Integrating morphology and gene expression of neural cells in unpaired single-cell data using GeoAdvAE

Cellular morphological transitions are widely observed in many diseases; however, the functional role of these morphologies remains unclear, as most technologies are unable to profile both form and function simultaneously. However, computationally linking single-cell morphology and transcriptomics of neural cells is challenging due to a lack of feature correspondences. We present GeoAd-vAE, a geometry-aware adversarial autoencoder for diagonal (unpaired) integration of single-cell morphology and single-cell RNA sequencing. GeoAdvAE combines modality-specific variational autoencoders with a Gromov-Wasserstein regularizer and an adversarial discriminator to embed unpaired morphologies and transcriptomes into a shared latent space, preserving both reconstruction fidelity and cross-modal geometry. To validate the correctness of integration, we leverage Patch-seq neurons with joint morphology-RNA measurements. Using these ground-truth pairings, GeoAdvAE achieves the best cross-modal cell-type matching accuracy compared to other diagonal integration methods, outperforming optimal transport, latent alignment, and adversarial baselines. We then apply GeoAdvAE to microglia from the 5xFAD mouse model, a model system of Alzheimer’s disease. We integrate 98 CAJAL-quantified morphologies, spanning amoeboid and ramified forms, with 31,948 single-cell RNA-seq profiles across homeostatic, proliferating, and disease-associated states to recover a one-dimensional axis that aligns the two modalities. We uncover novel biology by using integrated gradient attribution, where we highlight transcriptomic shifts (DNA repair in ramified; cell killing in amoeboid) and nominate gene markers ( Ms4a6b ; Ftl1 / Fth1 ) corresponding with morphological changes. Our integration also enables us to identify DAM signatures that do not correspond to morphological changes. GeoAdvAE provides a scalable and interpretable approach to connecting cellular “form” and “function” when joint profiling of morphology and transcriptomics is impractical. Our method is publicly available at https://github.com/turbodu222/GeoAdVAE