Abstract 4552: Profiling signalling protein expression, modifications and interactions with multi-dimensional antibody microarrays
Notice bibliographique
Résumé
Abstract Antibody microarrays permit sensitive and semi-quantitative analysis of the expression, covalent modification and interactions of proteins in lysates of cells and tissues. At Kinexus, we have developed high content Kinex KAM microarrays that feature nearly 900 pan- and phosphosite-specific antibodies for monitoring protein kinases, phosphatases and other low abundance cell signalling proteins with combinations of different detection systems. One method involved capture of in vitro dye-labeled proteins (e.g. with Cy3) from lysates from cells subjected to diverse treatments. Another method involved the detection of changes in their total phosphorylation with biotinylated pIMAGO stain and an anti-biotin antibody that is labeled with a different dye (e.g. Cy5). Alteration in protein-tyrosine phosphorylation were monitored with a dye-labelled, generic phosphotyrosine-specific PYK antibody in a sandwich antibody microarray (SAM) format. The SAM technique was also used to explore the interactions of adapter, scaffolding and chaperone proteins with hundreds of potential target signal transduction proteins with dye-labeled reporter antibodies for these highly interactive proteins. We used several human cancer cell lines (e.g. A431, HeLa, Jurkat, MCF7) subjected to diverse treatments (e.g. growth factors) to identify biomarkers for the actions of these agents. Reproducible results were obtained with as little as 25 μg of lysate protein, with a dynamic range of detection exceeding 6000-fold, and a median error range for duplicates measurements of ±12%. Typically 10-15% of the proteins tracked with these arrays demonstrated perturbations exceeding 50%. More than a third of the leads from our antibody microarrays were confirmed by immunoblotting studies. The major limitation associated with validation by Western blotting was the much lower sensitivity with immunoblotting compared with antibody microarrays. We also explored the specific interactions of heat shock proteins, adapter proteins, 14-3-3 and calcium-binding proteins with the antibody microarray captured lysate proteins from cancer cell lines. By combining these detection strategies, it was feasible to obtain over 7000 data points from use of a single antibody microarray slide with two lysate samples and duplicate measurements. The goal of our proteomics and bioinformatics studies is to use the experimental results from the application of these microarrays to map the architecture of signalling networks in a cell- or tissue-specific manner. Such multi-tiered microarray-based analyses permit target-directed identification of diverse regulatory protein changes in different experimental model systems with greater sensitivity, breadth, selectivity and economy when compared to any other competing proteomics methodologies. Citation Format: Steven Pelech, Lambert Yue, Jeffrey White, Ryan Hounjet, Dirk Winkler. Profiling signalling protein expression, modifications and interactions with multi-dimensional antibody microarrays. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4552.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».