MétaCan
Menu
← Back to cohort
Record W3110689298 · doi:10.1002/alz.041789

Single‐cell profiling of circulating and brain‐resident immune cells in a mouse model for amyloidosis and in aged mice

2020· article· en· W3110689298 on OpenAlexaff
Lynn van Olst, Jan Verhoeff, Sjoerd Schetters, Lianne A. Hulshof, Roland van Dijk, Susanne M. A. van der Pol, Marijn Schouten, Juan J. García‐Vallejo, Wiesje M. van der Flier, Charlotte E. Teunissen, Jinte Middeldorp, Helga E. de Vries

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsImmune systemMass cytometryAmyloidosisGenetically modified mouseImmunologyBiologyT cellMedicinePathologyTransgenePhenotype

Abstract

fetched live from OpenAlex

Abstract Background Emerging evidence suggests that peripheral immunity plays an important role in the progression of Alzheimer’s disease (AD) (Gate et al., 2020). However, more understanding on how peripheral immune cells respond to AD pathology is needed before utilizing the immune system further for early diagnosis and therapeutic treatment. Here, we aimed to map the immune system in the early stages of amyloidosis. In addition, we aimed to characterize the aged immune system and to study the effect of age‐related circulating immune factors on the immune response. Method We used 6‐month‐old APPswe/PS1dE9 (APP/PS1) transgenic and wild type (WT) mice to study the immune response to amyloid‐beta pathology and 20 month‐old WT mice for characterization of the aged immune system. In addition, both 6‐month‐old WT and APP/PS1 mice received multiple injections with aged blood plasma derived from 20‐month‐old WT mice. Next, immunohistochemical and high‐dimensional single‐cell analysis using time‐of‐flight mass cytometry (CyTOF), allowing detection of different immune populations and their activation status, was performed to map the circulating and brain‐resident immune cells in the different conditions (Figure 1). Result Immunohistochemical analysis revealed that T cell infiltration in the 6‐month‐old APP/PS1 brain coincided with early amyloid‐beta plaque formation. Single cell analysis of circulating and brain‐resident immune cells of APP/PS1 mice showed that especially T cells clustered in different cell‐subsets and had increased expression of activation and cell‐adhesion molecules compared to young WT mice. In contrast, circulating and brain‐resident T cells of aged mice contained more regulatory cell subsets and increasingly expressed molecules involved in immune inhibition. Interestingly, initial analysis revealed that injections with aged WT blood plasma partially reduced the aforementioned differences in T cells between young WT and APP/PS1 mice. Conclusion We found that circulating and brain‐resident T cells were different in their subsets and expression profile in early amyloid‐beta accumulation and in aging. In addition, the activation status of APP/PS1‐derived T cells was reduced after exposure to aged WT blood plasma resulting in a T‐cell signature more similar to young WT mice. Currently, we are further investigating the peripheral immune response to amyloid‐beta pathology by lipid‐ and RNA profiling of the circulating immune cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.257
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicNeuroinflammation and Neurodegeneration Mechanisms→French-language works237,207→