Biology subjects

Xavier-Magalhaes, A.

Publications and source records attributed to Xavier-Magalhaes, A..

3 recordsLinked to original sources

bioRxiv2024

Comparative Analysis of Commercial Single-Cell RNA Sequencing Technologies

This study evaluates ten commercially available single-cell RNA sequencing (scRNA-seq) approaches across four technology groups: Emulsion-based kits from 10x Genomics and Fluent Biosciences; Microwell-based kits from Becton Dickinson, Honeycomb Technologies and Singerlon Technologies; Combinatorial-indexing kits from Parse Biosciences and Scale Biosciences; and a Matrigel-based kit from Scipio Biosciences. Peripheral blood mononuclear cells (PBMCs) from a single donor were used to assess analytical performance. Key features such as sample compatibility, cost, and experimental duration were also compared. Notably, superior analytical performance was demonstrated by the Chromium Fixed RNA Profiling kit from 10x Genomics, which uniquely features probe hybridization for transcript detection. Additionally, the Rhapsody WTA kit from Becton Dickinson provided a cost-effective balance of performance and expense per cell. With a rich dataset of 218,154 cells, this work provides a basis for differentiating commercial scRNA-seq technologies, which is intended to facilitate the effective application and further methodological development of single cell transcriptomics.

molecular biology↗

bioRxiv2024

Overloading And unpacKing (OAK) - droplet-based combinatorial indexing for ultra-high throughput single-cell multiomic profiling

Multiomic profiling of single cells by sequencing is a powerful technique for investigating cellular diversity in complex biological systems. Although the existing droplet-based microfluidic methods have advanced single-cell sequencing, they produce a plethora of cell-free droplets and underutilize barcoding capacities due to their low cell concentration prerequisites. Meanwhile, combinatorial indexing on microplates can index cells in a more effective way; however, it requires time-consuming and laborious protocols involving multiple splitting and pooling steps. Addressing these constraints, we have developed "Overloading And unpacKing" (OAK). With reduced labor intensity, OAK can provide cost-effective multiomic profiling for hundreds of thousands of cells, offering detection sensitivity on par with commercial droplet-based methods. To demonstrate OAKs versatility, we conducted single-cell RNA sequencing (scRNA-Seq) as well as joint single-nucleus RNA sequencing (snRNA-Seq) and single-nucleus Assay for Transposase Accessible Chromatin with sequencing (snATAC-Seq) using cell lines. We further showcased OAKs performance on more complex samples, including in vitro differentiated bronchial epithelial cells and primary retinal tissues. Finally, we examined transcriptomic responses of 408,000 melanoma cells across around 1,000 starting lineages over a 90-day treatment with a RAF inhibitor, belvarafenib. We discovered a rare cell population (0.12%) that underwent a sequence of transcriptomic changes, resulting in belvarafenib resistance. Ultra-high throughput, broad compatibility with diverse molecular modalities, high detection sensitivity, and simplified experimental procedures distinguish OAK from previous methods, and render OAK a powerful tool for large-scale analysis of molecular signatures, even for rare cells.

molecular biology↗

bioRxiv2023

Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we developed Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of unique transcripts per cell by 29-fold. We used scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we revealed associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. In addition, phasing of SNVs across transcripts facilitated measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.

genomics↗