Dissecting the dynamic cellular transcriptional atlas of adult teleost testis development throughout the annual reproductive cycle

Teleost testis development during the annual cycle involves dramatic changes in cellular compositions and molecular events. In this study, the testicular cells derived from adult black rockfish at distinct stages - regressed, regenerating and differentiating - were meticulously dissected via single-cell transcriptome sequencing. A continuous developmental trajectory of spermatogenic cells, from spermatogonia to spermatids, was delineated, elucidating the molecular events involved in spermatogenesis. Subsequently, the dynamic regulation of gene expression associated with spermatogonia proliferation and differentiation was observed across spermatogonia subgroups and developmental stages. A bioenergetic transition from glycolysis to mitochondrial respiration of spermatogonia during the annual developmental cycle was demonstrated, and a deeper level of heterogeneity and molecular characteristics was revealed by re-clustering analysis. Additionally, the developmental trajectory of Sertoli cells was delineated, alongside the divergence of Leydig cells and macrophages. Moreover, the interaction network between testicular micro-environment somatic cells and spermatogenic cells was established. Overall, our study provides detailed information on both germ and somatic cells within teleost testes during the annual reproductive cycle, which lays the foundation for spermatogenesis regulation and germplasm preservation of endangered species.

Prediction of immunotherapy responsiveness in melanoma through single-cell sequencing-based characterization of the tumor immune microenvironment

Immune checkpoint inhibitors (ICB) therapy have emerged as effective treatments for melanomas. However, the response of melanoma patients to ICB has been highly heterogenous. Here, by analyzing integrated scRNA-seq datasets from melanoma patients, we revealed significant differences in the TiME composition between ICB-resistant and responsive tissues, with resistant or responsive tissues characterized by an abundance of myeloid cells and CD8+ T cells or CD4+ T cell predominance, respectively. Among CD4+ T cells, CD4+ CXCL13+ Tfh-like cells were associated with an immunosuppressive phenotype linked to immune escape-related genes and negative regulation of T cell activation. We also develop an immunotherapy response prediction model based on the composition of the immune compartment. Our predictive model was validated using CIBERSORTx on bulk RNA-seq datasets from melanoma patients pre- and post-ICB treatment and showed a better performance than other existing models. Our study presents an effective immunotherapy response prediction model with potential for further translation, as well as underscores the critical role of the TiME in influencing the response of melanomas to immunotherapy.

Antigen/HLA-agnostic strategies for Characterizing Tumor-responsive T cell receptors in PDAC patients via single-cell sequencing and autologous organoid application

Characterization of tumor-responsive T cell receptors (TCRs) is a critical step in personalized TCR-T cell therapy, and remains challenging for pancreatic ductal adenocarcinoma (PDAC). Here we report a proof-of-concept study to identify and validate antitumor TCRs in two representative PDAC patients using ultradeep single-cell TCR/RNA sequencing and autologous organoids, and reveal the phenotypic dynamics of TCR repertoire in different T cell expansions from the same patient. We first performed comparative sequencing on freshly harvested peripheral blood mononuclear cells (PBMCs) and uncultured tumor infiltrating lymphocytes (TILs), followed by reactivity tests of TIL-enriched TCRs with autologous organoids, in which two tumor-responsive TCRs were successfully characterized and the corresponding TILs were mostly tissue-resident memory-like T cells, and partially expressed both naïve and exhausted T cell markers. For the PDAC patient without high-quality TILs, PBMCs were cultured with neoantigen peptide (KRASG12D), organoids, or anti-CD3 antibody in presence, and experienced extensive clonal expansions within ten days. All derived PBMCs were sequenced in parallel (>82,000 cells), and TCRs enriched in both peptide- and organoid-experienced, but not anti-CD3-treated CD8 T cells, were assessed for their reactivity to antigen-presenting cells (APCs) and organoids, in which three neoantigen-reactive TCRs were identified as tumor-responsive, and the corresponding T cells were characterized by mixed transcriptional signatures including but not limited to typical exhausted T cell markers. Together, our study revealed that the combination of ultradeep single-cell sequencing and organoid techniques enabled rapid characterization of tumor-responsive TCRs for developing practical personalized TCR-T therapy in an antigen/human leukocyte antigen (HLA)-agnostic manner.

Association of preoperative aspartate aminotransferase to platelet ratio index with outcomes and tumour microenvironment among colorectal cancer with liver metastases

This study aims to investigate applicable robust biomarkers that can improve prognostic predictions for colorectal liver metastasis (CRLM) patients receiving simultaneous resection. A total of 1323 CRLM patients from multiple centres were included. The preoperative aspartate aminotransferase to platelet ratio index (APRI) level from blood of patients were obtained. Patients were stratified into a high APRI group and a low APRI group, and comparisons were conducted by analyzing progression-free survival (PFS), overall survival (OS) and postoperative early recurrence. Tumour samples of CRLM were collected to perform single-cell RNA sequencing and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) to investigate the association of APRI levels and the tumour microenvironment of CRLM. Compared with APRI <0.33, PFS disadvantage (IPTW-adjusted HR = 1.240, P = 0.015) and OS disadvantage (IPTW- adjusted HR = 1.507, P = 0.002) of APRI ≥0.33 were preserved in the IPTW-adjusted Cox hazards regression analyses. An APRI ≥0.25 was associated with a significantly increased risk of postoperative early recurrence after adjustment (IPTW-adjusted OR = 1.486, P = 0.001). The external validation showed consistent results with the training cohort. In the high APRI group, the single-cell RNA sequencing results revealed a heightened malignancy of epithelial cells, the enrichment of inflammatory-like cancer-associated fibroblasts and SPP1+ macrophages associated with activation of malignant cells and fibrotic microenvironment, and a more suppressed-function T cells; mIHC/IF showed that PD1+ CD4+ T cells, FOXP3+ CD4+ T cells, PD1+ CD8+ T cells, FOXP3+ CD8+ T cells, SPP1+ macrophages and iCAFs were significantly increased in the intratumoral region and peritumoral region. This study contributed valuable evidence regarding preoperative APRI for predicting prognoses among CRLM patients receiving simultaneous resection and provided underlying clues supporting the association between APRI and clinical outcomes by single-cell sequencing bioinformatics analysis and mIHC/IF.

Integrative Analyses of Bulk and Single-Cell RNA Seq Identified the Shared Genes in Acute Respiratory Distress Syndrome and Rheumatoid Arthritis

Acute respiratory distress syndrome (ARDS), a progressive status of acute lung injury (ALI), is primarily caused by an immune-mediated inflammatory disorder, which can be an acute pulmonary complication of rheumatoid arthritis (RA). As a chronic inflammatory disease regulated by the immune system, RA is closely associated with the occurrence and progression of respiratory diseases. However, it remains elusive whether there are shared genes between the molecular mechanisms underlying RA and ARDS. The objective of this study is to identify potential shared genes for further clinical drug discovery through integrated analysis of bulk RNA sequencing datasets obtained from the Gene Expression Omnibus database, employing differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). The hub genes were identified through the intersection of common DEGs and WGCNA-derived genes. The Random Forest (RF) and least absolute shrinkage and selection operator (LASSO) algorithms were subsequently employed to identify key shared target genes associated with two diseases. Additionally, RA immune infiltration analysis and COVID-19 single-cell transcriptome analysis revealed the correlation between these key genes and immune cells. A total of 59 shared genes were identified from the intersection of DEGs and gene clusters obtained through WGCNA, which analyzed the integrated gene matrix of ALI/ARDS and RA. The RF and LASSO algorithms were employed to screen for target genes specific to ALI/ARDS and RA, respectively. The final set of overlapping genes (FCMR, ADAM28, HK3, GRB10, UBE2J1, HPSE, DDX24, BATF, and CST7) all exhibited a strong predictive effect with an area under the curve (AUC) value greater than 0.8. Then, the immune infiltration analysis revealed a strong correlation between UBE2J1 and plasma cells in RA. Furthermore, scRNA-seq analysis demonstrated differential expression of these nine target genes primarily in T cells and NK cells, with CST7 showing a significant positive correlation specifically with NK cells. Beyond that, transcriptome sequencing was conducted on lung tissue collected from ALI mice, confirming the substantial differential expression of FCMR, HK3, UBE2J1, and BATF. This study provides unprecedented evidence linking the pathophysiological mechanisms of ALI/ARDS and RA to immune regulation, which offers novel understanding for future clinical treatment and experimental research.

Spatial transcriptomics reveals segregation of tumor cell states in glioblastoma and marked immunosuppression within the perinecrotic niche

Glioblastoma (GBM) remains an untreatable malignant tumor with poor patient outcomes, characterized by palisading necrosis and microvascular proliferation. While single-cell technology made it possible to characterize different lineage of glioma cells into neural progenitor-like (NPC-like), oligodendrocyte-progenitor-like (OPC-like), astrocyte-like (AC-like) and mesenchymal like (MES-like) states, it does not capture the spatial localization of these tumor cell states. Spatial transcriptomics empowers the study of the spatial organization of different cell types and tumor cell states and allows for the selection of regions of interest to investigate region-specific and cell-type-specific pathways. Here, we obtained paired 10x Chromium single-nuclei RNA-sequencing (snRNA-seq) and 10x Visium spatial transcriptomics data from three GBM patients to interrogate the GBM microenvironment. Integration of the snRNA-seq and spatial transcriptomics data reveals patterns of segregation of tumor cell states. For instance, OPC-like tumor and NPC-like tumor significantly segregate in two of the three samples. Our differentially expressed gene and pathway analyses uncovered significant pathways in functionally relevant niches. Specifically, perinecrotic regions were more immunosuppressive than the endogenous GBM microenvironment, and perivascular regions were more pro-inflammatory. Our gradient analysis suggests that OPC-like tumor cells tend to reside in areas closer to the tumor vasculature compared to tumor necrosis, which may reflect increased oxygen requirements for OPC-like cells. In summary, we characterized the localization of cell types and tumor cell states, the gene expression patterns, and pathways in different niches within the GBM microenvironment. Our results provide further evidence of the segregation of tumor cell states and highlight the immunosuppressive nature of the necrotic and perinecrotic niches in GBM.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

BACKGROUND

Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.

RESULTS

We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal .

CONCLUSIONS

Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

MGP+ and IDO1+ tumor-associated macrophages facilitate immunoresistance in breast cancer revealed by single-cell RNA sequencing

Immunotherapy is widely applied for the treatment of breast cancer, but to which some patients respond poorly or develop resistance. Therefore, the mechanism needs to be further studied. Transcriptomic data of 31 breast cancer patients treated with anti-programmed death receptor 1 (PD-1) was downloaded from the VIB-KULeuven Center for Cancer Biology to analyze the changes in myeloid cells in tumor tissues before and after immunotherapy. And 24 cell populations that may be immune-related were further identified. Representative cell populations were also screened and validated through cellular and animal experiments to evaluate the relevant molecular expression and pathways of tumor-associated macrophages (TAMs) in the tumor microenvironment. The results demonstrated that MGP+ TAMs and IDO1+ TAMs influenced the efficacy of immunotherapy in breast cancer patients. After anti-PD-1 treatment, Increased numbers of MGP+ TAMs and IDO1+ TAMs in breast cancer patients upregulated pro-tumorigenic factors associated with resistance to immunosuppressive therapy. This study provides new biomarkers for immunotherapy to predict therapeutic responses and overcome potential resistance to immunotherapy. It is an important complement to the immunosuppression caused by TAMs after immunotherapy for breast cancer.

[Dynamic trajectory and cell communication of different cell clusters in malignant progression of glioblastoma]

OBJECTIVE

To delve deeply into the dynamic trajectories of cell subpopulations and the communication network among immune cell subgroups during the malignant progression of glioblastoma (GBM), and to endeavor to unearth key risk biomarkers in the GBM malignancy progression, so as to provide a more profound understanding for the treatment and prognosis of this disease by integrating transcriptomic data and clinical information of the GBM patients.

METHODS

Utilizing single-cell sequencing data analysis, we constructed a cell subgroup atlas during the malignant progression of GBM. The Monocle2 tool was employed to build dynamic progression trajectories of the tumor cell subgroups in GBM. Through gene enrichment analysis, we explored the biological processes enriched in genes that significantly changed with the malignancy progression of GBM tumor cell subpopulations. CellChat was used to identify the communication network between the different immune cell subgroups. Survival analysis helped in identifying risk molecular markers that impacted the patient prognosis during the malignant progression of GBM. This method ological approach offered a comprehensive and detailed examination of the cellular and molecular dynamics within GBM, providing a robust framework for understanding the disease' s progression and potential therapeutic targets.

RESULTS

The analysis of single-cell sequencing data identified 6 different cell types, including lymphocytes, pericytes, oligodendrocytes, macrophages, glioma cells, and microglia. The 27 151 cells in the single-cell dataset included 3 881 cells from the patients with low-grade glioma (LGG), 10 166 cells from the patients with newly diagnosed GBM, and 13 104 cells from the patients with recurrent glioma (rGBM). The pseudo-time analysis of the glioma cell subgroups indicated significant cellular heterogeneity during malignant progression. The cell interaction analysis of immune cell subgroups revealed the communication network among the different immune subgroups in GBM malignancy, identifying 22 biologically significant ligand-receptor pairs across 12 key biological pathways. Survival analysis had identified 8 genes related to the prognosis of the GBM patients, among which SERPINE1, COL6A1, SPP1, LTF, C1S, AEBP1, and SAA1L were high-risk genes in the GBM patients, and ABCC8 was low-risk genes in the GBM patients. These findings not only provided new theoretical bases for the treatment of GBM, but also offered fresh insights for the prognosis assessment and treatment decision-making for the GBM patients.

CONCLUSION

This research comprehensively and profoundly reveals the dynamic changes in glioma cell subpopulations and the communication patterns among the immune cell subgroups during the malignant progression of GBM. These findings are of significant importance for understanding the complex biological processes of GBM, providing crucial new insights for precision medicine and treatment decisions in GBM. Through these studies, we hope to provide more effective treatment options and more accurate prognostic assessments for the patients with GBM.

Exploring natural killer cell-related biomarkers in multiple myeloma: a novel nature killer cell-related model predicting prognosis and immunotherapy response using single-cell study

BACKGROUND

Natural killer cells (NKs) may be involved in multiple myeloma (MM) progression. The present study elucidated the correlation between NKs and the progression of MM using single-cell binding transcriptome probes to identify NK cell-related biomarkers.

METHODS

Single-cell analysis was performed including cell and subtype annotation, cell communication, and pseudotime analysis. Hallmark pathway enrichment analysis of NKs and NKs-related differentially expressed genes (DEGs) were conducted using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction (PPI) networks. Then, a risk model was structured based on biomarkers identified through univariate Cox regression analysis and least absolute shrinkage and selection operator regression analysis and subsequently validated. Additionally, correlation of clinical characteristics, gene set enrichment analysis, immune analysis, regulatory network, and drug forecasting were explored.

RESULTS

A total of 13 cell clusters were obtained and annotated, including 8 cell populations that consisted of NKs. Utilizing 123 PPI network node genes, 8 NK-related DEGs were selected to construct a prognostic model. Immune cell infiltration results suggested that 11 immune cells exhibited marked differences in the high and low-risk groups. Finally, the model was used to screen potential drug targets to enhance immunotherapy efficacy.

CONCLUSION

A new prognostic model for MM associated with NKs was constructed and validated. This model provides a fresh perspective for predicting patient outcomes, immunotherapeutic response, and candidate drugs.

Single-cell profile reveals the landscape of cardiac immunity and identifies a cardio-protective Ym-1hi neutrophil in myocardial ischemia-reperfusion injury

Myocardial ischemia-reperfusion injury (MIRI) is a major hindrance to the success of cardiac reperfusion therapy. Although increased neutrophil infiltration is a hallmark of MIRI, the subtypes and alterations of neutrophils in this process remain unclear. Here, we performed single-cell sequencing of cardiac CD45+ cells isolated from the murine myocardium subjected to MIRI at six-time points. We identified diverse types of infiltrating immune cells and their dynamic changes during MIRI. Cardiac neutrophils showed the most immediate response and largest changes and featured with functionally heterogeneous subpopulations, including Ccl3hi Neu and Ym-1hi Neu, which were increased at 6 h and 1 d after reperfusion, respectively. Ym-1hi Neu selectively expressed genes with protective effects and was, therefore, identified as a novel specific type of cardiac cell in the injured heart. Further analysis indicated that neutrophils and their subtypes orchestrated subsequent immune responses in the cardiac tissues, especially instructing the response of macrophages. The abundance of Ym-1hi Neu was closely correlated with the therapeutic efficacy of MIRI when neutrophils were specifically targeted by anti-Lymphocyte antigen 6 complex locus G6D (Ly6G) or anti-Intercellular cell adhesion molecule-1 (ICAM-1) neutralizing antibodies. In addition, a neutrophil subtype with the same phenotype as Ym-1hi Neu was detected in clinical samples and correlated with prognosis. Ym-1 inhibition exacerbated myocardial injury, whereas Ym-1 supplementation significantly ameliorated injury in MIRI mice, which was attributed to the tilt of Ym-1 on the polarization of macrophages toward the repair phenotype in myocardial tissue. Overall, our findings reveal the anti-inflammatory phenotype of Ym-1hi Neu and highlight its critical role in myocardial protection during the early stages of MIRI.

Single-cell profiling of response to neoadjuvant chemo-immunotherapy in surgically resectable esophageal squamous cell carcinoma

BACKGROUND

The efficacy of neoadjuvant chemo-immunotherapy (NAT) in esophageal squamous cell carcinoma (ESCC) is challenged by the intricate interplay within the tumor microenvironment (TME). Unveiling the immune landscape of ESCC in the context of NAT could shed light on heterogeneity and optimize therapeutic strategies for patients.

METHODS

We analyzed single cells from 22 baseline and 24 post-NAT treatment samples of stage II/III ESCC patients to explore the association between the immune landscape and pathological response to neoadjuvant anti-PD-1 combination therapy, including pathological complete response (pCR), major pathological response (MPR), and incomplete pathological response (IPR).

RESULTS

Single-cell profiling identified 14 major cell subsets of cancer, immune, and stromal cells. Trajectory analysis unveiled an interesting link between cancer cell differentiation and pathological response to NAT. ESCC tumors enriched with less differentiated cancer cells exhibited a potentially favorable pathological response to NAT, while tumors enriched with clusters of more differentiated cancer cells may resist treatment. Deconvolution of transcriptomes in pre-treatment tumors identified gene signatures in response to NAT contributed by specific immune cell populations. Upregulated genes associated with better pathological responses in CD8 + effector T cells primarily involved interferon-gamma (IFNγ) signaling, neutrophil degranulation, and negative regulation of the T cell apoptotic process, whereas downregulated genes were dominated by those in the immune response-activating cell surface receptor signaling pathway. Natural killer cells in pre-treatment tumors from pCR patients showed a similar upregulation of gene expression in response to IFNγ but a downregulation of genes in the neutrophil-mediated immunity pathways. A decreased cellular contexture of regulatory T cells in ESCC TME indicated a potentially favorable pathological response to NAT. Cell-cell communication analysis revealed extensive interactions between CCL5 and its receptor CCR5 in various immune cells of baseline pCR tumors. Immune checkpoint interaction pairs, including CTLA4-CD86, TIGIT-PVR, LGALS9-HAVCR2, and TNFSF4-TNFRSF4, might serve as additional therapeutic targets for ICI therapy in ESCC.

CONCLUSIONS

This pioneering study unveiled an intriguing association between cancer cell differentiation and pathological response in esophageal cancer patients, revealing distinct subgroups of tumors for which neoadjuvant chemo-immunotherapy might be effective. We also delineated the immune landscape of ESCC tumors in the context of clinical response to NAT, which provides clinical insights for better understanding how patients respond to the treatment and further identifying novel therapeutic targets for ESCC patients in the future.

Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes

Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.

Identification of estrogen response-associated STRA6+ granulosa cells within high-grade serous ovarian carcinoma by single-cell sequencing

Background

High-grade serous ovarian carcinoma (HGSOC) is a pathologic subtype of ovarian cancer (OC) with a more lethal prognosis. Extensive heterogeneity results in HGSOC being more susceptible to treatment resistance and adverse treatment effects. Revealing the heterogeneity involved is crucial.

Methods

We downloaded the single-cell RNA-seq (scRNA) data from GEO database and performed a scRNA analysis for cell landscape of HGSOC by using the Seurat package. The highly expressed genes were uploaded into the DAVID and KEGG database for enrichment analysis, and the AUCell package was used to calculate cancer-associated hallmark score. The SCENIC analysis was used for key regulons, the estrogen response enrichment scores in TCGA-OV RNA-seq dataset were calculated by using the GSVA package. Besides, the expression of STRA6 and IRF1 and the cell invasion and migration in si-STRA6 OC cells were detected by using the quantitative reverse transcription (qRT)-PCR method and Transwell assay respectively.

Results

We successfully constructed a single-cell atlas of HGSOC and delineated the heterogeneity of epithelial cells therein. There were five epithelial cell subpopulations, GLDC + Epithelial cells, PEG3+ leydig cells, STRA6+ granulosa cells, POLE2+ Epithelial cells, and AURKA + Epithelial cells. STRA6+ granulosa cells have the potential to promote tumor growth as well as the highest estrogen response early activity through the biological pathways analysis of highly expressed genes and estrogen response score of ssGSEA. We found that IRF1 and STRA6 expression was remarkably upregulated in the OC cancer cell line HEY. Silencing of STRA6 markedly decreased the invasion and migration ability of the OC cancer cell line HEY.

Conclusion

There is extreme heterogeneity of epithelial cells in HGSOC, and STRA6+ granulosa cells may be able to promote cancer progression. Our findings are benefit to the heterogeneity identification of HGSOC and develop targeted therapy strategy for HGSOC patients.

Single-cell sequencing analysis of chronic subdural hematoma cell subpopulations and their potential therapeutic mechanisms

BACKGROUND

Chronic subdural hematoma (CSDH) is a prevalent form of intracranial haemorrhage encountered in neurosurgical practice, and its incidence has notably risen in recent years. Currently, there is a lack of studies that have comprehensively classified the cells present in hematomas removed during surgery, and their correlation with CSDH recurrence remains elusive. This study aims to analyse the subcellular populations and occupancy levels within peripheral blood.

METHODS

This study analyses the subcellular populations and occupancy levels within peripheral blood and postoperatively removed hematomas by single-cell sequencing and attempts to analyse the effect of different cell occupancies within peripheral blood and intraoperatively removed hematomas on CSDH.

RESULTS

The single-cell sequencing results showed that the cells were classified into 25 clusters by differential gene and UMAP dimensionality reduction clustering analyses and further classified into 17 significant cell populations by cell markers: pDCs, CD8 T cells, CD4 T cells, MigDCs, cDC2s, cDC1s, plasma cells, neutrophils, naive B cells, NK cells, memory B cells, M2 macrophages, CD8 Teffs, CD8 MAIT cells, CD4 Tregs, CD19 B cells, and monocytes. Further research showed that the presence of more cDC2 and M2 macrophages recruited at the focal site in patients with CSDH and the upregulation of the level of T-cell occupancy may be a red flag for further brain damage. ROS, a marker of oxidative stress, was significantly upregulated in cDC2 cells and may mediate the functioning of transcription proteins of inflammatory factors, such as NFκB, which induced T cells' activation. Moreover, cDC2 may regulate M2 macrophage immune infiltration and anti-inflammatory activity by secreting IL1β and binding to M2 macrophage IL1R protein.

CONCLUSION

The detailed classification of cells in the peripheral blood and hematoma site of CSDH patients helps us to understand the mechanism of CSDH generation and the reduction in the probability of recurrence by regulating the ratio of cell subpopulations.

Single-cell profiling transcriptomic reveals cellular heterogeneity and cellular crosstalk in choroidal neovascularization model

Choroidal neovascularization (CNV) is a hallmark of neovascular age-related macular degeneration (nAMD) and a major contributor to vision loss in nAMD cases. However, the identification of specific cell types associated with nAMD remains challenging. Herein, we performed single-cell sequencing to comprehensively explore the cellular diversity and understand the foundational components of the retinal pigment epithelium (RPE)/choroid complex. We unveiled 10 distinct cell types within the RPE/choroid complex. Notably, we observed significant heterogeneity within endothelial cells (ECs), fibroblasts, and macrophages, underscoring the intricate nature of the cellular composition in the RPE/choroid complex. Within the EC category, four distinct clusters were identified and EC cluster 0 was tightly associated with choroidal neovascularization. We identified five clusters of fibroblasts actively involved in the pathogenesis of nAMD, influencing fibrotic responses, angiogenic effects, and photoreceptor function. Additionally, three clusters of macrophages were identified, suggesting their potential roles in regulating the progression of nAMD through immunomodulation and inflammation regulation. Through CellChat analysis, we constructed a complex cell-cell communication network, revealing the role of EC clusters in interacting with fibroblasts and macrophages in the context of nAMD. These interactions were found to govern angiogenic effects, fibrotic responses, and inflammatory processes. In summary, this study reveals noteworthy cellular heterogeneity in the RPE/choroid complex and provides valuable insights into the pathogenesis of CNV. These findings will open up potential avenues for deep understanding and targeted therapeutic interventions in nAMD.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer

BACKGROUND

The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health.

METHODS

In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116).

RESULTS

According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29).

CONCLUSIONS

Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Clinical implications of single cell sequencing for bladder cancer

Bladder cancer (BC) is the 10th most common cancer worldwide, with about 0.5 million reported new cases and about 0.2 million deaths per year. In this scoping review, we summarize the current evidence regarding the clinical implications of single-cell sequencing for bladder cancer based on PRISMA guidelines. We searched PubMed, CENTRAL, Embase, and supplemented with manual searches through the Scopus, and Web of Science for published studies until February 2023. We included original studies that used at least one single-cell technology to study bladder cancer. Forty-one publications were included in the review. Twenty-nine studies showed that this technology can identify cell subtypes in the tumor microenvironment that may predict prognosis or response to immune checkpoint inhibition therapy. Two studies were able to diagnose BC by identifying neoplastic cells through single-cell sequencing urine samples. The remaining studies were mainly a preclinical exploration of tumor microenvironment at single cell level. Single-cell sequencing technology can discriminate heterogeneity in bladder tumor cells and determine the key molecular properties that can lead to the discovery of novel perspectives on cancer management. This nascent tool can advance the early diagnosis, prognosis judgment, and targeted therapy of bladder cancer.

Single-cell transcriptomic sequencing data reveal aberrant DNA methylation in SMAD3 promoter region in tumor-associated fibroblasts affecting molecular mechanism of radiosensitivity in non-small cell lung cancer

OBJECTIVE

Non-small cell lung cancer (NSCLC) often exhibits resistance to radiotherapy, posing significant treatment challenges. This study investigates the role of SMAD3 in NSCLC, focusing on its potential in influencing radiosensitivity via the ITGA6/PI3K/Akt pathway.

METHODS

The study utilized gene expression data from the GEO database to identify differentially expressed genes related to radiotherapy resistance in NSCLC. Using the GSE37745 dataset, prognostic genes were identified through Cox regression and survival analysis. Functional roles of target genes were explored using Gene Set Enrichment Analysis (GSEA) and co-expression analyses. Gene promoter methylation levels were assessed using databases like UALCAN, DNMIVD, and UCSC Xena, while the TISCH database provided insights into the correlation between target genes and CAFs. Experiments included RT-qPCR, Western blot, and immunohistochemistry on NSCLC patient samples, in vitro studies on isolated CAFs cells, and in vivo nude mouse tumor models.

RESULTS

Fifteen key genes associated with radiotherapy resistance in NSCLC cells were identified. SMAD3 was recognized as an independent prognostic factor for NSCLC, linked to poor patient outcomes. High expression of SMAD3 was correlated with low DNA methylation in its promoter region and was enriched in CAFs. In vitro and in vivo experiments confirmed that SMAD3 promotes radiotherapy resistance by activating the ITGA6/PI3K/Akt signaling pathway.

CONCLUSION

High expression of SMAD3 in NSCLC tissues, cells, and CAFs is closely associated with poor prognosis and increased radiotherapy resistance. SMAD3 is likely to enhance radiotherapy resistance in NSCLC cells by activating the ITGA6/PI3K/Akt signaling pathway.

Bulk anda single-cell transcriptome profiling reveals the molecular characteristics of T cell-mediated tumor killing in pancreatic cancer

Background

Despite the potential of immune checkpoint blockade (ICB) as a promising treatment for Pancreatic adenocarcinoma (PAAD), there is still a need to identify specific subgroups of PAAD patients who may benefit more from ICB. T cell-mediated tumor killing (TTK) is the primary concept behind ICB. We explored subtypes according to genes correlated with the sensitivity to TKK and unraveled their underlying associations for PAAD immunotherapies.

Methods

Genes that control the responsiveness of T cell-induced tumor destruction (GSTTK) were examined in PAAD, focusing on their varying expression levels and association with survival results. Moreover, samples with PAAD were separated into two subsets using unsupervised clustering based on GSTTK. Variability was evident in the tumor immune microenvironment, genetic mutation, and response to immunotherapy among different groups. In the end, we developed TRGscore, an innovative scoring system, and investigated its clinical and predictive significance in determining sensitivity to immunotherapy.

Results

Patients with PAAD were categorized into 2 clusters based on the expression of 52 GSTTKs, which showed varying levels and prognostic relevance, revealing unique TTK patterns. Survival outcome, immune cell infiltration, immunotherapy responses, and functional enrichment are also distinguished among the two clusters. Moreover, we found the CATSPER1 gene promotes the progression of PAAD through experiments. In addition, the TRGscore effectively predicted the responses to chemotherapeutics or immunotherapy in patients with PAAD and overall survival.

Conclusions

TTK exerted a vital influence on the tumor immune environment in PAAD. A greater understanding of TIME characteristics was gained through the evaluation of the variations in TTK modes across different tumor types. It highlights variations in the performance of T cells in PAAD and provides direction for improved treatment approaches.

Single-cell RNA sequencing reveals critical modulators of extracellular matrix of penile cavernous cells in erectile dysfunction

Erectile dysfunction (ED) is a common and difficult to treat disease, and has a high incidence rate worldwide. As a marker of vascular disease, ED usually occurs in cardiovascular disease, 2-5 years prior to cardiovascular disease events. The extracellular matrix (ECM) network plays a crucial role in maintaining cardiac homeostasis, not only by providing structural support, but also by promoting force transmission, and by transducing key signals to intracardiac cells. However, the relationship between ECM and ED remains unclear. To help fill this gap, we profiled single-cell RNA-seq (scRNA-seq) to obtain transcriptome maps of 82,554 cavernous single cells from ED and non-ED samples. Cellular composition of cavernous tissues was explored by uniform manifold approximation and projection. Pseudo-time cell trajectory combined with gene enrichment analysis were performed to unveil the molecular pathways of cell fate determination. The relationship between cavernous cells and the ECM, and the changes in related genes were elucidated. The CellChat identified ligand-receptor pairs (e.g., PTN-SDC2, PTN-NCL, and MDK-SDC2) among the major cell types in the cavernous tissue microenvironment. Differential analysis revealed that the cell type-specific transcriptomic changes in ED are related to ECM and extracellular structure organization, external encapsulating structure organization, and regulation of vasculature development. Trajectory analysis predicted the underlying target genes to modulate ECM (e.g., COL3A1, MDK, MMP2, and POSTN). Together, this study highlights potential cell-cell interactions and the main regulatory factors of ECM, and reveals that genes may represent potential marker features of ED progression.

Identification and validation of RNA-binding protein SLC3A2 regulates melanocyte ferroptosis in vitiligo by integrated analysis of single-cell and bulk RNA-sequencing

BACKGROUND

The pathogenesis of vitiligo remains unclear. The genes encoding vitiligo-related RNA-binding proteins (RBPs) and their underlying pathogenic mechanism have not been determined.

RESULTS

Single-cell transcriptome sequencing (scRNA-seq) data from the CNCB database was obtained to identify distinct cell types and subpopulations and the relative proportion changes in vitiligo and healthy samples. We identified 14 different cell types and 28 cell subpopulations. The proportion of each cell subpopulation significantly differed between the patients with vitiligo and healthy groups. Using RBP genes for unsupervised clustering, we obtained the specific RBP genes of different cell types in vitiligo and healthy groups. The RBP gene expression was highly heterogeneous; there were significant differences in some cell types, such as keratinocytes, Langerhans, and melanocytes, while there were no significant differences in other cells, such as T cells and fibroblasts, in the two groups. The melanocyte-specific RBP genes were enriched in the apoptosis and immune-related pathways in the patients with vitiligo. Combined with the bulk RNA-seq data of melanocytes, key RBP genes related to melanocytes were identified, including eight upregulated RBP genes (CDKN2A, HLA-A, RPL12, RPL29, RPL31, RPS19, RPS21, and RPS28) and one downregulated RBP gene (SLC3A2). Cell experiments were conducted to explore the role of the key RBP gene SLC3A2 in vitiligo. Cell experiments confirmed that melanocyte proliferation decreased, whereas apoptosis increased, after SLC3A2 knockdown. SLC3A2 knockdown in melanocytes also decreased the SOD activity and melanin content; increased the Fe2+, ROS, and MDA content; significantly increased the expression levels of TYR and COX2; and decreased the expression levels of glutathione and GPX4.

CONCLUSION

We identified the RBP genes of different cell subsets in patients with vitiligo and confirmed that downregulating SLC3A2 can promote ferroptosis in melanocytes. These findings provide new insights into the pathogenesis of vitiligo.

Granzyme B+ B cells detected by single-cell sequencing are associated with prognosis in patients with intrahepatic cholangiocarcinoma following liver transplantation

B cells possess anti-tumor functions mediated by granzyme B, in addition to their role in antigen presentation and antibody production. However, the variations in granzyme B+ B cells between tumor and non-tumor tissues have been largely unexplored. Therefore, we integrated 25 samples from the Gene Expression Omnibus database and analyzed the tumor immune microenvironment. The findings uncovered significant inter- and intra-tumoral heterogeneity. Notably, single-cell data showed higher proportions of granzyme B+ B cells in tumor samples compared to control samples, and these levels were positively associated with disease-free survival. The elevated levels of granzyme B+ B cells in tumor samples resulted from tumor cell chemotaxis through the MIF- (CD74 + CXCR4) signaling pathway. Furthermore, the anti-tumor function of granzyme B+ B cells in tumor samples was adversely affected, potentially providing an explanation for tumor progression. These findings regarding granzyme B+ B cells were further validated in an independent clinic cohort of 40 liver transplant recipients with intrahepatic cholangiocarcinoma. Our study unveils an interaction between granzyme B+ B cells and intrahepatic cholangiocarcinoma, opening up potential avenues for the development of novel therapeutic strategies against this disease.

Single-cell RNA sequencing of mid-to-late stage spider embryos: new insights into spider development

BACKGROUND

The common house spider Parasteatoda tepidariorum represents an emerging new model organism of arthropod evolutionary and developmental (EvoDevo) studies. Recent technical advances have resulted in the first single-cell sequencing (SCS) data on this species allowing deeper insights to be gained into its early development, but mid-to-late stage embryos were not included in these pioneering studies.

RESULTS

Therefore, we performed SCS on mid-to-late stage embryos of Parasteatoda and characterized resulting cell clusters by means of in-silico analysis (comparison of key markers of each cluster with previously published information on these genes). In-silico prediction of the nature of each cluster was then tested/verified by means of additional in-situ hybridization experiments with additional markers of each cluster.

CONCLUSIONS

Our data show that SCS data reliably group cells with similar genetic fingerprints into more or less distinct clusters, and thus allows identification of developing cell types on a broader level, such as the distinction of ectodermal, mesodermal and endodermal cell lineages, as well as the identification of distinct developing tissues such as subtypes of nervous tissue cells, the developing heart, or the ventral sulcus (VS). In comparison with recent other SCS studies on the same species, our data represent later developmental stages, and thus provide insights into different stages of developing cell types and tissues such as differentiating neurons and the VS that are only present at these later stages.

Deciphering the heterogeneity of neutrophil cells within circulation and the lung cancer microenvironment pre- and post-operation

Neutrophils play a crucial role in the immune system within tumor microenvironment. At present, numerous studies have explored the changes of neutrophils' automatic killing effect and cellular communication with other immune cells under pathological conditions through single-cell sequencing. However, there remains a lack of definite conclusion about the identification criteria of neutrophil subgroups. Here, we collected tumor and para-carcinoma tissues, pre- and postoperative blood from patients with non-small cell lung cancer (NSCLC), and performed single-cell RNA (scRNA) sequencing to evaluate the distribution of neutrophil subgroups. We have developed a computational method of over expression rate (OER) to evaluate the specificity of neutrophil subgroups, in order to target gene panels with potential clinical application value. In addition, OER was used to evaluate specificity of neutrophil subsets in healthy people and patients with various diseases to further validate the feasibility of this evaluation system. As a result, we found the specificity of Neu_ c1_ IL1B and Neu_ c2_ cxcr4 (low) in postoperative blood has increased, while that of IL-7R + neutrophils has decreased, indicating that these groups of cells possibly differentiated or migrated to other subgroups in the state of lung cancer. In addition, seven gene panels (Neu_c3_CST7, RSAD2_Neu, S100A2/Pabpc1_Neu, ISG15/Ifit3_Neu, CD74_Neu, PTGS2/Actg1_Neu, SPP1_Neu) were high specific in all the four NSCLC-associated samples, meaning that changes in the percentage of these cell populations would have a high degree of confidence in assessing changes of disease status. In conclusion, combined consideration of the distribution characteristics of neutrophil subgroups could help evaluate the diagnosis and prognosis of NSCLC.

Progress in single-cell multimodal sequencing and multi-omics data integration

With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.

A novel protein encoded by circKANSL1L regulates skeletal myogenesis via the Akt-FoxO3 signaling axis

Skeletal muscle is one the largest organs of the body and is involved in animal production and human health. Circular RNAs (circRNAs) have been implicated in skeletal myogenesis through largely unknown mechanisms. Herein, we report the phenotypic and metabolomic analysis of porcine longissimus dorsi muscles in Lantang and Landrace piglets, revealing a high-content of slow-oxidative fibers responsible for high-quality meat product in Lantang piglets. Using single-cell transcriptomics, we identified four myogenesis-related cell types, and the Akt-FoxO3 signaling axis was the most significantly enriched pathway in each subpopulation in the different pig breeds, as well as in fast-twitch glycolytic fibers. Using the multi-dimensional bioinformatic tools of circRNAome-seq and Ribo-seq, we identified a novel circRNA, circKANSL1L, with a protein-coding ability in porcine muscles, whose expression level correlated with myoblast proliferation and differentiation in vitro, as well as the transformation between distinct mature myofibers in vivo. The protein product of circKANSL1L could interact with Akt to decrease the phosphorylation level of FoxO3, which subsequently promoted FoxO3 transcriptional activity to regulate skeletal myogenesis. Our results established the existence of a protein encoded by circKANSL1L and demonstrated its potential functions in myogenesis.

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

Leukemias are refractory hematological malignancies, characterized by marked intrinsic heterogeneity which poses significant obstacles to effective treatment. However, traditional bulk sequencing techniques have not been able to effectively unravel the heterogeneity among individual tumor cells. With the emergence of single-cell sequencing technology, it has bestowed upon us an unprecedented resolution to comprehend the mechanisms underlying leukemogenesis and drug resistance across various levels, including the genome, epigenome, transcriptome and proteome. Here, we provide an overview of the currently prevalent single-cell sequencing technologies and a detailed summary of single-cell studies conducted on leukemia, with a specific focus on four key aspects: (1) leukemia's clonal architecture, (2) frameworks to determine leukemia subtypes, (3) tumor microenvironment (TME) and (4) the drug-resistant mechanisms of leukemia. This review provides a comprehensive summary of current single-cell studies on leukemia and highlights the markers and mechanisms that show promising clinical implications for the diagnosis and treatment of leukemia.

Integrating bulk-RNA sequencing and single-cell sequencing analyses to characterize adenosine-enriched tumor microenvironment landscape and develop an adenosine-related prognostic signature predicting immunotherapy in lung adenocarcinoma

The adenosine-signaling axis has been recognized as an important immunomodulatory pathway in tumor immunity. However, the biological role of the adenosine-signaling axis in the remodeling of the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unclear. Here, we quantified adenosine signaling (ado_sig) in LUAD samples using the GSVA method and assessed the prognostic value of adenosine in LUAD. Afterward, we explored the heterogeneity of the tumor-immune microenvironment at different adenosine levels. In addition, we analyzed the potential biological pathways engaged by adenosine. Next, we established single-cell transcriptional profiles of LUAD and analyzed cellular composition and cell-cell communication analysis under different adenosine microenvironments. Moreover, we established adenosine-related prognostic signatures (ARS) based on comprehensive bioinformatics analysis and evaluated the efficacy of ARS in predicting immunotherapy. The results demonstrated that adenosine signaling adversely impacted the survival of immune-enriched LUAD. The high-adenosine microenvironment exhibited elevated pro-tumor-immune infiltration, including M2 macrophages and displayed notably increased epithelial-mesenchymal transition (EMT) transformation. Furthermore, adenosine signaling displayed significant associations with the expression patterns and prognostic value of immunomodulators within the TME. Single-cell sequencing data revealed increased fibroblast occupancy and a prominent activation of the SPP1 signaling pathway in the high adenosine-signaling microenvironment. The ARS exhibited promising effectiveness in prognostication and predicting immunotherapy response in LUAD. In summary, overexpression of adenosine can cause a worsened prognosis in the LUAD with abundant immune infiltration. Moreover, increased adenosine levels are associated with pro-tumor-immune infiltration, active EMT transformation, pro-tumor angiogenesis, and other factors promoting cancer progression, which collectively contribute to the formation of an immunosuppressive microenvironment. Importantly, the ARS developed in this study demonstrate high efficacy in evaluating the response to immunotherapy.

Emerging applications of single-cell profiling in precision medicine of atherosclerosis

Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.

Single-cell data revealed CD14-type and FCGR3A-type macrophages and relevant prognostic factors for predicting immunotherapy and prognosis in stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD) exhibits profound tumor heterogeneity and represents a great therapeutic challenge. Single-cell sequencing technology is a powerful tool to identify characteristic cell types.

Methods

Single-cell sequencing data (scRNA-seq) GSE167297 and bulk RNA-seq data from TCGA, GTEx, GSE26901 and GSE15459 database were included in this study. By downscaling and annotating the cellular data in scRNA-seq, critical cell types in tumor progression were identified by AUCell score. Relevant gene modules were then identified by weighted gene co-expression network analysis (WGCNA). A prognostic scoring system was constructed by identifying prognostic factors in STAD by Least absolute shrinkage and selection operator (LASSO) COX model. The prognosis and model performance in the RiskScore groups were measured by Kaplan-Meier (K-M) curves and Receiver operating characteristic (ROC) curves. Nomogram was drawn based on RiskScore and prognosis-related clinical factors. In addition, we evaluated patient's feedback on immunotherapy in the RiskScore groups by TIMER, ESTIMATE and TIDE analysis. Finally, the expression levels of prognostic factors were verified in gastric cancer cell lines (MKN7 and MKN28) and human normal gastric mucosal epithelial cells (GES-1), and the effects of prognostic factors on the viability of gastric cancer cells were examined by the CCK8 assay and cell cycle.

Results

scRNA-seq analysis revealed that 11 cell types were identified, and macrophages exhibited relatively higher AUCell scores and specifically expressed CD14 and FCGR3A. High macrophage scores worsened the prognosis of STAD patients. We intersected the specifically expressed genes in macrophages subgroups (670) and macrophage module genes (2,360) obtained from WGCNA analysis. Among 86 common genes, seven prognostic factors (RGS2, GNAI2, ANXA5, MARCKS, CD36, NRP1 and PDE4A) were identified and composed a RiskScore model. Patients in low Risk group showed a better survival advantage. Nomogram also provided a favorable prediction for survival at 1, 3 and 5 years in STAD patients. Besides, we found positive feedback to immunotherapy in patients with low RiskScore. The expression tendency of the seven prognostic factors in MKN7 and MKN28 was consistent with that in the RNA-seq data in addition to comparison of protein expression levels in the public HPA (The Human Protein Atlas) database. Further functional exploration disclosed that MARCKS was an important prognostic factor in regulating cell viability in STAD.

Conclusion

This study preliminary uncovered a single cell atlas for STAD patients, and Macrophages relevant gene signature and nomogram displayed favorable immunotherapy and prognostic prediction ability. Collectively, our work provides a new insight into the molecular mechanisms and therapeutic approach for LUAD patients.

Single-cell sequencing reveals the heterogeneity of B cells and tertiary lymphoid structures in muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease with a poor prognosis. B cells are crucial factors in tumor suppression, and tertiary lymphoid structures (TLSs) facilitate immune cell recruitment to the tumor microenvironment (TME). However, the function and mechanisms of tumor-infiltrating B cells and TLSs in MIBC need to be explored further.

METHODS

We performed single-cell RNA sequencing analysis of 11,612 B cells and 55,392 T cells from 12 bladder cancer patients and found naïve B cells, proliferating B cells, plasma cells, interferon-stimulated B cells and germinal center-associated B cells, and described the phenotype, gene enrichment, cell-cell communication, biological processes. We utilized immunohistochemistry (IHC) and immunofluorescence (IF) to describe TLSs morphology in MIBC.

RESULTS

The interferon-stimulated B-cell subtype (B-ISG15) and germinal center-associated B-cell subtypes (B-LMO2, B-STMN1) were significantly enriched in MIBC. TLSs in MIBC exhibited a distinct follicular structure characterized by a central region of B cells resembling a germinal center surrounded by T cells. CellChat analysis showed that CXCL13 + T cells play a pivotal role in recruiting CXCR5 + B cells. Cell migration experiments demonstrated the chemoattraction of CXCL13 toward CXCR5 + B cells. Importantly, the infiltration of the interferon-stimulated B-cell subtype and the presence of TLSs correlated with a more favorable prognosis in MIBC.

CONCLUSIONS

The study revealed the heterogeneity of B-cell subtypes in MIBC and suggests a pivotal role of TLSs in MIBC outcomes. Our study provides novel insights that contribute to the precision treatment of MIBC.

Construction and validation of a mitochondria-associated genes prognostic signature and immune microenvironment characteristic of sepsis

BACKGROUND

Sepsis is a common critical condition seen in clinical settings, with mitochondrial dysfunction playing an important role in the progression of sepsis. However, a mitochondrial prognosis model related to sepsis has not been established yet, and the relationship between the sepsis immune microenvironment and mitochondria remains unclear.

METHODS

Sepsis prognostic mitochondria-associated genes (MiAGs) were screened by univariate Cox, multivariate Cox, and LASSO analysis from the GEO dataset. Consensus Cluster was used to analyze MiAGs-based molecular subtypes for sepsis. The ESTIMATE and ssGSEA algorithms were used to analyze the situation of sepsis immune cell infiltration and its relation to MiAGs. Further, MiAGs score was calculated to construct a sepsis prognosis risk model to predict the prognosis of sepsis patients. Clinical blood samples were used to investigate the expression level of selected MiAGs in sepsis. Single-cell sequencing analysis, mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and ATP detection were used to verify the influence of MiAGs on mitochondrial dysfunction in sepsis.

RESULTS

A total of 5 MiAGs of sepsis were screened. Based on MiAGs, sepsis MiAGs subtypes were analyzed, where Cluster A had a better prognosis than Cluster B, and there were significant differences in immune infiltration between the two clusters. The sepsis mitochondrial prognosis model suggested that the high MiAG score group had a shorter survival time compared to the low MiAG score group. Significant differences were also observed in the immune microenvironment between the high and low MiAG score groups. Prognostic analysis and the Nomogram indicated that the MiAG score is an independent prognostic factor in sepsis. Single-cell sequencing analysis exhibited the possible influence of MiAGs on the mitochondrial function of monocytes. Finally, the downregulation of the COX7B could effectively improve mitochondrial function in the LPS-stimulated sepsis model.

CONCLUSION

Our findings suggest that MiAGs can be used to predict the prognosis of sepsis and that regulating the mitochondrial prognostic gene COX7B can effectively improve the mitochondrial function of immune cells in sepsis.

Single-Cell Transcriptome Reveals Cell Type-Specific Molecular Pathology in a 2VO Cerebral Ischemic Mouse Model

Post-ischemia memory impairment is a major sequela in cerebral ischemia patients. However, cell type-specific molecular pathology in the hippocampus after ischemia is poorly understood. In this study, we adopted a mouse two-vessel occlusion ischemia model (2VO model) to mimic cerebral ischemia-induced memory impairment and investigated the single-cell transcriptome in the hippocampi in 2VO mice. A total of 27,069 cells were corresponding 14 cell types with neuronal, glial, and vascular lineages. We next analyzed cell-specific gene alterations in 2VO mice and the function of these cell-specific genes. Differential expression analysis identified cell type-specific genes with altered expression in neurons, astrocytes, microglia, and oligodendrocytes in 2VO mice. Notably, four subtypes of oligodendrocyte precursor cells with distinct differentiation pathways were suggested. Taken together, this is the first single-cell transcriptome analysis of gene expression in a 2VO model. Furthermore, we suggested new types of oligodendrocyte precursor cells with angiogenesis and neuroprotective potential, which might offer opportunities to identify new avenues of research and novel targets for ischemia treatment.

An aging-related immune landscape in the hematopoietic immune system

BACKGROUND

Aging is a holistic change that has a major impact on the immune system, and immunosenescence contributes to the overall progression of aging. The bone marrow is the most important hematopoietic immune organ, while the spleen, as the most important extramedullary hematopoietic immune organ, maintains homeostasis of the human hematopoietic immune system (HIS) in cooperation with the bone marrow. However, the overall changes in the HIS during aging have not been described. Here, we describe a hematopoietic immune map of the spleen and bone marrow of young and old mice using single-cell sequencing and flow cytometry techniques.

RESULTS

We observed extensive, complex changes in the HIS during aging. Compared with young mice, the immune cells of aged mice showed a marked tendency toward myeloid differentiation, with the neutrophil population accounting for a significant proportion of this response. In this change, hypoxia-inducible factor 1-alpha (Hif1α) was significantly overexpressed, and this enhanced the immune efficacy and inflammatory response of neutrophils. Our research revealed that during the aging process, hematopoietic stem cells undergo significant changes in function and composition, and their polymorphism and differentiation abilities are downregulated. Moreover, we found that the highly responsive CD62L + HSCs were obviously downregulated in aging, suggesting that they may play an important role in the aging process.

CONCLUSIONS

Overall, aging extensively alters the cellular composition and function of the HIS. These findings could potentially give high-dimensional insights and enable more accurate functional and developmental analyses as well as immune monitoring in HIS aging.

Single Cell Micro RNA Sequencing Library Preparation

Micro RNAs represent important post-transcriptional regulators in health and are involved in the onset of many diseases. Therefore, the further characterization of physiological miRNA functions is an important basic research question, and miRNAs even have high potential as biomarkers both for prognosis and diagnosis. In order to exploit this potential, it is mandatory to accurately quantify the miRNA expression not only in bulk but also on the single-cell level. Here, we describe a protocol, which facilitates miRNA sequencing library preparation of very low input samples, single cells, and even clinical samples such as circulating tumor cells. The protocol can be combined with different single-cell isolation methods (e.g., micromanipulation and FACS sorting). After cell lysis, sequencing adapters are ligated to the miRNAs, other ncRNA species, and adapter dimers are reduced by exonuclease digest, the miRNA library is reverse transcribed, amplified, and purified. Furthermore, quality controls are described to select only high-quality samples for sequencing.

Mapping enhancer and chromatin accessibility landscapes charts the regulatory network of Alzheimer's disease

BACKGROUND

Enhancers are regulatory elements that target and modulate gene expression and play a role in human health and disease. However, the roles of enhancer regulatory circuit abnormalities driven by epigenetic alterations in Alzheimer's disease (AD) are unclear.

METHODS

In this study, a multiomic integrative analysis was performed to map enhancer and chromatin accessibility landscapes and identify regulatory network abnormalities in AD. We identified differentially methylated enhancers and constructed regulatory networks across brain regions using AD brain tissue samples. Through the integration of snATAC-seq and snRNA-seq datasets, we mapped enhancers with DNA methylation alterations (DMA) and chromatin accessibility landscapes. Core regulatory triplets that contributed to AD neuropathology in specific cell types were further prioritized.

RESULTS

We revealed widespread DNA methylation alterations (DMA) in the enhancers of AD patients across different brain regions. In addition, the genome-wide transcription factor (TF) binding profiles showed that enhancers with DMA are pervasively regulated by TFs. The TF-enhancer-gene regulatory network analysis identified core regulatory triplets that are associated with brain and immune cell proportions and play important roles in AD pathogenesis. Enhancer regulatory circuits with DMA exhibited distinct chromatin accessibility patterns, which were further characterized at single-cell resolutions.

CONCLUSIONS

Our study comprehensively investigated DNA methylation-mediated regulatory circuit abnormalities and provided novel insights into the potential pathogenesis of AD.

Single-Cell Sequencing Data Analysis Unveiled HDAC1 as the Therapeutic Target for Chronic Pancreatitis

Chronic pancreatitis (CP) as a progressive inflammatory disorder, remains untreatable. The novel treatment strategy for CP is imperative. We attempted to explore the therapeutic biomarkers for CP. The single-cell sequencing data were retrieved from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in idiopathic CP were identified, followed by function and pathway annotation, and PPI network established. DEGs of interest were verified in human tissue samples. The function of candidate biomarker was determined in the murine model with CP. A total of 208 genes were specially differentially expressed in idiopathic patients. Functional enrichment analysis showed DEGs were mainly enriched in glycogen catabolic process, RNA splicing, and glucagon signaling pathway. A PPI network centered on HDAC1 was constructed. HDAC1 was overexpressed in CP patients. The murine model with CP was induced by repetitive cerulein treatment. Silencing sh-HDAC1 treatment reversed cerulein-induced inflammatory cells accumulation, high expression of TGF-β1, and collagen 1 in pancreas in vivo. HDAC1 might be served as potential biomarker for CP. The present study provided insights into the molecular mechanism of CP that may be useful in further investigations.

Profiling cardiomyocytes at single cell resolution reveals COX7B could be a potential target for attenuating heart failure in cardiac hypertrophy

Cardiac hypertrophy can develop to end-stage heart failure (HF), which inevitably leading to heart transplantation or death. Preserving cardiac function in cardiomyocytes (CMs) is essential for improving prognosis in hypertrophic cardiomyopathy (HCM) patients. Therefore, understanding transcriptomic heterogeneity of CMs in HCM would be indispensable to aid potential therapeutic targets investigation. We isolated primary CM from HCM patients who had extended septal myectomy, and obtained transcriptomes in 338 human primary CM with single-cell tagged reverse transcription (STRT-seq) approach. Our results revealed that CMs could be categorized into three subsets in nonfailing HCM heart: high energy synthesis cluster, high cellular metabolism cluster and intermediate cluster. The expression of electron transport chain (ETC) was up-regulated in larger-sized CMs from high energy synthesis cluster. Of note, we found the expression of Cytochrome c oxidase subunit 7B (COX7B), a subunit of Complex IV in ETC had trends of positively correlation with CMs size. Further, by assessing COX7B expression in HCM patients, we speculated that COX7B was compensatory up-regulated at early-stage but down-regulated in failing HCM heart. To test the hypothesis that COX7B might participate both in hypertrophy and HF progression, we used adeno associated virus 9 (AAV9) to mediate the expression of Cox7b in pressure overload-induced mice. Mice in vivo data supported that knockdown of Cox7b would accelerate HF and Cox7b overexpression could restore partial cardiac function in hypertrophy. Our result highlights targeting COX7B and preserving energy synthesis in hypertrophic CMs could be a promising translational direction for HF therapeutic strategy.

Single-Cell Mapping of Brain Myeloid Cell Subsets Reveals Key Transcriptomic Changes Favoring Neuroplasticity after Ischemic Stroke

Interactions between brain-resident and peripheral infiltrated immune cells are thought to contribute to neuroplasticity after cerebral ischemia. However, conventional bulk sequencing makes it challenging to depict this complex immune network. Using single-cell RNA sequencing, we mapped compositional and transcriptional features of peri-infarct immune cells. Microglia were the predominant cell type in the peri-infarct region, displaying a more diverse activation pattern than the typical pro- and anti-inflammatory state, with axon tract-associated microglia (ATMs) being associated with neuronal regeneration. Trajectory inference suggested that infiltrated monocyte-derived macrophages (MDMs) exhibited a gradual fate trajectory transition to activated MDMs. Inter-cellular crosstalk between MDMs and microglia orchestrated anti-inflammatory and repair-promoting microglia phenotypes and promoted post-stroke neurogenesis, with SOX2 and related Akt/CREB signaling as the underlying mechanisms. This description of the brain's immune landscape and its relationship with neurogenesis provides new insight into promoting neural repair by regulating neuroinflammatory responses.

Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis

PURPOSE

Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets.

METHODS

ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors.

RESULTS

Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001).

CONCLUSIONS

This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

Single-Cell Sequencing Technology and Its Application in the Study of Central Nervous System Diseases

The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.

Identification of hub genes based on integrated analysis of single-cell and microarray transcriptome in patients with pulmonary arterial hypertension

BACKGROUND

Pulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution.

METHODS

Single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram.

RESULTS

Nineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439.

CONCLUSION

WDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.

Proximal tubule cells in blood and urine as potential biomarkers for kidney disease biopsy

Early diagnosis and treatment are crucial for managing kidney disease, yet there remains a need to further explore pathological mechanisms and develop minimally invasive diagnostic methods. In this study, we employed single-cell RNA sequencing (scRNA-seq) to assess the cellular heterogeneity of kidney diseases. We analyzed gene expression profiles from renal tissue, peripheral blood mononuclear cells (PBMCs), and urine of four patients with nephritis. Our findings identified 12 distinct cell subsets in renal tissues and leukocytes. These subsets encompassed fibroblast cells, mesangial cells, epithelial cells, proximal tubule cells (PTCs), and six immune cell types: CD8+ T cells, macrophages, natural killer cells, dendritic cells, B cells, and neutrophils. Interestingly, PTCs were present in both PBMCs and urine samples but absent in healthy blood samples. Furthermore, several populations of fibroblast cells, mesangial cells, and PTCs exhibited pro-inflammatory or pro-apoptotic behaviors. Our gene expression analysis highlighted the critical role of inflammatory PTCs and fibroblasts in nephritis development and progression. These cells showed high expression of pro-inflammatory genes, which could have chemotactic and activating effect on neutrophils. This was substantiated by the widespread in these cells. Notably, the gene expression profiles of inflammatory PTCs in PBMCs, urine, and kidney tissues had high similarity. This suggests that PTCs in urine and PBMCs hold significant potential as alternative markers to invasive kidney biopsies.

A Unified Bayesian Framework for Bi-overlapping-Clustering Multi-omics Data via Sparse Matrix Factorization

The advances of modern sequencing techniques have generated an unprecedented amount of multi-omics data which provide great opportunities to quantitatively explore functional genomes from different but complementary perspectives. However, distinct modalities/sequencing technologies generate diverse types of data which greatly complicate statistical modeling because uniquely optimized methods are required for handling each type of data. In this paper, we propose a unified framework for Bayesian nonparametric matrix factorization that infers overlapping bi-clusters for multi-omics data. The proposed method adaptively discretizes different types of observations into common latent states on which cluster structures are built hierarchically. The proposed Bayesian nonparametric method is able to automatically determine the number of clusters. We demonstrate the utility of the proposed method using simulation studies and applications to a single-cell RNA-sequencing dataset, a combination of single-cell RNA-sequencing and single-cell ATAC-sequencing dataset, a bulk RNA-sequencing dataset, and a DNA methylation dataset which reveal several interesting findings that are consistent with biological literature.

Methods, bioinformatics tools and databases in ecDNA research: An overview

Extrachromosomal DNA (ecDNA), derived from chromosomes, is a cancer-specific circular DNA molecule. EcDNA drives tumor initiation and progression, which is associated with poor clinical outcomes and drug resistance in a wide range of cancers. Although ecDNA was first discovered in 1965, tremendous technological revolutions in recent years have provided crucial new insights into its key biological functions and regulatory mechanisms. Here, we provide a thorough overview of the methods, bioinformatics tools, and database resources used in ecDNA research, mainly focusing on their performance, strengths, and limitations. This study can provide important reference for selecting the most appropriate method in ecDNA research. Furthermore, we offer suggestions for the current bioinformatics analysis of ecDNA and provide an outlook to the future research.

Integrating bulk-seq and single-cell-seq reveals disulfidptosis potential index associating with neuroblastoma prognosis and immune infiltration

PURPOSE

Neuroblastoma is a challenging pediatric tumor with a need for improved treatment strategies. This study explores the role of disulfidptosis, a form of cell death induced by intracellular disulfide accumulation, in neuroblastoma and its implications for prognosis and immune infiltration.

METHODS

We subgrouped neuroblastoma samples based on disulfidptosis-related gene expression and constructed a disulfidptosis potential index (DPI) to quantify disulfidptosis levels in neurobalstoma. The correlation between DPI, outcome, immune infiltration, and drug sensitivity were explored.

RESULTS

Combing RNA-seq and single-cell dataset, we found that higher disulfidptosis potential index (DPI) is associated with poorer outcomes in neuroblastoma patients, indicating the detrimental impact of enhanced disulfide stress and cellular dysfunction. Furthermore, we found that higher DPI is correlated with reduced immune infiltration within the tumor microenvironment, highlighting an immunosuppressive milieu in high DPI neuroblastomas. The DPI-high neuroblastoma may benefit from the estrogen pathway related drug fulvestrant.

CONCLUSION

Overall, this study highlights the significance of disulfidptosis as a potential therapeutic target and underscores the importance of integrating immune modulation strategies, offering new avenues for improved management of neuroblastoma.

The combined characteristics of cholesterol metabolism and the immune microenvironment may serve as valuable biomarkers for both the prognosis and treatment of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) being a complex disease, commonly exhibits multifaceted presentations, rendering its treatment challenging and necessitating specific approaches. The tumor immune microenvironment is crucial in cancer treatment, and cholesterol metabolism is a key component that helps cells grow and produce vital metabolites. However, the reprogramming of cholesterol metabolism in the tumor microenvironment (TME) can promote HCC development, and cancer classifiers relating to cholesterol metabolism are currently limited. Despite significant progress, further research is needed to improve early detection, liver function, and treatment options to improve patient outcomes.

Methods

To evaluate the expression abundance of tumor immune microenvironment (TIME) and cholesterol metabolism in 8 types of liver cancer cells, we comprehensively evaluated the immune cell composition, extracellular matrix alterations, and activity of relevant signaling pathways in the TIME through nine liver cancer patients, stromal scoring, immune scoring, tumor purity scoring, immune infiltration analysis, and pathway enrichment. Subsequently, we utilized machine learning techniques to construct prognostic models for both cholesterol metabolism and the tumor immune microenvironment, further exploring the tumor mutation burden, immune infiltration levels, and drug sensitivity in different subtypes of HCC patients.

Results

Our study constructed three cancer screening models to identify HCC patients with high cholesterol metabolism and low TIME, who have a poorer prognosis. On the contrary, patients with low cholesterol metabolism and high TIME often have better prognosis. Furthermore, we identified chemical compounds, such as BPD-00008900, ML323, Doramapimod, and AZD2014, which display better chemotherapy results for high-risk patients in specific sub-groups.

Role of arachidonic acid metabolism in intervertebral disc degeneration: identification of potential biomarkers and therapeutic targets via multi-omics analysis and artificial intelligence strategies

BACKGROUND

Intervertebral disc degeneration (IVDD) is widely recognized as the primary etiological factor underlying low back pain, often necessitating surgical intervention as the sole recourse in severe cases. The metabolic pathway of arachidonic acid (AA), a pivotal regulator of inflammatory responses, influences the development and progression of IVDD.

METHODS

Initially, a comparative analysis was conducted to investigate the relationship between AA expression patterns and different stages of IVDD using single-cell sequencing (scRNA-seq) data. Additionally, three machine learning methods (LASSO, random forest, and support vector machine recursive feature elimination) were employed to identify hub genes associated with IVDD. Subsequently, a novel artificial intelligence prediction model was developed for IVDD based on an artificial neural network algorithm and validated using an independent dataset. The identified hub genes were further subjected to functional enrichment, immune infiltration, and Connectivity Map analysis. Moreover, external validation was performed using flow cytometry and real-time reverse transcription polymerase chain reaction analysis.

RESULTS

Both scRNA-seq and bulk RNA-seq data revealed a positive correlation between the severity of IVDD and the AA metabolic pathway. They also revealed increased AA metabolic activity in macrophages and neutrophils, as well as enhanced intercellular communication with nucleus pulposus cells. Utilizing advanced machine learning algorithms, five hub genes (AKR1C3, ALOX5, CYP2B6, EPHX2, and PLB1) were identified, and an incipient diagnostic model was developed with an AUC of 0.961 in the training cohort and 0.72 in the validation cohort. An in-depth exploration of the functionality of these hub genes revealed their notable association with inflammatory responses and immune cell infiltration. Lastly, AH6809 was found to delay IVDD by inhibiting AKR1C3.

CONCLUSIONS

This study offers comprehensive insights into potential biomarkers and small molecules associated with the early pathogenesis of IVDD. The identified biomarkers and the developed integrated diagnostic model hold great promise in predicting the onset of early IVDD. AH6809 was established as a therapeutic target for AKR1C3 in the treatment of IVDD, as evidenced by computer simulations and biological experiments.