Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

Single cell RNA sequencing of human microglia uncovers a subset that is associated with Alzheimer’s disease

Alzheimer's & Dementia(2020)10.1002/alz.038589Source: DataRank Database

Single cell RNA sequencing of human microglia uncovers a subset that is associated with Alzheimer’s disease is a research paper published in Alzheimer's & Dementia (2020). On theSindex it has a DataRank of 0.505. It has been cited 28 times.

N/A

0.505DataRank · unranked

0.505

Open Access28 citations · base score 3.4

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

AbstractBackgroundThe extent of microglial heterogeneity in the aging human brain remains a central, yet poorly explored question in light of the development of therapies targeting this cell type in age related neurodegenerative diseases, such as Alzheimer’s disease.MethodsUsing single cell RNA sequencing, we investigated the population structure of microglia purified from human cerebral cortex of aged donors with and without Alzheimer’s disease.ResultsWe describe the population structure of microglia in the aged human brain and establish the divergent association of the different microglia subsets to age related neuropathologies and measures of cognitive decline. We confirm the presence of the identified microglial subpopulations histologically and explore their topological relationship to histopathological hallmarks of aging and AD in situ. Based on our data we prioritize one microglial cluster, cluster 7, which is enriched for genes involved in endosomal/vacuolar pathway. Interestingly we find that the signature gene set of cluster 7 is depleted in the cortical transcriptomic profile of individuals with AD. Histologically, these cluster 7 microglia are reduced in frequency in AD tissue, and we validate this observation in an independent set of single nucleus data.ConclusionsThus, single cell transcriptomic profiling of live human microglia identifies a range of microglia subtypes in the aged human brain, and we prioritize one of these as being altered in AD.

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.505

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

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(C_q) ÷ max(outdegree_q, 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.

Read the full methodology →