Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 257

The DOI matches its Crossref record; 248 of the 248 references it cites were found in a registry or an index.

Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery

Open access
Bai, Bing, Vanderwall, David, Li, Yuxin, Wang, Xusheng, Poudel, Suresh, Wang, Hong, Dey, Kaushik Kumar, Chen, Ping-Chung, Yang, Ka, Peng, Junmin·2021·Molecular Neurodegeneration

Abstract

Mass spectrometry-based proteomics empowers deep profiling of proteome and protein posttranslational modifications (PTMs) in Alzheimer’s disease (AD). Here we review the advances and limitations in historic and recent AD proteomic research. Complementary to genetic mapping, proteomic studies not only validate canonical amyloid and tau pathways, but also uncover novel components in broad protein networks, such as RNA splicing, development, immunity, membrane transport, lipid metabolism, synaptic function, and mitochondrial activity. Meta-analysis of seven deep datasets reveals 2,698 differentially expressed (DE) proteins in the landscape of AD brain proteome (n = 12,017 proteins/genes), covering 35 reported AD genes and risk loci. The DE proteins contain cellular markers enriched in neurons, microglia, astrocytes, oligodendrocytes, and epithelial cells, supporting the involvement of diverse cell types in AD pathology. We discuss the hypothesized protective or detrimental roles of selected DE proteins, emphasizing top proteins in “amyloidome” (all biomolecules in amyloid plaques) and disease progression. Comprehensive PTM analysis represents another layer of molecular events in AD. In particular, tau PTMs are correlated with disease stages and indicate the heterogeneity of individual AD patients. Moreover, the unprecedented proteomic coverage of biofluids, such as cerebrospinal fluid and serum, procures novel putative AD biomarkers through meta-analysis. Thus, proteomics-driven systems biology presents a new frontier to link genotype, proteotype, and phenotype, accelerating the development of improved AD models and treatment strategies.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s13024-021-00474-z

Cites

248 of 248 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 248 references

Research works printed but not linked: 5

  1. Bellenguez C, et al. New insights on the genetic etiology of Alzheimer’s and related dementia. medRxiv. 2020;Manuscript submitted online.
  2. Barber M, et al. Determination of amino acid sequences in oligopeptides by mass spectrometry. II. The structure of peptidolipin NA. Tetrahedron Lett. 1965;6:1331–6. · doi:10.1016/s0040-4039(00)77207-8
  3. Senn M, et al. Automatic amino-acid-sequence determination in peptides. Biochem Biophys Res Commun. 1966;23:381–5. · doi:10.1016/0006-291x(66)90737-6
  4. Shemyakin MM, et al. Mass spectrometric determination of the amino-acid sequence of peptides. Nature. 1966;211:361–6. · doi:10.1038/211361a0
  5. Litvinov Y, et al. 100 years of Mass Spectrometry. Int J Mass Spectrom. 2013;349–350:1–276.

Cited by

See all 257

Metadata

Authors
Bai, B., Vanderwall, D., Li, Y., Wang, X., Poudel, S., Wang, H., Dey, K. K., Chen, P.-C., Yang, K., & Peng, J.
Venue
Molecular Neurodegeneration
Published
2021-08-12
Volume
16
Issue
1
Pages
55-55
ISSN
1750-1326 (ISSN-L)
OpenAlex
W3190031729
Cited by
257
References
248
FWCI
20.36
Type
journal article
Language
en
Open access
gold
Licence
Funders
National Institutes of Health (R01AG047928, R01AG053987, R01AG068581, R01GM114260, RF1AG064909, U54NS110435)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery. https://app.cochat.ai/papers/10.1186/s13024-021-00474-z
The paper
Bai, B., Vanderwall, D., Li, Y., Wang, X., Poudel, S., Wang, H., Dey, K. K., Chen, P.-C., Yang, K., & Peng, J. (2021). Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery. Molecular Neurodegeneration. https://doi.org/10.1186/s13024-021-00474-z

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.