TY - JOUR
T1 - Allograft tissue under the microscope
T2 - only the beginning
AU - Virmani, Sarthak
AU - Rao, Arundati
AU - Menon, Madhav C.
N1 - Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.
PY - 2023/4/1
Y1 - 2023/4/1
N2 - 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.
AB - 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.
KW - digital pathology
KW - imaging mass spectrometry
KW - machine learning
KW - transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85149384328&partnerID=8YFLogxK
U2 - 10.1097/MOT.0000000000001052
DO - 10.1097/MOT.0000000000001052
M3 - Review article
C2 - 36787238
AN - SCOPUS:85149384328
SN - 1087-2418
VL - 28
SP - 126
EP - 132
JO - Current Opinion in Organ Transplantation
JF - Current Opinion in Organ Transplantation
IS - 2
ER -