Biology subjects

Cao, S.

Publications and source records attributed to Cao, S..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Spatial transcriptomics reveals heterogeneous cell-cell interactions among brain regions in a cuprizone model consistent with multiple sclerosis lesions

The cuprizone (CPZ) model is widely used for modeling demyelination in multiple sclerosis (MS) and for testing potential remyelination therapies. We integrated single-cell and spatial transcriptomics (ST) to fine map the spatial cellular and molecular responses during de and remyelination. ST revealed global demyelination and neuroinflammation in the brain beyond the corpus callosum, with region-specific differences. We identified oligodendroglia and microglia as two major cell types with significant transcriptomic changes in the model. Ligand receptor pairing analyses predicted growth factor and phagocytic pathway enrichment during demyelination, which is consistent with changes in MS lesions. During remyelination, while mature oligodendrocytes nearly reversed their phenotype back to the control state, microglia remained associated with the demyelination phenotype. Finally, astrocytes in the CPZ model had the greatest preservation of disease-associated modules to MS lesions, while the MOL, OPC, and microglia showed moderate to low preservation, which overall suggested that the CPZ model had moderate translatability to chronically active MS lesions.

neuroscience↗

bioRxiv2025

ScaleSC: A superfast and scalable single cell RNA-seq data analysis pipeline powered by GPU.

The rise of large-scale single-cell RNA-seq data has introduced challenges in data processing due to its slow speed. Leveraging advancements in GPU computing ecosystems, such as CuPy, and building on Scanpy and rapids-singlecell package, we developed ScaleSC, a GPU-accelerated solution for single-cell data processing. ScaleSC delivers over a 20x speedup through GPU computing and significantly improves scalability, handling datasets of 10-40 million cells with over 1000 batches by overcoming the memory bottleneck on a single A100 card- far surpassing rapids-singlecells capacity of processing only 1 million cells without multi-GPU support. We also resolved discrepancies between GPU and CPU algorithm implementations to ensure consistency. In addition to core optimizations, we developed new advanced tools for marker gene identification, cluster merging, and more, with GPU-optimized implementations seamlessly integrated. Designed for ease of use, the ScaleSC package is compatible with Scanpy workflows, requiring minimal adaptation from users. The ScaleSC package (https://github.com/interactivereport/ScaleSC) promises significant benefits for the single-cell RNA-seq computational community.

bioinformatics↗

bioRxiv2025

Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2

Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the three-dimensional structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for these residues. Then, it incorporates an additional neural network to characterize the atom-level features of peptides, allowing for multi-scale modeling of peptide molecules while enabling the distinction between various unnatural amino acids. Besides, HighFold2 constructs a relative position encoding matrix for cyclic peptides based on different cyclization constraints. Except for training using spatial structures with unnatural amino acids, HighFold2 also parameterizes the unnatural amino acids to relax the predicted structure by energy minimization for clash elimination. Extensive empirical experiments demonstrate that HighFold2 can accurately predict the three-dimensional structures of cyclic peptide monomers containing unnatural amino acids and their complexes with proteins, with the median RMSD for C reaching 1.891 [A]. All these results indicate the effectiveness of HighFold2, representing a significant advancement in cyclic peptide-based drug discovery.

bioinformatics↗

bioRxiv2024

High-resolution imaging atlas reveals context-dependent role of pancreatic sympathetic innervation in diabetic mice

Gaining a better understanding of how sympathetic nerves impact pancreatic function is helpful for understanding diabetes. However, there is still uncertainty and controversy surrounding the roles of sympathetic nerves within the pancreas. To address this, we utilize high-resolution imaging and advanced three-dimensional (3D) reconstruction techniques to study the patterns of sympathetic innervation and morphology in islets of adult WT and diabetic mice. Our data shows that more than [~]30% /{beta}-cells are innervated by sympathetic nerves in both WT and diabetic mice. Also, sympathetic innervated /{beta}-cells are reduced in DIO mice, whereas sympathetic innervated {beta}-cells are increased in db/db mice. Besides, in situ chemical pancreatic sympathetic denervation (cPSD) improves glucose tolerance in WT and db/db mice, but decreases in DIO mice. In situ cPSD also enhances insulin sensitivity in diabetic mice without affecting WT mice. Overall, our findings advance our comprehension of diabetes by highlighting the distinctive impact of pancreatic sympathetic innervation on glucose regulation. HighlightsPancreatic sympathetic innervation plays an important role in glucose homeostasis. In our study, we systematically investigate sympathetic innervation in the islets of WT and diabetic mice. O_LI45.9% -cells and 31.8% {beta}-cells are innervated by sympathetic nerves in 10-week-old WT mice. C_LIO_LISympathetic innervated /{beta}-cells are reduced in DIO mice, whereas sympathetic innervated {beta}-cells are increased in db/db mice. C_LIO_LIUnlike the core-mantle structure observed in WT and DIO mice, -cells and {beta}-cells are intermixed in db/db mice. C_LIO_LIPancreatic sympathetic denervation elicits varied responses across diabetic models, where it improves glucose tolerance and insulin sensitivity in db/db mice, yet it increases insulin sensitivity but reduces glucose tolerance in DIO mice. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/623708v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@183be78org.highwire.dtl.DTLVardef@4dc76borg.highwire.dtl.DTLVardef@5f5ca0org.highwire.dtl.DTLVardef@ba3cc7_HPS_FORMAT_FIGEXP M_FIG Summary of morphological and physiological phenotypes in diabetic mice. All data are normalized to that of WT-10 mice. The numbers of "+" refer to their relative numbers. {uparrow} or {downarrow} refers to increase or decrease, ns refers to "not significant". "-" refers to the experiment has not been carried out. C_FIG

neuroscience↗

bioRxiv2024

Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygote with few-shot learning

The CRISPR-Cas system has revolutionized gene editing, while its outcome prediction remains unsatisfactory, especially in new cell states due to their distinct DNA repair preferences. In this study, we introduce inDecay, a flexible system for predicting CRISPR editing outcomes from target sequence, returning probabilities of nearly the full spectrum of indel events. Uniquely, inDecay utilizes informative and parameter-efficient features for each indel event and incorporates cell-type-specific repair preferences through a multi-stage design. While both inDecay and existing methods achieve accurate results for prediction within cell lines, only inDecay with transfer learning can retain the high performance for cross-cell line prediction. We then applied inDecay to mouse embryo editing using our newly generated data and observed remarkable accuracy by including as few as 30 fine-tuning embryonic samples. Notably, inDecay is the first software to predict embryonic editing. Therefore, our few-shot learning-supported system may accelerate guide RNA prioritization in mouse model generation, mammal embryonic gene editing, and cellular therapeutics.

bioinformatics↗

bioRxiv2024

Single-Cell and Spatial Multi-omics Reveal Interferon Signaling in the Pathogenesis of Perianal Fistulizing Crohn's Disease

Background & AimsPerianal fistulizing Crohns disease (PCD) is a common and debilitating complication with elusive pathophysiology. We examined mucosal cells from patients with PCD and related conditions using a multi-omics approach. MethodsWe recruited patients with PCD (n = 24), CD without perianal disease (NPCD, n = 10), and idiopathic perianal fistulas (IPF, n = 29). Biopsies were taken from fistula tracts, fistula opening, and rectal mucosa. Single-cell RNA-sequencing (scRNA-seq), mass cytometry (CyTOF), spatial transcriptomics (ST), immunohistochemistry, and integrated analysis were performed. ResultsScRNA-seq, CyTOF, and ST unraveled immune and non-immune cell compartments in PCD and IPF fistula tracts. PCD fistulas showed hyperactivated pathogenic pathways including interferon (IFN)G response and TNF signaling in myeloid and stromal cells. Intestinal cells from PCD patients also expressed greater levels of IFNG-responsive and EMT genes compared to NPCD patients. Furthermore, both fistula tracts and ileal mucosa from PCD patients harbored expanded IFNG+ pathogenic Th17 cells, which expressed elevated inflammatory mediators. CyTOF also identified skewed immune cell phenotypes in the fistula tracts, fistula opening, and rectum in PCD patients including expanded Th17 cells, increased pathogenic myeloid cells, and altered T cell exhaustion markers. Further analysis also revealed cellular modules associated with anti-TNF therapy in PCD patients. ConclusionMulti-omics analysis revealed immune, stromal, and epithelial cell landscapes of PCD, which highlight the pathogenic role of hyperactivated IFNG signaling in both fistula tracts and luminal mucosa. This study identified IFNG as a potential therapeutic target for PCD.

systems biology↗

bioRxiv2024

A revision of the African Russula radicans and allies in subgen. Heterophyllidiae provides an example of a clade that exhibits recent diversification and extensive phenotypic plasticity.

Russula subsection Radicantinae is described for R. radicans and allies, while subsection Aureotactinae is again restricted to R. aureotacta, the subsectional type species that has never been recollected since its original description. Russula radicans, originally described from Madagascar, as well as the Central African R. brunneoannulata, are epitypified. Russula xylophila is the first confirmed annulate species belonging to Sect. Ingratae and a close relative to R. oleifera. An ITS sequence has been obtained from the R. xylophila holotype collection made in 1928, but it is slightly different from those obtained from all new collections. For this reason and some minor morphological differences, we refrained from epitypification. Russula acriannulata, another species attributed to Aureotactinae in the past, is now also excluded from the subsection, but needs more sequencing to solve its systematic placement. The Malagasy Russula cibaensis and R. tapiae, as well as the Central African R. cameroonensis and R. afrovinacea are new species in Radicantinae.At least three more undescribed species are revealed by environmental sequences. The West African R. sankarae is reduced to a form of R. radicans. The latter species exhibits an impressive phenotypic plasticity in response to differences in habitat or host associations. It has a very wide distribution covering littoral rain forest in eastern Madagascar (forma radicans), Zambezian woodlands in Central and Eastern Africa (forma miomboensis fo. nov.) and gallery forests in West Africa (forma sankarae).

molecular biology↗

bioRxiv2024

Domain-specific Schema Reuse Supports Flexible Learning to Learn in Primate Brain

Prior knowledge accelerates subsequent learning of similarly structured problems - a phenomenon termed "learning to learn" - by forming and reusing generalizable neural representations, i.e., the schemas. However, the stability-plasticity dilemma, i.e., how to exploit stable schemas to facilitate learning while remaining flexible towards possible changes, is not well understood. We hypothesize that restricting schemas to specific functional, e.g., decision-making, subspace and making it orthogonal to other subspaces allows the brain to balance stability and plasticity. To test it, we trained three macaques on visuomotor mapping tasks and recorded neural activity in the dorsolateral premotor cortex. By delineating decision and stimulus subspaces, we identified a schema-like manifold within only the decision subspace. The reuse of decision schemas significantly facilitated subsequent learning. In addition, the decision subspace exhibited a trend to be orthogonal to the stimulus subspace, minimizing interference between these two domains. Our results revealed that restricting schemas to specific functional domains can preserve useful knowledge while maintaining orthogonality with other subspaces, allowing for flexible adaptation to new environments, thereby resolving the stability-plasticity dilemma. This finding provides new insights into the mechanisms underlying brains capability to learn both fast and flexibly, which can also inspire more efficient learning algorithms for artificial intelligence systems towards working in open, dynamic environments.

neuroscience↗

bioRxiv2024

Hand Position Fields of Neurons in the Premotor Cortex of Macaques during Natural Reaching

Planning and execution for goal-directed movements requires the integration of the current position of body or body parts with various kinematic parameters of the movement itself. In the hippocampus, the field-based representation of spatial information plays an essential role in this process during navigation. However, if a similar, field-based encoding framework is also utilized by the motor areas during hand reaching remains unclear. In this study, we investigated the hand position tuning in the dorsal premotor cortex (PMd) neurons (n = 601) when four monkeys performing a naturalistic reach-and-grasp task. We show that 132/601 (22%) of PMd neurons increased their firing rates when the monkeys hand occupied specific positions in the space, forming the "position fields" in their spatial firing maps that can be well described by 2D Gaussian functions/kernels. We further analyzed how the field-tuning of hand position is co-represented with other task-related parameters including the hand moving direction, speed, and reward location in the same population of PMd neurons, revealing a mixed-selective framework also similar to that discovered in the hippocampus. These position-tuned cells demonstrated high efficiency in encoding hand position, with ~10% of overall recorded neurons contributing >80% of accuracy in decoding instantaneous hand moving trajectories. These results suggest that a field-based encoding framework of position may be a common component to representing spatial information and integrating it with kinematic parameters for guiding goal-directed movements of the body or body parts.

neuroscience↗

bioRxiv2024

stFormer: a foundation model for spatial transcriptomics

Recent foundation models for single-cell transcriptomics data generate informative, context-aware representations for genes and cells. The Spatial Transcriptomics (ST) data offer extra positional insights, which were not considered by these single-cell models. Here, we introduce stFormer, a transformer model tailored for ST data. stFormer employs the cross-attention module to incorporate spatial ligand genes into the transformer encoder of single-cell transcriptomics. To unify different ST technologies with trade-off between resolution and gene coverage, we propose a biased cross-attention method that enables the model to do learning with single-cell resolution on whole-transcriptome but low-resolution Visium data. We collected human Visium datasets from a public ST database and performed cell type deconvolution, generating ~4.1 million pretraining samples. As a foundation model for ST data, stFormer improved performance upon the state-of-the-art single-cell foundation model, scFoundation, across a variety of tasks, including cell clustering, batch effect correction, cell type prediction, and gene function prediction. stFormer also revealed intercellular ligand-receptor signaling responses via in silico perturbation.

bioinformatics↗

bioRxiv2024

Deep learning facilitates precise identification of disease-resistance genes in plants

The identification of plant disease-resistance genes is essential for understanding the plant immune system and accelerating crop breeding with disease resistance. Therefore, there is a pressing need for a method capable of accurately and comprehensively annotating resistance genes at a genome-wide scale. In this study, we propose Evolutionary Scale Modeling for LRR (ESM-LRR), a novel approach based on the deep protein language model to accurately identify LRR domains which are substantially variable structures in disease-resistance proteins. ESM-LRR achieved its highest F1 score of 0.80 on a test set using the 90% identity as the matching threshold. Building upon ESM-LRR, we developed the Plant Disease-Resistance Gene Predictor (R-Predictor), a framework designed to simultaneously annotate 15 diverse domain topologies, extensively covering recently characterised resistance genes across the entire genome. R-Predictor integrates four modules, each employing superior methods that outperform existing methods (F1 score of 0.89 for RLKs and 0.88 for NLRs), demonstrating its high accuracy and practicality in annotating disease-resistance genes (R genes). R-Predictor were also applied to identify R genes in rice, tomato, and grapevine. Furthermore, AlphaFold3 was employed to screen interactions between 1,116 protein pairs involving three NLRs identified exclusively by R-Predictor and 372 literature-reported plant-pathogen effectors, resulting in the identification of 15 putative NLR-effector complexes. Overall, this study presents a novel approach for advancing our understanding of plant immune mechanisms.

bioinformatics↗

bioRxiv2024

Somatic mutation phasing and haplotype extension using linked-reads in multiple myeloma

Somatic mutation phasing informs our understanding of cancer-related events, like driver mutations. We generated linked-read whole genome sequencing data for 23 samples across disease stages from 14 multiple myeloma (MM) patients and systematically assigned somatic mutations to haplotypes using linked-reads. Here, we report the reconstructed cancer haplotypes and phase blocks from several MM samples and show how phase block length can be extended by integrating samples from the same individual. We also uncover phasing information in genes frequently mutated in MM, including DIS3, HIST1H1E, KRAS, NRAS, and TP53, phasing 79.4% of 20,705 high-confidence somatic mutations. In some cases, this enabled us to interpret clonal evolution models at higher resolution using pairs of phased somatic mutations. For example, our analysis of one patient suggested that two NRAS hotspot mutations occurred on the same haplotype but were independent events in different subclones. Given sufficient tumor purity and data quality, our framework illustrates how haplotype-aware analysis of somatic mutations in cancer can be beneficial for some cancer cases.

bioinformatics↗

bioRxiv2024

Zinc and Metformin Co-Functionalized Polyetheretherketone: A Novel Dental Implant Material Tailored for the Elderly

This study focuses on addressing the challenges of dental implants in the geriatric population by enhancing the bioactivity of polyetheretherketone (PEEK) through surface modification. PEEK, with its elastic modulus close to alveolar bone, mitigates stress shielding but faces limitations in osseointegration due to low bioactivity. We introduced zinc (Zn) and metformin (MF) onto PEEK surfaces via a dopamine-assisted physical adhesion method, creating a functionalized derivative called ZnMF@PEEK. This combination targets diminished osteogenic potential, persistent inflammation, and cell senescence, which are common issues in elderly patients. Comprehensive physicochemical characterizations confirmed the successful preparation of ZnMF@PEEK, and in vitro and in vivo experiments systematically evaluated its biocompatibility and bioactivity. The results indicate that ZnMF@PEEK holds promise as a dental implant material tailored to the specific needs of the elderly, addressing multifaceted challenges in osseointegration.

bioengineering↗

bioRxiv2024

The AP2/ERF transcription factor HcERF5 confers drought tolerance via ABA-mediated signaling in kenaf

The APETALA2/ethylene response factor (AP2/ERFs) are pivotal in regulating abiotic stress responses in plants. However, the specific role of ERFs in kenafs response to drought stress remains unclear. In this study, a transcription factors HcERF5 was isolated from kenaf and its role in drought stress tolerance was analyzed. HcERF5 was found to localize in both nucleus and cytoplasm and could be significantly induced by polyethylene glycol-6000 (PEG-6000) and abscisic acid (ABA) in kenaf seedlings. In transgenic Arabidopsis expressing the HcERF5 promoter-driven {beta}-glucuronidase (GUS), strong GUS activity was observed in roots, stems, and leaves. Overexpression of HcERF5 in Arabidopsis enhanced seed germination rates under drought or ABA stress and improved drought tolerance in seedlings by increasing antioxidant enzyme activities, whereas aterf5 knockout lines exhibited the opposite trend. Additionally, HcERF5 overexpressing Arabidopsis showed significantly increased drought tolerance and reduced sensitivity to ABA. Furthermore, virus-induced gene silencing (VIGS) of HcERF5 in kenaf reduced drought tolerance, as evidenced by decreased antioxidant enzyme activity, increased stomatal aperture, and elevated levels of malondialdehyde (MDA), reactive oxygen species (ROS), and proline under drought stress. RNA-seq analysis further revealed that HcERF5 directly regulated ABA signaling pathway. Yeast-two-hybrid (Y2H) assays revealed 29 proteins that interact with HcERF5. Among them, the expression of downstream drought stress-related genes HcPRK, HcRD22, HcMAP2, HcCAB, HcCS, and HcCCoAOMT3 were significantly reduced in HcERF5-silenced plants. Overall, this study highlights the significant potential of HcERF5 in enhancing drought tolerance in kenaf. HighlightO_LIHcERF5 overexpression enhanced drought tolerance in kenaf, however silencing increased drought sensitivity. C_LIO_LIHcERF5 regulates ABA synthesis and increases kenaf stomatal conductance and density under drought stress. C_LIO_LIHcERF5 regulates plant hormone signal transduction, MAPK signalling, and phenylpropanoid biosynthesis in kenaf under drought stress. C_LIO_LIProtein interaction revealed HcERF5 interacts with six stress-response genes. C_LI

molecular biology↗

bioRxiv2024

Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome

Intra-tumor heterogeneity is characterized by a diverse population of tumor clones and subclones which are important drivers of tumor evolution and therapeutic response. However, accurate subclonal reconstruction at scale remains challenging. We developed a machine learning tool, CliPP, and surveyed 9,972 tumors from 32 cancer types. We found that high subclonal mutation load (sML), the fraction of subclonal single nucleotide variants (SNVs) to all SNVs in the coding region, was prognostic of survival (progression free survival or overall survival) in 18 cancer types. In 14 cancers with low to moderate tumor mutation burden (TMB), high sML was associated with better prognosis. In immunotherapy trials for 42 metastatic prostate cancer (mCRPC), high sML was predictive of favorable response to ipilimumab and associated with increased CD8+ T-cell infiltration and decreased macrophage population. A validation using 613 whole-genomes of esophageal adenocarcinoma confirms the favorable effect of high sML and the observed tumor-associated macrophage. Our study identifies sML as a key feature of cancer, suggesting a biphasic relationship between evolutionary dynamics and differential immune environments. Finally, sML may serve as an orthogonal approach to identify likely responders of immune checkpoint blockade in low to moderate TMB tumors.

genomics↗

bioRxiv2024

Distinct Perception Mechanisms of BACH1 Quaternary Structure Degrons by Two F-box Proteins under Oxidative Stress

The transcription factor BACH1 regulates heme homeostasis and oxidative stress responses and promotes cancer metastasis upon aberrant accumulation. Its stability is controlled by two F-box protein ubiquitin ligases, FBXO22 and FBXL17. Here we show that the homodimeric BTB domain of BACH1 functions as a previously undescribed quaternary structure degron, which is deciphered by the two F-box proteins via distinct mechanisms. After BACH1 is released from chromatin by heme, FBXO22 asymmetrically recognizes a cross-protomer interface of the intact BACH1 BTB dimer, which is otherwise masked by the co-repressor NCOR1. If the BACH1 BTB dimer escapes the surveillance by FBXO22 due to oxidative modifications, its quaternary structure integrity is probed by a pair of FBXL17, which simultaneously engage and remodel the two BTB protomers into E3-bound monomers for ubiquitination. By unveiling the multifaceted regulatory mechanisms of BACH1 stability, our studies highlight the abilities of ubiquitin ligases to decode high-order protein assemblies and reveal therapeutic opportunities to block cancer invasion via compound-induced BACH1 destabilization.

cancer biology↗

bioRxiv2024

Engineered Flt3L Drives Tolerogenic State to Attenuate Anti-drug Antibody Responses

Immune reactions to protein drugs present substantial challenges to protein replacement for treating congenital diseases and metabolic deficiencies, due to the lack of endogenous tolerance or the protein drugs partial or total non-human origin. We sought to transiently modify the immune environment when the adaptive response to the drug antigen is mounted to lessen future reactions upon continued therapeutic treatment, without modifying the drug itself. Herein, we characterize a recombinant fusion of the cytokine Flt3L to serum albumin and describe a novel pathway of Flt3L-mediated immune regulation. We highlight reduced activation of dendritic cells (DC) as well as an increased frequency of DCs expressing LAP, a TGF-{beta} precursor. These effects in combination with low doses of the exogenous antigen led to less TH2 differentiation. This enabled a tolerance-biasing induction regimen to significantly decrease anti-drug antibodies upon repeated exposure to a clinically used, immunogenic fungal enzyme, rasburicase. This induction regimen reduced the Tfh compartment and increased Tfh cells expressing Foxp3 and PD-L1, suggesting a regulatory response. Overall, we introduce the use of a Flt3L variant as an induction therapeutic to modulate the innate immune response, thereby attenuating the adaptive reaction to antigenic protein drugs and addressing an unmet clinical need.

bioengineering↗

bioRxiv2024

Integrated multi-omics analyses reveal the pro-inflammatory and pro-fibrotic pulmonary macrophage subcluster in silicosis

BackgroundSilicosis is a lethal occupational disease caused by long-term exposure to respirable silica dust. Pulmonary macrophages play a crucial role in mediating the initiation of silicosis. However, the phenotypic and functional heterogeneities of pulmonary macrophages in silicosis have not been well-studied. MethodsThe silicosis mouse model was established by intratracheal administration of silica suspension. Bronchoalveolar lavage fluids (BALFs) of mice were collected for the multiplex cytokine analysis. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics were performed to reveal the heterogeneity and spatial localization of macrophages in the lung tissues. The formation of the fibrotic nodules was characterized by histology, hydroxyproline assay, and immunohistochemical staining, respectively. The expression of the pro-inflammatory or pro-fibrotic genes was investigated by quantitative polymerase chain reaction (qPCR). ResultsWe found that the level of pro-inflammatory cytokines and chemokines is significantly increased in the BALFs of silicosis mice. Apparent collagen deposition can also be observed in the silicotic lung tissues. By scRNA-seq, we have identified a subpopulation of Mmp12hi macrophages significantly expanding in the lung tissues of mice with silicosis. Spatial transcriptomics analysis further confirmed that the Mmp12hi macrophages are mainly enriched in silicosis nodules. Pseudotime trajectory showed that these Mmp12hi macrophages, highly expressing both pro-inflammatory and pro-fibrotic genes are derived from Ly6c+ monocytes. Additionally, 4-octyl itaconate (4-OI) treatment, which can alleviate pulmonary fibrosis in silicosis mice, also reduces the enrichment of the Mmp12hi macrophages. Moreover, we found a subset of macrophages in BALFs derived from patients with silicosis exhibited similar characteristics of Mmp12hi macrophages in silicosis mice models. ConclusionsOur study suggested that a group of Mmp12hi macrophages highly express both pro-inflammatory and pro-fibrotic factors in silicosis mice, and thus may contribute to the progression of fibrosis. The findings have proposed new insights for understanding the heterogeneity of lung macrophages in silicosis, suggesting that the subset of Mmp12hi macrophages may be a potential therapy target to further halt the progression of silicosis.

bioinformatics↗