If cell-cell interactions are to inform therapy development more directly, it is not enough to acknowledge their importance. The more difficult question is what it would actually mean to measure them in a way that is useful, scalable, and interpretable in practice. Not all interaction information is equally valuable. Cells can come into contact for many reasons, and not every contact is functionally meaningful. An interaction-aware technology therefore needs to distinguish between proximity and engagement. It must capture whether cells merely encounter one another or whether they establish interactions that lead to downstream functional consequences. Time is a critical dimension. Current assays provide static snapshots, but cell-cell interactions unfold dynamically. Productive engagement may require sustained contact, repeated encounters, or specific temporal patterns of signaling. A technology that collapses interactions into a single time point risks missing distinctions that are essential for understanding function. Context is equally important. Cell-cell interactions do not occur in isolation, but within complex cellular environments. Target cell density, effector cell state, and the presence of competing or suppressive cell types all influence how interactions proceed. Measuring interactions without preserving at least some contextual information limits interpretability and transferability across systems.

Scale presents another constraint. Interaction-aware measurements must operate across sufficiently large numbers of cells and samples to support robust conclusions. Capturing a small number of interactions in high detail can be informative, but therapy development requires population-level insight. This creates a tension between resolution and throughput that any practical technology must address. Equally important is interpretability. An interaction readout must map onto decisions that researchers and developers actually need to make. Data that are rich but difficult to interpret risk remaining descriptive rather than actionable. For interaction-aware technologies to be adopted, their outputs must connect clearly to questions of efficacy, safety, or mechanism. Integration with existing workflows also matters. Technologies that require extensive changes to sample handling, assay design, or analysis pipelines face high barriers to adoption. Interaction-aware measurements are more likely to be used if they complement, rather than replace, established experimental approaches. Finally, robustness and reproducibility cannot be secondary considerations. Interaction measurements must be resilient to biological variability and technical noise. Without clear standards for quality control and comparison across experiments, cellular interaction data risk being difficult to trust, particularly in translational or regulated settings.

Taken together, these requirements illustrate why measuring cellular interactions has remained challenging. They also clarify what progress would need to look like. An effective technology would capture engagement rather than proximity, preserve temporal and contextual information, operate at scale, and produce readouts that are interpretable within existing experimental and computational frameworks. The question is no longer whether such technologies would be useful. It is whether they can be designed in a way that aligns with the practical constraints of therapy development. That question will shape how interaction-centric measurements move from concept to routine use.