Browsing by Author "Saykin, Andrew J"

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    Cell-specific transcriptional signatures of vascular cells in Alzheimer’s disease: perspectives, pathways, and therapeutic directions
    (Springer Nature, 2025-01-29) Chaudhuri, Soumilee; Cho, Minyoung; Stumpff, Julia C; Bice, Paula J; İş, Özkan; Ertekin-Taner, Nilüfer; Saykin, Andrew J; Nho, Kwangsik
    Alzheimer’s disease (AD) is a debilitating neurodegenerative disease that is marked by profound neurovascular dysfunction and significant cell-specific alterations in the brain vasculature. Recent advances in high throughput single-cell transcriptomics technology have enabled the study of the human brain vasculature at an unprecedented depth. Additionally, the understudied niche of cerebrovascular cells, such as endothelial and mural cells, and their subtypes have been scrutinized for understanding cellular and transcriptional heterogeneity in AD. Here, we provide an overview of rich transcriptional signatures derived from recent single-cell and single-nucleus transcriptomic studies of human brain vascular cells and their implications for targeted therapy for AD. We conducted an in-depth literature search using Medline and Covidence to identify pertinent AD studies that utilized single-cell technologies in human post-mortem brain tissue by focusing on understanding the transcriptional differences in cerebrovascular cell types and subtypes in AD and cognitively normal older adults. We also discuss impaired cellular crosstalk between vascular cells and neuroglial units, as well as astrocytes in AD. Additionally, we contextualize the findings from single-cell studies of distinct endothelial cells, smooth muscle cells, fibroblasts, and pericytes in the human AD brain and highlight pathways for potential therapeutic interventions as a concerted multi-omic effort with spatial transcriptomics technology, neuroimaging, and neuropathology. Overall, we provide a detailed account of the vascular cell-specific transcriptional signatures in AD and their crucial cellular crosstalk with the neuroglial unit.
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    Elevated Cerebrospinal Fluid Tau Protein Concentrations on Admission Are Associated With Long-term Neurologic and Cognitive Impairment in Ugandan Children With Cerebral Malaria
    (Oxford, 2020-03-15) Datta, Dibyadyuti; Conroy, Andrea L; Castelluccio, Peter F; Ssenkusu, John M; Park, Gregory S; Opoka, Robert O; Bangirana, Paul; Idro, Richard; Saykin, Andrew J; John, Chandy C; Pediatrics, School of Medicine
    Background Elevated concentrations of cerebrospinal fluid (CSF) tau, a marker of axonal injury, have been associated with coma in severe malaria (cerebral malaria [CM]). However, it is unknown whether axonal injury is related to long-term neurologic deficits and cognitive impairment in children with CM. Methods Admission CSF tau concentrations were measured in 145 Ugandan children with CM and compared to clinical and laboratory factors and acute and chronic neurologic and cognitive outcomes. Results Elevated CSF tau concentrations were associated with younger age, increased disease severity (lower glucose and hemoglobin concentrations, malaria retinopathy, acute kidney injury, and prolonged coma duration, all P < .05), and an increased CSF:plasma albumin ratio, a marker of blood–brain barrier breakdown (P < .001). Admission CSF tau concentrations were associated with the presence of neurologic deficits at hospital discharge, and at 6, 12, and 24 months postdischarge (all P ≤ .02). After adjustment for potential confounding factors, elevated log10-transformed CSF tau concentrations correlated with worse cognitive outcome z scores over 2-year follow-up for associative memory (β coefficient, –0.31 [95% confidence interval [CI], –.53 to –.10]) in children <5 years of age, and for overall cognition (–0.69 [95% CI, –1.19 to –.21]), attention (–0.78 [95% CI, –1.34 to –.23]), and working memory (–1.0 [95% CI, –1.68 to –.31]) in children ≥5 years of age (all P < .006). Conclusions Acute axonal injury in children with CM is associated with long-term neurologic deficits and cognitive impairment. CSF tau concentrations at the time of the CM episode may identify children at high risk of long-term neurocognitive impairment.
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    Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification
    (Oxford, 2020-07-13) Du, Lei; Liu, Fang; Liu, Kefei; Yao, Xiaohui; Risacher, Shannon L; Han, Junwei; Guo, Lei; Saykin, Andrew J; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Motivation Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype–phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype–phenotype associations. Results In this article, we propose a new joint multitask learning method, named MT–SCCALR, which absorbs the merits of both SCCA and logistic regression. MT–SCCALR learns genotype–phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype–phenotype pattern. Meanwhile, MT–SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT–SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype–phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders.