Phenotype Scoring of Population Scale Single-Cell Data Dissects Alzheimer's Disease Complexity
Chenfeng He, Athan Z Li, Kalpana Hanthanan Arachchilage, Chirag Gupta, Xiang Huang, Xinyu Zhao, PsychAD Consortium, Kiran Girdhar et al.. medRxiv : the preprint server for health sciences, 2024-11-02
Topic: Genetics and APOE
The complexity of Alzheimer's disease (AD) manifests in diverse clinical phenotypes, including cognitive impairment and neuropsychiatric symptoms (NPSs). However, the etiology of these phenotypes remains elusive. To address this, the PsychAD project generated a population-level single-nucleus RNA-seq dataset comprising over 6 million nuclei from the prefrontal cortex of 1,494 individual brains, covering a variety of AD-related phenotypes that capture cognitive impairment, severity of pathological lesions, and the presence of NPSs. Leveraging this dataset, we developed a deep learning framework, called Phenotype Associated Single Cell encoder (PASCode), to score single-cell phenotype associations, and identified ∼1.5 million phenotype associate cells (PACs). We compared PACs within 27 distinct brain cell subclasses and prioritized cell subpopulations and their expressed genes across various AD phenotypes, including the upregulation of a reactive astrocyte subtype with neuroprotective function in AD resilient donors. Additionally, we identified PACs that link multiple phenotypes, including a subpopulation of protoplasmic astrocytes that alter their gene expression and regulation in AD donors with depression. Uncovering the cellular and molecular mechanisms underlying diverse AD phenotypes has the potential to provide valuable insights towards the identification of novel diagnostic markers and therapeutic targets. All identified PACs, along with cell type and gene expression information, are summarized into an AD-phenotypic single-cell atlas for the research community.
Key findings
- Leveraging this dataset, we developed a deep learning framework, called Phenotype Associated Single Cell encoder (PASCode), to score single-cell phenotype associations, and identified ∼1.5 million phenotype associate cells (PACs).
- We compared PACs within 27 distinct brain cell subclasses and prioritized cell subpopulations and their expressed genes across various AD phenotypes, including the upregulation of a reactive astrocyte subtype with neuroprotective function in AD resilient donors.
- Additionally, we identified PACs that link multiple phenotypes, including a subpopulation of protoplasmic astrocytes that alter their gene expression and regulation in AD donors with depression.
- Uncovering the cellular and molecular mechanisms underlying diverse AD phenotypes has the potential to provide valuable insights towards the identification of novel diagnostic markers and therapeutic targets.
- All identified PACs, along with cell type and gene expression information, are summarized into an AD-phenotypic single-cell atlas for the research community.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 22 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 13 of the 60 points we could judge it on, which is 22 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv : the preprint server for health sciences) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39574856) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 13 / 60 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 13 of the 60 points we could judge it on is 22 out of 100. |
| Total | 22 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
He, C., Li, A. Z., Arachchilage, K. H., Gupta, C., Huang, X., Zhao, X., et al. (2024). Phenotype Scoring of Population Scale Single-Cell Data Dissects Alzheimer's Disease Complexity. medRxiv : the preprint server for health sciences. https://doi.org/10.1101/2024.11.01.24316586
Vancouver
He C, Li AZ, Arachchilage KH, Gupta C, Huang X, Zhao X, et al. Phenotype Scoring of Population Scale Single-Cell Data Dissects Alzheimer's Disease Complexity. medRxiv : the preprint server for health sciences. 2024. doi:10.1101/2024.11.01.24316586. PMID: 39574856.
BibTeX
@article{he2024-39574856,
author = {He, Chenfeng and Li, Athan Z and Arachchilage, Kalpana Hanthanan and Gupta, Chirag and Huang, Xiang and Zhao, Xinyu and {PsychAD Consortium} and Girdhar, Kiran and others},
title = {{Phenotype Scoring of Population Scale Single-Cell Data Dissects Alzheimer's Disease Complexity}},
journal = {medRxiv : the preprint server for health sciences},
year = {2024},
doi = {10.1101/2024.11.01.24316586},
pmid = {39574856},
url = {https://doi.org/10.1101/2024.11.01.24316586}
}