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Ribeiro, M. M.

Publications and source records attributed to Ribeiro, M. M..

3 recordsLinked to original sources

Striving towards improved full-length single-cell RNA-sequencing

Full-length single-cell RNA-sequencing (scRNA-seq) methods provide superior transcript coverage and isoform resolution compared to their 3-end counterparts, but are typically limited to short-read platforms. Here, we report efforts to improve the FLASH-seq protocol and adapt it for long-read sequencing on the Oxford Nanopore Technologies (ONT) platform (FLASH-seq-ONT). We developed two plate-barcoding strategies enabling higher multiplexing: a custom PCR-ligation approach (PCR-LIG) and ONT native barcoding (NB-ONT). To support data processing, we built FSNanoporeR, a comprehensive bioinformatics pipeline for barcode demultiplexing, chimeric read detection and splitting, UMI extraction, and transcript quantification. Both barcoding strategies produced high-quality transcriptomic data from HEK293T cells, with notable differences in read length distributions. We further demonstrated that monomeric and trimeric UMIs can be reliably detected in >82% of reads, enabling accurate molecular counting at isoform resolution. However, both multiplexing approaches exhibited also critical limitations, including high chimeric read rates and index-swapping artifacts. Our results highlight both the promise and current technical hurdles of full-length single-cell long-read sequencing, and provide a practical framework for researchers considering ONT-based scRNA-seq workflows.

genomics↗

SEQURNA enhances FLASH-seq gene detection while eliminating DTT dependence

Effective RNase inhibition is critical for single-cell RNA-sequencing, yet commercial recombinant RNase inhibitors (RRIs) require reducing agents for stability and impose substantial costs. Here, we systematically benchmark SEQURNA, a synthetic thermostable RNase inhibitor, against commercial alternatives using FLASH-seq in human retinal organoids and peripheral blood mononuclear cells (PBMCs). SEQURNA at 0.5-1 U/l achieved 14-50% higher gene detection than Takara RRI, with the greatest improvement in low-RNA PBMCs. Surprisingly, DTT supplementation at standard concentrations (5-10 mM) significantly impaired gene detection across all SEQURNA concentrations without improving RNA quality metrics, challenging established reverse transcription protocols. SEQURNA preserved biological heterogeneity, maintained sample stability during one-month storage at -80{degrees}C, and reduced reagent costs by 70%. We recommend SEQURNA at 1 U/l without DTT as an optimized formulation that simultaneously enhances data quality and cost-effectiveness for full-length single-cell RNA sequencing.

genomics↗

RNetDys: identification of disease-related impaired regulatory interactions due to SNPs

The dysregulation of regulatory mechanisms due to Single Nucleotide Polymorphisms (SNPs) can lead to diseases and does not affect all cell (sub)types equally. Current approaches to study the impact of SNPs in diseases lack mechanistic insights. Indeed, they do not account for the regulatory landscape to decipher cell (sub)type specific regulatory interactions impaired due to disease-related SNPs. Therefore, characterizing the impact of disease-related SNPs in cell (sub)type specific regulatory mechanisms would provide novel therapeutical targets, such as promoter and enhancer regions, for the development of gene-based therapies directed at preventing or treating diseases. We present RNetDys, a pipeline to decipher cell (sub)type specific regulatory interactions impaired by disease-related SNPs based on multi-OMICS data. RNetDys leverages the information obtained from the generated cell (sub)type specific GRNs to provide detailed information on impaired regulatory elements and their regulated genes due to the presence of SNPs. We applied RNetDys in five disease cases to study the cell (sub)type differential impairment due to SNPs and leveraged the GRN information to guide the characterization of dysregulated mechanisms. We were able to validate the relevance of the identified impaired regulatory interactions by verifying their connection to disease-related genes. In addition, we showed that RNetDys identifies more precisely dysregulated interactions linked to disease-related genes than expression Quantitative Trait Loci (eQTL) and provides additional mechanistic insights. RNetDys is a pipeline available at https://github.com/BarlierC/RNetDys.git

bioinformatics↗