Profiling Signaling Protein Expression, Modifications and Interactions with Multi‐dimensional Antibody Microarrays
Bibliographic record
Abstract
Antibody microarrays permit the sensitive and semi‐quantitative analysis of the expression, covalent modification and interactions of proteins recovered from lysates of cells and tissues. At Kinexus, we have developed high content antibody microarrays that feature pan‐and phosphosite‐specific antibodies for tracking protein kinases, phosphatases and other low abundance cell signalling proteins for their expressions, covalent modifications and interactions with multiplex detection systems. Kinex™ KAM antibody microarrays with 880 printed antibodies were used to monitor protein levels, phosphorylation and interactions 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 general changes in their protein phosphorylation with biotinylated pIMAGO stain and an anti‐biotin antibody that is labeled with a different dye (e.g. Cy5). Alterations in protein‐tyrosine phosphorylation were monitored with a dye‐labelled, generic phosphotyrosine‐specific PYK rabbit polyclonal 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 of 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 demonstrate 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 measurements from use of a single antibody microarray slide. 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 signaling 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. Support or Funding Information Supported by Kinexus Bioinformatics Corporation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".