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Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Bulk short- and long-read RNA-sequencing of iNeurons in Fragile-X and control cell-lines

ABSTRACT: 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.

INSTRUMENT(S): Illumina NovaSeq X, Sequel IIe, PromethION

ORGANISM(S): Homo sapiens

SUBMITTER: Rajib Schubert

PROVIDER: E-MTAB-16791 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Single cell short- and long-read RNA-sequencing of iNeurons in Fragile-X and control cell-lines

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

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2025-08-26 | E-MTAB-15190 | biostudies-arrayexpress

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2025-06-01 | E-MTAB-14725 | biostudies-arrayexpress

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2020-04-14 | E-MTAB-8735 | biostudies-arrayexpress

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