Molecular estimation of neurodegeneration pseudotime in older brains

Abstract

26Therapeutic treatments for late-onset Alzheimer's disease (LOAD) are hindered by an incomplete 27 understanding of the temporal molecular changes that lead to disease onset and progression. Here, we 28 evaluate the ability of manifold learning to develop a molecular model for the unobserved temporal 29 disease progression from RNA-Seq data collected from human postmortem brain samples collected 30 within the ROS/MAP and Mayo Clinic RNA-Seq studies of the AMP-AD consortium. This approach 31 defines a cross-sectional ordering across samples based on their relative similarity in RNA-Seq profiles 32 and uses this information to define an estimate of molecular disease stage -or disease progression 33 pseudotime -for each sample. This transcriptional estimate of disease progression is strongly concordant 34 with burden of tau pathology (Braak score, P = 1.0x10 -5 ), amyloid pathology (CERAD score, P = 1.8x10 -35 5 ), and cognitive diagnosis (P = 3.5x10 -7 ) of LOAD. Further, the disease progression estimate 36 recapitulates known changes in cell type abundance and impact of genes that harbor known AD risk loci. 37Samples estimated to reside early in disease progression were enriched for control and early stage AD 38 cases, and demonstrated changes in basic cellular functions. Samples estimated to reside late in disease 39 progression were enriched for late-stage AD cases, and demonstrated changes in known disease processes 40 including neuroinflammation and amyloid pathology. We also identified a set of control samples with 41 late-stage estimated disease progression who also showed compensatory changes in genes involved in 42 affected pathways are protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid 43 cleavage. In summary, we present a disease specific method for ordering patients based on their LOAD 44 disease progression from CNS transcriptomic data. 45

Gene expression signatures identify biologically and clinically distinct tuberculosis endotypes

“…Cell trajectory (also termed pseudotime trajectory) based on transcriptomic profiles is widely used to assess development and disease(30). We employed the pseudotime analytical method Monocle and identified an increase in trajectory score from healthy controls to Endotype B to Endotype A (Figure 3); this result led to a search for specific molecular properties, such as enriched pathways, that follow the predicted disease trajectory.…”

Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer’s disease

“…Interestingly, the transcriptomic signatures obtained from our study show high similarity with the signatures obtained from a recent single-nucleus transcriptome analysis from the prefrontal cortical samples of AD patients and normal control subjects (Lau et al ., 2020), where higher proportion of endothelial nuclei were sampled and dysregulated pathways are associated with blood vessel morphogenesis, angiogenesis and antigen presentation. Notably, these functions are also implicated in the top common blood-brain functional pathways relevant for LOAD progression in the recent study of gene expression trajectories in AD (Mukherjee et al ., 2020). Additionally, the enriched functions of proton transport implicated in mitochondrial functions and cell signaling pathway in the turquoise module were also found to be associated with the overlapped DEGs in neurons from two independent single nuclei transcriptomic studies from AD patients (Mathys et al ., 2019; Lau et al ., 2020).…”

“…This further exacerbates the challenge we face in studying LOAD, or dementia in general as a continuous spectrum to find novel biomarker and drug targets. Recent efforts have begun to model AD progression as a continuous trajectory using cross-sectional transcriptomic data (Iturria-Medina et al ., 2020; Mukherjee et al ., 2020), by leveraging the methods developed in single-cell genomics (Campbell and Yau, 2018; Van den Berge et al ., 2020) and machine learning (Eraslan et al ., 2019). Iturria-Medina et al (Iturria-Medina et al ., 2020) adopted an unsupervised machine learning algorithm applied to gene expression microarray data and discovered a contrastive trajectory in multiple cohorts respectively.…”

“…The trajectory has been demonstrated to strongly predict neuropathological severity in AD in each dataset. Mukherjee et al (Mukherjee et al ., 2020) applied a manifold learning method to RNA-seq data to define ordering across samples based on gene expression similarity and estimate the disease pseudotime for each sample. Disease pseudotime was strongly correlated with the burden of Aβ, tau, and cognitive dementia within subjects with LOAD.…”

A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

“…When the cells in a sample come from a continuous biological process, such as a temporal or spatial process, computationally placing cells along a pseudotemporal trajectory based on their progressively changing transcriptomes is a powerful approach to reconstructing the dynamic gene expression programs of the underlying biological process. This approach, also known as pseudotime analysis [1][2][3] , is now widely used to study cell differentiation [4][5][6] , immune responses 7,8 , disease development [9][10][11][12] and many other biological systems with temporal or spatial dynamics. A systematic review and comparison of these methods can be found in a recent benchmark study 3 , but the majority of the methods were designed to infer gene expression changes along the reconstructed trajectory within one biological sample.…”