Zhu et al. Journal of Neuroinflammation(2024) 21:169
https://doi.org/10.1186/s12974-024-03161-0

Journal of Neuroinflammation

Open Access

RESEARCH

Integrating single-nucleus RNA sequencing
and spatial transcriptomics to elucidatea specialized subpopulation of astrocytes,
microglia and vascular cells in brains of mousemodel of lipopolysaccharide-induced sepsisassociated encephalopathy
Yanyan Zhu1,2,3,4,5,6†, Yin Zhang8†, Sheng He1,2,3,4,5,6, Sanjun Yi1,2,3,4,5,6, Hao Feng9, Xianzhu Xia1,2,3, Xiaodong Fang8*,
Xiaoqian Gong7* and Pingsen Zhao1,2,3,4,5,6*
Abstract
Background Understanding the mechanism behind sepsis-associated encephalopathy (SAE) remains a formidable
task. This study endeavors to shed light on the complex cellular and molecular alterations that occur in the brains of amouse model with SAE, ultimately unraveling the underlying mechanisms of this condition.
Methods We established a murine model using intraperitoneal injection of lipopolysaccharide (LPS) in wild type
and Anxa1−/− mice and collected brain tissues for analysis at 0-hour, 12-hour, 24-hour, and 72-hour post-injection.
Utilizing advanced techniques such as single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq, we conducted acomprehensive characterization of the cellular responses and molecular patterns within the brain.
Results Our study uncovered notable temporal differences in the response to LPS challenge between Anxa1−/−(annexin A1 knockout) and wild type mice, specifically at the 12-hour and 24-hour time points following injection. We
observed a significant increase in the proportion of Astro-2 and Micro-2 cells in these mice. These cells exhibited acolocalization pattern with the vascular subtype Vas-1, forming a distinct region known as V1A2M2, where Astro-2 andMicro-2 cells surrounded Vas-1. Moreover, through further analysis, we discovered significant upregulation of ligands†

Yanyan Zhu and Yin Zhang contributed equally to this work.

Pingsen Zhao is the Lead contact.
*Correspondence:
Xiaodong Fangfangxd@genomics.cn
Xiaoqian Gong
drgx8008@163.com
Pingsen Zhao
zhaopingsen01@163.com; zhaopingsen@hotmail.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Zhu et al. Journal of Neuroinflammation(2024) 21:169

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and receptors such as Timp1-Cd63, Timp1-Itgb1, Timp1-Lrp1, as well as Ccl2-Ackr1 and Cxcl2-Ackr1 within this region.
In addition, we observed a notable increase in the expression of Cd14-Itgb1, Cd14-Tlr2, and Cd14-C3ar1 in regions
enriched with Micro-2 cells. Additionally, Cxcl10-Sdc4 showed broad upregulation in brain regions containing both
Micro-2 and Astro-2 cells. Notably, upon LPS challenge, there was an observed increase in Anxa1 expression in themouse brain. Furthermore, our study revealed a noteworthy increase in mortality rates following Anxa1 knockdown.
However, we did not observe substantial differences in the types, numbers, or distribution of other brain cells
between Anxa1−/− and wildtype mice over time. Nevertheless, when comparing the 24-hour post LPS injection time
point, we observed a significant decrease in the proportion and distribution of Micro-2 and Astro-2 cells in the vicinityof blood vessels in Anxa1−/− mice. Additionally, we noted reduced expression levels of several ligand-receptor pairs
including Cd14-Tlr2, Cd14-C3ar1, Cd14-Itgb1, Cxcl10-Sdc4, Ccl2-Ackr1, and Cxcl2-Ackr1.
Conclusions By combining snRNA-seq and Stereo-seq techniques, our study successfully identified a distinctive
cellular colocalization, referred to as a special pathological niche, comprising Astro-2, Micro-2, and Vas-1 cells.
Furthermore, we observed an upregulation of ligand-receptor pairs within this niche. These findings suggest apotential association between this cellular arrangement and the underlying mechanisms contributing to SAE or theincreased mortality observed in Anxa1 knockdown mice.
Keywords Sepsis, SAE, Single-nucleus RNA sequencing, Spatial transcriptomics, Anxa1, Glial cell response, Cellular
colocalization

Background
Sepsis, a critical condition marked by life-threateningorgan dysfunction resulting from an imbalanced host
response to infection, is a significant contributor to
global health burdens [1]. In 2017, the Global Burden of
Diseases, Injuries, and Risk Factors Study (GBD) documented an alarming estimate of 48.9 million incident
cases of sepsis worldwide, with a staggering 11 million
sepsis-related deaths [2]. These figures represent approximately 19.7% of all reported global deaths, solidifying
sepsis as one of the primary causes of critical illness andmortality. SAE, Sepsis-associated encephalopathy (SAE),
a frequent complication in sepsis, manifests througha spectrum of symptoms including alterations in consciousness, ranging from mild cognitive impairment toprofound coma, as well as a variety of neurological disturbances such as confusion, cognitive dysfunction, sleep
disturbances, hallucinations, and delirium [3–6]. Clinicalmanifestations of SAE are observed in approximately 70%
of septic patients, and this condition has been linked toincreased mortality rates. Additionally, survivors often
experience long-term neurological impairments. The
pathophysiology of SAE is not completely understood,
but it is likely multifactorial, involving mechanisms suchas oxidative stress, increased cytokines, and a dysregulated host response to infection [7]. Further research isneeded to better understand the etiology and pathophysiology of SAE in order to develop more effective prevention and treatment strategies.
The advent of snRNA-seq has revolutionized thefield of cell biology, The advent of single-nucleus RNA
sequencing (snRNA-seq) has revolutionized the field ofcell biology, enabling researchers to unravel the intricate cellular heterogeneity observed in various organs

and diseases. In recent years, snRNA-seq has gained
significant traction for its ability to dissect the complex
landscape of immune cell subsets and uncover distinct
cellular signatures associated with a multitude of humandiseases [8], including cancers, autoimmune disorders,cardiovascular diseases, and infectious diseases [9–14].
Within the realm of sepsis research, the application ofsnRNA-seq technology has yielded valuable insights
and discoveries [15–18]. Nonetheless, it is paramount toacknowledge that these methods, reliant on tissue dissociation, inherently sacrifice spatial information. Consequently, the precise pathophysiological mechanisms
governing the recruitment of inflammatory cells to sitesof tissue injury during sepsis remain unknown, as thespatial context is not fully preserved. Spatial transcriptomic platforms offer a powerful means of simultaneously assessing the comprehensive mRNA expression
profiles of thousands of genes and overlaying this information onto histological data from adjacent tissue sections. By mapping gene expression back to their original
locations, these platforms establish a direct association
between histology and gene expression [19]. Integrationof snRNA-seq with spatial transcriptomics enhances analytical capabilities and facilitates the in situ-visualizationof molecular signatures, resulting in the mapping of alarger repertoire of cell types compared to spatial transcriptomics alone [20]. However, a comprehensive exploration of spatiotemporal omics in the context of sepsis or
SAE in the brain remains limited. Therefore, our study
aims to employ a combined approach of snRNA-seq andspatial transcriptomics (ST) to map the transcriptionalchanges occurring in the brains of mice subjected to an
endotoxin-induced sepsis model.

Zhu et al. Journal of Neuroinflammation(2024) 21:169

Annexin A1 (ANXA1) is a protein that has been extensively studied for its role in various biological processes.
In the context of recent studies, ANXA1 is recognized for
its anti-inflammatory properties due to its control overleukocyte-mediated immune responses. In the context ofviral infections, ANXA1 has a multifaceted role, including promoting apoptosis of granulocytes, efferocytosis
of apoptotic cells, and inhibiting the production of proinflammatory cytokines [21]. It’s also involved in cellularsignaling, hormonal secretion, fetal development, aging,and disease development. Specifically, ANXA1 affects the
hypothalamic–pituitary–adrenal (HPA) axis physiology
and regulates the blood–brain barrier (BBB) tightness[22]. The multifaceted role of ANXA1 in neuroinflammation has garnered considerable attention in recent
literature. The presence of ANXA1 in microglia of aged
human brains, as reported by McArthur et al. [23], andits increased expression in Alzheimer’s disease suggest asignificant role in the phagocytosis of apoptotic neurons.
Zhang et al. [24] further elucidated the neuroprotective
effects of ANXA1 tripeptide, highlighting its capacity toinhibit NF-κB p65 and ameliorate neuroinflammation
in a rat model of cardiopulmonary bypass. The work ofDing et al. [25] supports these findings, demonstratingthat ANXA1 can attenuate neuroinflammation throughthe FPR2/p38/COX-2 pathway following intracerebral
hemorrhage in mice. Recent studies have also shed lighton the pathological mechanisms underlying neurodegenerative dementias. Chua et al. [26] observed elevatedlevels of cleaved ANXA1, indicating a potential failure in
the resolution of inflammation and establishing a mechanistic link between ANXA1, amyloid pathology, neuroinflammation, and apoptosis. Moreover, Mao et al. [27]identified SENP6-mediated ANXA1 de-SUMOylation asa contributor to microglial polarization and subsequentneuroinflammation after cerebral ischemia-reperfusion
injury. COVID-19 has the potential to induce severe
immune reactions, known as cytokine storms [28]. Our
previous single-cell sequencing articles on covid-19
patients, anxa1 and its receptor were found to be up-regulated in expression after cytokine storm [29]. Moreover,
our recent molecular studies have revealed significant
upregulation of ANXA1 in septic encephalopathy. We
demonstrated a substantial increase in ANXA1 protein
and mRNA levels in a mouse model of septic encephalopathy, with a 2.5-fold and 3-fold elevation respectively
at 24 h post-induction. These findings suggest an active
involvement of ANXA1 in the central nervous system’s
acute phase response to septic challenges.
In this study, we utilized snRNA-seq datasets to spatially map cell types onto transcriptomic anchoring landmarks (spots) within the murine brain. Specifically, weinvestigated the differentially expressed genes (DEGs)
and pathways in the brains of mice subjected to an

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endotoxin-induced sepsis model. To precisely define the
distribution of immune cell transcript expression in thesemodels, we utilized the transcriptomic signatures derived
from the Stereo-seq spatial gene expression platform,
allowing for the colocalization of glial cells with vascularassociated cells. Through our investigation, we successfully identified key ligand-receptor pairs expressed inglial cells and vascular-associated cells within the brainsof mice subjected to an endotoxin-induced sepsis model.
These identified pairs play a significant role in facilitating
immune cell migration and intercellular communication.
By harnessing the high-throughput capabilities of singlenucleus and spatial transcriptome technologies, we have
not only advanced our understanding of SAE, but alsoestablished a pioneering framework for leveraging spatial
information to enhance the interpretation of cellular activation in neuroinflammatory conditions. This approachholds immense potential in deepening our insights into
the intricate mechanisms underlying neuroinflammation.

Materials and methods
Mice and treatments

C57BL/6 mice were obtained from the Guangdong Medical Laboratory Animal Center, while Anxa1−/− mice were
procured from GemPharmatech. The mice were housedin the animal care facility at Yuebei People’s Hospital
Affiliated to Shantou University Medical College. The
mice were maintained under specific conditions, including a regular 12-hour light/dark cycle, a controlled ambient temperature ranging from 23 to 26 °C, and a humidity
level maintained between 50 and 60%. The mice used inthis study were 8-week-old, which is equivalent to thelate adolescence stage in humans (young adult, postnatal
day 46–59). Neuroinflammation was induced by a single
intraperitoneal injection (IP) of LPS at a dose of 15 mg/
kg, obtained from Sigma Aldrich. LPS was diluted in a
0.9% saline solution. The control group, referred to as
the Ctrl group in the text, received an injection of normal saline (NaCl). All experimental procedures were conducted in accordance with the guidelines and regulations
set by The Medical Animal Care & Welfare Committee of
Shantou University Medical College (Approval Number:
SUMC2021-332).
Preparation, sequencing, and alignment for ST libraries

Fresh brain tissues from mice were collected and immediately frozen in OCT compound (Tissue-Tek; Sakura
Finetek USA, Torrance, CA, USA). The frozen tissues
were sliced transversely into 10 μm thick sections usinga CM1950 cryostat (Leica, Wetzlar, Germany). These tissue sections were then affixed to the surface of the Stereo-seq chip. After a 3-minute incubation at 37 °C, thesections were fixed in methanol and further incubated at-20 °C for 40 min. H&E staining was performed on theseZhu et al. Journal of Neuroinflammation(2024) 21:169

sections as well as the adjacent tissue sections. Both procedures were imaged using a Ti-7 Eclipse microscope(Nikon, Tokyo, Japan). The libraries were prepared using
established methods [30] and sequenced on an MGI
DNBSEQ-Tx sequencer. Read 1 contained the coordinate
identity (CID) barcode and molecular identifiers (MIDs)(CID: 1–25 bp, MID: 26–35 bp), while read 2 consisted of
the cDNA sequences. The raw sequencing reads were filtered and demultiplexed using the SAW pipeline (https://github.com/BGIResearch/SAW). For each library, we
generated an expression profile matrix that included the
CID information.
Preparation, sequencing, and alignment of snRNA-seq
libraries

The snRNA-seq samples were collected from adjacentfrozen sections to those used for Stereo-seq. These sections were carefully transferred into plastic wells on
dry ice and subsequently stored at -80 °C refrigerator.
Single nucleus suspensions were prepared as previously
described [31]. Briefly, mouse brain tissue pieces were
homogenized in a Dounce homogenizer with pre-chilled
homogenization buffer on ice. The resulting homogenate was filtered through 30 μm MACS SmartStrainers(Miltenyi Biotech, #130-110-915) into a 15 ml conicaltube. Subsequently, the tube was centrifuged to pellet
nuclei. The pellet was resuspended in blocking buffer andcentrifuged again to pellet nuclei. The nuclei were thenresuspended in cell resuspension buffer for subsequentsnRNA-seq library preparation.
The DNBelab C Series High-throughput SingleNucleus RNA Library Preparation Kit (MGI, #940000047-00) was used to construct the sequencing
libraries as per the manufacturer’s instructions. In summary, single-nucleus suspensions were employed for
droplet generation, emulsion breakage, beads collection,
reverse transcription, second-strand synthesis, cDNA
amplification, and droplet index products amplification
to generate barcodes libraries. The sequencing libraries were quantified using the Qubit™ ssDNA Assay Kit(Thermo Fisher Scientific, #Q10212) and sequenced on
the ultra-high-throughput DIPSEQ T1 or DIPSEQ T10
sequencers at the China National GeneBank (Shenzhen,
China). Each library had a read length of 41-bp for read
1 and 100-bp for read 2. The raw sequencing reads were
filtered and demultiplexed using PISA (v0.2). The reads
were then aligned to the reference human genome using
STAR (v2.7.4a) and sorted using sambamba (v0.7.0).
Finally, a cell versus gene UMI count matrix was generated for each library using PISA.

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SnRNA-seq data analysis
Raw data processing

The raw snRNA-seq data were processed using the R
package Seurat (v4.1.1) [32]. We merged the gene expression matrix of libraries from each sample, calculated thepercentage of reads that mapped to the mitochondrial
genome (percent.mt), and counted the number of genes
in each cell. The gene number was recorded as Nexpress
based on the cell with the highest number of genes.
Finally, we filtered out cells with less than 400 genes orwith more than 0.9*Nexpress genes, or with a percent.mt
larger than 0.2.
To remove reads originating from ambient RNA, weapplied the R package DecontX [33] on each sample
with default parameter settings. For the identification
and removal of doublets, we used the R package DoubletFinder [34] with default parameters and excluded the
top 7.5% of cells that exhibited the closest resemblance topseudo-doublets.
Integration, clustering and annotation of snRNA-seq data

For the integration, clustering, and annotation of snRNAseq data, we utilized the Seurat package. Firstly, we performed log-normalization using the NormalizeData
function, with a scale factor of 10,000. Next, we identified
highly variable features using the FindVariableFeatures
function, selecting the top 2,000 features. To integrate
the data from each dataset, we used the FindIntegrationAnchors and IntegratedData functions. Counts werescaled and centered in the integrated dataset using the
ScaleData function. Dimensionality reduction was performed using the RunPCA function, utilizing the first 50
principal components. Subsequently, graph-based clustering was applied using the FindClusters function, witha resolution of 2. Next, we used RunUMAP function to
perform Uniform manifold approximation and projection (UMAP) plots. Cluster marker genes were identified
using the FindAllMarkers function, and cell types wereannotated by comparing the cluster markers with theirknown canonical markers. Lastly, we merged cell clusters
that shared the same markers.
Differential gene expression and gene set enrichment
analyses of snRNA-seq data

To perform differential gene expression analysis, weemployed the FindMarkers function in Seurat. This
allowed us to compare gene expression between cells
under different conditions, including different cell types(subtypes), time periods, and cells with or without aligand-receptor (LR). Genes with adjusted P-values < 0.05
were considered as differentially expressed genes (DEGs).
To perform gene set enrichment analysis, we utilized theenrichGO functions from the R package clusterProfiler(v4.4.4) [35].

Zhu et al. Journal of Neuroinflammation(2024) 21:169

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Calculation of signature score

Integration and clustering of sixteen ST datasets

We initially extracted gene signatures for Astro-1, Astro2, Micro-1, Micro-2, Neuron, OligoD, OPC, Vas-1, andCdkn1a+ Serpina3n+ OligoD from the snRNA-seq data
(Supplementary Table 1). To determine the signaturescore for each cell type, we summed the log-normalized
expression values of the corresponding gene signature ineach cell. Then, we identified the 99th percentile of thesummed value distribution, denoted as x. By dividing thesummed value of each cell by x and replacing any values
greater than 1 with 1, we obtained the signature score foreach cell. Using this approach, we calculated signaturescores for all cells across the nine cell types.

A total of 20,000 spots were randomly selected fromeach dataset of ST. These spots were then integrated with
Seurat software. To reduce the dimensions of the dataset,
we employed RunPCA function with the initial 50 PCs.
For graph-based clustering, we used FindClusters function with a resolution of 0.1. Subsequently, we performed
UMAP using RunUMAP function.

ST data analysis
Raw data processing

To precisely capture the gene expression patterns identified by Stereo-seq, we utilized a convolution-based
approach to expand the raw spatial expression matrix ofsingle DNB spots into larger pseudo-spots. The coordinates of a segmented-nuclei spot was determined fromthe single-strand DNA (ssDNA) image of the ST datausing CELL BIN Studio (v1.0.6). By merging the DNB
spots within each segmented-cells spot, we generated thespatial expression matrix of the pseudo-spots. For each
pseudo-spot, its center coordinates were computed as its
x-y coordinates.
Inferring cell composition in a ST dataset using RCTD
algorithm

To determine the cellular composition of each spot, we
employed the RCTD algorithm implemented in thespacexr R package (v 2.2.0) [36]. The RCTD algorithm
was applied to each dataset using the full model, with
our in-house snRNA-seq data serving as the reference.
Cell type scores for each spot were normalized using thenormalized weights function, and cell type labels wereassigned to a spot if the scores surpassed the corresponding threshold value. The threshold values varied depending on the cell type, with values of 0.5 for neurons, 0.25
for Astro-1, 0.2 for OligoD and Vas-1, 0.1 for Astro-2,
and 0.05 for OPC, Micro-1, and Micro-2.
Inferring cell composition in a ST dataset with cell typesignature genes

To identify the cellular composition of a ST dataset using
gene signature scores, we initially extracted gene signatures for neurons, Astro-1, OligoD, Vas-1, Astro-2, OPC,
Micro-1, and Micro-2 (Supplementary Table 1). We thenconducted log-normalization on the ST dataset using
the NormalizeData function in Seurat. Using the same
approach as with the snRNA-seq data, we calculated thesignature score for each cell type in the ST dataset.

Differential gene expression and gene set enrichment
analyses of ST data

We conducted the same differential gene expression andgene set enrichment analyses on the ST data as we did on
the snRNA-seq data.
Detection of gene co-localization module (Co-locM)

We converted each ST dataset into a pseudo-bulk dataset and performed differential gene expression analysis
separately for wild type and Anxa1−/− mice (see “Identification of DEGs from bulk and pseudo-bulk data” partin methods for detail). For wild type mice, we compared
the pseudo-bulk datasets between 12- or 24-hour and
0- or 72-hour, identifying up-regulated genes with foldchange values greater than 2 and p-values less than 0.01.
The same approach was used for Anxa1−/− mice. The
up-regulated genes from both sets were then merged tocreate a candidate gene set. To calculate the co-localization score between two genes (Gene A and Gene B), we
analyzed one 12-hour wild type mouse ST dataset. We
counted the number of spots ( Nspots ) and extracted thespatial expression matrix. We sorted the spots based on
Gene A’s expression and identified the top 0.05 ∗ Nspots
spots as {A1, A2, . . . , A0.05∗Nspots } . We followed a similar process for Gene B, obtaining the top 0.05 ∗ Nspots
spots as {B1 , B2, . . . , B0.05∗Nspots } . We then determined the overlap between these two sets as NA∩B andthe total unique spots as NA∪B . The co-location score( S_colocAB ) was calculated as 0 when NA∩B was lessNthan 50, and as NA∩B
when NA∩B was 50 or greater. ThisA∪B
calculation was repeated for all genes in the candidate
gene set. Using hierarchical clustering analysis, we identified a gene module consisting of 130 genes based on the
co-localization scores. This module was named Co-locM(Supplementary Table 6). We visualized the Co-locM by
calculating the co-localization scores of the candidate
gene set in all sixteen ST datasets.
Grouping spots based on cell type label combinations

From a 12-hour wild type mouse ST dataset, we gatheredspots with various labels such as Astro-1, Astro-2, Micro1, Micro-2, Neuron, OligoD, OPC, Vas-1, or Cdkn1a+
Serpina3n+ OligoD. These spots were then categorized
into different groups based on their label combinations.
We performed a count of spots within each group andZhu et al. Journal of Neuroinflammation(2024) 21:169

specifically examined spot groups containing more than
100 spots. Following the same approach, we identified
spot groups in eight ST datasets from both wild type
and Anxa1−/− mice at 12- and 24-hour time points. We
objective was to identify spot groups that consistently
appeared in all eight datasets. As a result, we identified a
total of 46 spot groups across these datasets.
Calculation of module score

To calculate the module score for Co-locM, we employed
the same method that we used for calculating the signature score.
Identification of ligand-receptor pairs specifically activated inwild type or Anxa1−/− mice at 12- and 24-hour time points

We extracted a total of 2,033 literature-supported
ligand-receptor pairs (LRs) specific to mice from the
CellTalkDB database [37]. To calculate the activity ofa LRs in a 12-hour wild type mouse ST dataset, we followed a specific procedure. Firstly, we calculated theco-localization score ( S_colocLR ) between the ligand
and the receptor, using the same method described in
the “Detection of gene co-localization module (ColocM)” section of our methods. Next, we randomly
selected 10,000 gene pairs from the ST dataset, ensuring that the two genes in each pair were not identical.
We then calculated the co-location scores for these randomly selected gene pairs, resulting in a random score
set denoted as {S_coloc1 , S_coloc2, . . . , S_coloc10,000 }.
The
frequency
atwhich{S_coloc1 , S_coloc2, . . . , S_coloc10,000 } ≥ S_colocLR
was determined as f reqLR . Finally, the LR activity wascalculated as −log2 (f reqLR ) . In the same manner, we
calculated the activity of all 2,033 LRs across the sixteen
ST datasets obtained from wild type and Anxa1−/− miceat different time points following LPS treatment. We
identified LRs that were specifically activated in wild
type mice at 12- and 24-hour time points. To do this, wegrouped the LR activity values obtained from 12- and24-hour wild type mice ST datasets as Set1, and the values from the other two time points as Set2. We replacedvalues larger than −log2 (0.05) with 1 and other values
with 0 in both sets. We then used Wilcox rank sum test
to compare the two sets. If Set1 had a higher rank thanSet2, and the P-value was less than 0.05, we considered
the LR as specifically activated in wild type mice at 12and 24-hour time points. We applied this method to all
LRs to identify those that were specifically activated ineither wild type or Anxa1−/− mice at 12- and 24-hourtime points.

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Bulk and pseudo-bulk gene expression data analysis
Data acquisition and processing

We retrieved bulk RNA-seq and microarray data, alongwith their corresponding sample information, for mousebrain tissue under various conditions from the GEO
database(https://www.ncbi.nlm.nih.gov/geo/).
The
datasets we used were identified with the accession IDs
GSE153369, GSE7814, GSE6019, GSE53874, GSE205140,
and GSE9524. We downloaded and utilized the processedand normalized matrices from these datasets, specificallyselecting relevant samples for further analysis.
To generate the pseudo-bulk RNA-seq dataset for eachST dataset, we summed the expression matrix of spots
based on genes. We then calculated the rpm (reads per
million mapped reads) value for each gene, which wasutilized for subsequent analysis.
Identification of DEGs from bulk and pseudo-bulk data

We identified DEGs between two groups of mice using
both bulk and pseudo-bulk data. For example, we compared the gene expression levels between different time
periods following LPS treatment and between wild type
and Anxa1−/− mice. We calculated the fold change values(between the two groups) and P-values. Specifically, we
conducted t-tests when there were more than two samples in each group, enabling us to ascertain significant
differences.
Western blotting

Brains of mouse were dissected and homogenized using
tissue grinders (Kimble) in 1.5 ml tubes. The homogenization process involved the use of lysis buffer containing
protease inhibitors (Complete Mini; Roche). After 1 minof homogenization, cellular debris was removed by centrifugation at 12,000×g for 10 min at 4 °C. The resulting
supernatant was collected and subjected to denaturation
at 95 °C for 10 min. Tissue lysates were separated using
SDS-PAGE (Bio-Rad) and transferred to a polyvinylidene
fluoride membrane. To prevent non-specific binding, themembranes were blocked for 1 h using 5% non-fat dry
milk in phosphate buffered saline (PBS) containing 0.5%
Tween-20 (PBST). Subsequently, the membranes wereprobed with primary antibodies against Anxa1 (NR1,
Abcam, ab109182) and glyceraldehyde 3-phosphate
dehydrogenase (GAPDH, Beyotime, AF0006), which
served as a loading control. After three washes within
PBST, a HRP-labeled secondary antibody (CWS) wasapplied at room temperature (RT) for 1 h using a dilution buffer for secondary antibodies (Beyotime). Subsequently, the membranes were washed three more times
with PBST. The bands were visualized using the Immobilon Western ECL system (Millipore) and analyzed using
ImageJ Analysis software.

Zhu et al. Journal of Neuroinflammation(2024) 21:169

For real-time Polymerase Chain Reaction (PCR), we
added 1 μL of template DNA and 1 μL of primer mixture
to the 2x Taq Plus Master Mix II (Vazyme, P213). Ultrapure water was then added to achieve a final reaction volume of 25 μL. The reaction mixture underwent an initialdenaturation at 95 °C for 5 min, followed by 32 cycles ofdenaturation at 95 °C for 30 s, annealing at 55 °C for 30 s,
and extension at 72 °C for 30 s. Eventually, there was a
final extension at 72 °C for 10 min. After the reaction,
7 μL of the sample was electrophoresed on a 1.5% agarose gel containing 0.01% ethidium bromide at 120 V for
35 min. The gel was then subjected to UV imaging andsubsequent analysis.
Immunofluorescence

Mice were anesthetized using an intraperitoneal injectionof 20% urethane (0.01 ml/g) and subsequently perfusedthrough the heart with 0.01 M PBS (pH 7.4), followed by
4% paraformaldehyde (PFA). After perfusion, the brains
were dissected and post-fixed in 4% PFA overnight.
To protect the tissue during freezing, they were thenimmersed in a 30% sucrose solution at 4 °C. Serial coronal sections, with a thickness of 20 μm, were obtainedusing a Leica CM3050S. The slides were warmed to RT
and washed three times with PBS. To enhance permeability of the tissue, the sections were washed with 0.3% Triton X-100 in PBS for 10 min. Non-specific binding was
blocked by incubating the tissue sections with QuickBlock™ Blocking Buffer (P0260) at RT for 10 min. The
sections were then incubated overnight at 4 °C with rabbit anti-Iba1 (Wako, 019-19741, 1:1000), CD68 (Abcam,
ab53444, 1:250), and anti-GFAP (CST, #3670, 1:1000)
antibodies in Primary Antibody Dilution Buffer (P0103).
After three washes with PBS, a secondary antibody(Invitrogen) was applied and incubated at RT for 1 h.
Afterward, the sections were washed three more times
with PBS for 10 min. The sections were then washedand mounted using ProLong™ Gold Antifade Mountant(P36930) and imaged using a fluorescence microscope.

Results
snRNA-seq analysis of the mouse brain over the course of
lipopolysaccharide challengeThis study employed both wild type and Anxa1 knockout(Anxa1−/−) C57BL/6 mice as experimental models. To
validate the successful generation of the Anxa1−/− mousemodel, Western blotting was performed on brain tissue
samples obtained from both wild type and Anxa1−/−
mice. As anticipated, the ANXA1 protein band was notdetected in the Anxa1−/− mice, while it was observedin the wild type mice, thereby confirming the successful construction of Anxa1−/− mouse model. Furthermore, Real-time Polymerase Chain Reaction (PCR) was
utilized to validate the absence of Anxa1 in the tails ofPage 7 of 24

the mice, providing further support to the findings
obtained through Western blot analysis. This additional
analysis further strengthens the evidence and confirms
the successful establishment of the Anxa1−/− model(Supplementary Fig. 1C). Both wild type and Anxa1−/−
mice were intraperitoneally injected with either Escherichia coli lipopolysaccharide (LPS; serotype 0111: B4,
Sigma; 15 mg/kg body weight) or a control solution ofendotoxin-free saline. During the 132-hour observation period, notable weight loss was observed in bothwild type and Anxa1−/− mice following LPS administration. However, after 72 h, their weights stabilized or evenincreased, suggesting the occurrence of intense inflammatory responses during the initial phases of simulated
endotoxemia (Supplementary Fig. 1D).
We employed both wild type and Anxa1−/− C57BL/6
mice as experimental models to investigate the intricate
cellular composition and transcriptional pathways in
the central nervous system (CNS) during sepsis or SAE.
Through snRNA-seq and high-resolution spatial transcriptomic analyses, we examined brain tissues obtained
at 12 h, 24 h, and 72 h post LPS challenge. After rigorous
quality control and filtering procedures, a toral of 376,909
high-quality single nucleus cells were obtained. On average, each cell contained approximately 3028 uniquemolecular identifiers (UMIs) and expressed 1,431 genes(Supplementary Table 2). Through our rigorous analysis, we successfully identified nine clusters representing
six distinct cell types within the CNS. These cell types
encompassed neurons, microglia, astrocytes, oligodendrocytes, oligodendrocyte precursor cells, and vascular
cells (Fig. 1B). Specifically, neurons were characterized
by the expression of Syt1 [38], oligodendrocytes exhibited the marker Cldn1 [39], and oligodendrocyte precursor cells displayed Pdgfra expression [40–42]. Notably,
microglia and astrocytes were further subdivided into
two subclusters based on their distinctive gene expression
patterns. Microglia were classified as Tmem119+ (Micro1) [43, 44] and Gpr84+ (Micro-2) [45], while astrocytes
were divided into Cldn10+ (Astro-1) and Igtp+ (Astro-2)
subclusters [46]. Furthermore, vascular-associated cells
were also classified into two subclusters: Col1a2+ (Vas1) [47] and Vtn+ (Vas-2) [48] (Fig. 1B and C). Our findings are consistent with previous studies investigating
cellular diversity in the mouse cortex and hippocampus[49]. Interestingly, no substantial differences in cell types
were observed between Anxa1−/− and wild type micefollowing LPS challenge, suggesting that Anxa1 does
not influence the composition of cells involved in brain
inflammatory responses (Fig. 1D). However, notablechanges were detected in the UMAP plots (Fig. 1E), particularly in the Astro-2 and Micro-2 populations. These
subpopulations initially exhibited an increase in abundance, followed by a subsequent decrease at 24 h, andZhu et al. Journal of Neuroinflammation(2024) 21:169

eventually returned to baseline levels by 72 h in bothmouse groups. At the 12-hour time point, we observed
a comparable increase in the astro-2 and micro-2 populations in both Anxa1−/− and wild type mice. This initial
response suggests a shared early reaction in these populations irrespective of the Anxa1 status. However, thesubsequent responses diverged significantly between thetwo groups. In wild type mice, the astro-2 and micro-2
populations reached a stable state at 24 h, indicating acontrolled and self-regulatory response in the presenceof Anxa1. Contrastingly, Anxa1−/− mice exhibited a rapid
decline in both astro-2 and micro-2 populations during
this same time point. This decline underscores the potential critical role of Anxa1 in sustaining these cell populations beyond the initial response phase (Fig. 1F-G). We
conducted a thorough analysis of differentially expressed
genes (DEGs) between Astro-2 and Astro-1 subpopulations, which uncovered significant upregulation of genes
such as Gbp2, Cxcl10, Serping1, and MHC-associatedgenes H2-D1 and H2-T23 in Astro-2 (Supplementary
Table 3). In contrast, a comparison with Micro-1 revealed
the downregulation of steady-state genes Tmem119 andP2ry12, alongside upregulation of inflammatory genes
including TNF, Il1a, Il1, and chemokines Ccl3, Ccl4, andCcl5 in Micro-2 (Supplementary Table 4). These alterations in the Micro-2 subpopulation align with the wellknown characteristics of microglia activated by LPS, aspreviously reported [50]. Furthermore, pathway analysis
highlighted that the upregulated genes in both Astro-2
and Micro-2 were primarily enriched in pathways associated with viral response, biotic stimulus regulation,and innate immune response (Supplementary Fig. 1E).
Moreover, to investigate the transcriptional impact of
LPS stimulation on oligodendrocytes as a whole, we also
conducted a differential expression analysis on a clusterof oligodendrocytes previously identified. This analysis revealed dynamic changes in their transcriptional
profiles. For instance, we found that oligodendrocytes
exhibit distinct gene expression patterns at different time
points, with genes showing high expression at 12 h and
24 h, while displaying deceased expression or no expression at 0 h and 72 h following the challenge (Fig. 1I).
Within this set of genes, we identified Serpina3n andCdkn1a, which have established associations with specific diseases and differentiation processes. In addition,
genes such as Sox4, Apod, and Klf9 were also observed.
These findings suggest that oligodendrocytes undergo
distinct differentiation states at 12 h and 24 h comparedto other time points. These particular oligodendrocytes,
referred to as Cdkn1a+Serpina3n+ OligoD, exhibitedenriched pathways related to cytoplasmic translation,
macromolecule biosynthetic process regulation, and viral
response (Supplementary Figure S1F). Our snRNA-seq
results indicate a significant increase in the proportions

Page 8 of 24

of Astro-2, Micro-2, and Cdkn1a+Serpina3n+ OligoD
within 24 h of LPS challenge, emphasizing their pivotal
roles during the initial phases of simulated endotoxemia.
Spatially-resolved transcriptomics of the mouse brain overthe course of lipopolysaccharide challenge

Spatial information is critical for understanding cell-cellinteractions that occur within tissues. Regrettably, thisvaluable information is absent from the snRNA-seq data.
To gain deeper insights into the specific cell type thatexerts the most significant impact on the inflammatory
response at 12 h and 24 h, as well as to investigate thespatial relationships and potential mechanisms of interaction among these cell types, we employed Stereo-seq
technology. This advanced method allowed us to capturein situ gene expression profiles. The Stereo-seq chips
used were designed with capture probes incorporatinga 25 bp coordinate identity barcode, a 10 bp molecular identity barcode, and a 22 bp poly T tail to facilitatemRNA hybridization. Brain tissues were collected fromboth wild-type and Anxa1−/− mice at 12 h, 24 h, and
72 h following LPS challenge. These tissues were subsequently embedded in optimal cutting temperature (OCT)compound. Utilizing this approach, we prepared serial
cryosections with a thickness of 10 μm for Stereo-seq,
hematoxylin and eosin (H&E) staining, and immunohistochemical (IHC) staining. Following the initial snRNAseq analysis performed on 16 samples (including wild
type and Anxa1−/− at 0 h, 12 h, 24 h, and 72 h, with two
samples at each time point), we proceeded to conductspatial transcriptome sequencing. The Stereo-seq chips
utilized in this study were designed with capture spots
measuring 220 nm in diameter and spaced at a distanceof 500 nm from each other. To generate a comprehensive
expression matrix with single-nucleus cell resolution, we
employed a registration process that aligned the ssDNA
image with the DNB image. We then utilized the StereoCell package for precise cell segmentation, thereby
enabling the creation of a spatial gene expression matrix.
To ensure the accuracy and reliability of gene annotation, we established stringent criteria of a minimum of
660 gene types and 1,300 counts per cell bin across all
utilized chips. Notably, the gene capture and UMI numbers demonstrated a consistent pattern across samples
obtained from both wild type and Anxa1−/− mice. Thisobservation serves as a strong indicator of the acquisitionof high-quality spatial transcriptomic data (Supplementary Figures S2A and S2B). To conduct cell annotation,
we generated distinct marker gene sets for each cell cluster by utilizing dimensionality-reduced snRNA-seq data.
These gene sets were further visualized, demonstrating their remarkable specificity (Supplementary Figure
S2C). To ensure comprehensive analysis, we examined
20,000 spots per sample for UMAP graph visualization,

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Zhu et al. Journal of Neuroinflammation(2024) 21:169

Page 10 of 24(See figure on previous page.)
Fig. 1 Single-nucleus transcriptomic profiling of the mouse brain following peripheral LPS stimulation. (A) Schematic diagram illustrating the acquisition
and analysis workflow of the spatial transcriptomics (ST) maps. Brain tissue samples from 8 wide type and 8 Anxa1 knockout C57BL6 mice were subjected
to Stereo-seq, resulting in a total of 1,943,116 spots. Paired brain tissue samples of the wild type and Anxa1−/− mice were also subjected to snRNA-seq,
and 376,909 qualified nuclei were identified. The processed data was subjected to dimensionality reduction using Uniform Manifold Approximation and
Projection (UMAP) and spatial cell types mapping for visualization, respectively. Finally, a combined analysis of the single-nucleus sequencing results andthe spatial transcriptome sequencing results was performed. (B) UMAP plot of the snRNA-seq data. Each cluster in the plot corresponds to a specific cell
type and is color-coded accordingly. The cell types included in the plot are Oligodendrocytes (OligoD), Vascular cells (Vas), Microglial cells (Microglia),
Astrocytes (Astro), and Oligodendrocyte precursor cells (OPC). (C) Expression of canonical markers in each cell type cluster. (D) The UMAP plot displays
cells from both the wild type and Anxa1−/− mice, with each cell color-coded based on the genotype of the respective mouse. (E) The UMAP plots demonstrate the changes in cell dynamics after administering LPS to both wild type and Anxa1−/− mice. (F-G) The relative proportions of Astro-1 and Astro-2
cells (F), as well as Micro-1 and Micro-2 cells (G), were compared at different time intervals following LPS treatment in both wild type and Anxa1−/− mice.
(H) Volcano plots were generated to visualize the differentially expressed genes (DEGs) between Astro-2 and Astro-1 cells, as well as between Micro-2
and Micro-1 cells. The X-axis and Y-axis represent the log2 fold-change differences between the compared cell types and the statistical significance as thenegative log10 of DEG P-values, respectively. Significantly up-regulated and down-regulated genes are indicated by orange and green dots, respectively,
while non-significant genes are represented as black dots. (I) Heatmap displaying the DEGs between two groups of Oligodendrocytes. The first groupconsists of Oligodendrocytes from wild type and Anxa1−/− mice at 12- or 24-hour, while the second group comprises Oligodendrocytes from wild type
and Anxa1−/− mice at 0- or 72-hour. Only the up-regulated genes with a log2 fold-change value greater than 1 and an adjusted p-value smaller than 0.05
are shown. Oligodendrocytes from wild type mice are colored in yellow, while those from Anxa1−/− mice are colored in red

successfully mitigating any potential batch effects (Supplementary Figures S2D and S2E). Additionally, we
devised a marker gene signature score for each cell cluster using spatial transcriptomics data. The accuracy ofthis annotation was confirmed through robust cell typedecomposition (RCTD) [51] (Supplementary Figures S2F
and S2G). Furthermore, by comparing the RCTD scoreswith the marker gene signature scores, we established theconsistency and validity of our annotation outcomes.
H&E staining unveiled a considerable populationof ependymal cells within the third ventricle, encompassed by astrocytes, aligning with spatial transcriptomic
annotations. Spatially resolved transcriptomic analysis revealed the distribution of neurons, astrocytes, andmicroglia throughout the entire brain slice, with neurons
exhibiting the highest prevalence, in accordance withprevious research (Fig. 2A) [52]. We conducted a thorough analysis of the cellular clusters within the spatialtranscriptome data to ascertain their respective proportions, and observed no notable disparities in terms of cell
type composition or quantities between wild type andAnxa1−/− mice. Neurons constituted the largest proportion, followed by glial cells, with vascular cells being theleast abundant (Fig. 2B and Supplementary Figure S2H).
We examined the ratios of Astro-2 and Micro-2 subtypes
at different time points, indicating a noteworthy upregulation of both subtypes in both mouse brains at 12 hfollowing LPS challenge. Nevertheless, while Micro-2
levels continued to be significantly elevated at 24 h inboth groups, Astro-2 levels remained sustained in thewild type mice but reverted to baseline in the Anxa1−/−
mice. This observed pattern was consistently supportedby the results obtained from the snRNA-seq analysis. At
the 72-hour mark following the challenge, both Astro-2
and Micro-2 levels reverted to their baseline levels(Fig. 2C and D). To validate and ascertain the observed
patterns during simulated endotoxemia, we employedspatial visualization to analyze the temporal trends of theinflammation-related cell clusters (Fig. 2E and F). Furthermore, for the analysis of oligodendrocytes, we useda specific gene set, Cdkn1a+Serpina3n+ OligoD, derived
from the snRNA-seq data, which demonstrated a highdegree of specificity (Supplementary Figure S2J). Genemodule scores were assessed in the spatial transcriptome
data, utilizing a predefined threshold of 0.4 for the specific gene set. Clusters surpassing this threshold wereidentified as Cdkn1a+Serpina3n+ OligoD (Supplementary
Figure S2K). Within a 24-hour timeframe, this particular
population exhibited a significantly increase, amounting to approximately 50% in both mouse brains, thereby
corroborating the findings obtained through snRNA-seq
analysis. Moreover, the spatial visualization of these oligodendrocytes consistently demonstrated similar trends(Fig. 2G and H). The analysis of the spatial transcriptome
data further confirmed that alterations in cell proportionsof Astro-2, Micro-2, and Cdkn1a+Serpina3n+ OligoD
were in concordance with those observed in snRNA-seq
analysis. The Gene Ontology (GO) enrichment analysis
revealed that these clusters were primarily associatedwith pathways such as “response to virus”, “regulationof neuron death” and “defense response to virus”, which
are all pertinent to neuron death and defense responses,
particularly evident 24 h following LPS stimulation. In
contrast, Astro-2 and Micro-2 subtypes were implicatedin additional pathways related to the regulation of neuron
death and inflammatory responses, including “neutrophil
chemotaxis” and “myeloid leukocyte migration” (Fig. 2I).
Furthermore, the spatial transcriptome gene expression
mapping revealed that while a significant proportion of
spots in both mouse strains comprised a single nucleus
cell type, there were also a notable number of spots containing multiple cell types (Supplementary Figure S2I).
The integrated findings from our snRNA-seq
and spatial transcriptomics analyses demonstrate

Zhu et al. Journal of Neuroinflammation(2024) 21:169

a significant upregulation of Astro-2, Micro-2, andCdkn1a+Serpina3n+ OligoD in both wild-type andAnxa1−/− mice at 12 h following LPS challenge. By the
24-hour timepoint, Micro-2 and Cdkn1a+Serpina3n+OligoD remain elevated, while Astro-2 exhibits upregulation in wild-type mice but returns to baseline levels in
Anxa1−/− mice. The spatial visualization of the transcriptome data further confirms that within 24 h post-challenge, the proportions of these three cell types, namely
Astro-2, Micro-2, and Cdkn1a+Serpina3n+ OligoD, experience a significant increase, thereby playing key roles
in neuron death and defense response pathways in bothexperimental groups.
snRNA-seq combined spatial transcriptome analysis
reveals special pathological niche in mouse brain duringsepsisTo delve deeper into the spatial distribution of inflammatory responses, we conducted a meticulous spatial
co-localization analysis of 682 up-regulated genes at
12 h and 24 h after LPS challenge, using the spatial transcriptomic data. This comprehensive analysis revealed a
specialized gene module consisting of 130 genes, which
demonstrated consistent expression patterns in bothwild-type and Anxa1−/− mice (Fig. 3A and Supplementary Figure S3A). Analysis of gene expression profiles
in brain slices collected at 0 h, 12 h, 24 h, and 72 h after
the challenge substantiated the augmented expression ofthis particular gene module at the 12-hour and 24-hourtime points (Supplementary Figure S3B). Furthermore,
the snRNA-seq data revealed that these genes exhibiteda high level of expression in the Astro-2 and Micro-2
clusters, further reinforcing their involvement in mediating inflammatory responses at the spatial transcriptomic
level. Importantly, these findings indicate that there wereno significant transcriptional changes observed in theother cell clusters following LPS stimulation (Fig. 3B).
GO analysis provided valuable insights into the functional properties of this gene module, indicating its
involvement in crucial inflammatory pathways including“response to interferon-beta”, “response to type II interferon”, “response to virus”, and “cytokine-mediated signaling pathway”. These findings suggest the regulatoryrole of this module in inflammatory responses within
the mouse brain, particularly 24 h after the stimulation (Fig. 3C). Subsequent analysis aimed to examine
the distribution patterns of this module across different
cell types within brain tissue slices. Notably, Astro-2,
Micro-2, and Cdkn1a+Serpina3n+ OligoD demonstrated
elevated module scores compared to non-inflammatory
subpopulations at both the 12-hour and 24-hour time
points following the stimulation. Significantly, the Vas-1
cluster showed pronounced upregulation and displayed
the highest scores within the identified gene module

Page 11 of 24(Fig. 3D). Spatial visualization analysis conducted at 12 h
and 24 h post stimulation substantiated these findings,
illustrating that the regions with the highest scores wereprimarily localized around blood vessels. This observation implies a vascular-centric distribution pattern of thismodule within the brain tissue (Fig. 3E). A parallel trend
was observed in Anxa1−/− mice, providing evidence thatAnxa1 plays a negligible role in the regulation of inflammatory responses within these specific cell types during endotoxemia simulation (Supplementary Figure S3C
and Figure S3D). In summary, our integrated analysis of
snRNA-seq and Stereo-seq revealed that identified gene
module showcased heightened expression not only in the
Astro-2 and Micro-2 clusters but also manifested augmented scores within the Vas-1 cell cluster. These findings collectively indicate a notable inflammatory activity
within these regions in both wild type and Anxa1−/− miceat the 12-hour and 24-hour time points following LPS
stimulation.
Given the existence of diverse cell types within certain
spatial transcriptome spots (Supplementary Figure S2I),
it became imperative to explore the precise distribution
pattern of a 130-gene module within Vas-1 cell spots.
To this end, we implemented a threshold value of 0.5,
wherein any spot score surpassing this threshold denoted
the presence of expression for the forementioned 130
genes (Fig. 3F). To substantiate the distribution pattern within spatial transcriptome spots comprising multiple cell types, a comprehensive analysis of the data
was undertaken. Notably, it was observed that the gene
module under investigation exhibited pronounced
enrichment in a specific set of 46 mixed cell type spots(Fig. 3G). Furthermore, the application of paired t-tests
provided compelling evidence of the module’s substantial
enrichment (P < 0.05) in spots containing mixed Astro-2/
Micro-2/Vas-1 cells, particularly in brain slices derived
from both wild type and Anxa1−/− mice, at 12 h and 24 h
subsequent to LPS stimulation (Fig. 3H). To facilitate
spatial visualization, our attention was directed towards
the hippocampus, lateral ventricle, and third ventricle
regions. Intriguingly, a closer examination revealed ahigher concentration of Astro-2 and Micro-2 cells in thevicinity of blood vessels at the 12-hour mark following
stimulation in wild type mice, which aligns consistently
with previous findings (Fig. 3I and J). Furthermore, an indepth analysis of the snRNA-seq data pertaining to thegenes upregulated in Astro-2, Micro-2, and Vas-1 cell
types (Fig. 3K) was followed by a subsequent GO enrichment analysis. Remarkably, the results of this analysis
indicated notable associations with “viral response” and“response to biotic stimulus” (Fig. 3L). The observed
enrichments serve to validate the findings of the ColocM module obtained from spatial transcriptomic analysis, thereby highlighting the coherence and agreement

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Zhu et al. Journal of Neuroinflammation(2024) 21:169

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Fig. 2 Spatially-resolved transcriptomic analysis following peripheral LPS stimulation. (A) Hematoxylin and eosin (H&E) staining, single-stranded DNA(ssDNA) picture, and cell type mapping of the ST slide from a wild type mouse at 72-hour. The spatial expression signal in the slide was detected using
Stereo-seq technology, and the raw spatial expression matrix was transformed into spots based on the ssDNA picture. The cell types within the spots
were then identified using the robust cell type decomposition (RCTD) method. In the right panel, the H&E staining and cell type labels around the thirdventricle are emphasized. (B) The number of spots in the spatial transcriptome (ST) data with the eight assigned cell type labels. Spots that are not categorized into any of the eight cell type labels, namely Astro-1, Astro-2, Micro-1, Micro-2, Neuron, OligoD, OPC, and Vas-1, are labeled as “other”. (C-D) The
relative proportions of Astro-1 and Astro-2 spots (C), as well as Micro-1 and Micro-2 spots (D), were compared at different time intervals following LPS
treatment in both wild type and Anxa1−/− mice. (E-F) Spatial map of Astro-1 or Astro-2 spots (E), as well as Micro-1 or Micro-2 spots (F) at different timeintervals following LPS treatment in both wild type and Anxa1−/− mice. (G) Proportions of Cdkn1a+ Serpina3n+ OligoD spots relative to all Oligodendrocyte spots across the ST data. (H) Spatial map of Cdkn1a+ Serpina3n+ OligoD spots across the ST data. (I) The enriched GO BP-terms for the DEGs between
Astro-2 and Astro-1, Micro-2 and Micro-1, as well as Cdkn1a+ Serpina3n+ OligoD and OligoD in the ST data. The top six GO BP-terms for the up-regulated
genes in each category-Astro-2, Micro-2, and Cdkn1a+ Serpina3n+ OligoD are presented

between different data modalities. Notably, the examination of brain slices derived from Anxa1−/− mice exhibited
analogous patterns of cell distribution surrounding Vas-1
cells at both 12-hour and 24-hour intervals post LPS
stimulation, with no significant differences compared towild type mice group. This suggests that the knockout of
Anxa1 did not induce any alterations in the distributionof inflammatory cells around Vas-1 cells during the simulated sepsis condition (Supplementary Figures S3E, S3F).
Moreover, a subsequent GO enrichment analysis conducted on the snRNA-seq data further reinforces thesefindings by reaffirming the involvement of these genes inthe “viral response” process, in accordance with the spatial transcriptomic analysis (Supplementary Figures S3G,
S3H).
By employing co-localization analysis, differential
expression analysis and GO enrichment analysis, wewere able to discern that a module comprising 130 genes
primarily associated with inflammation response exhibited heightened expression in Astro-2, Micro-2 andVas-1 subtypes (specifically, the V1A2M2 colocalization
structure) at both 12-hour and 24-hour time points subsequent to LPS stimulation, in both the wild type andAnxa1−/− mouse brain. Intriguingly, an exploration of
the 130-gene module across various published databases
revealed its presence in mouse models infected withparasites or viruses, while no occurrence was observedin models stimulated with LPS. This indicates a potential
protective role of the V1A2M2 complex in response toinfection scenarios (Supplementary Figure S6).
Cell-cell interaction analysis revealed potential ligandreceptor interactions within this niche involving Timp1 or
Ackr1

To gain insights into cellular interactions, we conducted
an analysis of co-localization scores for over 2,000 ligandreceptor gene pairs and identified 19 pairs activatedpairs in wild-type brains and 15 pairs in Anxa1−/− miceat both the 12-hour and 24-hour time points following
LPS challenge, with 11 pairs exhibiting activation in bothgroups. Notably, among the identified pairs, we observed
the presence of three pairs involving Timp1 and Cd14,

five pairs with Ackr1, and two pairs with Csf1r and Sdc4
(Fig. 4A). Intriguingly, an in-depth analysis of the snRNAseq data uncovered high expression levels of Timp1 in the
Astro-2 and Vas-1 clusters, while its receptor exhibiteda more widespread expression pattern across multiple
cell types. This intriguing expression pattern implies a
potential regulatory interaction between Astro-2 andVas-1 cells that may exert an influence on other cellular
entities. Likewise, we observed a predominant expression of Ackr1 in neurons and Vas-1 cells, accompaniedby the noteworthy presence of its ligands in Astro-2 andMicro-2 subtypes, suggesting the existence of bidirectional interactions. Furthermore, our analysis revealed
the expression of Cd14 and its receptors within theMicro-2 subtype, indicating the potential involvement ofself-regulatory mechanisms within this specific cluster. In
addition, the expression of Csf1 and Cxcl10, along with
their respective receptors, was detected in both Astro-2
and Micro-2 subtypes, implying the possibility of facilitating mutual regulation or autoregulation processes.
Moreover, we observed a predominant expression of
Csf1 in the Astro-2 subtype and Cdkn1a+Serpina3n+ OligoD cell population, Intriguingly, the receptor for Csf1,
namely Csf1r, exhibited specific localization in Micro-2
subtype. This finding suggests a potential regulatory relationship, wherein Astro-2 cells and Cdkn1a+Serpina3n+OligoD population may govern the activity of Micro-2
cells through the Csf1-Csf1r ligand-receptor pathway.
Additionally, our analysis revealed the specific presenceof Cxcl10 and Sdc4 in both Astro-2 and Micro-2 subtypes, further implicating their potential involvement ineither mutual regulation or autoregulation mechanisms.
(Supplementary Figure S4A). Following the initial analysis, spatial co-expression analysis was performed to validate the co-expression patterns of these ligand-receptor
genes in both wild type and Anxa1−/− mice at 12-hourand 24-hour time points post-challenge. Notably, certain
gene pairs, including Col4a1-Cd93 and Pdgfa-Pdgfra,
maintained their co-expression status until 72-hour time
point in wild type mice. Conversely, in Anxa1−/− mice,the co-expression of some gene pairs diminished before
the 24-hour mark. Collectively, no significant disparity

Zhu et al. Journal of Neuroinflammation(2024) 21:169

Fig. 3 (See legend on next page.)

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Zhu et al. Journal of Neuroinflammation(2024) 21:169

Page 15 of 24(See figure on previous page.)
Fig. 3 Diversity and spatial distribution of microglia, astrocytes, and Oligodendrocytes following peripheral LPS stimulation. (A) Heatmap illustrating
the gene co-localization module (Co-locM) in the ST data of wild type mice from different time periods after LPS treatment. (B) Visualization of Co-locM
module score in the UMAP plot of the snRNA-seq data. (C) The enriched GO BP-terms in the genes of the Co-locM module. The top ten GO BP-terms
are shown. (D) Distribution of the Co-locM module score in spots assigned with different cell type labels in the ST data of wild type mice from different
time periods. The boxplot for each spot type is color-coded based on its label type. T tests (unpaired samples, two-tailed) were conducted between different spot types, and the P-value or the maximum P-value of a test set was reported. Significance levels are indicated as follows: *** P < 0.001. (E) Spatial
distribution of the Co-locM module score and the spatial map of Vas-1 in the ST data obtained from wild type mice at 12- and 24-hour time points. (F)Distribution of the Co-locM module score in all spots within the ST data. The ST data is color-coded based on the mice genotype. Spots with a score above
0.5 were assigned the Co-locM label. (G) Distribution of Co-locM spot proportion across 46 spot groups. The spots were selected based on the presenceof any of the nine labels (Astro-1, Astro-2, Micro-1, Micro-2, Neuron, OligoD, OPC, Vas-1, and Cdkn1a+ Serpina3n+ OligoD) and grouped according to the
combination of labels present in each ST dataset. The Co-locM spot proportion in each spot group was calculated and plotted across eight ST datasetsfrom mice at 12- and 24-hour time points. The top five spot types with the highest Co-locM spot proportion are highlighted. (H) Distribution of Co-locM
spot proportion in the top five spot groups. Spot Group 1 (S1) consists of spots labeled with Micro-2, Astro-2, and Vas-1; Spot Group 2 (S2) comprises
spots labeled with OligoD, Micro-2, Astro-2, and Cdkn1a+ Serpina3n+ OligoD; Spot Group 3 (S3) includes spots labeled with Astro-2 and Vas-1; Spot Group4 (S4) consists of spots labeled with OligoD, Astro-2, and Cdkn1a+ Serpina3n+ OligoD; Spot Group 5 (S5) encompasses spots labeled with Micro-2 andVas-1. The Co-locM proportion between S1 and S2, S3, S4, and S5 is compared using a T-test (paired samples, one-tailed). Significance levels are indicatedas follows: *P < 0.05, ***P < 0.001. (I-J) Spatial maps of Astro.2, Micro-2, and Vas-1 spots in twFo different regions from wild type mice. The first region, asa Co-locM hot spot (I), is from a 12-hour mouse and represents a region where all three cell types are co-localized. The second region is a paired region
from a 0-hour mouse (J). The maps illustrate the distribution and co-localization of these three cell types in each region. (K) DEG analysis showing up- anddown-regulated genes across Astro-2, Micro-2 and Vas-1 clusters in snRNA-seq data of wild type mice. An adjusted P value < 0.01 is indicated in red, while
an adjusted P value ≥ 0.01 is indicated in black. DEG, differentially expressed genes. (L) The GO terms obtained by performing GO enrichment analysis on
the top 30 genes of Astro-2, Micro-2 and Vas-1 clusters based on the log2 FC value in the snRNA-seq data of wild type micein gene co-expression was observed at the 12-hour postchallenge time point between the two mouse groups(Fig. 4B). Subsequently, we assessed the differential
gene expression levels of these ligand-receptor pairs andobserved that majority of genes in the brains of bothwild-type and Anxa1−/− mice exhibited upregulation
at both the 12-hour and 24-hour time points following
LPS challenge. To confirm the robustness of these findings, we cross-validated our results by analyzing the
GSE153369 dataset, which yielded consistent outcomes(Fig. 4C). Furthermore, a GO enrichment analysis on
the upregulated genes shed light on their involvement in
immune cell migration pathways, including leukocyte,
myeloid leukocyte, granulocyte migration, and chemotaxis (Fig. 4D). Consequently, we further investigated
the spatial distribution of these genes at the 12-hour and24-hour time points, specifically focusing on the expression patterns within clusters such as Astro-2, Micro-2,
and Vas-1 (collectively referred to as V1A2M2). Notably, hub genes such as Timp1 and Ackr1 exhibited highly
expression levels within these clusters. Similarly, theTgm2-Sdc4 pair demonstrated elevated expression within
the same clusters. These findings imply that the intricate
interactions among these cell types within the V1A2M2
structure are potentially facilitated through the involvement of Timp1 and Ackr1 genes (Fig. 4E).
Our analysis of snRNA-seq data revealed that Ackr1
exhibited predominant expression (Supplementary Figure S4A). However, further investigation through spatial transcriptomic analysis unveiled its presence within
the V1A2M2 region. To validate its expression in Vas-1,
we performed an examination of Ackr1 across 46 mixed
cell type groups. Our findings confirmed its expression in Vas-1, as illustrated in the fourth box plot of

Supplementary Figure S4B. Additionally, to explore
the potential role of the hub gene Timp1 and its associated receptors in the V1A2M2 region, we conducted a
GO analysis focusing on the upregulated genes present in Vas-1 and Astro-2 cells expressing Timp1. Notably, we identified Cd63, Itgb1, and Lrp1 as receptor
genes associated with Timp1 in this context. The analysis revealed a significant enrichment of pathways related
to wound healing and amoeboid cell migration in boththe snRNA-seq and Stereo-seq datasets, suggesting thatVas-1 and Astro-2 cell populations may play a crucialrole in inflammatory repair processes and potentiallyfacilitate the migration of Micro-2 cells towards Vas-1
through Timp1-mediated signaling (Fig. 4F and G). This
study hypothesizes that Vas-1 and Astro-2 populations,characterized by their expression of the Timp1 receptor
gene, may play a role in the regulation of inflammatory
repair. Furthermore, considering the well-establishedevidence of amoeboid-type movement exhibited by activated microglia in previous investigations [53–56], it isplausible to suggest that the expression of Timp1 in Vas-1
and Astro-2 cells potentially facilitates the migration of
Micro-2 cells. Spatial transcriptomic analysis revealed
that the expression of ligand-receptor genes within the
V1A2M2 region exhibited a peak at 12 h following LPS
stimulation, followed by a decrease by the 24-hour time
point, and eventual disappearance by 72 h. Notably, no
significant disparities in gene expression were observed
between wild type and Anxa1−/− mice (Supplementary Figure S4C). Furthermore, the hub gene Cd14 andits associated receptors, primarily localized in microglial spots, indicated a potential self-regulatory mechanism of Micro-2 cells through the involvement of thesegenes during the inflammatory response (SupplementaryZhu et al. Journal of Neuroinflammation(2024) 21:169

Fig. 4 (See legend on next page.)

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Zhu et al. Journal of Neuroinflammation(2024) 21:169

Page 17 of 24(See figure on previous page.)
Fig. 4 Crosstalk among microglia, astrocytes, and vascular components within the pathological microstructure. (A) Network diagram of ligand-receptor
pairs that are specifically activated in wild type or Anxa1−/− mice at 12- and 24-hour time points (referred to as responsive-LRs). Each node in the diagram
represents either a ligand or receptor, with the node from which the arrow originates representing the ligand and the node to which the arrow points
representing the receptor. (B) Heatmap displaying the -log2 (p-value) of the activity of responsive ligand-receptor (LR) pairs in wild type and knockout(Anxa1−/−) mice at different time points. LR pairs were identified as responsive if they showed significant changes in activity compared to baseline levels.
The heatmap uses a color-coding scheme to indicate whether a responsive LR is specific to wild type (blue) or Anxa1−/− (red) mice. The heatmap provides
a visual representation of the changes in activity of specific LR pairs in wild type and Anxa1−/− mice over time. (C) Fold change values of genes composedof responsive ligand-receptor (LR) pairs. Fold change values were calculated in wild type and Anxa1−/− mice at 12- and 24-hour time points compared to
0- and 72-hour time points. Additionally, fold change values were calculated in a publicly available bulk RNA-seq dataset comparing LPS-treated wild typemice to PBS-treated wild type mice. The genes included in the analysis are those that are composed of the responsive LR pairs identified in the previous
analysis. The figure provides a visual representation of the changes in gene expression in response to LPS treatment in wild type and Anxa1−/− mice, as
well as in a separate dataset. (D) The enriched GO BP-terms in the genes composed of responsive-LRs in wild type mice. Top ten GO BP-terms are shown.
(E) Distribution of ligand-receptor (LR) co-localization spot percent in different spot groups. The spots were selected based on the presence of any of thenine labels (Astro-1, Astro-2, Micro-1, Micro-2, Neuron, OligoD, OPC, Vas-1, and Cdkn1a+ Serpina3n+ OligoD) and grouped according to the combinationof labels present in each ST dataset. LR co-localization spot percent in each spot group was calculated and plotted across eight ST datasets from miceat 12- and 24-hour time points. The top five spot types with the highest percentages are shown in the figure and labeled as S1, S2, S3, S4, and S5. The
LR co-localization spot percent between S1 and S2, S3, S4, and S5 is compared by T-test (paired samples, one-tailed); statistical significance is indicatedby asterisks (*P < 0.05; ***P < 0.001). In each subgraph, S1 represents the spot group composed of Micro-2, Astro-2, and Vas-1 labels. The figure provides
insights into the co-localization patterns of LR pairs within specific cell types and across different ST datasets. (F-G) The GO BP-terms that are enriched in
the up-regulated genes between Vas-1 cells (spots) with and without the ligand-receptor pair(LR) (F) and between Astro-2 cells (spots) with and without
the LR (G). A cell or a spot with a LR pair means that the two genes composed the LR are co-expressed in the cell or spot. The analysis was performed
to identify the biological processes that are associated with the up-regulated genes between cells with and without LR pairs. The top six enriched GO
BP-terms are shown for both ST data and snRNA-seq data. The figure provides insights into the biological processes that are affected by the presence orabsence of LR pairs in Vas-1 and Astro-2 cells

Figure S5A). Subsequent GO analysis focused on theactivation of Micro-2 cells mediated by Cd14 unveiled
significant associations with immune cell migration processes. Notably, these processes encompassed the regulation of inflammatory response, neutrophil migration,and myeloid leukocyte migration, suggesting a potential
role for Micro-2 cells in facilitating immune cell movement. Consequently, the heightened activity of Micro-2
cells observed in the vicinity of blood vessels may be
attributable to their migratory behavior (Supplementary Figures S5C, S5B, and S5D). Simultaneous analysis
revealed the widespread expression of the ligand-receptor pair Cxcl10-Sdc4 across diverse cellular populations
within the spatial transcriptome spots. Notably, this pair
exhibited specific and high expression levels in Astro-2
and Micro-2 cells. GO enrichment analysis conducted on
the co-expressed genes within these clusters indicated anaugmented migration capacity in Micro-2 cells, alongside
an elevated immune response in Astro-2 cells due to the
influence of Cxcl10-Sdc4 pair. This co-expression pattern
was found to be associated with inflammatory responses
and signaling pathways related to cytokines or type IIinterferon. These findings underscore the crucial role
played by Cxcl10-Sdc4 in the inflammatory dynamics ofAstro-2 and Micro-2 cells (Supplementary Figures S5E
and S5F).
Our findings highlight the upregulation and co-expression of genes belonging to ligand-receptor pairs, including hub genes Timp1 and Ackr1. These genes play crucial
roles in processes such as immune cell migration, chemotaxis, inflammatory responses, and signaling pathways
associated with cytokines or type II interferon. Notably,

increased expression of these genes was observed within
the specialized pathological niche V1A2M2 in the brainsof both wild type and Anxa1−/− mice at 12 h and 24 h following LPS challenge. The absence of Anxa1 resulted in amore rapid decline of the protective structure composedof Aastro-2/Micro-2/Vas-1 during the reactive phase,potentially leading to reduced survival.
To elucidate the significance of ANXA1 during thisprocess, we conducted a survival analysis to compare
the outcomes between LPS stimulated Anxa1−/− andwild type mice. Notably, the survival rates of Anxa1−/−
mice were notably lower than those of wild type mice,
underscoring the protective role of Anxa1 in the context
of LPS-induced sepsis (Fig. 5A). Subsequently, we performed a comparative analysis to assess the impact of
Anxa1 on the inflammatory response in the brain following endotoxin stimulation. Through a comparative analysis of the ratios of Astro-2 to Astro-1 in brain tissues
from both wild type and Anxa1−/− mice, we identified asignificant increase in the proportion of Astro-2 at 12 hpost-stimulation, with consistent trends observed in both
snRNA-seq and Stereo-seq datasets. However, at 24 hpost-stimulation, the levels of Astro-2 in Anxa1−/− mice
exhibited a significant decrease compared to those inwild type mice, indicating a substantial down-regulation
linked to the knockout of Anxa1 (Fig. 5B). A similar trend
was observed in the proportional shifts between Micro-2
and Micro-1 cells, which exhibited significant increases
in both mouse types at 12 h post-stimulation. However,at 24 h, there was a slight reduction in the proportionof Micro-2 cells specifically in Anxa1−/− mice (Fig. 5C).
Subsequent analyses conducted using snRNA-seq andZhu et al. Journal of Neuroinflammation

Fig. 5 (See legend on next page.)(2024) 21:169

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Zhu et al. Journal of Neuroinflammation(2024) 21:169

Page 19 of 24(See figure on previous page.)
Fig. 5 Role of Anxa1 in the pathogenesis of pathological microstructure formation. (A) Survival Analysis of murine model through intraperitoneal administration of lipopolysaccharide (LPS) in wild type and Anxa1−/− group. Graphical representation is as follows: wild type control mice receiving saline(purple dashed line), wild type mice administered lipopolysaccharide (LPS) (blue solid line), Anxa1−/− control mice receiving saline (green dashed line),and Anxa1−/− mice administered LPS (red solid line). On the survival plot, the x-axis signifies the duration of post-injection observation in hours, while they-axis denotes the survival percentage. A statistical significance, indicated by three asterisks, corresponds to a P value of less than 0.001. (B-C) The ratio
between cells (spots) of different cell subtypes across time periods. Specifically, it shows the ratio of Astro-2 cells (spots) with Astro-1 cells (spots) (B andthe ratio of Micro-2 cells (spots) with Micro-1 cells (spots) (C). Each dot in the figure represents the ratio calculated from a mouse, and the color of the dot
indicates the genotype of the mouse. The ratio calculation was performed on two different types of data: “snRNA” represents the ratio calculated in thesnRNA-seq data, while “ST” indicates that the ratio was calculated in the ST data. (D) UMAP plot showing Astro-2 and Astro-1 cells from 12- and 24-hour
mice in the snRNA-seq data. The Astro-2 and Astro-1 cells are indicated by different colors in the plot. (E) Spatial map showing the distribution of Astro-2
and Astro-1 labeled spots in the ST datasets from 12- and 24-hour mice. The Astro-2 and Astro-1 labeled spots are represented by different colors on themap, indicating their respective locations. (F) Percents of Vas-1, Astro-2, and Micro-2 labeled spots in the ST data from different time periods. Each dot in
the figure represents a mouse, and the color of the dot represents the mouse genotype. The percentages of Vas-1, Astro-2, and Micro-2 labeled spots were
calculated for each mouse and are displayed in the figure. (G) Spatial map of Vas-1, Astro-2, and Micro-2 labeled spots in the ST datasets from 12- and 24hours mice. (H) Fold change values of genes composed of responsive ligand-receptor pairs between 24-hour wild type mice and 24-hour Anxa1−/− mice.
The genes that showed a fold change of two times or more, either up-regulated (red) or down-regulated (green), were identified. The corresponding
ligand-receptor pairs associated with these genes were highlighted. (I-J) Percentages of Micro-2 cells (spots) with the LR in 24-hour wild type mice and24-hour Anxa1−/− mice (I), as well as the percentages of Astro-2 cells (spots) with Cxcl10-Sdc4 in 24-hour wild type mice and 24-hour Anxa1−/− mice. (K)Percentages of Vas-1, Astro-2, and Micro-2 labeled spots with the ligand-receptor pair in 24-hour wild type mice and 24-hour Anxa1−/− mice. Each dot in
the figure represents a mouse, and the color of the dot indicates the mouse genotype. A spot with a ligand-receptor pairs indicates that the two genes
composing the LR are co-localized in that particular spot

spatial transcriptome visualization approaches confirmedthat the decline in Astro-2 at 24 h post-stimulation in
Anxa1−/− mice was significantly more pronounced compared to the wild-type group, establishing a direct correlation with the genetic knockout of the Anxa1 gene(Fig. 5D and E). We analyzed the temporal expression
patterns of a 130-gene module, referred to as V1A2M2,
in the brain tissues of wild type and Anxa1−/− mice. This
module was identified as the highest scoring cluster in
our analysis. At 12 h post-LPS stimulation, we observed
a significant upregulation of these genes in both mousegroups. However, by the 24-hour time point, the extent
of gene downregulation in Anxa1−/− mice was notably
more pronounced compared to wild type mice (Fig. 5F).
These findings were further confirmed through spatialtranscriptome visualization, providing additional support
for the observed patterns (Fig. 5G). Additionally, at 24 hfollowing LPS challenge, we observed significantly lower
expression levels of certain ligand-receptor pairing genes
in Anxa1−/− mice compared to wild type mice, which
could potentially be attributed to the decreased numbersof the specialized pathological niche V1A2M2 (Fig. 5H).
Further analysis of cell percentages associated with thesegenes showed significant decreases in the proportions ofMicro-2 cells expressing Cd14 and its receptors, as well
as Astro-2 and Micro-2 cells expressing Cxcl10-Sdc4.
Similarly, the proportions of Astro-2/Micro-2/Vas-1 cells
expressing Ccl2-Ackr1 also showed a significant decline.
These findings suggest that the knockout of Anxa1 influenced the expression of Cd14, Cxcl10-Sdc4, and Ccl2Ackr1, ultimately leading to diminished proportions of
the V1A2M2 structure (Fig. 5I, J and K).

Discussion
SAE, a severe complication of sepsis, poses a considerable challenge in clinical practice due to its intricate
pathophysiology and substantial implications for morbidity and mortality. Given the potential confounding
factors in neurological assessment within the intensive
care unit (ICU) setting, a comprehensive and multimodal
approach is necessary for accurate diagnosis and effective management. Standard diagnostic measures encompass clinical evaluation, metabolic tests, and, in selectedcases, neuroimaging and electroencephalography. SAE
is defined by a complex array of neurological symptoms that manifest as a consequence of systemic infection. However, the precise mechanisms that underlie thedevelopment of septic encephalopathy remain incompletely understood, posing a significant challenge to theadvancement of accurate diagnostic approaches and theformulation of effective therapeutic interventions [57]. By
employing keyword burst detection, it was revealed thatneuroinflammation, blood-brain barrier (BBB) integrity,and mitochondria dysfunction have emerged as prominent areas of research focus [58]. Notably, significant
progress has been made in elucidating the underlying
mechanisms of neuroinflammation, which have provided
valuable insights into the pathogenesis of SAE. A prevailing hypothesis posits that dysregulation of the immune
response, characterized by an imbalance between proinflammatory and anti-inflammatory pathways, plays apivotal role in the development and progression of SAE.
Uncontrolled neuroinflammation and ischemic injury
have been identified as key contributors to the development of brain dysfunction in SAE. Additionally, investigations have delved into the intricate interplay between
SAE and peripheral immunity, aiming to unravel thecomplex mechanisms underlying the systemic impact ofZhu et al. Journal of Neuroinflammation(2024) 21:169

sepsis on brain function. Emerging scientific literature
has brought attention to the pivotal role of various mediators in the disruption of the BBB during sepsis. Notably,
recent studies propose that therapeutic interventions
aimed at modulating microglial activation, ameliorating
endothelial cell dysfunction, and preventing BBB permeability hold considerable promise in improving the clinical outcomes of SAE [59].
The Anxa1 gene, recognized for its anti-inflammatory
properties within the nervous system, has gained attention as a potential modulator in SAE. Previous studies
have provided evidence of the protective effects of Anxa1
in models of neuroinflammation, indicating its involvement in modulating the progression of the disease. In
a recent study, researchers made a finding wherein theadministration of recombinant ANXA1 (rANXA1) exhibited robust protective effects on the BBB in animal models. This intervention was associated with a reduction in
BBB disruption, brain edema, and the loss of endothelial junction proteins [60]. Furthermore, parallel studies
have demonstrated the critical role of ANXA1 in brain
physiology, particularly its involvement in the endothelium of the BBB. These investigations have emphasized
the potential of ANXA1 to exert protective actions in thecontext of neuroinflammatory, neurovascular, and metabolic diseases [61]. Additionally, ANXA1 levels have been
observed to be higher in the brains of individuals with
Alzheimer’s disease (AD) as well as in animal models
during the initial stages of the disease. ANXA1 has theability to enhance the breakdown of amyloid-beta (Aβ)
through neprilysin in N2a cells, and it also assists in theengulfment of Aβ by microglia. These effects are mediated by the involvement of FPRL1 receptors. Moreover,
ANXA1 acts as an inhibitor by reducing the release of
inflammatory substances by microglia stimulated by Aβ[62]. Researchers have found that the interaction between
SIRT5 and ANXA1 at K166 causes the desuccinylation of
ANXA1, leading to a reduction in its SUMOylation level.
This process prevents ANXA1 from being recruited tothe membrane and secreted outside the cell, resulting inan overactive response from microglia, increased production of proinflammatory cytokines and chemokines,
and ultimately damage to neuronal cells in cases of ischemic stroke [63]. In another study, researchers aimed toinvestigate whether the ANXA1 tripeptide could hinder
the activation of NLRP3 inflammasome, which in turn
could prevent the activation of microglial and memory
deficits related to the hippocampal-dependent [64, 65].
The specific involvement of Anxa1 in SAE, particularly regarding the progression of the disease over time,remains largely unexplored. This knowledge gap emphasizes the necessity for comprehensive research that investigates the temporal patterns of inflammatory responses
and examines the potential role of Anxa1 in these

Page 20 of 24

processes. To address this concern, our study utilized acomprehensive experimental approach by employing amouse model of SAE induced by intraperitoneal injectionof LPS [66]. This mouse model enables us to investigate
the development of SAE at critical time intervals, withspecial emphasis on the initial phases of the disease. We
employed advanced techniques such as snRNA-seq and
Stereo-seq [67–70] to gain a comprehensive understanding of the cellular composition and diversity following
LPS stimulation in the brain. SnRNA-seq enables indepth analysis of individual cells, allowing us to explore
the heterogeneity within the brain. This is essential tocomprehend intricate neurological mechanisms andidentify specific cell types that may contribute to disease
processes [71]. Although snRNA-seq offers detailed cellular information, it lacks spatial context. Spatial transcriptomics, on the other hand, provides data on the
precise locations of different cells within the tissue. This
spatial information is particularly significant in the brain,
as the organization and location of cells are closely tied totheir functions [72, 73]. By combining these technologies,
we can gain a more comprehensive understanding of thefunctional aspects of the brain. Our approach provides
a more holistic perspective by analyzing both the individual cellular responses and their spatial organization
within the complex brain microenvironment. This allows
us to capture the diversity of cell types and their spatial
distribution in a more comprehensive manner.
In our study, detailed findings in the results section
uncovered several important discoveries regarding theactivation of glial cells, their spatial distribution, and thedistinct response observed in knockout mice. The dataobtained from snRNA-seq demonstrated a significant
increase in activated microglial cells and astrocytes at
12 h and 24 h after LPS injection. These findings were
further supported by spatial transcriptome sequencing, which provided spatial context to the observed cellular activation in both wild-type and Anax1 knockout
mice. The timing of glial cell activation following LPS
stimulation can vary. Previous studies have reported
that changes in glial cells can be detected as early as 3 hafter LPS injection [74]. Another study documented anelevation in specific activation markers in the brain 4 hafter LPS administration [75]. Additionally, one study
examined mice that were sacrificed 24 h after intracerebroventricular (ICV) LPS injection for further analysis[76]. Activated microglia have been found to provide
neuroprotection against brain injuries. For example, a
study demonstrated that mice injected with LPS experienced decreased neuronal cell death and lesion volumes
following experimental brain injury [77]. This neuroprotective effect is thought to be achieved by reducing inhibitory axosomatic synapses. LPS stimulation can induce
neuroinflammation in microglia by activating certainZhu et al. Journal of Neuroinflammation(2024) 21:169

pathways and suppressing others, thereby inhibiting
autophagosome formation [78]. Such neuroinflammation is associated with the development of neurodegenerative diseases. LPS stimulation of microglia primarily
triggers a pro-inflammatory response, activating otherglial cells and peripheral immune response, which collectively contributes to the establishment of a neuroinflammatory phenotype [79]. Furthermore, spatial
analysis uncovered a unique pattern of activated glial
cell distribution around blood vessels at 12 h and 24 h,suggesting a targeted response in that area. This finding contrasts with a previous study, which establishedthat glial cells have the capability to both dilate and constrict blood vessels, a mechanism involved in neurovascular coupling. This important discovery emphasizes theclose interaction between glial cells and blood vessels[80]. Analysis of ligand profiles unveiled a simultaneous increase in specific receptor-ligand pairs at 12 h andPage 21 of 24

24 h, suggesting potential signaling pathways implicatedin glial cell activation (Fig. 6). Further examination ofthese receptor-ligand pairs showed that both Astro-2 andVas-1 upregulated Timp1-Itgb1/Cd63 after 24 h, which is
linked to wound healing and amoeboid cell migration. A
study demonstrated that TIMP-1 promotes hypermigration of primary dendritic cells infected with Toxoplasma
through CD63-ITGB1-FAK signaling [81]. This finding
implies a potential involvement of the Timp1-Itgb1/Cd63
interaction in microglia migration. In another study,
it was discovered that TIMP1 interacts with the CD63/
ITGB1 complex, leading to the activation of downstream FAK signaling. This activation, in turn, reduces
RhoA activation and promotes the stability of endothelial cell structure by preventing F-actin depolymerization. These findings imply that the interaction between
TIMP1 and ITGB1/CD63 may contribute to maintaining the integrity of the blood-brain barrier, which is vitalFig. 6 Hypothesized model of astroglial and microglial perivascular network interactions. Glial cell activation and interaction following intraperitoneal
injection of lipopolysaccharide (LPS) were compared to the control group. After intraperitoneal injection, microglia and astrocytes were observed to be
activated, proliferate, and enlarge in the peritoneal cavity. These activated glial cells were found to aggregate around blood vessels, forming a structurereferred to as V1A2M2. The activated glial cells present in the V1A2M2 structure may act by activating specific receptor ligands. Activated astrocytes in the
structure may interact through Timp1-Cd63 and Timp1-Lrp1, while activated microglia may engage in self-regulation through Cd14-Itgb1 and Cd14-C3ar1.
This response signifies the reaction to intracranial inflammation initiated by peripheral inflammation

Zhu et al. Journal of Neuroinflammation(2024) 21:169

for neuroprotection [82]. Additionally, we detected anincrease in the expression of ACKR1 receptor and chemokines in the CNS. Although the role of ACKRs in the
CNS is multifaceted and extends beyond inflammation
control, there are specific cases where CXCR7/ACKR3
has been shown to regulate T cell trafficking by modulating the internalization of CXCL12 at CNS endothelial barriers. ACKR1 plays a critical regulatory role inchemokine biology by capturing, scavenging, or transporting chemokines, thus modulating their availability
and signaling through conventional chemokine receptors [83]. An example of such a chemokine is CCL2, also
known as monocyte chemoattractant protein 1 (MCP1), which attracts monocytes and microglia to areasof brain inflammation. Notably, it has been associatedwith the recruitment of microglia and macrophages inchronic traumatic encephalopathy [84]. The interaction
between chemokines like CCL2 and ACKR1 can modulate the inflammatory response in the brain, which is a
crucial component of neuroinflammation. The increasein CD14 and Toll-Like Receptors 2 (TLR2) observed inthis study confirms previous findings that indicates theirinvolvement in microglial activation induced by fibrillar Aβ [85]. Additionally, this finding is consistent withthe positive regulation of defense responses identified in
the GO analysis conducted in this experiment. Notably,
when comparing wild-type and knockout mice, noticeable differences were primarily observed in the quantityand arrangement of activated astrocytes at 24 h, accompanied by the reduced expression of specific receptorligand pairs (Ccl2-Ackr1, Cd14-Tlr2). These findings
suggest that the knockdown of Anxa1 results in a reducein V1A2M2 structure, decreased astro-2 count, lessmicro-2 recruitment, and an attenuated inflammatory
response in microglial cells. Collectively, these findings
enhance our understanding of the temporal and spatial
dynamics in glial cell activation following LPS injection.
They also provide insights into potential signaling mechanisms and highlight distinctions between wild-type andAnax1 knockout mice. However, there are a few limitations in this study that need to be addressed. Firstly, thenumber of samples at each time point is limited. Additionally, this study only focuses on the effects of LPS challenge on the brain in mice. In future studies, it would be
beneficial to investigate the impact of sepsis on other tissues and organs, such as the intestine, heart, liver, kidneyand spleen etc. Finally, the use of spatial omics methods,
such as spatial proteomics, can be employed to directly
examine the expression changes of important regulatory
proteins in various brain regions at different time points
following LPS stimulation. Furthermore, employing spatial metabolomics can enable the examination of alterations in metabolite levels within various regions of the

Page 22 of 24

brain in mice, specifically those that are temporally regulated following LPS stimulation.
In conclusion, our study highlights the importance ofconsidering the temporal dynamics of neuroinflammatory responses in murine model of SAE. By integrating
snRNA-seq and Stereo-seq techniques, we have identified a special pathological niche composed of Astro-2
and Micro-2 and Vas-1 cells, accompanied by the upregulation of specific ligand-receptor pairs. These findings
suggest potential involvement of these mechanisms inthe development of SAE or the mortality associated with
Anxa1 knockdown. Future investigations focusing on theinterplay between Anxa1, different anti-inflammatory
pathways, and their contributions to the progression of
SAE could contribute further to our understanding of
this complex disease.
Abbreviations
BBB	Blood-brain barrier
CID	Coordinate identity
CNS	Central nervous system
DEGs	Differentially expressed genes
H&E	Hematoxylin and eosin
ICV	Intracerebroventricular
IHC	Immunohistochemical
IP	Intraperitoneal injection
LPS	LipopolysaccharideLRs	Ligand-receptor pairs
MIDs	Molecular identifiers
OCT	Optimal cutting temperature
OPC	Oligodendrocyte precursor cells
PBS	Phosphate buffered saline
PCR	Polymerase chain reaction
SAE	Sepsis-associated encephalopathy
snRNA-seq	Single nucleus RNA sequencing
ST	Spatial transcriptome sequencing
UMAP	Uniform Manifold Approximation and Projection
UMIs	Unique molecular identifiers

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12974-024-03161-0.
Supplementary Material 1: Supplementary Figure 1
Supplementary Material 2: Supplementary Figure 2
Supplementary Material 3: Supplementary Figure 3
Supplementary Material 4: Supplementary Figure 4
Supplementary Material 5: Supplementary Figure 5
Supplementary Material 6: Supplementary Figure 6
Supplementary Material 7: Supplementary Table 1. Display of genesignatures for Astro-1, Astro-2, Micro-1, Micro-2, Neuron, OligoD, OPC, Vas1, and Cdkn1a+ Serpina3n+ OligoD from the snRNA-seq data
Supplementary Material 8: Supplementary Table 2. Display of thefeatures and counts number per cell of snRNA-seq
Supplementary Material 9: Supplementary Table 3. Display of DEGs
across Astro-2 vs. Astro-1 clusters in snRNA-seq data
Supplementary Material 10: Supplementary Table 4. Display of DEGs
across Micro-2 vs. Micro-1 in snRNA-seq data

Zhu et al. Journal of Neuroinflammation(2024) 21:169

Supplementary Material 11: Supplementary Table 5. Display of primer
sequences used in PCR experiments
Supplementary Material 12: Supplementary Table 6. Module of 130
genes
Acknowledgements
None.
Author contributions
Involvement in conception and design of the study (PZ), acquisition of thedata (YZ, YZ, PZ, XG, XF, SH, SY, HF and XX), analysis and interpretation of thedata (YZ, YZ, PZ, XG and XF), substantial involvement in the writing and/or
revision of the article (PZ, YZ, YZ, XG and XF); responsible for content of themanuscript including data and analysis (PZ). All authors read and approved
the final manuscript.
Funding
This study was supported by Natural Science Foundation of Guangdong
Province, China (Grant No.: 2021A1515012429 to Prof. Pingsen Zhao);
Shaoguan Municipal Science and Technology Program, China (Grant No.:
211102114530659 to Prof. Pingsen Zhao); Shaoguan Municipal Science andTechnology Program, China (Grant No.: 220610154531525 to to Prof. Pingsen
Zhao); Shaoguan Engineering Research Center for Research and Developmentof Molecular and Cellular Technology in Rapid Diagnosis of Infectious Diseases
and Cancer Program, China (Grant No.: 20221807 to Prof. Pingsen Zhao);Research Fund for Joint Laboratory for Digital and Precise Detection of Clinical
Pathogens, Yuebei People’s Hospital Affiliated to Shantou University Medical
College, China (Grant No.: KEYANSHEN (2023) 01 to Prof. Pingsen Zhao);Research Project for Outstanding Scholar of Yuebei People’s Hospital, Shantou
University Medical College, China (Grant No.: RS202001 to Prof. Pingsen Zhao).
Data availability
No datasets were generated or analysed during the current study.

Declarations
Ethics approval and consent to participate
All experimental procedures were approved by The Medical Animal Care &Welfare Committee of Shantou University Medical College (Approval Number:
SUMC2021-332).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Department of Laboratory Medicine, Yuebei People’s Hospital, Shantou
University Medical College, No 133, Huimin Road South, Wujiang District,
Shaoguan 512025, China
2
Laboratory for Diagnosis of Clinical Microbiology and Infection, Yuebei
People’s Hospital, Shantou University Medical College, Shaoguan
512025, China
3
Research Center for Interdisciplinary & High-quality Innovative
Development in Laboratory Medicine, Shaoguan 512025, China
4
Shaoguan Municipal Quality Control Center for Laboratory Medicine,
Yuebei People’s Hospital, Shantou University Medical College,
Shaoguan 512025, China
5
Shaoguan Municipal Quality Control Center for Surveillance of Bacterial
Resistance, Shaoguan 512025, China
6
Shaoguan Engineering Research Center for Research and Developmentof Molecular and Cellular Technology in Rapid Diagnosis of Infectious
Diseases and Cancer, Shaoguan 512025, China
7
Yuebei People’s Hospital, Shantou University Medical College,
Shaoguan 512025, China
8
BGI-Shenzhen, Shenzhen 518083, China
9
Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese
Medical University, Jiaxing 314001, China

Page 23 of 24

Received: 22 February 2024 / Accepted: 26 June 2024

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