Project description:DNA methylation can give rise to robust biomarkers of aging, yet most studies profile it at the bulk tissue level, which masks cell type-specific alterations that may follow distinct aging trajectories. Long-read sequencing technology enables methylation profiling of extended DNA fragments, enabling mapping to their cell type of origin. In this study, we introduce a framework for evaluating cell type-specific aging using long-read sequencing data, without the need for cell sorting. Leveraging cell type-specific methylation patterns, we map long-read fragments to individual cell types and generate cell type-specific methylation profiles, which are used as input to a newly developed probabilistic aging model, LongReadAge, capable of predicting epigenetic age at the cell-type level. We use LongReadAge to track aging of myeloid cells and lymphocytes from bulk leukocyte data as well as circulating cell-free DNA, demonstrating robust performance in predicting age despite limited shared features across samples. This approach provides a novel method for profiling the dynamics of epigenetic aging at cell-type resolution.
Project description:Ongoing improvements to next generation sequencing technologies are leading to longer sequencing read lengths, but a thorough understanding of the impact of longer reads on RNA sequencing analyses is lacking. To address this issue, we generated and compared two RNA sequencing datasets of differing read lengths -- 2x75 bp (L75) and 2x262 bp (L262) -- and investigated the impact of read length on various aspects of analysis, including the performance of currently available read-mapping tools, gene and transcript quantification, and detection of allele-specific expression patterns. Our results indicate that, while the scalability of read-mapping tools and the cost-effectiveness of long read protocol is an issue that requires further attention, longer reads enable more accurate quantification of diverse aspects of gene expression, including individual-specific patterns of allele-specific expression and alternative splicing. Two RNA-Seq datasets of differing read lengths (2x262 bp and 2x75 bp)
Project description:This study presents the highest-resolution chromatin map of cellular senescence to date, shedding light on how genomic architecture is altered with this damaging phenotype. Senescence, a driver of aging, is a pro-inflammatory state of proliferative arrest caused by DNA damage; it is associated with epigenetic changes, including those to chromatin organization. We created ~3kb Hi-C contact maps of proliferating, quiescent, and replicative senescent lung fibroblasts, and also compared these to oncogene-induced senescence. Our findings confirm a loss of heterochromatin, with a shift towards the A compartment and A subcompartments. We establish a novel loop analysis framework, revealing the ~six times more unique loops with senescence, which lose methylation at their anchors. Additionally, we present a custom long-read reference genome highlighting structural changes supporting retrotransposon derepression, particularly at a defined ‘hotspot’. These architectural changes contribute to senescence, as they promote cell cycle arrest and inflammation, as well as epigenetic drift.
Project description:In recent years, long-read sequencing technologies have detected transcript isoforms with unprecedented accuracy and resolution. However, it remains unclear whether long-read sequencing can effectively disentangle the isoform landscape of complex allele-specific loci that arise from genetic or epigenetic differences between alleles. Here, we combine the PacBio Iso-Seq workflow with the established phasing approach WhatsHap to assign long reads to the corresponding allele in polymorphic F1 mouse hybrids. Upon comparing the long-read sequencing results with matched short reads, we observed general consistency in the allele-specific information and were able to confirm the imprinting status of known imprinted genes. We then explored the complex imprinted Gnas locus known for allele-specific non-coding and coding isoforms and were able to benchmark historical observations. This approach also allowed us to detect isoforms from both the active and inactive X chromosomes of genes that escape X chromosome inactivation. The described workflow offers a promising framework and demonstrates the power of long-read transcriptomic data to provide mechanistic insight into complex allele-specific loci.
Project description:Ongoing improvements to next generation sequencing technologies are leading to longer sequencing read lengths, but a thorough understanding of the impact of longer reads on RNA sequencing analyses is lacking. To address this issue, we generated and compared two RNA sequencing datasets of differing read lengths -- 2x75 bp (L75) and 2x262 bp (L262) -- and investigated the impact of read length on various aspects of analysis, including the performance of currently available read-mapping tools, gene and transcript quantification, and detection of allele-specific expression patterns. Our results indicate that, while the scalability of read-mapping tools and the cost-effectiveness of long read protocol is an issue that requires further attention, longer reads enable more accurate quantification of diverse aspects of gene expression, including individual-specific patterns of allele-specific expression and alternative splicing.
Project description:Alternative splicing significantly contributes to transcriptome complexity and has critical implications for cellular functions. Recent advancements in single-cell isolation and capture techniques have enabled high-throughput quantification of gene expression at single-cell resolution. Long-read sequencing technologies can further be combined with single-cell technologies and enable an unambiguous identification of complete exon structures. Several computational methods have been developed to specifically address bioinformatics challenges associated with the processing of long read scRNA-seq data. Evaluating and comparing these computational methods becomes crucial. The goal of this study was to benchmark state-of-the-art computational tools for single-cell and spatial long-read transcriptomics. The scRNA-seq data were generated from two tumors developed by a mouse model, and designated as MPNST1 and MPNST2. Data were obtained by using the 10X Genomics technology, then generating sequencing libraries using either Illumina, Oxford Nanopore Technology (ONT) or scNaUmi-Seq protocols. Raw data were obtained after sequencing the libraries on Illumina, MinION or PromethION sequencing platforms. The two Illumina data were uploaded as part of the related submission E-MTAB-14222, with sample MPNST1 corresponding to 2020_23 and MPNST2 to 2022_26. This current submission contains the four long-read raw data et the data processed using the wf-single-cell pipeline. For the additional processed data, please refer to https://github.com/GenomiqueENS/scKenver.
Project description:This submission comprises the raw and processed mass spectrometry data from a high-resolution proteogenomic atlas established in iPSC-derived cortical neurons (a model of human neuron development). The data was collected using quantitative Tandem Mass Tag (TMT) proteomics and integrated with deep long-read RNA sequencing to expand the human proteome's functional landscape. Crucially, the mass spectrometry data provided direct peptide evidence supporting the translation of hundreds of novel protein-coding sequences derived from previously unknown mRNA isoforms. This resource was used to characterize dynamic protein isoform switching (including microexon usage and intron retention) within ASD risk genes during neuronal maturation, offering a critical foundation for reinterpreting pathogenic genetic variation in neurodevelopmental disorders.