MétaCan
Menu
← Retour à la cohorte
Enregistrement W6920714847 · doi:10.6084/m9.figshare.14314649

Additional file 1 of The CD33 short isoform is a gain-of-function variant that enhances Aβ1–42 phagocytosis in microglia

2021· article· en· W6920714847 sur OpenAlexaff

Notice bibliographique

RevueFigshare · 2021
Typearticle
Langueen
DomaineNeuroscience
ThématiqueNeuroinflammation and Neurodegeneration Mechanisms
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMicrogliaPhagocytosisFlow cytometryGenetically modified mouseImmunofluorescenceGreen fluorescent proteinTransgene

Résumé

récupéré en direct d'OpenAlex

Additional file 1: Fig. 1. Quantifying microglia in the brain of human CD33 transgenic mice by immunofluorescence staining. The density of microglia was assessed by the number of IBA1 + cells per mm2 of tissue from the average of a minimum of five sections from each genotype. The microglia density was evaluated in (a) whole brain, and in three selected regions: (b) cortex, (c) hippocampus, and (d) midbrain. No significant difference was detected amongst the four cohorts that were analyzed (N = 5 mice per genotype). Fig. 2. Quantifying GFP expression in the brain of human CD33 transgenic mice by immunofluorescence staining. (a) The percentage of GFP positive cells in the IBA1+ or NeuN+populations were assessed in a minimum of five individual sections from five mice per genotype. Representative images are shown for each genotype. (b,c) Quantifying the percentage of GFP + cells in the (b) IBA1+ and (c) NeuN+ reveals minimal leakiness outside of the microglial cell lineage. Fig. 3. hCD33m transgenic microglia have an enhanced ability to phagocytose fluorescent beads. A competitive flow cytometry-based phagocytosis assay between primary hCD33m+ (blue) and WT (black) microglia, showing the (a) representative flow cytometry data for uptake of fluorescent polystyrene beads and (b) quantification of uptake. % Phagocytosis represents the cytochalasin-D subtracted values referenced to the average of the WT microglia set to 100%. (N=6). Fig. 4. Cell annotation for BAMs, monocytes and microglia. (a) BAM specific gene markers, Ms4a7, H2-Eb1 and Mrc1 exclusively expressed in cluster 9. (b) Ly6c2, Plac8 and Ace, genes representative of monocytes expressed in cluster 11. (c) Validation for the microglia specific genes, Sall1, Tmem119 and P2ry12 for clusters 0–8. Fig. 5. Differential gene expression of the microglia clusters from Experiment 1. (a) UMAP projection of the 12 identified clusters from Experiment 1. (b) Heatmap showing the top 50 differentially expressed genes in each cluster. Fig. 6. The top 30 DEGs in Cluster 0 from Experiment 1. Fig. 7. Single cell analysis of control, hCD33M and hCD33m in Experiment 2 reveals differences in isoform gene expression. (a) UMAP projections of the 13,982 cells in the merged Experiment 2 datasets showing 13 individual clusters. (b) Bar graphs showing the absolute number of cells from each isoform present in each cluster (top) and their respective proportions (bottom). (c) UMAP projection of the individual Control, hCD33M and hCD33m datasets. (d) Heatmap of representative genes. (e) Violin plots of hCD33m specific cluster 0 genes. Fig. 8. Feature plots showing the differentially expressed genes for each of the 11 lusters. Cluster were defined by the unsupervised SCCAF clustering and the expression of two representative genes were chosen for each cluster. Cluster 0–8, and 10 expressed microglial genes, whereas cluster 9 expressed border associated macrophage genes and cluster 11 expressed monocyte genes. Fig. 9. Anti-CD33 clone HIM3–4 does not recognize hCD33m. U937 cells overexpressing either hCD33M or hCD33m tested with anti-CD33 antibody clone HIM3–4 before and after pre-treatment with neuraminidase. Fig. 10. Optimizing and quantifying intracellular staining with S503 on U937 and THP1 cells. (a) CD33−/− U937 cells overexpressing CD33m were used to optimize a procedure with trypsin to remove cell surface antigens. Cells were treated with or without trypsin prior to staining with S503 (blue) or isotype control (grey). Cells were not fixed or permeabilized in this experiment. (b) Quantification of the mean fluorescence intensity (MFI) values for S503 staining of U937 cells with the indicated genotypes, taken from Fig. 5e of the main manuscript. MFI values are isotype control-subtracted. (c) An independent experiment showing that intracellular staining of hCD33m can be detected within hCD33m-overexpressing CD33−/− U937 cells. (d) Extracellular (left panel) and intracellular (right panel) staining with S503 and isotype control (grey) on WT and CD33−/− THP1 cells. Fig. 11. Cell surface staining of hCD33m + and hCD33M+ transgenic primary mouse microglia with antibody S503. (a) Flow cytometry histograms of WT (black), hCD33M+ (red), and hCD33m + (blue) primary microglia stanine with either isotype or S503. (b) Quantification of the mean fluorescence intensity (MFI) values for n = 3 samples for each condition. N.S. = no statistical significance (P > 0.05). Fig. 12. hCD33M+ mouse microglia express hCD33M at levels modestly lower than human peripheral blood monocytes. (a) Peripheral blood mononuclear cells were assessed for CD33M expression (red) on CD14+ peripheral blood monocytes relative to an isotype control (grey). (b) hCD33M+ primary mouse microglia were assessed for CD33M expression (red) relative to an isotype control (grey). (c) Mean fluorescence intensity (MFI) values from the flow cytometry graphs of CD33M signal. Data represents three different healthy human subjects and three different CD33M+ mice. Fig. 13. CD33 transcript levels in transgenic mouse microglia. (a) mCd33 transcript levels in primary microglia from hCD33 transgenic mice demonstrate that expression of neither hCD33 isoform alters the mCd33 transcript levels. (b) hCD33 transcript levels in primary microglia from hCD33 transgenic mice. Both datasets are derived from aligning our scRNAseq datasets with the inclusion of hCD33. Note that this method does not differentiate hCD33M and hCD33m transcripts due to extensive overlap between the two isoforms.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,884
Score d'incertitude au seuil0,165

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,025
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,000
Communication savante0,0020,002
Science ouverte0,0020,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,8840,177

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,058
Tête enseignante GPT0,248
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueFigshare→Même sujetNeuroinflammation and Neurodegeneration Mechanisms→Travaux en français237 207→