It used to cost $ 5-10k to process one spatial biology slide 5 years ago, and it's still costs $ 5-10k today. Spatial biology hasn't seen the same Moore's law-like drop in costs that NGS had (see image). Many of the diseases with the greatest burden on health are multifactorial, involving complex interactions between cell types inside 3D tissues, and spatial biology is critical for understanding diseases & designing drugs to solve these multifactorial challenges. Data per spatial biology slide has definitely increased, but more data per slide doesn't replace the need to measure biological heterogeneity across tissues, patients and conditions. We still need to process a huge number of independent samples and slides. Unlike NGS, where the growth in data density per flow cell enabled barcoding and profiling of greater numbers of samples, spatial is limited by the number of tissue sections you can plate per slide and not easily multiplexed in the same way. This constraint might explain why spatial biology adoption has been more concentrated on applications with high-value samples (e.g. patient samples from clinical trials), rather than early drug discovery, where hundreds or thousands of slides might be needed (e.g. in high-throughput screening). Spatial data is obviously extremely valuable and fills a key gap missed by other measurement technologies, but this bottleneck is a key blocker slowing the discovery of better drugs against complex diseases. Understanding complex disease requires systematically measuring how tissue biology varies across samples, disease states, and lots of perturbations. This is also one of the reasons that we ended up building our own in-house custom hardware & automation at Waypoint, to make it possible to profile hundreds (and later thousands!) of spatial biology slides per month. The goal is to enable very large-scale spatial perturbational screens & new types of AI models that learn how tissues respond to these interventions.