Mario Acera-Mateos, Xian Adiconis, J.J. Li, Domenica Marchese, Ginevra Caratù, Chung-Chau Hon, Prabha Tiwari, Miki Kojima, Beate Vieth, Michael A. Murphy, Sean K. Simmons, Thomas Lefevre, Irene Claes, Christopher L. O’Connor, Rajasree Menon, Edgar A. Otto, Yoshinari Ando, Katy Vandereyken, Matthias Kretzler, Markus Bitzer, Ernest Fraenkel, Thierry Voet, Wolfgang Enard, Piero Carninci, Holger Heyn, Joshua Z. Levin, Elisabetta Mereu
BACKGROUND: The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. Using the kidney as an emblematic example of a complex organ, we perform a systematic evaluation of multimodal single-cell integration strategies, with heart tissue used for additional methodological validation. RESULTS: We generate a benchmarking dataset for the renal cortex by integrating 3' and 5' scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we develop the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assess integration strategies. "Horizontal" integration of scRNA and snRNA-seq improves cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq has an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration is especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. CONCLUSIONS: Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.