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. 2022 Jan 21;1(1):100005. doi: 10.1016/j.cellin.2022.100005

Single-cell transcriptomic landscape identifies the expansion of peripheral blood monocytes as an indicator of HIV-1-TB co-infection

Qinglong Guo a,1, Yu Zhong b,1, Zhifeng Wang b,1, Tingzhi Cao a,1, Mingyuan Zhang b, Peiyan Zhang a, Waidong Huang b,c, Jing Bi a, Yue Yuan b,c, Min Ou a, Xuanxuan Zou b,c, Guohui Xiao a, Yuan Yang b, Shiping Liu b,d, Longqi Liu b, Zhaoqin Wang a, Guoliang Zhang a,∗, Liang Wu b,d,∗∗
PMCID: PMC10120323  PMID: 37192986

Abstract

Certain circulating cell subsets are involved in immune dysregulation in human immunodeficiency virus type 1 (HIV-1) and tuberculosis (TB) co-infection; however, the characteristics and role of these subclusters are unknown. Peripheral blood mononuclear cells (PBMCs) of patients with HIV-1 infection alone (HIV-pre) and those with HIV-1-TB co-infection without anti-TB treatment (HIV-pre & TB-pre) and with anti-TB treatment for 2 weeks (HIV-pre & TB-pos) were subjected to single-cell RNA sequencing (scRNA-seq) to characterize the transcriptome of different immune cell subclusters. We obtained > 60,000 cells and identified 32 cell subclusters based on gene expression. The proportion of immune-cell subclusters was altered in HIV-1-TB co-infected individuals compared with that in HIV-pre-group, indicating immune dysregulation corresponding to different disease states. The proportion of an inflammatory CD14+CD16+ monocyte subset was higher in the HIV-pre & TB-pre group than in the HIV-pre group; this was validated in an additional cohort (n = 80) via a blood cell differential test, which also demonstrated a good discriminative performance (area under the curve, 0.8046). These findings depicted the atlas of immune PBMC subclusters in HIV-1-TB co-infection and demonstrate that monocyte subsets in peripheral blood might serve as a discriminating biomarker for diagnosis of HIV-1-TB co-infection.

Keywords: Single-cell RNA sequencing, Peripheral blood mononuclear cells, HIV-1-TB co-infection, CD14+CD16+ monocytes

Graphical abstract

Image 1

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Highlights

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    PBMC scRNA-seq performed on patients with HIV-1 alone and HIV-1-TB co-infection.

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    Immune dysregulation corresponds to different disease states (HIV/HIV-1-TB).

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    An inflammatory CD14+CD16+ monocyte subset elevated in HIV-pre & TB-pre group.

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    Monocyte subsets can distinguish HIV-1-TB co-infection from HIV-pre group.

1. Introduction

Human immunodeficiency virus type 1 (HIV-1), the causative agent of acquired immunodeficiency syndrome (AIDS), was discovered in the 20th century (Korber et al., 2000). Mycobacterium tuberculosis (Mtb), the obligate pathogen of tuberculosis (TB), has co-evolved with its host for millennia (Russell, 2007). AIDS and TB remain the major health threats globally, with a global count of 37.7 million HIV-1-positive people in 2020 and approximately 20 million deaths from AIDS (Nwimo et al., 2020). There were estimated 10 million new TB cases and 1.40 million deaths worldwide in 2019 (World Health Organization, 2020). The synergy between HIV-1 and Mtb leads to further aggravation of immunological functions and greatly enhances mortality; further, Mtb infection is the primary cause of death in people living with HIV-1. HIV-1-infected people show higher frequency (∼26 times) of active TB cases than those without HIV-1 infection (Lawn et al., 2011). Approximately 0.21 million deaths resulted from HIV-1 co-infection with Mtb in 2020 (World Health Organization, 2020).

Both these pathogens can manipulate host immune responses via mechanisms that are not sufficiently understood. HIV-1 induces dysfunction of the host immune system, particularly destroying CD4+ T cells. Moreover, macrophages and monocytes are also targeted by HIV-1, maintaining an HIV-1 reservoir and contributing to HIV-1 persistence. An increasing number of reports have indicated that HIV-1 infection is a chronic inflammatory disease leading to immunodeficiency (Bell & Noursadeghi, 2018). Inflammatory identifiers increase during the asymptomatic stage of infection (Douek et al., 2009), and they are related to progressive HIV-1 infection (Deeks et al., 2004). The primary mechanism considers that microbial products translocate through the damaged gastrointestinal tract, and incomplete HIV-1 reverse transcripts are recognized by IFI16, which further activate inflammasome, causing pyroptosis and IL-1β secretion (Doitsh et al., 2014; Monroe et al., 2014). This leads to CD4 T cell death and activation of chronic inflammation (Bell & Noursadeghi, 2018; Doitsh & Greene, 2016; Galloway et al., 2015). Moreover, T cell death can result from HIV-1 proviral DNA integration-mediated apoptosis (Cooper et al., 2013). Chronic immune activation may cause premature immune senescence or compensatory immunoregulation (Beyer et al., 2016; Deeks, 2011; Khaitan & Unutmaz, 2011).

Mtb pathogenesis involves modulating and escaping host immune responses to facilitate the spread of bacteria by expressing diverse virulent factors (Esmail et al., 2018). Alveolar macrophages are the first innate immune cells encountered by Mtb after its inhalation. After phagocytosis by macrophages, Mtb is either cleared or proliferates within the macrophage. If macrophages fail to clear the bacteria, a mass of inactivated macrophages and neutrophils is accumulated at the infection site (O'Garra et al., 2013) and immature resident dendritic cells and other antigen-presenting cells convey bacteria via the lymphangion to local lymph nodes, which contribute to bacterial replication and transmission (Smith et al., 1970). Approximately four weeks after infection, Mtb-specific cell-induced immune responses can be identified within animals, including humans (Marais et al., 2004). During this stage, antigen-specific lymphocytes are quickly recruited to the site of infection while the number of neutrophils reduces. Particularly, CD4+ T cells exert the primary function to control bacterial infection by stimulating macrophages via IFN-γ and other cytokines.

Given that macrophages and CD4+ T cells are crucial for HIV-1 and Mtb infection, the two pathogens may interact with each other to impair host immunity in patients with HIV-1-TB co-infection. HIV-1-TB co-infection affects the clinical phenotype of TB; for instance, adenopathy, military pattern, and lower lung field disease became more common along with the reduced CD4 T cell number (Chamie et al., 2010). This poses challenges for diagnosis of TB in individuals with HIV-1-TB co-infection. Moreover, immunosuppression leads to decreased cavitation and sensitivity of any sputum-based Mtb detection in the context of HIV-1-TB co-infection (Esmail et al., 2018). Therefore, it is essential to identify potential new biomarkers for TB diagnosis in HIV-1 co-infected persons. Furthermore, HIV-1 may have a marked impact on host immune responses against Mtb during co-infection, including phagocytic ability, antigen presentation, cytokine generation, effect on lymphocytes of innate immune system, and role of B and T cells, which makes it more complicated to cure, leading to worse consequences (Esmail et al., 2018). In addition, Mtb infection can improve HIV-1 replication and accelerate AIDS progression (Collins et al., 2002; Goletti et al., 1996; Lawn et al., 2001; Marais et al., 2016; Meng et al., 2016; Nakata et al., 1997; Sullivan et al., 2015; Toossi et al., 2013). Better characterization of the immune responses in individuals with HIV-1-TB co-infection will contribute to finding novel immune-based methods to improve diagnosis and reduce pathological immunity.

Blood samples have high immune cell content. Better characterization of blood immune cells and their transcriptome in healthy individuals and those with different diseases will offer new insights in HIV-1-TB co-infection and will contribute to the identification of new TB biomarkers and improvement of immune-based therapy. In the present study, we applied scRNA-seq to PBMCs from individuals with HIV-1 alone and HIV-1-TB co-infection with or without anti-TB treatment in an unbiased, surface-marker-free method to characterize the transcriptome of different immune cell subclusters.

2. Results

2.1. Landscape of peripheral immune profiles in HIV-pre, HIV-pre & TB-pre, and HIV-pre & TB-pos groups

A total of 61,397 PBMCs from 11 individuals, including five patients with HIV-1 infection alone (HIV-pre, 27,934 cells), three patients with HIV-1-TB co-infection without anti-TB treatment (HIV-pre & TB pre, 19,723 cells), and three patients with HIV-1-TB co-infection and anti-TB treatment for two weeks (HIV-pre & TB pos, 9435 cells), were obtained and sequenced (Fig. 1A). A proportion of non-singlet and low-quality cells was removed, and finally, 57,092 cells were subjected for further analysis (Fig. S1). Publicly available scRNA-seq data of PBMCs from healthy controls (HC) were obtained and analyzed using the same method (Zhu et al., 2020). After unsupervised clustering, we identified three major different cell clusters for all groups (Fig. 1A, Fig. S2A). T cells expressed CD3D and CD3G (Fig. 1B, C, Fig. S2B); myeloid cells expressed CD68, LYZ, S100A9, FCGR3A, and S100A8 (Fig. 1B and C, Fig. S2B); and B cells expressed CD79A, MS4A1, and CD79B (Fig. 1B and C, Fig. S2B).

Fig. 1.

Fig. 1

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Landscape of peripheral immune profiles in HIV-pre, HIV-pre & TB-pre, and HIV-pre & TB-pos groups. (A) Uniform manifold approximation and projection (UMAP) embedding of 67,810 single cells from 14 individuals (HC, n = 3; HIV-pre, n = 5; HIV-pre & TB-pre, n = 3; HIV-pre & TB-pos, n = 3). Cell types, cells from different sample groups, and individuals are indicated by color. (B) Bubble plot showing the percentage (size) and relative expression levels (color) of marker genes in each cell type. (C) Differential gene expression analysis was used to assay contrasting cells from HIV-pre, HIV-pre & TB-pre, and HIV-pre & TB-pos groups for different cell types. Heatmaps demonstrate differentially regulated genes in different cell types. (D) Boxplot showing the abundance of each cell type in peripheral blood mononuclear cells (PBMCs) from four sample groups (Wilcoxon test).

Additionally, many cell-type-specific marker genes were identified (Fig. 1C), including myeloid cell markers (FCN1, AIF1, and LST1), B cell markers (BANK1, RALGPS2, TCF4, and IRF8), and T cell markers (IL-17R). These additional markers might be used as PBMC-specific markers in future studies.

T cells were the major part of PBMCs from HC, followed by myeloid and B cells (Figs. 1A and S2A), consistent with previous studies (Cai et al., 2020). In contrast, we also found a lower proportion of CD4 naïve T cells within HIV-1 and HIV-1-TB co-infections (Fig. 1D), which is consistent with the case that the number of CD4 T cells decline in HIV-1 infection. A small proportion of B cells was found in HIV-pre & TB-pos group, followed by HIV-pre and HIV-pre & TB-pre groups as compared to that in HC (Fig. 1D). Moreover, B cells significantly decreased in HIV-pre & TB-pos group than in HIV-pre group (Fig. 1D). However, a higher level of CD8+ T cells was observed in HIV-pre and HIV-pre & TB-pre groups as compared to that in HIV-pre & TB-pos group and HC (Fig. 1D). Higher frequencies of cycling cells, erythrocytes, and plasma cells were also detected in HIV-pre and HIV-1-TB co-infection than in HC (Fig. 1D). Moreover, the frequencies of cycling and erythrocyte cells significantly increased in HIV-pre group than in HC group (Fig. 1D). The atlas of scRNA-seq data of PBMCs from HIV-pre group was similar to that reported in a previous study (Wang et al., 2020); however, the atlas of PBMCs from HIV-1-TB co-infection is reported for the first time in the present study. Together, these results suggest the dysregulation of HIV-1- and HIV-1-TB-associated circulating cell subclusters in HIV-1 and HIV-1-TB disease progression.

2.2. Number of plasma B cells are gradually increased from HIV-pre to HIV-pre & TB-pos groups

Since it is difficult to manage B cells, only a small proportion of studies have investigated the functions of B cells in TB; thus, their effect against Mtb infection remains unclear (du Plessis et al., 2016; Cai et al., 2020). The proportion of B cell subsets change, and consistent B cell dysfunction is observed during HIV-1 infection (Townsley et al., 2021; Sharan et al., 2020). In this study, we identified six different B cell subsets displaying various states of B cell functions (Fig. 2A, Fig. S3A). Plasma B cells (subset 3) expressed high levels of CD79A, MS4A1, KCNN4, and JCHAIN and low levels of TCL1A and VPREB3 (Fig. 2B, 2D, 2E), suggestive of effective B cells (Teitell, 2005; Liu et al., 2021). Immature B cells (subset 0, 1, 5) expressed high levels of TCL1A and BTG1 and low levels of JCHAIN and SIGLEC6 (Fig. 2B, D, 2E), indicating that these may be early B cells (pre-B cells and naïve B cells) (Hystad et al., 2007; Chong & Sciammas, 2011). Memory B cells (subset 2) expressed high levels of CD19, CD79A, MS4A1, FGR, FCRL3, FCRL5, and SIGLEC6 and low levels of TCL1A (Culton et al., 2007; Kim et al., 2019) (Fig. 2B, 2D, 2E), suggesting that these may be dysfunctional atypical memory B cells (Portugal et al., 2017). Subset 4 expressed high levels of B cell markers (CD79A and MS4A1) and T cell markers (CD3D, CD8A, and GZMH etc.), indicative of a T & B doublet, possibly due to a technical limitation.

Fig. 2.

Fig. 2

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PlasmaB cells are graduallyincreased from the HIV-pre to HIV-pre & TB-pos group. (A) UMAP plot showing subclusters of B cells in different groups. Cell clusters and cells from different groups are indicated by color. (B) Bubble plot showing the percentage (size) and relative expression levels (color) of marker genes in each subcluster of B cells. (C) Proportion of cells for B cell subsets in HIV-pre, HIV-pre & TB-pre, and HIV-pre & TB-pos groups. (D) Violin plot showing the relative expression level of marker genes in each subcluster of B cells. (E) Differential gene expression analysis was performed on contrasting B cell subclusters from different groups. Heatmaps of differentially regulated genes in B cell subclusters are presented.

According to scRNA-seq data, all six B cell subclusters existed within all three groups, and they had variable cell subset proportions (Fig. 2C, Fig. S3A). The number of immature B cell subsets (0, 1, and 5) gradually decreased from HIV-pre group to HIV-pre & TB-pre and HIV-pre & TB-pos groups (Fig. S3B), although it was not significant. A opposite trend was observed in plasma B cell subset among the three groups (Fig. S3B). Moreover, the frequency of plasma B cell subset significantly decreased in HIV-pre group as compared to that in HIV-pre & TB-pre group (Fig. S3B). Taken together, our scRNA-seq data confirmed six B cell subclusters expressing different markers in PBMCs and a increase in the number of plasma B cell subset in HIV-pre & TB-pre group, which may facilitate investigation of the functions of B cell subsets in HIV-1-TB co-infection.

2.3. Ten T/NK cell subclusters confirmed via PBMC scRNA-seq

T cells play a crucial part in restricting Mtb infection in TB patients (Cai et al., 2020), and HIV-1 infection leads to a progressive decrease in absolute CD4+ T cell numbers (Sonnenberg et al., 2005). Moreover, during HIV-1-TB co-infection, CD4+ T cells contribute to HIV-1 proliferation and Mtb-specific CD4+ T cells are depleted, whereas the function of CD8+ T cells has rarely been studied (Esmail et al., 2018). In this study, scRNA-seq data revealed that T cells could be sub-divided into 10 subsets based on the distinct expression of associated markers (Fig. 3A, Fig. S4A). Nine of these subsets expressed high levels of CD3D and CD3G (Fig. 3B, 3D, 3E). We then identified eight different CD8+ T cell subsets (0–2 and 5–9). Subset 0 (CD8 T cell) expressed high levels of CD8A and NKG7 (Fig. 3B, 3D, 3E, Figs. S4B and S4C). Subset 1 (active CD8 T cells) produced high levels of CD8A, CD8B, PRF1, and TIGIT (Zheng et al., 2020; Inoue et al., 2016), suggesting that these may be activated CD8 T cells (Fig. 3B, 3D, 3E, Figs. S4B and S4C). Subset 2 (cytotoxic CD8 T cells) expressed high levels of CD8A, CD8B, GZMA, GZMB, and FCGR3A, indicative of cytotoxic activity in response to pathogen infection (Ikeda et al., 2017; Thomas & Massagué, 2005; Weng et al., 2012; Pizzolato et al., 2019) (Fig. 3B, 3D, 3E, Figs. S4B and S4C). Subset 4 (Naïve T cells) expressed high levels of CCR7, LEF1, and SELL, which are known to exert a critical effect on the early stages of T cell development and homing of naïve T cells to peripheral lymphoid organs (Singh et al., 2020; Ramirez et al., 2014) (Fig. 3B, 3D, 3E, Figs. S4B and S4C). Subset 8 (exhausted CD8 T cells) expressed high levels of CD8A, HAVCR2, CTLA4, LAG3, and TIGIT, suggesting functional dysfunction of pathogen-specific CD8 T cells in chronic infection (Naing et al., 2018; Kong et al., 2016; Zhang et al., 2019; Khan et al., 2017; Blackburn et al., 2009) (Fig. 3B, D, 3E, Figs. S4B and S4C). Subset 9 (IFN ​+ ​CD8 T cells) expressed high levels of CD8A, GZMA, PRF1, and NKG7. Moreover, this subset also highly expressed IFN-inducible genes, including ISG15, MX1, IFIT3, IFIT1, and RSAD2, suggesting the activation of the IFN signaling pathway (Pinto et al., 2011) (Fig. 3B, 3D, 3E, Figs. S4B and S4C). Subsets 6 and 7 expressed high levels of CD8A, PPBP, and HBB, revealing megakaryocyte-like and erythrocyte-like T cells, respectively. MAIT cell (Subset 5) expressed high levels of SLC4A10. Additionally, a proportion of NK cells (Subset 3) expressing high levels of KLRF1 (Kirkham & Carlyle, 2014; Vitale et al., 2001), which plays a critical part in the control of pathogen infection, was identified (Fig. 3B, 3D, 3E, Figs. S4B and S4C). Ten T/NK cell subsets were present in all the three groups, albeit at varying proportions (Fig. 3C). Our data showed that cytotoxic and active CD8 T cell numbers decreased in HIV-pre group compared to those in the HIV-pre & TB-pre group, while IFN CD8 T cell numbers were augmented in the HIV-pre group compared to that in the HIV-pre & TB-pre group (Fig. S4D). Although the differences did not reach statistical significance among the three groups due to the limited sample size, these data will serve as a reference to investigate the role of T/NK cell subsets in HIV-1-TB co-infection in future studies.

Fig. 3.

Fig. 3

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Ten T/NK cell subclusters confirmed via PBMC scRNA-seq. (A) UMAP plot showing the subclusters of T/NK cells in different groups. Cell clusters and cells from different groups are indicated by color. (B) Bubble plot showing the percentage (size) and relative expression levels (color) of marker genes in each subcluster of T/NK cells. (C) Proportion of cells of the T/NK cell subsets in HIV-pre, HIV-pre & TB-pre, and HIV-pre & TB-pos groups. (D) Violin plot showing the relative expression levels of marker genes in each subcluster of T/NK cells. (E) Differential gene expression analysis of contrasting T/NK cell subclusters in different groups. Heatmaps of differentially regulated genes in T/NK cell subclusters are presented.

2.4. Seven subclusters of naïve T cells identified using PBMC scRNA-seq

Early activation of naïve T cells involves better control of Mtb growth (Urdahl et al., 2011), while lymphoid tissue injury caused due to HIV-1 infection depletes the number of naïve T cells (Zeng et al., 2012). In this study, naïve T cells were further divided into seven subsets, and all of them were present in the three groups (Fig. 4A, Fig. S5A). Subset 0 (Naïve CD4+ T cell) expressed high levels of CD3D, CD4, and SELL (Elyahu et al., 2019) (Fig. 4B, 4D, 4E, Figs. S5B and S5C). Subset 2 (Naïve CD8+ T cell) expressed high levels of CD8A, LEF1, and SELL (Fig. 4B, 4D, 4E, Figs. S5B and S5C). Subset 3 (Cytotoxic CD8 T cells) expressed high levels of CD8A, GZMA, and NKG7 (Deng et al., 2021) (Fig. 4B, 4D, 4E, Figs. S5B and S5C). Subset 4 (regulatory T cells) produced high levels of CD3D, FOXP3, IL2RA, CTLA4, RTKN2, IKZF2, and TIGIT, suggesting that these cells may repress protective responses that contribute to pathogen replication and dissemination (Schumann et al., 2020; Shelyakin et al., 2021) (Fig. 4B, 4D, 4E, Figs. S5B and S5C). Subset 6 (IFN high T cells) expressed high levels of CD3D, IFIT3, ISG15, RSAD2, MX1, and IFIT1 (Fig. 4B, 4D, 4E, Figs. S5B and S5C). Subset 1 (Undefined T cells) expressed high levels of CD3D. Subset 5 (Megakaryocyte) expressed high levels of CD3D and PPBP, suggesting that these may be megakaryocyte-like cells. We also found that the number of naïve CD4 T cells increased but that of regulatory T cells decreased in the HIV-pre & TB-pre group compared to that in the HIV-pre group without statistical significance (Fig. S5D). Further investigations are required to elucidate the function of naïve CD4 and regulatory T cells in HIV-1-TB co-infection.

Fig. 4.

Fig. 4

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Seven subclusters of naïve T cells identified via PBMC scRNA-seq. (A) UMAP plot showing subclusters of naïve T cells in different groups. Cell clusters and cells from different sample groups are indicated with color. (B) Bubble plot showing the percentage (size) and relative expression levels (color) of marker genes in each subcluster of naïve T cells. (C) Proportion of naïve T cell subsets in HIV-pre, HIV-pre & TB-pre, and HIV-pre & TB-pos groups. (D) Violin plot showing the relative expression levels of marker genes in each subcluster of naïve T cells. (E) Differential gene expression analysis of contrasting naïve T cell subsets in different groups. Heatmaps of differentially regulated genes in naïve T cell subclusters are presented.

2.5. Nine subclusters of myeloid-derived cells identified via PBMC scRNA-seq

A large number of myeloid cells highly express the inflammatory biomarkers S100A8, S100A9, and S100A12 (Maertzdorf et al., 2016; Kaforou et al., 2013; Cai et al., 2020) in Mtb infection. Myeloid cells are also associated with inhibiting an early step of HIV-1 life cycle (Laguette et al., 2011). Moreover, increased cell death of macrophages and monocyte turnover have been observed in HIV-1-TB co-infection (Khan & Divangahi, 2018). To further characterize the remodeling of the myeloid cell cluster, we sub-clustered the myeloid cell cluster and investigated the changes in the frequencies of subsets among HIV-pre vs HIV-pre & TB-pre vs HIV-pre & TB-pos groups (Fig. 5A, Fig. S6A). We detected two monocyte subsets, characterized as non-classical (CD14low FCGR3A (CD16)+) and intermediate (CD14+CD16+) monocytes, and the classical (CD14+CD16−) monocytes were not obviously observed among the three groups (Fig. 5B and C, Figs. S6C–E). The classical monocyte subset might be included in the intermediate monocyte subset. scRNA-seq analysis showed that non-classical CD14lowCD16+ cells could re-cluster into five subsets (0–4) according to different levels of marker expression (Fig. 5B and C, Figs. S6C–E). Particularly, subsets 0 and 2–4 expressed high levels of LYZ, S100A8 and S100A9, suggesting that these may be inflammatory monocytes (Cai et al., 2020) (Fig. 5B and C, Figs. S6C–E). Subset 1 contained inflammatory monocytes/macrophages highly expressing CTSD, JUND, and CD68 markers (Cai et al., 2020) (Fig. 5B and C, Figs. S6C–E). Intermediate monocytes (5) expressed high levels of PF4, CD14, and FCGR3A markers (Fig. 5B and C, Figs. S6C–E). Moreover, this subset also expressed high levels of inflammatory markers, including S100A8, S100A9, S100A12, and LYZ, which are associated with disease progression. Based on specific marker expression, we also found a DC subset (6) (CD1C and CLEC10A) (Piccioli et al., 2007; Villani et al., 2017), a pDC subset (7) (JCHAIN, LILRA4, and CLEC4C) (Chappell et al., 2014; Källberg & Leanderson, 2008; Reizis, 2010) and a stem cell-like subset (8) (CD34; among myeloid cells) (Brown et al., 1991). Taken together, scRNA-seq analysis identified nine subsets in myeloid cells expressing different markers.

Fig. 5.

Fig. 5

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Nine subclusters of myeloid-derived cells identified via PBMC scRNA-seq. (A) UMAP plot showing subclusters of myeloid cells in different groups. Cell clusters and cells from different sample groups are indicated with color. (B) Bubble plot showing the percentage (size) and relative expression levels (color) of marker genes in each subcluster of myeloid cells. (C) Violin plot showing the relative expression levels of marker genes in each subcluster of myeloid cells. (D) Boxplot showing the abundance of each cell type among PBMCs from four sample groups (Wilcoxon test). (E) Differential gene expression analysis of the contrasting CD14 Mono subset in different groups. Heatmaps of up- and down-regulated genes among the three groups are presented. (F) Bar plot showing the top ten GO biological process enrichment terms for differentially expressed genes of CD14 Mono cluster among the three groups.

2.6. Increased CD14 Mono and decreased Mono_MФ of myeloid cells observed in HIV-pre & TB-pre group

We found that the frequency of CD14 Mono cell subset in whole PBMCs from the HIV-pre & TB-pre group was augmented compared to that in the HIV-pre group (Fig. 5D). Although the proportion of the CD14 Mono cell subset in myeloid cells increased, the difference between the two groups was no significant (Figs. S6B and S7A). Interestingly, we found that the proportion of Mono_MФ in myeloid cells significantly decreased in patients with HIV-TB co-infection (HIV-pre & TB-pre group) compared to that in the HIV-pre group (Fig. S7A). Additionally, we found that the proportion of a DC subset decreased in patients with HIV-1-TB co-infection (HIV-pre & TB-pre) compared to that in the HIV-pre group, although the difference was not significant (Fig. S7A). Biological process (BP) enrichment analysis revealed that Mono_CD14 cells were mainly enriched in response to virus, viral replication, and type Ⅰ interferon signaling, suggesting their involvement in the activation of HIV-1-contained CD14 Mono (CD14+, CD16+) cells and chronic immune activation (Imp et al., 2017) (Fig. 5E and F). Further, cells in the HIV-pre & TB-pre group were mostly enriched in neutrophil activation and degranulation, antigen processing and presentation, and interferon-gamma-mediated signaling, indicating their involvement in bacterial infection and subsequent monocyte recruitment, activation, and inflammation (Henderson et al., 2003) (Fig. 5E and F). Similarly, BP enrichment analysis demonstrated that Mono_MФ was mainly enriched in type Ⅰ interferon signaling pathway and response to virus in the HIV-pre group, while it was mainly enriched in antigen processing and presentation and intrinsic apoptotic signaling pathway in the HIV-pre & TB-pre group. This result indicated that Mono_MФ cells were activated in response to HIV-1 and HIV-1-TB co-infection, and apoptosis likely contributes to the decreased Mono_MФ numbers in the HIV-pre & TB-pre group (Figs. S7B and S7C). Altogether, our data show that the numbers of CD14 Mono and Mono_MФ particularly increased and decreased, respectively, in the HIV-pre & TB-pre group, and this finding warrants further investigation.

2.7. Monocyte subset proportions sufficiently distinguish HIV-1 from HIV-1-TB co-infection

We validated the role of monocyte cell subset in HIV-1/HIV-1-TB co-infection using another cohort. Consistent with our original sc-RNAseq results, our validation results demonstrated that the frequency of monocytes significantly increased in the HIV-pre & TB-pre group as compared to that in HIV-pre group (Fig. 6A). To further estimate the diagnostic efficacy of the monocyte subcluster in discriminating HIV-1-TB co-infection from HIV-1 infection, receiver-operating characteristic (ROC) curve analysis was performed for this cohort. The ROC curve analysis of the HIV-pre vs HIV-pre & TB-pre group suggested that monocytes could act as a potential molecular marker (area under the curve, 0.8046; Fig. 6B). Therefore, the monocyte cell subset is an effective marker for distinguishing HIV-1-TB co-infection from HIV-1 infection.

Fig. 6.

Fig. 6

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Monocyte subsetproportionssufficiently distinguish HIV-1 from HIV-1-TB co-infection. (A) Blood routine analysis of the counts of monocytes from HIV-pre and HIV-pre & TB-pre groups in the second cohort. Unpaired t-test was performed, and the data is represented as mean ​± ​SEM. ∗∗∗P < 0.001. (B) ROC curve of monocyte subsets to distinguish the HIV-pre & TB-pre group from the HIV-pre group (AUC = 0.8046). AUC, area under curve.

3. Discussion

In the present study, to characterize the effect of HIV-1-TB co-infection on circulating immune-cell profiles, we subjected > 60,000 PBMCs obtained from donors in HIV-pre, HIV-pre & TB-pre, and HIV-pre & TB-pos groups to scRNA-seq analysis and determined their transcriptomic characteristics and the ratio of PBMC subclusters among the three groups. The scRNA-seq analysis identified three primary cell types—T cells, B cells, and myeloid cells—and they were further sub-divided into 32 subclusters according to specific gene expression. Moreover, our scRNA-seq data confirmed many additional markers, including myeloid cell markers (FCN1, AIF1, and LST1), B cell markers (BANK1, RALGPS2, TCF4, and IRF8), and T cell markers (IL-17R). These markers may be used for further studies to differentiate distinct immune cell subsets of PBMCs and explore their role in the diagnosis and protective immunity against HIV-1-TB co-infection.

The frequency of circulating cell subsets alters upon HIV-1 or Mtb infection, which causes dysregulation of the host immune system (Cai et al., 2020; Wang et al., 2020). Wang's study showed that the proportion and role of CD4+, CD8+ T, and B cell subclusters are significantly altered in HIV-1 infection (Wang et al., 2020). Cai et al. found that a natural killer cell subset was depleted, while the numbers of inflammatory monocytes and a B cell subset were increased in TB (Cai et al., 2020). Consistent with previous studies, our scRNA-seq data demonstrated that fluctuations in subsets of these cell types among HIV-pre, HIV-pre & TB-pre, and HIV-pre & TB-pos groups, such as increased CD14 Mono and decreased Mono_MФ subset proportion in the HIV-pre & TB-pre group compared with that in the HIV-pre group, were closely associated with different disease states, particularly those that are likely specific for diseases. Our PBMC atlas offers new perspectives into cell subsets involved in HIV-1-TB co-infection.

The CD14+CD16+ monocyte subcluster accounts for approximately 10% of the total monocyte population in normal subjects, and these proportions are altered during HIV-1 or Mtb infection. HIV-1 can infect and replicate in CD14+CD16+ monocytes (Zhu et al., 2002). Moreover, these monocytes are critical to HIV-1 pathogenesis and comorbidities (León-Rivera et al., 2020) and involved in disease progression in long-term HIV-1 infection (Han et al., 2009). The monocytes are augmented in numbers in peripheral blood during HIV-1 infection and preferentially infected with HIV-1, which may serve as a viral reservoir (León-Rivera et al., 2020). Castano et al. found that the frequency of intermediate and non-classical monocytes increased while that of classical monocytes decreased in TB patients, and intermediate monocytes may play a role in T cell activation, proliferation, and antigen presentation (Sampath et al., 2018). Similarly, another report mentioned that an increased proportion of CD14+CD16+ monocytes, which is involved in disease severity, in the total monocyte population has been observed in TB patients (Balboa et al., 2011). This subset barely stimulates a respiratory burst, shows no response to the early stages of infection, and has little potential to differentiate into functional dendritic cells (Sampath et al., 2018). Moreover, this monocyte subset tends to differentiate into M2-like macrophages, which facilitate bacterial persistence and the establishment of chronic infection (Lastrucci et al., 2015). Consistent with previous studies, our study also showed an increased frequency of CD14+CD16+ monocytes (CD14 Mono) among PBMCs of the HIV-pre group as compared to those of the HC. However, to date, very few studies have investigated the function of CD14+CD16+ monocytes in HIV-1-TB co-infection. A previous report showed that an elevated frequency of CD14+CD16+ monocytes in patients with severe HIV-1-TB is associated with mortality (Janssen et al., 2017). In this study, we first depicted the atlas of PBMCs from HIV-1-TB co-infection using scRNA-seq analysis. We also found altered proportions of circulating monocytes among the three groups. Similarly, a high proportion of CD14+CD16+ monocytes was observed in the HIV-pre & TB-pre group as compared to that in the HIV-pre group. CD14+CD16+ monocytes expressed high levels of S100A8, S100A9, S100A12, LYZ, PF4, and PPBP, indicating that these cells have an inflammatory phenotype. Inflammatory CD14+CD16 monocytes have also been found in patients who died from severe HIV-1-TB co-infection (Janssen et al., 2017), indicating that this subset may contribute to Mtb survival in HIV-1-TB co-infection. Interestingly, HIV-1-infected CD14+CD16+ monocytes primarily migrate across the blood-brain barrier, which is involved in the development of cognitive impairment (León-Rivera et al., 2021). In this case, TB might facilitate the development of HIV-1-associated neurocognitive disorders in patients with HIV-1-TB co-infection. Additionally, we found that Mono_MФ highly expressed the inflammatory markers of S100A8, S100A9, and CD68 (Kaforou et al., 2013; Maertzdorf et al., 2016), the levels of which were decreased in the HIV-pre & TB-pre group as compared to that in the HIV-pre patients. This indicates that the inflammatory effect may be caused by the immediate loss of the membrane integrity of macrophages upon death, which may facilitate bacterial survival (Mahamed et al., 2017). Together, our sc-RNA-seq data revealed that the interaction between HIV-1 and Mtb in the host induced the expansion of the inflammatory CD14+CD16+ monocyte subcluster, which may contribute to both HIV-1 and Mtb pathogenesis.

Although a large number of reports are focused on the search for new TB biomarkers, the usefulness of these biomarkers remains limited. The available methods for TB examination, such as culture, smear microscopy, and nucleic acid amplification technique, are not sufficient for TB diagnosis, specifically for patients without sputum and HIV-1-TB co-infection. In this study, we found that the variation in the monocyte subset levels was distinct and specific enough to distinguish HIV-1-TB co-infection from HIV-1 infection, and it might be regarded as a useful biomarker for this purpose. Moreover, our results showed that anti-TB treatment might not affect the frequency of monocyte subsets (Fig. 5D), suggesting that the frequency of monocyte subsets is an efficient biomarker for TB diagnosis in HIV-1-TB co-infections following anti-TB therapy.

Our study has a few limitations. First, gender bias might exist, because a small number of samples were used in the study. Second, due to restrictions imposed by some objective factors, alterations in certain subsets identified in our scRNA-seq data, such as plasma B cell subset, could not be further validated. Third, our scRNA-seq data could not identify low-frequency immune-cell populations due to the limitation of resolution. More cells and high-resolution single-cell analysis tools are required to better characterize these cell subsets. Additionally, serial PBMCs were not obtained from the same patients.

In summary, to the best of our knowledge, our scRNA-seq data depicted the first atlas of PBMC immune-cell subclusters in HIV-1-TB co-infection via scRNA-seq analysis. Better characterization of circulating cell subsets may clarify the role of cell subclusters and mechanisms underlying the dysfunction of the immune response in HIV-1-TB co-infection, such as the increase in inflammatory CD14+CD16+ monocyte numbers, which might offer novel insights into TB diagnosis and immune-based therapy of HIV-1-TB co-infections.

4. Materials and methods

4.1. Subjects and specimen acquisition

The discovery cohort used for scRNA-seq analysis included patients with HIV-1 infection alone (HIV-pre, n = 5), HIV-1-TB co-infection without anti-TB treatment (HIV-pre & TB-pre, n = 3), and HIV-1-TB co-infection with anti-TB treatment for 2 weeks (HIV-pre & TB-pos, n = 3). The validation cohort comprised HIV-pre (n = 40) and HIV-pre & TB-pre (n = 40) for a routine blood differential test. HIV-1 diagnosis was based on positive HIV-1 RNA levels in the absence of results for positive enzyme-linked immunosorbent antibody assay and confirmatory Western-blot antibody test for HIV-1. None of the enrolled participants had received antiretroviral therapy. Diagnosis of active pulmonary TB was performed according to clinical characteristics and based on the results obtained for chest radiography, microscopy for acid-fast bacillus, nucleic acid amplification test, and Mtb culture. All confirmed pulmonary TB patients were drug-sensitive and followed a standard 6-month treatment regimen recommended by WHO (a 2-month intensive phase of isoniazid, rifampin, pyrazinamide, and ethambutol followed by a 4-month continuation phase of rifampin and isoniazid). Detailed clinical information is shown in Table S1. Peripheral blood samples were collected from the recruited individuals via venipuncture. PBMCs from the discovery cohort were isolated via density gradient centrifugation over Ficoll-Hypaque, and whole blood from the validation cohort was used for blood differential tests performed on COULTER LH 755 (Beckman Coulter, Fullerton, CA, USA).

4.2. Single-cell RNA library construction and sequencing

PBMCs were obtained from fresh blood as depicted previously (Cai et al., 2020). Cell viability (>90%) was evaluated by trypan blue staining. DNBelab C4 system was utilized for scRNA-seq library preparation as previous discribed (Liu et al., 2019, p. 818450). Briefly, the single-cell suspensions were transformed into barcoded scRNA-seq libraries via steps including droplet generation, emulsion breakage, mRNA captured beads accumulation, inverse transcription, cDNA amplification, and purification. cDNA production was sheared to short fragments with 250–400 bp, and indexed sequencing libraries were constructed in line with the manufacturer's instruction. Qualification was performed using Qubit ssDNA Assay Kit (Thermo Fisher Scientific) and Agilent 2100 Bioanalyzer. All libraries were further sequenced by the DIPSEQ T1 sequencing platform (China National GeneBank) with pair-end sequencing.

4.3. scRNA-seq data processing and cell clustering

The scRNA-seq data of PBMCs from HCs generated using the DNBelab C4 system were obtained from CNGB Nucleotide Sequence Archive (CNSA: https://db.cngb.org/cnsa) under the accession number CNP0001102 (Zhu et al., 2020). The sequenced data were processed using an open-source pipeline (https://github.com/MGI-tech-bioinformatics/DNBelab_C_Series_HT_scRNA-analysis-software). Briefly, all samples were subjected to sample de-multiplexing, barcode processing, and single-cell 3′ unique molecular identifier (UMI) counting with the default parameters. Next, the obtained reads were aligned to the GRCh38 genome reference using STAR (v2.5.3). The available cells were automatically acquired according to the UMI number distribution of each cell using the “barcodeRanks()” function of the DropletUtils tool. Finally, we used PISA to calculate the gene UMI count of cells and create a gene × cell UMI count matrix. The gene expression matrix was used for cell clustering analysis using Seurat (version 3.2.2) (Butler et al., 2018). Cells harboring less than 200 genes (UMI >0) or more than 3000 genes or over 5% UMI originated from the mitochondrial genome were identified as low quality cells. Following the removal of cells, the gene count matrix was normalized using log1p normalization, and the top 2000 highly variable genes were then selected to perform principal component analysis. We used harmony (https://github.com/immunogenomics/harmony) (Korsunsky et al., 2019) to integrate the healthy control data with our data using 30 PCs. Following this, 30 dimensions of harmony reduction were used for subsequent Louvain clustering and Uniform Manifold Approximation and Projection (UMAP) based-visualization using UMAP. The cell types were identified using known markers that were highly expressed in a specific subset. For B cell, T/NK cell, naïve T cell and myeloid cell subtypes sub-clustering, 30 PCs were firstly used for harmony reduction, and then top 20 dimensions of harmony reduction were for clustering and UMAP visualization.

4.4. Differential gene expression analysis

Differentially expressed gene (DEG) analysis in each subset/group was confirmed through the FindAllMarkers function of the Seurat package (v3.2.2) with default parameter setting.

4.5. Pathway analysis

ClusterProfiler(Yu et al., 2012) was applied to GO enrichment analysis with differentially expressed genes of a cell type among different groups. GO terms with a p value < 0.05 were significant and the top ten GO terms of biological process were shown.

4.6. Statistical analysis

The differences in the cell ratio between two groups were confirmed through Wilcoxon test. The comparison of the number of monocyte between two groups was performed through unpaired two-tailed t-test. Receive-operating characteristic (ROC) analysis was performed to determine the power of peripheral monocyte to distinguish HIV-1-TB co-infection from HIV-1 infection. All statistical analyses and presentations were performed using R3.6.1 or GraphPad Prism 7.

Data availability

The scRNA-seq processed expression data of HIV/TB infection patients can also obtain from CNSA with accession number CNP0002505.

Author contributions

Qinglong Guo, Yu Zhong, Zhifeng Wang, Tingzhi Cao: Methodology, Software, Validation, Formal analysis, Investigation and Writing; Mingyuan Zhang, Peiyan Zhang, Waidong Huang, Jing Bi, Yue Yuan, Min Ou, Xuanxuan Zou, Guohui Xiao, Yuan Yang, Shiping Liu: Methodology, Visualization, Validation; Longqi Liu, Zhaoqin Wang, Guoliang Zhang, Liang Wu: Conceptualization, Methodology, Writing, Supervision, Project administration and Funding acquisition.

Declaration of competing interest

None.

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 82170009, 81873958, 81802058), the National Key Research and Development Plan (No. 2020YFA0907200), the Guangdong Scientific and Technological Foundation (No. 2019B1515120041, 2020B1111170014), the Shenzhen Scientific and Technological Foundation (No. KCXFZ202002011007083), the Sanming Project of Medicine in Shenzhen (No. SZSM201911009), and Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831). We also thank the support provided by China National GeneBank.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cellin.2022.100005.

Contributor Information

Guoliang Zhang, Email: szdsyy@aliyun.com.

Liang Wu, Email: wuliang@genomics.cn.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Multimedia component 1
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Data Availability Statement

The scRNA-seq processed expression data of HIV/TB infection patients can also obtain from CNSA with accession number CNP0002505.

Articles from Cell Insight are provided here courtesy of Elsevier

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