Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology
Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology is a dataset published in Cell (2023). On theSindex it has a DataRank of 5.2, placing it in the top 28.6% of the data-sharing corpus. It has been cited 467 times, with 179 citing works in its 1-hop citation network. Its calibrated FAIR score is 35/100.
Abstract
Alzheimer's disease (AD) is the most common cause of dementia worldwide, but the molecular and cellular mechanisms underlying cognitive impairment remain poorly understood. To address this, we generated a single-cell transcriptomic atlas of the aged human prefrontal cortex covering 2.3 million cells from postmortem human brain samples of 427 individuals with varying degrees of AD pathology and cognitive impairment. Our analyses identified AD-pathology-associated alterations shared between excitatory neuron subtypes, revealed a coordinated increase of the cohesin complex and DNA damage response factors in excitatory neurons and in oligodendrocytes, and uncovered genes and pathways associated with high cognitive function, dementia, and resilience to AD pathology. Furthermore, we identified selectively vulnerable somatostatin inhibitory neuron subtypes depleted in AD, discovered two distinct groups of inhibitory neurons that were more abundant in individuals with preserved high cognitive function late in life, and uncovered a link between inhibitory neurons and resilience to AD pathology.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.905
From this paper's citation signal
Citation Network Contribution
4.3
From 179 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2Genome Biology201497,097 citationsDataRank 1.7
- <tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression dataBioinformatics200944,025 citationsDataRank 1.6
- Comprehensive Integration of Single-Cell DataCell201916,515 citationsDataRank 1.5
- Integrated analysis of multimodal single-cell dataCell202115,542 citationsDataRank 1.4
- Metascape provides a biologist-oriented resource for the analysis of systems-level datasetsNature Communications201915,474 citationsDataRank 14.7Top 13%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 18% comes from its base citations and 82% from the citation network (179 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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