Elyas Heidari
Elyas Heidari is a PhD student specialising in AI in Oncology at the German Cancer Research Center (DKFZ) in Heidelberg. He completed his Bachelor's in Computer Science and Mathematics at Sharif University of Technology in Iran and holds a Master’s degree from ETH Zurich, department of Biological Science and Systems Engineering. During his internship at EMBL-EBI, he focused on spatial omics and computational genomics. Elyas has developed packages such as MUVIS (R) and SageNet (python) for biomedical data science and machine learning and is currently interested in developing computational methods for the analysis and integration of spatial omics data at scale.
Sessions
Imaging-based spatial omics datasets present challenges in reliably segmenting single cells. Achieving accurate segmentation at single-cell resolution is crucial to unravelling multicellular mechanisms and understanding cell-cell communications in spatial omics studies. Despite the considerable progress and the variety of methods available for cell segmentation, challenges persist, including issues of over-segmentation, under-segmentation, and contamination from neighbouring cells. While combining multiple segmentation methods with distinct advantages has been proposed, it does not completely resolve these issues. Additionally, scalability remains an obstacle, particularly when applying these methods to larger tissues and gene panels in targeted studies.
Here we introduce Segger, a cell segmentation model designed for single-molecule resolved datasets, leveraging the co-occurrence of nucleic and cytoplasmic molecules (e.g., transcripts). It employs a heterogeneous graph structure on molecules and nuclei, integrating fixed-radius nearest neighbor graphs for nuclei and molecules, along with edges connecting transcripts to nuclei based on spatial proximity. A heterogeneous graph neural network (GNN) is then used to propagate information across these edges to learn the association of molecules with nuclei. Post-training, the model refines cell borders by regrouping transcripts based on confidence levels, overcoming issues like nucleus-less cells or overlapping cells. Benchmarks on 10X Xenium and MERSCOPE technologies reveal Segger's superiority in accuracy and efficiency over other segmentation methods, such as Baysor, Cellpose, BIDcell, and simple nuclei-expansion. Segger can be pre-trained on one or more datasets and fine-tuned with new data, even acquired via different technologies. Its highly parallelizable nature allows for efficient training across multiple GPU machines, facilitated by recent graph learning techniques. Compared to other model-based methods like Baysor and BIDCell Segger's training is orders of magnitude faster, and more accurate making it ideal for integration into preprocessing pipelines for comprehensive spatial omics atlases.