Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas

Spatiotemporal transcriptome atlas of developing mouse lung

The functional development of the mammalian lung is a complex process that relies on the spatial and temporal organization of multiple cell types and their states. However, a comprehensive spatiotemporal transcriptome atlas of the developing lung has not yet been reported. Here we apply high-throughput spatial transcriptomics to allow for a comprehensive assessment of mouse lung development comprised of two critical developmental events: branching morphogenesis and alveologenesis. We firstly generate a spatial molecular atlas of mouse lung development spanning from E12.5 to P0 based on the integration of published single cell RNA-sequencing data and identify 10 spatial domains critical for functional lung organization. Furthermore, we create a lineage trajectory connecting spatial clusters from adjacent time points in E12.5-P0 lungs and explore TF (transcription factor) regulatory networks for each lineage specification. We observe the establishment of pulmonary airways within the developing lung, accompanied by the proximal-distal patterning with distinct characteristics of gene expression, signaling landscape and transcription factors enrichment. We characterize the alveolar niche heterogeneity with maturation state differences during the later developmental stage around birth and demonstrate differentially expressed genes, such as Angpt2 and Epha3, which may perform a critical role during alveologenesis. In addition, multiple signaling pathways, including ANGPT, VEGF and EPHA, exhibit increased levels in more maturing alveolar niche. Collectively, by integrating the spatial transcriptome with corresponding single-cell transcriptome data, we provide a comprehensive molecular atlas of mouse lung development with detailed molecular domain annotation and communication, which would pave the way for understanding human lung development and respiratory regeneration medicine.

Post-gastrulation amnioids as a stem cell-derived model of human extra-embryonic development

The amnion, an extra-embryonic tissue in mammalian embryos, is thought to provide crucial signaling, structural, and nutritional support during pregnancy. Despite its pivotal importance, studying human amnion formation and function has been hampered by the lack of accurate in vitro models. Here, we present an embryonic stem cell-derived 3D model of the post-gastrulation amnion, post-gastrulation amnioids (PGAs), that faithfully recapitulates extra-embryonic development up to 4 weeks post-fertilization, closely mimicking the functional traits of the human amniotic sac. PGAs self-organize, forming the amnion and the yolk sac, and are surrounded by the extra-embryonic mesoderm. Using PGAs, we show that GATA3 is required and sufficient for amniogenesis and that an autoregulatory feedback loop governs amnion formation, whereby extra-embryonic signals promote amnion specification. The reproducibility and scalability of the PGA system, with its precise cellular, structural, and functional integrity, opens avenues for investigating embryo-amnion interactions beyond gastrulation and offers an ideal platform for large-scale pharmacological and clinical studies.

Sacubitril/Valsartan attenuates progression of diabetic cardiomyopathy through immunomodulation properties: an opportunity to prevent progressive disease

BACKGROUND AND AIMS

Diabetic cardiomyopathy (DbCM) is recognised as a key mediator and determinant of heart failure (HF), particularly HF with preserved ejection fraction (HFpEF). Improved understanding of mechanisms underlying transition from early-stage DbCM to HFpEF will inform innovative evidence-based treatment approaches, which are urgently required to alleviate increasing disease burden. This study aimed to determine whether inhibition of neprilysin activity by Sacubitril/Valsartan in both experimental and clinical DbCM attenuates adverse remodelling through promotion of cardioprotective signalling.

METHODS AND RESULTS

Sacubitril/Valsartan effectively reduced plasma neprilysin activity in both diabetic patients with pre-clinical HFpEF from the PARABLE trial (baseline (Val n = 25; Sac/Val n = 35) and 3 months after treatment (Val n = 21/25; Sac/Val n = 33/35)) and DbCM (high-fat diet and streptozotocin) mice. Plasma neprilysin activity at baseline was correlated with worsening cardiac performance at 18 months indicated by left atrial stiffness index in patients (n = 44/60), whilst diastolic dysfunction and pathological remodelling in DbCM mice were improved by Sacubitril/Valsartan, but not Valsartan. snRNA-sequencing showed that progressive experimental DbCM is characterised by chronic low-grade inflammation, reflected by increased infiltration of pro-inflammatory monocytes (Ccr2+ Ly6chi) and reduction in MHC-II macrophages, which was prevented by Sacubitril/Valsartan. Informatics analysis implicated IRF7 as a central mediator of Sacubitril/Valsartan-induced immunomodulation in DbCM, whilst treatment of M2-like pro-repair macrophages with the neprilysin inhibitor, LBQ657 and Valsartan suppressed glucose-induced IRF7 expression and paracrine activation of cardiac fibroblast differentiation in vitro.

CONCLUSION

Immune cells are significantly involved in DbCM progression, impacting myocardial homeostasis and HF progression. Neprilysin inhibition by Sacubitril/Valsartan improved adverse cardiac remodelling in experimental DbCM through direct regulation of inflammation, highlighting immunomodulation as a novel mechanism underlying established its cardioprotective actions.

ONTraC characterizes spatially continuous variations of tissue microenvironment through niche trajectory analysis

Recent technological advances enable mapping of tissue spatial organization at single-cell resolution, but methods for analyzing spatially continuous microenvironments are still lacking. We introduce ONTraC, a graph neural network-based framework for constructing spatial trajectories at niche-level. Through benchmarking analyses using multiple simulated and real datasets, we show that ONTraC outperforms existing methods. ONTraC captures both normal anatomical structures and disease-associated tissue microenvironment changes. In addition, it identifies tissue microenvironment-dependent shifts in gene expression, regulatory network, and cell-cell interaction patterns. Taken together, ONTraC provides a useful framework for characterizing the structural and functional organization of tissue microenvironments.

Single-cell analysis reveals the implication of vascular endothelial cell-intrinsic ANGPT2 in human intracranial aneurysm

AIMS

While previous single-cell RNA sequencing (scRNA-seq) studies have attempted to dissect intracranial aneurysm (IA), the primary molecular mechanism for IA pathogenesis remains unknown. Here, we uncovered the alterations of cellular compositions, especially the transcriptome changes of vascular endothelial cells (ECs), in human IA.

METHODS AND RESULTS

We performed scRNA-seq to compare the cell atlas of sporadic IA and the control artery. The transcriptomes of 43 462 cells were profiled for further analysis. In general, IA had increased immune cells (T/NK cells, B cells, myeloid cells, mast cells, neutrophils) and fewer vascular cells (ECs, vascular smooth muscle cells, and fibroblasts). Based on the obtained high-quantity and high-quality EC data, we found genes associated with angiogenesis in ECs from IA patients. By EC-specific expression of candidate genes in vivo, we observed the involvement of angpt2a in causing cerebral vascular abnormality. Furthermore, an IA zebrafish model mimicking the main features of human IA was generated through targeting pdgfrb gene, and knockdown of angpt2a alleviated the vascular dilation in the IA zebrafish model.

CONCLUSION

By performing a landscape view of the single-cell transcriptomes of IA and the control artery, we contribute to a deeper understanding of the cellular composition and the molecular changes of ECs in IA. The implication of angiogenic regulator ANGPT2 in IA formation and progression, provides a novel potential therapeutical target for IA interventions.

scCompass: An Integrated Multi-Species scRNA-seq Database for AI-Ready

Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the single-cell resolution. Advances in artificial intelligence enhance life sciences research by delivering critical insights and optimizing data analysis processes. However, inconsistent data processing quality and standards remain to be a major challenge. Here scCompass is proposed, which provides a comprehensive resource designed to build large-scale, multi-species, and model-friendly single-cell data collection. By applying standardized data pre-processing, scCompass integrates and curates transcriptomic data from nearly 105 million single cells across 13 species. Using this extensive dataset, it is able to identify stable expression genes (SEGs) and organ-specific expression genes (OSGs) in humans and mice. Different scalable datasets are provided that can be easily adapted for AI model training and the pretrained checkpoints with state-of-the-art single-cell foundation models. In summary, scCompass is highly efficient and scalable database for AI-ready, which combined with user-friendly data sharing, visualization, and online analysis, greatly simplifies data access and exploitation for researchers in single-cell biology (http://www.bdbe.cn/kun).

Single‑cell RNA‑Seq reveals PBMC profile alterations in a patient following a radiation accident

Nuclear technology has been extensively used in various fields, increasing the possibility of radiation exposure to humans. Radiation exposure outcomes may be classified as whole-body irradiation or local irradiation. Clinically, local irradiation refers to the exposure of a relatively limited portion of the body, with injury confined to the directly exposed tissues. However, locally irradiated tissues can trigger systemic reactions through the release of inflammatory factors or damage to blood cells at the irradiated site. The circulating population of peripheral blood mononuclear cells (PBMCs), a component of normal tissue, is particularly sensitive to ionizing radiation. The present study applied single-cell RNA sequencing (scRNA-Seq) to profile PBMCs from one irradiated patient and 10 healthy controls matched for sex and age. In total, 6,447 and 7,892 cells were collected for analysis from the PBMCs of the irradiated patient on the 113rd and 631st days post radiation, respectively, whereas 9,101 cells were obtained from 10 healthy controls. Following scRNA-Seq, five cell types were annotated via representative markers, revealing distinct cell types whose proportions changed markedly in the irradiated patient. Trajectory analysis indicated that the dysregulation of multiple signaling pathways was associated with radiation exposure. Furthermore, single-cell regulatory network inference and clustering analysis revealed gene regulatory networks and suggested the involvement of several signaling pathways, such as those related to viral infection, in the context of radiation exposure. The present study elucidated the dynamic landscape of human blood immune responses to ionizing radiation and provides evidence of its therapeutic potential for treating radiation injury.

Specification of human brain regions with orthogonal gradients of WNT and SHH in organoids reveals patterning variations across cell lines

The repertoire of neurons and their progenitors depends on their location along the antero-posterior and dorso-ventral axes of the neural tube. To model these axes, we designed the Dual Orthogonal-Morphogen Assisted Patterning System (Duo-MAPS) diffusion device to expose spheres of induced pluripotent stem cells (iPSCs) to concomitant orthogonal gradients of a posteriorizing and a ventralizing morphogen, activating WNT and SHH signaling, respectively. Comparison with single-cell transcriptomes from the fetal human brain revealed that Duo-MAPS-patterned organoids generated an extensive diversity of neuronal lineages from the forebrain, midbrain, and hindbrain. WNT and SHH crosstalk translated into early patterns of gene expression programs associated with the generation of specific brain lineages with distinct functional networks. Human iPSC lines showed substantial interindividual and line-to-line variations in their response to morphogens, highlighting that genetic and epigenetic variations may influence regional specification. Morphogen gradients promise to be a key approach to model the brain in its entirety.

Malignant Hepatoblast-Like Cells Sustain Stemness via IGF2-Dependent Cholesterol Accumulation in Hepatoblastoma

Hepatoblastoma, the most aggressive childhood liver tumor, poses significant challenges due to limited knowledge of its pathogenesis, particularly in poorly differentiated advanced tumors where the prognosis is dismal. Single-cell sequencing provides an in-depth exploration at the single-cell level and offers a deep understanding of tumor heterogeneity. Herein, single-cell transcriptomics analysis is used to identify a unique malignant-hepatoblast (HB)-like cell subpopulation as the possible origin of poorly differentiated hepatoblastoma. These cells are associated with an unfavorable clinical prognosis in hepatoblastoma patients. The malignant-HB-like cell subpopulation generated insulin-like growth factor 2 (IGF2) to sustain stem-like features by promoting abnormal cholesterol accumulation via SREBF2. IGF2 also stimulated fibroblast 2 to secrete collagen 1, intensifying tumor malignancy via the collagen 1/integrin α1 signaling pathway. This suggests that targeting malignant HB-like cells by inhibiting IGF2-induced pathways can lead to promising treatments for hepatoblastoma. Additionally, serum IGF2 levels may serve as a diagnostic biomarker for advanced hepatoblastoma. In summary, these findings provide valuable insight into the genesis and malignancy of hepatoblastoma and a foundation for more effective diagnostic tools and therapeutic strategies for this challenging disease.

Single-cell RNA-seq analysis identifies the atlas of lymph fluid and reveals a sepsis-related T cell subset

The lymphoid cycle serves as a sentinel of the immune response, yet the cell subtypes and immune properties within lymph fluid remain unclear. This study describes a comprehensive characterization of immune cells in rat lymph fluid using single-cell RNA sequencing, identifying a unique subset of CD4+ T cells (CD4_Icos) that suppresses inflammation in early sepsis. Trajectory analysis reveals that CD4+Icos+ T cells can differentiate into regulatory T cells (Tregs). Transferring CD4+Icos+ T cells alleviates CLP-induced organ injury, while CD4+ Icos-knockout (KO) mice show reduced Treg numbers, increased inflammation, and higher mortality. Further experiments identify Npas2 as an Icos-specific transcription factor regulating Icos expression and promoting the differentiation of CD4+Icos+ T cells. Clinical data show a negative correlation between ICOS expression in CD4+ T cells and clinical outcomes in septic patients. These findings highlight the protective role of CD4+ T cells in modulating immune responses and mitigating sepsis progression.

SpaGRN: Investigating spatially informed regulatory paths for spatially resolved transcriptomics data

Cells spatially organize into distinct cell types or functional domains through localized gene regulatory networks. However, current spatially resolved transcriptomics analyses fail to integrate spatial constraints and proximal cell influences, limiting the mechanistic understanding of tissue organization. Here, we introduce SpaGRN, a statistical framework that reconstructs cell-type- or functional-domain-specific, dynamic, and spatial regulons by coupling intracellular spatial regulatory causality with extracellular signaling path information. Benchmarking across synthetic and real datasets demonstrates SpaGRN's superior precision over state-of-the-art tools in identifying context-dependent regulons. Applied to diverse spatially resolved transcriptomics platforms (Stereo-seq, STARmap, MERFISH, CosMx, Slide-seq, and 10x Visium), complex cancerous samples, and 3D datasets of developing Drosophila embryos and larvae, SpaGRN not only provides a versatile toolkit for decoding receptor-mediated spatial regulons but also reveals spatiotemporal regulatory mechanisms underlying organogenesis and inflammation.

Multiomics Analysis Reveals Neuroblastoma Molecular Signature Predicting Risk Stratification and Tumor Microenvironment Differences

Neuroblastoma (NB) remains associated with high mortality and low initial response rate, especially for high-risk patients, thus warranting exploration of molecular markers for precision risk classifiers. Through integrating multiomics profiling, we identified a range of hub genes involved in cell cycle and associated with dismal prognosis and malignant cells. Single-cell transcriptome sequencing revealed that a subset of malignant cells, subcluster 1, characterized by high proliferation and dedifferentiation, was strongly correlated with the hub gene signature and orchestrated an immunosuppressive tumor microenvironment (TME). Furthermore, we constructed a robust malignant subcluster 1 related signature (MSRS), which was an independent prognostic factor and superior to other clinical characteristics and published signatures. Besides, TME differences conferred remarkably distinct therapeutic responses between high and low MSRS groups. Notably, polo-like kinase-1 (PLK1) was one of the most crucial contributors to MSRS and remarkably correlated with malignant subcluster 1, and PLK1 inhibition was effective for NB treatment as demonstrated by in silico analysis and in vitro experiments. Overall, our study constructs a novel molecular model to further guide the clinical classification and individualized treatment of NB.

Revealing the Transcriptional and Metabolic Characteristics of Sebocytes Based on the Donkey Cell Transcriptome Atlas

Worldwide, donkeys (Equus asinus) are valued for their meat and milk, and in China also for the medical value of their skin. Physiological characteristics are key to the donkey's adaptability, including their digestive, respiratory, and reproductive systems, which enable them to survive and work in a variety of environments. However, the understanding of donkey physiological characteristics at the cellular level remains poor. Thus, single-cell transcriptome sequencing is used to construct a detailed transcriptional atlas based on 20 tissues from the Dezhou donkey (in total 84 cell types and 275 050 high quality cells) to perform an in-depth investigation of molecular physiology. Cross-species and cross-tissue comparative analyses reveal SOX10 to be an evolutionally conserved regulon in oligodendrocytes and illuminate the distinctive transcriptional patterns of donkey sebocytes. Moreover, through multispecies skin metabolomics, highly abundant, species-specific metabolites in donkey skin are identified, such as arachidonic acid and gamma-glutamylcysteine, and the pivotal role of sebocytes in donkey skin metabolism is highlighted. In summary, this work offers new insights into the unique metabolic patterns of donkey skin and provides a valuable resource for the conservation of donkey germplasm and the advancement of selective breeding programs.

FOS-driven inflammatory CAFs promote colorectal cancer liver metastasis via the SFRP1-FGFR2-HIF1 axis

Rationale: Cancer-associated fibroblasts (CAFs) exhibit diverse functions, yet their roles in colorectal cancer liver metastasis (CRLM) remain poorly understood. Methods: Through integrated analysis of single-cell RNA sequencing and spatial transcriptomics from colorectal cancer patients (CRCP: non-metastatic primary tumors; CRCM: metastatic primary tumors with liver metastases), combined with in vitro and in vivo models to investigate the role of CAFs in CRLM. In vitro experiments included six groups to reveal the role of SFRP1-producing CAFs, comprising PBS (control) and recombinant human SFRP1 (rhSFRP1) treated SW480 cells, PBS (control) and recombinant mouse SFRP1 (rmSFRP1) treated CT26 cells, and conditioned medium (CM) derived from CAF-NC and CAF-Sfrp1 treated CT26 cells. Preclinical models were further employed to elucidate the role of SFRP1 in CRLM. Subcutaneous xenografts models were constructed from PBS (control) and rhSFRP1 treated SW480 cells. For orthotopic tumor metastasis models, CT26 cells were pre-cultured with CAF-NC or CAF-Sfrp1 and then orthotopically injected into BALB/c mice. Results: We identified an inflammatory CAF subtype (CFD+ iCAFs) associated with poor clinical outcomes, advanced staging, and metastasis. Transcriptional regulation analysis revealed FOS-mediated differentiation of CFD+ iCAFs drives SFRP1 overexpression. In vitro and in vivo experiments confirmed that SFRP1-producing CAFs promote tumor stemness and epithelial-mesenchymal transition (EMT). Mechanistically, SFRP1 from CFD+ iCAFs binds FGFR2, activating the HIF1 signaling pathway to enhance tumor stemness, EMT, and CRLM progression. Conclusion: This study highlights CFD+ iCAFs as key regulators of tumor-stromal interactions and identifies SFRP1 as a potential therapeutic target in CRLM.

FOXJ3, a novel tumor suppressor in neuroblastoma

Neuroblastoma (NB) poses a significant challenge in pediatric cancer care due to its aggressive nature and poor prognosis. While advances have been made in clinical treatments, therapy resistance remains a tough hurdle in NB treatment. While much research has focused on identifying oncogenes in NB, there has been less emphasis on understanding tumor suppressors. This study aimed to discover a new transcription factor that could address patient stage, risk level, and MYCN amplification status while exhibiting tumor-suppressive properties in NB patients. Using advanced bioinformatics techniques, we identified unique transcription factor signature that corresponded to patient characteristics. By analyzing regulon specificity scores, we prioritized Forkhead Box J3 (FOXJ3) as a potential novel driver transcription factor with tumor-suppressive functions in NB. Validation experiments on NB patients and patient-derived xenograft (PDX) tumors confirmed higher FOXJ3 expression in low-risk versus high-risk patients and in PDXs from diagnostic tumors versus relapse-specific tumors. Notably, the overexpression of FOXJ3 was associated with reduced cell density, proliferation, cells in S phase, colony-formation ability, transwell migration, neurosphere formation, spheroid diameter, and inhibition of AKT signaling in NB cells. Overall, these findings suggest that FOXJ3 functions as a novel tumor suppressor in NB, holding promise for potential therapeutic interventions.

Multiomic single-cell profiling identifies critical regulators of postnatal brain

Human brain development spans from embryogenesis to adulthood, with dynamic gene expression controlled by cell-type-specific cis-regulatory element activity and three-dimensional genome organization. To advance our understanding of postnatal brain development, we simultaneously profiled gene expression and chromatin accessibility in 101,924 single nuclei from four brain regions across ten donors, covering five key postnatal stages from infancy to late adulthood. Using this dataset and chromosome conformation capture data, we constructed enhancer-based gene regulatory networks to identify cell-type-specific regulators of brain development and interpret genome-wide association study loci for ten main brain disorders. Our analysis connected 2,318 cell-specific loci to 1,149 unique genes, representing 41% of loci linked to the investigated traits, and highlighted 55 genes influencing several disease phenotypes. Pseudotime analysis revealed distinct stages of postnatal oligodendrogenesis and their regulatory programs. These findings provide a comprehensive dataset of cell-type-specific gene regulation at critical timepoints in postnatal brain development.

Development of a tertiary lymphoid structure-based prognostic model for breast cancer: integrating single-cell sequencing and machine learning to enhance patient outcomes

Background

Breast cancer, a highly prevalent global cancer, poses significant challenges, especially in advanced stages. Prognostic models are crucial to enhance patient outcomes. Tertiary lymphoid structures (TLS) within the tumor microenvironment have been associated with better prognostic outcomes.

Methods

We analyzed data from 13 independent breast cancer cohorts, totaling over 9,551 patients. Using single-cell RNA sequencing and machine learning algorithms, we identified critical TLS-associated genes and developed a TLS-based predictive model. This model stratified patients into high and low-risk groups. Genomic alterations, immune infiltration, and cellular interactions within the tumor microenvironment were assessed.

Results

The TLS-based model demonstrated superior accuracy compared to traditional models, predicting overall survival. High TLS patients had higher tumor mutation burden and more chromosomal alterations, correlating with poorer prognosis. High-risk patients exhibited a significant depletion of CD4+ T cells, CD8+ T cells, and B cells, as evidenced by single-cell and bulk transcriptomic analyses. In contrast, immune checkpoint inhibitors demonstrated greater efficacy in low-risk patients, whereas chemotherapy proved more effective for high-risk individuals.

Conclusions

The TLS-based prognostic model is a robust tool for predicting breast cancer outcomes, highlighting the tumor microenvironment's role in cancer progression. It enhances our understanding of breast cancer biology and supports personalized therapeutic strategies.

Single-cell transcriptome atlas of male mouse pituitary across postnatal life highlighting its stem cell landscape

The pituitary represents the master gland governing the endocrine system. We constructed a single-cell (sc) transcriptomic atlas of male mouse endocrine pituitary by incorporating existing and new data, spanning important postnatal ages in both healthy and injured condition. We demonstrate strong applicability of this new atlas to unravel pituitary (patho)biology by focusing on its stem cells and investigating their complex identity (unveiling stem cell markers) and niche (pinpointing regulatory factors). Importantly, we functionally validated transcriptomic findings using pituitary stem cell organoids, revealing roles for Krüppel-like transcription factor 5 (KLF5), activator protein-1 (AP-1) complex and epidermal growth factor (EGF) pathways in pituitary stem cell regulation. Our investigation substantiated changes in stem cell dynamics during aging, reinforcing the inflammatory/immune nature in elderly pituitary and stem cells. Finally, we show translatability of mouse atlas-based findings to humans, particularly regarding aging-associated profile. This pituitary sc map is a valuable tool to unravel pituitary (patho)biology.

Integrative machine learning model of RNA modifications predict prognosis and treatment response in patients with breast cancer

BACKGROUND

Breast cancer, a highly heterogeneous and complex disease, remains the leading cause of cancer-related death among women worldwide. Despite advances in treatment modalities, effective prognostic models and therapeutic strategies are still urgently needed.

METHODS

We retrospectively analyzed 15 independent breast cancer cohorts to explore the role of RNA modifications in the prognosis of patients with breast cancer. By integrating nine types of RNA modifications, we developed a comprehensive machine learning-based RNA modification signature (CMRS). Furthermore, single-cell RNA sequencing data were analyzed to understand the biological mechanisms underlying CMRS. In addition, immune infiltration levels were evaluated via six different algorithms, and immune checkpoint inhibitor responsiveness was predicted. Moreover, the response of high-CMIS patients to chemotherapy was predicted via multiple datasets. Finally, immunohistochemistry was performed on tissue samples from breast cancer patients to validate protein expression levels.

RESULTS

Our analysis revealed five key RNA modification-related genes (ENO1, ARAF, WT1, GADD45A, and BIRC3) associated with breast cancer prognosis. The CMRS model demonstrated high predictive accuracy across multiple cohorts and was significantly correlated with patient survival outcomes. Multiomics analysis revealed that high CMRS was associated with increased tumor mutational burden and distinct mutational signatures, particularly in pathways related to TP53, MYC, and cell proliferation. Single-cell analysis highlighted the involvement of epithelial cells and MYC signaling in high CMRS activity. Cell‒cell communication analysis revealed reduced interaction strength in hig CMRS patients, indicating poor prognosis. Furthermore, low CMRS patients presented increased immune cell infiltration and improved responsiveness to immune checkpoint inhibitors, whereas high CMRS patients were identified as potential candidates for treatment with panobinostat and vincristine.

CONCLUSION

Our study elucidates the significant role of RNA modifications in breast cancer prognosis and treatment. The CMRS model serves as a sensitive biomarker for predicting patient survival and treatment responsiveness, offering a new avenue for personalized therapy in patients with breast cancer.

Single-cell transcriptome analysis reveals the dysregulated monocyte state associated with tuberculosis progression

BACKGROUND

In tuberculosis (TB) infection, monocytes play a crucial role in regulating the balance between immune tolerance and immune response through various mechanisms. A deeper understanding of the roles of monocyte subsets in TB immune responses may facilitate the development of novel immunotherapeutic strategies and improve TB prevention and treatment.

METHODS

We retrieved and processed raw single-cell RNA-seq data from SRP247583. Single-cell RNA-seq combined with bioinformatics analysis was employed to investigate the roles of monocytes in TB progression.

RESULTS

Our findings revealed that classical monocytes expressing inflammatory mediators increased as the disease progressed, whereas non-classical monocytes expressing molecules associated with anti-pathogen infection were progressively depleted. Pseudotime analysis delineated the differentiation trajectory of monocytes from classical to intermediate to non-classical subsets. An abnormal differentiation trajectory to non-classical monocytes may represent a key mechanism underlying TB pathogenesis, with CEBPB and CORO1A identified as genes potentially related to TB development. Analysis of key transcription factors in non-classical monocytes indicated that IRF9 was the only downregulated transcription factor with high AUC activity in this subset. The expression of IRF9 exhibited a decreasing trend in both latent TB infection (LTBI) and active TB groups. Furthermore, dysregulation of transcription factor regulatory networks appeared to impair ferroptosis, with ferroptosis-associated genes MEF2C, MICU1, and PRR5 identified as potential targets of IRF9. Through cell communication analysis, we found that interactions between non-classical monocytes and other subpopulations may mediate TB progression, with MIF and LGALS9 highlighted as potential signaling pathways.

CONCLUSION

This study employs bioinformatics analysis in conjunction with single-cell sequencing technology to uncover the crucial role of monocyte subsets in tuberculosis infection.

The landscape of cell regulatory and communication networks in the human dental follicle

Introduction

The dental follicle localizes the surrounding enamel organ and dental papilla of the developing tooth germ during the embryonic stage. It can differentiate and develop to form the periodontal ligament, cementum, and alveolar bone tissues. Postnatally, the dental follicle gradually degenerates, but some parts of the dental follicle remain around the impacted tooth. However, the specific cellular components and the intricate regulatory mechanisms governing the postnatal development and biological function of the dental follicle have not been completely understood.

Methods

We analyzed dental follicles with single-cell RNA sequencing (scRNA-seq) to reveal their cellular constitution molecular signatures by cell cycle analysis, scenic analysis, gene enrichment analysis, and cell communication analysis.

Results

Ten cell clusters were identified with differential characteristics, among which immune and vessel-related cells, as well as a stem cell population, were revealed as the main cell types. Gene regulatory networks (GRNs) were established and defined four regulon modules underlying dental tissue development and microenvironmental regulation, including vascular and immune responses. Cell-cell communication analysis unraveled crosstalk between vascular and immune cell components in orchestrating dental follicle biological activities, potentially based on COLLAGAN-CD44 ligand-receptor pairs, as well as ANGPTL1-ITGA/ITGB ligand-receptor pairs.

Conclusion

We establish a landscape of cell regulatory and communication networks in the human dental follicle, providing mechanistic insights into the cellular regulation and interactions in the complex dental follicle tissue microenvironment.

Mendelian randomization and multiomics comprehensively reveal the causal relationship and potential mechanism between atrial fibrillation and gastric cancer

Objective

Gastric cancer is a harmful disease, the comorbidity mechanism and causality relationship between this disease and other diseases are worth studying.

Methods

Using a two-sample Mendelian Randomization method, this study revealed the potential causal effect of atrial fibrillation (AF) on gastric cancer (GC) risk by constructing a genetic instrument containing 136 AF associated SNPs. Subsequently, analysis identifies 62 AF-GC co-associated genes and constructs a protein-protein interaction network of key genes. High-throughput sequencing data were further used to analyze the association between the two and their impact on the survival outcome of gastric cancer.

Results

The results showed that AF was negatively associated with gastric cancer, and further analysis revealed that this relationship was independent of GC risk factors such as chronic gastritis, Helicobacter pylori infection, and alcohol consumption. Enrichment analysis reveals associations of key genes with pathways related to cardiovascular disease, inflammatory gastrointestinal diseases, and tumorigenesis. Through single-cell sequencing data analysis, fibroblast subpopulations associated with the key gene set are identified in GC, showing significant correlations with cancer progression and inflammation regulation pathways. Transcription factor analysis and developmental trajectory analysis reveal the potential role of fibroblasts in GC development. Finally, prognosis analysis and gene mutation analysis using TCGA-STAD data indicate an adverse prognosis associated with the key gene set in GC.

Conclusion

This study provides new insights into the association between AF and GC and offers novel clues for understanding its impact on the pathogenesis and therapeutic strategies of GC.

Subtype-specific transcription factors affect polyamine metabolism and the tumor microenvironment in breast cancer

Background

Polyamines play important roles in cell growth and proliferation. Polyamine metabolism genes are dysregulated in various tumors. Some polyamine metabolism genes are regulated by transcription factors. However, the transcription factors that regulate polyamine metabolism genes have not been completely identified. Additionally, whether any of the transcriptional regulations depend on tumor heterogeneity and the tumor microenvironment has not been investigated.

Methods

We used bulk RNA-seq data to identify dysregulated polyamine metabolism genes and their transcription factors across breast cancer subtypes. Genes highly correlated with polyamine changes were obtained, and their subtype-specific expressions were checked in tumor microenvironment cells using single-cell RNA (scRNA)-seq data. Gene Ontology enrichment analysis was used to explore their molecular functions and biological processes, and survival analysis was used to examine the impact of these genes on therapeutic outcome.

Results

We first analyzed the dysregulation of polyamine synthesis, catabolism, and transport in four breast cancer subtypes. Genes such as AGMAT and CAV1 were dysregulated across all subtypes, while APRT, SAT1, and other genes were dysregulated in the more lethal subtypes. Among the dysregulated genes of polyamine metabolism, we focused on three genes (SRM, APRT, and SAT1) and identified their transcription factors (SPI1 and IRF1 correspond to SAT1, and IRF3 corresponds to SRM and APRT). With scRNA-seq data, we verified that these three transcription factors also regulated these three polyamine metabolism genes in the tumor microenvironment. Both bulk RNA-seq and scRNA-seq data indicated that these genes were specifically upregulated in high-risk breast cancer subtypes, such as the basal-like type. High expression of these genes corresponded to worse outcomes in the basal-like subtype under chemotherapy and radiation treatment.

Conclusion

Our work identified three subtype-specific transcription factors that regulate three polyamine metabolism genes in high-risk breast cancer subtypes and the tumor microenvironment. Our results deepen the understanding of the role of polyamine metabolism in breast cancer and may help the clinical therapy of advanced breast cancer subtypes.

PD-L1 promotes tumor metastasis by regulating the infiltration of FGFBP2(+)Tm cells in colorectal cancer

Tumor-infiltrating lymphocytes can influence tumorigenesis and progression. We found PD-L1 can inhibit the infiltration of memory T (Tm) cells in vivo and in vitro by reducing the secretion of CXCL9, CXCL10 in colorectal cancer. Patients with high PD-L1 expression have minimal Tm cell infiltration, accompanied with a higher incidence of tumor metastasis. Single-cell sequencing revealed that PD-L1 mainly inhibited the infiltration of a specific Tm cell subset characterized by the expression of FGFBP2 gene. To clarify the distribution of FGFBP2(+)Tm cells, peripheral blood, lymph nodes, colon polyps, primary tumor, and liver metastases samples were collected. As the tumor progressed, the infiltration of FGFBP2(+) memory T cells gradually increased and accumulated in liver metastases. By establishing a mouse metastasis model, we found in high PD-L1 expression group, the luciferin intensity of metastatic tumor was significantly higher, the number of metastatic nodules and the weight of metastases were also increased. The number of FGFBP2(+) Tm cells in peripheral blood and in liver/lung metastases were increased. Therefore, the expression of PD-L1 in primary tumor can promote the occurrence of metastases, and FGFBP2(+)Tm cells may be involved in the formation of metastases. Furthermore, the result showed that the number of FGFBP2(+) Tm cells in metastases was positively correlated with the number of vessels in liver/lung metastases. In conclusion, we confirmed that the expression of PD-L1 in primary tumor can increase the number of FGFBP2(+) Tm cells in peripheral blood and promote tumor metastasis, which is likely to be caused by the angiogenesis of FGFBP2(+) Tm cells in metastases.

Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer

Background

The rising incidence of breast cancer and its heterogeneity necessitate precise tools for predicting patient prognosis and tailoring personalized treatments. Epigenetic changes play a critical role in breast cancer progression and therapy responses, providing a foundation for prognostic model development.

Methods

We developed the Machine Learning-derived Epigenetic Model (MLEM) to identify prognostic epigenetic gene patterns in breast cancer. Using multi-cohort transcriptomic datasets, MLEM was constructed with rigorous machine learning techniques and validated across independent datasets. The model's performance was further corroborated through immunohistochemical validation on clinical samples.

Results

MLEM effectively stratified breast cancer patients into high- and low-risk groups. Low-MLEM patients exhibited improved prognosis, characterized by enhanced immune cell infiltration and higher responsiveness to immunotherapy. High-MLEM patients showed poorer prognosis but were more responsive to chemotherapy, with vincristine identified as a promising therapeutic option. The model demonstrated robust performance across independent validation datasets.

Conclusion

MLEM is a powerful prognostic tool for predicting breast cancer outcomes and tailoring personalized treatments. By integrating epigenetic insights with machine learning, this model has the potential to improve clinical decision-making and optimize therapeutic strategies for breast cancer patients.

Metabolic reprogramming and macrophage expansion define ACPA-negative rheumatoid arthritis: insights from single-cell RNA sequencing

Background

Anti-citrullinated peptide antibodies (ACPA)-negative (ACPA-) rheumatoid arthritis (RA) presents significant diagnostic and therapeutic challenges due to the absence of specific biomarkers, underscoring the need to elucidate its distinctive cellular and metabolic profiles for more targeted interventions.

Methods

Single-cell RNA sequencing data from peripheral blood mononuclear cells (PBMCs) and synovial tissues of patients with ACPA- and ACPA+ RA, as well as healthy controls, were analyzed. Immune cell populations were classified based on clustering and marker gene expression, with pseudotime trajectory analysis, weighted gene co-expression network analysis (WGCNA), and transcription factor network inference providing further insights. Cell-cell communication was explored using CellChat and MEBOCOST, while scFEA enabled metabolic flux estimation. A neural network model incorporating key genes was constructed to differentiate patients with ACPA- RA from healthy controls.

Results

Patients with ACPA- RA demonstrated a pronounced increase in classical monocytes in PBMCs and C1QChigh macrophages (p < 0.001 and p < 0.05). Synovial macrophages exhibited increased heterogeneity and were enriched in distinct metabolic pathways, including complement cascades and glutathione metabolism. The neural network model achieved reliable differentiation between patients with ACPA- RA and healthy controls (AUC = 0.81). CellChat analysis identified CD45 and CCL5 as key pathways facilitating macrophage-monocyte interactions in ACPA- RA, prominently involving iron-mediated metabolite communication. Metabolic flux analysis indicated elevated beta-alanine and glutathione metabolism in ACPA- RA macrophages.

Conclusion

These findings underscore that ACPA-negative rheumatoid arthritis is marked by elevated classical monocytes in circulation and metabolic reprogramming of synovial macrophages, particularly in complement cascade and glutathione metabolism pathways. By integrating single-cell RNA sequencing with machine learning, this study established a neural network model that robustly differentiates patients with ACPA- RA from healthy controls, highlighting promising diagnostic biomarkers and therapeutic targets centered on immune cell metabolism.

Innovative strategies to optimise colorectal cancer immunotherapy through molecular mechanism insights

Background

Colorectal cancer (CRC) is a leading cause of cancer-related deaths globally. The heterogeneity of the tumor microenvironment significantly influences patient prognosis, while the diversity of tumor cells shapes its unique characteristics. A comprehensive analysis of the molecular profile of tumor cells is crucial for identifying novel molecular targets for drug sensitivity analysis and for uncovering the pathophysiological mechanisms underlying CRC.

Methods

We utilized single-cell RNA sequencing technology to analyze 13 tissue samples from 4 CRC patients, identifying key cell types within the tumor microenvironment. Intercellular communication was assessed using CellChat, and a risk score model was developed based on eight prognostic genes to enhance patient stratification for immunotherapeutic approaches. Additionally, in vitro experiments were performed on DLX2, a gene strongly associated with poor prognosis, to validate its potential role as a therapeutic target in CRC progression.

Results

Eight major cell types were identified across the tissue samples. Within the tumor cell population, seven distinct subtypes were recognized, with the C0 FXYD5+ tumor cells subtype being significantly linked to cancer progression and poor prognosis. CellChat analysis indicated extensive communication among tumor cells, fibroblasts, and immune cells, underscoring the complexity of the tumor microenvironment. The risk score model demonstrated high accuracy in predicting 1-, 3-, and 5-year survival rates in CRC patients. Enrichment analysis revealed that the C0 FXYD5+ tumor cell subtype exhibited increased energy metabolism, protein synthesis, and oxidative phosphorylation, contributing to its aggressive behavior. In vitro experiments confirmed DLX2 as a critical gene associated with poor prognosis, suggesting its viability as a target for improving drug sensitivity.

Conclusion

In summary, this study advances our understanding of CRC progression by identifying critical tumor subtypes, molecular pathways, and prognostic markers that can inform innovative strategies for predicting and enhancing drug sensitivity. These findings hold promise for optimizing immunotherapeutic approaches and developing new targeted therapies, ultimately aiming to improve patient outcomes in CRC.

Targeting the immune privilege of tumor-initiating cells to enhance cancer immunotherapy

Tumor-initiating cells (TICs) possess the ability to evade anti-tumor immunity, potentially explaining many failures of cancer immunotherapy. Here, we identify CD49f as a prominent marker for discerning TICs in hepatocellular carcinoma (HCC), outperforming other commonly used TIC markers. CD49f-high TICs specifically recruit tumor-promoting neutrophils via the CXCL2-CXCR2 axis and create an immunosuppressive milieu in the tumor microenvironment (TME). Reciprocally, the neutrophils reprogram nearby tumor cells toward a TIC phenotype via secreting CCL4. These cells can evade CD8+ T cell-mediated killing through CCL4/STAT3-induced and CD49f-stabilized CD155 expression. Notably, while aberrant CD155 expression contributes to immune suppression, it also represents a TIC-specific vulnerability. We demonstrate that either CD155 deletion or antibody blockade significantly enhances sensitivity to anti-PD-1 therapy in preclinical HCC models. Our findings reveal a new mechanism of tumor immune evasion and provide a rationale for combining CD155 blockade with anti-PD-1/PD-L1 therapy in HCC.

Cross-species single-cell analysis reveals divergence and conservation of peripheral blood mononuclear cells

BACKGROUND

Single-cell transcriptome sequencing (scRNA-seq) has revolutionized the study of immune cells by overcoming the limitations of traditional antibody-based identification and isolation methods. This advancement allows us to obtain comprehensive gene expression profiles from a diverse array of vertebrate species, facilitating the identification of various cell types. Comparative immunology across vertebrates presents a promising approach to understanding the evolution of immune cell types. In this study, we conducted a comparative transcriptome analysis of peripheral blood mononuclear cells (PBMCs) at the single-cell level across 12 species.

RESULTS

Our findings shed light on the cellular compositional features of PBMCs, spanning from fish to mammals. Notably, we identified genes that exhibit vertebrate universality in characterizing immune cells. Moreover, our investigation revealed that monocytes have maintained a conserved transcriptional regulatory program throughout evolution, emphasizing their pivotal role in orchestrating immune cells to execute immune programs.

CONCLUSIONS

This comprehensive analysis provides valuable insights into the evolution of immune cells across vertebrates.

Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model

Background

Breast cancer prognosis remains a significant challenge due to the disease's molecular heterogeneity and complexity. Accurate predictive models are critical for improving patient outcomes and tailoring personalized therapies.

Methods

We developed a Machine Learning-assisted Telomerase Signature (MLTS) by integrating multi-omics data from nine independent breast cancer datasets. Using multiple machine learning algorithms, we identified six telomerase-related genes significantly associated with patient survival. The predictive performance of MLTS was evaluated against 66 existing breast cancer prognostic models across diverse cohorts.

Results

The MLTS demonstrated superior predictive accuracy, stability, and reliability compared to other models. Patients with high MLTS scores exhibited increased tumor mutational burden, chromosomal instability, and poor survival outcomes. Single-cell RNA sequencing analysis further revealed higher MLTS scores in aneuploid tumor cells, suggesting a role in cancer progression. Immune profiling indicated distinct tumor microenvironment characteristics associated with MLTS scores, potentially guiding therapeutic decisions.

Conclusions

Our findings highlight the utility of MLTS as a robust prognostic biomarker for breast cancer. The ability of MLTS to predict patient outcomes and its association with key genomic and cellular features underscore its potential as a target for personalized therapy. Future research may focus on integrating MLTS with additional molecular signatures to enhance its clinical application in precision oncology.

VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell-cell communication network

Cell-cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell-cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell-cell communication. Furthermore, other methods for analyzing cell-cell communication mainly focus on individual tissue sections, neglecting cell-cell communication across multiple tissue layers, and fail to comprehensively elucidate cell-cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell-cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell-cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell-cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell-cell communication.

Machine learning unveils key Redox signatures for enhanced breast Cancer therapy

BACKGROUND

Breast cancer remains a leading cause of mortality among women worldwide, necessitating innovative prognostic models to enhance treatment strategies.

METHODS

Our study retrospectively enrolled 9,439 breast cancer patients from 12 independent datasets and single-cell data from 12 patients (64,308 cells). Moverover, 30 in-house clinical cohort were collected for validation. We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. To affirm the efficacy of our developed model, immunohistochemistry assays were performed. Additionally, we investigated various potential immunotherapeutic and chemotherapeutic interventions.

RESULTS

This study introduces an Artificial Intelligence-aided Redox Signature (AIARS) as a novel prognostic tool, leveraging machine learning to identify critical redox-related gene signatures in breast cancer. Our results demonstrate that AIARS significantly outperforms existing prognostic models in predicting breast cancer outcomes, offering a robust tool for personalized treatment planning. Validation through immunohistochemistry assays on samples from 30 patients corroborated our results, underscoring the model's applicability on a wider scale. Furthermore, the analysis revealed that patients with low AIARS expression levels are more responsive to immunotherapy. Conversely, those exhibiting high AIARS were found to be more susceptible to certain chemotherapeutic agents, including vincristine.

CONCLUSIONS

Our study underscores the importance of redox biology in breast cancer prognosis and introduces a powerful machine learning-based tool, the AIARS, for personalized treatment strategies. By providing a more nuanced understanding of the redox landscape in breast cancer, the AIARS paves the way for the development of redox-targeted therapies, promising to enhance patient outcomes significantly. Future work will focus on clinical validation and exploring the mechanistic roles of identified genes in cancer biology.

Single-cell resolution of intestinal regeneration in pythons without crypts illuminates conserved vertebrate regenerative mechanisms

Canonical models of intestinal regeneration emphasize the critical role of the crypt stem cell niche to generate enterocytes that migrate to villus ends. Burmese pythons possess extreme intestinal regenerative capacity yet lack crypts, thus providing opportunities to identify noncanonical but potentially conserved mechanisms that expand our understanding of regenerative capacity in vertebrates, including humans. Here, we leverage single-nucleus RNA sequencing of fasted and postprandial python small intestine to identify the signaling pathways and cell-cell interactions underlying the python's regenerative response. We find that python intestinal regeneration entails the activation of multiple conserved mechanisms of growth and stress response, including core lipid metabolism pathways and the unfolded protein response in intestinal enterocytes. Our single-cell resolution highlights extensive heterogeneity in mesenchymal cell population signaling and intercellular communication that directs major tissue restructuring and the shift out of a dormant fasted state by activating both embryonic developmental and wound healing pathways. We also identify distinct roles of BEST4+ enterocytes in coordinating key regenerative transitions via NOTCH signaling. Python intestinal regeneration shares key signaling features and molecules with mammalian gastric bypass, indicating that conserved regenerative programs are common to both. Our findings provide different insights into cooperative and conserved regenerative programs and intercellular interactions in vertebrates independent of crypts which have been otherwise obscured in model species where temporal phases of generative growth are limited to embryonic development or recovery from injury.

A spatiotemporal molecular atlas of mouse spinal cord injury identifies a distinct astrocyte subpopulation and therapeutic potential of IGFBP2

Spinal cord injury (SCI) triggers a cascade of intricate molecular and cellular changes that determine the outcome. In this study, we resolve the spatiotemporal organization of the injured mouse spinal cord and quantitatively assess in situ cell-cell communication following SCI. By analyzing existing single-cell RNA sequencing datasets alongside our spatial data, we delineate a subpopulation of Igfbp2-expressing astrocytes that migrate from the white matter (WM) to gray matter (GM) and become reactive upon SCI, termed Astro-GMii. Further, Igfbp2 upregulation promotes astrocyte migration, proliferation, and reactivity, and the secreted IGFBP2 protein fosters neurite outgrowth. Finally, we show that IGFBP2 significantly reduces neuronal loss and remarkably improves the functional recovery in a mouse model of SCI in vivo. Together, this study not only provides a comprehensive molecular atlas of SCI but also exemplifies how this rich resource can be applied to endow cells and genes with functional insight and therapeutic potential.

Cross-disorder and disease-specific pathways in dementia revealed by single-cell genomics

The development of successful therapeutics for dementias requires an understanding of their shared and distinct molecular features in the human brain. We performed single-nuclear RNA-seq and ATAC-seq in Alzheimer's disease (AD), frontotemporal dementia (FTD), and progressive supranuclear palsy (PSP), analyzing 41 participants and ∼1 million cells (RNA + ATAC) from three brain regions varying in vulnerability and pathological burden. We identify 32 shared, disease-associated cell types and 14 that are disease specific. Disease-specific cell states represent glial-immune mechanisms and selective neuronal vulnerability impacting layer 5 intratelencephalic neurons in AD, layer 2/3 intratelencephalic neurons in FTD, and layer 5/6 near-projection neurons in PSP. We identify disease-associated gene regulatory networks and cells impacted by causal genetic risk, which differ by disorder. These data illustrate the heterogeneous spectrum of glial and neuronal compositional and gene expression alterations in different dementias and identify therapeutic targets by revealing shared and disease-specific cell states.

Neuronal subtype-specific transcriptomic changes in the cerebral neocortex associated with sleep pressure

Sleep is homeostatically regulated by sleep pressure, which increases during wakefulness and dissipates during sleep. Recent studies have suggested that the cerebral neocortex, a six-layered structure composed of various layer- and projection-specific neuronal subtypes, is involved in the representation of sleep pressure governed by transcriptional regulation. Here, we examined the transcriptomic changes in neuronal subtypes in the neocortex upon increased sleep pressure using single-nucleus RNA sequencing datasets and predicted the putative intracellular and intercellular molecules involved in transcriptome alterations. We revealed that sleep deprivation (SD) had the greatest effect on the transcriptome of layer 2 and 3 intratelencephalic (L2/3 IT) neurons among the neocortical glutamatergic neuronal subtypes. The expression of mutant SIK3 (SLP), which is known to increase sleep pressure, also induced profound changes in the transcriptome of L2/3 IT neurons. We identified Junb as a candidate transcription factor involved in the alteration of the L2/3 IT neuronal transcriptome by SD and SIK3 (SLP) expression. Finally, we inferred putative intercellular ligands, including BDNF, LSAMP, and PRNP, which may be involved in SD-induced alteration of the transcriptome of L2/3 IT neurons. We suggest that the transcriptome of L2/3 IT neurons is most impacted by increased sleep pressure among neocortical glutamatergic neuronal subtypes and identify putative molecules involved in such transcriptional alterations.

Single-cell transcriptome analysis reveals evolving tumour microenvironment induced by immunochemotherapy in nasopharyngeal carcinoma

BACKGROUND

Combinatory therapeutic strategy containing immunochemotherapy as part of induction therapy components is one of the current trends in the treatment of high-risk metastatic locally advanced nasopharyngeal carcinoma (NPC). However, the mechanism underlying the heterogeneity of response at the single-cell level has not been underexplored.

METHODS

18 bulks and 11 single-cell RNA sequencing from paired before-treatment and on-treatment samples in patients with treatment-naive high-risk metastatic locally advanced NPCs were obtained. Following quality control, a total of 87 191 cells were included in the subsequence bioinformatics analysis.

RESULTS

Immunochemotherapy was associated with on-treatment tumour microenvironment (TME) remodelling, including upregulation of anti-TMEs signatures, downregulation of pro-TMEs signatures, reversing CD8+ T exhaustion, and repolarizing proinflammatory TAMs. For the patients achieving a complete response, the cytotoxic activity of CD8+ T cells was stimulated and more interferon-gamma was provided, which would be the key for TAMs proinflammatory repolarization and eventually promote the CD8+ T cells maturation in turn. Among patients who did not reach complete response, differentiation and hypoxia signatures for endothelial cells were elevated after therapy. These patients exhibited higher levels of immune checkpoint genes in malignant cells at the baseline (before treatment), and decreased tumour antigen presentation activity, which may underlie the resistance mechanism to therapy.

CONCLUSIONS

This study pictures a map of TME modulation following immunochemotherapy-based combination induction therapy and provides potential future approaches.

HIGHLIGHTS

Immunochemotherapy remodeled T cell phenotypes. For the patients achieving complete response, more interferon gamma was provided by CD8+ T cells after therapy, which would be the key for TAMs pro-inflammatory repolarization and eventually promote the CD8+ T cells maturation in turns. Among patients who did not reach complete response, malignant cells exhibited higher level of immune checkpoint genes before therapy, and decreased tumor antigen presentation activity, which may underlie the resistance mechanism to therapy.

Characteristics of PD-1+CD4+ T cells in peripheral blood and synovium of rheumatoid arthritis patients

Objectives

PD-1 plays a crucial role in the immune dysregulation of rheumatoid arthritis (RA), but the specific characteristics of PD-1+CD4+ T cells remain unclear and require further investigation.

Methods

Circulating PD-1+CD4+ T cells from RA patients were analysed using flow cytometry. Plasma levels of soluble PD-1 (sPD-1) were measured using enzyme-linked immunosorbent assay (ELISA). Single-cell RNA sequence data from peripheral blood mononuclear cells (PBMCs) and synovial tissue of patients were obtained from the GEO and the ImmPort databases. Bioinformatics analyses were performed in the R studio to characterise PD-1+CD4+ T cells. Expression of CCR7, KLF2 and IL32 in PD-1+CD4+ T cells was validated by flow cytometry.

Results

RA patients showed an elevated proportion of PD-1+CD4+ T cells in peripheral blood, along with increased plasma sPD-1 levels, which positively correlated with TNF-α and erythrocyte sedimentation rate. Bioinformatic analysis revealed PD-1 expression on CCR7+CD4+ T cells in PBMCs, and on both CCR7+CD4+ T cells and CXCL13+CD4+ T cells in RA synovium. PD-1 was co-expressed with CCR7, KLF2, and IL32 in peripheral CD4+ T cells. In synovium, PD-1+CCR7+CD4+ T cells had higher expression of TNF and LCP2, while PD-1+CXCL13+CD4+ T cells showed elevated levels of ARID5A and DUSP2. PD-1+CD4+ T cells in synovium also appeared to interact with B cells and fibroblasts through BTLA and TNFSF signalling pathways.

Conclusion

This study highlights the increased proportion of PD-1+CD4+ T cells and elevated sPD-1 levels in RA. The transcriptomic profiles and signalling networks of PD-1+CD4+ T cells offer new insights into their role in RA pathogenesis.

Multi‑omics identification of a signature based on malignant cell-associated ligand-receptor genes for lung adenocarcinoma

PURPOSE

Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches.

METHODS

We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts.

RESULTS

We developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells.

CONCLUSION

We have identified malignant cell-associated ligand-receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

The role of proliferating stem-like plasma cells in relapsed or refractory multiple myeloma: Insights from single-cell RNA sequencing and proteomic analysis

The management and comprehension of relapsed or refractory multiple myeloma (RRMM) continues to pose a significant challenge. By integrating single-cell RNA sequencing (scRNA-seq) data of 15 patients with plasma cell disorders (PCDs) and proteomic data obtained from mass spectrometry-based analysis of CD138+ plasma cells (PCs) from 144 PCDs patients, we identified a state of malignant PCs characterized by high stemness score and increased proliferation originating from RRMM. This state has been designated as proliferating stem-like plasma cells (PSPCs). NUCKS1 was identified as the gene marker representing the stemness of PSPCs. Comparison of differentially expressed genes among various PC states revealed a significant elevation in LGALS1 expression in PSPCs. Survival analysis on the MMRF CoMMpass dataset and GSE24080 dataset established LGALS1 as a gene associated with unfavourable prognostic implications for multiple myeloma. Ultimately, we discovered three specific ligand-receptor pairs within the midkine (MDK) signalling pathway network that play distinct roles in facilitating efficient cellular communication between PSPCs and the surrounding microenvironment cells. These insights have the potential to contribute to the understanding of molecular mechanism and the development of therapeutic strategies involving the application of stem-like cells in RRMM treatment.

Activity-dependent transcriptional programs in memory regulate motor recovery after stroke

Stroke causes death of brain tissue leading to long-term deficits. Behavioral evidence from neurorehabilitative therapies suggest learning-induced neuroplasticity can lead to beneficial outcomes. However, molecular and cellular mechanisms that link learning and stroke recovery are unknown. We show that in a mouse model of stroke, which exhibits enhanced recovery of function due to genetic perturbations of learning and memory genes, animals display activity-dependent transcriptional programs that are normally active during formation or storage of new memories. The expression of neuronal activity-dependent genes are predictive of recovery and occupy a molecular latent space unique to motor recovery. With motor recovery, networks of activity-dependent genes are co-expressed with their transcription factor targets forming gene regulatory networks that support activity-dependent transcription, that are normally diminished after stroke. Neuronal activity-dependent changes at the circuit level are influenced by interactions with microglia. At the molecular level, we show that enrichment of activity-dependent programs in neurons lead to transcriptional changes in microglia where they differentially interact to support intercellular signaling pathways for axon guidance, growth and synaptogenesis. Together, these studies identify activity-dependent transcriptional programs as a fundamental mechanism for neural repair post-stroke.

Innovative aspects and applications of single cell technology for different diseases

Recent developments in single-cell technologies have provided valuable insights from cancer genomics to complex microbial communities. Single-cell technologies including the RNA-seq, next-generation sequencing (NGS), epigenomics, genomics, and transcriptomics can be used to uncover the single cell nature and molecular characterization of individual cells. These technologies also reveal the cellular transition states, evolutionary relationships between genes, the complex structure of single-cell populations, cell-to-cell interaction leading to biological discoveries and more reliable than traditional bulk technologies. These technologies are becoming the first choice for the early detection of inflammatory biomarkers affecting the proliferation and progression of tumor cells in the tumor microenvironment and improving the clinical efficacy of patients undergoing immunotherapy. These technologies also hold a central position in the detection of checkpoint inhibitors and thus determining the signaling pathways evoked by tumor invasion. This review addressed the emerging approaches of single cell-based technologies in cancer immunotherapies and different human diseases at cellular and molecular levels and the emerging role of sequencing technologies leading to drug discovery. Advancements in these technologies paved for discovering novel diagnostic markers for better understanding the pathological and biochemical mechanisms also for controlling the rate of different diseases.

Single-cell RNA sequencing highlights the immunosuppression of IDO1+ macrophages in the malignant transformation of oral leukoplakia

Rationale: Immunosuppressive tumor microenvironment (iTME) plays an important role in carcinogenesis, and some macrophage subsets are associated with iTME generation. However, the sub-population characterization of macrophages in oral carcinogenesis remains largely unclear. Here, we investigated the immunosuppressive status with focus on function of a macrophage subset that expressed indoleamine 2,3 dioxygenase 1 (Macro-IDO1) in oral carcinogenesis. Methods: We built a single cell transcriptome atlas from 3 patients simultaneously containing oral squamous cell carcinoma (OSCC), precancerous oral leukoplakia (preca-OLK) and paracancerous tissue (PCA). Through single-cell RNA sequencing and further validation using multicolor immunofluorescence staining and the in vitro/in vivo experiments, the immunosuppressive cell profiles were built and the role of a macrophage subset that expressed indoleamine 2,3 dioxygenase 1 (Macro-IDO1) in the malignant transformation of oral leukoplakia was evaluated. Results: The iTME formed at preca-OLK stage, as evidenced by increased exhausted T cells, Tregs and some special subsets of macrophages and fibroblasts. Macro-IDO1 was predominantly enriched in preca-OLK and OSCC, distributed near exhausted T cells and possessed tumor associated macrophage transformation potentials. Functional analysis revealed the established immunosuppressive role of Macro-IDO1 in preca-OLK and OSCC: enriching the immunosuppression related genes; having an established level of immune checkpoint score; exerting strong immunosuppressive interaction with T cells; positively correlating with the CD8-exhausted. The immunosuppression related gene expression of macrophages also increased in preca-OLK/OSCC compared to PCA. The use of the IDO1 inhibitor reduced 4NQO induced oral carcinogenesis in mice. Mechanistically, IFN-γ-JAK-STAT pathway was associated with IDO1 upregulation in OLK and OSCC. Conclusions: These results highlight that Macro-IDO1-enriched in preca-OLK possesses a strong immunosuppressive role and contributes to oral carcinogenesis, providing a potential target for preventing precancerous legions from transformation into OSCC.

Transcriptional regulation of the postnatal cardiac conduction system heterogeneity

The cardiac conduction system (CCS) is a network of specialized cardiomyocytes that coordinates electrical impulse generation and propagation for synchronized heart contractions. Although the components of the CCS, including the sinoatrial node, atrioventricular node, His bundle, bundle branches, and Purkinje fibers, were anatomically discovered more than 100 years ago, their molecular constituents and regulatory mechanisms remain incompletely understood. Here, we demonstrate the transcriptomic landscape of the postnatal mouse CCS at a single-cell resolution with spatial information. Integration of single-cell and spatial transcriptomics uncover region-specific markers and zonation patterns of expression. Network inference shows heterogeneous gene regulatory networks across the CCS. Notably, region-specific gene regulation is recapitulated in vitro using neonatal mouse atrial and ventricular myocytes overexpressing CCS-specific transcription factors, Tbx3 and/or Irx3. This finding is supported by ATAC-seq of different CCS regions, Tbx3 ChIP-seq, and Irx motifs. Overall, this study provides comprehensive molecular profiles of the postnatal CCS and elucidates gene regulatory mechanisms contributing to its heterogeneity.

Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts

BACKGROUND

Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear.

METHODS

Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed.

RESULTS

We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis.

CONCLUSION

By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

Single-cell multiregion dissection of Alzheimer's disease

Alzheimer's disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood1-3. Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer's disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer's disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer's disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer's disease pathology.

Inflammatory Alterations to Renal Lymphatic Endothelial Cell Gene Expression in Mouse Models of Hypertension

INTRODUCTION

Hypertension (HTN) is a major cardiovascular disease that can cause and be worsened by renal damage and inflammation. We previously reported that renal lymphatic endothelial cells (LECs) increase in response to HTN and that augmenting lymphangiogenesis in the kidneys reduces blood pressure and renal pro-inflammatory immune cells in mice with various forms of HTN. Our aim was to evaluate the specific changes that renal LECs undergo in HTN.

METHODS

We performed single-cell RNA sequencing. Using the angiotensin II-induced and salt-sensitive mouse models of HTN, we isolated renal CD31+ and podoplanin+ cells.

RESULTS

Sequencing of these cells revealed three distinct cell types with unique expression profiles, including LECs. The number and transcriptional diversity of LECs increased in samples from mice with HTN, as demonstrated by 597 differentially expressed genes (p < 0.01), 274 significantly enriched pathways (p < 0.01), and 331 regulons with specific enrichment in HTN LECs. These changes demonstrate a profound inflammatory response in renal LECs in HTN, leading to an increase in genes and pathways associated with inflammation-driven growth and immune checkpoint activity in LECs.

CONCLUSION

These results reinforce and help to further explain the benefits of renal LECs and lymphangiogenesis in HTN.

Transcriptional dynamics orchestrating the development and integration of neurons born in the adult hippocampus

The adult hippocampus generates new granule cells (aGCs) with functional capabilities that convey unique forms of plasticity to the preexisting circuits. While early differentiation of adult radial glia-like cells (RGLs) has been studied extensively, the molecular mechanisms guiding the maturation of postmitotic neurons remain unknown. Here, we used a precise birthdating strategy to study aGC differentiation using single-nuclei RNA sequencing. Transcriptional profiling revealed a continuous trajectory from RGLs to mature aGCs, with multiple immature stages bearing increasing levels of effector genes supporting growth, excitability, and synaptogenesis. Analysis of differential gene expression, pseudo-time trajectory, and transcription factors (TFs) revealed critical transitions defining four cellular states: quiescent RGLs, proliferative progenitors, immature aGCs, and mature aGCs. Becoming mature aGCs involved a transcriptional switch that shuts down pathways promoting cell growth, such SoxC TFs, to activate programs that likely control neuronal homeostasis. aGCs overexpressing Sox4 or Sox11 remained immature. Our results unveil precise molecular mechanisms driving adult RGLs through the pathway of neuronal differentiation.

Landscape of tumoral ecosystem for enhanced anti-PD-1 immunotherapy by gut Akkermansia muciniphila

Gut Akkermansia muciniphila (Akk) has been implicated in impacting immunotherapy or oncogenesis. This study aims to dissect the Akk-associated tumor immune ecosystem (TIME) by single-cell profiling coupled with T cell receptor (TCR) sequencing. We adopted mouse cancer models under anti-PD-1 immunotherapy, combined with oral administration of three forms of Akk, including live Akk, pasteurized Akk (Akk-past), or its membrane protein Amuc_1100 (Amuc). We show that live Akk is most effective in activation of CD8 T cells by rescuing the exhausted type into cytotoxic subpopulations. Remarkably, only live Akk activates MHC-II-pDC pathways, downregulates CXCL3 in Bgn(+)Dcn(+) cancer-associated fibroblasts (CAFs), blunts crosstalk between Bgn(+)Dcn(+) CAFs and PD-L1(+) neutrophils by a CXCL3-PD-L1 axis, and further suppresses the crosstalk between PD-L1(+) neutrophils and CD8 T cells, leading to the rescue of exhausted CD8 T cells. Together, this comprehensive picture of the tumor ecosystem provides deeper insights into immune mechanisms associated with gut Akk-dependent anti-PD-1 immunotherapy.