TY - GEN
T1 - Correlation of glomerular histomorphometry changes with spatially resolved transcriptomic profiles in diabetic nephropathy
AU - Naglah, Ahmed
AU - Mimar, Sayat
AU - Paul, Anindya
AU - Ferreira, Ricardo Melo
AU - Rosenberg, Avi Z.
AU - Han, Seung Seok
AU - Ray, Jessica
AU - Eadon, Michael T.
AU - Sarder, Pinaki
N1 - Publisher Copyright:
© 2024 SPIE.
PY - 2024
Y1 - 2024
N2 - Diabetic nephropathy (DN), a common complication of diabetes mellitus, remains a leading cause of endstage renal disease. Histopathological assessment of renal biopsy remains the gold standard for diagnosis. Accurate diagnosis is crucial for timely intervention and personalized management plans. Machine learning (ML) models can analyze digital pathology slides, learn DN biomarkers, and aid in DN staging. Developing ML models can be challenging for the limited availability of annotated images, subjectivity in histopathology interpretation, and histology artifacts. Molecular profiling such as single-cell RNA sequencing (SC) and spatial transcriptomics (ST) can contribute to better understanding of cellular heterogeneity and molecular pathways. Clinical use of molecular tests is limited due to the absence of well-established protocols specific to DN diagnosis. In this study, we propose a framework for correlating glomerular histomorphometry with spatially resolved transcriptomics to better understand the histologic spectrum of DN. The framework uses manual tissue labels by experienced users, and hybrid labels by combining user input and unsupervised clustering of molecular data. Clustering is performed on the gene expression levels of disease biomarkers and on the cell type decomposition of tissue by integration with SC reference data from KPMP. We used a dataset of 6 DN and 3 normal cases, with frozen section histology, ST, and SC collected at Seoul National University Hospital, Seoul, South Korea. Our initial experiments identified a correlation between the imaging features histomorphometry and disease label. Our cloud-based prototype visualizes both gene markers and cell type decomposition as a heatmap on histology, enables molecular-informed validation of structures, enables adding manual labels, and visualizes the clusters on histology. In conclusion, our framework can analyze the correlation between histomorphometry and tissue labels generated in a molecular-informed environment. Our cloud-based prototype can aid the diagnosis process by visualizing these correlations overlaid on digital slides.
AB - Diabetic nephropathy (DN), a common complication of diabetes mellitus, remains a leading cause of endstage renal disease. Histopathological assessment of renal biopsy remains the gold standard for diagnosis. Accurate diagnosis is crucial for timely intervention and personalized management plans. Machine learning (ML) models can analyze digital pathology slides, learn DN biomarkers, and aid in DN staging. Developing ML models can be challenging for the limited availability of annotated images, subjectivity in histopathology interpretation, and histology artifacts. Molecular profiling such as single-cell RNA sequencing (SC) and spatial transcriptomics (ST) can contribute to better understanding of cellular heterogeneity and molecular pathways. Clinical use of molecular tests is limited due to the absence of well-established protocols specific to DN diagnosis. In this study, we propose a framework for correlating glomerular histomorphometry with spatially resolved transcriptomics to better understand the histologic spectrum of DN. The framework uses manual tissue labels by experienced users, and hybrid labels by combining user input and unsupervised clustering of molecular data. Clustering is performed on the gene expression levels of disease biomarkers and on the cell type decomposition of tissue by integration with SC reference data from KPMP. We used a dataset of 6 DN and 3 normal cases, with frozen section histology, ST, and SC collected at Seoul National University Hospital, Seoul, South Korea. Our initial experiments identified a correlation between the imaging features histomorphometry and disease label. Our cloud-based prototype visualizes both gene markers and cell type decomposition as a heatmap on histology, enables molecular-informed validation of structures, enables adding manual labels, and visualizes the clusters on histology. In conclusion, our framework can analyze the correlation between histomorphometry and tissue labels generated in a molecular-informed environment. Our cloud-based prototype can aid the diagnosis process by visualizing these correlations overlaid on digital slides.
KW - Diabetic Nephropathy
KW - Digital Pathology
KW - Machine Learning
KW - Spatial-Omics
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85191290115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191290115&partnerID=8YFLogxK
U2 - 10.1117/12.3008461
DO - 10.1117/12.3008461
M3 - Conference contribution
AN - SCOPUS:85191290115
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2024
A2 - Tomaszewski, John E.
A2 - Ward, Aaron D.
PB - SPIE
T2 - Medical Imaging 2024: Digital and Computational Pathology
Y2 - 19 February 2024 through 21 February 2024
ER -