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Mimar S, Paul AS, Lucarelli N, Border S, Santo BA, Naglah A, Barisoni L, Hodgin J, Rosenberg AZ, Clapp W, Sarder P. ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology. bioRxiv 2024:2024.03.21.586102. [PMID: 38585837 PMCID: PMC10996469 DOI: 10.1101/2024.03.21.586102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.
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Affiliation(s)
- Sayat Mimar
- Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL
| | - Anindya S. Paul
- Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL
| | - Nicholas Lucarelli
- Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL
| | - Samuel Border
- Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL
| | - Briana A. Santo
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY
| | - Ahmed Naglah
- Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL
| | - Laura Barisoni
- Department of Pathology, Division of AI & Computational Pathology, Duke University, Durham, NC
- Department of Medicine, Division of Nephrology, Duke University, Durham, NC
| | - Jeffrey Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, MI
| | - Avi Z Rosenberg
- Department of Pathology, Johns Hopkins University, Baltimore, MD
| | - William Clapp
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, College of Medicine, Gainesville, FL
| | - Pinaki Sarder
- Quantitative Health Section, Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL
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Santo BA, Jenkins TD, Ciecierska SSK, Baig AA, Levy EI, Siddiqui AH, Tutino VM. MicroCT and Histological Analysis of Clot Composition in Acute Ischemic Stroke : A Comparative Study of MT-Retrieved Clots and Clot Analogs. Clin Neuroradiol 2024:10.1007/s00062-023-01380-1. [PMID: 38294532 DOI: 10.1007/s00062-023-01380-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/26/2023] [Indexed: 02/01/2024]
Abstract
PURPOSE Assessing clot composition on prethrombectomy computed tomography (CT) imaging may help in stroke treatment planning. In this study we seek to use microCT imaging of fabricated blood clots to understand the relationship between CT radiographic signals and the biological makeup. METHODS Clots (n = 10) retrieved by mechanical thrombectomy (MT) were collected, and 6 clot analogs of varying RBC composition were made. We performed paired microCT and histological image analysis of all 16 clots using a ScanCo microCT 100 (4.9 µm resolution) and standard H&E staining (imaged at 40×). From these data types, first order statistic (FOS) radiomics were computed from microCT, and percent composition of RBCs (%RBC) was computed from histology. Polynomial and linear regression (LR) were used to build statistical models based on retrieved thrombus microCT and %RBC that were evaluated for their ability to predict the %RBC of clot analogs from mean HU. Correlation analyses of microCT FOS with composition were completed for both retrieved clots and analogs. RESULTS The LR model fits relating MT-retrieved clot %RBC with mean (R2 = 0.625, p = 0.006) and standard deviation (R2 = 0.564, p < 0.05) in HUs on microCT were significant. Similarly, LR models relating analog histological %RBC to analog protocol %RBC (R2 = 0.915, p = 0.003) and mean HUs on microCT (R2 = 0.872, p = 0.007) were also significant. When the LR model built using MT-retrieved clots was used to predict analog %RBC from mean HUs, significant correlation was observed between predictions and actual histological %RBC (R2 = 0.852, p = 0.009). For retrieved clots, significant correlations were observed for energy and total energy with %RBC and %FP (|R| > 0.7, q < 0.01). Analogs further demonstrated significant correlation between FOS energy, total energy, variance and %WBC (|R| > 0.9, q < 0.01). CONCLUSION MicroCT can be used to build models that predict AIS clot composition from routine CT parameters and help us to better understand radiomic signatures associated with clot composition and first pass outcomes. In future work, such observations can be used to better infer clot composition and inform thrombectomy prognostics from pretreatment CTs.
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Affiliation(s)
- Briana A Santo
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, 14203, Buffalo, NY, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA
| | - TaJania D Jenkins
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, 14203, Buffalo, NY, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Shiau-Sing K Ciecierska
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, 14203, Buffalo, NY, USA
| | - Ammad A Baig
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, 14203, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Elad I Levy
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, 14203, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Adnan H Siddiqui
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, 14203, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Vincent M Tutino
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, 14203, Buffalo, NY, USA.
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA.
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA.
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Santo BA, Janbeh Sarayi SMM, McCall AD, Monteiro A, Donnelly B, Siddiqui AH, Tutino VM. Multimodal CT imaging of ischemic stroke thrombi identifies scale-invariant radiomic features that reflect clot biology. J Neurointerv Surg 2023; 15:1257-1263. [PMID: 36787955 PMCID: PMC10659055 DOI: 10.1136/jnis-2022-019967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/30/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND Biological interpretability of ischemic stroke clot imaging remains challenging. OBJECTIVE To carry out paired CT/micro-CT imaging of ischemic stroke clots retrieved by thrombectomy with the aim of identifying interpretable image features that are correlated among pretreatment image modalities and post-treatment histopathology. METHODS We performed multimodal CT imaging and histology for 10 stroke clots retrieved by mechanical thrombectomy. Clots were manually segmented from co-registered, pretreatment CT angiography (CTA) and non-contrast CT (NCCT). For the same cases, retrieved clots were iodine-stained, and imaged with a ScanCo micro-CT 100 (4.9 µm resolution). Afterwards, clots were subjected to histological processing (hematoxylin and eosin staining) and whole slide scanned (40X). Clot radiomic features (RFs) (n=93 per modality, 279 total) were extracted using PyRadiomics and histological composition was computed using Orbit Image Analysis. Correlation analysis was used to test associations between micro-CT and CTA (or NCCT) RFs as well as between RFs and histological composition. Statistical significance was considered at R≥0.65 and q<0.05. RESULTS From paired RF correlation analysis, we identified 23 scale-invariant RFs with significant correlation between micro-CT and CTA (18), and micro-CT and NCCT (5). Correlation of unpaired RFs identified 377 positively and 36 negatively correlated RFs between micro-CT and CTA, and 168 positively and 41 negatively correlated RFs between micro-CT and NCCT. Scale-invariant RFs computed from CTA and NCCT demonstrated significant correlation with red blood cell and fibrin-platelet components, while micro-CT RFs were found to be correlated with white blood cell percent composition. CONCLUSION Multimodal CT, radiomic, and histological analysis of stroke clots can help to bridge the gap between pretreatment imaging and clot pathobiology.
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Affiliation(s)
- Briana A Santo
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA
| | | | - Andrew D McCall
- Optical Imaging and Analysis Facility, University at Buffalo, Buffalo, NY, USA
| | - Andre Monteiro
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA
| | - Brianna Donnelly
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA
| | - Adnan H Siddiqui
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA
| | - Vincent M Tutino
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA
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Patel TR, Santo BA, Baig AA, Waqas M, Monterio A, Levy EI, Siddiqui AH, Tutino VM. Histologically interpretable clot radiomic features predict treatment outcomes of mechanical thrombectomy for ischemic stroke. Neuroradiology 2023; 65:737-749. [PMID: 36600077 DOI: 10.1007/s00234-022-03109-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/21/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE Radiomics features (RFs) extracted from CT images may provide valuable information on the biological structure of ischemic stroke blood clots and mechanical thrombectomy outcome. Here, we aimed to identify RFs predictive of thrombectomy outcomes and use clot histomics to explore the biology and structure related to these RFs. METHODS We extracted 293 RFs from co-registered non-contrast CT and CTA. RFs predictive of revascularization outcomes defined by first-pass effect (FPE, near to complete clot removal in one thrombectomy pass), were selected. We then trained and cross-validated a balanced logistic regression model fivefold, to assess the RFs in outcome prediction. On a subset of cases, we performed digital histopathology on the clots and computed 227 histomic features from their whole slide images as a means to interpret the biology behind significant RF. RESULTS We identified 6 significantly-associated RFs. RFs reflective of continuity in lower intensities, scattered higher intensities, and intensities with abrupt changes in texture were associated with successful revascularization outcome. For FPE prediction, the multi-variate model had high performance, with AUC = 0.832 ± 0.031 and accuracy = 0.760 ± 0.059 in training, and AUC = 0.787 ± 0.115 and accuracy = 0.787 ± 0.127 in cross-validation testing. Each of the 6 RFs was related to clot component organization in terms of red blood cell and fibrin/platelet distribution. Clots with more diversity of components, with varying sizes of red blood cells and fibrin/platelet regions in the section, were associated with RFs predictive of FPE. CONCLUSION Upon future validation in larger datasets, clot RFs on CT imaging are potential candidate markers for FPE prediction.
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Affiliation(s)
- Tatsat R Patel
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Briana A Santo
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Ammad A Baig
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Muhammad Waqas
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Andre Monterio
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Elad I Levy
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Adnan H Siddiqui
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Vincent M Tutino
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA.
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA.
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, USA.
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA.
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA.
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Santo BA, Ciecierska SSK, Mousavi Janbeh Sarayi SM, Jenkins TD, Baig AA, Monteiro A, Koenigsknecht C, Pionessa D, Gutierrez L, King RM, Gounis M, Siddiqui AH, Tutino VM. Tectonic infarct analysis: A computational tool for automated whole-brain infarct analysis from TTC-stained tissue. Heliyon 2023; 9:e14837. [PMID: 37025889 PMCID: PMC10070917 DOI: 10.1016/j.heliyon.2023.e14837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/06/2023] [Accepted: 03/20/2023] [Indexed: 03/29/2023] Open
Abstract
Background Infarct volume measured from 2,3,5-triphenyltetrazolium chloride (TTC)-stained brain slices is critical to in vivo stroke models. In this study, we developed an interactive, tunable, software that automatically computes whole-brain infarct metrics from serial TTC-stained brain sections. Methods Three rat ischemic stroke cohorts were used in this study (Total n = 91 rats; Cohort 1 n = 21, Cohort 2 n = 40, Cohort 3 n = 30). For each, brains were serially-sliced, stained with TTC and scanned on both anterior and posterior sides. Ground truth annotation and infarct morphometric analysis (e.g., brain-Vbrain, infarct-Vinfarct, and non-infarct-Vnon-infarct volumes) were completed by domain experts. We used Cohort 1 for brain and infarct segmentation model development (n = 3 training cases with 36 slices [18 anterior and posterior faces], n = 18 testing cases with 218 slices [109 anterior and posterior faces]), as well as infarct morphometrics automation. The infarct quantification pipeline and pre-trained model were packaged as a standalone software and applied to Cohort 2, an internal validation dataset. Finally, software and model trainability were tested as a use-case with Cohort 3, a dataset from a separate institute. Results Both high segmentation and statistically significant quantification performance (correlation between manual and software) were observed across all datasets. Segmentation performance: Cohort 1 brain accuracy = 0.95/f1-score = 0.90, infarct accuracy = 0.96/f1-score = 0.89; Cohort 2 brain accuracy = 0.97/f1-score = 0.90, infarct accuracy = 0.97/f1-score = 0.80; Cohort 3 brain accuracy = 0.96/f1-score = 0.92, infarct accuracy = 0.95/f1-score = 0.82. Infarct quantification (cohort average): Vbrain (ρ = 0.87, p < 0.001), Vinfarct (0.92, p < 0.001), Vnon-infarct (0.80, p < 0.001), %infarct (0.87, p = 0.001), and infarct:non-infact ratio (ρ = 0.92, p < 0.001). Conclusion Tectonic Infarct Analysis software offers a robust and adaptable approach for rapid TTC-based stroke assessment.
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Yoshida T, Latt KZ, Santo BA, Shrivastav S, Zhao Y, Fenaroli P, Chung JY, Hewitt SM, Tutino VM, Sarder P, Rosenberg AZ, Winkler CA, Kopp JB. APOL1 kidney risk variants in glomerular diseases modeled in transgenic mice. bioRxiv 2023:2023.03.27.534273. [PMID: 37090576 PMCID: PMC10120684 DOI: 10.1101/2023.03.27.534273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
APOL1 high-risk variants partially explain the high kidney disease prevalence among African ancestry individuals. Many mechanisms have been reported in cell culture models, but few have been demonstrated in mouse models. Here we characterize two models: (1) HIV-associated nephropathy (HIVAN) Tg26 mice crossed with bacterial artificial chromosome (BAC)/APOL1 transgenic mice and (2) interferon-γ administered to BAC/APOL1 mice. Both models showed exacerbated glomerular disease in APOL1-G1 compared to APOL1-G0 mice. HIVAN model glomerular bulk RNA-seq identified synergistic podocyte-damaging pathways activated by the APOL1-G1 allele and by HIV transgenes. Single-nuclear RNA-seq revealed podocyte-specific patterns of differentially-expressed genes as a function of APOL1 alleles. Eukaryotic Initiation factor-2 pathway was the most activated pathway in the interferon-γ model and the most deactivated pathway in the HIVAN model. HIVAN mouse model podocyte single-nuclear RNA-seq data showed similarity to human focal segmental glomerulosclerosis (FSGS) glomerular bulk RNA-seq data. Furthermore, single-nuclear RNA-seq data from interferon-γ mouse model podocytes (in vivo) showed similarity to human FSGS single-cell RNA-seq data from urine podocytes (ex vivo) and from human podocyte cell lines (in vitro) using bulk RNA-seq. These data highlight differences in the transcriptional effects of the APOL1-G1 risk variant in a model specific manner. Shared differentially expressed genes in podocytes in both mouse models suggest possible novel glomerular damage markers in APOL1 variant-induced diseases. Transcription factor Zbtb16 was downregulated in podocytes and endothelial cells in both models, possibly contributing to glucocorticoid-resistance. In summary, these findings in two mouse models suggest both shared and distinct therapeutic opportunities for APOL1 glomerulopathies.
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Affiliation(s)
- Teruhiko Yoshida
- Kidney Disease Section, Kidney Diseases Branch, NIDDK, NIH, Bethesda, MD
| | - Khun Zaw Latt
- Kidney Disease Section, Kidney Diseases Branch, NIDDK, NIH, Bethesda, MD
| | - Briana A. Santo
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, NY
| | - Shashi Shrivastav
- Kidney Disease Section, Kidney Diseases Branch, NIDDK, NIH, Bethesda, MD
| | - Yongmei Zhao
- Frederick National Laboratory for Cancer Research, NCI, NIH, Frederick, MD
| | - Paride Fenaroli
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD
- S.C. Nefrologia e Dialisi, AUSL-IRCCS, Reggio Emilia, Italy
| | | | | | - Vincent M. Tutino
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, NY
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, NY
- College of Medicine, University of Florida, Gainesville, FL
| | - Avi Z. Rosenberg
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD
| | - Cheryl A. Winkler
- Frederick National Laboratory for Cancer Research, NCI, NIH, Frederick, MD
| | - Jeffrey B. Kopp
- Kidney Disease Section, Kidney Diseases Branch, NIDDK, NIH, Bethesda, MD
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Poppenberg KE, Chien A, Santo BA, Baig AA, Monteiro A, Dmytriw AA, Burkhardt JK, Mokin M, Snyder KV, Siddiqui AH, Tutino VM. RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood. J Pers Med 2023; 13:jpm13020266. [PMID: 36836499 PMCID: PMC9967913 DOI: 10.3390/jpm13020266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/24/2023] [Accepted: 01/27/2023] [Indexed: 02/04/2023] Open
Abstract
After detection, identifying which intracranial aneurysms (IAs) will rupture is imperative. We hypothesized that RNA expression in circulating blood reflects IA growth rate as a surrogate of instability and rupture risk. To this end, we performed RNA sequencing on 66 blood samples from IA patients, for which we also calculated the predicted aneurysm trajectory (PAT), a metric quantifying an IA's future growth rate. We dichotomized dataset using the median PAT score into IAs that were either more stable and more likely to grow quickly. The dataset was then randomly divided into training (n = 46) and testing cohorts (n = 20). In training, differentially expressed protein-coding genes were identified as those with expression (TPM > 0.5) in at least 50% of the samples, a q-value < 0.05 (based on modified F-statistics with Benjamini-Hochberg correction), and an absolute fold-change ≥ 1.5. Ingenuity Pathway Analysis was used to construct networks of gene associations and to perform ontology term enrichment analysis. The MATLAB Classification Learner was then employed to assess modeling capability of the differentially expressed genes, using a 5-fold cross validation in training. Finally, the model was applied to the withheld, independent testing cohort (n = 20) to assess its predictive ability. In all, we examined transcriptomes of 66 IA patients, of which 33 IAs were "growing" (PAT ≥ 4.6) and 33 were more "stable". After dividing dataset into training and testing, we identified 39 genes in training as differentially expressed (11 with decreased expression in "growing" and 28 with increased expression). Model genes largely reflected organismal injury and abnormalities and cell to cell signaling and interaction. Preliminary modeling using a subspace discriminant ensemble model achieved a training AUC of 0.85 and a testing AUC of 0.86. In conclusion, transcriptomic expression in circulating blood indeed can distinguish "growing" and "stable" IA cases. The predictive model constructed from these differentially expressed genes could be used to assess IA stability and rupture potential.
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Affiliation(s)
- Kerry E. Poppenberg
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, USA
| | - Aichi Chien
- Department of Radiology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Briana A. Santo
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY 14203, USA
| | - Ammad A. Baig
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, USA
| | - Andre Monteiro
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, USA
| | - Adam A. Dmytriw
- Neuroendovascular Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Jan-Karl Burkhardt
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Maxim Mokin
- Department of Neurosurgery, University of South Florida, Tampa, FL 33620, USA
| | - Kenneth V. Snyder
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, USA
| | - Adnan H. Siddiqui
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, USA
| | - Vincent M. Tutino
- Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY 14203, USA
- Correspondence: ; Tel.: +1-716-829-5400
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Poppenberg KE, Chien A, Santo BA, Chaves L, Veeturi SS, Waqas M, Monteiro A, Dmytriw AA, Burkhardt JK, Mokin M, Snyder KV, Siddiqui AH, Tutino VM. Profiling of Circulating Gene Expression Reveals Molecular Signatures Associated with Intracranial Aneurysm Rupture Risk. Mol Diagn Ther 2023; 27:115-127. [PMID: 36460938 PMCID: PMC9924426 DOI: 10.1007/s40291-022-00626-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2022] [Indexed: 12/04/2022]
Abstract
BACKGROUND Following detection, rupture risk assessment for intracranial aneurysms (IAs) is critical. Towards molecular prognostics, we hypothesized that circulating blood RNA expression profiles are associated with IA risk. METHODS We performed RNA sequencing on 68 blood samples from IA patients. Here, patients were categorized as either high or low risk by assessment of aneurysm size (≥ 5 mm = high risk) and Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, Site (PHASES) score (≥ 1 = high risk). Modified F-statistics and Benjamini-Hochberg false discovery rate correction was performed on transcripts per million-normalized gene counts. Protein-coding genes expressed in ≥ 50% of samples with a q value < 0.05 and an absolute fold-change ≥ 2 were considered significantly differentially expressed. Bioinformatics in Ingenuity Pathway Analysis was performed to understand the biology of risk-associated expression profiles. Association was assessed between gene expression and risk via Pearson correlation analysis. Linear discriminant analysis models using significant genes were created and validated for classification of high-risk cases. RESULTS We analyzed transcriptomes of 68 IA patients. In these cases, 31 IAs were large (≥ 5 mm), while 26 IAs had a high PHASES score. Based on size, 36 genes associated with high-risk IAs, and two were correlated with the size measurement. Alternatively, based on PHASES score, 76 genes associated with high-risk cases, and nine of them showed significant correlation to the score. Similar ontological terms were associated with both gene profiles, which reflected inflammatory signaling and vascular remodeling. Prediction models based on size and PHASES stratification were able to correctly predict IA risk status, with > 80% testing accuracy for both. CONCLUSIONS Here, we identified genes associated with IA risk, as quantified by common clinical metrics. Preliminary classification models demonstrated feasibility of assessing IA risk using whole blood expression.
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Affiliation(s)
- Kerry E Poppenberg
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Aichi Chien
- Department of Radiology, University of California Los Angeles, Los Angeles, CA, USA
| | - Briana A Santo
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Lee Chaves
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Sricharan S Veeturi
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA
| | - Muhammad Waqas
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Andre Monteiro
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Adam A Dmytriw
- Neuroendovascular Program, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jan-Karl Burkhardt
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Maxim Mokin
- Department of Neurosurgery, University of South Florida, Tampa, FL, USA
| | - Kenneth V Snyder
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Adnan H Siddiqui
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Vincent M Tutino
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA.
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA.
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA.
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA.
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9
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Andrews M, Yoshida T, Henderson CM, Pflaum H, McGregor A, Lieberman JA, de Boer IH, Vaisar T, Himmelfarb J, Kestenbaum B, Chung JY, Hewitt SM, Santo BA, Ginley B, Sarder P, Rosenberg AZ, Murakami T, Kopp JB, Kuklenyik Z, Hoofnagle AN. Variant APOL1 protein in plasma associates with larger particles in humans and mouse models of kidney injury. PLoS One 2022; 17:e0276649. [PMID: 36279295 PMCID: PMC9591058 DOI: 10.1371/journal.pone.0276649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 10/11/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Genetic variants in apolipoprotein L1 (APOL1), a protein that protects humans from infection with African trypanosomes, explain a substantial proportion of the excess risk of chronic kidney disease affecting individuals with sub-Saharan ancestry. The mechanisms by which risk variants damage kidney cells remain incompletely understood. In preclinical models, APOL1 expressed in podocytes can lead to significant kidney injury. In humans, studies in kidney transplant suggest that the effects of APOL1 variants are predominantly driven by donor genotype. Less attention has been paid to a possible role for circulating APOL1 in kidney injury. METHODS Using liquid chromatography-tandem mass spectrometry, the concentrations of APOL1 were measured in plasma and urine from participants in the Seattle Kidney Study. Asymmetric flow field-flow fractionation was used to evaluate the size of APOL1-containing lipoprotein particles in plasma. Transgenic mice that express wild-type or risk variant APOL1 from an albumin promoter were treated to cause kidney injury and evaluated for renal disease and pathology. RESULTS In human participants, urine concentrations of APOL1 were correlated with plasma concentrations and reduced kidney function. Risk variant APOL1 was enriched in larger particles. In mice, circulating risk variant APOL1-G1 promoted kidney damage and reduced podocyte density without renal expression of APOL1. CONCLUSIONS These results suggest that plasma APOL1 is dynamic and contributes to the progression of kidney disease in humans, which may have implications for treatment of APOL1-associated kidney disease and for kidney transplantation.
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Affiliation(s)
- Michael Andrews
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Teruhiko Yoshida
- Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Clark M. Henderson
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Hannah Pflaum
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Ayako McGregor
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Joshua A. Lieberman
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Ian H. de Boer
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Kidney Research Institute, University of Washington, Seattle, Washington, United States of America
| | - Tomas Vaisar
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Jonathan Himmelfarb
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Kidney Research Institute, University of Washington, Seattle, Washington, United States of America
| | - Bryan Kestenbaum
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Kidney Research Institute, University of Washington, Seattle, Washington, United States of America
| | - Joon-Yong Chung
- Center for Cancer Research, NCI, NIH, Bethesda, Maryland, United States of America
| | - Stephen M. Hewitt
- Center for Cancer Research, NCI, NIH, Bethesda, Maryland, United States of America
| | - Briana A. Santo
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Brandon Ginley
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
| | - Avi Z. Rosenberg
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, Maryland, United States of America
| | - Taichi Murakami
- Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
- Department of Nephrology, Ehime Prefectural Central Hospital, Ehime, Japan
| | - Jeffrey B. Kopp
- Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Zsuzsanna Kuklenyik
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Andrew N. Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Kidney Research Institute, University of Washington, Seattle, Washington, United States of America
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10
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Tutino VM, Santo BA, Snyder KV, Siddiqui AH. Multi-Omic Investigation of Retrieved Blood Clots May Identify Complex Traits Associated with Ischemic Stroke Etiology. World Neurosurg 2022; 168:311-313. [DOI: 10.1016/j.wneu.2022.08.132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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11
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Santo BA, Govind D, Daneshpajouhnejad P, Yang X, Wang XX, Myakala K, Jones BA, Levi M, Kopp JB, Yoshida T, Niedernhofer LJ, Manthey D, Moon KC, Han SS, Zee J, Rosenberg AZ, Sarder P. PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool. Kidney Int Rep 2022; 7:1377-1392. [PMID: 35694561 PMCID: PMC9174049 DOI: 10.1016/j.ekir.2022.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 02/25/2022] [Accepted: 03/04/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction Podocyte depletion is a histomorphologic indicator of glomerular injury and predicts clinical outcomes. Podocyte estimation methods or podometrics are semiquantitative, technically involved, and laborious. Implementation of high-throughput podometrics in experimental and clinical workflows necessitates an automated podometrics pipeline. Recognizing that computational image analysis offers a robust approach to study cell and tissue structure, we developed and validated PodoCount (a computational tool for automated podocyte quantification in immunohistochemically labeled tissues) using a diverse data set. Methods Whole-slide images (WSIs) of tissues immunostained with a podocyte nuclear marker and periodic acid–Schiff counterstain were acquired. The data set consisted of murine whole kidney sections (n = 135) from 6 disease models and human kidney biopsy specimens from patients with diabetic nephropathy (DN) (n = 45). Within segmented glomeruli, podocytes were extracted and image analysis was applied to compute measures of podocyte depletion and nuclear morphometry. Computational performance evaluation and statistical testing were performed to validate podometric and associated image features. PodoCount was disbursed as an open-source, cloud-based computational tool. Results PodoCount produced highly accurate podocyte quantification when benchmarked against existing methods. Podocyte nuclear profiles were identified with 0.98 accuracy and segmented with 0.85 sensitivity and 0.99 specificity. Errors in podocyte count were bounded by 1 podocyte per glomerulus. Podocyte-specific image features were found to be significant predictors of disease state, proteinuria, and clinical outcome. Conclusion PodoCount offers high-performance podocyte quantitation in diverse murine disease models and in human kidney biopsy specimens. Resultant features offer significant correlation with associated metadata and outcome. Our cloud-based tool will provide end users with a standardized approach for automated podometrics from gigapixel-sized WSIs.
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Affiliation(s)
- Briana A. Santo
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Darshana Govind
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York, USA
| | | | - Xiaoping Yang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Xiaoxin X. Wang
- Department of Biochemistry, Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
| | - Komuraiah Myakala
- Department of Biochemistry, Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
| | - Bryce A. Jones
- Department of Pharmacology and Physiology, Georgetown University, Washington, District of Columbia, USA
| | - Moshe Levi
- Department of Biochemistry, Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
| | - Jeffrey B. Kopp
- Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Teruhiko Yoshida
- Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Laura J. Niedernhofer
- Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Kyung Chul Moon
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seung Seok Han
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jarcy Zee
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Avi Z. Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Correspondence: Avi Z. Rosenberg, Department of Pathology, Johns Hopkins University School of Medicine, 720 Rutland Avenue, Ross Building, Room 632D, Johns Hopkins Medical Institutions, Baltimore, Maryland 21205, USA.
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York, USA
- Pinaki Sarder, Department of Pathology and Anatomical Sciences, University at Buffalo, 955 Main Street, Room 4204, Buffalo, New York 14203, USA.
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12
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Govind D, Santo BA, Ginley B, Yacoub R, Rosenberg AZ, Jen KY, Walavalkar V, Wilding GE, Worral AM, Mohammad I, Sarder P. Automated detection and quantification of Wilms' Tumor 1-positive cells in murine diabetic kidney disease. Proc SPIE Int Soc Opt Eng 2021; 11603. [PMID: 34366543 DOI: 10.1117/12.2581387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
In diabetic kidney disease (DKD), podocyte depletion, and the subsequent migration of parietal epithelial cells (PECs) to the tuft, is a precursor to progressive glomerular damage, but the limitations of brightfield microscopy currently preclude direct pathological quantitation of these cells. Here we present an automated approach to podocyte and PEC detection developed using kidney sections from mouse model emulating DKD, stained first for Wilms' Tumor 1 (WT1) (podocyte and PEC marker) by immunofluorescence, then post-stained with periodic acid-Schiff (PAS). A generative adversarial network (GAN)-based pipeline was used to translate these PAS-stained sections into WT1-labeled IF images, enabling in silico label-free podocyte and PEC identification in brightfield images. Our method detected WT1-positive cells with high sensitivity/specificity (0.87/0.92). Additionally, our algorithm performed with a higher Cohen's kappa (0.85) than the average manual identification by three renal pathologists (0.78). We propose that this pipeline will enable accurate detection of WT1-positive cells in research applications.
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Affiliation(s)
- Darshana Govind
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY
| | - Briana A Santo
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY
| | - Brandon Ginley
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY
| | - Rabi Yacoub
- Department of Internal Medicine, University at Buffalo, Buffalo, NY
| | - Avi Z Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kuang-Yu Jen
- Department of Pathology and Laboratory Medicine, University of California at Davis, CA
| | - Vignesh Walavalkar
- Department of Pathology, University of California San Francisco, San Francisco, CA
| | | | - Amber M Worral
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY
| | - Imtiaz Mohammad
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY
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13
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Santo BA, Segal BH, Tomaszewski JE, Mohammad I, Worral AM, Jain S, Visser MB, Sarder P. Neutrophil Extracellular Traps (NETs): An unexplored territory in renal pathobiology, a pilot computational study. Proc SPIE Int Soc Opt Eng 2020; 11320. [PMID: 32377029 DOI: 10.1117/12.2549340] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In the age of modern medicine and artificial intelligence, image analysis and machine learning have revolutionized diagnostic pathology, facilitating the development of computer aided diagnostics (CADs) which circumvent prevalent diagnostic challenges. Although CADs will expedite and improve the precision of clinical workflow, their prognostic potential, when paired with clinical outcome data, remains indeterminate. In high impact renal diseases, such as diabetic nephropathy and lupus nephritis (LN), progression often occurs rapidly and without immediate detection, due to the subtlety of structural changes in transient disease states. In such states, exploration of quantifiable image biomarkers, such as Neutrophil Extracellular Traps (NETs), may reveal alternative progression measures which correlate with clinical data. NETs have been implicated in LN as immunogenic cellular structures, whose occurrence and dysregulation results in excessive tissue damage and lesion manifestation. We propose that renal biopsy NET distribution will function as a discriminate, predictive biomarker in LN, and will supplement existing classification schemes. We have developed a computational pipeline for segmenting NET-like structures in LN biopsies. NET-like structures segmented from our biopsies warrant further study as they appear pathologically distinct, and resemble non-lytic, vital NETs. Examination of corresponding H&E regions predominantly placed NET-like structures in glomeruli, including globally and segmentally sclerosed glomeruli, and tubule lumina. Our work continues to explore NET-like structures in LN biopsies by: 1.) revising detection and analytical methods based on evolving NETs definitions, and 2.) cataloguing NET morphology in order to implement supervised classification of NET-like structures in histopathology images.
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Affiliation(s)
- Briana A Santo
- Department of Pathology and Anatomical Sciences - University at Buffalo, The State University of New York
| | - Brahm H Segal
- Departments of Medicine, Immunology - Roswell Park Comprehensive Cancer Center
| | - John E Tomaszewski
- Department of Pathology and Anatomical Sciences - University at Buffalo, The State University of New York
| | - Imtiaz Mohammad
- Department of Pathology and Anatomical Sciences - University at Buffalo, The State University of New York
| | - Amber M Worral
- Department of Pathology and Anatomical Sciences - University at Buffalo, The State University of New York
| | - Sanjay Jain
- Departments of Medicine, Nephrology - Washington University School of Medicine
| | - Michelle B Visser
- Department of Oral Biology - University at Buffalo School of Dental Medicine
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences - University at Buffalo, The State University of New York
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14
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Maraszek KE, Santo BA, Yacoub R, Tomaszewski JE, Mohammad I, Worral AM, Sarder P. The Presence and Location of Podocytes in Glomeruli as Affected by Diabetes Mellitus. Proc SPIE Int Soc Opt Eng 2020; 11320:1132018. [PMID: 32362706 PMCID: PMC7194214 DOI: 10.1117/12.2548904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The primary purpose of the kidney, specifically the glomerulus, is filtration. Filtration is accomplished through the glomerular filtration barrier, which consists of the fenestrated endothelium, glomerular basement membrane, and specialized epithelial cells called podocytes. In pathologic states, such as Diabetes Mellitus (DM) and diabetic kidney disease (DKD), variable glomerular conditions result in podocyte injury and depletion, followed by progressive glomerular injury and DKD progression. In this work we quantified glomerulus and podocyte structural changes in histopathology image data derived from a murine model of DM. Using a variety of image processing techniques, we studied changes in podocyte morphology and intra-glomerular distribution across healthy, mild DM, and DM glomeruli. Our feature analysis provided feature trends which we believe are reflective of DKD pathology; while glomerular area peaked in mild DM, average podocyte number and distance from the urinary pole continued to decrease and increase, respectively, throughout DM. Ultimately, this study aims to augment the set of quantifiable image biomarkers used for evaluation of DKD progression in digital pathology, as well as underscore the importance of engineering biologically-inspired image features.
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Affiliation(s)
- Kathryn E. Maraszek
- Department of Pathology and Anatomical Sciences, University
at Buffalo – The State University of New York
| | - Briana A. Santo
- Department of Pathology and Anatomical Sciences, University
at Buffalo – The State University of New York
| | - Rabi Yacoub
- Medicine – Nephrology, University at Buffalo
– The State University of New York
| | - John E. Tomaszewski
- Department of Pathology and Anatomical Sciences, University
at Buffalo – The State University of New York
| | - Imtiaz Mohammad
- Department of Pathology and Anatomical Sciences, University
at Buffalo – The State University of New York
| | - Amber M. Worral
- Department of Pathology and Anatomical Sciences, University
at Buffalo – The State University of New York
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, University
at Buffalo – The State University of New York
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