Single-cell multiregion dissection of Alzheimer’s disease
Single-cell multiregion dissection of Alzheimer’s disease is a dataset published in Nature (2024). On theSindex it has a DataRank of 4.2, placing it in the top 29.9% of the data-sharing corpus. It has been cited 256 times, with 189 citing works in its 1-hop citation network. Its calibrated FAIR score is 39/100.
Abstract
Alzheimer's disease is the leading cause of dementia worldwide, but the cellular pathways that underlie its pathological progression across brain regions remain poorly understood1-3. Here we report a single-cell transcriptomic atlas of six different brain regions in the aged human brain, covering 1.3 million cells from 283 post-mortem human brain samples across 48 individuals with and without Alzheimer's disease. We identify 76 cell types, including region-specific subtypes of astrocytes and excitatory neurons and an inhibitory interneuron population unique to the thalamus and distinct from canonical inhibitory subclasses. We identify vulnerable populations of excitatory and inhibitory neurons that are depleted in specific brain regions in Alzheimer's disease, and provide evidence that the Reelin signalling pathway is involved in modulating the vulnerability of these neurons. We develop a scalable method for discovering gene modules, which we use to identify cell-type-specific and region-specific modules that are altered in Alzheimer's disease and to annotate transcriptomic differences associated with diverse pathological variables. We identify an astrocyte program that is associated with cognitive resilience to Alzheimer's disease pathology, tying choline metabolism and polyamine biosynthesis in astrocytes to preserved cognitive function late in life. Together, our study develops a regional atlas of the ageing human brain and provides insights into cellular vulnerability, response and resilience to Alzheimer's disease 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
0.801
From this paper's citation signal
Citation Network Contribution
3.4
From 139 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
- Fitting Linear Mixed-Effects Models Using<b>lme4</b>Journal of Statistical Software201582,566 citationsDataRank 1.7
- <tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression dataBioinformatics200944,025 citationsDataRank 1.6
- <b>lmerTest</b> Package: Tests in Linear Mixed Effects ModelsJournal of Statistical Software201722,751 citationsDataRank 1.5
- Massively parallel digital transcriptional profiling of single cellsNature Communications20177,641 citationsDataRank 1.3
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 19% comes from its base citations and 81% from the citation network (139 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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