Begin your next experiment with a wider view.

Most teams still choose samples from morphology alone, before any molecular read exists. When samples outnumber assay slots, morphology and cohort knowledge usually pick the block. That reasoning works, but nobody writes it down, and it runs before anyone has a molecular reading of any candidate.

Choosing badly costs more than the kit. A section that turns out mostly stroma consumes a sample nobody can recover, and so does one that misses the boundary the question was about. Because it also takes a slot in a queue, everything behind it waits. In a study sized by budget, the selection step quietly decides what the study can conclude.

Ranking candidates on expression wants the measurement you have not yet paid for. That loop is why selection rarely gets a molecular rehearsal. Ranking on expression needs expression, and expression is what the assay you have not yet paid for would deliver. So the decision falls back on what a section looks like at low magnification, and the record of it lives in somebody's memory.

Predicting first breaks the circle and leaves something you can inspect. DeepSpot-M reads every candidate for the same genes, at every retained tile, before anyone cuts any of them, so you compare candidates on the quantity your question turns on. However, how a section looks still matters. It stops carrying the decision on its own.

An order can hold while the individual values stay uncertain. Assay spend asks two different questions. Which sample ranks higher is an ordering. How much of a gene is there is a measurement. Virtual spatial transcriptomics helps with the first, where to spend the kit, while the assay you run still settles the second.

The same reasoning places one square. A capture area reaches a fixed piece of the section and misses the rest, so where that square goes amounts to a decision in its own right, usually taken from the stain alone. Read the whole section with DeepSpot-M first and the placement becomes a choice between places.