CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type's preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs' risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

Identification of an immune-related gene panel for the diagnosis of pulmonary arterial hypertension using bioinformatics and machine learning

OBJECTIVE

This study aimed to screen an immune-related gene (IRG) panel and develop a novel approach for diagnosing pulmonary arterial hypertension (PAH) utilizing bioinformatics and machine learning (ML).

METHODS

Gene expression profiles were retrieved from the Gene Expression Omnibus (GEO) database to identify differentially expressed immune-related genes (IRG-DEGs). We employed five machine learning algorithms-LASSO, random forest (RF), boosted regression trees (BRT), XGBoost, and support vector machine recursive feature elimination (SVM-RFE) to identify biomarkers derived from IRG-DEGs associated with the diagnosis of PAH, incorporating them into the IRG-DEGs panel. Validation of these biomarker levels in lung tissue was conducted in a hypoxia-induced mouse model of PAH, investigating the correlation between AIMP1, IL-15, GLRX, SOD1, Fulton's index (RVHI), and the ratio of pulmonary artery medial thickness to external diameter (MT%). Subsequently, we developed a nomogram model based on the IRG-DEGs panel in lung tissue for diagnosing PAH. The expression, distribution, and pseudotime analysis of these biomarkers across various immune cell types were assessed using single-cell sequencing datasets. Finally, we evaluated the diagnostic utility of the nomogram model based on the IRG-DEGs panel in peripheral blood mononuclear cells (PBMCs) for diagnosing PAH.

RESULTS

A total of 36 upregulated and 17 downregulated IRG-DEGs were identified in lung tissue from patients with PAH. AIMP1, IL-15, GLRX, and SOD1 were subsequently selected as novel immune-related biomarkers for PAH through the aforementioned machine learning algorithms and incorporated into the IRG-DEGs panel. Experimental results from mice with PAH validated that the expression levels of AIMP1, IL-15, and GLRX in lung tissue were elevated, while SOD1 expression was significantly reduced. Additionally, GLRX and AIMP1 exhibited positive correlations with Fulton's index (RVHI). The expression levels of GLRX, IL-15, and AIMP1 showed positive correlations with MT%, whereas SOD1 exhibited negative correlations with MT%. Analysis of single-cell sequencing data further revealed that the levels of IRG-DEG panel members gradually increased during the pseudotime trajectory from PBMCs to macrophages, correlating with macrophage activation. The area under the curve (AUC) for diagnosing PAH using a nomogram model based on the IRG-DEGs panel derived from lung tissue samples and PBMCs was ≥0.969 and 0.900, respectively.

CONCLUSIONS

We developed an IRG-DEGs panel containing AIMP1, IL-15, GLRX, and SOD1, which may facilitate the diagnosis of pulmonary arterial hypertension (PAH). These findings provide novel insights that may enhance diagnostic and therapeutic approaches for PAH.

FAP+ gastric cancer mesenchymal stromal cells via paracrining INHBA and remodeling ECM promote tumor progression

Gastric cancer (GC) mesenchymal stromal cells (GCMSCs) are the predominant components of the tumor microenvironment (TME) and play a role in the occurrence, development, and metastasis of tumors. However, GCMSCs exhibit phenotypic and functional heterogeneity. The key population of GCMSCs which are vital to tumor progression remains elusive. The expression of fibroblast activation protein (FAP) in gastric cancer was analyzed and verified using clinical pathology data and single-cell RNA sequencing database of gastric cancer patients. FAP positive GCMSCs (FAP+ GCMSCs) were isolated via flow cytometry and characterized through transcriptomic sequencing. The impact of conditioned medium from FAP+ GCMSCs on gastric cancer cell lines was assessed using Enzyme-linked immunosorbent assay (ELISA) and Western blot analyses. Additionally, immunohistochemistry (IHC) and Masson's trichrome staining were employed to explore the association between FAP+ GCMSCs and extracellular matrix (ECM) deposition in gastric cancer tissues. Our study demonstrates that FAP is predominantly expressed in the mesenchymal stromal cells within the gastric cancer milieu. FAP+ GCMSCs exhibited enhanced proliferation, migration, contraction, and tumor-promoting capabilities compared to their FAP- counterparts. These cells significantly increased proliferation and migration of gastric cancer cells through the paracrine secretion of Inhibin Subunit Beta A (INHBA) and activation of the SMAD2/3 signaling pathway. Moreover, FAP+ GCMSCs also induced collagen deposition in ECM and then up-regulated invasion and stemness of GC cells. Mechanistically, this process was mediated by the interaction of collagen with Integrin Subunit Beta 1 (ITGB1), triggering the phosphorylation of Focal Adhesion Kinase (FAK) and Yes Associated Transcriptional Regulator (YAP). Our findings reveal that FAP+ GCSMCs enhanced the GC progression via releasing cytokine INHBA and remodeling ECM providing a theoretical basis for further exploration of tumor stromal-targeting therapy of gastric cancer.

IDO1 inhibitor enhances the effectiveness of PD-1 blockade in microsatellite stable colorectal cancer by promoting macrophage pro-inflammatory phenotype polarization

Microsatellite stable (MSS) colorectal cancer (CRC) is a subtype of CRC that generally exhibits resistance to immunotherapy, particularly immune checkpoint inhibitors such as PD-1 blockade. This study investigates the effects and underlying mechanisms of combining PD-1 blockade with IDO1 inhibition in MSS CRC. Bioinformatics analyses of TCGA-COAD and TCGA-READ cohorts revealed significantly elevated IDO1 expression in CRC tumors, correlating with tumor mutation burden across TCGA datasets. In vivo experiments demonstrated that the combination of IDO1 inhibition and PD-1 blockade significantly reduced tumor growth and increased immune cell infiltration, particularly pro-inflammatory macrophages and CD8+ T cells. IDO1 knockdown in CRC cell lines impaired tolerance to interferon-γ and increased apoptosis in vitro, which were rescued by the application of kynurenine, the end product of IDO1. IDO1 knockdown in MSS CRC enhanced the effectiveness of PD-1 blockade therapy in vivo. IDO1 knockdown cancer cells promoted pro-inflammatory macrophage polarization and enhanced phagocytic activity in vitro, associated with the upregulation of JAK2-STAT3-IL6 signaling pathway. These findings highlight the role of IDO1 in modulating the tumor immune microenvironment in MSS CRC and suggest that combining PD-1 blockade with IDO1 inhibition could enhance therapeutic efficacy by promoting macrophage pro-inflammatory polarization and infiltration through the JAK2-STAT3-IL6 pathway.

High order expression dependencies finely resolve cryptic states and subtypes in single cell data

Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells' local proximity in transcriptome space. Stator labels the same single cell multiply, not just by type and subtype, but also by state such as activation, maturity or cell cycle sub-phase, through deriving higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. Stator's finer resolution is clear from analyses of mouse embryonic brain, and human healthy or diseased liver. Rather than only coarse-scale labels of cell type, Stator further resolves cell types into subtypes, and these subtypes into stages of maturity and/or cell cycle phases, and yet further into portions of these phases. Among cryptically homogeneous embryonic cells, for example, Stator finds 34 distinct radial glia states whose gene expression forecasts their future GABAergic or glutamatergic neuronal fate. Further, Stator's fine resolution of liver cancer states reveals expression programmes that predict patient survival. We provide Stator as a Nextflow pipeline and Shiny App.

Bulk integrated single-cell-spatial transcriptomics reveals the impact of preoperative chemotherapy on cancer-associated fibroblasts and tumor cells in colorectal cancer, and construction of related predictive models using machine learning

BACKGROUND

Preoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear.

METHODS

This study utilized bulk, single-cell, and spatial transcriptomic sequencing data from 22 independent cohorts of CRC. Through bioinformatics analysis and in vitro experiments, the research investigated the impact of PC on fibroblast and epithelial cells in CRC. Subpopulations associated with PC and CRC prognosis were identified, and a predictive model was constructed using machine learning.

RESULTS

PC significantly attenuated the pathways related to tumor progression in fibroblasts and epithelial cells. NOTCH3 + Fibroblast (NOTCH3 + Fib), TNNT1 + Epithelial (TNNT1 + Epi), and HSPA1A + Epithelial (HSPA1A + Epi) subpopulations were identified in the adjacent spatial region and were associated with poor prognosis in CRC. PC effectively diminished the presence of these subpopulations, concurrently inhibiting pathway activity and intercellular crosstalk. A risk signature model, named the Preoperative Chemotherapy Risk Signature Model (PCRSM), was constructed using machine learning. PCRSM emerged as an independent prognostic indicator for CRC, impacting both overall survival (OS) and recurrence-free survival (RFS), surpassing the performance of 89 previously published CRC risk signatures. Additionally, patients with a high PCRSM risk score showed sensitivity to fluorouracil-based adjuvant chemotherapy (FOLFOX) but resistance to single chemotherapy drugs (such as Bevacizumab and Oxaliplatin). Furthermore, this study predicted that patients with high PCRSM were resistant to anti-PD1therapy.

CONCLUSION

In conclusion, this study identified three cell subpopulations (NOTCH3 + Fib, TNNT1 + Epi, and HSPA1A + Epi) associated with PC, which can be targeted to improve the prognosis of CRC patients. The PCRSM model shows promise in enhancing the survival and treatment of CRC patients.

SITP: A single cell bioinformatics analysis flow captures proteasome markers in the development of breast cancer

Single cell sequencing and related databases have been widely used in the exploration of cancer occurrence and development, but there is still no in-depth explanation of specific and complicated cellular protein modification processes. Ubiquitin-Proteasome System (UPS), as a specific and precise protein modification and degradation process, plays an important role in the biological functions of cancer cell proliferation and apoptosis. Proteasomes, vital multi-catalytic proteinases in eukaryotic cells, play a crucial role in protein degradation and contribute to tumor regulation. The 26S proteasome, part of the ubiquitin-proteasome system. In this study, we have enrolled a common SITP process including analysis of single cell sequencing to elucidate a flow that can capture typical proteasome markers in the oncogenesis and progression of breast cancer. PSMD11, a key component of the 26S proteasome regulatory particle, has been identified as a critical survival factor in cancer cells. Results suggest that PSMD11's rapid degradation is linked to acute apoptosis in cancer cells, making it a potential target for cancer treatment. Our study explored the potential mechanisms of PSMD11 in breast cancer development. The findings revealed the feasibility of disclosing ubiquitinating biomarkers from public database, as well as presented new evidence supporting PSMD11 as a potential therapeutic biomarker for breast cancer.

Inhibition of CDH11 Activates cGAS-STING by Stimulating Branched Chain Amino Acid Catabolism and Mitigates Lung Metastasis of Adenoid Cystic Carcinoma

Salivary adenoid cystic carcinoma (SACC) is an intractable malignant tumor originates in the secretory glands and frequently metastasizes to the lungs. Hybrid epithelial-mesenchymal transition (EMT) cells within the tumors are correlated with augmented proliferative capacity and facilitation of lung metastasis. Single-cell RNA sequencing and spatial transcriptomic sequencing are employed to reveal the hybrid EMT subsets within the vascular fibroblast microenvironment. These hybrid EMT cells exhibit a pro-tumorigenic impact in vitro. Notably, cadherin 11 (CDH11), a specific marker for hybrid EMT cells, may exert its regulatory role in cellular function by interfering with branched-chain amino acids (BCAA) metabolism by inhibiting branched-chain ketoacid dehydrogenase to activate the mammalian target of the rapamycin pathway, thus making it a potential therapeutic target for SACC. Furthermore, celecoxib and its derivatives are specific CDH11 inhibitors that regulate BCAA metabolism, increase reactive oxygen species production, and subsequently activate the cyclic GMP-AMP synthase-stimulator of the interferongene pathway (cGAS-STING). They also inhibit lung metastasis in NOD-SCID mice in vivo. Overall, these findings suggest a promising treatment strategy that targets hybrid EMT cells to mitigate lung metastasis in SACC. Celecoxib may serve as a promising clinical intervention for the treatment of lung metastases in patients with SACC.

Liver cancer multiomics reveals diverse protein kinase A disruptions convergently produce fibrolamellar hepatocellular carcinoma

Fibrolamellar Hepatocellular Carcinoma (FLC) is a rare liver cancer characterized by a fusion oncokinase of the genes DNAJB1 and PRKACA, the catalytic subunit of protein kinase A (PKA). A few FLC-like tumors have been reported showing other alterations involving PKA. To better understand FLC pathogenesis and the relationships among FLC, FLC-like, and other liver tumors, we performed a massive multi-omics analysis. RNA-seq data of 1412 liver tumors from FLC, hepatocellular carcinoma, hepatoblastoma and intrahepatic cholangiocarcinoma are analyzed, obtaining transcriptomic signatures unrestricted by experimental processing methods. These signatures reveal which dysregulations are unique to specific tumors and which are common to all liver cancers. Moreover, the transcriptomic FLC signature identifies a unifying phenotype for all FLC tumors regardless of how PKA was activated. We study this signature at multi-omics and single-cell levels in the first spatial transcriptomic characterization of FLC, identifying the contribution of tumor, normal, stromal, and infiltrating immune cells. Additionally, we study FLC metastases, finding small differences from the primary tumors.

A novel biomarker of COVI-19: MMP8 emerged by integrated bulk RNAseq and single-cell sequencing

COVID-19 has been emerging as the most influential illness which has caused great costs to the heath of population and social economy. Sivelestat sodium (SS) is indicated as an effective cure for lung dysfunction, a characteristic symptom of COVID-19 infection, but its pharmacological target is still unclear. Therefore, a deep understanding of the pathological progression and molecular alteration is an urgent issue for settling the diagnosis and therapy problems of COVID-19. In this study, the bulk ribonucleic acid sequencing (RNA-seq) data of healthy donors and non-severe and severe COVID-19 patients were collected. Then, target differentially expressed genes (DEGs) were screened through integrating sequencing data and the pharmacological database. Besides, with the help of functional and molecular interaction analyses, the potential effect of target gene alteration on COVID-19 progression was investigated. Single-cell sequencing was performed to evaluate the cell distribution of target genes, and the possible interaction of gene-positive cells with other cells was explored by intercellular ligand-receptor pattern analysis. The results showed that matrix metalloproteinase 8 (MMP8) was upregulated in severe COVID-19 patients, which was also identified as a targeting site to SS. Additionally, MMP8 took a core part in the regulatory interaction network of the screened DEGs in COVID-19 and was dramatically correlated with the inflammatory signaling pathway. The further investigations indicated that MMP8 was mainly expressed in myelocytes with a high degree of heterogeneity. MMP8-positive myelocytes interacted with other cell types through RETN-TLR4 and RETN-CAP1 ligand-receptor patterns. These findings emphasize the important role of MMP8 in COVID-19 progression and provide a potential therapeutic target for COVID-19 patients.

Single-cell transcriptomics reveals tumor microenvironment changes and prognostic gene signatures in hepatocellular carcinoma

BACKGROUND

Hepatocellular Carcinoma (HCC) is the most common type of primary liver cancer, accounting for the majority of liver cancer cases. Hepatocellular Carcinoma not only exhibits high heterogeneity but also possesses an immune-suppressive tumor microenvironment that promotes tumor evasion, posing substantial difficulties for efficient therapy. Our aim is to utilize single-cell RNA transcriptome data to investigate the dynamic changes in the tumor microenvironment during the malignant progression of HCC, the communication among immune cells, and the marker genes associated with patient prognosis.

METHODS

We constructed expression matrices from open single-cell RNA transcriptome data (GSE149614) of HCC patients (representing stages I-IV), establishing single-cell RNA transcriptional atlases for different stages of HCC progression. For each stage, we conducted cell subgroup analysis to identify cell types at each stage. Horizontally, we explored the dynamic changes of the same cell type across different stages, performing trajectory analysis and prognosis analysis. Vertically, we investigated pairwise comparisons of different stages of HCC progression, probing the dynamic alterations in tumor microenvironment immune cell signaling pathways. Finally, potential drugs for the treatment of HCC were predicted based on relevant genes.

FINDINGS

As the HCC advances towards increased malignancy, there is a shift in the predominant composition of the tumor microenvironment, with a decline in the dominance of hepatic cells. Tumor-infiltrating immune cells migrate and accumulate within the tumor microenvironment, where T cells and myeloid cells display distinct patterns of change. Genes associated with cancer-associated fibroblasts (CAFs) and T cells are correlated with adverse patient outcomes. In the late stages of HCC, the tumor microenvironment is infiltrated by more myeloid-derived suppressor cells (MDSCs), and a prognostic model constructed based on genes related to myeloid cells can predict patient outcomes. Additionally, in the analysis of transcription factors, YY1 and MYC are found to be highly expressed. Cell communication analysis among tumor-infiltrating immune cells reveals significant differences in the main signaling pathways at different stages of HCC progression. Finally, drug sensitivity analysis based on key genes identifies Acetalax, Allopurinol, and Amonafide as potential candidates for HCC treatment.

The conserved wobble uridine tRNA thiolase Ctu1 is required for angiogenesis and embryonic development

Cytosolic thiouridylase is a conserved cytoplasmic tRNA thiolase composed of two different subunits, CTU1 and CTU2. CTU2 serves as a scaffold protein, while CTU1 catalyzes the 2-thiolation at the 34th wobble uridine of the anticodon loop. tRNAGlnUUG, tRNAGluUUC, and tRNALysUUU are the tRNA substrates that are modified with a thiol group at the C2 positions (s2) by CTU1, and also with a methoxycarbonylmethyl group at the C5 positions (mcm5) by Elongator and ALKBH8. mcm5s2U34 modification of the three tRNAs, and their modifying enzymes are involved in human disease and development. Elongator mutant animals exhibit severe phenotypes, while the biological function of Ctu1 in vertebrate animal models remains poorly characterized. Here, we applied antisense morpholino oligonucleotides targeting cytosolic thiouridylase subunit1 (ctu1) transcripts in a zebrafish model and small interfereing RNA against CTU1 transcript in human endothelial cells to define the phenotypes. We found that deficiency of ctu1 causes impaired angiogenesis and development in zebrafish embryos, and CTU1 is involved in proliferation, migration, and tube formation of human endothelial cells. We employed single-cell RNA sequencing to acquire the transcriptomic atlas from ctu1 and control morphant zebrafish. Comprehensive bioinformatics analysis, including pseudo-time, RNA velocity, cell-cell communication, and gene regulatory network inference revealed that ctu1 deficiency leads to the arrest of cell cycle, and the defects of nerve development and erythrocyte differentiation and the attenuation of several pro-angiogenic signaling pathways, e.g., angpt-tek and dll4-notch. Our findings show for the first time that CTU1 is essential for angiogenesis and embryonic development in vertebrates.

Characterisation of GPR17-expressing oligodendrocyte precursors in human ischaemic lesions and correlation with reactive glial responses

White matter damage and subsequent demyelination significantly contribute to long-term functional impairment after ischaemic stroke. Identifying novel pharmacological targets to restore myelin integrity by promoting the maturation of oligodendrocyte precursor cells (OPCs) into new myelinating oligodendrocytes may open new perspectives for ischaemic stroke treatment. In this respect, previous studies highlighted the role of the G protein-coupled membrane receptor 17 (GPR17) as a key regulator of OPC differentiation in experimental models of brain injury, including ischaemic stroke. To determine the translational value of GPR17 as a possible target in the context of human disease, we exploited immunohistochemistry to characterise the distribution of GPR17-expressing cells in brain tissue samples from ischaemic stroke cases and correlated it with the reactive state of neighbouring glial cells. The results showed that GPR17 specifically decorates a subpopulation of differentiation-committed OPCs, labelled by the peculiar marker breast carcinoma-amplified sequence 1 (BCAS1), that accumulates in the peri-infarct region in the later stages after the ischaemic event. Interestingly, the response of GPR17-expressing cells appears to be paralleled by the switch of reactive microglia/macrophages from a phagocytic to a dystrophic phenotype and by astrocytic scar formation. A negative correlation was found between GPR17-expressing OPCs and reactive microglia/macrophages and astrocytes surrounding chronic ischaemic lesions in female subjects, while the same relationship was less pronounced in males. These results were reinforced by bioinformatic analysis of a publicly available transcriptomic dataset, which implicated a possible role of inflammation and defective neuron-to-OPC communication in remyelination failure after ischaemic damage. Hence, these data strengthen the relevance of GPR17-based remyelinating therapies for the treatment of ischaemic stroke. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Transcriptomic profiling of thyroid eye disease orbital fat demonstrates differences in adipogenicity and IGF-1R pathway

Despite recent advances in the treatment of thyroid eye disease thyroid-related eye disease (TED), marked gaps remain in our understanding of the underlying molecular mechanisms, particularly concerning the insulin-like growth factor-1 receptor (IGF-1R) pathway. To dissect the pathophysiology of TED, we used single-nucleus RNA-Seq to analyze orbital fat specimens from both patients with TED and matched individuals acting as controls. The analysis demonstrated a marked increase in the proportion of fibroblasts transitioning to adipogenesis in the orbital fat of patients with TED compared with that in control patients. This was associated with diverse alterations in immune cell composition. Significant alterations in the IGF-1R signaling pathway were noted between TED specimens and those from control patients, indicating a potential pathological mechanism driven by IGF-1R signaling abnormalities. Additionally, our data showed that linsitinib, a small-molecule inhibitor of IGF-1R, effectively reduced adipogenesis in TED orbital fibroblasts in vitro, suggesting its potential utility as a therapeutic agent. Our findings reveal that, beyond immune dysfunction, abnormal IGF-1R signaling leading to enhanced adipogenesis is a crucial pathogenic mechanism in TED.

Exploring tumor microenvironment interactions and apoptosis pathways in NSCLC through spatial transcriptomics and machine learning

BACKGROUND

The most common type of lung cancer is non-small cell lung cancer (NSCLC), accounting for 85% of all cases. Programmed cell death (PCD), an important regulatory mechanism for cell survival and homeostasis, has become increasingly prominent in cancer research in recent years. As such, exploring the role of PCD in NSCLC may help uncover new mechanisms for therapeutic targets.

METHODS

We utilized the GEO database and TCGA NSCLC gene data to screen for co-expressed genes. To delve deeper, single-cell sequencing combined with spatial transcriptomics was employed to study the intrinsic mechanisms of programmed cell death in cells and their interaction with the tumor microenvironment. Furthermore, Mendelian randomization was applied to screen for causally related genes. Prognostic models were constructed using various machine learning algorithms, and multi-cohort multi-omics analyses were conducted to screen for genes. In vitro experiments were then carried out to reveal the biological functions of the genes and their relationship with apoptosis.

RESULTS

Cells with high programmed cell death activity primarily activate pathways related to apoptosis, cell migration, and hypoxia, while also exhibiting strong interactions with smooth muscle cells in the tumor microenvironment. Based on a set of programmed cell death genes, the prognostic model NSCLCPCD demonstrates strong predictive capabilities. Moreover, laboratory experiments confirm that SLC7A5 promotes the proliferation of NSCLC cells, and the knockout of SLC7A5 significantly increases tumor cell apoptosis.

CONCLUSIONS

Our data indicate that programmed cell death is predominantly associated with pathways related to apoptosis, tumor metastasis, and hypoxia. Additionally, it suggests that SLC7A5 is a significant risk indicator for the prognosis of non-small cell lung cancer (NSCLC) and may serve as an effective target for enhancing apoptosis in NSCLC tumor cells.

Integration of Microarray Data and Single-Cell Sequencing Analysis to Explore Key Genes Associated with Macrophage Infiltration in Heart Failure

Background

Cardiac macrophages are a heterogeneous population with high plasticity and adaptability, and their mechanisms in heart failure (HF) remain poorly elucidated.

Methods

We used single-cell and bulk RNA sequencing data to reveal the heterogeneity of non-cardiomyocytes and assess the immunoreactivity of each subpopulation. Additionally, we employed four integrated machine learning algorithms to identify macrophage-related genes with diagnostic value, and in vivo validation was performed. To assess the immune infiltration characteristics in HF, we utilized the CIBERSORT and single sample gene set enrichment analysis (ssGSEA). An unsupervised consensus clustering algorithm was applied to identify the macrophage-related HF subtypes. Furthermore, the scMetabolism was employed to explore the specific metabolic patterns of the macrophage subtypes. Finally, CellChat was used to investigate cell-cell interactions among the identified subtypes.

Results

The immunoreactivity score of macrophages in the HF was higher than that in the other cell types. GSEA of macrophage clusters indicated a significant enrichment of leukocyte-mediated immune processes, antigen processing, and presentation. The intersection of the results from machine learning revealed that SERPINA3, GPAT3, ANPEP, and FCER1G can serve as feature genes and form a diagnostic model with a good predictive capability. Unsupervised consensus clustering algorithms reveal the immune and metabolic subtypes of macrophages. The metabolic heterogeneity of macrophage subpopulations can lead to macrophage polarization into different types, which may be related to the metabolic reprogramming between glycolysis and mitochondrial oxidative phosphorylation. Cellular communication revealed that macrophages form a network of interactions with neutrophils to support each other's functions and maintenance. The complex efferent and afferent signals are closely associated with myocardial fibrosis.

Conclusion

SERPINA3, GPAT3, ANPEP, and FCER1G can potentially serve as immune therapeutic targets and central biomarkers. The immunological and metabolic heterogeneity of macrophages may offer a more precise direction to explore the mechanisms underlying HF and novel immunotherapies.

Coupling of response biomarkers between tumor and peripheral blood in patients undergoing chemoimmunotherapy

Platinum-based chemotherapy in combination with anti-PD-L1 antibodies has shown promising results in mesothelioma. However, the immunological mechanisms underlying its efficacy are not well understood and there are no predictive biomarkers to guide treatment decisions. Here, we combine time course RNA sequencing (RNA-seq) of peripheral blood mononuclear cells with pre-treatment tumor transcriptome data from the single-arm, phase 2 DREAM trial (N = 54). Single-cell RNA-seq and T cell receptor sequencing (TCR-seq) reveal that CD8+ T effector memory (TEM) cells with stem-like properties are more abundant in peripheral blood of responders and that this population expands upon treatment. These peripheral blood changes are linked to the transcriptional state of the tumor microenvironment. Combining information from both compartments, rather than individually, is most predictive of response. Our study highlights complex interactions between the tumor and immune cells in peripheral blood during objective tumor responses to chemoimmunotherapy. This trial is registered with the Australian New Zealand Clinical Trials Registry, number ACTRN12616001170415.

Intestinal metaplasia key molecules and UPP1 activation via Helicobacter pylori /NF-kB: drivers of malignant progression in gastric cancer

Gastric cancer (GC) remains a significant global health challenge due to its high morbidity and mortality rates. The development of GC is a multi-hit process and the exploration of precancerous lesions is crucial. To elucidate the molecular and cellular dynamics underlying gastric carcinogenesis, we conducted an integrative single-cell RNA sequencing analysis of 26,028 high-quality cells from gastric antral mucosa biopsies across various stages, including non-atrophic gastritis, chronic atrophic gastritis, intestinal metaplasia, and early gastric cancer. By constructing a detailed single-cell atlas, we identified distinct epithelial cell subpopulations and their corresponding molecular signatures. We focused on the biological link between gastric epithelial cells and cancer cells. Notably, we observed that gland mucous cells acquired an intestinal-like stem cell phenotype during metaplasia, with MUC6, MUC2 and OLFM4 emerging as the specific markers for unique endocrine cells in early malignant lesions. Additionally, our analysis highlighted UPP1 as a key oncogene, with its expression progressively increasing from normal epithelial cells to malignant cells. UPP1 upregulation was shown to promote GC cell proliferation and migration, implicating it in the oncogenic process. Further, we explored the impact of Helicobacter pylori infection on gene expression, revealing that Helicobacter pylori infection upregulates UPP1 via the NF-κB pathway. Our cell-cell communication analysis underscored the significant role of the Macrophage migration inhibitory factor pathway in the tumor microenvironment, contributing to GC progression. Various key molecules involved in intestinal metaplasia, along with UPP1 and the Macrophage migration inhibitory factor pathway, collectively illustrate the multifaceted nature and complexity of gastric cancer evolution, highlighting the cumulative impacts that drive tumorigenesis.

Diverse Epithelial Lymphocytes in Zebrafish Revealed Using a Novel Scale Biopsy Method

Zebrafish (Danio rerio) are a compelling model for studying lymphocytes because zebrafish and humans have similar adaptive immune systems, including their lymphocytes. Antibodies that recognize zebrafish proteins are sparse, so many investigators use transgenic, lymphocyte-specific fluorophore-labeled lines. Human and zebrafish lymphocyte types are conserved, but many aspects of zebrafish lymphocyte biology remain uninvestigated, including lymphocytes in peripheral tissues, like epidermis. This study is, to our knowledge, the first study to focus on zebrafish epidermal lymphocytes, using scales. Obtaining zebrafish blood via nonlethal methods is difficult; scales represent a source to longitudinally sample live fish. We developed a novel biopsy technique, collecting scales to analyze epithelial lymphocytes from several transgenic lines. We imaged scales via confocal microscopy and demonstrated multiple lymphocyte types in scales/epidermis, quantifying them flow cytometrically. We profiled gene expression of scale, thymic, and kidney-marrow (analogous to mammalian bone marrow) lymphocytes from the same animals, revealing B- and T-lineage signatures. Single-cell quantitative real-time PCR and RNA sequencing show not only canonical B and T cells but also novel lymphocyte populations not described previously. To validate longitudinal scale biopsies, we serially sampled scales from fish treated with dexamethasone, demonstrating epidermal lymphocyte responses. To analyze cells functionally, we employed a bead-ingestion assay, showing that thymic, marrow, and epidermal lymphocytes have phagocytic activity. In summary, we establish a novel, nonlethal technique to obtain zebrafish lymphocytes, providing the first quantification, expression profiling, and functional data from zebrafish epidermal lymphocytes.

Integration of single-cell and bulk analysis reveals TBXAS1 as a key platelet-related gene causing poor prognosis in osteosarcoma

Background

Platelets are associated with poor prognosis in most tumors, but their specific pathogenic mechanism in osteosarcoma is not yet clear. The objective of this study is to conduct an in-depth analysis of how genes closely related to platelet function impact the prognosis of osteosarcoma patients. We hope that through this research, we can uncover the potential mechanisms of these genes in the development and progression of osteosarcoma, thereby providing new therapeutic strategies and theoretical foundations for improving the prognosis of osteosarcoma patients.

Method

We collected the blood routine test data of patients who were initially diagnosed with osteosarcoma at the Department of Bone Tumors, West China Hospital, from January 2012 to January 2022. By applying the LASSO-COX regression analysis, a statistical method, we found that the platelet count is associated with the prognosis of osteosarcoma patients. To further explore this relationship, we obtained single-cell data and bulk RNA data of osteosarcoma patients from the TARGET database and GEO database, respectively. By analyzing these data, we revealed at the transcriptomic level how platelets contribute to the poor prognosis in osteosarcoma patients.

Result

Platelets are associated with the prognosis of osteosarcoma patients (HR = 3.9, 95% CI = 1.9-8.1, P < 0.001). Through the analysis of transcriptomic data from the TARGET database and GEO database, we found significant heterogeneity in tumor-specific pathways and immune infiltration under different platelet-related gene expression patterns. Among these, TBXAS1 was identified as a key gene that affects the prognosis of osteosarcoma patients. In addition, single-cell data analysis showed that the platelet-related gene TBXAS1 is mainly enriched in macrophages, and markers of macrophages are significantly associated with poor prognosis in osteosarcoma patients.

Conclusion

TBXAS1 is a key platelet-related gene that leads to poor prognosis in osteosarcoma, and this gene may affect the prognosis of osteosarcoma patients by interacting with macrophages.

Computer-aided analysis reveals metallothionein-positive cancer-associated fibroblasts promote angiogenesis in gastric adenocarcinoma

Gastric adenocarcinoma (GC), along with its tumor microenvironment (TME), poses great challenges for clinical treatment strategies. Single-cell sequencing has become an important tool for analyzing TME heterogeneity, cell subpopulation, and gene expression patterns. 56 GC single-cell sequencing samples were analyzed, focusing on TME by delineating cancer cells, cancer-associated fibroblasts (CAFs), and macrophages. The spatial transcriptome was used to clarify the distribution characteristics of each cellular component in the tissue slice. Despite the widespread genetic mutations observed in cancer cells, certain recurrent alterations were identified in specific chromosomal regions. The heterogeneity among GC cells is profound, four cancer cell subpopulations were identified through drug sensitivity profiling. Subtype 4, although only present in some samples, demonstrates the strongest stemness and metabolic activity, possibly indicative of an early-stage cancer subpopulation. Their drug sensitivity profiles may hold promise for guiding clinical intervention. In addition, robust spatial co-localization patterns were observed between CAFs, M2 macrophages, and endothelial cells. CAFs were further categorized into six subgroups, among which a novel subgroup termed metallothionein(mt)-positive CAF (mtCAF), characterized by elevated expression of metallothionein 1X (MT1X) and subsequent vascular endothelial growth factor A (VEGFA) secretion, was identified. Immunohistochemistry preliminary confirmed the presence of this unique CAF subgroup. Additionally, M2d macrophages, besides exhibiting high VEGFA expression, also demonstrated various growth factors such as Aamphiregulin (AREG). The M2d-mtCAF axis may play an important role in GC angiogenesis. This study not only enhances our understanding of the TME heterogeneity in GC but also sheds light on the interaction between CAFs and tumor-associated macrophages (TAMs) in tumor angiogenesis.

Multi-omics joint screening of biomarkers related to M2 macrophages in gastric cancer

BACKGROUND

Due to high mortality rate and limited treatments in gastric cancer (GC), call for deeper exploration of M2 macrophages as biomarkers is needed.

METHODS

The data for this study were obtained from the Gene Expression Omnibus (GEO) and Genomic Data Commons (GDC). The Seurat package was utilized for single-cell RNA sequencing (scRNA-seq) analysis. FindAllMarkers was used to identify genes highly expressed among different cell subsets. DESeq2 package was leveraged to screen differentially expressed genes (DEGs), while limma package was utilized for identifying differentially expressed proteins (DEPs). Enrichment analyses of the genes were conducted using KOBAS-i database. MultipleROC was applied to evaluate the diagnostic potential of biomarkers, and rms package was utilized to construct diagnostic models. hTFtarget database was utilized to predict potential transcription factors (TFs). Finally, cell-based assays were performed to validate the expression and potential biological functions of the screened key markers.

RESULTS

This study found that M2 macrophages were enriched in protein, endoplasmic reticulum, and virus-related pathways. A total of 4146 DEGs and 1946 DEPs were obtained through screening, with 254 common DEGs/DEPs. The results of gene function enrichment analysis suggested that it may affect the occurrence and development of GC through DNA replication and cell cycle. This study identified three biomarkers, HSPH1, HSPD1, and IFI30, and constructed a diagnostic model based on these three genes. The AUC value greater than 0.8 proved the reliability of the model. Through screening TFs, SPI1 and KLF5 were found to be the common TFs for the three biomarkers. The expression of the three genes IFI30, HSPD1 and HSPH1 was up-regulated in GC cells, and IFI30 may play a facilitating role in the migration and invasion of GC cells.

CONCLUSION

This study identified three biomarkers and constructed a diagnostic model, providing a new perspective for the research and treatment of GC.

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.

SMART: spatial transcriptomics deconvolution using marker-gene-assisted topic model

While spatial transcriptomics offer valuable insights into gene expression patterns within the spatial context of tissue, many technologies do not have a single-cell resolution. Here, we present SMART, a marker gene-assisted deconvolution method that simultaneously infers the cell type-specific gene expression profile and the cellular composition at each spot. Using multiple datasets, we show that SMART outperforms the existing methods in realistic settings. It also provides a two-stage approach to enhance its performance on cell subtypes. The covariate model of SMART enables the identification of cell type-specific differentially expressed genes across conditions, elucidating biological changes at a single-cell-type resolution.

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

TYROBP promotes the spread of pancreatic cancer by causing M2 TAM polarization

BACKGROUND AND AIM

M2-polarized tumor-associated macrophages (M2 TAMs) are known to promote cancer progression, and exosomes are crucial mediators of communication within the tumor microenvironment (TME). However, the specific role of exosomes derived from M2 TAMs in pancreatic cancer (PC) progression remains poorly understood. Tyrosine kinase binding protein (TYROBP, also known as DAP12 for DNAX activating protein-12) is a transmembrane signal transduction polypeptide that interacts with immune cell receptors, influencing cellular functions via signal transduction pathways. TYROBP is prominently found in M2 TAMs exosomes, facilitating its transfer to PC cells and suggesting a potential role in PC pathogenesis.

METHODS

This study initially confirmed the presence of TYROBP in M2 TAMs exosomes and its transfer to PC cells via exosomes. The impact of TYROBP on PC proliferation, apoptosis, migration, and invasion was investigated. Special attention was given to TYROBP's influence on PC metastasis and its underlying mechanisms, focusing particularly on the CD44/AKT/ERK signaling pathway.

RESULTS

TYROBP expression in PC cells did not significantly affect tumor cell proliferation or apoptosis but demonstrated a notable inhibitory effect on migration and invasion, which was mediated through the CD44/AKT/ERK pathway. Both in vivo and in vitro experiments consistently showed that TYROBP enhanced PC metastasis.

CONCLUSIONS

This study elucidates that TYROBP plays a direct role in promoting PC metastasis through its association with M2 TAMs polarization. Therefore, TYROBP represents a potential novel therapeutic target for interventions aimed at combatting PC progression.

Single-cell 5' RNA sequencing of camelid peripheral B cells provides insights into cellular basis of heavy-chain antibody production

Camelids produce both conventional tetrameric antibodies (Abs) and dimeric heavy-chain antibodies (HCAbs). Although B cells that generate these two types of Abs exhibit distinct B cell receptors (BCRs), whether these two B cell populations differ in their phenotypes and developmental processes remains unclear. Here, we performed single-cell 5' RNA profiling of peripheral blood mononuclear cell samples from Bactrian camels before and after immunization. We characterized the functional subtypes and differentiation trajectories of circulating B cells in camels, and reconstructed single-cell BCR sequences. We found that in contrast to humans, the proportion of T-bet+ B cells was high among camelid peripheral B cells. Several marker genes of human B cell subtypes, including CD27 and IGHD, were expressed at low levels in the corresponding camel B cell subtypes. Camelid B cells expressing variable genes of HACbs (VHH) were widely present in various functional subtypes and showed highly overlapping differentiation trajectories with B cells expressing variable genes of conventional Abs (VH). After immunization, the transcriptional changes in VHH+ and VH+ B cells were largely consistent. Through structure modeling, we identified a variety of scaffold types among the reconstructed VHH sequences. Our study provides insights into the cellular context of HCAb production in camels and lays the foundation for developing single-B cell-based camelid single-domain Ab screening.

BrainCellR: A precise cell type nomenclature pipeline for comparative analysis across brain single-cell datasets

Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained cell types and establish standardized annotations at the cluster level, hindering comprehensive understanding of the brain's cellular composition. To facilitate data integration across multiple models and datasets, we designed BrainCellR. This pipeline provides researchers with a powerful and user-friendly tool for efficient cell type classification and nomination from single-cell transcriptomic data. While initially focused on brain studies, BrainCellR is applicable to other tissues with complex cellular compositions. BrainCellR goes beyond conventional classification approaches by incorporating a standardized nomenclature system for cell types at the cluster level. This feature enables consistent and comparable annotations across different studies, promoting data integration and providing deeper insights into the complex cellular landscape of the brain. All documents for BrainCellR, including source code, user manual and tutorials, are freely available at https://github.com/WangLab-SINH/BrainCellR.

scBlood: A comprehensive single-cell accessible chromatin database of blood cells

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

scRNA-Seq reveals age-dependent microglial evolution as a determinant of immune response following spinal cord injury

Spinal cord injury (SCI) is a debilitating condition of the central nervous system (CNS) that leads to severe impairments in sensory and motor functions. Previous studies have pointed out that patient age is a critical factor influencing SCI prognosis. However, the role of microglia in age-related differences in SCI outcomes remains unclear. The current study aims to identify specific microglial subtypes and investigate their responses and functional differences in SCI recovery across different age groups. Single-cell RNA sequencing (scRNA-seq) data were obtained from the Gene Expression Omnibus (GEO) database, integrating multiple datasets to identify microglial subtypes. We performed pseudotime trajectory analysis and cell-cell communication analysis to understand microglial differentiation and interactions. Finally, immunofluorescence staining of mouse model samples was conducted to validate our bioinformatics findings. Microglia were classified into four subtypes: Homeostatic, Proliferating, Inflammatory A, and Inflammatory B. The Young SCI group exhibited a higher proportion of Homeostatic microglia and Inflammatory microglia A, whereas the old SCI group had more Inflammatory Microglia B but lacked Homeostatic Microglia. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that markers for homeostasis microglia were enriched in immune modulation pathways. While makers for Inflammatory Microglia were enriched in immune response pathways. Specifically, markers for Inflammatory microglia B were enriched in pathways associated with overactive immune response. Pseudotime analysis indicated that microglia in young mice predominantly differentiated into Inflammatory Microglia A and Homeostatic Microglia, whereas in old mice, they tended to only differentiate into Inflammatory Microglia B. CellChat analysis showed increased pro-inflammatory signaling generated by Inflammatory Microglia B, exclusively in the old group. Our study demonstrates significant differences in microglial subtypes and functions between different age groups following SCI. These findings provide novel insights into the development of age-related therapeutic strategies and microglia-targeted biological treatments for SCI.

Cellular and Molecular Basis of Environment-Induced Color Change in a Tree Frog

Background color matching is essential for camouflage and thermoregulation in ectothermic vertebrates, yet several key cellular-level questions remain unresolved. For instance, it is unclear whether the number of chromatophores or the activity of individual chromatophores plays a more critical role in this process. Using single-cell RNA sequencing (scRNA-seq), we investigated the cellular and molecular mechanisms underlying color change in Rhacophorus dugritei, which adapted to its background by displaying light-green skin on white and black skin on black within two days. We identified two types of chromatophores in their skin, both responsible for the observed color differences. Our findings reveal that morphological color change (MCC) is the dominant process, with the number of chromatophores being more influential in driving color change than the transcriptional activity of melanogenesis in individual cells. Additionally, melanophores from darker individuals exhibited increased activity in energy metabolism pathways, while those from lighter individuals showed stronger immune-related gene expression, suggesting that background adaptation involves more than just morphological changes. Overall, this study successfully applied single-cell sequencing technology to investigate skin pigmentation in a non-model organism. Our results suggest that MCC driven by chromatophore proliferation is a key mechanism of background adaptation, offering new insights into amphibian color adaptation and environmental adaptation in other vertebrates.

Comprehensive gene set enrichment and variation analyses identify SUV39H1 as a potential prognostic biomarker for glioblastoma immunorelevance

Glioblastoma (GBM) is the most common intracranial malignancy. SUV39H1 encodes a histone H3 lysine 9 methyltransferase that acts as an oncogene in several cancers; however, its role in GBM remains unknown. We obtained GBM transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database on the UCSC Xena platform to perform differential and enrichment analyses of genes in the SUV39H1 high- and low-expression groups to construct a prognostic risk model. Analysis of SUV39H1 related biological processes in GBM was performed by gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). High- and low-risk subgroup mutation signatures were analyzed using maftools. Immune infiltration was evaluated using IOBR and CIBERSORT algorithms. We analyzed the cell types and intercellular communication networks in glioma stem cells (GSCs) using scRNA-seq. The effects on GBM cells and GSCs after inhibition of SUV39H1 were investigated in vitro. SUV39H1 was significantly overexpressed in GBM and associated with poor prognosis. SUV39H1-related differentially expressed genes were enriched in immune and inflammation related pathways, and GSEA revealed that these genes were significantly enriched in signaling pathways such as IL-18, oxidative phosphorylation, and regulation of TP53 activity. Mutational analysis revealed frequent alterations in TP53 and PTEN expression. In addition, the infiltration abundances of the five immune cell types were significantly different between the high- and low-expression groups. Analysis of cellular communication networks by scRNA-seq revealed a strong interaction between CRYAB-GSC and PTPRZ1-GSC in GSCs. In vitro experiments verified that knockdown of SUV39H1 inhibited the viability and proliferation of U87 and U251 glioblastoma cells and downregulated the expression of stemness markers Nestin and SOX2 in CSC1589 and TS576 GSC lines. Increased SUV39H1 expression is associated with immune cell infiltration and poor prognosis in patients with GBM. Inhibition of SUV39H1 restrains GBM growth and reduces the stem cell properties of GSC. Thus, SUV39H1 might be a prognostic predictor and immunotherapeutic target in patients with GBM.

Mechanistic study of the regulation of mitochondrial function by the GPNMB/Nrf2/NF-κB signaling pathway mediated by Quzhi Tang to alleviate chondrocyte senescence

ETHNOPHARMACOLOGICAL RELEVANCE

Quzhi Tang (QZT) is a compound formula consisting of six traditional Chinese medicinal herbs. It has achieved good clinical results in the treatment of knee osteoarthritis (KOA), and the potential drug mechanisms involved are worth exploring in depth.

MATERIALS AND METHODS

Using single-cell transcriptome analysis, this study identified the key target of senescence, GPNMB. Then, it investigated the mechanism by which QZT regulates the GPNMB/Nrf2/NF-κB signaling pathway to repair mitochondrial damage and ameliorate the process of chondrocyte senescence.

RESULTS

We collected cartilage tissues from mice and identified GPNMB as a key target of chondrocyte senescence by combining transcriptomics, histopathology, molecular biology, and immunology methods. The effects of QZT on the level of chondrocyte senescence in mice and its ameliorative effect on KOA were studied. In in vivo experiments, we explored the mechanism of GPNMB in the development of senescence in detail and revealed that, after siRNA-GPNMB interference, chondrocytes exhibited reduced impairment of mitochondrial function and senescence under equal amounts of stimuli, increasing Nrf2 expression and reducing NF-κB expression. In addition, the level of oxidative stress increased in chondrocytes overexpressing GPNMB after lentiviral infiltration, aggravating the impairment of mitochondrial function. After treatment with QZT, chondrocytes overexpressing GPNMB were able to increase Nrf2 expression, decrease NF-κB expression, repair mitochondrial damage, and improve the degree of chondrocyte aging.

CONCLUSION

We concluded that the GPNMB/Nrf2/NF-κB signaling pathway plays an important role in chondrocyte senescence and that QZT was able to reduce intracellular oxidative stress and restore impaired mitochondrial function by regulating the expression level of the GPNMB/Nrf2/NF-κB signaling pathway, reducing the level of chondrocyte senescence in the KOA process.

Evaluation of large language models for discovery of gene set function

Gene set enrichment is a mainstay of functional genomics, but it relies on gene function databases that are incomplete. Here we evaluate five large language models (LLMs) for their ability to discover the common functions represented by a gene set, supported by molecular rationale and a self-confidence assessment. For curated gene sets from Gene Ontology, GPT-4 suggests functions similar to the curated name in 73% of cases, with higher self-confidence predicting higher similarity. Conversely, random gene sets correctly yield zero confidence in 87% of cases. Other LLMs (GPT-3.5, Gemini Pro, Mixtral Instruct and Llama2 70b) vary in function recovery but are falsely confident for random sets. In gene clusters from omics data, GPT-4 identifies common functions for 45% of cases, fewer than functional enrichment but with higher specificity and gene coverage. Manual review of supporting rationale and citations finds these functions are largely verifiable. These results position LLMs as valuable omics assistants.

Deciphering the impact of senescence in kidney transplant rejection: An integrative machine learning and multi-omics analysis via bulk and single-cell RNA sequencing

BACKGROUND

The demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft survival. KTx rejection poses a significant threat to allograft function and longevity of the kidney graft. The relationship between senescence and rejection remains elusive and controversial.

METHODS

Gene Expression Omnibus (GEO) provided microarray and single-cell RNA sequencing datasets. After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). A cluster analysis of rejection samples was conducted using the consensus clustering algorithm. Subsequently, we utilized multiple machine learning methods (RF, SVM, XGB, GLM and LASSO) based on pSRGs to develop the optimal Acute Rejection (AR) diagnostic model and long-term graft survival predictive signatures. Finally, we validated the role of pSRGs and senescence in kidney rejection through the single-cell landscape.

RESULTS

Thirteen pSRGs were identified, correlating with rejection. Two rejection clusters were divided (Cluster C1 and C2). GSVA analysis of two clusters underscored a positive correlation between senescence, KTx rejection occurrence and worse graft survival. A non-invasive diagnostic model (AUC = 0.975) and a prognostic model (1- Year AUC = 0.881; 2- Year AUC = 0.880; 3- Year AUC = 0.883) for graft survival were developed, demonstrating significant predictive capabilities to early detect acute rejection and long-term graft outcomes. Single-cell sequencing analysis provided a detailed cellular-level landscape of rejection, supporting the conclusions drawn from above.

CONCLUSION

Our comprehensive analysis underscores the pivotal role of senescence in KTx rejection, highlighting the potential of SRGs as biomarkers for diagnosing rejection and predicting graft survival, which may enhance personalized treatment strategies and improve transplant outcomes.

Constructing methylation-driven ceRNA networks unveil tumor heterogeneity and predict patient prognosis

Cancer development involves a complex interplay between genetic and epigenetic factors, with emerging evidence highlighting the pivotal role of competitive endogenous RNA (ceRNA) networks in regulating gene expression. However, the influence of ceRNA networks by aberrant DNA methylation remains incompletely understood. In our study, we proposed DMceNet, a computational method to characterize the effects of DNA methylation on ceRNA regulatory mechanisms and apply it across eight prevalent cancers. By integrating methylation and transcriptomic data, we constructed methylation-driven ceRNA networks and identified a dominant role of lncRNAs within these networks in two key ways: (i) 17 cancer-shared differential methylation lncRNAs (DMlncs), including PVT1 and CASC2, form a Common Cancer Network (CCN) affecting key pathways such as the G2/M checkpoint, and (ii) 24 cancer-specific DMlncs construct unique ceRNA networks for each cancer type. For instance, in LUAD and STAD, hypomethylation drives DMlncs like PCAT6 and MINCR, disrupting the Wnt signaling pathway and apoptosis. We further investigated the characteristics of these methylation-driven ceRNA networks at the cellular level, revealing how methylation-driven dysregulation varies across distinct cell populations within the tumor microenvironment. Our findings also demonstrate the prognostic potential of cancer-specific ceRNA relationships, highlighting their relevance in predicting patient survival outcomes. This integrated transcriptomic and epigenomic analysis provides new insights into cancer biology and regulatory mechanisms.

IL7 as a Risk Factor for Prostate Cancer: Implications for T Cell Apoptosis and Infiltration in the Tumor Microenvironment

BACKGROUND

Prostate cancer's complex interplay with the immune microenvironment prompted an investigation into immune-related pathogenic mechanisms and potential therapeutic targets.

METHODS

Within the GSE176031 data set, Seurat meticulously dissected single-cell profiles from radical prostatectomy patients. Leveraging CellMarker and SingleR cell identities were precisely annotated. Then, monocle traced pseudotime trajectories, illuminating cellular paths, complemented by CellChat's insights into intricate intercellular communications. Furthermore, mendelian randomization (MR) robustly substantiated causal associations within prostate cancer contexts.

RESULTS

Employing single-cell analysis on intraoperative tumor and normal tissue, we identified 15 distinct cell types, notably observing a significant T cell reduction in tumor samples. Intercellular communication analysis revealed multiple pathways between epithelial cells and T cells, highlighting interleukin (IL)-IL7R-IL2RG interactions. IL7R, crucial in T cell apoptosis, showed differential expression across T cell development stages. Patients with IL7 amplification had poorer outcomes (p < 0.05), supported by MR in two cohorts (ieu-b-4809 cohort: odds ratio [OR] = 1.005, p = 0.002, 95% confidence interval [CI] [1.002-1.008]; ebi-a-GCST90018905: OR = 1.063, p = 0.032, 95% CI [1.005-1.125]), confirming IL7 as a prostate cancer risk factor.

CONCLUSIONS

These findings suggest T cell depletion via IL7-IL7R signaling may drive prostate cancer progression, offering novel therapeutic insights.

Single-cell RNA sequencing reveals immune microenvironment niche transitions during the invasive and metastatic processes of ground-glass nodules and part-solid nodules in lung adenocarcinoma

BACKGROUND

Radiographically, ground-glass nodules (GGN) and part-solid nodules (PSN) in lung adenocarcinoma (LUAD) have significant heterogeneity in their clinical manifestations, biological characteristics, and prognosis. This study aimed to explore the heterogeneity of LUAD in different radiological phenotypes and associated factors influencing tumor evolution.

METHODS

We performed single-cell RNA sequencing (scRNA-seq) on tumor tissues from eight and seven cases of GGN- and PSN-LUAD, respectively, at different disease stages, including minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IAC), and metastatic lung cancer (MLC). Additionally, we analyzed adjacent normal tissues from four cases. Immunohistochemistry, multiplex immunofluorescence, and external scRNA-seq data were employed to confirm the expression of signature genes as well as the distribution patterns of CXCL9 + TAMs and TREM2 + TAMs. A LUAD mouse model was generated using gene editing, organoid culture, and orthotopic transplantation techniques, and comprehensive analyses such as histopathology, RNA sequencing, and Western blotting were performed to validate key pathways.

RESULTS

Diverse cellular compositions were observed in the tumor microenvironment (TME) during GGN- and PSN-LUAD invasion and metastasis. Notably, CXCL9 + and TREM2 + tumor-associated macrophages (TAMs) exhibited the most significant enrichment changes. It was found that GGN-LUAD exhibited a stronger immune response than PSN-LUAD, with increased interaction between CXCL9 + TAMs and CD8 + tissue-resident memory T cells during invasion stage (MIA-IAC). Conversely, greater interactions between TREM2 + TAMs and tumor cells were observed in PSN-LUAD during the MLC stage. Additionally, TREM2 + TAMs were found to differentiate into TREM2 + /SPP1 + and TREM2 + /SPP1- TAMs at different stages, which promotes tumor progression. This study also emphasizes that during the transdifferentiation process of GGN- and PSN-LUAD, IFN-γ activates the STAT1 signaling pathway to regulate the activation of CXCL9 + TAMs, and further recruiting CD8 + Trm cells and activating T cells through MHC class I antigen presentation. The role of the IFN-γ/STAT1 pathway in the occurrence and development of LUAD was further validated by animal experiments.

CONCLUSIONS

Our findings offer a potential therapeutic strategy to maintain a dynamic balance within the TME and improve the immunotherapy efficacy by modulating the relative proportions and functional states of CXCL9 + TAMs and TREM2 + TAMs.

Identification of early Alzheimer's disease subclass and signature genes based on PANoptosis genes

Introduction

Alzheimer's disease (AD) is one of the most prevalent forms of dementia globally and remains an incurable condition that often leads to death. PANoptosis represents an emerging paradigm in programmed cell death, integrating three critical processes: pyroptosis, apoptosis, and necroptosis. Studies have shown that apoptosis, necroptosis, and pyroptosis play important roles in AD development. Therefore, targeting PANoptosis genes might lead to novel therapeutic targets and clinically relevant therapeutic approaches. This study aims to identify different molecular subtypes of AD and potential drugs for treating AD based on PANoptosis.

Methods

Differentially expressed PANoptosis genes associated with AD were identified via Gene Expression Omnibus (GEO) dataset GSE48350, GSE5281, and GSE122063. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to construct a risk model linked to these PANoptosis genes. Consensus clustering analysis was conducted to define AD subtypes based on these genes. We further performed gene set variation analysis (GSVA), functional enrichment analysis, and immune cell infiltration analysis to investigate differences between the identified AD subtypes. Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and the DGIdb database was consulted to identify potential therapeutic compounds targeting these hub genes. Single-cell RNA sequencing analysis was utilized to assess differences in gene expression at the cellular level across subtypes.

Results

A total of 24 differentially expressed PANoptosis genes (APANRGs) were identified in AD, leading to the classification of two distinct AD subgroups. The results indicate that these subgroups exhibit varying disease progression states, with the early subtype primarily linked to dysfunctional synaptic signaling. Furthermore, we identified hub genes from the differentially expressed genes (DEGs) between the two clusters and predicted 38 candidate drugs and compounds for early AD treatment based on these hub genes. Single-cell RNA sequencing analysis revealed that key genes associated with the early subtype are predominantly expressed in neuronal cells, while the differential genes for the metabolic subtype are primarily found in endothelial cells and astrocytes.

Conclusion

In summary, we identified two subtypes, including the AD early synaptic abnormality subtype as well as the immune-metabolic subtype. Additionally, ten hub genes, SLC17A7, SNAP25, GAD1, SLC17A6, SLC32A1, PVALB, SYP, GRIN2A, SLC12A5, and SYN2, were identified as marker genes for the early subtype. These findings may provide valuable insights for the early diagnosis of AD and contribute to the development of innovative therapeutic strategies.

TriTan: an efficient triple nonnegative matrix factorization method for integrative analysis of single-cell multiomics data

Single-cell multiomics have opened up tremendous opportunities for understanding gene regulatory networks underlying cell states by simultaneously profiling transcriptomes, epigenomes, and proteomes of the same cell. However, existing computational methods for integrative analysis of these high-dimensional multiomics data are either computationally expensive or limited in interpretation. These limitations pose challenges in the implementation of these methods in large-scale studies and hinder a more in-depth understanding of the underlying regulatory mechanisms. Here, we propose TriTan (Triple inTegrative fast non-negative matrix factorization), an efficient joint factorization method for single-cell multiomics data. TriTan implements a highly efficient factorization algorithm, greatly improving its computational performance. Three matrix factorization produced by TriTan helps in clustering cells, identifying signature features for each cell type, and uncovering feature associations across omics, which facilitates the identification of domains of regulatory chromatin and the prediction of cell-type-specific regulatory networks. We applied TriTan to the single-cell multiomics data obtained from different technologies and benchmarked it against the state-of-the-art methods where it shows highly competitive performance. Furthermore, we showed a range of downstream analyses conducted utilizing TriTan outputs, highlighting its capacity to facilitate interpretation in biological discovery.

ADM: adaptive graph diffusion for meta-dimension reduction

Dimension reduction is essential for analyzing high-dimensional data, with various techniques developed to address diverse data characteristics. However, individual methods often struggle to capture all intricate patterns and complex structures simultaneously. To overcome this limitation, we introduce ADM (Adaptive graph Diffusion for Meta-dimension reduction), a novel meta-dimension reduction method grounded in graph diffusion theory. ADM integrates results from multiple dimension reduction techniques, leveraging their individual strengths while mitigating their specific weaknesses.ADM utilizes dynamic Markov processes to transform Euclidean space results into an information space, revealing intrinsic nonlinear manifold structures that are hard to capture by conventional methods. A critical advancement in ADM is its adaptive diffusion mechanism, which dynamically selects optimal diffusion time scales for each sample, enabling effective representation of multi-scale structures. This approach generates robust, high-quality low-dimensional representations that capture both local and global data structures while reducing noise and technique-specific distortions. We demonstrate ADM's efficacy on simulated and real-world datasets, including various omics data types. Results show that ADM provides clearer separation between biological groups and reveals more meaningful patterns compared to existing methods, advancing the analysis and visualization of complex biological data.

Exploring the Unknown: How Can We Improve Single-cell RNAseq Cell Type Annotations in Non-model Organisms?

Single-cell RNA sequencing (scRNAseq) is a powerful tool to describe cell types in multicellular organisms across the animal kingdom. In standard scRNAseq analysis pipelines, clusters of cells with similar transcriptional signatures are given cell type labels based on marker genes that infer specialized known characteristics. Since these analyses are designed for model organisms, such as humans and mice, problems arise when attempting to label cell types of distantly related, non-model species that have unique or divergent cell types. Consequently, this leads to limited discovery of novel species-specific cell types and potential mis-annotation of cell types in non-model species while using scRNAseq. To address this problem, we discuss recently published approaches that help annotate scRNAseq clusters for any non-model organism. We first suggest that annotating with an evolutionary context of cell lineages will aid in the discovery of novel cell types and provide a marker-free approach to compare cell types across distantly related species. Secondly, machine learning has greatly improved bioinformatic analyses, so we highlight some open-source programs that use reference-free approaches to annotate cell clusters. Lastly, we propose the use of unannotated genes as potential cell markers for non-model organisms, as many do not have fully annotated genomes and these data are often disregarded. Improving single-cell annotations will aid the discovery of novel cell types and enhance our understanding of non-model organisms at a cellular level. By unifying approaches to annotate cell types in non-model organisms, we can increase the confidence of cell annotation label transfer and the flexibility to discover novel cell types.

sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human

Identifying cell populations associated with risk variants is essential for uncovering cell-specific mechanisms that drive disease development and progression. Integrating genome-wide association studies (GWAS) with single-cell RNA sequencing (scRNA-seq) has become an effective strategy for detecting trait-cell relationships. The accumulation of trait-related single cell data has led to an urgent need for its comprehensively processing. To address this, we developed sc2GWAS (https://bio.liclab.net/sc2GWAS/), which aims to document large-scale GWAS trait-cell regulatory pairs at single-cell resolution and provide comprehensive annotations and enrichment analyses for these related pairs. The current version of sc2GWAS curates a total of 15 078 310 candidate trait-cell pairs from > 6 300 000 individual cells, offering a valuable resource for exploring complex regulatory relationships between traits and cells. We applied strict quality control measures on both scRNA-seq data and GWAS data, ensuring the reliability and accuracy of the datasets for the identification of trait-relevant cells and genes. In addition, sc2GWAS provides ranked lists of trait-relevant genes and extensive (epi) genetic annotations, making it a valuable resource for downstream analyses. We demonstrate the utility of the platform by investigating Alzheimer's disease, where we identified significant associations between the disease and microglial cells, with the APOE gene emerging as particularly significant. This platform facilitates detailed research into complex trait-cell and trait-gene interactions, we anticipate that sc2GWAS will become a comprehensive and valuable platform for exploring GWAS trait-cell regulatory mechanisms.

Unveiling Leydig cell heterogeneity and its role in male infertility: A single-cell transcriptomic study of human testicular tissue

Male infertility and impaired spermatogenesis are significant concerns in reproductive health, often linked to disruptions in the cellular and molecular processes within the testis. The cellular composition and transcriptional dynamics of human testicular tissue are crucial for understanding these issues. Previous studies have largely relied on bulk tissue analysis, which obscures the distinct roles and interactions of specific cell types. Here, through a comprehensive single-cell transcriptomic analysis of human testes across various developmental stages and pathological conditions, we reveal the intricate cellular heterogeneity and the molecular mechanisms underlying testicular function. Our study identifies significant disruptions in the differentiation trajectories of Germ cells in conditions such as Klinefelter syndrome (KS), AZFa deletion, and Sertoli-cell-only syndrome (SCOS). We further uncover key transcription factors and regulatory networks governing Leydig cell function, particularly those related to steroidogenesis and hormonal regulation. These findings highlight the organized yet complex cellular and molecular landscape of the testis and uncover critical pathways altered in male infertility. Collectively, our data suggest that targeted therapeutic strategies could be developed to address specific disruptions in testicular cell populations and their associated regulatory networks.

Single-cell data revealed the regulatory mechanism of TNK cell heterogeneity in liver metastasis from gastric cancer

AIM

The present work set out to classify cell subpopulations related to liver metastasis from gastric cancer (GC) and the mechanisms of their interactions with other immune cell subpopulations.

BACKGROUND

GC is characterized by a high degree of heterogeneity and liver metastasis. Exploring the mechanism of liver metastasis of GC from the perspective of heterogeneity of the tumor microenvironment (TME) might help improve the efficacy of GC treatment.

OBJECTIVE

Based on the cellular subpopulation characteristics of GC with liver metastasis, the regulatory mechanisms contributing to GC progression were analyzed, with special focuses on the roles of signaling pathways, transcription factors (TFs) and ligand-receptor pairs.

METHODS

The GSE163558 dataset was downloaded from the Gene Expression Omnibus (GEO) database to collect single-cell transcriptomic data of GC patients and their metastasis groups for cell clustering and relevant analyses. Differentially expressed genes (DEGs) in the GC and GC liver metastasis groups were screened and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. SCENIC analysis was used to mine TFs that affected cellular subpopulations during liver metastasis from GC. The relative expression levels of TFs in GC were determined using qRT-PCR. Transwell and wound healing assays were utilized to verify the regulation of the TFs on the migration and invasion of GC cells. Interaction network between the cellular subpopulations was developed applying CellChat.

RESULTS

Single-cell clustering was performed to group six major cell subpopulations, namely, Myeloid cells, B cells, Mast cells, Epithelial cells, Fibroblasts, and TNK cells, among which the number of TNK cells was significantly increased in the GC liver metastasis group. Differentially enriched pathways of TNK cells between GC and GC liver metastasis groups mainly included IL-17 and Pi3k-Akt signaling pathways. TNK cell subsets could be further categorized into CD8 T cells, Exhausted T cells, NK cells, NKT cells, and Treg cells, with the GC liver metastasis group showing significantly more CD8 T cells and NKT cells. FOS and JUNB were the TFs of TNK cell marker genes that contributed to liver metastasis from GC and the invasion and migration of GC cell lines. Significant differences in immune cell communication ligand-receptor pairs existed between the GC and GC liver metastasis groups.

CONCLUSION

This study revealed the critical role of TNK cell subsets in GC with liver metastasis applying single-cell transcriptomics analysis. The findings provided an important theoretical basis for developing novel therapies to inhibit liver metastasis from GC.

Distinguishable topology of the task-evoked functional genome networks in HIV-1 reservoirs

HIV-1 reservoirs display a heterogeneous nature, lodging both intact and defective proviruses. To deepen our understanding of such heterogeneous HIV-1 reservoirs and their functional implications, we integrated basic concepts of graph theory to characterize the composition of HIV-1 reservoirs. Our analysis revealed noticeable topological properties in networks, featuring immunologic signatures enriched by genes harboring intact and defective proviruses, when comparing antiretroviral therapy (ART)-treated HIV-1-infected individuals and elite controllers. The key variable, the rich factor, played a pivotal role in classifying distinct topological properties in networks. The host gene expression strengthened the accuracy of classification between elite controllers and ART-treated patients. Markov chain modeling for the simulation of different graph networks demonstrated the presence of an intrinsic barrier between elite controllers and non-elite controllers. Overall, our work provides a prime example of leveraging genomic approaches alongside mathematical tools to unravel the complexities of HIV-1 reservoirs.

Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data

As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such datasets include diversity of content, size and privacy. Effective transformation of data into information requires harmonization of stakeholder goals, implementation, enforcement of standards regarding quality and completeness, and availability of resources for maintenance and updates. Systems such as the Biomedical Data Translator leverage knowledge graphs (KGs), structured and machine learning readable knowledge representation, to encode knowledge extracted through inference. We focus here on the transformation of data from multiomics datasets and EHRs into compact knowledge, represented in a KG data structure. We demonstrate this data transformation in the context of the Translator ecosystem, including clinical trials, drug approvals, cancer, wellness, and EHR data. These transformations preserve individual privacy. We provide access to the five resulting KGs through the Translator framework. We show examples of biomedical research questions supported by our KGs, and discuss issues arising from extracting biomedical knowledge from multiomics data.

Gamma-aminobutyric acid-mediated neuro-immune interactions in glioblastoma: Implications for prognosis and immunotherapy response

AIMS

This study aimed to investigate the role of gamma-aminobutyric acid (GABA) in the glioblastoma (GBM) tumor immune microenvironment (TIME) and its impact on prognosis and response to immunotherapy.

MAIN METHODS

This study employed single-cell RNA sequencing (scRNA-seq) to delineate the TIME of GBM, utilized non-negative matrix factorization (NMF) for GABA-associated cell clustering, and performed pseudotime analysis for cellular trajectories. Additionally, we integrated immunohistochemistry (IHC), immunofluorescence (IF), and protein-protein interaction (PPI) analysis to explore the regulatory mechanisms within the tumor microenvironment.

KEY FINDINGS

The study identified distinct GABA-associated immune cell subtypes, particularly macrophages and T-cells, with unique gene expression and developmental trajectories. The development of the GABA-associated scoring model (GABAAS), introduced novel prognostic indicators, enhancing our ability to predict patient outcomes. This study also suggests that GABA-related genes, including NDRG2 and TIMP1, play a crucial role in immune modulation, with potential implications for immunotherapy responsiveness.

SIGNIFICANCE

The findings underscore the potential of targeting GABA-related genes (NDRG2 and TIMP1) and M2 macrophage to reshape the glioblastoma immune landscape, offering a new frontier in personalized neuro-immunotherapy. This approach holds promise to counter individual tumor immunosuppressive mechanisms, enhancing patient outcomes.

SPathDB: a comprehensive database of spatial pathway activity atlas

Spatial transcriptomics sequencing technology deepens our understanding of the diversity of cell behaviors, fates and states within complex tissue, which is often determined by the fine-tuning of regulatory network functional activities. Therefore, characterizing the functional activity within tissue space is helpful for revealing the functional features that drive spatial heterogeneity, and understanding complex biological processes. Here, we describe a database, SPathDB (http://bio-bigdata.hrbmu.edu.cn/SPathDB/), which aims to dissect the pathway-mediated multidimensional spatial heterogeneity in the context of functional activity. We manually curated spatial transcriptomics datasets and biological pathways from public data resources. SPathDB consists of 1689 868 spatial spots of 695 slices from 84 spatial transcriptome datasets of human and mouse, which involves 36 tissues, and also diseases such as cancer, and provides interactive analysis and visualization of the functional activities of 114 998 pathways across these spatial spots. SPathDB provides five flexible interfaces to retrieve and analyze pathways with highly variable functional activity across spatial spots, the distribution of pathway functional activities along pseudo-space axis, pathway-mediated spatial intercellular communications and the associations between spatial pathway functional activity and the occurrence of cell types. SPathDB will serve as a foundational resource for identifying functional features and elucidating underlying mechanisms of spatial heterogeneity.