Single-cell transcriptomics of blood reveals a natural killer cell subset depletion in tuberculosis

Basic information
Cell
68,369
Sample
7

Technology
10X Genomics
Omics
scRNA-seq
Source
PBMCs

Dataset ID
35228015
Platform
HiSeq X Ten
Species
Human
Disease
Tuberculosis (TB),LTBI,Healthy
Age range
26 - 51
Update date
2022-02-25
Summary

Background: Tuberculosis (TB) continues to be a critical global health problem, which killed millions of lives each year. Certain circulating cell subsets are thought to differentially modulate the host immune response towards Mycobacterium tuberculosis (Mtb) infection, but the nature and function of these subsets is unclear. Methods: Peripheral blood mononuclear cells (PBMC) were isolated from healthy controls (HC), latent tuberculosis infection (LTBI) and active tuberculosis (TB) and then subjected to single-cell RNA sequencing (scRNA-seq) using 10 × Genomics platform. Unsupervised clustering of the cells based on the gene expression profiles using the Seurat package and passed to tSNE for clustering visualization. Flow cytometry was used to validate the subsets identified by scRNA-Seq. Findings: Cluster analysis based on differential gene expression revealed both known and novel markers for all main PBMC cell types and delineated 29 cell subsets. By comparing the scRNA-seq datasets from HC, LTBI and TB, we found that infection changes the frequency of immune-cell subsets in TB. Specifically, we observed gradual depletion of a natural killer (NK) cell subset (CD3-CD7+GZMB+) from HC, to LTBI and TB. We further verified that the depletion of CD3-CD7+GZMB+ subset in TB and found an increase in this subset frequency after anti-TB treatment. Finally, we confirmed that changes in this subset frequency can distinguish patients with TB from LTBI and HC. Interpretation: We propose that the frequency of CD3-CD7+GZMB+ in peripheral blood could be used as a novel biomarker for distinguishing TB from LTBI and HC.

Overall design

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Contributors

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Contact

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snRNA-Seq
Sample nameSample titleDiseaseGenderAgeSourceTreatmentTechnologyPlatformOmicsSample IDDataset IDAction
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