Biology subjects

Lio, C. T.

Publications and source records attributed to Lio, C. T..

6 recordsLinked to original sources

bioRxiv2025

Refinement Strategies for Tangram for Reliable Single-Cell to Spatial Mapping

MotivationSingle-cell RNA sequencing (scRNA-seq) provides comprehensive gene expression data at a single-cell level but lacks spatial context. In contrast, spatial transcriptomics captures both spatial and transcriptional information but is limited by resolution, sensitivity, or feasibility. No single technology combines both the high spatial resolution and deep transcriptomic profiling at the single-cell level without trade-offs. Spatial mapping tools that integrate scRNA-seq and spatial transcriptomics data are crucial to bridge this gap. However, we found that Tangram, one of the most prominent spatial mapping tools, provides inconsistent results over repeated runs. ResultsWe refine Tangram to achieve more consistent cell mappings and investigate the challenges that arise from data characteristics. We find that the mapping quality depends on the gene expression sparsity. To address this, we (1) train the model on an informative gene subset, (2) apply cell filtering, (3) introduce several forms of regularization, and (4) incorporate neighborhood information. Evaluations on real and simulated mouse datasets demonstrate that this approach improves both gene expression prediction and cell mapping. Consistent cell mapping strengthens the reliability of the projection of cell annotations and features into space, gene imputation, and correction of low-quality measurements. Our pipeline, which includes gene set and hyperparameter selection, can serve as guidance for applying Tangram on other datasets, while our benchmarking framework with data simulation and inconsistency metrics is useful for evaluating other tools or Tangram modifications. AvailabilityThe refinements for Tangram and our benchmarking pipeline are available in https://github.com/daisybio/Tangram_Refinement_Strategies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/634996v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@5ef8a8org.highwire.dtl.DTLVardef@11707d7org.highwire.dtl.DTLVardef@103e1f2org.highwire.dtl.DTLVardef@1e713e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Alternative splicing in mechanically stretched podocytes as a model of glomerular hypertension

BackgroundAlterations in pre-mRNA splicing play an important role in disease pathophysiology. However, the role of alternative splicing (AS) for podocytes in hypertensive nephropathy (HN) has not been investigated. The purpose of the Sys_CARE project was to identify AS events that play a role in the development and progression of HN. MethodsMurine podocytes were exposed to mechanical stretch, after which proteins and mRNA were analyzed by proteomics, RNA-Seq and several bioinformatic AS tools. ResultsBased on transcriptomics and proteomics analysis we could observe significant changes in gene expression and abundance of proteins under mechanical stretch compared to unstretched conditions. By RNA-Seq, we identified over 3,000 alternative spliced genes after mechanical stretch, including all types of AS events. We found 17 genes that showed an AS event in four different splicing analysis tools. From these, we focused on Myl6, a component of the myosin protein complex, and Shroom3, an actin-binding protein crucial for podocyte function. We found two Shroom3 isoforms that showed significant changes in expression upon mechanical stretch, which was verified by qRT-PCR and in situ hybridization. Furthermore, we observed an expression switch of two Myl6 isoforms after mechanical stretch. This switch is accompanied by a change in a C-terminally located amino acid sequence. ConclusionsIn summary, mechanical stretch of cultured podocytes is an excellent model to simulate hypertensive nephropathy. In depth RNA-Seq analysis disclosed alternative splicing events, such as in Shroom3 and Myl6, which may play a crucial role in the pathophysiology of hypertension-induced nephropathy.

cell biology↗

bioRxiv2024

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions

RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is non-trivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool for their task, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that DEXSeq, edgeR, and LimmaDS are better choices for paired-end data, while DSGseq and DEXSeq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/IS analysis based on the evidence provided using GO terms enrichment analysis.

bioinformatics↗

bioRxiv2022

Systematic analysis of alternative splicing in time course data using Spycone

During disease progression or organism development, alternative splicing (AS) may lead to isoform switches (IS) that demonstrate similar temporal patterns and reflect the AS co-regulation of such genes. Tools for dynamic process analysis usually neglect AS. Here we propose Spycone (https://github.com/yollct/spycone), a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection.

bioinformatics↗

bioRxiv2022

Alternative splicing analysis benchmark with DICAST

Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for the analysis of alternative splicing integrating 11 splice-aware mapping and eight event detection tools. We benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, we propose the first reporting standard to unify existing formats and to guide future tool development.

bioinformatics↗

bioRxiv2021

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with protein-protein and domain-domain interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.

bioinformatics↗