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Single-cell transcriptome sequencing reveals altered peripheral blood immune cells in patients with severe tuberculosis

European Journal of Medical Research volume 29, Article number: 434 (2024) Cite this article

Abstract

Tuberculosis is a serious global health burden, resulting in millions of deaths each year. Several circulating cell subsets in the peripheral blood are known to modulate the host immune response to Mycobacterium tuberculosis (Mtb) infection in different ways. However, the characteristics and functions of these subsets to varying stages of tuberculosis infection have not been well elucidated. Peripheral blood immune cells (PBICs) were isolated from healthy donors (HD group), individuals with mild tuberculosis (MI group), and individuals with severe tuberculosis (SE group). CD4+ naive T cells and CD8+ T cells were decreased in the SE and MI groups, while CD14+ monocytes were increased in the SE group. Further analysis revealed increased activated CD4+ T cells, transitional CD8+ T cells, memory-like NK cells, and IGHG3highTTNhighFCRL5high B cells were increased in all patients with tuberculosis (SE and MI group). In contrast, Th17 cells, cytotoxic NK cells, and cytotoxic CD4+ T cells were decreased. Moreover, the increase of CD14+CD16+ monocytes correlated with severe tuberculosis, and the GBP5highRSAD2high neutrophils were unique to patients with severe tuberculosis. Cellular communication analysis revealed that CD8+ T cells exhibited the highest incoming interaction strength in the SE group. The increased CD8+ T cell incoming interactions are associated with the MHC-I and LCK pathways, with HLA-(A-E)-CD8A, HLA-(A-E)-CD8B, and LCK-(CD8A+CD8B) being ligand–receptor pairs. Patients with tuberculosis, especially severe tuberculosis, have profound changes in peripheral blood immune cell profiles. CD8+ T cells showed the highest incoming interaction strength in patients with severe tuberculosis, with the main signals being MHC-I and LCK pathways.

Introduction

Mycobacterium tuberculosis (Mtb) is the causative organism of tuberculosis, which infects about a quarter of the world’s population [1,2,3]. Meanwhile, it is the leading cause of death globally from a single infectious pathogen, accounting for approximately 1.5 million deaths annually. Currently, the global control of Mtb is not optimistic, and there are over 10 million new cases reported each year [4].

Most patients with primary tuberculosis are asymptomatic or have mild symptoms such as fever and night sweats. However, a minority of tuberculosis patients suffer from more severe symptoms such as caseous necrosis of the lungs and concomitant caseous liquefaction, which usually leads to a worsening of prognosis or death. Mortality rates for severe tuberculosis requiring Intensive Care Unit were reported to range from 15.5 to 65.9% [5]. Mtb invasion and anti-Mtb host immune responses interact to influence disease severity and clinical outcomes, creating barriers to the design of effective strategies for the prevention and control of Mtb infection. Therefore, understanding the host immune response in patients with severe tuberculosis is important for better designing prognostic and diagnostic indicators and appropriate therapeutic interventions.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for profiling immune cell responses and is leading the field of pathogen infections [5,6,7]. In the present study, scRNA-seq was conducted to obtain a high-resolution transcriptomic landscape of peripheral immune cells in peripheral blood immune cells (PBICs) from healthy donors, patients with mild tuberculosis, and patients with severe tuberculosis (Fig. 2A). Our study reveals the core of peripheral blood immune alterations in tuberculosis patients, especially in severe tuberculosis patients, and provides potential new therapeutic targets.

Methods

Study design and participants

The present study recruited two normal donors and six newly diagnosed patients from January 2020 to March 2020 at Shanghai Pulmonary Hospital. All tuberculosis patients included in the study were primary susceptible tuberculosis patients with Xpert positive and rifampicin resistance negative. In addition, none of the patients had received effective anti-tuberculosis medication in the 3 months before hospital admission. Three of these patients were included in the SE group, with manifestations of cavitary tuberculosis or caseous pneumonia and involvement of more than 2 pulmonary lobes (Fig. 1). The other three patients were included in the MI group, with lesions confined to one pulmonary lobe and presenting as hypodense patchy shadows (Fig. 1). Other inclusion criteria were as follows: (1) patients with a clear diagnosis (positive sputum microscopy or sputum culture or PCR; (2) patients between 20 and 70 years of age; and (3) patients with normal immune function (as assessed by routine blood counts and lymphocyte subpopulation counts). Exclusion criteria were as follows: (1) patients with extrapulmonary Mtb infection; (2) patients with diabetes mellitus, hyperlipidemia, and other metabolic disorders; (3) HIV-positive patients; (4) patients with co-infections; (5) patients with co-infections with other severe lung diseases; (6) patients with other comorbidities; (7) patients with smoking history, and alcohol consumption > 100 mL/day; and (8) patients who were receiving anti-tuberculosis treatment at the time of enrolment.

Fig. 1
figure 1

Radiographs of chest CT of the six tuberculosis patients in the present study

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Single-cell transcriptomes were generated from PBICs from 2 healthy donors and 6 patients with mild (n = 3) and severe (n = 3) active tuberculosis (Fig. 2A). Thus, the 8 participants were classified into three groups: healthy donors (HD), mild tuberculosis (MI), and severe tuberculosis (SE). The demographic characteristics of study populations are included in Table S1.

Fig. 2
figure 2

Study design and overall results of single-cell transcriptional profiling of PBICs from 8 participants. A Schematic diagram illustrating the overall study design. 8 subjects, including 2 healthy donors, 6 mild tuberculosis patients, and 3 severe tuberculosis patients. B The t-SNE plot of single-cell profiles, with cells divided by 23 clusters. C t-SNE plots of single-cell profiles, with cells divided by cell annotation. D Total cell proportions of the 12 cell types in the three comparison groups. E t-SNE plot of single-cell profiles, with cells divided by 5 main cell types

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The study was approved by the Ethics Committee of Tongji University affiliated Shanghai Pulmonary Hospital (No. K23-198), with the informed written consent of each participant. The study was conducted following the principles of the Declaration of Helsinki and the ethical and biosafety agreements of the institution.

Single-cell suspension preparation and scRNA-seq library construction

Peripheral blood was collected from the donor into a heparin anticoagulation tube. After adding 10 mL of 6% hydroxyethyl solution, the sample was inverted several times and kept at room temperature for 30 min. Subsequently, the supernatant was centrifuged continuously at 290×g for 5 min. The cells were washed twice and then dissolved with ACK (ammonium–chloride–potassium) lysis buffer (Thermo Fisher Scientific, Waltham, MA) for 5 min at room temperature to completely remove erythrocytes. The de-erythropoietic cells were then centrifuged at 300×g for 5 min at 4 °C, and the cell microspheres were resuspended in 1 mL of PBS (HyClone). Peripheral blood de-erythropoietic cell suspension was counted using a TC20 automated cell counter (Bio-Rad) to determine cell concentration and viability.

The concentration of the cell suspension was adjusted to 1 × 105 cells/mL with PBS. Then, the single-cell suspension was loaded onto a microfluidic chip, and a scRNA-seq library was constructed following the manufacturer's instructions (Singleron Biotechnologies, Nanjing, China). The scRNA-seq library was sequenced on an Illumina HiSeq × 10 instrument.

Single-cell data analysis

Quality control was first performed on the raw data generated by the sequencing machine. Cell barcodes, UMI, and mRNA sequences were extracted, and the cell barcode sequences were corrected. The mRNA sequence was then aligned with the human reference genome, and the corresponding gene and transcript tags were added to the sequence. UMI counts were determined based on the combination of cells and genes. The gene expression matrix was obtained, and subsequent cell-type clustering and identification of major cell types were carried out.

Cells expressing less than 200 or more than 6000 genes were removed. In addition, mitochondria-expressed genes higher than 20% were discarded. Finally, a total of 28,417 cells were retained, with an average of 3552 cells in each sample. Gene expression matrices of the remaining cells were normalized using a linear regression model (Seurat R package version 4.3.0.1). Principal component analysis (PCA) was performed, and dimensionality reduction was carried out using UMAP and t-SNE [22]. Following that, clusters were identified and annotated based on the composition of marker genes. The cells were clustered using the FindCluster function, and the FindMarkers function was used to identify marker genes within the cell clusters. Cell annotation was performed using the SingleR package (version 3.17), with the Human Primary Cell Atlas (https://www.humancellatlas.org/) serving as the reference data. The online database CellMarker (http://xteam.xbio.top/CellMarker/) was used to further identify the cells [8]. Finally, the annotated platelets were removed to obtain the final gene expression matrix of PBICs.

The FindMarkers tool was used to calculate the differentially expressed genes (DEGs) between each cluster or group. Pathway analyses were performed on 50 hallmark pathways using the Genome Variation Analysis (GSVA) package (version 1.26.0) [9].

Pseudotime trajectory analysis

To construct single-cell trajectories and identify gene expression changes among different cell subtypes, Monocle 3 (version 1.3.1) was applied to the cell subtypes of the PBICs.

Cell communication analysis

Cell communication analysis was performed using the R package CellChat (version 1.6.1) [35]. The CellChatDB human was used for analysis. All three groups of samples were normalized together, and then each group was extracted, analyzed, and compared in parallel.

Statistical analysis

Statistical analyses and visualizations are performed in R software (version 4.3).

Results

Integrated analysis of scRNA-seq data

A total of 29,150 cells were isolated and sequenced from 8 participants (2 healthy donors, HD group; 3 patients with mild tuberculosis, MI group; 3 patients with severe tuberculosis, SE group). After removing duplicate cells, empty droplets, and low-quality cells, a total of 28,218 cells were filtered out for further analysis. Using the Seurat package for unsupervised cell clustering [10], 22 cell clusters were obtained (Fig. 2B).

After clustering, CD14+ monocytes, CD16+ monocytes, CD4+ memory T cells, CD4+ naive T cells, CD8+ T cells, Gamma-delta T cells, neutrophils, NK cells, and B cells, were annotated based on the expression of the canonical marker genes and variable genes (Fig. 2C, Table S2). Cell proportion analysis showed that CD4+ naive T cells and CD8+ T cells were decreased in the SE and MI groups (CD4+ naive T cells: SE 5.53%, MI 4.78%, HD 8.07%; CD8+ T cells: SE 7.12%, MI 5.98%, HD 11.10%). In addition, CD14+ monocytes were increased in the SE group (Fig. 2D, SE 16.84%, MI 10.00%, HD 13.15%).

The 22 cell clusters were then divided into five main cell types, including neutrophils (CXCR2highCXCL8highFCGR3Bhigh, cluster 0, 1, 2, 4, 8, and 21), T cells (CD3DhighCD2highIL32high, cluster 5, 6, 9, 11, 16, and 18), monocytes (LYZhighVCANhighCD14high, cluster 3, 14, 17, 19, and 22), B cells (CD79AhighCD79BhighMS4A1high, cluster 10, 13, and 20), and NK cells (GNLYhighNKG7high, cluster 7) (Table S3, Fig. 2E, S1). Subsequently, the five main cell types were re-clustered and analyzed for more detailed information.

The increase of CD14+CD16+ monocyte cells correlated with the severity of tuberculosis

Monocytes play an important role in the pathogenesis of tuberculosis [11]. Tuberculosis induces the accumulation of monocytes in the peripheral blood, and these monocytes express high levels of the inflammatory markers S100A12 [12, 13]. Therefore, further analysis of monocytes in our scRNA-seq was performed to reveal the differences in monocyte subsets occurring among the three groups. All monocytes were further divided into 10 subsets, M0-9 (Fig. 3A, C).

Fig. 3
figure 3

Further analysis of monocytes from HD, MI, and SE groups. A Monocytes were highlighted and extracted for further clustering. B Differences in GSVA pathway analysis among monocyte subsets. C The t-SNE plot of monocyte profiles, with cells divided by group. D Feature plots of two well-known monocyte marker genes, CD14 and FCGR3A, and their corresponding expression levels. E Heatmap displaying marker genes for cluster M0–9. F Proportions of monocyte subsets in the three comparison groups. G Pseudotime trajectory analysis of monocyte subsets. The Arabic numerals represent the pseudotime. Larger Arabic numerals represent an increase in the pseudotime. The solid black lines represent the pseudotime trajectory

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Based on the expression levels of the marker genes CD14 and FCGR3A (also known as CD16), three monocyte subpopulations were identified, i.e., classical (CD14+CD16−), non-classical (CD14lowCD16+), and intermediate (CD14+CD16+) monocytes [13]. As shown in Fig. 3D, the classical CD14+CD16− cell subset is the predominantly abundant cell subset, comprising clusters M0, M1, M2, and M5. Notably, M0, M1, M2, and M5 represent inflammatory monocytes that express high levels of inflammation-related markers, such as S100A12 [14,15,16]. There are also differences among these three subsets. M0 and M2, especially M0, are the most classic monocytes with high expression of S100A9, S100A8, and S100A12 (Fig. 3E). M1 was a subset that highly expressed LGALS2 (Figure S2). M5 highly expressed interferon-induced or regulated molecules such as ISG15 [17], IFI44L [18], and IFIT1 [19], suggesting that M5 is a subset regulated by interferon (Figure S2). CD14lowCD16+ monocytes, mainly clustered in M4, are characterized by high expression of FCGR3A (Figure S2). CD14+CD16+ monocytes, mainly clustered in M3, exhibit high expression of HLA molecules such as HLA-DRA, HLA-DPB1, HLA-DRB1, HLA-DRB5, HLA-DPA1, HLA-DQB1, HLA-DMA, and HLA-DQA1 (Figure S2).

The cell proportion of CD14+CD16+ monocytes is reported to increase in inflammatory diseases [20]. And another scRNA-seq study demonstrated a similar phenomenon in patients with tuberculosis [13]. Consistently, the proportion of M3 monocytes was observed to be higher in the SE group (12.72%) compared to the HD group (9.60%) and the MI group (8.95%) (Fig. 3F). These results further suggest that the increase of CD14+CD16+ intermediate monocyte cells correlated with the severity of tuberculosis. Pathway analysis showed that M3 had up-regulated WNT-β-catenin signaling, Notch signaling, and MYC-Target signaling (Fig. 3B).

In addition, the M0 cell subpopulation proportion was highest in the SE group (26.48%). Pathway analysis showed that M0 showed down-regulated interferon alpha response and interferon-gamma response, suggesting relatively insensitive to interferon. In addition, M0 showed up-regulated glycolysis and down-regulated fatty acid metabolism.

Pseudotime trajectory analysis further highlighted the heterogeneity and connectivity of cellular differentiation among monocyte subsets. As shown in Fig. 3G, there are three main cellular trajectories in the prominent monocytes (M0–6): M2 to M0, M2 to M1 to M0, and M2 to M1 to M3 to M4. Genes that contribute most to the trajectory alignment were LYZ, S100A8, S100A9, VCAN, etc. The expression of these genes decreases with trajectory, implying that the inflammatory responsiveness of cells decreases with trajectory.

Tuberculosis patients exhibited increased activated CD4+ T cells and transitional CD8+ T cells and decreased Th17 cells and cytotoxic CD4+ T cells

T cells play a key role in controlling the infection of Mtb in patients with tuberculosis [21]. The present scRNA-seq analysis detected 5235 T cells in the three groups, which could be subdivided into 10 subsets (Fig. 4A, C). Five distinct CD4+ T cell subsets were identified: T0, T1, T3, T6, and T8 (Fig. 4A, D). Both T0 and T6 expressed high levels of the activated CD4+ T cell marker CCR6 [22], as well as functional markers such as LTB, AQP3, GPR183, and LDHB [23] (Figure S3). T1 expresses high levels of CCR7, indicating that it is a naive CD4+ T cell subset (Fig. 4E) [24]. T3 expresses high levels of KLRB1 (CD161), indicating that it is a subset of T helper 17 (Th17) cells. T8 might be a cytotoxic CD4+ T cell subset that expresses high levels of SYNE1 [25]. Four distinct CD8+ T cell subsets were identified as well, including naive CD8+ T cells (T4, CCR7high) [26], cytotoxic CD8+ T cells (T2, GZMHhighNKG7highFGFBP2high), transitional CD8+ T cells (T5, GZMKhigh) [27], and memory CD8+ T cells (T7, FCGR3Bhigh) [25].

Fig. 4
figure 4

Further analysis of T cells from the HD, MI, and SE groups. A T cells were highlighted and extracted for further clustering. B Differences in GSVA pathway analysis among T cell subsets. C The t-SNE plot of T cell profiles, with cells divided by group. D Feature plots of two well-known T cell marker genes, CD4 and CD8, and their corresponding expression levels. E Heatmap displaying marker genes for cluster T0–9. F Proportions of T cell subsets in the three comparison groups. G Pseudotime trajectory analysis of T cell subsets. The Arabic numerals represent the pseudotime. Larger Arabic numerals represent an increase in the pseudotime. The solid black lines represent the pseudotime trajectory

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Pseudotime trajectory analysis showed that there are two cellular trajectories in the prominent monocytes: T2 to T8 and T2 to T5 to T3 to T6 to T0 to T1 (Fig. 3G). Genes that contribute most to the trajectory alignment were CCL5, CST7, FGFBP2, GZMA, GZMH, NKG7, etc. The expression of these genes decreases with trajectory, indicating that the cellular cytotoxicity decreases with trajectory.

The proportion of activated CD4+ T cells (T0) and transitional CD8+ T cells (T5) were increased in the MI and SE groups compared to the HD group (Fig. 4F, T0: HD14.80%, MI 23.80%, SE 24.32%; T5: HD 6.95%, MI 10.16%, SE 9.11%). The cellular proportion of Th17 cells (T3) and cytotoxic CD4+ T cells (T8) was significantly decreased in the MI and SE groups (T3: HD 17.01%, MI 9.96%, SE 8.67%; T8: HD 10.82%, MI 1.55%, SE 1.93%). GSEA revealed that T0 exhibited up-regulated KRAS signaling and down-regulated HEDGEHOG signaling (Fig. 3B). T5 displayed up-regulated pathways including peroxisome, reactive oxygen species, MYC-targets, and E2F-targets. T3 displayed down-regulated NOTCH signaling, while T8 showed down-regulation of peroxisome, reactive oxygen species, adipogenesis, fatty acid metabolism, oxidative phosphorylation, and MYC-targets. Overall, tuberculosis patients (MI and SE groups) exhibited an increase in activated CD4+ T cells and transitional CD8+ T cells and a decrease in Th17 cells and cytotoxic CD4+ T cells, and these trends appeared to be independent of disease severity.

GBP5highRSAD2high neutrophil subset was unique to patients with severe tuberculosis

Neutrophils are a major component of the innate immune system and, as the most numerous cell type of circulating leukocytes, are the host's first line of defense against invading bacteria and other pathogens [28]. Therefore, further analyses were performed on neutrophils. Neutrophils were divided into 14 cell subsets: N0–N13 (Fig. 5A, C). The top 10 marker genes of N0–N13 subsets are shown with heatmaps in Fig. 5D. As shown in Fig. 5E, there were no significant differences in the cellular proportions in the N0–N6 and N8 subsets among the three groups. However, compared to the HD and MI groups, the N7 (SLPIhighPTGS2highLST1high) subset was significantly decreased in the SE group, and the N10 (GBP5highRSAD2high) subset was only present in the SE group. In addition, the MI and SE groups had additional N11, N12, and N13 subsets compared to the HD group. GSVA analysis revealed that N7 exhibited up-regulated oxidative phosphorylation and down-regulated glycolysis (Fig. 4B). N10 displayed up-regulated IL2-STAT5 signaling and MYC-targets and down-regulated NOTCH signaling and inflammatory response.

Fig. 5
figure 5

Further analysis of neutrophils from the HD, MI, and SE groups. A Neutrophils were highlighted and extracted for further clustering. B Differences in GSVA pathway analysis among neutrophil subsets. C The t-SNE plot of neutrophil profiles, with cells divided by group. D Heatmap displaying marker genes for cluster N0–13. E Proportions of neutrophil subsets in the three comparison groups

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Cytotoxic NK cells are decreased while memory-like NK cells are increased in tuberculosis patients

NK cells can kill Mtb-infected cells directly or through antibody-dependent cellular cytotoxicity [29]. Sub-cluster analysis revealed five distinct NK cell clusters: NK0–4 (Fig. 6A, B). NK0 expressed high levels of GZMB and GNLY, indicative of high cytotoxic activity, while NK1 selectively expressed high levels of KLRC2, indicating the presence of a memory-like NK cell subset (Fig. 6E, S5). The proportion of the NK0 subset decreased, while the proportion of the NK1 subset increased in the MI and SE groups compared to the HD group (NK0: HD 61.75%, MI 49.6%, SE 56.84%; NK1: HD 20.37%, MI 28.90%, SE 24.47%, Fig. 6I), which suggests that cytotoxic NK cell subset is decreased in all tuberculosis patients, and memory-like NK cell subset was increased. GSVA analysis (Fig. 6G) showed that NK0 displayed up-regulated glycolysis, while NK1 displayed up-regulated fatty acid metabolism. In addition, NK1 displayed up-regulated reactive oxygen species, MTORC1 signaling, the P53 pathway, and peroxisome.

Fig. 6
figure 6

Further analysis of NK and B cells from HD, MI, and SE groups. Figure 5 Further analysis of neutrophils from HD, MI, and SE groups. A NK cells were highlighted and extracted for further clustering. B The t-SNE plot of NK cell profiles, with cells divided by group. C B cells were highlighted and extracted for further clustering. D The t-SNE plot of B cell profiles, with cells divided by group. E Heatmap displaying marker genes for cluster NK0–4. F Heatmap displaying marker genes for cluster B0–4. G Differences in GSVA pathway analysis among NK cell subsets. H Differences in GSVA pathway analysis among B cell subsets. I Proportions of NK cell subsets in the three comparison groups. J Proportions of B cell subsets in the three comparison groups

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IGHG3highTTNhighFCRL5high B cell subset was increased in all tuberculosis patients

Recently, the dynamics of B cell memory responses have been characterized at different stages of the clinical spectrum of Mtb infection, suggesting that B cells play an important role in human tuberculosis [30]. Five different B cell subsets, B0-4, were identified in the present study, each representing a distinct stage of B cell development (Fig. 6C, D). B0 expressed high levels of TCL1A, CD79A, CD79B, and MS4A1 but lacked CD27 expression, indicating that B0 is a subset of follicular B cells [13, 31, 32]. B1 expressed high levels of MS4A1, CD79A, and CRIP1, and low levels of TCL1A (Fig. 6F, S6), indicating a mature B cell subset [32]. B2 is a cell subset that exhibits a relatively high expression of IGHG3, TTN, and FCRL5. B4 expresses high levels of CD38 and low levels of CD19, indicating that it is probably an abnormal plasma cell subset [33]. As observed in t-SNE plots and cell proportion plots (Fig. 6J), the B2 subset was significantly increased in the MI group (10.88%) and the SE group (6.54%) compared to the HD group (1.75%). GSVA analysis (Fig. 6H) showed that B2 displayed up-regulated pathways including MYC-targets, WNT-beta-catenin, and interferon alpha response.

CD8+ T cells from patients with severe tuberculosis exhibit the highest outgoing interaction strengths

During Mtb infection, numerous crucial interactions take place between Mtb and immune cells, as well as among different immune cells [34]. Understanding global communication among PBICs requires an accurate representation of intercellular signaling links. Therefore, an effective system-level analysis has been applied to quantitatively infer and analyze intercellular communication networks [35]. To demonstrate cellular communication among different cell subsets, all PBICs were reclassified as neutrophils, CD14+ monocytes, CD16+ monocytes, CD8+ T cells, CD4+  naive T cells, CD4+ memory T cells, B cells, NK cells, and plasmas. The numbers of significant interactions among different cell types in the HD, MI, and SE groups are shown in Fig. 7A, B, respectively.

Fig. 7
figure 7

Cellular communication analysis. A Interactions strength between cell populations in the HD group. B Interactions strength between cell populations in the MI group. C Interactions strength between cell populations in the SE group. The width of the connecting line is directly related to the strength of the interaction. The wider the line, the stronger the interaction. D Incoming and outgoing interaction strength of all pathways in the HD group. E Incoming and outgoing interaction strength of all pathways in the MI group. F Incoming and outgoing interaction strength of all pathways in the SE group. The horizontal coordinates represent the outgoing interaction strength. The vertical coordinates represent the incoming interaction strength. The size of the circle represents the number of pathways. G The inferred incoming communication patterns of target cells are visualized by an alluvial plot. The thickness of the flow indicates the contribution of the cell group or signaling pathway to each latent pattern. The height of each pattern is proportional to the number of its associated cell groups or signaling pathways. H The incoming communication patterns of target cells in the SE group. The size of the dots represents the contribution of the pattern to intercellular communication. I Ligand–receptor pairs of MHC-I pathway targeting CD8+ T cells in the SE group. J Ligand–receptor pairs of LCK pathway targeting CD8+ T cells in the SE group. The horizontal coordinates represent the direction of the ligand–receptor pairs. The vertical coordinates represent ligand–receptor pairs of the MHC-I (I) and LCK pathway (J). The size of the dot represents the p-value. Colors represent communication probabilities

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Notably, there was the highest incoming interaction strength exhibited by CD8+ T cells in the SE group (Fig. 7D–F), which was significantly higher than that of CD14+ monocytes and FCGR3A+ monocytes. The latter two cell types exhibited the highest and second-highest incoming interaction strengths in both the HD and MI groups. These results indicated that CD8+ T cells receive more cellular interactions in severe tuberculosis.

Further analysis of incoming communication patterns of receiver cells showed that CD8+ T cells in the SE group received incoming communications through pattern 2 (Fig. 7G). And pattern 2 is comprised of MHC-I, ANNEXIN, CD45, LCK, and CD86 pathways. Since there was no cellular communication received from the ANNEXIN, CD45, and CD86 pathways by CD8+ T cells in the SE group, the increase in CD8+ T cell incoming interactions was associated with MHC-I and LCK pathways (Fig. 7H).

We further analyzed the ligand–receptor pairs of MHC-I and LCK pathways targeting CD8+ T cells in the SE group. HLA-(A-E)-CD8A, HLA-(A-E)-CD8B, and LCK-(CD8A+CD8B) are ligand–receptor pairs that targeting CD8+ T cells in the SE group (Fig. 7I, J). These results suggest that the above ligand–receptor pairs that target CD8+ T cells are associated with severe tuberculosis.

Discussion

Our understanding of the mechanisms by which humans coordinate immune responses to pathogens is limited due to a lack of information about the immune landscape in peripheral blood [36]. With the assistance of scRNA-seq, the peripheral blood immune profiles of MI and SE patients were mapped, revealing differences in peripheral blood immune cell subpopulations and their functions between MI and SE patients as well as between healthy donors. In addition, cellular communication analyses revealed peripheral blood immune cell interactions, pathways, and receptor–ligand pairs.

Monocytes/macrophages play an essential role in the control of Mtb infection. Paradoxically, macrophages also serve as the natural habitat of Mtb [37]. This phenomenon has aroused tremendous interest among scientists. Huang et al. [38] studied alveolar macrophage (AM) and interstitial macrophage (IM) profiles using fluorescent Mtb reporter strains. According to their finding, IMs have glycolytic activity, while AMs are dedicated to fatty acid oxidation. IMs exhibit nutrient limitation and control of bacterial growth, while AMs represent more nutrient-permissive environments. In vitro macrophage infection, treatment with the glycolysis inhibitor 2-deoxyglucose increased bacterial growth, whereas the fatty acid oxidation inhibitor etomoxir inhibited bacterial growth. Consistently, Mtb survival in monocytes is also mainly fueled by fatty acids and cholesterol [11]. The above strongly suggests that macrophages/monocytes in preference for glycolytic metabolism are more efficient in clearing Mtb, whereas fatty acid oxidative metabolism is the opposite. These findings are consistent with our results that the most classic M0 subset showed up-regulated glycolysis and down-regulated fatty acid metabolism. In addition, our results also show that M0 has a higher proportion in SE.

Neutrophils, the most abundant innate immune cells, are strongly heterogeneous [39,40,41]. Neutrophils migrate from the circulating blood to infected tissues in response to inflammatory stimuli and protect the host by phagocytizing, killing, and digesting bacteria [42]. Differentiation and maturation of neutrophils give rise to different neutrophil subpopulations that might be functionally preprogrammed differently. The discrete microenvironment can alter neutrophil function and behavior. In addition, the rapid aging of neutrophils, short lifespan, and mechanically induced cellular responses as they enter and exit capillaries contribute to neutrophil heterogeneity. Some neutrophil subpopulations overlap and lead to controversy regarding neutrophil function. As a result, scRNA-seq studies of neutrophils are difficult, leading to the majority of scRNA-seq studies only investigating peripheral blood mononuclear cells (PBMCs). Currently, the full heterogeneity and differentiation status of neutrophils is still not fully determined [7]. To demonstrate the complete immune landscape of the peripheral blood of MI and SE patients, neutrophils were not discarded in the present study. Our findings revealed, for the first time, that a SLPIhighPTGS2highLST1high subset was significantly reduced in SE patients, and a GBP5highRSAD2high subset was only present in SE patients. It has implications for subsequent research as well as treatment.

Human NK cells are the effector cells of innate immunity and have a potential role in immunosurveillance against Mtb infection. According to our data, cytotoxic NK cells and cytotoxic CD4+ T cells are decreased in all tuberculosis patients. This might be related to the lack of protective immune response in patients with tuberculosis infection. It is consistent with the findings of Kathamuthu [43] et al. that the number of T cells and NK cells expressing cytotoxic markers decreased at the site of Mtb infection, which might reflect the lack of protective immune response. B cells mediate adaptive immune activation and participate in host defense against Mtb, however, fewer studies have been conducted on B cells. Based on scRNA-seq, it is revealed that a B cell subset with relatively high expression of IGHG3, TTN, and FCRL5 was increased in tuberculosis patients. It has been reported that overexpression of FCRL5 by B cells is associated with low antibody titers following HCV infection [44]. Therefore, it seems that the B-cell-mediated adaptive immune response has been hampered in tuberculosis patients.

CD8+ T cells have been proven to play a direct role in the response to Mtb infection and coordinate many different functions in the overall host immune response. CD8+ T cells can recognize Mtb-specific antigens presented by classical (HLA-A, -B, -C) and non-classical MHC molecules (HLA-E) [45]. CD8+ T cells can lyse Mtb-infected macrophages and kill the intracellular bacilli in an antigen-specific fashion. Our findings revealed that CD8+ T cells from SE patients received more MHC cell signaling, which might be associated with enhanced killing of infected macrophages and Mtb. Lymphocyte-specific protein tyrosine kinase (LCK) is a member of the Src family of protein tyrosine kinases (PTKs), encoding a key signaling protein in the selection and maturation of developing T-cells. Studies using LCK knock-out mice or LCK deficient T-cell lines have shown that LCK regulates the initiation of TCR signaling, T-cell development, and T-cell homeostasis [46]. Enhanced LCK signaling promotes the activation of CD8+ T cells [47]. Therefore, enhancement of the LCK pathway targeting CD8+ T cells in SE patients is also related to the activation of CD8+ T cells.

Patients with tuberculosis, especially those with severe tuberculosis, have the highest intensity of CD8+ T-cell interactions, which gives us a hint that CD8+ T cells play a critical role in tuberculosis, and that intervention in CD8+ T cells might be the key to the treatment of tuberculosis patients. Meanwhile, the MHC-I and LCK pathways are the main signals involved in the significantly increased CD8+ T cell interactions, suggesting that these are valuable targets for therapeutic intervention in tuberculosis.

The present study has several limitations and further optimization and expansion of the tuberculosis scRNA-seq dataset. Firstly, our scRNA-seq dataset consists of relatively small samples with gender bias, and inter-individual differences might harm the results. Secondly, this study is only an exploratory experiment based on unbiased and unlabeled scRNA-seq, and a further combination of other techniques, such as multiparameter flow cytometry analyses, is required to provide valid results to draw more reliable conclusions. Thirdly, the resolution of the study data is still insufficient for cell populations with lower frequencies. Some cell clusters may be composed of more than one phenotype of cells. Additional single-cell analysis tools such as surface protein labeling will improve our ability to detect and identify these important cell populations in future analyses.

Conclusion

The present study mapped the peripheral blood immune profiles of healthy donors and patients with mild tuberculosis and severe tuberculosis, revealing that patients with tuberculosis, especially severe tuberculosis, have profound changes in peripheral blood immune cell profiles. CD8+ T cells showed the highest incoming interaction strength in patients with severe tuberculosis, with the main signals being MHC-I and LCK pathways.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

Not applicable.

Funding

This study was supported by the funds from: (1) National Key R&D Program of China (2023YFC2307300). (2) Shanghai Shenkang Hospital Development Center Emerging Frontier Technology Joint Research Project (SHDC12022108). (3) Shanghai 2020 “science and technology innovation action plan” technological innovation fund: Clinical Study on New Short-Course treatment regimens and Host-Directed Therapy for MDR-TB (Grant ID: 20Z11900500). (4) Shanghai Clinical Research Center for Infectious Disease (tuberculosis) (Grant ID: 19MC1910800). (5) Shanghai’s 2023 “Science and Technology Innovation Action Plan”, special general project of medical innovation research (Grant No.23Y11900300).

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Author notes

  1. Li Wang, Ya He, and Peng Wang contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Clinic and Research Center of Tuberculosis, School of Medicine, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China

    Li Wang, Ya He, Peng Wang, Hai Lou & Wei Sha

  2. Department of Tuberculosis, School of Medicine, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China

    Li Wang, Ya He, Peng Wang, Hai Lou & Wei Sha

  3. Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China

    Wei Sha

  4. Central Laboratory, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China

    Haipeng Liu

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Contributions

WL and HY wrote the main manuscript text. PW contributes to data curation. HL contributes to the investigation. All authors read and approved the final manuscript. SW and HPL contributes to conceptualization. All authors reviewed the manuscript.

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Correspondence to Haipeng Liu or Wei Sha.

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The study was approved by the Ethics Committee of Tongji University affiliated Shanghai Pulmonary Hospital (No. K23-198), with the informed written consent of each participant. The study was conducted in accordance with the principles of the Declaration of Helsinki and the ethical and biosafety agreements of the institution.

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Wang, L., He, Y., Wang, P. et al. Single-cell transcriptome sequencing reveals altered peripheral blood immune cells in patients with severe tuberculosis. Eur J Med Res 29, 434 (2024). https://doi.org/10.1186/s40001-024-01991-5

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European Journal of Medical Research

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