Single-cell RNA-seq provides insight into the underdeveloped immune system of germ-free mice

doi:10.21203/rs.3.rs-5288875/v1

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Single-cell RNA-seq provides insight into the underdeveloped immune system of germ-free mice

https://doi.org/10.21203/rs.3.rs-5288875/v1

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Background

Germ-free mice feature a profoundly underdeveloped immune system. Despite recent studies that emphasize the role of specific bacteria-derived metabolites in immune cell development and differentiation, it remains unclear how the lack of microbiota leads to immune deficiencies.

Results

Here we performed droplet-based single-cell RNA sequencing to analyze the bone marrow and peripheral blood of both germ-free and specific-pathogen-free mice, identifying 25 distinct cell types. Our findings indicate that neutrophil apoptosis in germ-free mice is strongly associated with the absence of niacin dehydrogenase, which derived primarily from Pseudomonas. In addition, germ-free mice exhibited elevated excretion of 5’-methylthioadenosine, increased ERK activation induced by reactive oxygen species, and cessation of the bone marrow stromal antigen 2 signaling pathway in germ-free mice. The responses of monocytes and CD8 + T cells to interferon β and interferon γ were reduced in germ-free mice, which accounted for their increased susceptibility to viruses.

Conclusions

Together, we identified a regulatory mechanism connecting immunodeficiency to the absence of microbiota in germ-free mice and validated these findings via multiple techniques.

bone marrow

germ-free mice

microbiota

peripheral blood

single-cell RNA sequencing

underdeveloped immune system

The eukaryotic host and its microbiota do not exist in isolation. Instead, they constitute a complex meta-organism known as the “holobiont”, which regulates multiple aspects of mammalian physiology, such as metabolism, immune system development, and nervous system function [1]. In different microbiome configurations, a broad range of metabolites are produced, modulated, and degraded, providing functional complements to the host’s metabolic capabilities. For example, the microbial community can metabolize complex proteins and carbohydrates that the host cannot degrade [2, 3]. A secondary function of this complex network of metabolites is to communicate intimately with their eukaryotic hosts, which is mediated by metabolite signaling through a series of innate immune receptors [4]. Therefore, it is increasingly apparent that the microbiota and its metabolites are essential coordinators of host physiology and pathophysiology because they control a wide range of metabolic, inflammatory, and even behavioral processes [5].

As a result of their extensive co-evolutionary history with microbes, Louis Pasteur concluded that normal germ-free (GF) animals would not be able to survive [6], whereas Reyniers achieved the first successful rearing of healthy GF rats in 1946 [7]. Since then, studies on such animals have illustrated how microbes play a critical role in mammalian development and health. GF mice are free of all the microorganisms available within the limits of the detection methods available and are confined to a tightly monitored and controlled environment to prevent contamination. GF mice feature a profoundly underdeveloped immune system [8], which can be primarily reversed by conventionalizing the species-specific microbiota in adults [9]. In the 1960s, reports already showed that GF mice had increased susceptibility to influenza A virus [10], coxsackie B virus [11], and Friend virus [12]. There is limited understanding of how the microbiome influences immune system ontogeny, but several studies have suggested that bacteria-derived metabolites, such as short-chain fatty acids [13–16], tryptophan metabolites [17–19] and retinoic acid [20–24], play essential roles in the regulation of immune cell development and differentiation [25].

Several studies have used single-cell RNA sequencing (scRNA-seq) to compare GF and specific-pathogen-free (SPF) mice, but they have focused mainly on specific immune subsets of peripheral tissues, such as colonic CD4 + T cells [26] and brain microglia [27]. However, the immune profiles of the peripheral blood (PB) and bone marrow (BM) of GF and SPF mice have not yet been characterized. In this study, we aimed to reveal the immune landscape of adult GF and SPF mice and to elucidate the causes of the impaired immune system in GF mice. We performed two stages of analysis: discovery and validation. In the discovery stage, we first conducted routine blood tests to understand the differences in immune cell content between GF and SPF mice. We then analyzed approximately 35,000 high-quality single-cell transcriptomes from the PB and BM of GF and SPF mice and obtained some interesting findings. In the validation stage, we used data from non-targeted metabolomics, scRNA-seq, and spatial transcriptomics of different organs or tissues to confirm our findings.

Study design

The aim of this study was to unravel the mechanisms underlying the underdevelopment of the immune system in GF mice. We designed a comprehensive experiment to analyze immune cells in PB and BM of GF mice and SPF mice using scRNA-seq technology. Experiments were performed in a tightly controlled environment to ensure that the sterile mice were free from any microbial contamination. By analyzing the transcriptomes of more than 35,000 high-quality single cells, we compared the immune cell composition and transcriptional profiles of germ-free and SPF mice with the aim of revealing the effects of microbiota deletion on the immune system and validating our findings by a variety of technical means.

Mice

GF and SPF male Kunming mice (aged 10 weeks) were provided by the Huazhong Agricultural University Experimental Animal Center. Our GF mice met all the currently known characteristics that a recognized germ-free mouse should have, including an enlarged cecum and a profoundly underdeveloped immune system.

GF mice were kept in specialized sterile isolators, and any exposure to microorganisms was prevented. SPF mice were kept in a pathogen-free isolator in specific pathogen-free rooms to prevent cross- contamination. All the GF and SPF mice were housed under a 12 h light: dark cycle and controlled climate (temperature: 25 ± 2°C, humidity: 45–60%) with ad libitum access to water and food. GF and SPF mice were fed GF diets sterilized by Co60-γ irradiation at 50 kGy to ensure the elimination of microorganisms. Peripheral blood and bone marrow were isolated from donor mice euthanized by cervical spine dislocation.

Sample preparation and cell isolation for scRNA-seq

Cervical dislocation was used to euthanize the mice. Blood was collected via retro-orbital puncture. The mice were then sprayed with 75% alcohol until they were wet. The skin and muscle between the two hip joints were cut open with sterile ophthalmic scissors, and the lower-limb specimens were snipped at the hip. The complete stripping of the muscles was accomplished by cutting them off with scissors and cleaning them with gauze. The thigh bones and tibias were isolated separately after being cut at the ankle and 1 cm distal to the tibiofibular junction to expose the marrow cavity. The cartilage at both ends was carefully cut to expose the red bone marrow cavity, and the marrow was flushed out from the bone cavity with DMEM (Thermo Fisher Scientific). Fresh blood and bone marrow aspirates were transported and stored at 4°C for 24 h.

For cell isolation, samples were filtered through 40-µm sterile strainers and centrifuged at 300 g and 4◦C for 5 minutes. Afterward, the supernatants were discarded, and the cell pellets were suspended in 1 mL of phosphate-buffered saline (PBS, Invitrogen). For the removal of red blood cells (RBCs), 3 mL of RBC lysis buffer (eBioscience) was added, and the cells were incubated at 25°C for 5 minutes. The cell suspension was gently pipetted into eight mL of PBS to stop the lysis reaction and then centrifuged at 300 g and 4°C for 5 minutes. The bone marrow cells were washed twice with PBS and centrifuged at 300 g and 4°C for 5 minutes, after which the supernatant was discarded. Single cells were resuspended in 0.04% bovine serum albumin (BSA) in PBS and loaded on a 10x Chromium chip. We stained the samples with trypan blue (Invitrogen) and evaluated the cellular viability under an inverted microscope (Zeiss).

Library preparation

ScRNA-seq was performed via the 10x Genomics Chromium Next GEM Single Cell 3’ Reagent Kit v3.1, followed by library construction. Approximately 16,000 cells were loaded for gel bead-in-emulsion (GEM) generation and barcoding. Final libraries with sizes ranging between 200 and 9,000 bp were size-selected. The average fragment size, used as the insert size for library quantification, was determined by running a 1 µL sample at a 1:10 dilution on an Agilent Bioanalyzer High Sensitivity chip. To capture paired-end reads, the libraries were sequenced via an MGISEQ-2000 system.

Statistical method

The p-values mentioned are all from the Student’s t-test to compare the means of two independent groups. All t-tests were two-sided and assumed equal variances. The statistical analyses were performed using R software (version 4.0.5).

Non-targeted metabolomics

We performed a non-targeted metabolomics analysis on cecal content, feces, and serum from GF (n = 11) and SPF (n = 10) mice. Feces were harvested within one hour after food intake, while serum and cecal content were harvested after fasting overnight. We used LC-MS/MS technology for nontargeted metabolomics analysis via a high-resolution Q Exactive HF mass spectrometer (Thermo Fisher Scientific, USA) to collect positive and negative ion data and improve metabolite coverage. LC-MS/MS data processing was performed via The Compound Discoverer 3.1 (Thermo Fisher Scientific, USA) software, which mainly included peak extraction, peak alignment, and compound identification. Data preprocessing, statistical analysis, metabolite classification annotations, and functional annotations were performed via the metabolomics R package metaX [28] and the metabolome bioinformatic analysis pipeline. PCA dimensionally reduces the multivariate raw data to analyzeXXX the groupings, trends, and outliers of the observed variables in the dataset. Using partial least squares method-discriminant analysis (PLS-DA), the variable importance in projection (VIP) values of the first two principal components of the model, combined with the variability analysis, the fold change, and Student’s t-test results to screen for differentially abundant metabolites.

Preprocessing of scRNA-seq data

Cell Ranger v6.0.2 (10x Genomics) was used to align the reads to the mm10 mouse reference genome. Unique molecular identifier (UMI) gene expression profiles were generated with default parameters for each cell that met the standard sequencing quality threshold. Only confidently mapped, non-PCR duplicates with valid barcodes and UMIs were used to generate a gene-barcode matrix for further analysis. For PB, we obtained data for 10,504 cells from the GF mouse sample and 11,323 cells from the SPF mouse sample. For BM, we got data for 10,693 cells from the GF mouse sample and 9,247 cells from the SPF mouse sample. Cells with UMI/gene numbers out of the limit of the mean value ± threefold standard deviations and a percentage of mitochondrial gene expression over 10% were filtered out to remove low-quality, likely multiplet, or dying captures. After applying the QC criteria, 18,344 high-quality cells with 1,426 median genes per cell from PB and 16,537 high-quality cells with 1,391 median genes per cell from BM remained and were included in downstream analyses. UMI counts were normalized via the NormalizeData function with log transformation using the natural log (base e) in the Seurat (v4.0.3) package.

Data integration and dimensional reduction

To integrate the datasets, we applied canonical correlation analysis (CCA) using the FindIntegrationAnchors and IntegrateData functions in the Seurat package (v4.0.3) [29]. PCA was performed on the variable genes using the RunPCA function in Seurat, and then dimensionality reduction was subsequently performed via umap-learn (v0.5.1) within Python (v3.8.11). For example, a UMAP plot was obtained using 30 PCs with a resolution parameter set at 1.0, as shown in Figure S1A. With the help of the CellMarker database [30], most cell clusters were manually characterized by known marker genes in published articles.

Quantifying differences between large clusters in GF and SPF mice

We quantified the differences between GF and SPF mice by calculating the similarity of the distributions of cell identities from each kind of mouse. The Bhattacharyya distance was used to measure the distance between GF and SPF mice from each cell identity, and clusters with more than 400 cells in both mice were calculated [31]. We obtained the PCA embedding derived from highly variable genes in the dataset and used the top 30 PCs for the following calculation. Specifically, we first randomly sampled 50 cells from each cluster 100 times to compare any given two clusters. To generate a background distribution for statistical comparison, another 50 cells were randomly sampled twice without replacement to simulate two sham samples after two clusters were mixed. P-values from Student’s t-test were calculated to compare the GF vs. SPF groups to the random selection for each cluster.

Differential expression analysis

The FindAllMarkers function in Seurat with the Wilcoxon rank sum test identified cluster-specific marker genes for transcriptional subpopulations. For specific cluster comparisons between GF and SPF mice, we used the MAST (v1.18.0) method implemented in the Seurat FindMarkers function based on normalized data to identify differentially expressed genes (DEGs). Significant DEGs were filtered by |log fold change| > 0.26 and p-value < 0.05.

Gene functional annotation

For each DEG group identified by MAST, we used the clusterProfiler package (v4.0.5) [32] for the analysis of the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with the significant DEGs. The biological process from the org.Mm.eg.db genome-wide annotation was used for the ontology database. The gene ratio is the proportion of genes in a given set annotated with a specific GO term compared with the proportion of genes in the whole genome annotated with that term. The p-values were calculated with the enrichGO function in the clusterProfiler package and adjusted via Benjamini-Hochberg for multiple testing.

Cell-cell interaction analysis

We used CellChat (v1.1.3) [33] to evaluate cell-to-cell interactions and how individual signaling pathways interact across cell identities between the two scRNA-seq datasets, one from GF mice and the other from SPF mice. We followed the official CellChat workflow to merge the GF and SPF datasets, created the CellChat object, and used the CellChatDB.mouse database for subsequent analysis. To determine if the microbiota can interfere with or influence potential cell-cell interactions, we identified the most significant signaling pathways in GF and SPF mice, and visualized the results as chord plots, such as BST2, ICAM, SELPLG, TGFb, and THBS.

The underdeveloped immune system was confirmed by routine blood tests in GF mice

First, blood was drawn from GF (n = 11) and SPF mice (n = 10) for routine blood tests (Table 1; Fig. 1A). The results revealed that the white blood cell (WBC) count was significantly lower in GF mice than in SPF mice (1.80×10⁹/L and 3.40×10⁹/L on average, p-value = 0.040). Specifically, the germ-free condition resulted in a significant decrease in the number of monocytes (p-value = 0.027) and neutrophils (p-value = 0.014). However, only a minor (statistically nonsignificant, p-value = 0.097) reduction in lymphocyte count was observed. Recent research has shown that GF mice have fewer platelets than SPF (both wild-type and IL-1 receptor 1 knockout) mice do [34]. In our study (Table 1), although the reduction in platelet count was not significant (p-value = 0.233) in GF mice, it was significant for the mean platelet volume (MPV) and platelet distribution width (PDW).

Table 1

Routine blood tests of germ-free (GF) and specific-pathogen-free (SPF) Kunming mice
Test (unit)	GF (n = 11)	SPF (n = 10)	p-value
WBC (10⁹/L)	1.80 ± 1.08	3.40 ± 1.98	0.040
LYM (10⁹/L)	1.44 ± 0.96	2.45 ± 1.56	0.097
MON (10⁹/L)	0.06 ± 0.03	0.17 ± 0.14	0.027
NEU (10⁹/L)	0.30 ± 0.14	0.78 ± 0.48	0.014
EOS (10⁹/L)	0.00 ± 0.00	0.00 ± 0.00	NA
BAS (10⁹/L)	0.00 ± 0.00	0.00 ± 0.00	NA
LYM% (%)	77.77 ± 6.57	69.66 ± 12.05	0.080
MON% (%)	3.44 ± 2.17	5.72 ± 2.57	0.042
NEU% (%)	18.81 ± 6.46	24.62 ± 10.61	0.156
EOS% (%)	0.00 ± 0.00	0.00 ± 0.00	NA
BAS% (%)	0.00 ± 0.00	0.00 ± 0.00	NA
RBC (10¹²/L)	10.98 ± 0.84	10.68 ± 1.05	0.477
HGB (g/L)	14.44 ± 1.31	13.76 ± 1.69	0.324
HCT (%)	51.62 ± 6.29	50.62 ± 5.70	0.705
MCV (fL)	47.00 ± 3.58	47.50 ± 2.80	0.724
MCH (pg)	13.15 ± 0.34	12.89 ± 0.82	0.361
MCHC (g/L)	28.12 ± 1.65	27.23 ± 1.94	0.277
RDWc (%)	20.13 ± 0.99	19.43 ± 0.93	0.113
RDWs (fL)	35.37 ± 3.45	34.53 ± 1.68	0.482
PLT (10⁹/L)	464.55 ± 144.97	540.00 ± 135.75	0.233
MPV (fL)	6.78 ± 0.28	7.77 ± 1.13	0.023
PCT (%)	0.32 ± 0.10	0.43 ± 0.15	0.073
PDWc (%)	31.89 ± 2.48	35.03 ± 3.30	0.026
PDWs (fL)	10.47 ± 1.98	13.66 ± 3.80	0.033

WBC, white blood cell count; LYM, lymphocyte count; MON, monocyte count; NEU, neutrophil count; EOS, eosinophil count; BAS, basophil count; LYM%, lymphocyte ratio; MON%, monocyte ratio; NEU%, neutrophil ratio; EOS%, eosinophil ratio; BAS%, basophil ratio; RBC, red blood cell count; HGB, hemoglobin; HCT, hematocrit; MCV, mean cell volume; MCH, mean cell hemoglobin; MCHC, mean cell hemoglobin concentration; RDWc, red cell distribution width (CV), RDWs, red cell distribution width (SD); PLT, platelet count; MPV, mean platelet volume; PCT, phrombocytosis; PDWc, platelet distribution width (CV); PDWs, platelet distribution width (SD); NA, not available. P-values were calculated using Student’s t-test.

Major changes in the transcriptional profiles of GF and SPF mice compared at the single-cell level

To explore how the microbiota affects the composition and transcription of the immune microenvironment, we established a comprehensive single-cell transcriptome reference for the hematopoietic system of adult GF and SPF mice by performing scRNA-seq of PB and BM (Figs. 1A and 1D). We isolated and sequenced 21,827 cells from PB cell suspensions and 19,940 cells from BM cell suspensions from GF (n = 1) and SPF (n = 1) mice. After stringent quality control and data integration, we further analyzed 18,344 high-quality cells with 1,426 median genes per cell from PB and 16,537 high-quality cells with 1,391 median genes per cell from BM.

Using an unsupervised clustering method, we first partitioned single cell profiles composed of a total of 34,881 cells into 18 major cell identities (Figure S1B). Furthermore, we divided 18 main cell groups into 25 subpopulations of cells (Fig. 1B). These cell identities were identified by known marker genes for blood cells (Figs. 1C and S2). Eosinophils were not detected via scRNA-seq data, which is consistent with the results of routine blood tests (Table 1).

Next, we evaluated the differences in transcriptional profiles between GF and SPF mice in two ways. First, we calculated DEGs for each cell identity and then visualized the number of DEGs via uniform manifold approximation and projection (UMAP) plots (Fig. 1E). The numbers of DEGs revealed broad transcriptional changes in blood cells, most notably in neutrophils, monocytes, and B cells. The number of DEGs also progressively increased as differentiation progressed, especially in neutrophils (Figure S1D). In addition, we measured the distance between the major identities via the Bhattacharyya distance [31]. Owing to the limited number of cells available, clusters with fewer than 400 cells were excluded (see Methods). These results revealed more significant differences between GF and SPF mice in terms of the expression of neutrophils, monoblasts, monocytes, myeloid cells, B cells, CD4 + T cells, and NK cells (Fig. 1F). All comparisons were significant on 100 replicates of testing, while the mean fold change varied from 1.82-fold (myeloid cells) to 1.19-fold (granulocytopoietic cells). Although not as significant as those of the immune cells listed above, the expression of hematopoietic stem and progenitor cells considerably changed, indicating that this cell identity was highly likely to be directly or indirectly regulated by the microbiota and its metabolites.

As a result, we identified primary blood cells in GF mice and characterized broad differences in cell proportions and transcriptional profiles compared with those in SPF mice, highlighting notable changes in neutrophils and monocytes (Figs. 1E-1G). Interestingly, neutrophil and monocyte happen to be the cell identities that are significantly reduced in GF mice in routine blood. Therefore, we hypothesize that the microbiota modulates gene expression in immune cells, thus affecting the proliferation or apoptosis of immune cells.

HCAR2 activation as a potential mechanism for the regulation of neutrophil number

The bone marrow can continuously generate neutrophils by expressing some cytokines (such as G-CSF) and releasing them into the blood circulation [35]. Previous studies have shown that the microbiota can regulate neutrophil production and function throughout life [36, 37]. However, the exact molecular mechanisms, pathways, and genes involved remain unclear. To better understand the role of the microbiota in bone marrow neutrophil development, we extracted neutrophils from all the data for downstream analysis. Clustering of neutrophils revealed four distinct clusters (neutrophil P1-P4, Fig. 2A), which we identified with marker genes reported by Grieshaber-Bouyer et al. [38]. The four stages were ordered according to their developmental stages (Figure S1D). Neutrophil P1 corresponds to the early stage, which develops from granulocytopoietic cells, and neutrophil P4 corresponds to the late stage, which approaches mature neutrophils in terms of gene expression.

To determine the molecular basis underlying the difference between GF and SPF mice, we performed differential expression analysis and identified sets of up-regulated and down-regulated genes. Next, we performed a GO enrichment analysis on the identified up-regulated and down-regulated genes (Fig. 2C). We found that signaling pathways related to interleukin-1 beta (IL-1β) production were enriched in up-regulated genes, which showed a more potent antibacterial effect in GF mice. Specifically, the genes Arg2, Egr1, and Tlr2, which are involved in the IL-1β production pathway, were up-regulated in GF mice (Figs. 2B and 2D). On the other hand, actin-related signalingXXX pathways, such as regulation of actin cytoskeleton organization and regulation of actin filament-based processes, were enriched in down-regulated genes (Figure S2), especially in the early stages, which means that early neutrophils can be inactive in terms of cell motility and cytokinesis in GF mice.

Compared with that in SPF mice, the expression of Hcar2 was up-regulated in GF mice neutrophil P4 (Figs. 2B and 2D). HCAR2 (also known as GPR109A) acts as a high-affinity receptor for both nicotinic acid (NA; also called niacin) and (D)-beta-hydroxybutyrate and mediates NA-induced apoptosis in mature neutrophils [39]. HCAR2 activation by NA results in reduced cAMP levels, thereby affecting the activity of cAMP-dependent protein kinase A and phosphorylating target proteins, resulting in neutrophil apoptosis [40]. We hypothesized that the expression of Hcar2 is regulated by NA or related metabolites. To validate the up-regulation of Hcar2, we performed scRNA-seq on BM cells from GF (n = 1) and SPF (n = 1) mice via the DNBelab C4 platform. We confirmed that neutrophils were significantly reduced in the GF mouse and that Hcar2 was significantly up-regulated in the GF mouse in the validation data (Figure S3). Furthermore, a recent study performing scRNA-seq and spatial transcriptomics on the spleen of GF (n = 1) and SPF (n = 1) mice reported a significant decrease of neutrophils in GF mice (Figs. 2E and 2F) [41]. We demonstrated the spatial expression of three marker genes of neutrophils, Il1b, Ccl6, and Ly6g, in the spleen to determine the difference in the number of neutrophils in the spleen between GF and SPF mice (Fig. 2F). More importantly, we verified the significant up-regulation of Hcar2 in GF mouse neutrophils via the spleen scRNA-seq data (Fig. 2E).

To further confirm the metabolic changes associated with NA, we performed a non-targeted metabolomics analysis on the cecal content, feces, and serum of GF (n = 11) and SPF (n = 10) mice (Figs. 1A and 2G; Supplementary Data). In fecal and cecal samples from GF mice, we detected decreased levels of NA and 6-hydroxynicotinic acid, the 6-hydroxy derivative of NA produced by the action of niacin dehydrogenase, which is derived mainly from Pseudomonas. On the other hand, nicotinamide (NAM) which is converted from NA when it is overtaken, and trigonelline, a product of NA metabolism that is excreted in the urine of mammals, are increased.

These results suggest that the nicotinate degradation pathway is down-regulated as a result of the absence of microbiota in GF mice. Excess NA is subsequently converted to trigonelline, NAM, or even nicotinuric acid (NUA). In summary, the accumulation of NA and its derivative due to a lack of niacin dehydrogenase in GF mice is a potential regulatory mechanism for up-regulation of Hcar2, which can enhance mature neutrophil apoptosis.

Enhanced bacterial recognition and impaired viral defense in GF mouse monocytes

Monocytes and macrophages play indispensable roles in immunosuppression, tissue repair, inflammation regression, bacterial removal, atherosclerosis, and fibrosis. We separated monoblasts from monocytes based on the fact that monoblasts expressed more Irf8 and Elane (Fig. 1C). Monoblasts and monocytes from PB and BM were extracted for further analysis, but macrophages whose cell number was insufficient for statistical analysis were excluded (Fig. 3A).

The differential expression analysis of both monoblasts and monocytes revealed that GF mice expressed high levels of the monocyte marker gene Vcan (versican, Fig. 3B). The versican gene encodes a large chondroitin sulfate/dermatan sulfate proteoglycan belonging to the aggrecan/lexican family [42]. Previous studies have indicated that up-regulation of versican is correlated with inflammation and cancer aggressiveness [42, 43]. In addition, versican blocks cell migration and is associated with slow cell proliferation and cytodifferentiation, and promotes the adhesion of monocytes in the extracellular matrix (ECM) [44]. The up-regulation of versican may contribute to impaired monocyte development. Moreover, GF mice expressed a high level of Cd14 in monocytes but not in monoblasts (Fig. 3B). CD14 is a coreceptor for the detection of bacterial lipopolysaccharide (LPS), and it also recognizes other pathogen-associated molecular patterns, such as lipoteichoic acid. Monocytes produce sCD14, a soluble form of the receptor, which confers LPS responsiveness to cells that do not express Cd14. Generally, high levels of sCD14 in monocytes reflect monocyte activation [45]. Under germ-free conditions, there is increased recognition of microbes, which may be associated with the potential risk of inflammation. The up-regulation of Cd14 was also detected in neutrophils (P1-P4). To further validate this conclusion, we performed differential expression analysis on a cecum dataset containing GF and SPF mice [46]. The results revealed that Cd14 was up-regulated in enterocytes (fold change = 1.28, p-value = 6.65e-17).

GO enrichment of monocytes revealed that the up-regulated genes were enriched mainly in pathways related to reactive oxygen species (ROS), the ERK1 and ERK2 cascades, myeloid cell differentiation, and apoptotic cell clearance (Fig. 3C). ROS are well-known inducers of inflammation [47], and ERK signalingXXX is believed to promote primarily cell proliferation and survival [48]. However, it has been well documented that ROS-induced ERK activation generally results in cell cycle arrest and apoptosis in a variety of cells [49, 50], which may explain the decreased number of monocytes in GF mice shown in routine blood tests. On the other hand, the down-regulated genes were enriched mainly in pathways related to antigen processing and presentation and interferons (Figs. 3D and 3E). These findings indicate that monocytes have a reduced capacity to mount an immune defense against viral pathogens and endogenous peptide antigens, which is mediated by interferon β (IFN-β) and interferon γ (IFN-γ), respectively. Consistent with these findings, previous studies reported increased susceptibility of GF mice to influenza A virus [10], coxsackie B virus [11], and Friend virus [12].

Elevated excretion of 5’-methylthioadenosine is associated with immunodeficiency

All mammalian tissues contain 5’-methylthioadenosine (MTA), a sulfur-containing nucleoside. In humans, MTA functions as a powerful inhibitor of several enzymatic reactions, and is synthesized mainly through the polyamine biosynthetic pathway from S-adenosylmethionine. For example, MTA can influence the regulation of gene expression, proliferation, differentiation, and apoptosis, as reported previously [51]. Elevated excretion of MTA has been detected in children with severe combined immunodeficiency syndrome (SCID) [52]. This is consistent with our findings in GF mice with underdeveloped immune systems. MTA elevation was observed in the serum (fold change = 1.5304, p-value = 0.0121), feces (fold change = 2.3591, p-value = 0.0211) and cecal content (fold change = 4.4906, p-value = 0.0023). This finding suggested a direct or indirect association between elevated MTA and the immunodeficiency phenotype.

Cell-cell interaction analysis revealed that decreased BST2 signaling pathway activity in GF mice may inhibit immune response activation

In view of transcriptomic changes in genes encoding receptor-ligand pairs in response to the absence of microbiota, we performed an unbiased inference analysis of cell-cell interactions via CellChat [33] across all cell identities from PB and BM except plasma cells, which were found to be absent in the immune system of GF mice in this study.

To measure the degree of cellular communication between samples, we compared the total number and strength of interactions in the inferred cell-cell communication network. Compared with those SPF mice, the interaction numbers and strength in GF mice were lower (Fig. 4A). To specify the interaction between which cell populations showed notable changes, we compared the number and strength of the interactions between different cell populations (Fig. 4B). Interestingly, when NKT cells act as receivers, communication between NKT cells and all other cells is reduced or shut down. This finding suggests that NKT cells in GF mice cannot receive most extracellular signals. In terms of interaction strength, there was also a significant reduction in CD8 + T cell interactions with other cell types, particularly lymphocytes and NK cells, in GF mice, the reasons for which need to be further investigated. Macrophages in GF mice exhibit enhanced interactions with various cell types, either as senders or receivers.

To further explore the differences in cellular interactions between cell identities in GF versus SPF mice, we used CellChat to calculate the distance of signaling pathways between the 24 cell groups in the two datasets (Fig. 4C). The results revealed that the BST2 signaling pathway was the most significant in terms of information flow. The ligand gene Bst2 expressed by bone marrow stromal cells can promote the growth of murine pre-B cells [53], and Pira2 is the receptor gene. We found that the BST2 signaling pathway was inhibited in basophils, platelets, Vpreb3- naïve B cells, and all T cell subpopulations in GF mice, which was caused by the down-regulation of Bst2 gene expression in these cell groups (Figs. 4D and 4E). It has been reported that the acquisition of alloantigen-specific memory by murine macrophages requires the interaction between MHC-I and PIR-A [54]. In conclusion, the reduction in the BST2 signaling pathway between cells may inhibit the immune response activation of BST2 signaling pathway receptor cells in GF mice, which are monoblasts, monocytes, macrophages, myeloid cells, and neutrophils, to be more specific.

Nearly 80 years have passed since germ-free rodent colonies were first established, and tremendous progress in understanding microbial function has been achieved. However, the interactions between the microbiota and host immune system are complex, dynamic, and context-dependent. Our study provides the first attempt to use scRNA-seq along with non-targeted metabolomics in GF mice to investigate how the lack of microbiota affects transcription during immune cell development. By comparing GF mice with SPF mice, we confirmed the underdeveloped immune system in GF mice and revealed the potential reason for this. Fewer immune cells are found in GF mice from routine blood results, as evidenced by the fact that the number of immune cells, such as neutrophils and monocytes is only approximately 40% of that in SPF mice. However, the decrease in lymphocytes was not significant. Based on the above results, the underdevelopment of the immune system in GF mice was widespread across cell types, and different subtypes presented varying degrees of deficiency.

Myeloid cell populations, particularly neutrophils and monocytes, were dramatically reduced in GF mice, similar to those observed in systemic sites after antibiotic treatment [14]. Neutrophils are essential for the innate immune response to bacteria and fungi, and it takes approximately 5–6 days for neutrophils to mature in the bone marrow as post-mitotic precursor cells. At this point, they are released into the circulation as mature neutrophils [55]. The endpoint for neutrophils under steady-state conditions is spontaneous apoptosis and removal by other phagocytes, a default process typically altered by interactions with microbes or their products [56]. Our study revealed that the toxicity of the accumulation of niacin and its derivatives due to the lack of niacin dehydrogenase may contribute to excessive neutrophil apoptosis. HCAR2, the receptor for niacin, was also found to be up-regulated in GF mice, and was reported to mediate niacin-induced neutrophil apoptosis [39]. Notably, in our study, neutrophil P4 did not represent mature neutrophils. It is a near-mature state present only in the BM.

Monocytes in mice receiving antibiotics have less migratory capacity and are less abundant in peripheral tissues [57, 58], even though their numbers in the bone marrow and peripheral blood are not significantly reduced [57, 59]. However, our study revealed a significant decrease in monocytes in the routine blood tests of GF mice. We found that monocyte numbers increased in GF mice, as shown in Fig. 1G. The absolute number of cells identified via scRNA-seq can be affected by various factors, such as cell viability, library preparation, sequencing depth, and data processing. Therefore, we do not use absolute cell numbers in scRNA-seq to compare different samples, especially if there are other better and more direct methods (e.g., routine blood tests). Therefore, we believe that the two main reasons for this result are impaired monocyte development due to ROS-induced ERK activation and the slow migration of monocytes due to the up-regulation of versican.

We assume that microbiota-derived metabolites affect the development of immune cells such as neutrophils through systemic circulation. On the basis of the above assumption, the accumulation of metabolites should be observed not only in the feces but also in the serum. However, the results we observed for serum were not significant enough, and some were not even significant. This is because we collect serum from the whole body, the final serum metabolite concentration obtained is an average result, and the areas where the concentration changes significantly should be in local organs, such as the ileum, liver, etc. However, the metabolites in the cecal content and feces are concentrated, which is why we observe significant changes in the feces, which are not evident in the serum.

In summary, GF mice are natural immunodeficient model mice, and our study provides a foundation for understanding the underdeveloped immune system of GF mice. Our reference dataset will be invaluable for studying the crosstalk between the microbiota and the immune system. Collectively, our work establishes a foundation for exploring the connections between the microbiota and the immune system and provides a reference for future interventions for immune deficiencies caused by microbial dysbiosis.

Limitations of the study

First, the sample size was relatively small; only one pair of GF and SPF mice was collected in the discovery group and another pair was collected for validation. However, it should be mentioned that we took blood and bone marrow from the same mouse in the discovery stage, ensuring that there was no sample bias between the blood and bone marrow. Second, our scRNA-seq data generated by the Chromium platform lack completeness, especially the lack of neutrophils in the peripheral blood subset. ScRNA-seq data obtained via the droplet-based 10x Genomics Chromium platform usually have difficulty detecting mature neutrophils in peripheral blood. This is due to the relatively low levels of RNA in neutrophils, as well as other granulocytes, and the relatively high levels of Rnases and other inhibitory compounds, leading to fewer transcripts being detected and fewer sequencing reads being available.

In summary, in this study, we revealed the underlying mechanisms of immune system underdevelopment in GF mice by scRNA-seq and non-targeted metabolomics analysis. We found that the number of immune cells, especially neutrophils and monocytes, was significantly reduced in GF mice. The reduction of neutrophils was associated with the accumulation of niacin and its derivatives due to niacin dehydrogenase deficiency. In addition, we observed that upregulation of HCAR2 in GF mice may promote neutrophil apoptosis. Gene expression analysis of monocytes showed enhanced bacterial recognition but diminished viral defense. Analysis of cell-cell interactions showed reduced activity of the BST2 signaling pathway in GF mice, which may inhibit activation of the immune response. Our study provides new insights into understanding the interactions between the microbiota and the host immune system and provides the foundation for future interventions targeting immune deficiencies caused by microbial dysregulation.

Bone Marrow

BSA

Bovine Serum Albumin

CCA

Canonical Correlation Analysis

DEGs

Differentially Expressed Genes

ERK

Extracellular Signal-Regulated Kinase

GEM

Gel Bead-In-Emulsion

Germ-Free

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

LC-MS/MS

Liquid Chromatography-Tandem Mass Spectrometry

MPV

Mean Platelet Volume

MTA

5’-Methylthioadenosine

MHC-I

Major Histocompatibility Complex Class I

NKT

Natural Killer T

PCA

Principal Component Analysis

PBS

Phosphate-Buffered Saline

Peripheral Blood

PDW

Platelet Distribution Width

PIR-A

Paired Immunoglobulin-like Receptor A

PLS-DA

Partial Least Squares Discriminant Analysis

ROS

Reactive Oxygen Species

SCID

Severe Combined Immunodeficiency Syndrome

scRNA-seq

Single-Cell RNA Sequencing

SPF

Specific-Pathogen-Free

UMAP

Uniform Manifold Approximation and Projection

UMI

Unique Molecular Identifier

Ethics approval and consent to participate

All animal experiments were approved by the Institutional Review Board of BGI (BGI-IRB A21028) and the Institutional Animal Care and Use Committee of Huazhong Agricultural University (HZAUMO-2020-0050) on the basis of the principles of animal welfare and ethics.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by a grant from the Science Technology and Innovation Commission of Shenzhen Municipality, China (SGCX20190919142801722).

Author Contribution

Y.F. Sheng, W. Cheng, H. Wei, and X.D. Fang contributed to the conception and design of the study. J. Shen, Y.F. Sheng, R.Z. Zhao, T.L. Chai, X. Huang, and S.S. Pan participated in the experimental design, and W. Cheng’s team was responsible for sampling. Y.F. Sheng, Y. Zhang, Q.J. Liao, and B. Wei performed the statistical analysis. Y.F. Sheng wrote the draft of the manuscript. J. Shen, Q.J. Liao, L.J. Han, Y.R. Zhang, R.X. Chen, J.P. Mei and X.D. Fang revised the manuscript. All the authors contributed to the article and approved the submitted version.

Acknowledgement

We thank Zhifeng Wu, Xiang Tan, and Hang Zhang for the mouse dissection. We are grateful for the data support from the China National GeneBank, BGI, Shenzhen 518120, China. Figure 1A was created with BioRender.com.

Data Availability

The single-cell expression data generated by 10x Chromium and DNBelab C4 and non-targeted metabolomics data have been deposited in the CNGB Nucleotide Sequence Archive (CNSA) under accession code CNP0002818 (https://db.cngb.org/cnsa/). The spleen scRNA-seq and spatial transcriptomics data used in this study are available from CNSA under the accession number CNP0003930. The cecum scRNA-seq data used in this study are available from CNSA under the accession number CNP0004192. All the data were analysed with standard programs and packages, as detailed above. Additional information and code supporting the findings of this study are available from the corresponding author upon reasonable request.

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Single-cell RNA-seq provides insight into the underdeveloped immune system of germ-free mice

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Version 1

Abstract

Background

Results

Conclusions

Figures

Background

Methods

Study design

Mice

Sample preparation and cell isolation for scRNA-seq

Library preparation

Statistical method

Non-targeted metabolomics

Preprocessing of scRNA-seq data

Data integration and dimensional reduction

Quantifying differences between large clusters in GF and SPF mice

Differential expression analysis

Gene functional annotation

RESULTS

The underdeveloped immune system was confirmed by routine blood tests in GF mice

HCAR2 activation as a potential mechanism for the regulation of neutrophil number

Enhanced bacterial recognition and impaired viral defense in GF mouse monocytes

Elevated excretion of 5’-methylthioadenosine is associated with immunodeficiency

Discussion

Limitations of the study

Conclusions

Abbreviations

Declarations

Competing interests

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1