Allograft tissue under the microscope: only the beginning

Sarthak Virmani, Arundati Rao, Madhav C. Menon

Research output: Contribution to journalReview articlepeer-review

Abstract

Purpose of reviewTo review novel modalities for interrogating a kidney allograft biopsy to complement the current Banff schema.Recent findingsNewer approaches of Artificial Intelligence (AI), Machine Learning (ML), digital pathology including Ex Vivo Microscopy, evaluation of the biopsy gene expression using bulk, single cell, and spatial transcriptomics and spatial proteomics are now available for tissue interrogation.SummaryBanff Schema of classification of allograft histology has standardized reporting of tissue pathology internationally greatly impacting clinical care and research. Inherent sampling error of biopsies, and lack of automated morphometric analysis with ordinal outputs limit its performance in prognostication of allograft health. Over the last decade, there has been an explosion of newer methods of evaluation of allograft tissue under the microscope. Digital pathology along with the application of AI and ML algorithms could revolutionize histopathological analyses. Novel molecular diagnostics such as spatially resolved single cell transcriptomics are identifying newer mechanisms underlying the pathologic diagnosis to delineate pathways of immunological activation, tissue injury, repair, and regeneration in allograft tissues. While these techniques are the future of tissue analysis, costs and complex logistics currently limit their clinical use.

Original languageEnglish
Pages (from-to)126-132
Number of pages7
JournalCurrent Opinion in Organ Transplantation
Volume28
Issue number2
DOIs
StatePublished - 1 Apr 2023
Externally publishedYes

Keywords

  • digital pathology
  • imaging mass spectrometry
  • machine learning
  • transcriptomics

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