV infection peripheral 33 immune responses of multiple cell types in four untreated individuals. Onset of viremia induces a 34 strong transcriptional interferon response integrated across most cell types, with subsequent pro-35 inflammatory T cell differentiation, monocyte MHC-II upregulation, and cytolytic killing. With 36 longitudinal sampling, we nominate key intra-and extracellular drivers that induce these 37 programs, and assign their multi-cellular targets, temporal ordering, and duration in acute 38 infection. Two individuals studied developed spontaneous viral control, associated with initial 39 elevated frequencies of proliferating cytotoxic cells, inclusive of a previously unappreciated 40 proliferating natural killer (NK) cell subset. Our study presents a unified framework for 41 characterizing immune evolution during a persistent human viral infection at single-cell resolution, 42 and highlights programs that may drive response coordination and influence clinical trajectory. 43 44 54Recently, high-throughput single-cell RNA-sequencing (scRNA-Seq) has emerged as a 55 powerful tool to characterize, transcriptome-wide, complex human systems in health and disease 56 at single-cell resolution [3][4][5][6][7][8][9] . When applied to a collection of samples across a disease setting, this 57 approach provides a platform for investigating cell types, states, interactions, and drivers 58 associated with that disease; this information can be used to develop testable hypotheses on 59 therapeutic modulations that may ameliorate disease state 7,8 . Meanwhile, within an individual, 60longitudinal sampling provides an opportunity to decipher, at unprecedented resolution and 61 absent potentially confounding inter-individual variability 7 , shifts in these same variables, and to 62 3 associate observed changes with internal or external perturbations 10-12 . Such sampling of a host's 63 exposure to a pathogen could provide foundational insights into essential cellular response 64 features and their coordination, empowering the rational design of improved prophylactic 65 interventions. 66Illustratively, a better understanding of the interplay between innate and adaptive immune 67 responses at the very earliest stages of a viral infection, and its impact on long-term disease, 68 could reveal principles to accelerate prevention efforts. Human Immunodeficiency Virus (HIV) has 69 been the subject of thorough study, and thus is a well-considered model system for examining 70 host responses to a pathogen. Moreover, although the development of antiretroviral therapy 71 (ART) 13 , as well as implementation of pre-exposure prophylaxis (PrEP) 14 and combination 72 prevention efforts, has improved the lives of persons living with HIV, increased life expectancie...

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“…Because T cell phenotypes can be characterized by both transcriptional and surface protein markers [27][28][29][30][31][32][33] , we profiled single cells with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), a multimodal method that combines unbiased single-cell RNA-seq with surface marker profiles obtained via oligonucleotide-tagged antibodies 34,35 . We optimized a panel of 31 surface markers ( Supplementary Table 3), including markers of lineage (e.g., CD4, CD8), activation (e.g., CD25, HLA-DR), migration (e.g., CCR6, CXCR3), and other functions, and mouse immunoglobulin G (IgG) as a control.…”

Section: Multimodal Sequencing Produces 500k Robust T Cell Profilesmentioning

confidence: 99%

“…2b, c) 38 . The first CV is computed such that it captures the most variation shared between modalities, and accordingly, scores on this dimension correlated with a previously defined signature of cytotoxic potential (Pearson r = 0.90) 27 (Extended Data Fig. 2d, e).…”

Section: Multimodal Integration Defines the Memory T Cell Landscapementioning

confidence: 99%

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

Nathan

Beynor

Baglaenko

et al. 2020

Preprint

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Mycobacterium tuberculosis (M.tb) results in 10 million active tuberculosis (TB) cases and 1.5 million deaths each year 1 , making it the world's leading infectious cause of death 2 . 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 IFNγ 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. Main textInterindividual immune differences may underlie host variation in response to pathogens, such as M.tb. Only 5-15% of individuals who are infected with M.tb develop TB disease during their lifetime 2 . Disease progression is influenced by host immune and genetic factors that implicate T cells, which are major contributors to defense against intracellular pathogens 3-11 . These markedly different outcomes of infection raise the question of whether steady-state differences in T cell composition underlie divergent host response to M.tb.Studies examining immunophenotypes in active TB have identified numerous memory T cell changes, including in the CD4+ 12 , activated 13 , exhausted 14-16 , Th1 17,18 , and IL-17+ compartments [19][20][21][22][23] . However, these studies typically profiled patients during ongoing infection and focused on antigen-specific T cells, rather than broad, intrinsic differences in memory T cell composition outside of acute infection or active disease.Moreover, it is challenging to acquire an adequate sample size, account for confounders influencing T cell composition 24 , and overcome limitations of surface marker or bulk RNA-seq-based technologies that only capture certain cell state changes.Here, we profiled total memory T cells at single-cell resolution from patients more than four years after TB disease in order to identify broad steady-state differences in progre...

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Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions

Cited by 139 publications

References 71 publications

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Integrated Single-Cell Analysis of Multicellular Immune Dynamics during Hyper-Acute HIV-1 Infection

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

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