Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Journal: Science advances
PMID: 39772690

Abstract

Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained neural network that digitally transforms dark-field images of unstained bacteria into their Gram-stained equivalents matching bright-field image contrast. After a one-time training, the virtual Gram staining model processes an axial stack of dark-field microscopy images of label-free bacteria (never seen before) to rapidly generate Gram staining, bypassing several chemical steps involved in the conventional staining process. We demonstrated the success of virtual Gram staining on label-free bacteria samples containing and by quantifying the staining accuracy of the model and comparing the chromatic and morphological features of the virtually stained bacteria against their chemically stained counterparts. This virtual bacterial staining framework bypasses the traditional Gram staining protocol and its challenges, including stain standardization, operator errors, and sensitivity to chemical variations.

Authors

  • Çağatay Işıl
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Hatice Ceylan Koydemir
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Merve Eryilmaz
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Kevin de Haan
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Nir Pillar
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Koray Mentesoglu
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Aras Firat Unal
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Yair Rivenson
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Sukantha Chandrasekaran
    Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, CA, USA.
  • Omai B Garner
    Department of Pathology and Laboratory Medicine , University of California , Los Angeles , California 90025 , United States.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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