GIDER: GUIDE-INFORMED DUAL-STAGE MODELING OF CRISPR–CAS9 EDITING RESPONSES IN SINGLE CELLS

Predicting single-cell transcriptional responses to specific CRISPR–Cas9 editing designs can support the assessment of intervention outcomes before experimentation. However, target-gene identity alone cannot distinguish different single-guide RNAs (sgRNAs) targeting the same gene under identical cellular backgrounds,limiting design-specific response prediction. Sequence information provides finer-grained conditions, but how to organize sgRNA–site relationships and their corresponding DNA contexts into predictive conditions for unseen editing designs remains underexplored. We therefore introduce GIDER, a two-stage framework inspired by gene-editing mechanisms that models targeting relationships and the corresponding site contexts as interdependent conditioning information. First, GIDER incorporates target-gene priors to encode sgRNA relationships with intended target sites and computationally predicted candidate off-target sites. It then uses each site’s query to read its own long-range DNA context and reuses the first-stage site weights for aggregation. Auxiliary supervision is provided by observed differences in mean expression between perturbed and background-matched control populations in the training data. Conditioned on these representations and control expression, GIDER combines discrete change-state prediction with continuous magnitude generation to learn perturbed expression distributions from unpaired data. Across unseen-sgRNA and unseen-target-gene tasks on Frangieh and Papalexi, GIDER achieves the lowest all-gene EMD, defined as the mean gene-wise Wasserstein-1 distance, in all four settings, with reductions of 6.00%–19.62% relative to the best baseline in each setting. On Papalexi, it leads all six error metrics and the perturbation discrimination score (PDS cosine) in both tasks. Controlled ablations assess the contributions of editing information, conditioning structure, and response supervision. These results suggest that structured editing representations improve prediction of the transcriptional consequences of specific editing designs, providing a computational basis for virtual gene editing to inform experimental design.