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Background <p>Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptomic analysis but loses spatial context due to tissue dissociation, thereby limiting insights into spatially regulated cell–cell interactions critical for tissue function and disease, including cancer. Consequently, developing approaches capable of extracting spatially relevant intercellular interaction information from scRNA-seq data is of substantial importance for elucidating mechanisms underlying disease initiation and progression.</p> Methods <p>Here, we present DeepPNCC, a novel deep learning framework that reconstructs pseudo-spatial cell-cell interaction networks (PSCCIs) from scRNA-seq data by leveraging latent spatial cues preserved in undissociated cell aggregates. Built on a variational graph autoencoder (VGAE) enhanced with adversarial regularization, DeepPNCC uniquely integrates local adjacency matrices derived from multiplet data, without relying on prior knowledge such as ligand-receptor pairs, to infer global, spatially informed interaction landscapes. DeepPNCC is available as an open-source Python package.</p> Results <p>DeepPNCC outperforms existing methods in recovering interactions aligned with spatial transcriptomics across mouse brain and breast cancer datasets. In breast cancer, the inferred cell-cell interaction network reveals a closed-loop signaling axis in triple-negative breast cancer, in which cancer-associated fibroblasts (CAFs) and perivascular-like cells (PVL) promote angiogenesis through the ADM-CALCRL-STAT1-CD40/CCL2/ICAM1 pathway. These findings provide important clues for elucidating the regulatory mechanisms of the tumor microenvironment.</p> Conclusions <p>DeepPNCC provides an efficient tool for spatially informed analysis of scRNA-seq data, enabling the reconstruction of pseudo-spatial cell–cell interaction networks from datasets lacking explicit spatial information. This approach expands the spatial analytical scope of scRNA-seq and facilitates mechanistic dissection of cellular ecosystems in both health and disease.</p>

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DeepPNCC: reconstructing pseudo-spatial cell-cell interaction landscapes from single-cell data to decipher breast cancer pathogenesis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • Xu-hua Li,
  • Xiao-ling Gao,
  • Deng-hui Guo,
  • Jie Shen,
  • Si-rui Li,
  • Na Wang,
  • Xiao-ling Wen,
  • Jiang-ying Liang,
  • Hong-jiu Wang,
  • Zhen-zhen Wang

摘要

Background

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptomic analysis but loses spatial context due to tissue dissociation, thereby limiting insights into spatially regulated cell–cell interactions critical for tissue function and disease, including cancer. Consequently, developing approaches capable of extracting spatially relevant intercellular interaction information from scRNA-seq data is of substantial importance for elucidating mechanisms underlying disease initiation and progression.

Methods

Here, we present DeepPNCC, a novel deep learning framework that reconstructs pseudo-spatial cell-cell interaction networks (PSCCIs) from scRNA-seq data by leveraging latent spatial cues preserved in undissociated cell aggregates. Built on a variational graph autoencoder (VGAE) enhanced with adversarial regularization, DeepPNCC uniquely integrates local adjacency matrices derived from multiplet data, without relying on prior knowledge such as ligand-receptor pairs, to infer global, spatially informed interaction landscapes. DeepPNCC is available as an open-source Python package.

Results

DeepPNCC outperforms existing methods in recovering interactions aligned with spatial transcriptomics across mouse brain and breast cancer datasets. In breast cancer, the inferred cell-cell interaction network reveals a closed-loop signaling axis in triple-negative breast cancer, in which cancer-associated fibroblasts (CAFs) and perivascular-like cells (PVL) promote angiogenesis through the ADM-CALCRL-STAT1-CD40/CCL2/ICAM1 pathway. These findings provide important clues for elucidating the regulatory mechanisms of the tumor microenvironment.

Conclusions

DeepPNCC provides an efficient tool for spatially informed analysis of scRNA-seq data, enabling the reconstruction of pseudo-spatial cell–cell interaction networks from datasets lacking explicit spatial information. This approach expands the spatial analytical scope of scRNA-seq and facilitates mechanistic dissection of cellular ecosystems in both health and disease.