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Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia

ABSTRACT: Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia

PROVIDER: PRJNA1206456 | ENA |

REPOSITORIES: ENA

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Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia

Project description:DNA methylation (DNAm) is a crucial epigenetic mark and plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across five single-nucleus DNAm datasets from the human brain and mouse generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. We applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, advancing our understanding of the epigenetic bases of schizophrenia within specific brain cell types.

2025-01-08 | GSE285847 | GEO

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

Project description: Not available

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scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia.

Project description: Not available

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ReDD: RNA editing detection by direct RNA sequencing and deep learning

Project description:Many previous studies, including the Next Generation Sequencing (NGS)-based ones, have shown the critical roles of RNA editing in biomedicine. Direct RNA sequencing emerges as another powerful technique to advance the understanding of RNA editing by new paradigms, especially in single-molecule and long-range characterization. The urgent gap is the accurate and robust identification of RNA editing at the single-molecule and single-nucleotide resolution from direct RNA sequencing. This is challenging due to the inherent nature of the context-dependence on the raw signals, which requires enormous training data with considerable diversity. Here we propose two coupled measures to address them: 1) an abductive deep learning strategy implemented as the software ReDD fully utilizes the widely accessible NGS-based RNA editing data as indirect labels of direct RNA sequencing to achieve the detection at the single-molecule level; 2) a cloud-based platform Argo-ReDD serves as a central database for assembling large and diverse data from the community to continuously train the abductive deep learning model, which also meets the community demand of a user-friendly way to perform RNA editing analyses, such as co-occurrence analysis, quantitative analysis and gene isoform-resolved analysis, based on the specific information from direct RNA sequencing.

2024-05-08 | GSE267030 | GEO

Peripheral mRNA expression of first-episode drug-naïve patients with schizophrenia

Project description:Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral blood, is of great significance. This study aimed to identify immune-related molecular biomarkers of schizophrenia in peripheral blood. Eighty-four Peripheral blood leukocytes of first-episode drug-naïve (FEDN) patients with schizophrenia and 97 healthy controls were collected and examined using high-throughput RNA-sequencing. Differentially-expressed genes (DEGs) were analysed. Weighted correlation network analysis (WGCNA) was employed to identify schizophrenia-associated module genes. The CIBERSORT algorithm was adopted to analyse immune cell proportions. Then, machine-learning algorithms including random forest, LASSO, and SVM-RFE were employed to screen immune-related predictive genes of schizophrenia. The RNA-seq analyses revealed 734 DEGs. Further machine-learning-based bioinformatic analyses screened out three immune-related predictive genes of schizophrenia (FOSB, NUP43, and H3C1), all of which were correlated with neutrophils and natural killer cells resting.

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Extracellular Vesicle Biomarkers for Complement System Dysfunction in Schizophrenia

Project description:Schizophrenia has a high rate of misdiagnosis, leading to inappropriate or delayed treatment. Combining with extracellular vesicles’ (EVs) proteome profiling and XGBoost-based machine learning, we developed new pan-EVs markers and yielded personalized discrimination scores (PDS) for schizophrenia diagnosis, subtyping and predicting treatment response.

2023-02-20 | PXD040261 |

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ReDD: RNA editing detection by direct RNA sequencing and deep learning

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| PRJNA1109379 | ENA

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Developmental Alterations in the Transcriptome of Three Distinct Rodent Models of Schizophrenia

Project description:Schizophrenia is a debilitating disorder affecting just under 1% of the population. While the symptoms of this disorder do not appear until late adolescence, pathological alterations likely occur earlier, during development in utero. While there is an increasing literature examining transcriptome alterations in patients, it is not possible to examine the changes in gene expression that occur during development in humans that will develop schizophrenia. Here we utilize three distinct rodent developmental disruption models of schizophrenia to examine potential overlapping alterations in the transcriptome, with a specific focus on markers of interneuron development. Specifically, we administered either methylazoxymethanol acetate (MAM), Polyinosinic:polycytidylic acid (Poly I:C), or chronic protein malnutrition, on GD 17 and examined mRNA expression in the developing hippocampus of the offspring 18 hours later. Here, we report alterations in gene expression that may contribute to the pathophysiology of schizophrenia, including significant alterations in interneuron development and ribosome function.

2020-05-05 | GSE149828 | GEO

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