Virtual staining of unstained tissue for histologic assessment is the subject of burgeoning research and has been approached using various methodologies. This technology has the potential to reduce laboratory turnaround time, reduce consumption of chemicals and water, and improve occupational health and safety for laboratory personnel. In addition, the technology presents the alluring prospect of non-destructive hematoxylin and eosin histologic examination, allowing unlimited multiplexing on the same section, and improved image analysis techniques that are unimpeded by inter- and intra-laboratory stain variation. Recent advancements in this field and projections of applicability to nonclinical pharmaceutical development and discovery pathology settings warrant a brief review. Virtual staining has been applied most widely to unlabeled (unstained) tissue but has also been used in stain-to-stain transformation. Specimen input varies from conventional formalin-fixed paraffin-embedded tissue to partially processed or intact tissue. Imaging is commonly traditional brightfield or fluorescence, although other modalities are available. Depending on the imaging modality, computational methods such as deep learning neural networks are used to infer the virtual stain that is ultimately viewed as a digitized histologic image. Current barriers to applicability include qualification, histologic quality, generative artificial intelligence concerns, training material acquisition, and infrastructure.
Esther E. V. Crouch (Sat,) studied this question.
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