Single-cell transcriptome reveals highly complement activated microglia cells in association with pediatric tuberculous meningitis

Abstract

Background
Tuberculous meningitis (TBM) is a devastating form of tuberculosis (TB) causing high mortality and disability. TBM arises due to immune dysregulation, but the underlying immune mechanisms are unclear.

Methods
We performed single-cell RNA sequencing on peripheral blood mononuclear cells (PBMCs) and cerebrospinal fluid (CSF) cells isolated from children (n=6) with TBM using 10 xGenomics platform. We used unsupervised clustering of cells and cluster visualization based on the gene expression profiles, and validated the protein and cytokines by ELISA analysis.

Results
We revealed for the first time 33 monocyte populations across the CSF cells and PBMCs of children with TBM. Within these populations, we saw that CD4_C04 cells with Th17 and Th1 phenotypes and Macro_C01 cells with a microglia phenotype, were enriched in the CSF. Lineage tracking analysis of monocyte populations revealed myeloid cell populations, as well as subsets of CD4 and CD8 T-cell populations with distinct effector functions. Importantly, we discovered that complement-activated microglial Macro_C01 cells are associated with a neuroinflammatory response that leads to persistent meningitis. Consistently, we saw an increase in complement protein (C1Q), inflammatory markers (CRP) and inflammatory factor (TNF-α and IL-6) in CSF cells but not blood. Finally, we inferred that Macro_C01 cells recruit CD4_C04 cells through CXCL16/CXCR6.

Discussion
We proposed that the microglial Macro_C01 subset activates complement and interacts with the CD4_C04 cell subset to amplify inflammatory signals, which could potentially contribute to augment inflammatory signals, resulting in hyperinflammation and an immune response elicited by Mtb-infected tissues.

Full text

Single-cell transcriptome
reveals highly complement
activated microglia cells in
association with pediatric
tuberculous meningitis
Siwei Mo
1,2
†
, Chenyan Shi
2,3
†
,YiCai
2
, Maozhu Xu
4
,
Hongmei Xu
5
, Yuzhong Xu
6
, Kehong Zhang
6
, Yue Zhang
2
,
Jiao Liu
7
, Siyi Che
8
, Xiangyu Liu
1
, Chaonan Xing
2
, Xiaoru Long
1
*,
Xinchun Chen
2
*and Enmei Liu
1
*
1
Department of Respiratory Medicine, Children’s Hospital of Chongqing Medical University, National
Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of
Child Development and Disorders, China International Science and Technology Cooperation Base of
Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University,
Chongqing Key Laboratory of Pediatrics, Chongqing, China,
2
Guangdong Provincial Key Laboratory of
Regional Immunity and Diseases, Department of Pathogen Biology, School of Medicine, Shenzhen
University, Shenzhen, China,
3
School of Public Health, Shenzhen University Medical School, Shenzhen
University, Shenzhen, Guangdong, China,
4
Maternal and Child Care Health Hospital of Zunyi City,
Zunyi, Guizhou, China,
5
Department of Infectious Diseases, Children’s Hospital of Chongqing Medical
University, Chongqing, China,
6
Department of Clinical Laboratory, Shenzhen Baoan Hospital, The
Second Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China,
7
Pediatric
Research Institute, Children’s Hospital of Chongqing Medical University, Chongqing, China,
8
Department of Radiology, Children’s Hospital of Chongqing Medical University, National Clinical
Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child
Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
Background: Tuberculous meningitis (TBM) is a devastating form of tuberculosis
(TB) causing high mortality and disability. TBM arises due to immune
dysregulation, but the underlying immune mechanisms are unclear.
Methods: We performed single-cell RNA sequencing on peripheral blood
mononuclear cells (PBMCs) and cerebrospinal fluid (CSF) cells isolated from
children (n=6) with TBM using 10 xGenomics platform. We used unsupervised
clustering of cells and cluster visualization based on the gene expression profiles,
and validated the protein and cytokines by ELISA analysis.
Results: We revealed for the first time 33 monocyte populations across the CSF
cells and PBMCs of children with TBM. Within these populations, we saw that
CD4_C04 cells with Th17 and Th1 phenotypes and Macro_C01 cells with a
microglia phenotype, were enriched in the CSF. Lineage tracking analysis of
monocyte populations revealed myeloid cell populations, as well as subsets of
CD4 and CD8 T-cell populations with distinct effector functions. Importantly, we
discovered that complement-activated microglial Macro_C01 cells are
associated with a neuroinflammatory response that leads to persistent
meningitis. Consistently, we saw an increase in complement protein (C1Q),
inflammatory markers (CRP) and inflammatory factor (TNF-aand IL-6) in CSF
cells but not blood. Finally, we inferred that Macro_C01 cells recruit CD4_C04
cells through CXCL16/CXCR6.
Frontiers in Immunology frontiersin.org01
OPEN ACCESS
EDITED BY
Qiushui He,
University of Turku, Finland
REVIEWED BY
Debora Decote-Ricardo,
Federal Rural University of
Rio de Janeiro, Brazil
Danielle Oliveira Nascimento,
Federal Rural University of
Rio de Janeiro, Brazil
*CORRESPONDENCE
Enmei Liu
emliu186@126.com
Xinchun Chen
chenxinchun@szu.edu.cn
Xiaoru Long
2387207074@qq.com
†
These authors have contributed equally to
this work
RECEIVED 18 February 2024
ACCEPTED 16 April 2024
PUBLISHED 30 April 2024
CITATION
Mo S, Shi C, Cai Y, Xu M, Xu H, Xu Y, Zhang K,
Zhang Y, Liu J, Che S, Liu X, Xing C, Long X,
Chen X and Liu E (2024) Single-cell
transcriptome reveals highly complement
activated microglia cells in association with
pediatric tuberculous meningitis.
Front. Immunol. 15:1387808.
doi: 10.3389/fimmu.2024.1387808
COPYRIGHT
© 2024 Mo, Shi, Cai, Xu, Xu, Xu, Zhang, Zhang,
Liu, Che, Liu, Xing, Long, Chen and Liu. This is
an open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Original Research
PUBLISHED 30 April 2024
DOI 10.3389/fimmu.2024.1387808

Discussion: We proposed that the microglial Macro_C01 subset activates
complement and interacts with the CD4_C04 cell subset to amplify
inflammatory signals, which could potentially contribute to augment
inflammatory signals, resulting in hyperinflammation and an immune response
elicited by Mtb-infected tissues.
KEYWORDS
pediatric tuberculous meningitis, cerebrospinal fluid, microglia, inflammation, single
Cell RNA sequencing, complement
Introduction
Tuberculous meningitis (TBM) is the most severe manifestation
of extrapulmonary tuberculosis (TB). Although the incidence of
TBM among individuals infected with Mycobacterium tuberculosis
(Mtb) is low, this pathology is characterized by a disability and
mortality rate (1) reaching as high as 20% in affected children under
5 years-of-age (2). TBM affects the central nervous system (CNS)
and manipulates the activity of immune response (3); however, the
specific role played by the immune system in this context is unclear.
So far, we know that lymphocytes —predominantly T/natural
killer (NK) cells/natural killer T (NKT) cells—are the main
infiltrating leukocytes in TBM (4–6). Flow cytometry results from
a cohort study revealed that abT and gdT cells, NK cells and MAIT
cells were decreased in the cerebrospinal fluid (CSF) of those with
TBM patients compared to those with pulmonary TB (5).
Moreover, TBM is characterized by high production of pro- and
anti-inflammatory cytokines such as tumor necrosis factor-a(TNF-
a), interferon-g(IFN-g), IL-1b, IL-6, IL-8 and IL-10, which
ultimately leads to immune disruption (7,8). Specifically,
increased inflammasome activation and decreased T-cell
activation has been reported in the peripheral blood, and a high
concentration of cytokines and chemokines in the CSF, along with
brain injury biomarkers in the ventricular CSF (4). Interestingly,
both an insufficient and excessive host inflammatory response has
been associated with poor outcomes, thus implying that a delicate
balance is crucial for a favorable prognosis (9,10). While these
observations provide a certain level of insight into TBM pathology,
a mechanistic understanding is lacking. Methods used in previous
studies have predominantly relied on bulk-RNA sequencing and
flow cytometry, which have limited resolution to resolve the
heterogeneity of immune cells in complex microenvironments.
Unveiling the profiles of the local and systemic immune
response in TBM patients is urgently needed to improve the
clinical care of affected patients. In this study, we aimed to
determine the characterization of local immune responses to
Mycobacterium tuberculosis, leading to insights into the specific
immune landscape at the site of local Mtb infection. To do so, we
performed massively parallel single-cell RNA sequencing (scRNA-
seq) to compare the immune cell landscape of CSF cells and
peripheral blood mononuclear cells (PBMCs) in a cohort of six
children with TBM to determine the anti-Mtb response at the tissue
level. We identified that a CD4 T-cell subset (with Th17 and Th1
phenotypes) and microglia were specifically enriched in the CSF of
TBM patients compared to PBMCs. These cells exhibited a stronger
interaction with tissue-resident CD4 T cells than other cell
subclusters based on ligand-receptors analysis. Importantly, we
observed that microglia participated in complement activation,
thereby amplifying inflammatory signals and potentially
contributing to pathological inflammation and brain damage.
Materials and methods
Patient recruiting, diagnostic criteria
Children with tuberculous meningitis (TBM) and non-TBM
[viral meningitis (VM) and cryptococcal meningitis (CM)]
admitted to the Affiliated Children’s Hospital of Chongqing
Medical University, China, from September 2021 to October 2022
were recruited into this study. The study cohort included six children
with TBM and one with CM whose PBMCs and lumbar CSF samples
were collected, and one pediatric VM with only CSF sample. All
collected PBMCs and CSF samples were used for scRNA-seq. A TBM
diagnosis was confirmed if the child had a positive CSF culture for
Mtb, or a positive CSF gene X-pert result, or a positive CSF Mtb
macrogene test result, or a positive CSF antacid staining assay
showing evidence of TB plus TBM clinical symptoms evident 1
week after anti-tuberculosis drug therapy. A VM diagnosis was
confirmed if the child had a positive CSF macro gene test. A CM
diagnosis was confirmed if the child had a positive CSF cryptococcal
culture, or a positive CSF cryptococcal macrogene test result, or if the
CM clinical symptoms were still evident after 1 week of lumbar spinal
sheath injection therapy. At the time of sample collection, all children
had received antimicrobial treatment, anti-cancer treatment,
antibiotic treatment, corticosteroids or non-steroidal anti-
inflammatory drugs for no longer than 2 weeks. The characteristics
of all children are shown in Supplementary Table 1.
Mo et al. 10.3389/fimmu.2024.1387808
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PBMC and CSF cell isolation
EDTA-whole blood samples were collected by venipuncture.
PBMCs were obtained from 2 ml of whole blood samples by Ficoll-
Hypaque density gradient separation (Ficoll-Paque Plus; Amersham
Biosciences) (11). Lumbar CSF samples were collected during routine
lumbar punctures for diagnostic or treatment purposes. CSF cells and
the supernatant were separated by centrifugation of ≤1mlCSFat500g
for 5 min at 4°C within 1 h of fresh CSF collection. The isolated PBMCs
and CSF cells were immediately stored at - 195°C until further use.
Plasma from PBMCs and CSF cells supernatant were stored at - 80°C
until further use.
Processing and sequencing of single-
cell libraries
Cell viability was assessed by Trypanosoma cruzi blue staining
and samples (with cell viability ≥90%) were prepared using a 10x
Genomics Single Cell 30 v2 kit according to the manufacturer’s
instructions (BGI, China). The single cell libraries were prepared as
previously described (12) and sequenced on an Illumina HiSeq X
Ten system (Illumina).
scRNA-seq data processing and
quality control
Reads from single cells isolated using 10x chromium were
demultiplexed and then aligned to the human genome (GRCh38),
version 32 (Ensembl 98) using Cell Ranger (version 6.1.2, 10x
Genomics) with default parameters, and counted by molecular
identifier (UMI). The UMI matrix was then analyzed using the
Seurat package (version 4.1.1) in R software (version 4.1.2). Low-
quality cells were filtered out if they expressed fewer than 200 genes
or had a percent.mt (The Percentage of Reads that Map to The
Mitochondrial Genome) >10% (12).
Dimensionality reduction, unsupervised
clustering, and cell type determination
The gene expression matrices of the remaining cells were
normalized with default parameters using the “NormalizeData”
(normalization.method = “LogNormalize”, scale.factor = 10000)
function. Then, the normalized values were used to select highly
variable genes (HVGs) with the “FindVariableFeatures”function
(election.method = “vst”). The expression profilesofHVGswere
converted to z-scores using the function “ScaleData”while unwanted
variables were regressed out with the function vars.to.regress =
“percent.mt”.Principlecomponents(PCs) were estimated based on
the selected HVGs with the function “RunPCA”,andthefirst 20 PCs
explaining most of the overall changes were selected for downstream
analysis. Harmony package (version 1.0) (13) was used to remove the
batch effects of the samples. The two functions “FindNeighbors”and
“FindClusters”were then used to find clusters of expression-similar
cells with empirically set resolutions, and “resolution = 0.8”was used in
the downstream analyses.
For visualization, t-distributed stochastic neighbor embedding (t-
SNE) was used to generate a two-dimensional cell atlas. Two rounds of
clustering were performed to identify the cell type of each cluster; if a
cluster from the second round needed to be further subdivided, the
clustering analysis was performed again. The main immune cell types
were annotated based on the expression pattern of differentially
expressed genes (DEGs) and the canonical gene markers for various
cell types. The first round of clustering identified four main immune
cell types including T cells (CD3D, CD3E, and CD3G), NK cells (CD16
and CD56), B cells (CD79A, CD20, MZB1, and JCHAIN), and myeloid
cells (CD68, CD14, LYZ, and CST3). Several clusters were removed,
including platelets (PPBP, PF4, and NRGN), erythroblast (HBB,
HBA1, and HBA2), and doublets (cells expressing more than one
major cell type marker were considered as doublets). A second-round
clustering was performed with the same method to identify subclusters
of T cells, NK cells, B cells and myeloid cells
Differential composition analysis
A T-test and the Wilcox to test were used for differences in cluster
abundance (cell counts) between CSF donors and PBMC donors. Both
methods were used as the estimated proportion of one cell type in each
donor might be over-dispersed when cell types are scarce. As the
outcomes from both methods were consistent, only the difference
component analysis from the Wilcoxon comparisons is presented (14).
Differential expression, gene ontology
enrichment and KEGG enrichment analysis
The significantly overexpressed marker genes for clusters were
identified using the “FindMarkers”or “FindAllMarkers”function of
Seurat. Genes with an adjusted p value < 0.05 by Wilcoxon rank-
sum test were defined as cluster-specific signature genes. To identify
the potential functions of cell clusters, an enrichment analysis was
performed with marker genes for cell clusters. Gene Ontology (GO)
enrichment and Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway enrichment were performed with the R package
“cluster Profiler (version 4.7.1.2)”.
Gene set enrichment analysis
For the single cluster enrichment analysis, normalized and
centered expression data were converted to z-scores; for each
cluster, the z-score for each cell was the mean value for each gene.
The Wilcoxon rank sum test in the presto package (v1.0.0) was used
to obtain the rank of all genes before the fgsea package (v1.17.1) was
used to calculate GSEA enrichment scores and p values for each gene
set collection. Overall, activity scores for each cell were compared
using generalized linear models and visualized in heatmaps.
Mo et al. 10.3389/fimmu.2024.1387808
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AddModuleScore
Enrichment scores were calculated using Seurat function
AddModuleScore, which calculated the average expression of a
gene set by subtracting the aggregated expression of control gene
sets, which could be deemed as the average relative expression. The
results are visualized in heatmaps.
Developmental trajectory inference
The monocle2 R package (version 2.22.0) was used to infer the
developmental trajectories of each major cell type or cell
populations. The monocle function, “Differential Gene Test”, was
used to detect genes with differential expression between clusters to
construct the cluster-based trajectory.
Cell-cell interaction analysis
To study intercellular communication among cell populations,
the R package “Cell Chat (version 1.6.1)”was applied (15). The
aggregated cell-cell communication network was calculated using the
“aggregate Net”function in Cell Chat. The results were visualized
using the function “net Visual circle”and “net Visual chord gene”.
Measurement of C1Q, C-reactive protein,
TNF-aand IL-6
The concentrations of C1Q, C-Reactive Protein (CRP), TNF-a,
and IL-6 in paired blood plasma and CSF cells supernatant from
patients with TBM (n=8, see Supplementary Table 1 for Cohort II)
were determined using an enzyme-linked immunosorbent assay
(ELISA) kit (Elabscience/Liankebio, China) according to the
manufacturer’s instructions.
Statistical analysis
The statistical tools, methods, and thresholds for each analysis
are described within the respective results, figure legends or
methods sections. Differences between two groups were analyzed
by paired t-test. All statistical analyses were performed in GraphPad
Prism (v8.0). Two-sided statistical tests were conducted, and P
values <0.05 were considered statistically significant.
Results
Single-cell transcriptional profiles of paired
PBMCs and CSF cells in pediatric
TBM patients
Our first aim was to objectively characterize the specific
composition and expression of CSF cells compared to PBMCs
from pediatric patients with TBM. To do so, we performed
single-cell RNA sequencing (scRNA-seq) on paired CSF cells and
PBMCs from six patients (one patient lacked a PBMC sample)
(Figure 1A). We obtained 57,797 single-cell transcriptomes of CSF
cells (six samples) and 64,399 of PBMCs (five samples)
(Supplementary Figure 1A and Supplementary Tables 2,3). After
removing approximately 12.94% of the non-immune cells (0.06%
basal cells, 1.95% erythroblast and 0.59% platelets), duplex cells
(7.05% doublet cells, 0.36% Mix/HSC-like cells, and 2.93%
undefined cells), empty droplets and poor quality droplets (see
Materials and Methods), we extracted 106,386 cells from CSF and
PBMC datasets for further analysis (Supplementary Figures S1B–D
and Supplementary Table 3).
We classified all remaining cells into six distinct clusters by
unsupervised clustering, which were annotated as CD4, CD8, NK,
Myeloid, and B cells as well as proliferating T cells from the cell
cycle based on marker gene expression (Figures 1B,C). We
observed a moderate increase in T cells in the CSF, which
consisted of 36.28% and 32.14% CD4 T cells (n=36556), followed
by 36.47% and 29.59% CD8 T cells (n=35407) and 3.96% and 0.74%
proliferating T cells (n=2624) in CSF cells and PBMCs, respectively.
Notably, myeloid cells and B cells were significantly reduced among
CSF cells: Myeloid cells (n=8604) accounted for 11.95% in PBMCs
but only 4.76% in CSF cells, while B cells (n= 11868) accounted for
17.81% of PBMCs but only 5.32% in CSF cells. Meanwhile, the NK
cell population was increased in CSF cells compared to PBMCs, but
the difference was not statistically significant (Figure 1D and
Supplementary Table 3).
High heterogeneity of CD4 T cells in paired
PBMCs and CSF cells from patients
with TBM
We saw that CD4 T-cells accounted for the largest proportion of
immune cells in the CSF and PBMCs, and thus likely played a
crucial role in regulating Mtb infection. We next further separated
the CD4 T cells into six subsets in order to reveal high level of
heterogeneity (Figure 2A and Supplementary Tables 4,5). To
annotate the subsets, we collected marker gene signatures from
FindAllMarkers in the Seurat package and from published datasets,
which we used for cellular identification or phenotypic
determination (Figures 2B,Cand Supplementary Table 6). Based
on classical markers and published signatures (16–22), we identified
two naive CD4 T-cell subsets, CD4_C01 and CD4_C02, which
highly expressed CCR7, SELL, LEF1, and TCF7 and were enriched
in the naive signatures. CD4_C03 was enriched in the central
memory signature and highly expressed ANXA1, ANXA2, IL7R,
CD74 and CD69. Meanwhile CD4_C04, a tissue-resident subset
(CD4_TRM) (22), was characterized by high HOPX, ID2, CXCR6,
ITGAE, ITGA1, MYADM and PTGER4 expression. According to
previous studies (18,19), CD4_C04 also exhibited Th17 and Th1
phenotypic signatures (Figure 2C and Supplementary Table 6). CD4
regulatory T cells (CD4_C05) (18) had high FOXP3, CTLA4, IL2RA
and RTKN2 expression, while CD4_C06 resembled exhausted T
cells with high LAG3, PDCD1, TIMD4, HAVCR2 expression
Mo et al. 10.3389/fimmu.2024.1387808
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(Figures 2B,Cand Supplementary Tables 4–6). Although all subsets
were found in both PBMCs and CSF cells, their relative proportions
varied depending on the tissue. CD4_C04 and CD4_C06 were
significantly enriched in CSF cells, and CD4_C05 had a slight
increase, although it was significantly enriched in CSF cells
compared to PBMCs. By contrast, CD4_C01 were significantly
reduced in the CSF cells compared to PBMCs (Figure 2D and
Supplementary Table 3).
We next performed functional enrichment analyses of the DEGs
of these subsets. To do so, we examined each state of each subcluster
of CD4 cells across multiple datasets (23)(Figure 2E and
Supplementary Table 8). Among the CSF-enriched CD4 clusters,
we discovered that CD4_C04 and CD4_C06 exhibited high
expression of activation and effector gene signatures, while
CD4_C05 and CD4_C06 cells were predominantly in the
exhaustion state. Additionally, when compared to other CD4 cells,
CD4_C04 and CD4_C06 clusters not only displayed strong
chemotactic, migratory and adhesion features, but also significantly
expressed genes related to cytolytic activity, chemokines, a strong
inflammatory response/IFN response, and co-stimulatory molecules.
CD4_C04 cells exhibited stronger glycolysis and lipid metabolism, as
well as stronger expression of pro- and anti-apoptotic gene
signatures. CD4_C05 showed typical Treg marker expression and
high cytokine expression. TCR signaling was only observed in
CD4_C01 and CD4_C02 (Figure 2E and Supplementary Table 8).
Because the CD4_C06 cluster had a low number of cells
(n=384), we investigated the gene signature of the characteristic
CD4_C04 subcluster in CSF cells. Nine genes were defined by
selected from DEGs between CSF and PBMC (with threshold
adjusted P value <0.05 and the absolute value of log2 fold change
>1.5). Here, two known IFN-g-related genes (GBP5 and CCL5), and
four genes with cytotoxic functions (ALOX5AP, CEBPD, GZMA
and GZMB) were more highly expressed in CSF CD4_C04 cells
compared to the corresponding PBMCs (Figure 2F and
Supplementary Table 7). Gene ontology (GO) enrichment
analysis revealed that genes upregulated in CSF CD4_C04 were
associated with regulation of lymphocyte proliferation, leukocyte
differentiation, chemotaxis and positive regulation of lymphocyte
activation and migration compared to the corresponding
PBMCs (Figure 2G).
A
B
DC
FIGURE 1
Single-cell transcriptional profiles of paired PBMCs and CSF cells from pediatric TBM patients. (A) Experimental procedure for defining and
comparing immune PBMCs and CSF cells in TBM and Non-TBM samples. (B) Immune cell profiles of tSNE, color-coded for each major cell type and
associated cell subpopulations from TBM patients (samples of PBMCs, n = 6; samples of CSF cells n = 5). (C) Dot plots depicting selected marker
genes in cell clusters. The dot size encodes the percentage of cells expressing the gene, and the color encodes the average level of gene expression
per cell. (D) Volcano plots depicting differences in cluster abundance in CSF cells versus PBMCs based on b-binomial regression plotting the change
in multiplicity of differences (log10) versus p-value (- log10) (Methods). Horizontal lines indicate significance thresholds.
Mo et al. 10.3389/fimmu.2024.1387808
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A
B
DE
FG
H
C
FIGURE 2
High heterogeneity of CD4 T cells in paired PBMCs and CSF cells from patients with TBM. (A) The subset strategy and flow diagram of CD4 T-cell
lineage after clustering from paired PBMCs and CSF cells. (B) Dot plots of the average expression of selected typical markers and the percentage of
cells expressing these markers in each labeled CD4 T-cell subtype. (C) CD4 T-cell subsets enriched for specific markers or marker genes from
published datasets; enrichment scores are shown in heatmaps. TRM, tissue resident memory; Tex, exhaustion. (D) Volcano plots showing differences
in CSF versus PBMC CD4 T-cell cluster abundance based on t-tests, plotted as the fold change (log10) versus the p-value (- log10). (E) Heatmaps
showing the expression of 24 curated gene signatures (see Supplementary Table 8) in CD4 T-cell clusters. (F) Volcano plots showing the
differentially expressed genes in CSF CD4_C04 versus PBMC CD4_C04 cells (n = 6). Each red dot indicates passing the P-value and fold difference
(P < 0.01; average fold-change >1.5) thresholds. (G) Enrichment of GO bioprocess (BP) terms in CD4_C04 cells between CSF cells and PBMCs
(options shown; adjusted p-values are indicated by colored bars). (H) Monocle2 analysis of CD4 T-cell cluster pseudotimes: associated cell types
and corresponding states (samples of PBMCs, n = 6; samples of CSFs, n = 5) are shown.
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Complete transcriptomic data for most T cells allowed us to
gain insight into the functional states and interrelationships of these
cells. Using this information, we could construct intercellular
developmental trajectories (24) to model the course of CD4 T
cells over time and to determine their differential trajectories
between CSF cells and PBMCs. By generating a monocle2 plot,
we saw a connecting node between the CD4 T-cell subsets that
represents a potential bridge to transdifferentiation. Among them,
CD4_C03 and CD4_C05 were on the trajectory differentiation
pathway, connecting with other naïve (CD4_C01 and CD4_C02)
CD4 T-cell subsets. We also observed that tissue-resident CD4_C04
cells, regulatory CD4_C05 T cells, and CD4_C06 T cells with
exhaustion features appeared at one end of the pseudo-temporal
node and were in a state of terminal differentiation (Figure 2H).
Together, we could conclude that the developmental states of CD4
T cells in the CSF are ultimately CD4_C04, CD4_C05 and
CD4_C06, with CD4_C04 cells being more enriched in the CSF
compared to PBMC (Figure 2H).
Heterogeneity of CD8 T cells in paired
PBMCs and CSFs from patients with TBM
We next focused our attention on CD8 T cells, as optimal
immunization against Mtb infection involving CD8 T cells (25). We
were able to classify CD8 T cells into six subclusters (Figure 3A and
Supplementary Tables 4,5), and according to the FindAllMarkers
labeling and published signatures, we could annotate CD8_C02 and
CD8_C03 as naive CD8 T cells (CCR7, SELL, LEF1, TCF7),
CD8_C01 and CD8_C05 as effector memory CD8 T cells
(GZMK, CCL5, CCL4, NKG7), CD8_C04 as mucosal-associated
invariant CD8 T (MAIT) cells (SLC4A10, NCR3, ZBTB16, RORA,
KLRB1), and CD8_C06 as exhausted T cells (LAG3, PDCD1,
TIMD4, HAVCR2; Figure 3B and Supplementary Tables 5,6).
We then confirmed the phenotypes of these cells by referring to
published signatures (17,22,26–28). Specifically, CD8_C01 was
characterized by terminal effector CD8 T cells, whereas CD8_C04
was characterized by tissue-resident CD8 T cells and MAIT cells
(Figure 3C and Supplementary Table 6). In terms of the proportions
of CD8 T cells between the CSF and PBMCs, we noticed that subset
CD8_C02 were significantly decreased. Meanwhile, CD8_C01,
CD8_C03, CD8_C05 and CD8_C06 were increased in CSF, with
the latter being significantly increased (Figure 3D and
Supplementary Table 3). Compared to PBMCs, the CSF CD8 T-
cell subsets C01-C05 highly expressed inflammation-associated
genes (GBP1, GBP4 and GBP5) and IFN-related genes (ISG20,
SOCS1, STAT1, IFI16, IFI44L) in CSF (Table S7 in the
Supplementary Appendix). In addition, CSF CD8_C01 exhibited
high GZMK and low GNLY expression, indicative of cytotoxicity,
while CD8_C04 showed high GZMB and CXCR6, indicative of
cytotoxicity and residency (Supplementary Table 7).
We next analyzed the state of each subcluster of CD8 cells using
24 curated gene signatures (23). Among the CD8 clusters enriched
in the CSF, we discovered that CD8_C01 exhibited stronger
responses in all four cell states, as characterized by high
inflammatory/IFN responses (Figure 3E and Supplementary
Table 8). For the DEGs of CD8_C03, these were enriched in
senescence and Alzheimer’s disease. Meanwhile, the CD8_C06
subset represented an activated state with characteristics of
exhaustion. Moreover, CD8_C06, enriched in CSF, not only
displayed enhanced Pro-apoptosis/Anti-apoptosis signaling and
metabolism, but also demonstrated strong signaling states in
Chemotaxis, Migration, Stress response, Anergy, TNF signaling,
Adhesion, IFN response, and Co-stimulatory molecules compared
to all other CD8 subsets. The MAIT cells (CD8_C04) (which were
in a lower proportion in CSF compared to PBMCs) displayed
stronger cytokine, inflammatory, and IFN responses and fatty
acid metabolic states. Finally, we observed that the TCR signaling
of CD8 subclusters was mainly concentrated in CD8_C01 and
CD8_C04 of PBMC (Figure 3E and Supplementary Table 8).
We then utilized the same method to track the trajectory of CD8
T-cell development. Monocle2 analysis revealed the differentiation
gradient of CD8 T cells from the initial clusters (CD8_C02 and
CD8_C03) to the activated clusters, as well as the potential
differentiation trajectories among the CD8 subsets (Figure 3F).
The developmental trajectories of CD8 T cells was similar for
CD8_C01 cells (predominantly from the CSFs); by contrast,
CD8_C04 cells (predominantly from PBMCs) and CD8_C05 cells
(from CSFs) exhibited different trajectories according to the
Pseudotime trajectory nodes and were connected to other CD8
subsets. Further analysis revealed that CD8_C01 and CD8_C05
were located at one end of terminal differentiation (the opposite end
of the initial CD8 cluster) and that CD8_C01 and CD8_C05 were
more enriched in CSFs (Figure 3F), suggestive of distinct cell fates of
CD8_C01 and CD8_C05 cells in CSF cells and PBMCs.
Single-cell immune profiling reveals a
unique myeloid cell population in paired
PBMCs and CSF
We next aimed to resolve the composition and transcriptome of
the myeloid cells present in the PBMCs and CSF. Again, using
FindAllMarkers, we sorted the available myeloid cells into 15 cell
subsets comprising five monocyte, four macrophage, four dendritic
cell, and two neutrophil clusters (Figure 4A and Supplementary
Tables 4,5). Mono_C01, C02, C03 and C05 contained the CD14
+CD16- (“classical”) monocytes, as indicated by high VCAN,
FCN1, S100A12 and S100A9 marker expression. Mono_C04
represented non-classical (CD14-CD16+) monocytes, with high
HK3, CX3CR1, PILRA and LST1 expression (Figure 4B and
Supplementary Tables 4,5). As before, we confirmed the
monocyte cell subsets using published signatures (29)(Figure 4C
and Supplementary Table 6). Among the four defined macrophage
subsets, Macro_C01 exhibited characteristics of microglia (30–32)
with the expression of C1QA, C1QB, C1QC, APOE, C3, PLTP,
MAF and SLCO2B1 (Figures 4B,Cand Supplementary Tables 5,6).
Macro_C02 showed high expression of the inflammatory marker
S100A9, while Macro_C03 represented IFN-responsive
macrophages with high IFIT2, ISG15, IFIT3 and TNFSF10
expression. Finally, Macro_C04 showed high LYZ expression
(Figure 4C and Supplementary Table 6).
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We also determined that the dendritic cell subset DC_C01 and
DC_C03 represented classical cDC2 (CD1C, CLEC10A, FCER1A
and CD1E) and cDC1 (CLEC9A, XCR1, CLNK and CADM1),
respectively. A pDC (plasma-like DC) subset DC_C02 was also
identified based on high LILRA4, MZB1, ITM2C and CLEC
expression. Finally, DC_C04 showed cDC characteristics with
high LAMP3 expression (Figure 4B and Supplementary Table 6).
We further defined the characters of DC cell subsets using
published signatures (20,29,33,34)(Figure 4C and
Supplementary Table 6). We identified two neutrophil subsets
A
BD
E
F
C
FIGURE 3
Heterogeneity of CD8 T cells in paired PBMCs and CSF cells from patients with TBM. (A) Subset strategy and flow diagram of the CD8 T-cell lineage
after clustering from paired PBMCs and CSF cells. (B) Dot plots showing marker genes for each CD8 T-cell subcluster. (C) CD8 T-cell subsets
enriched for specific markers or marker genes from published datasets; enrichment scores are shown in heatmaps. Temra, effector memory or
effector; Teff, effector; TRM, tissue resident memory; Tex, exhaustion. (D) Volcano plots showing the differences in CSF versus PBMC CD8 T-cell
cluster abundance based on t-tests, plotted as the fold change (log10) versus the p-value (- log10). (E) Heatmaps showing the expression of 24
curated gene signatures (see Supplementary Table 8) in CD4 T-cell clusters. (F) Monocle2 analysis of CD8 T-cell cluster pseudotimes: associated
cell types and corresponding states (samples of PBMCs, n = 6; samples of CSFs, n = 5) are shown.
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based on our FindAllmarker analysis and published references (29,
35). Neu_C02 highly expressed neutrophil markers (CSF3R,
FCGR3B, G0S2 and NAMPT), whereas Neu_C01 represented a
neutrophil precursor characterized by CAMP, LCN2, RETN and
LTF (Figures 4B,Cand Supplementary Tables 4–6). In the
comparison analysis, we observed that Macro_C01 as microglia
and DC_C03 were specifically enriched in the CSF from TBM
patients, while Mono_C02, Mono_C03 and Mono_C05 cells were
significantly decreased (Figure 4D and Supplementary Table 3).
CSF-enriched Macro_C01 cells are
highly activated
To further investigate the function of microglia (Macro_C01) in the
CFS, we conducted a functional modules enrichment analysis using
multiple datasets and 22 curated gene signatures (Figure 5A and
Supplementary Table 8). The microglia significantly enriched in the
CSF exhibited a stronger response compared to other myeloid cells,
including stronger leukocyte differentiation and activation, as well as
enhanced chemotaxis, migration and chemokine expression. However,
another significantly enriched subset in the CSF, DC_C03, did not
exhibit any enhanced functional module (Figure 5A and Supplementary
Table 8). By comparing to other myeloid cells, we subsequently identified
36 CSF microglia-specific genes (adjusted P value <0.05 and fold-change
>1.5). In addition to the complement related genes (C1QA, C1QB,
C1QC and A2M), these genes included CCL3, APOC1 and APOE
(Figure 5B and Supplementary Table 7), suggesting that CSF microglia
might have pro-inflammatory functions. Our GO results confirmed that
CSF microglia exhibited an activated immune response and were
involved in positively regulating cytokine production and cytokine-
mediated signaling compared to PBMCs (Figure 5C).
CSF microglia also displayed robust fatty acid metabolism.
Interestingly, we discovered that CSF microglia exhibited a strong
A
B
DC
FIGURE 4
Single-cell immune profiling reveals the specific composition and transcriptome of myeloid cells in paired PBMCs and CSF cells. (A) Subset strategy
and flow diagram of myeloid cell lineage after clustering from paired PBMCs and CSF cells. (B) Dot plots showing marker genes for each myeloid
cell subcluster. (C) Myeloid cell subsets enriched for specific markers or marker genes from published datasets; enrichment scores are shown in
heatmaps; BAM, border-associated macrophages. (D) Volcano plots showing differences in CSF versus PBMC myeloid cell cluster abundance based
on t-tests, plotted as the fold change (log10) versus the p-value (- log10).
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phenotype related to the Alzheimer gene signature (Figure 5A and
Supplementary Table 8), suggesting a potential association between
CSF microglia and increased CSF inflammation in TBM. We
therefore calculated the Pearson correlation between complement
activation and the neuroinflammatory response (Figure 5D and
Supplementary Table 9), and found that CSF microglia enriched for
complement activation exhibited a significant correlation with
inflammatory signaling (R=0.734 and p-value=0). We confirmed
A
B
D
E
F
C
FIGURE 5
CSF-enriched Macro_C01 cells are highly activated. (A) Heatmaps showing the expression of 22 curated gene signatures (see Supplementary
Table 8) in myeloid cell clusters. (B) Volcano plot showing differentially expressed genes in CSF Macro_C01 versus other myeloid cells (n = 6). Each
red dot indicates passage of the P-value (val) and the fold difference (P < 0.01; average fold-change >1.5). (C) Enrichment of GO bioprocesses (BP) in
Macro_C01 cells between CSF cells and PBMCs (options shown; adjusted p-values are indicated by colored bars). (D) Pearson’s correlation
regression plot between the Macro_C01 cells complement activation and neuroinflammatory response enrichment score (log2 transformed). Each
point represents a unique cell. The Pearson’s test showed R2 > 0.6 as significant. (E) The levels of C1Q, CRP, TNF-aand IL-6 in paired Blood plasma
and CSF cells from TBM patients, detected by enzyme-linked immunosorbent assay (ELISA) (n = 8). Paired sample differences were analyzed using
by paired t-test; *, P < 0.05; **, P < 0.01; ***, P < 0.001. (F) Monocle2 analysis of myeloid cell cluster pseudotimes: associated cell types and
corresponding states (PBMCs, n = 6; CSF, n = 5) are shown.
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the possible link between complement activation and inflammatory
by ELISA of paired blood plasma and CSF samples from TBM
patients. Consistent with the scRNA-seq data, we detected
significantly higher levels of complement protein C1Q, the
inflammatory marker CRP, and inflammatory factors TNF-aand
IL-6 in the CSF compared to blood (n = 8, see Supplementary
Table 1,Figure 5E). These findings suggest that microglia-mediated
complement activation enhances the release of inflammatory
signals, leading to excessive inflammation in the CSF.
We also looked at the developmental trajectory of the monocyte
subsets. Both the DC and macrophage subsets followed the same
pathway, but eventually, Macro_C01 served as the two end states of
differentiation. In contrast to PBMCs, the CSF cells had the most
abundant end state of developmental differentiation with Macro_C01
cells, but exhibited two different developmental trajectory directions
(Figure 5F). Taken together, these data support the existence of an
enhanced inflammatory response of CSF microglia cells than other
myeloid cells in TBM, and their involvement in excessive
inflammation through complement activation.
A scRNA-seq data expose four B-cell
subsets in paired PBMCs and CSF cells
The role of B cells during Mtb infection is unclear, as only a few
studies have explored their function in TB (36) and even fewer in
TBM (37). Moreover, information on B-cell phenotype and
function is limited due to difficulties in maneuvering B cells. Our
scRNA-seq analysis identified four distinct B-cell subsets in TBM,
each representing a different stage of B-cell development
(Supplementary Figure 2A and Supplementary Tables 4,5). In
addition to expressing CD79A, CD79B, MS4A1 and BANK1,
B_C01 also had high levels of IGHD, FCER2, TCL1A and IL4R,
and were subsequently defined as naive B cells. By contrast, B_C02
showed high CD24, AIM2, TNFRSF13B and LRMP expression and
so were defined as memory B cells. B_C03 was specifically enriched
in MZB1, JCHAIN, CD27, SDC1 and CD38 and so was annotated
as comprising plasma B cells. B _C04 cells were annotated as
plasmablasts due to high MNKI67, TOP2A and TYMS expression
(Supplementary Figure 2B and Supplementary Tables 4,5). In our
cohort, all six TBM individuals had the four B-cell subsets present;
however, and in contrast to PBMCs, none of these four subsets were
significantly enriched in the CSF (Supplementary Figure 2C and
Supplementary Table 3).
Further functional modules analysis of the B-cell subsets
revealed that B_C01 cells exhibited weaker states of function,
metabolism and apoptosis. B_C02 showed stronger signaling in
antigen presentation, proliferation, chemotaxis, migration and
adhesion compared to other B cell subsets, while B_C03 showed
stronger BCR signaling, phagocytosis and senescence compared to
other B-cell subsets enriched in the CSF compared to PBMC.
Interestingly, we also observed that the CSF-enriched B_C03
subset exhibited strong stress, metabolism, and apoptosis
responses (Supplementary Figure 2D and Supplementary
Table 8). Finally, the trajectory analysis demonstrated that B_C01
and B_C02 subsets in the CSF differentiated toward the B_C03 and
B_C04 subsets, and reached the final developmentally differentiated
state of the cells (Supplementary Figure 2E).
Macro_C01 has stronger interactions with
CD4_04 T cells than other CD4 T cells
Thus far, we have gained an understanding of the landscape and
function of T cells and myeloid cells in TBM. In our final analyses,
we investigated how Macro_C01 cells regulate these T-cell subsets.
To do so, we utilized a set of ligand-receptors (L-R) to understand
the relationship between these cell clusters. We discovered that the
interaction between macrophages and T cells was particularly
prominent in the CSF, especially within the Macro_C01 cluster
(microglia). Furthermore, the number and strength of interactions
between Macro_C01 and CD4 T cells was stronger than those with
CD8 T cells in the CSF (Figures 6A,B), with strong interactions
between Macro_C01 and CD4_C04 (tissue-resident T cells) and
CD4_C06 (exhausted T cells) compared to other subsets in the CSF
(Figures 6C,D). The interaction between Macro_C01 and
CD4_C04, CD4_C06 in CSF was mediated by CXCL16 and
CXCR6; CXCL9, CXCL10 and CXCR3; CXCL12 and CXCR4;
and CCL3 and CCR5. The interaction between Macro_C01 and
CD4_C04, however, was primarily mediated by the ligand-receptor
CXCL16–CXCR6 (Figure 6E). These interaction analyses revealed
the axis of cellular plasticity and ligand pleiotropy in TBM.
Given that TBM and Non-TBM (VM, CM) are completely
different forms of meningitis, we further analyzed the available
single-cell data by mapping cell types (Supplementary Figures 3A,
B). We discovered that Macro_C01 and CD4_C04 were
significantly more abundant in the CSF than PBMCs, from those
with Non-TBM (n = 2; Supplementary Figures 3C,Dand
Supplementary Table 3). Interestingly, we also observed that
Macro_C01 and CD4_C04 were significantly more abundant in
TBM, rather than Non-TBM (n = 2; Supplementary Figures 3E,F
and Supplementary Table 3). These findings provide a reference
point for future studies on the specific roles of TBM and Non-TBM
in Macro_C01 and CD4_C04 cells.
Discussion
TBM is a deadly and challenging-to-treat form of tuberculosis
(TB) that occurs in parts of the body other than the lungs, but
immune insights into its development are still limited. In this study,
our objective was to characterize the local immune response in TBM
to gain insight into the Mtb-specific immune landscape at the site of
localized infection in TBM. We obtained paired PBMCs and CSF cells
from children with TBM and performed scRNA-seq and pathway
analysis to identify the immune cell composition. From >100,000
isolated cells, we distinguished three major cell types: T cells, B cells
and myeloid cells. We subsequently categorized these cells into 33
clusters based on quantitative gene expression and from here,
identified CSF-specific leukocyte transcriptomic, compositional and
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functional enrichments, including tissue-resident CD4 T cells
(CD4_C04) and microglia (Macro_C01). Furthermore, we revealed
the connections and potential developmental pathways of T cells and
myeloid cells in TBM. We most notably provide evidence that CSF-
enriched microglia (Macro_C01) might be involved in complement
activation, leading to the development of excessive inflammation. We
identified a distinctive myeloid cell subset in the CSF that partially
resembled CNS-associated macrophages, emphasizing the unique
immune microenvironment of the CSF. Our transcriptomics data,
combined with information on microglia (Macro_C01) and tissue-
resident CD4 T cell (CD4_C04) interactions, ultimately provide a
comprehensive, multidimensional cellular signature of local T-cell
and myeloid cell immunity, particularly in the context of Mtb
infection. This information could inform further studies on the role
of immune cell subsets in the pathogenesis of TBM and protective
immunity against TBM.
Numerous studies have shown that lymphoid infiltrating cell
populations in TBM CSF have high levels of IFN-gand other
associated cytokines (3,5,7). We know that the CSF contains large
numbers of CD4 T cells (38). Increasing evidence also suggests that
tissue-resident memory T cells (Trm) are superior in controlling
many pathogens, including Mtb (39–42). Consistent with these
reports, we newly identified a multifunctional subcluster CD4_C04
in CD4 T cells, which was enriched in the CSF compared to the
blood. CD4_C04 not only seems to act as a source of tissue-resident
memory T cells (ID2, CXCR6, ITGAE, ITGA1, MYADM and
PTGER4) (22), but also contains Th17-like and Th1-like signatures
(RORC, CXCR6, RHOC and HOPX) (18,19). Notably, we found that
CD4_C04 was also a cytotoxic CD4 T lymphocyte (CD4-CTL,
expressing GZMA, GZMB), with a similar gene expression feature
to that of cytotoxic CD8 T cells (43,44), which was reported in a
previous study of tuberculous pleural effusion (TPE) (12). CD4-CTL
A
B
D
E
C
FIGURE 6
Macro_C01 has stronger interactions with CD4_04 T cells than others. (A, B) The interactions made by CD4 and CD8 T cells with different
macrophage subpopulations in the CSF and blood. The number and strength of interactions between cell clusters are indicated. (C, D) The
interactions made by CD4 T cells and Macro_C01 cells in the CSF compared to in the blood. The number and strength of interactions between cell
clusters are indicated. (E) A dot plot of ligand–receptor (L–R) pairs between CD4 T cells and Macro_C01 cells in the CSF compared to in the blood.
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cells aid in the dormitory clearance of pathogens (45,46), while
several studies have shown that CD4-CTL cells exhibit Th1 and Th17
characteristics (47,48) and produce Th1 cytokines, Th17 cytokines,
IFN-gand TNF-a(49,50). Consistently, our results support that CTL
CD4_C04 cells exhibited robust cytokine and IFN-gexpression, as
well as an inflammatory response status. In addition, our analysis of
the developmental differentiation of CD4 T cells suggested that
CD4_C04 cells might originate from the differentiation pathway —
afinding that now warrants further investigation. It would now be
interesting to explore whether therapeutic strategies targeting these
intermediate populations could promote the killing and/or activation
of Mtb-infected macrophages. Nevertheless, our results clearly
highlight the persistent adaptive immune response in the CSF of
TBM patients, and prompt us to further investigate the role of CD4-
CTL cells in TB. In contrast to blood, we also identified a regulatory T
cell subset, CD4_C05, enriched in the CSF. Previous studies have
shown that cytokine activation/expansion and the recruitment of
specific chemokines is involved in the enrichment of regulatory T
cells (Treg) in local tissues or organs under pathological conditions
(51). Here, we saw that CD4_C05 was also characterized by
exhaustion, suggesting a reduced ability to inhibit inflammation
production and maintain immune cell homeostasis.
Mounting evidence suggests that the control of Mtb infection by
CD8 T cells is mediated by granzymes (25,52,53). From the
samples used in our study, we observed that heterogeneous CD8 T-
cell subsets in the CSF exhibited a different phenotype compared to
those derived from PBMCs. We observed upregulated GZMK in
CD8_C01 cells within the CSF, implying that the CSF
microenvironment influences the development of CD8 T cells
expressing GZMK. This phenomenon might signify distinctions
in antigens, tissue factors, or bacterial presence between the blood
and CSF. We know that CD8 T cells expressing GZMK can produce
cytokines in response to antigen-dependent and antigen-
independent stimuli, potentially driving inflammation (54,55).
Notably, our findings revealed that GZMK-expressing CD8_C01
cells in the CSF expressed IFN-like inflammatory genes. Moreover,
these cells were notably enriched for cytokines, IFN and
inflammatory response states. This compelling evidence strongly
suggests that this cell subset possesses the capacity to actively
promote and contribute to inflammatory responses.
The antimicrobial function of MAIT cells, a unique innate class
of T cells, was supported by the reported protective effects observed
in infection experimental models (56,57). MAIT cells produce
interferon-g(characteristic of Th1 cells) and interleukin IL-17
(characteristic of Th17 cells) with cytotoxic activity (58–60).
Consistent with these studies, we found that CD8_C04 cells were
characterized as MAIT cells (27,28) and expressed GZMA and
inflammatory genes. The developmental trajectory supported that
CD8_C04 cells were at a late stage of cell development in the CSF.
Thus, the enrichment of CD8_C01 and CD8_C04 cells in the CSF
might represent an active, tissue-specific effector mechanism that
mediates tissue inflammation. However, we cannot exclude the
possibility that GZMK-expressing CD8 T cells and GZMA
expressing CD8 MAIT cells contribute to immunopathology, even
though CD8 MAIT cells were fewer in the CSF than PBMCs.
Therefore, future studies should investigate the exact role of
GZMK/GZMA-expressing CD8 T cells in TB progression.
Nevertheless, our data identified CD8_C06 cells that were
enriched in the CSF, which were characterized by exhaustion.
This subset exhibited a strong inflammatory response, energy
metabolism and IFN response.However,manystudieshave
shown (61–63) that T-cell exhaustion manifests as a result of
inactivated T-cell proliferation, the secretion of suppressor
cytokines and decreased production of IFNg.
Microglia are a preferred target for Mtb and immune effector
cells in the CNS. Neuronal cell phenotypes have also been reported,
such as microglia activation (64–66). Once stimulated and activated
by Mtb, microglia secrete cytokines and chemokines that have a
central role in initiating, coordinating, and modulating immune
responses to TB (8,64). We identified a population of microglia
(Macro_C01) enriched in the CSF (19,30–32,67). Consistent with
previous reports (3,6,67), the Macro_C01 subset not only highly
expressed inflammatory genes (APOC1, APOE) and cytokines
(CCL3), but also the complement-activating genes C1QA, C1QB,
C1QC, A2M compared to other myeloid cells. Increasing evidence
supports the idea that pathogens might have a role in the
development of neurodegenerative diseases (68,69). Several
recent studies also found that proteins and metabolites typical of
neurodegenerative diseases (e.g. apolipoproteins) are significantly
associated with TBM (1,4,70). Here, we found a significant
enrichment of Macro_C01 in Alzheimer disease and fatty acid
metabolism states (71,72), suggesting that the TBM CSF is
characterized by intrinsic immune-mediated inflammation.
Others have shown that activated complement signaling by
microglia increases pro-inflammatory cytokine levels, leading to
neuroinflammation (72). Data derived from a mouse model also
found involvement of microglia and complement in Alzheimer
disease attributed to neuroinflammation (73). Others have shown
that tissues infected with Mtb, as well as other infectious meningitis
agents, cause complement activation and induce large amounts of
inflammatory factor production (74,75). Results of an ELISA
revealed the activation of the complement system in TPE (76),
with the complement genes C3a and C5a inducing large amounts of
inflammatory cytokines (77). Other studies on bacterial meningitis
have also shown a positive correlation with complement signaling
activation and the production of large amounts of inflammatory
factors in the CSF (78–80). One prospective study on
pneumococcal-induced meningitis found significantly higher
levels of complement (C3a, iC3b, and C5b-9) in the CSF of
affected patients (79). Notably, we found that activated
complement signaling in Macro_C01 cells was significantly
associated with neuroinflammation, thus suggesting that
microglial-mediated complement activation in the CSF is
responsible for persistent inflammation.
Based on our data, we cannot exclude the possibility that
complement-activated microglia promote pathological
inflammation, because activated microglia have the ability to
induce cell death and increase inflammatory responses (64,81),
which may in turn lead to brain tissue damage and infiltration in
TBM. Furthermore, our proposed temporal trajectory analysis of
myeloid cells indicated that Macro_C01 cells were in a terminal
differentiation state in the CSF compared to other myeloid cells,
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potentially implying that they originate from a distinct
differentiation pathway. It will now be crucial to track the
differentiation trajectory, cellular origin, phenotypic
transformation and antigenic specificity of Macro_C01 cells
during TBM progression and treatment in order to understand
whether excessive pathological inflammation. A particularly
intriguing discovery in our study was the identification of a more
robust and direct interaction between CD4 T cells and Macro_C01
cells in the CSF compared to CD8 T cells, which was characterized
by greater numbers and greater intensity. Supporting this finding,
others have shown that antigen-presenting cells populate the dural
sinus and upon activation can interact with patrolling lymphocytes
(38,82). Taken together, our results emphasize the possibility that
Macro_C01 cells sustain excessive pathological inflammation in the
CSF, but further studies are now needed to clarify the exact role of
microglia-associated functions in TBM.
There remains a lack of standardized diagnostic criteria for
distinguishing TBM and Non-TBM (VM and CM) in the clinic (2,
83,84)—both causative pathogens can potentially cross the blood-
brain barrier and invade the brain leading to meningitis
development (84). Remarkably, we found that microglia
Macro_C01 and tissue-resident CD4_C04 cells were significantly
enriched in the CSF of TBM patients, compared to Non-TBM
patients. This finding suggests that the two population of cells are
promising differential diagnostic markers for TBM and Non-TBM,
despite their exact roles need further investigation.
There are several limitations in this study to consider when
interpreting these findings. First, the cohort of Non-TBM patients
(n=2) resulted in relatively small samples for scRNA-seq. Second,
the analysis of the T cell receptor (TCR) group library was limited
by the absence of paired PBMC and CSF scTCR-seq data. This
meant that the interaction of TCR with MHC-presenting antigens is
crucial for acquired immunity; however, understanding the
characteristics of local and systemic immune responses in patients
with TBM still contribute to the therapeutic efficacy of TBM. Third,
we did not characterize non-immune cell populations. Indeed, non-
immune cell clusters may have some kind of regulatory role on the
immune cell population. We also removed certain immune cell
clusters due to cells expressing more than one major cell type
marker were considered as doublets, and these cells might also be
co-regulated by multiple cell clusters. If we wish to characterize
these removed subsets in the future, we will need to isolate more
cells and leverage additional single-cell analysis tools such as surface
protein labeling (10x Genomics Single Cell 30 v3 Reagent Kit).
Applying these methods will enhance our ability to detect and
identify these crucial cell populations in future analyses.
Our annotation of the molecular and functional differences
between paired PBMCs and CSFs in TBM confirm many important
previous observations, add new detail to the literature, and also
highlight key areas of ongoing research and challenges. For
example, the topic of how specific tissue-resident cell populations
change and contribute to the development of TB is currently under
discussion. To the best of our knowledge, our data represent the first
description of PBMCs and CSF immune-cell subpopulations in
TBM at the scRNA-seq resolution. We believe that our
characterization of tissue-resident cell subsets in TBM provides a
useful framework for studying the role of cell subsets in the
progression of TBM. A thorough understanding of the
mechanisms underlying the increase in TB-associated cell subsets,
such as tissue-resident CD4 T cells and microglia, is expected to
lead to new opportunities for therapeutic and diagnostic
interventions in TBM.
Data availability statement
The data presented in the study are deposited in the NCBI
repository, accession number PRJNA1100176.
Ethics statement
The studies involving humans were approved by The
Institutional Review Board of the Children’sHospitalof
Chongqing Medical University, China (Ethics number: 2023-273).
The studies were conducted in accordance with the local legislation
and institutional requirements. Written informed consent for
participation in this study was provided by the participants’legal
guardians/next of kin.
Author contributions
SM: Writing –original draft, Data curation, Formal analysis,
Investigation, Validation. CS: Writing –review & editing, Data
curation, Investigation. YC: Formal analysis, Writing –original
draft, Data curation. MX: Data curation, Writing –original draft,
Investigation. XH: Investigation, Writing –original draft. YX:
Investigation, Writing –original draft. KZ: Formal analysis,
Writing –original draft, Validation. YZ: Validation, Writing –
original draft. JL: Investigation, Writing –original draft. SC:
Investigation, Writing –original draft. XLi: Investigation, Writing
–original draft. CX: Investigation, Writing –original draft. XLo:
Data curation, Writing –original draft, Investigation. XC: Data
curation, Formal analysis, Supervision, Writing –original draft,
Writing –review & editing. EL: Data curation, Formal analysis,
Supervision, Visualization, Writing –original draft, Writing –
review & editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. This work
was supported by the National Key Research and Development
Program projects (2022YFC2302900), the Shenzhen Medical
Research Special Fund Project (A2304001), National Natural
Science Youth Foundation of China (82202574, 82304839),
General program of clinical Medical Research (NCRCCHD-2020-
Mo et al. 10.3389/fimmu.2024.1387808
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GP-06), and Chongqing Science and health joint medical scientific
research project (2023MSXM051).
Acknowledgments
The authors would like to thank Dr. Jessica Tamanini
(Shenzhen University and ET editing) for editing the manuscript
prior to submission.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
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