Dataset Information

Transcriptional programming and T cell receptor repertoires distinguish human lung and lymph node memory T cells.

ABSTRACT: Antigen-specific memory T cells persist for years after exposure to a pathogen and provide effective recall responses. Many memory T cell subsets have been identified and differ in abundance throughout tissues. This study focused on CD4 and CD8 memory T cells from paired human lung and lung draining lymph node (LDLN) samples and identified substantial differences in the transcriptional landscape of these subsets, including higher expression of an array of innate immune receptors in lung T cells which were further validated by flow cytometry. Using T cell receptor analysis, we determined the clonal overlap between memory T cell subsets within the lung and within the LDLN, and this was greater than the clonal overlap observed between memory T cell subsets compared across tissues. Our results suggest that lung and LDLN memory T cells originate from different precursor pools, recognize distinct antigens and likely have separate roles in immune responses.

SUBMITTER: Schoettler N

PROVIDER: S-EPMC6853923 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Transcriptional programming and T cell receptor repertoires distinguish human lung and lymph node memory T cells.

Schoettler Nathan N Hrusch Cara L CL Blaine Kelly M KM Sperling Anne I AI Ober Carole C

Communications biology 20191113

Antigen-specific memory T cells persist for years after exposure to a pathogen and provide effective recall responses. Many memory T cell subsets have been identified and differ in abundance throughout tissues. This study focused on CD4 and CD8 memory T cells from paired human lung and lung draining lymph node (LDLN) samples and identified substantial differences in the transcriptional landscape of these subsets, including higher expression of an array of innate immune receptors in lung T cells ...[more]

PMID: 31754641

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Project description:Single-cell transcriptomics promise to revolutionize our understanding of the vasculature. Emerging computational methods applied to high-dimensional single-cell data allow integration of results between samples and species and illuminate the diversity and underlying developmental and architectural organization of cell populations. Here, we illustrate these methods in the analysis of mouse lymph node (LN) lymphatic endothelial cells (LEC) at single-cell resolution. Clustering identifies five well-delineated subsets, including two medullary sinus subsets not previously recognized as distinct. Nearest neighbor alignments in trajectory space position the major subsets in a sequence that recapitulates the known features and suggests novel features of LN lymphatic organization, providing a transcriptional map of the lymphatic endothelial niches and of the transitions between them. Differences in gene expression reveal specialized programs for (1) subcapsular ceiling endothelial interactions with the capsule connective tissue and cells; (2) subcapsular floor regulation of lymph borne cell entry into the LN parenchyma and antigen presentation; and (3) pathogen interactions and (4) LN remodeling in distinct medullary subsets. LEC of the subcapsular sinus floor and medulla, which represent major sites of cell entry and exit from the LN parenchyma respectively, respond robustly to oxazolone inflammation challenge with enriched signaling pathways that converge on both innate and adaptive immune responses. Integration of mouse and human single-cell profiles reveals a conserved cross-species pattern of lymphatic vascular niches and gene expression, as well as specialized human subsets and genes unique to each species. The examples provided demonstrate the power of single-cell analysis in elucidating endothelial cell heterogeneity, vascular organization, and endothelial cell responses. We discuss the findings from the perspective of LEC functions in relation to niche formations in the unique stromal and highly immunological environment of the LN.

Project description:Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60-0.88), 0.72 (95% CI 0.57-0.87) and 0.64 (95% CI 0.48-0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61-0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61-0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66-0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

Dataset Information

Transcriptional programming and T cell receptor repertoires distinguish human lung and lymph node memory T cells.

Publications

Transcriptional programming and T cell receptor repertoires distinguish human lung and lymph node memory T cells.

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