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, Philip L. Jager, Nilüfer Ertekin-Taner, Gregory W. Carter, Lara M. Mangravite and Benjamin A. Logsdon ()
Additional contact information
Sumit Mukherjee: Sage Bionetworks
Laura Heath: Sage Bionetworks
Christoph Preuss: The Jackson Laboratory
Suman Jayadev: University of Washington
Gwenn A. Garden: University of Washington
Anna K. Greenwood: Sage Bionetworks
Solveig K. Sieberts: Sage Bionetworks
Philip L. Jager: Columbia University Irving Medical Center
Nilüfer Ertekin-Taner: Mayo Clinic Florid
Gregory W. Carter: The Jackson Laboratory
Lara M. Mangravite: Sage Bionetworks
Benjamin A. Logsdon: Sage Bionetworks

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract The temporal molecular changes that lead to disease onset and progression in Alzheimer’s disease (AD) 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 late-onset (LO) AD. 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.

Date: 2020
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DOI: 10.1038/s41467-020-19622-y

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