Path-Plex

Path-plex (30 phosphoproteins)

90 samples/plate

SERVICE INFO

The Path-Plex service allows relative quantitation of the phosphorylation levels of 30 phospho-proteins involved in major signaling pathways. This type of data can be used for pathway construction, monitor pathway alterations between disease states, identify patient-to-patient variability, or reveal cell- and drug-specific signalling pathway effects. See below "Supplementary Services" for more information and published work.  

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PHOSPHOPROTEINS COVERED

AKT1/2/3 S473, CHK2 T68, c-JUN S63, CREB1 S133, EGFR Y1068, eIF2α S51, ERK1/2 T202/Y204, GSK3α/β S9/S21, FAK1 Y397, HSP27 S78/S82, IKBA S32/S36, JNK T183/Y185, LCK Y505, MARCKS S170, MEK1 (MP2K1) S218/S222, mTOR S2448, NF-kB (TF65) S536, p38 T180/Y182, p53 S15, p70S6K (KS6B1) T389, PRAS40 (AKT1S1) T246, RSK1 (KS6A1) S380, SHP2 (PTN11) Y542, Smad3 S423/S425, SRC Y419, STAT1 Y701, STAT3 Y705, STAT5α Y694, STAT6 Y641, WNK1 T60.

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SAMPLE INFO

This type of assay has been optimized with specific lysis buffers in cultured cells and solid tissues. Please contact us to send you instructions on preparation of protein lysates as well as buffers to be used.

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SHIPPING INFO

Samples can be delivered in micro centrifuge tubes or 96-well microtitre plates.

Delivery should be arranged in dry ice between Mon-Wed to ensure adequate receipt of samples.

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SUPPLEMENTARY SERVICES & PUBLICATIONS

Our bioinformatics team can subsequently analyse your data to construct signaling pathways or identify drug effects. Please contact us for more info and refer to the following publications: 

• Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data A Mitsos et al. PLoS computational biology 5 (12), e1000591

• Comparing signaling networks between normal and transformed hepatocytes using discrete logical models J Saez-Rodriguez et al. Cancer research 71 (16), 5400-5411

• Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge K Rhrissorrakrai et al. Bioinformatics 31 (4), 471-483

• Crowdsourcing network inference: the DREAM predictive signaling network challenge RJ Prill et al. Science signaling 4 (189), mr7

• Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes LG Alexopoulos et al. Molecular & Cellular Proteomics 9 (9), 1849-1865

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