Single-cell technologies have transformed biology by making cellular heterogeneity measurable. They allow cells to be classified, compared, and linked to molecular states with a level of detail that was previously difficult to achieve. Yet most single-cell data still describe cells as individual units. This is powerful, but incomplete when biological function depends on relationships between cells. Cellular interaction data adds a different layer. It does not only ask what a cell is, or which state it occupies, but which other cells it engages with. This distinction matters because two cells with similar molecular profiles can behave differently depending on their interaction partners and local context.

In therapeutic settings, this could be particularly relevant. A therapeutic cell may express the expected markers, but its contribution to response depends on whether it engages productively with target cells. Similarly, a target cell may appear vulnerable based on its antigen profile, but remain poorly eliminated if the relevant cellular interaction does not occur or is not sustained. The added value of cell-cell interaction data is therefore not simply higher resolution. It is a different form of information. Molecular profiling describes cellular identity and potential. Interaction measurements describe how this potential is realized within a cellular system.

This perspective could help explain why similar cell populations sometimes produce different functional outcomes. It may also help identify which cellular relationships are associated with response, resistance, or toxicity. Cellular interaction data should not replace single-cell profiling but be combined with it. Together, these approaches could connect cellular state with cellular behavior and make it easier to study immune function as a coordinated process rather than as a collection of isolated profiles. The critical challenge is to make this layer interpretable. Not every interaction will be meaningful, and not every measurable relationship will be useful. The value will depend on whether interaction data can clarify mechanisms, support decision making and inform therapy development more broadly.