From art to optimization: information-theoretic principles for spectral cytometry panel design

Conventional polychromatic (filter-based) and spectral flow cytometry enable high-parameter single-cell analysis through fluorochrome multiplexing, with signal separation performed via spectral unmixing. However, principled optimization of fluorochrome panels remains challenging, and general theoretical guidance is limited. This presentation develops a theoretical framework for evaluating and optimizing fluorochrome panels, grounded in a linear mixing model and Fisher information theory. Using a simplified additive Gaussian noise model, we derive the associated Fisher information matrices and corresponding Cramer–Rao bounds for fluorochrome abundance estimates. Building on this foundation, we introduce the D-optimality criterion as a principled objective for panel design and outline a simple constructive algorithm for panel selection. Finally, we discuss limitations of these simplifying assumptions (e.g., intensity-dependent and correlated noise, background/autofluorescence, and model mismatch) and briefly summarize more sophisticated heuristics currently under development.