Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas.
Acera-Mateos, M.; Adiconis, X.; Li, J.-K.; Marchese, D.; Caratu, G.; Hon, C.-C.; Tiwari, P.; Kojima, M.; Vieth, B.; Murphy, M. A.; Simmons, S. K.; Lefevre, T.; Claes, I.; O'Connor, C. L.; Menon, R.; Otto, E. A.; Ando, Y.; Vandereyken, K.; Kretzler, M.; Bitzer, M.; Fraenkel, E.; Voet, T.; Enard, W.; Carnici, P.; Heyn, H.; Levin, J. Z.; Mereu, E.
Show abstract
The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. We generated 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 developed the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assessed integration strategies. "Horizontal" integration of scRNA and snRNA-seq improved cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq had an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration was especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.
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