Biology subjects

Defard, T.

Publications and source records attributed to Defard, T..

3 recordsLinked to original sources

bioRxiv2025

RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics

Imaging-based spatial transcriptomics (IST) enables high-resolution spatial mapping of RNA species. A key challenge in IST is accurate cell segmentation to assign each RNA molecule to the right cell. Here, we present RNA2seg, a novel segmentation algorithm trained on over 4 million cells from MERFISH and CosMx datasets across seven organs using a teacher-student training scheme. RNA2seg integrates RNA point clouds and all available membrane and nuclear stainings. Validation on manually annotated data shows superior performance including in zero-shot and few-shot settings. The method is available as a documented pip package: https://github.com/fish-quant/rna2seg.

bioinformatics↗

bioRxiv2024

autoFISH - a modular toolbox for sequential smFISH experiments

Fluorescence in situ hybridization (FISH) allows for spatial and quantitative profiling of gene expression by visualizing individual RNA molecules. Here, we introduce automated FISH (autoFISH), a comprehensive toolbox to conduct automated single molecule FISH (smFISH) experiments that is both cost-effective and versatile. This includes detailed plans for constructing the necessary equipment, open-source software for control, reliable experimental protocols, and analysis workflows based on our FISH-quant analysis package. Validation experiments with both cell lines and tissue samples confirmed the systems robustness. We demonstrate standard and amplified smFISH, along with a modified protocol for tissue clearing that enhances nuclear retention while preserving background reduction efficiency.

molecular biology↗

bioRxiv2023

A point cloud segmentation framework for image-based spatial transcriptomics

Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It hence presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is thus applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and cell type calling. On experimental data, our method also demonstrates proficiency in estimating RNA profiles that align with established scRNA-seq datasets. Importantly, ComSeg exhibits a particular efficiency in handling complex tissue, positioning it as a valuable tool for the community.

bioinformatics↗