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Background <p>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.</p> Results <p>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.</p> Conclusions <p>Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.</p>

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Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月13日

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  • Mario Acera-Mateos,
  • Xian Adiconis,
  • Jessica-Kanglin 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

摘要

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.