“Current spatial transcriptomics workflows will mature into robust, semi-automated platforms: AI-driven cell segmentation is a reliable proxy for true boundaries once specialized ‘bleed’ corrections (e.g. nuclear expansion vs. cytoplasmic detection) are applied; cell identity is best modeled as a continuous manifold with discrete cluster labels overlaid for interpretability; normalization protocols that explicitly model zero-inflation and guard against over-imputation will reveal genuine low-abundance signals without manufacturing spurious consensus; dissociation methods employing cold-active proteases will largely mitigate stress-induced early-response artifacts; and multimodal integration will deliver holistic insights if diagonal alignment algorithms and overfitting safeguards become standard. With these measures, the field will contain most risks associated with artifacts and maintain biological fidelity.”