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- A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes.
A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes.
Citation | “A High-Throughput Pixul-Matrix-Based Toolbox To Profile Frozen And Formalin-Fixed Paraffin-Embedded Tissues Multiomes.”. Laboratory Investigation; A Journal Of Technical Methods And Pathology, p. 100282. . |
Center | University of Washington |
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Featured
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Author | Daniel Mar, Ilona M Babenko, Ran Zhang, William Stafford Noble, Oleg Denisenko, Tomas Vaisar, Karol Bomsztyk |
Keywords | CryoGrid, FFPE, LC-MS/MS, Matrix, PIXUL, machine learning |
Abstract |
Large-scale high-dimensional multiomics studies are essential to unravel molecular complexity in health and disease. We developed an integrated system for tissue sampling (CryoGrid), analytes preparation (PIXUL), and downstream multiomic analysis in a 96-well plate format (Matrix), MultiomicsTracks96, which we used to interrogate matched frozen and formalin-fixed paraffin-embedded (FFPE) mouse organs. Using this system, we generated 8-dimensional omics data sets encompassing 4 molecular layers of intracellular organization: epigenome (H3K27Ac, H3K4m3, RNA polymerase II, and 5mC levels), transcriptome (messenger RNA levels), epitranscriptome (m6A levels), and proteome (protein levels) in brain, heart, kidney, and liver. There was a high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles confirmed known organ-specific superenhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic profiles, known to be poorly correlated with transcriptomic data, can be more accurately predicted by the full suite of multiomics data, compared with using epigenomic, transcriptomic, or epitranscriptomic measurements individually. |
Year of Publication |
2024
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Journal |
Laboratory investigation; a journal of technical methods and pathology
|
Volume |
104
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Issue |
1
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Number of Pages |
100282
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Date Published |
01/2024
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ISSN Number |
1530-0307
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DOI |
10.1016/j.labinv.2023.100282
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Alternate Journal |
Lab Invest
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PMID |
37924947
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PMCID |
PMC10872585
|
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