Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1256151 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This study benchmarks bulk and single-cell long-read RNA sequencing technologies in a human neuronal model of Fragile X syndrome. NGN2-induced neurons were generated from patient-derived iPSCs carrying a silenced FMR1 gene (FXS line E3) and an isogenic CRISPR-corrected rescue line (IsoB11) in which FMR1 expression is restored. These conditions provide a defined system to evaluate transcript detection and quantification across sequencing platforms. Bulk and single-cell RNA-seq datasets were generated using Illumina short-read sequencing and long-read sequencing from Pacific Biosciences (PB) and Oxford Nanopore Technologies (ONT). Single-cell libraries were prepared using the 10x Genomics Chromium platform. ERCC and SIRV spike-in controls were added to bulk samples to enable benchmarking of transcript quantification accuracy. Three biological replicates were sequenced for each condition. The dataset enables cross-platform comparisons of transcript detection, quantification methods, transcript length biases, and sequencing depth requirements for long-read transcriptomic analyses.
2025-03-30 | E-MTAB-16805 | biostudies-arrayexpress
Project description:This study benchmarks bulk and single-cell long-read RNA sequencing technologies in a human neuronal model of Fragile X syndrome. NGN2-induced neurons were generated from patient-derived iPSCs carrying a silenced FMR1 gene (FXS line E3) and an isogenic CRISPR-corrected rescue line (IsoB11) in which FMR1 expression is restored. These conditions provide a defined system to evaluate transcript detection and quantification across sequencing platforms. Bulk and single-cell RNA-seq datasets were generated using Illumina short-read sequencing and long-read sequencing from Pacific Biosciences (PB) and Oxford Nanopore Technologies (ONT). Single-cell libraries were prepared using the 10x Genomics Chromium platform. ERCC and SIRV spike-in controls were added to bulk samples to enable benchmarking of transcript quantification accuracy. Three biological replicates were sequenced for each condition. The dataset enables cross-platform comparisons of transcript detection, quantification methods, transcript length biases, and sequencing depth requirements for long-read transcriptomic analyses.
2026-03-25 | E-MTAB-16791 | biostudies-arrayexpress
Project description:To address how genetic variation alters gene expression in complex cell mixtures, we developed Direct Nuclear Tagmentation and RNA-sequencing (DNTR-seq), which enables whole genome and mRNA sequencing jointly in single cells. DNTR-seq readily identified minor subclones within leukemia patients. In a large-scale DNA damage screen, DNTR-seq was used to detect regions under purifying selection, and identified genes where mRNA abundance was resistant to copy number alteration, suggesting strong genetic compensation. mRNA-seq quality equals RNA-only methods, and the low positional bias of genomic libraries allowed detection of sub-megabase aberrations at ultra-low coverage. Each cell library is individually addressable and can be re-sequenced at increased depth, allowing multi-tiered study designs. Additionally, the direct tagmentation protocol enables coverage-independent estimation of ploidy, which can be used to identify cell singlets. Thus, DNTR-seq directly links each cell?s state to its corresponding genome at scale, enabling routine analysis of heterogeneous tumors and other complex tissues.
2020-10-22 | GSE144296 | GEO
Project description:Benchmarking single-cell RNA sequencing methods for the Human Cell Atlas
| PRJNA414779 | ENA
Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
| 2533916 | ecrin-mdr-crc
Project description:Missing values (MVs) remain a significant barrier to reliable proteomics analysis, particularly in single-cell proteomics, where small amounts of starting material and limits in detection drive Missing-Not-At-Random (MNAR) sparsity. Commonly used bulk proteomic imputation approaches typically address Missing-At-Random (MAR) MVs and improve replicate consistency at the expense of sensitivity for biological variation, whereas MNAR-specific strategies preserve group differences but compromise cross-replicate reproducibility. Existing imputation methods are commonly applied to bulk data and do not offer a generalised ‘off-the-shelf’ implementation that robustly addresses the significant sparsity observed in single-cell studies. Here, we introduce SoftHybrid, a continuous weighting framework that automatically balances MAR- and MNAR-oriented imputation across a dataset-derived model between missing rate and protein abundance model. Benchmarking across known ground truth samples (three-species mix) and real single-cell proteomics data showed that SoftHybrid outperforms existing methods at low inputs while meeting or exceeding previous state-of-the-art performance at the mini-bulk level. Preservation of proteomic patterns and enhanced replicate consistency improve testing significance and boost recovery of biological signals. SoftHybrid is implemented as an R package and freely available on GitHub.
2026-07-23 | PXD071819 | Pride
Project description:Kilian2024 - Immune cell dynamics in Cue-Induced Extended Human Colitis Model
Single-cell technologies such as scRNA-seq and flow cytometry provide critical insights into immune cell behavior in inflammatory bowel disease (IBD). However, integrating these datasets into computational models for dynamic analysis remains challenging. Here, Kilian et al., (2024) developed a deterministic ODE-based model that incorporates these technologies to study immune cell population changes in murine colitis. The model parameters were optimized to fit experimental data, ensuring an accurate representation of immune cell behavior over time. It was then validated by comparing simulations with experimental data using Pearson’s correlation and further tested on independent datasets to confirm its robustness. Additionally, the model was applied to clinical bulk RNA-seq data from human IBD patients, providing valuable insights into immune system dynamics and potential therapeutic strategies.
Figure 4c, obtained from the simulation of human colitis model is highlighted here.
This model is described in the article:
Kilian, C., Ulrich, H., Zouboulis, V.A. et al. Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease. npj Syst Biol Appl 10, 69 (2024). https://doi.org/10.1038/s41540-024-00395-9
Abstract:
Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.
This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.
2025-03-07 | MODEL2502180001 | BioModels
Project description:Background: Breast cancer stem cells (BCSCs) are considered responsible for cancer relapse and drug-resistance. Understanding the identity of BCSCs may open new avenues in breast cancer therapy. Although several discoveries have been made on BCSCs characterization, the factors critical to BCSCs is largely unclear. This study was aimed to determine whether genomic mutation contributes to the acquisition of cancer stem-like phenotype, and to investigate the genetic and transcriptional features of BCSCs. Methods: We detected the potential mutation hotspot regions by using whole genome sequencing on parental cancer cells and derived serial-generation spheroids in increasing order of BCSC frequency, and then performed target deep DNA sequencing in the level of bulk-cell and single-cell. To identify the transcriptional program associated with BCSCs, bulk-cell and single-cell RNA sequencing were performed. Results: By analyzing whole genome sequencing of bulk cells, potential BCSCs associated mutation hotspot regions were detected. Validation by target deep sequencing, in both bulk-cell and single-cell levels, revealed no genetic changes specifically associated with BCSC phenotype. Moreover, single-cell RNA sequencing showed that cancer cells display profound transcriptional variability at the single-cell level that predicts BCSC features. Notably, this transcriptomic variability is enriched in transcription of a number of genes, revealed as BCSC markers. Individuals with breast cancer in a high-risk recurrence group exhibited higher expression of these transcriptomic variabilities, highlighting their clinical significance. Conclusions: Transcriptional variability, not genetic mutations, distinguish BCSCs from non-BCSCs. The identified BCSCs markers can become novel targets for BCSCs.
2018-10-12 | GSE116180 | GEO
Project description:Cell size is tightly controlled in healthy tissues and single-celled organisms, but it remains unclear how cell size influences physiology. Increasing cell size was recently shown to remodel the proteomes of cultured human cells, demonstrating that large and small cells of the same type can be compositionally different. Here, we utilize the natural heterogeneity of hepatocyte ploidy and yeast genetics to establish that ploidy-to-cell size ratio is a highly conserved determinant of proteome composition. In both mammalian and yeast cells, genome dilution by cell growth elicits a starvation-like phenotype, suggesting that growth in large cells is restricted by genome concentration in manner that mimics a limiting nutrient. Moreover, genome dilution explains some proteomic changes ascribed to yeast aging. Overall, our data indicate that genome concentration drives changes in cell composition independently of external environmental cues.
2024-06-05 | PXD052786 | Pride
Project description:Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally-efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package. Benchmarking samples to compare MBD- and MeDIP-seq [GSE38679, GSE24546; PMID 21045081] datasets against 450k measurements
2014-01-25 | E-GEOD-54375 | biostudies-arrayexpress