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A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes.

Citation
Mar, D., et al. “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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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
Journal
Laboratory investigation; a journal of technical methods and pathology
Volume
104
Issue
1
Number of Pages
100282
Date Published
01/2024
ISSN Number
1530-0307
DOI
10.1016/j.labinv.2023.100282
Alternate Journal
Lab Invest
PMID
37924947
PMCID
PMC10872585
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