scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease

Jiang, Jing; Wang, Cankun; Qi, Ren; Fu, Hongjun; Ma, Qin

doi:10.1016/j.isci.2020.101769

Cited by 103 publications

(80 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(D) TREM2 upregulations in AD postmortem samples (p < 0.05). The snRNAseq gene expression data were acquired from scREAD [45]. (E) Trem2 upregulations in microglia of 5XFAD and APP/PS1 AD mouse models (p < 0.05).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TREM2 Mediates Microglial Anti-Inflammatory Activations in Alzheimer’s Disease: Lessons Learned from Transcriptomics

Xue

2021

Cells

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a lethal neurodegenerative disorder primarily affecting the aged population. The etiopathogenesis of AD, especially that of the sporadic type, remains elusive. The triggering receptor expressed on myeloid cells 2 (TREM2), a member of TREM immunoglobulin superfamily, plays a critical role in microglial physiology. Missense mutations in human TREM2 are determined as genetic risk factors associated with the development of sporadic AD. However, the roles of TREM2 in the pathogenesis of AD are still to be established. In this review, we outlined the influence of Trem2 on balance of pro- and anti-inflammatory microglial activations from a perspective of AD mouse model transcriptomics. On this basis, we further speculated the roles of TREM2 in different stages of AD, which may shed light to the development of TREM2-targeted strategy for the prevention and treatment of this neurodegenerative disorder.

show abstract

Section: Introductionmentioning

confidence: 99%

“…(E) Trem2 upregulations in microglia of 5XFAD and APP/PS1 AD mouse models (p < 0.05). The snRNAseq gene expression data were acquired from scREAD [45].…”

Section: Introductionmentioning

confidence: 99%

TREM2 Mediates Microglial Anti-Inflammatory Activations in Alzheimer’s Disease: Lessons Learned from Transcriptomics

Xue

2021

Cells

View full text Add to dashboard Cite

show abstract

“…For single-cell expression studies, the scREAD database (Available from: https://bmbls.bmi.osumc.edu/scread/) was interrogated, to further characterize IFITM3' and FYN's expression in AD-donated tissue 6 (See Supplementary Materials 1 -Expanded Methods for further details).…”

Section: Single Cell Rna Transcriptomicsmentioning

confidence: 99%

“…Dataset used in this study were anonymized; The specific protocol for informed consent and institutional review board approval can be found in detail elsewhere 5,6 .…”

Section: Ethical Approvalmentioning

confidence: 99%

“…Persistent neuroinflammation, viral latency in case of defective endosome -lysosome fusion in a vulnerable area (i.e. the entorhinal cortex in transcribial neuroinvasion) would then account for a switch from tau pathology to Aβ oligomer / Interferon "signal" spreading through neuronal projections to afferent sites, such as the hippocampi, as seen in SARS-CoV-2-related olfactory and memory impairment (6).…”

Section: Sars-cov-2's Relevancy In Ad Pathobiologymentioning

confidence: 99%

See 1 more Smart Citation

FYN, SARS-CoV-2, and IFITM3 in the Neurobiology of Alzheimer’s Disease: A Regulatory Feedback Loop Governing Tau and Aβ Pathology

Vavougios¹,

Breza²,

Nikou³

et al. 2020

Preprint

View full text Add to dashboard Cite

Introduction IFITM3, an innate immune protein linked to COVID-19 severity, has recently been identified as a novel γ-secretase modulator. Independent research has shown that IFITM3 may facilitate SARS-CoV-2 neurotropism in an ACE2-independent manner. In a previous study, we had detected perturbations in IFITM3 networks in both the CNS and peripheral immune cells donated by AD patients.The purpose of this study is to explore the transcriptomic evidence of the SARS-CoV-2 / IFITM3 / AD interplay, validating previous findings from our group. Methods Exploratory analyses involved meta-analysis of bulk and single cell RNA data for IFITM3 and FYN differential expression. For confirmatory analyses, we performed gene set enrichment analysis (GSEA) on an AD gene signature from AD Consensus transcriptomics; using the Enrichr platform, we scrutinized COVID-19 datasets for significant, overlapping enriched biological networks. Results Bulk RNA data analysis revealed that IFITM3 and FYN were differentially expressed in two CNS regions in AD: the temporal cortex (AD vs. Controls, adj.p-value=1.3e-6) and the parahippocampal cortex (AD vs. controls, adj.p-value=0.012). Correspondingly, single cell RNA analysis of IFITM3 and FYN revealed that it was differentially expressed in neuronal cells donated from AD patients (astrocytes, microglia and oligodendrocyte precursor cells), when compared to controls. Discussion IFITM3 and by extent FYN were found as interactors within biological networks overlapping between AD and SARS-CoV-2 infection. SARS-CoV-2 SARS-CoV-2-mediated IFITM3 induction would mechanistically result in increased Aβ production. FYN recruitment by viral processes results in abrogation of both fusion of IFITM3 vesicles with lysosomes; immunoevasion, by FYN-mediated impairment of autophagy would then serve to promote impaired detoxification from Aβ, while propagating Tau pathology in an IFITM3-independent manner.

show abstract

Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

Alzheimer's & Dementia

View full text Add to dashboard Cite

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well‐documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI‐enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co‐produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care.HIGHLIGHTS Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

show abstract

scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease

Cited by 103 publications

References 33 publications

TREM2 Mediates Microglial Anti-Inflammatory Activations in Alzheimer’s Disease: Lessons Learned from Transcriptomics

TREM2 Mediates Microglial Anti-Inflammatory Activations in Alzheimer’s Disease: Lessons Learned from Transcriptomics

FYN, SARS-CoV-2, and IFITM3 in the Neurobiology of Alzheimer’s Disease: A Regulatory Feedback Loop Governing Tau and Aβ Pathology

Artificial intelligence for dementia research methods optimization

Contact Info

Product

Resources

About