biostudies-arrayexpressgenomiqueENS IBENSMus musculushttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-15190Alternative 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.biostudies-arrayexpressLibrary Construction - Nanopore sequencing libraries were prepared with the Single Cell sequencing on Promethion protocol (Nanopore) with the SQK-PCS11 kit. We re-amplified 10 ng of the 10x Genomics PCR product for 4 cycles with 5′-CAGCTTTCTGTTGGTGCTGATATTGCAAGCAGTGGTATCAACGCAGAG-3′ and 5′ Biotine-CAGACACTTGCCTGTCGCTCTATCTTCCTACACGACGCTCTTCCGATCT 3′. After 0,8x AmpureXP purification to remove excess biotinylated primers, biotinylated is bound to Dynabeads™ M-280 Streptavidin beads (Invitrogen) and amplified with the primers cPRM for 4 cycles.Library Construction - As described in Lebrigand et al. 2020, we depleted the cDNA for variable extended (30–50%) cDNA that lacks poly(A) and poly(T) sequences. We re-amplified 10 ng of the 10x Genomics PCR product for 5 cycles with 5′-NNNAAGCAGTGGTATCAACGCAGAGTACAT-3′ and 5′ Biotine-AAAAACTACACGACGCTCTTCCGATCT 3′. After 0.6x SPRIselect purification to remove excess biotinylated primers, biotinylated cDNA (in 40 μl EB) is bound to 15 μl 1x SSPE washed Dynabeads™ M-270 Streptavidin beads (Thermo) resuspended in 10 μl 5x SSPE for 15 min at room temperature on a shaker. After two washes with 100 μl 1x SSPE and one wash with 100 μl EB, the beads are suspended in 100 μl 1x PCR mix and amplified for 9 cycles with the primers NNNAAGCAGTGGTATCAACGCAGAGTACAT and NNNCTACACGACGCTCTTCCGATCT to generate enough material for Nanopore sequencing library preparation. Amplified cDNA was purified with 0.6x SPRISelect and Nanopore sequencing libraries were prepared with the Oxford Nanopore SQK- LSK-110[MOU2] kit following the manufacturer’s instructions. All PCR amplifications for Nanopore library preparations were done with KapaHifi Hotstart polymerase (Roche Sequencing Solutions): initial denaturation, 3 min at 95 °C; cycles: 98 °C for 30 s, 64 °C for 30 s, 72 °C for 5 min; final elongation: 72 °C for 10 min, primer concentration was 1 μM.Sequencing - Libraries were individually loaded on PromethION flow cells version R9.4.1.Sequencing - Libraries were individually loaded on MinION flow cells version R9.4.1.OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - The wf-single-cell pipeline was executed with recommended parameters and the --expected_cells option set to 4,500 for the MPNST1 dataset, and 11,000 for the MPNST2 dataset. In addition, the --barcode_max_ed parameter was set to 1 to improve barcode assignment accuracy.MetabolomicsUnknownTranscriptomicsGenomicsProteomicsMinIONPromethIONAlternative splicing plays a crucial role in transcriptomic complexity, yet remains difficult to resolve at the single-cell level due to the limitations of short-read technologies. Coupling single-cell with long-read sequencing offers full-length transcript coverage, enabling more accurate isoform detection. Multiple specialized computational tools tailored for single-cell and spatial long-read transcriptomics have been developed, with diverse strategies. To compare the effectiveness of these approaches, we generated paired short-read and Nanopore long-read single-cell datasets, tailored for benchmarking bioinformatics tools. We evaluated ten state-of-the-art methods, spanning four analytical dimensions: barcodes and UMI detection, demultiplexing and UMI clustering, gene-level expression profiling, and isoform detection and quantification. Using real and simulated datasets across different protocols, sequencing depths and chemistries, we assessed the accuracy, robustness, and scalability of each tool. Our results revealed method-specific trade-offs, and highlight the importance of sequencing quality and UMI correction strategies. This benchmark provides a practical resource for optimizing isoform analysis and accurate gene expression profiling in single-cell and spatial transcriptomics using long-read sequencing. Our benchmarking workflow is designed to be reusable, thereby enabling method developers to compare their own approaches against the set of reference methods evaluated in this work.RNA-seq of coding RNA from single cellsMus musculusA systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq Nanopore long-read dataMorgane THOMAS-CHOLLIERCatherine SENAMAUD-BEAUFORTAli Hamraoui, Audrey Onfroy, Catherine Senamaud-Beaufort, Fanny Coulpier, Sophie Lemoine, Laurent Jourdren, Morgane Thomas-CholliergenomiqueENS IBENSFanny COULPIERfalseComparative analysis of single-cell and spatial Nanopore long-read methodsAlternative 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.2025-08-26T00:00:00Z2025-08-26T11:51:04.633Z2025-06-03T15:12:08.138ZE-MTAB-15190ERP173142E-MTAB-14222EFO_0004170EFO_0005684EFO_0003816EFO_000418410.1101/2025.07.21.665920