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Publications

Journal Article Ppublish

PubMed: 40749063 DOI: 10.1126/sciadv.adv0741

Virtual staining of label-free tissue in imaging mass spectrometry.

Sci Adv 11(31):eadv0741

Authors: Yijie Zhang, Luzhe Huang, Nir Pillar, Yuzhu Li, Yuhang Li, Lukasz G Migas, Raf Van de Plas, Jeffrey M Spraggins, Aydogan Ozcan

Affiliations

  • Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Bioengineering Department, University of California, Los Angeles, CA 90095, USA.
  • California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA.
  • Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA. Bioengineering Department, University of California, Los Angeles, CA 90095, USA. California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA.
  • Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA.
  • Delft Center for Systems and Control, Delft University of Technology, 2628 Delft, Netherlands.
  • Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA.
  • Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA.
  • Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA.
  • Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
  • Department of Surgery, University of California, Los Angeles, CA 90095, USA.

Abstract

Imaging mass spectrometry (IMS) enables untargeted, highly multiplexed mapping of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms lack microscopy-level spatial resolution and cellular morphological contrast, necessitating subsequent histochemical staining, microscopic imaging, and advanced image registration to correlate/link molecular distributions with specific tissue features and cell types. We present a diffusion model-based virtual histological staining approach that enhances spatial resolution and digitally introduces cellular morphological contrast into mass spectrometry images of label-free human tissue. Blind testing on human kidney tissue demonstrated that the virtually stained images of label-free samples closely match their histochemically stained counterparts (with periodic acid-Schiff staining), showing high concordance in identifying key renal pathology structures despite using IMS data with 10-fold larger pixel size. Additionally, our approach uses optimized noise sampling during the diffusion model's inference to achieve reliable and repeatable virtual staining. We believe this virtual staining method will open avenues for IMS-based biomedical research.

MeSH

Cells, Biological Science Disciplines, Tissues