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Molecular estimation of neurodegeneration pseudotime in older brains

Sumit Mukherjee, Laura Heath, Christoph Preuss, Suman Jayadev, Gwenn A. Garden, Anna K Greenwood, Solveig K Sieberts, Phillip L De Jager, Nilufer Ertekin-Taner, Gregory W Carter, Lara M Mangravite, View ORCID ProfileBenjamin A Logsdon
doi: https://doi.org/10.1101/686824
Sumit Mukherjee
1Sage Bionetworks, Seattle, WA, USA
2Microsoft, Redmond, WA, USA
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Laura Heath
1Sage Bionetworks, Seattle, WA, USA
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Christoph Preuss
3The Jackson Laboratory, Bar Harbor, ME, USA
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Suman Jayadev
4Department of Neurology, University of Washington, Seattle, WA, USA
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Gwenn A. Garden
4Department of Neurology, University of Washington, Seattle, WA, USA
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Anna K Greenwood
1Sage Bionetworks, Seattle, WA, USA
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Solveig K Sieberts
1Sage Bionetworks, Seattle, WA, USA
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Phillip L De Jager
5Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York City, USA
6Taub Institute, Columbia University Irving Medical Center, New York City, USA
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Nilufer Ertekin-Taner
7Mayo Clinic Florida, Department of Neurology, Jacksonville, FL, USA
8Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL, USA
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Gregory W Carter
3The Jackson Laboratory, Bar Harbor, ME, USA
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Lara M Mangravite
1Sage Bionetworks, Seattle, WA, USA
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Benjamin A Logsdon
1Sage Bionetworks, Seattle, WA, USA
9Cajal Neuroscience, Seattle, WA, USA
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  • ORCID record for Benjamin A Logsdon
  • For correspondence: ben@cajalneuro.com
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Abstract

The temporal molecular changes that lead to disease onset and progression in Alzheimer’s disease are still unknown. Here we develop a temporal model for these unobserved molecular changes with a manifold learning method applied to RNA-Seq data collected from human postmortem brain samples collected within the ROS/MAP and Mayo Clinic RNA-Seq studies. We define an ordering across samples based on their similarity in gene expression and use this ordering to estimate the molecular disease stage – or disease pseudotime - for each sample. Disease pseudotime is strongly concordant with the burden of tau (Braak score, P = 1.0×10−5), Aβ (CERAD score, P = 1.8×10−5), and cognitive diagnosis (P = 3.5×10−7) of LOAD. Early stage disease pseudotime samples are enriched for controls and show changes in basic cellular functions. Late stage disease pseudotime samples are enriched for late stage AD cases and show changes in neuroinflammation and amyloid pathologic processes. We also identify a set of late stage pseudotime samples that are controls and show changes in genes enriched for protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid cleavage pathways. In summary, we present a method for ordering patients along a trajectory of LOAD disease progression from brain transcriptomic data.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Editorial changes prior to acceptance at Nature Communications.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 23, 2020.
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Molecular estimation of neurodegeneration pseudotime in older brains
Sumit Mukherjee, Laura Heath, Christoph Preuss, Suman Jayadev, Gwenn A. Garden, Anna K Greenwood, Solveig K Sieberts, Phillip L De Jager, Nilufer Ertekin-Taner, Gregory W Carter, Lara M Mangravite, Benjamin A Logsdon
bioRxiv 686824; doi: https://doi.org/10.1101/686824
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Molecular estimation of neurodegeneration pseudotime in older brains
Sumit Mukherjee, Laura Heath, Christoph Preuss, Suman Jayadev, Gwenn A. Garden, Anna K Greenwood, Solveig K Sieberts, Phillip L De Jager, Nilufer Ertekin-Taner, Gregory W Carter, Lara M Mangravite, Benjamin A Logsdon
bioRxiv 686824; doi: https://doi.org/10.1101/686824

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