Browsing by Author "Barisoni, Laura"

Now showing 1 - 3 of 3
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    A multimodal and integrated approach to interrogate human kidney biopsies with rigor and reproducibility: guidelines from the Kidney Precision Medicine Project
    (American Physiological Society, 2021) El-Achkar, Tarek M.; Eadon, Michael T.; Menon, Rajasree; Lake, Blue B.; Sigdel, Tara K.; Alexandrov, Theodore; Parikh, Samir; Zhang, Guanshi; Dobi, Dejan; Dunn, Kenneth W.; Otto, Edgar A.; Anderton, Christopher R.; Carson, Jonas M.; Luo, Jinghui; Park, Chris; Hamidi, Habib; Zhou, Jian; Hoover, Paul; Schroeder, Andrew; Joanes, Marianinha; Azeloglu, Evren U.; Sealfon, Rachel; Winfree, Seth; Steck, Becky; He, Yongqun; D’Agati, Vivette; Iyengar, Ravi; Troyanskaya, Olga G.; Barisoni, Laura; Gaut, Joseph; Zhang, Kun; Laszik, Zoltan; Rovin, Brad H.; Dagher, Pierre C.; Sharma, Kumar; Sarwal, Minnie M.; Hodgin, Jeffrey B.; Alpers, Charles E.; Kretzler, Matthias; Jain, Sanjay; Medicine, School of Medicine
    Comprehensive and spatially mapped molecular atlases of organs at a cellular level are a critical resource to gain insights into pathogenic mechanisms and personalized therapies for diseases. The Kidney Precision Medicine Project (KPMP) is an endeavor to generate three-dimensional (3-D) molecular atlases of healthy and diseased kidney biopsies by using multiple state-of-the-art omics and imaging technologies across several institutions. Obtaining rigorous and reproducible results from disparate methods and at different sites to interrogate biomolecules at a single-cell level or in 3-D space is a significant challenge that can be a futile exercise if not well controlled. We describe a “follow the tissue” pipeline for generating a reliable and authentic single-cell/region 3-D molecular atlas of human adult kidney. Our approach emphasizes quality assurance, quality control, validation, and harmonization across different omics and imaging technologies from sample procurement, processing, storage, shipping to data generation, analysis, and sharing. We established benchmarks for quality control, rigor, reproducibility, and feasibility across multiple technologies through a pilot experiment using common source tissue that was processed and analyzed at different institutions and different technologies. A peer review system was established to critically review quality control measures and the reproducibility of data generated by each technology before their being approved to interrogate clinical biopsy specimens. The process established economizes the use of valuable biopsy tissue for multiomics and imaging analysis with stringent quality control to ensure rigor and reproducibility of results and serves as a model for precision medicine projects across laboratories, institutions and consortia.
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    FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology
    (bioRxiv, 2024-08-22) Border, Samuel; Ferreira, Ricardo Melo; Lucarelli, Nicholas; Manthey, David; Kumar, Suhas; Paul, Anindya; Mimar, Sayat; Naglah, Ahmed; Cheng, Ying-Hua; Barisoni, Laura; Ray, Jessica; Strekalova, Yulia; Rosenberg, Avi Z.; Tomaszewski, John E.; Hodgin, Jeffrey B.; HuBMAP consortium; El-Achkar, Tarek M.; Jain, Sanjay; Eadon, Michael T.; Sarder, Pinaki; Medicine, School of Medicine
    Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.
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    A reference tissue atlas for the human kidney
    (American Association for the Advancement of Science, 2022) Hansen, Jens; Sealfon, Rachel; Menon, Rajasree; Eadon, Michael T.; Lake, Blue B.; Steck, Becky; Anjani, Kavya; Parikh, Samir; Sigdel, Tara K.; Zhang, Guanshi; Velickovic, Dusan; Barwinska, Daria; Alexandrov, Theodore; Dobi, Dejan; Rashmi, Priyanka; Otto, Edgar A.; Rivera, Miguel; Rose, Michael P.; Anderton, Christopher R.; Shapiro, John P.; Pamreddy, Annapurna; Winfree, Seth; Xiong, Yuguang; He, Yongqun; de Boer, Ian H.; Hodgin, Jeffrey B.; Barisoni, Laura; Naik, Abhijit S.; Sharma, Kumar; Sarwal, Minnie M.; Zhang, Kun; Himmelfarb, Jonathan; Rovin, Brad; El-Achkar, Tarek M.; Laszik, Zoltan; He, John Cijiang; Dagher, Pierre C.; Valerius, M. Todd; Jain, Sanjay; Satlin, Lisa M.; Troyanskaya, Olga G.; Kretzler, Matthias; Iyengar, Ravi; Azeloglu, Evren U.; Kidney Precision Medicine Project; Medicine, School of Medicine
    Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.