Dataset Information

Single-cell immune profiling reveals functional diversity of T cells in tuberculous pleural effusion.

ABSTRACT: Orchestration of an effective T lymphocyte response at infection sites is critical for protection against Mycobacterium tuberculosis (Mtb) infection. However, the local T cell immunity landscape in human tuberculosis is poorly defined. Tuberculous pleural effusion (TPE), caused by Mtb, is characterized by an influx of leukocytes to the pleural space, providing a platform suitable for delineating complex tissue responses to Mtb infection. Using single-cell transcriptomics and T cell receptor sequencing, we analyzed mononuclear cell populations in paired pleural fluid and peripheral blood of TPE patients. While all major cell clusters were present in both tissues, their relative proportions varied significantly by anatomic location. Lineage tracking analysis revealed subsets of CD8 and CD4 T cell populations with distinct effector functions specifically expanded at pleural sites. Granzyme K-expressing CD8 T cells were preferentially enriched and clonally expanded in pleural fluid from TPE, suggesting that they are involved in the pathogenesis of the disease. The findings collectively reveal the landscape of local T cell immunity in tuberculosis.

SUBMITTER: Cai Y

PROVIDER: S-EPMC8789099 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Single-cell immune profiling reveals functional diversity of T cells in tuberculous pleural effusion.

Cai Yi Y Wang Yejun Y Shi Chenyan C Dai Youchao Y Li Fuxiang F Xu Yuzhong Y Zhang Peize P Kong Fanhui F Deng Guofang G Wen Zhihua Z Zhou Qi Q Kang Boxi Chris BC Singhal Amit A Yang Qianting Q Feng Carl G CG Chen Xinchun X

The Journal of experimental medicine 20220121 3

Orchestration of an effective T lymphocyte response at infection sites is critical for protection against Mycobacterium tuberculosis (Mtb) infection. However, the local T cell immunity landscape in human tuberculosis is poorly defined. Tuberculous pleural effusion (TPE), caused by Mtb, is characterized by an influx of leukocytes to the pleural space, providing a platform suitable for delineating complex tissue responses to Mtb infection. Using single-cell transcriptomics and T cell receptor sequ ...[more]

PMID: 35061012

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Project description:BackgroundTuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods often involving invasive surgery and being time-consuming. While various machine learning and statistical models have been proposed for TPE diagnosis, these methods are typically limited by complexities in data processing and difficulties in feature integration. Therefore, this study aims to develop a diagnostic model for TPE using ChatGPT-4, a large language model (LLM), and compare its performance with traditional logistic regression and machine learning models. By highlighting the advantages of LLMs in handling complex clinical data, identifying interrelationships between features, and improving diagnostic accuracy, this study seeks to provide a more efficient and precise solution for the early diagnosis of TPE.MethodsWe conducted a cross-sectional study, collecting clinical data from 109 TPE and 54 non-TPE patients for analysis, selecting 73 features from over 600 initial variables. The performance of the LLM was compared with logistic regression and machine learning models (k-Nearest Neighbors, Random Forest, Support Vector Machines) using metrics like area under the curve (AUC), F1 score, sensitivity, and specificity.ResultsThe LLM showed comparable performance to machine learning models, outperforming logistic regression in sensitivity, specificity, and overall diagnostic accuracy. Key features such as adenosine deaminase (ADA) levels and monocyte percentage were effectively integrated into the model. We also developed a Python package ( https://pypi.org/project/tpeai/ ) for rapid TPE diagnosis based on clinical data.ConclusionsThe LLM-based model offers a non-surgical, accurate, and cost-effective method for early TPE diagnosis. The Python package provides a user-friendly tool for clinicians, with potential for broader use. Further validation in larger datasets is needed to optimize the model for clinical application.

Project description:BACKGROUNDCurrent diagnostic tools for tuberculous pleural effusion (TPE) are often inadequate, making accurate diagnosis challenging. Effective identification of TPE is critical for ensuring proper treatment and preventing tuberculosis relapse. This study explored the potential of granzyme A (GZMA) as a biomarker for TPE.METHODSPatients with TPE, malignant pleural effusion (MPE), and parapneumonic pleural effusion (PPE) were recruited into discovery and validation cohorts. The discovery cohort consisted of 200 patients with TPE and 100 patients with MPE, while the validation cohort included 167 patients with TPE, 84 patients with MPE, and 69 patients with PPE.RESULTSIn the discovery cohort, GZMA levels were significantly elevated in TPE compared with MPE, demonstrating 90% sensitivity and 91% specificity at a cutoff of 102.29 ng/mL for effectively distinguishing between the two conditions. In the validation cohort, GZMA maintained high diagnostic performance, distinguishing TPE from MPE with 87% sensitivity and 87% specificity and from PPE with 87% sensitivity and 84% specificity. Incorporating GZMA, lactate dehydrogenase (LDH), and adenosine deaminase (ADA) into a random forest model further improved diagnostic accuracy. In the discovery cohort, this model achieved 92% sensitivity and 100% specificity, and in the validation cohort, it distinguished TPE from MPE with 87% sensitivity and 94% specificity and from PPE with 87% sensitivity and 91% specificity.CONCLUSIONOverall, GZMA is a promising biomarker for diagnosing TPE, with improved accuracy when combined with LDH and ADA, providing a robust tool for timely identification and effective management of patients with TPE.FUNDINGThe study was supported by Science and Technology Project of Shenzhen (KCXFZ20211020163545004, KQTD20210811090219022, JCYJ20220818095610021, JSGG20220822095200001, JCYJ20210324094614038), Shenzhen Medical Research Funding (B2302035, A2302004), Provincial Natural Science Foundation of Guangdong (2022A1515220034), and Shenzhen Third People's Hospital Research Foundation (G2022155).

Dataset Information

Single-cell immune profiling reveals functional diversity of T cells in tuberculous pleural effusion.

Publications

Single-cell immune profiling reveals functional diversity of T cells in tuberculous pleural effusion.

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