Integrating Morphology and Gene Expression of Neural Cells in Unpaired Single-Cell Data Using GeoAdvAE.

Authors

Jinqiu Turbo Du, Tom Chartrand, Suman Jayadev, Katherine E Prater, Kevin Z Lin

Published in

Journal of computational biology : a journal of computational molecular cell biology. Pages 15578666261492321. Sep 29, 2026. Epub Sep 29, 2026.

Abstract

Cellular morphological transitions are observed across many diseases, yet their functional role remains unclear because few technologies profile form and function in the same cell. Linking single-cell morphology to transcriptomics is difficult: The two modalities share no feature correspondence and are typically measured in different cells. We present GeoAdvAE, a geometry-aware adversarial autoencoder for diagonal (unpaired) integration of single-cell morphology and single-cell RNA sequencing. GeoAdvAE couples modality-specific variational autoencoders with a Gromov-Wasserstein regularizer and an adversarial discriminator to embed unpaired morphologies and transcriptomes into a shared latent space that preserves both reconstruction fidelity and cross-modal geometry. Using patch-seq neurons with joint morphology-RNA measurements as ground truth, GeoAdvAE attains the best cross-modal cell-type matching accuracy among diagonal integration methods, outperforming optimal-transport, latent-alignment, and adversarial baselines. Applied to 98 CAJAL-quantified microglial morphologies and 31,948 single-cell transcriptomes from the 5xFAD Alzheimer's disease model, GeoAdvAE recovers a one-dimensional axis that aligns the two modalities. Integrated-gradient attribution highlights transcriptomic shifts (DNA repair in ramified microglia; cell killing in amoeboid microglia), nominates gene markers (Ms4a6b; Ftl1/Fth1), and reveals disease-associated microglia signatures that are decoupled from morphology. GeoAdvAE provides a scalable, 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.

PMID:
42809409
Bibliographic data and abstract were imported from PubMed on 30 Sep 2026.

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