Recent advancements in spatial transcriptomics have been highlighted through various studies. Notably, the 'scGPT-spatial' model has been introduced as a continual pretrained foundation model specifically for spatial transcriptomics. This model builds on the existing scGPT framework and is continually pretrained on a large-scale spatial transcriptomic corpus, which includes the 'SpatialHuman30M' dataset consisting of 30 million human cells and spots derived from four sequencing protocols: Visium, Visium HD 14, MER-FISH, and Xenium. The scGPT-spatial model aims to harmonize different spatial technologies and offers enhancements such as the Mixture-of-Experts (MoE) architecture in its decoders, which captures expression profiles from diverse sequencing methods. Additionally, it employs a coordinate-based sampling and training strategy to facilitate spatially-aware learning, enabling the model to recognize complex spatial patterns from transcriptomic data. Other studies also explored topics such as axonal beading induced by photo-oxidation and the impact of ambient contamination on demultiplexing methods for single-nucleus multiome experiments, contributing to the broader understanding of spatial omics and its applications.