Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2
Long-read scRNA-seq enables the profiling of RNA isoform expression and alternative splicing at the single cell level. However, the diversity of single cell technologies and sparsity of isoform data requires flexible, accurate tools for data processing and analysis. Here we introduce FLAMESv2, a highly modular and protocol-agnostic framework for long-read scRNA-seq data processing. FLAMESv2 supports a wide range of single-cell and spatial experimental protocols, is highly configurable and scalable, allowing seamless multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate FLAMESv2 compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Applying FLAMESv2 to \textit{in vitro} differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA sequencing, unlocking this powerful methodology for RNA isoform characterisation.