Single-cell Sequencing Datasets Research Articles

Abstract B007: Reconstruction of cellular phylogenies and analysis of gene expression variation in single cells

Abstract In cancer, somatic mutations occur continuously, causing cell populations to evolve. These somatic mutations result in the evolution of cellular gene expression patterns that can also change due to epigenetic modifications and environmental changes. By exploring the concordance of gene expression changes with molecular evolutionary trajectories of cells, we can examine the role of somatic variation on the evolution of gene expression patterns. In these endeavors, reliable inference of the evolutionary relationship of single cells is a key step. However, single-cell sequences contain many errors and missing bases, which necessitate advancing standard molecular phylogenetics approaches for applications in analyzing these datasets. We first show our new computational approach (TopHap+) that integratively applies standard phylogenetic optimality principles and patterns of co-occurrence of sequence variations to produce more expansive and accurate cellular phylogenies from single-cell sequence datasets. We then present Multi-Omics Concordance Analysis (MOCA) software to jointly analyze gene expressions and genetic variations from single-cell RNA sequencing profiles. MOCA outputs cells and genes showing convergent and divergent gene expression patterns in functional genomics. Citation Format: Sayaka Miura, Sudhir Kumar. Reconstruction of cellular phylogenies and analysis of gene expression variation in single cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B007.

Comparative and integrative single cell analysis reveals new insights into the transcriptional immaturity of stem cell-derived β cells

Diabetes cell replacement therapy has the potential to be transformed by human pluripotent stem cell-derived β cells (SC-β cells). However, the precise identity of SC-β cells in relationship to primary fetal and adult β-cells remains unclear. Here, we used single-cell sequencing datasets to characterize the transcriptional identity of islets from in vitro differentiation, fetal islets, and adult islets. Our analysis revealed that SC-β cells share a core β-cell transcriptional identity with human adult and fetal β-cells, however SC-β cells possess a unique transcriptional profile characterized by the persistent expression and activation of progenitor and neural-biased gene networks. These networks are present in SC-β cells, irrespective of the derivation protocol used. Notably, fetal β-cells also exhibit this neural signature at the transcriptional level. Our findings offer insights into the transcriptional identity of SC-β cells and underscore the need for further investigation of the role of neural transcriptional networks in their development.

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

Elucidating key immunological biomarkers and immune microenvironment dynamics in aging-related intracranial aneurysm through integrated multi-omics analysis.

The exact cause of intracranial aneurysms (IA) is still unclear. However, pro-inflammatory factors are known to contribute to IA progression. The specific changes in the immune microenvironment of IAs remain largely unexplored. This study analyzed single-cell sequencing data from a male mouse model of brain aneurysm, focusing on samples before and after elastase-induced Willis aneurysms. The data helped identify eight distinct cell subpopulations: fibroblasts, macrophages, NK cells, endothelial cells, B cells, granulocytes, and monocytes. The study also involved bulk RNA sequencing of 97 IA samples, utilizing ssGSEA and CIBERSORT algorithms for analysis. Intercellular communication among these cells was inferred to understand the immune dynamics in IA. The study found that fibroblasts and macrophages are predominant in various disease states of IA. Notably, the onset of IA was marked by a significant increase in fibroblasts and a decrease in macrophages. There was a marked increase in cellular interactions, especially involving macrophages, at the onset of the disease. Through enrichment analysis, 12 potential immunogenic biomarkers were identified. Of these, Rgs1 emerged as a critical molecule in IA formation, confirmed through secondary validation in a single-cell sequencing dataset. This comprehensive analysis of immune cell composition and intercellular communication in IA tissues highlights the significant roles of macrophages and the molecule Rgs1. These findings shed light on the physiological and pathological conditions of IA, offering new insights into its immune microenvironment.

Batch-effect correction in single-cell RNA sequencing data using JIVE.

In single-cell RNA sequencing analysis, addressing batch effects-technical artifacts stemming from factors such as varying sequencing technologies, equipment, and capture times-is crucial. These factors can cause unwanted variation and obfuscate the underlying biological signal of interest. The joint and individual variation explained (JIVE) method can be used to extract shared biological patterns from multi-source sequencing data while adjusting for individual non-biological variations (i.e. batch effect). However, its current implementation is originally designed for bulk sequencing data, making it computationally infeasible for large-scale single-cell sequencing datasets. In this study, we enhance JIVE for large-scale single-cell data by boosting its computational efficiency. Additionally, we introduce a novel application of JIVE for batch-effect correction on multiple single-cell sequencing datasets. Our enhanced method aims to decompose single-cell sequencing datasets into a joint structure capturing the true biological variability and individual structures, which capture technical variability within each batch. This joint structure is then suitable for use in downstream analyses. We benchmarked the results against four popular tools, Seurat v5, Harmony, LIGER, and Combat-seq, which were developed for this purpose. JIVE performed best in terms of preserving cell-type effects and in scenarios in which the batch sizes are balanced. The JIVE implementation used for this analysis can be found at https://github.com/oconnell-statistics-lab/scJIVE.

Identification of diagnostic molecules and potential traditional Chinese medicine components for Alzheimer's disease by single cell RNA sequencing combined with a systematic framework for network pharmacology.

Single-cell RNA sequencing (scRNA-Seq) provides new perspectives and ideas to investigate the interactions between different cell types and organisms. By integrating scRNA-seq with new computational frameworks or specific technologies, better Alzheimer's disease (AD) treatments may be developed. The single-cell sequencing dataset GSE158234 was obtained from the GEO database. Preprocessing, quality control, dimensionality-reducing clustering, and annotation to identify cell types were performed on it. RNA-seq profiling dataset GSE238013 was used to determine the components of specific cell subpopulations in diverse samples. A set of genes included in the OMIM, Genecards, CTD, and DisGeNET databases were selected as highly plausible AD-related genes. Then, ROC curves were created to predict the diagnostic value using the significantly expressed genes in the KO group as hub genes. The genes mentioned above were mapped to the Coremine Medical database to forecast prospective therapeutic Chinese medicines, and a "Chinese medicine-ingredient-target" network was constructed to screen for potential therapeutic targets. The last step was to undertake Mendelian randomization research to determine the causal link between the critical gene IL1B and AD in the genome-wide association study. Using the scRNA-seq dataset, five unique cell clusters were discovered. These clusters were further subdivided into four distinct cell types using marker genes. The KO group showed a more substantial differential subgroup of macrophages than the WT group. By using the available datasets and PPI network analysis, 54 common genes were discovered. Four clusters were identified using the MCODE approach, and correlation analysis showed that seven genes in those four clusters had a significantly negative correlation with macrophages. Six genes in four sets had a significantly positive correlation. Five genes had different levels of expression in the WT and KO groups. The String database was used to identify the regulatory relationships between the four genes (IL10, CX3CR1, IL1B, and IL6) that were finally selected as AD hub genes. Screening identified potential traditional Chinese medicine to intervene in the transformation process of AD, including Radix Salviae, ginseng, Ganoderma, licorice, Coptidis Rhizoma, and Scutellariae Radix, in addition to promising therapeutic targets, such as PTGS1, PTGS2, and RXRA. Finally, it was shown that IL1B directly correlated with immune cell infiltration in AD. In inverse variance weighting, we found that IL1B was associated with a higher risk of AD, with an OR of 1.003 (95% CI = 1.001-1.006, p = 0.038). Our research combined network pharmacology and the scRNA-seq computational framework to uncover pertinent hub genes and prospective traditional Chinese medicine potential therapeutic targets for AD. These discoveries may aid in understanding the molecular processes behind AD genes and the development of novel medications to treat the condition.

Tumor-associated macrophages and PD-L1 in prostate cancer: a possible key to unlocking immunotherapy efficacy.

Prostate cancer (PCa) is often considered as a "cold" tumor with low responsiveness to immunotherapy. Recent evidence suggests the activation of specific immune cells, such as tumor-associated macrophages (TAMs), could potentially influence the efficacy of immunotherapy in PCa. However, the relationship between TAMs and PD-L1, a significant regulator in immunotherapy, within PCa remains unexplored. In this study, we assessed TAM infiltration and PD-L1 expression levels in a local cohort of 95 PCa tissue samples and two publicly available PCa datasets. We employed a combination of bioinformatics and experimental techniques, including gene set enrichment analysis, CIBERSORTx, tissue microarray, immunohistochemistry staining, and analysis of single-cell sequencing datasets, to provide a comprehensive understanding of the association between PD-L1 and TAMs in the PCa microenvironment. The study showed that CD68+ TAMs and CD163+ TAMs (M2-TAMs) were more abundant in the tumor microenvironment than in non-cancerous surrounding tissues. The infiltration of CD163+ TAMs was significantly associated with the Gleason score and risk stratification of PCa. Importantly, elevated PD-L1 expression correlated significantly with high infiltration of CD163+ TAMs. Furthermore, patients displaying high levels of CD163+ TAMs and PD-L1 expression exhibited shorter times to biochemical recurrence-free survival. Our study suggests that CD163+ TAMs are closely associated with PD-L1 expression and can act as a valuable prognostic indicator for PCa. The high infiltration of M2-TAMs, coupled with the overexpression of PD-L1, may contribute to immune escape mechanisms in PCa, thereby influencing disease prognosis.

Integrated bioinformatics analysis of nucleotide metabolism based molecular subtyping and biomarkers in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), a predominant subtype of non-small cell lung cancers, continues to challenge treatment outcomes due to its heterogeneity and complex tumor microenvironment (TME). Dysregulation in nucleotide metabolism has been identified as a significant factor in tumorigenesis, suggesting its potential as a therapeutic target. This study analyzed LUAD samples from The Cancer Genome Atlas (TCGA) using Non-negative Matrix Factorization (NMF) clustering, Weighted Correlation Network Analysis (WGCNA), and various machine learning techniques. We investigated the role of nucleotide metabolism in relation to clinical features and immune microenvironment through large-scale data analysis and single-cell sequencing. Using in vivo and in vitro experiments such as RT-qPCR, Western Blot, immunohistochemistry, and subcutaneous tumor formation in mice, we further validated the functions of key nucleotide metabolism genes in cell lines and animals. Nucleotide metabolism genes classified LUAD patients into two distinct subtypes with significant prognostic differences. The 'C1' subtype associated with active nucleotide metabolism pathways showed poorer prognosis and a more aggressive tumor phenotype. Furthermore, a nucleotide metabolism-related score (NMRS) calculated from the expression of 28 key genes effectively differentiated between patient outcomes and predicted associations with oncogenic pathways and immune responses. By integrating various immune infiltration algorithms, we delineated the associations between nucleotide metabolism signature genes and the tumor microenvironment, and characterized their distribution differences at the cellular level by analyzing single-cell sequencing dataset related to immunochemotherapy. Finally, we demonstrated the differential expression of the key nucleotide metabolism gene AUNIP acts as an oncogene to promote LUAD cell proliferation and is associated with tumor immune infiltration. The study underscores the pivotal role of nucleotide metabolism in LUAD progression and prognosis, highlighting the NMRS as a valuable biomarker for clinical outcomes and therapeutic responses. Specifically, AUNIP functions as a critical oncogene, offering a promising target for novel treatment strategies in LUAD.

Establishment of a tumor-associated fibroblast associated gene score based on scRNA-seq to predict prognosis in patients with triple-negative breast cancer.

The tumor microenvironment (TME) is emerging as a tool for the development of improved patient prognosis and the development of novel antitumor drugs. As the most important stromal cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play an important role in the development of TNBC. The rise of single-cell sequencing technology has facilitated our study of the various cell types in TME. In this study, we interpreted the heterogeneity of TNBCs from the perspective of tumor-associated fibroblasts in the tumor microenvironment based on the TNBC single-cell sequencing dataset GSE118389, in the hope of providing help for individualised treatment. Combining the TCGA database and the GSE103091 dataset, four genes associated with CAFs in TNBC (CERCAM, KLF10, ECM1,HGF) were identified using the R package Seurat as well as correlation consensus clustering analysis. Meanwhile, qRT-PCR, WB and IHC experiments confirmed their expression in TNBC. Based on these genes, CAFs Score was established and validated to correlate with the prognosis of patients with TNBC, with patients in the high score group surviving significantly worse than those in the low score group (P<0.001). In addition, there were significant differences in immune cell infiltration and expression of immune checkpoints between the high and low scoring groups. Compared to Stage I & II, the CAFs Score was higher in Stage III & IV TNBC patients (P = 0.043) and higher in N1-3 TNBC patients than in N0 TNBC patients (P = 0.035). EMT scores were higher within the high CAFs Score group (P = 1.4e-11) and there was a positive correlation between Stemness Score and CAFs Score (R = 0.61, P = 3.6e-09). Drug sensitivity analysis combining the GSE128099 showed a higher sensitivity to Gemcitabine in the low CAFs Score group (P = 0.0048). We speculate that these four CAFs-related genes are likely to be involved in regulating gemcitabine resistance in TNBC patients.

Batch correction of single-cell sequencing data via an autoencoder architecture.

Technical differences between gene expression sequencing experiments can cause variations in the data in the form of batch effect biases. These do not represent true biological variations between samples and can lead to false conclusions or hinder the ability to integrate multiple datasets. Since there is a growing need for the joint analysis of single-cell sequencing datasets from different sources, there is also a need to correct the resulting batch effects while maintaining the true biological variations in the data. We developed a semi-supervised deep learning architecture called Autoencoder-based Batch Correction (ABC) for integrating single-cell sequencing datasets. Our method removes batch effects through a guided process of data compression using supervised cell type classifier branches for biological signal retention. It aligns the different batches using an adversarial training approach. We comprehensively evaluate the performance of our method using four single-cell sequencing datasets and multiple measures for batch effect removal and biological variation conservation. ABC outperforms 10 state-of-the-art methods for this task including Seurat, scGen, ComBat, scanorama, scVI, scANVI, AutoClass, Harmony, scDREAMER, and CLEAR, correcting various types of batch effects while preserving intricate biological variations.

The Functional Roles of MDSCs in Severe COVID-19 Pathogenesis.

Severe COVID-19 is a major cause of morbidity and mortality worldwide, especially among those with co-morbidities, the elderly, and the immunocompromised. However, the molecular determinants critical for severe COVID-19 progression remain to be fully elucidated. Meta-analyses of transcriptomic RNAseq and single-cell sequencing datasets comparing severe and mild COVID-19 patients have demonstrated that the early expansion of myeloid-derived suppressor cells (MDSCs) could be a key feature of severe COVID-19 progression. Besides serving as potential early prognostic biomarkers for severe COVID-19 progression, several studies have also indicated the functional roles of MDSCs in severe COVID-19 pathogenesis and possibly even long COVID. Given the potential links between MDSCs and severe COVID-19, we examine the existing literature summarizing the characteristics of MDSCs, provide evidence of MDSCs in facilitating severe COVID-19 pathogenesis, and discuss the potential therapeutic avenues that can be explored to reduce the risk and burden of severe COVID-19. We also provide a web app where users can visualize the temporal changes in specific genes or MDSC-related gene sets during severe COVID-19 progression and disease resolution, based on our previous study.

SSR1 and CKAP4 as potential biomarkers for intervertebral disc degeneration based on integrated bioinformatics analysis.

Intervertebral disc degeneration (IDD) is a significant cause of low back pain and poses a significant public health concern. Genetic factors play a crucial role in IDD, highlighting the need for a better understanding of the underlying mechanisms. The aim of this study was to identify potential IDD-related biomarkers using a comprehensive bioinformatics approach and validate them in vitro. In this study, we employed several analytical approaches to identify the key genes involved in IDD. We utilized weighted gene coexpression network analysis (WGCNA), MCODE, LASSO algorithms, and ROC curves to identify the key genes. Additionally, immune infiltrating analysis and a single-cell sequencing dataset were utilized to further explore the characteristics of the key genes. Finally, we conducted in vitro experiments on human disc tissues to validate the significance of these key genes in IDD. we obtained gene expression profiles from the GEO database (GSE23130 and GSE15227) and identified 1015 DEGs associated with IDD. Using WGCNA, we identified the blue module as significantly related to IDD. Among the DEGs, we identified 47 hub genes that overlapped with the genes in the blue module, based on criteria of |logFC| ≥ 2.0 and p.adj <0.05. Further analysis using both MCODE and LASSO algorithms enabled us to identify five key genes, of which CKAP4 and SSR1 were validated by GSE70362, demonstrating significant diagnostic value for IDD. Additionally, immune infiltrating analysis revealed that monocytes were significantly correlated with the two key genes. We also analyzed a single-cell sequencing dataset, GSE199866, which showed that both CKAP4 and SSR1 were highly expressed in fibrocartilage chondrocytes. Finally, we validated our findings in vitro by performing real time polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) on 30 human disc samples. Our results showed that CKAP4 and SSR1 were upregulated in degenerated disc samples. Taken together, our findings suggest that CKAP4 and SSR1 have the potential to serve as disease biomarkers for IDD.

Comprehensive analyses and experimental verification of NETs and an EMT gene signature for prognostic prediction, immunotherapy, and chemotherapy in pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is an aggressive malignancy with high mortality and poor prognosis. Neutrophil extracellular traps (NETs) and the epithelial-mesenchymal transition (EMT) significantly influence on the progression of various cancers. However, the underlying relevance of NETs- and EMT-associated genes on the outcomes of patients with PAAD remains to be elucidated. Transcriptome RNA sequencing data, together with clinical information and single-cell sequencing data of PAAD were collected from public databases. In the TCGA-PAAD cohort, ssGSEA was used to calculate NET and EMT scores. WGCNA was used to determine the key gene modules. A risk model with eight NET- and EMT-related genes (NERGs) was established using LASSO and multivariate Cox regression analysis. Patients in the reduced risk (RR) group showed better prognostic values compared with those in the elevated risk (ER) group. The prognostic model exhibited reliable and robust prediction when validated using an external database. The distributions of risk genes were explored in a single-cell sequencing data set. Immune infiltration, immune cycle, and immune checkpoints were compared between the RR and ER groups. Moreover, potential chemotherapeutic drugs were examined. DCBLD2 was identified as a key gene in PAAD cell lines by qRT-PCR, and was highly expressed in PAAD tissues. GSEA demonstrated that DCBLD2 induced the EMT. Transwell assays and western blotting showed that cell invasion and EMT induction were significantly reduced after DCBLD2 knockdown. Collectively, we constructed a prognosis model based on a NET and EMT gene signature, providing a valuable perspective for the prognostic evaluation and management of PAAD patient.

Abstract B052: Single-cell analysis of multiple cancers in the upper gastrointestinal tract uncovers immune characteristics of tumor microenvironments linked to the predictive biomarkers for immunotherapy in esophageal cancers

Abstract Esophageal cancer is mainly composed of two subtypes – esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) – with distinct risk factors and cancer phenotypes despite arising from the same organ. The recent study by the Cancer Genome Atlas (TCGA) has revealed their distinct genomic characteristics and similarities to subsets of other nearby cancers such as head and neck squamous neck carcinoma (HNSCC) and gastric adenocarcinoma (GAC) for ESCC and EAC, respectively. Furthermore, recent clinical trials with anti-PD-1 monotherapy and combination treatment with anti-PD-1 and anti-CTLA-4 reported varying degrees of responses to immunotherapy for those cancers. Here we performed single-cell RNA sequencing of patients with ESCC, EAC, and HNSCC, and collected additional public datasets for comparative analysis of four cancer types in the upper gastrointestinal tract, especially in connection to responses to immunotherapy. In total, we integrated 35 patient samples from 4 different cohorts to comprehensively analyze malignant cells, stromal/endothelial cells, and immune cells. With the integrative analysis, we confirmed the similarities and differences among those cancer types in the upper gastrointestinal tract and expanded understanding of immune mechanisms at single-cell resolution. For malignant cells, we utilized matrix factorization analysis to identify underlying malignant cell programs related to each cancer type. We confirmed clear separation between malignant cells of squamous epithelial cell origins and glandular epithelial cell origins. We further identified the malignant cell programs related to both cancer cell origins and molecular mechanisms of cancer cells. For stromal and endothelial cells, we identified compositional differences in their cellular subtypes. With biological pathway and signature analyses, we revealed that their distinct cellular subtypes might be connected to distinct immune mechanisms in tumor microenvironments. For immune cells, despite having less compositional difference in cellular subtypes, we identified their underlying immune mechanisms that could explain key differences in responses to immunotherapy. With comprehensive analyses of various immune cell-types and their interactions, we identified several T cell populations and related tumor-associated macrophages that could serve as predictive markers of responses to cancer immunotherapy. In order to validate our findings, we utilized both bulk and single-cell sequencing datasets of various cancer types with treatments of immune checkpoint inhibitors (ICI) from previous studies and confirmed significance of those immune signatures and cellular compositions in responses to the ICI treatment. Citation Format: Seungbyn Baek, Gamin Kim, Sang Jun Ha, Hye Ryun Kim, Seong Yong Park, Insuk Lee. Single-cell analysis of multiple cancers in the upper gastrointestinal tract uncovers immune characteristics of tumor microenvironments linked to the predictive biomarkers for immunotherapy in esophageal cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B052.

LncPCD: a manually curated database ofexperimentally supported associations between lncRNA-mediated programmed cell death anddiseases.

Programmed cell death (PCD) refers to controlled cell death that is conducted to keep the internal environment stable. Long noncoding RNAs (lncRNAs) participate in the progression of PCD in a variety of diseases. However, no specialized online repository is available to collect and store the associations between lncRNA-mediated PCD and diseases. Here, we developed LncPCD, a comprehensive database that provides information on experimentally supported associations of lncRNA-mediated PCD with diseases. The current version of LncPCD documents 6666 associations between five common types of PCD (apoptosis, autophagy, ferroptosis, necroptosis and pyroptosis) and 1222 lncRNAs in 331 diseases. We also manually curated a wealth of information: (1) 7 important lncRNA regulatory mechanisms, (2) 310 PCD-associated cell types in three species, (3) detailed information on lncRNA subcellular locations and (4) clinical applications for lncRNA-mediated PCD in diseases. Additionally, 10 single-cell sequencing datasets were integrated into LncPCD to characterize the dynamics of lncRNAs in diseases. Overall, LncPCD is an extremely useful resource for understanding the functions and mechanisms of lncRNA-mediated PCD in diseases. Database URL: http://spare4.hospital.studio:9000/lncPCD/Home.jsp.

ScDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier

Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time, and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER (https://github.com/Zafar-Lab/scDREAMER), a data-integration framework that employs deep generative models and adversarial training for both unsupervised and supervised (scDREAMER-Sup) integration of multiple batches. Using six real benchmarking datasets, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches, nested batch-effects, large number of batches and conservation of development trajectory across batches. Our experiments also show that scDREAMER and scDREAMER-Sup outperform state-of-the-art unsupervised and supervised integration methods respectively in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we demonstrate that scDREAMER is scalable and can perform atlas-level cross-species (e.g., human and mouse) integration while being faster than other deep-learning-based methods.

Abstract 18691: Asporin Attenuates Pulmonary Arterial Smooth Muscle Cell Proliferation and Regulates Pulmonary Hypertension Development

Introduction: Pulmonary arterial hypertension (PAH) is a devastating pulmonary vascular lung disease characterized by increased pressure in the pulmonary arteries (PA) leading to cardiac right ventricular (RV) hypertrophy, RV failure, and ultimately death. Current PAH therapies merely improve symptoms temporarily and patients often need lung and heart transplantation. There is dire need for new insights into PAH pathophysiology to find novel therapeutic approaches. A key mechanism driving PAH is medial hyperplasia resulting from enhanced pulmonary arterial smooth muscle cell (PASMC) proliferation. Methods: We employed an unbiased bioinformatics approach in online-available microarray expression and single-cell sequencing datasets of dissected PA from PAH patients. In vitro experiments were performed in PASMC isolated from a female donor and PAH patient. Human lung tissue sections were obtained from the Pulmonary Hypertension Breakthrough Initiative Biobank. The Sugen/Hypoxia model was used to induce PH in rats. Results: We discovered that a novel gene, small leucine-rich proteoglycan Asporin (Aspn), is highly and significantly upregulated in PAH compared to donor PA and is most highly expressed in PASMC. We validated higher Aspn mRNA expression in human lungs and PASMC from PAH patients compared to controls, supporting our bioinformatics approach. Moreover, Aspn protein expression is elevated in PASMC in lung tissue sections from PAH patients compared to donors, as well as in PAH patient plasma . Functionally, silencing Aspn in PASMC from PAH patients promoted proliferation, whereas recombinant Aspn exerted opposite effects. To find potential downstream mediators of Aspn, STRING database analysis revealed that Aspn binds to the pro-proliferative mediator TGFβ1. Accordingly, TGFβ1 downstream mediator p-SMAD2/3 is enhanced upon Aspn silencing in PASMC. Intratracheal delivery of Aspn siRNA in rats aggravated Sugen/Hypoxia-induced PH, as assessed by RVSP, echocardiography and vascular remodeling. Conclusions: We hypothesize that elevated Aspn expression in PAH is a compensatory mechanism that inhibits PASMC proliferation, leading to decreased PA vascular remodeling, and that Aspn may be a novel therapeutic target against PAH.

Immunological role and clinical prognostic significance of P2RY6 in lung adenocarcinoma: a multi-omics studies and single-cell sequencing analysis

BackgroundThere is increasing evidence that recombinant human P2Y purinoceptor 6 (P2RY6) may be involved in inflammatory responses. However, the role of P2RY6 in lung adenocarcinoma (LUAD) remains unknown.MethodsWe used transcriptomic, genomic, single-cell transcriptomic, and methylation sequencing data from The Cancer Genome Atlas database to analyze the aberrant status and prognostic value of P2RY6 in a variety of tumors. The LUAD single-cell sequencing dataset was used to explore the effect of P2RY6 on the tumor microenvironment. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to quantify immune cells in the tumor microenvironment. We also analyzed the correlation of P2RY6 with immune checkpoints and immune regulation-related genes. The correlation of between tumor mutation burden (TMB), microsatellite instability (MSI), and P2RY6 expression was also analyzed simultaneously. Tissue microarray and immunohistochemistry were employed to assess the expression of P2RY6 in internal tumor samples.ResultsOur findings indicate that P2RY6 exhibits significantly higher expression levels in various cancer tissues, particularly in LUAD. High expression of P2RY6 was closely associated with a poor prognosis for patients, and it plays a role in regulating immune-related pathways, such as cytokine-cytokine receptor interaction. Notably, P2RY6 expression is closely linked to the abundance of CD8 + T cells. Furthermore, we have developed a P2RY6-related inflammation prediction model that demonstrates promising results in predicting the prognosis of LUAD patients, with an AUC (area under the curve) value of 0.83. This performance is significantly better than the traditional TNM staging system. Through single-cell transcriptome sequencing analysis, we observed that high P2RY6 expression is associated with increased intercellular communication. Additionally, pathway enrichment analysis revealed that P2RY6 influences antigen presentation and processing pathways within the LUAD microenvironment.ConclusionsThis study suggests that P2RY6 would be a new target for immunotherapy in LUAD.

Single-cell sequencing and transcriptome analyses in the construction of a liquid-liquid phase separation-associated gene model for rheumatoid arthritis.

Background: Rheumatoid arthritis (RA) is a disabling autoimmune disease that affects multiple joints. Accumulating evidence suggests that imbalances in liquid-liquid phase separation (LLPS) can lead to altered spatiotemporal coordination of biomolecular condensates, which play important roles in carcinogenesis and inflammatory diseases. However, the role of LLPS in the development and progression of RA remains unclear. Methods: We screened RA and normal samples from GSE12021, GSE55235, and GSE55457 transcriptome datasets and GSE129087 and GSE109449 single-cell sequencing datasets from Gene Expression Omnibus database to investigate the pathogenesis of LLPS-related hub genes at the transcriptome and single cell sequencing levels. Machine learning algorithms and weighted gene co-expression network analysis were applied to screen hub genes, and hub genes were validated using correlation studies. Results: Differential analysis showed that 36 LLPS-related genes were significantly differentially expressed in RA, further random forest and support vector machine identified four and six LLPS-related genes, respectively, and weighted gene co-expression network analysis identified 396 modular genes. Hybridization of the three sets revealed two hub genes, MYC and MAP1LC3B, with AUCs of 0.907 and 0.911, respectively. Further ROC analysis of the hub genes in the GSE55457 dataset showed that the AUCs of MYC and MAP1LC3B were 0.815 and 0.785, respectively. qRT-PCR showed that the expression of MYC and MAP1LC3B in RA synovial tissues was significantly lower than that in the normal control synovial tissues. Correlation analysis between hub genes and the immune microenvironment and single-cell sequencing analysis revealed that both MYC and MAP1LC3B were significantly correlated with the degree of infiltration of various innate and acquired immune cells. Conclusion: Our study reveals a possible mechanism for LLPS in RA pathogenesis and suggests that MYC and MAP1LC3B may be potential novel molecular markers for RA with immunological significance.

Aging-related genes revealed Neuroinflammatory mechanisms in ischemic stroke by bioinformatics

Ischemic stroke (IS) is a leading cause of disability, morbidity, and mortality globally. Aging affects immune function and contributes to poor outcomes of IS in elderly individuals. However, little is known about how aging-related genes (ARGs) are involved in IS. In this study, the relationship between ARGs and IS immune microenvironment biomarkers was explored by bioinformatics. Two IS microarray datasets (GSE22255, GSE16561) from human blood samples were analyzed and 502 ARGs were identified, from which 29 differentially expressed ARGs were selected. Functional analysis revealed that 7 of these ARGs (IL1B, FOS, JUN, CXCL5, PTGS2, TNFAIP3 and TLR4) were involved in five top enriched pathways (IL-17 signaling pathway, TNF signaling pathway, Rheumatoid arthritis, NF-kappa B signaling pathway and Pertussis) related to immune responses and inflammation. Five hub DE-ARGs (IL2RB, FOS, IL7R, ALDH2 and BIRC2) were identified using machine learning algorithms, and their association with immune-related characteristics was confirmed by additional tests. Single-cell sequencing dataset GSE129788 was retrieved to analyze aging molecular-related features, which was in accordance with microarray datasets. Clustering analysis revealed two subtypes of IS, which were distinguished by their differential expression of genes related to the NF-kappa B signaling pathway. These findings highlight the importance of ARGs in regulating immune responses in IS and suggest potential prevention and treatment strategies as well as guidelines for future research.