HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 26, 2025

Abstract

MOTIVATION: Histopathology, particularly hematoxylin and eosin (H&E) staining, is pivotal for diagnosing and characterizing pathological conditions by visualizing tissue morphology. However, H&E-stained images inherently lack molecular resolution, necessitating costly and labor-intensive technologies like spatial transcriptomics to uncover spatial gene expression patterns. There is a critical need for scalable computational methods that can bridge this imaging-transcriptomics gap.

Authors

  • Qing Wang
    School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China. qwang@163.com.
  • Wen-Jie Chen
    School of Biological and Behavioural Sciences, Queen Mary University of London, London, E1 4NS, United Kingdom.
  • Jing Su
    Indiana University School of Medicine.
  • Guangyu Wang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Qianqian Song
    Wake Forest School of Medicine.

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