A recent paper published in Proteomics provides an overview of the applications of protein microarrays in translational research. The high-density array format appears to be a highly advantageous platform in this type of study thanks to features such as:
- Multiplexing capabilities: concomitant but individual analysis of many markers can be performed simultaneously,
- Low volume required with less consumption of reagents, samples, and tissues,
- Quick and automatable testing that can be of benefit in clinical studies.
Small amounts of precious clinical samples can be effectively employed to discover or validate potential biomarkers that could aid in the diagnostic, prognosis and treatment of patients. The numerous examples cited in the paper are summarized in the following tables:
|Breast cancer||NAPPA||28 potential autoantibody biomarkers identified- could be used for early detection of breast cancer||Anderson et al. J. Proteome Res. 2011, 10,
|Breast cancer||RPPM||Two glycolysis and splicesome autoantibody signatures identified||Ladd et al. Cancer Res. 2013, 73, 1502–1513.||Needs further validation|
|Basal-like Breast cancer-||NAPPA||13 verified autoantibodies||Wang et al. Cancer Epidemiol. Biomarkers Prev. 2015, 24, 1332–
|HPV (human papillomavirus)||Bead-based assay||Association between cervical cancer and viral protein E6 identified.||Waterboer et al. Clin. Chem. 2005, 51, 1845–1853.||Very low sera volume required (2ul) sensitivity higher than GST-capture ELISA!|
|RPPA||Higher level of phosphorylated proteins (P13K/AKT/mTOR) associated with lower survival rates||Petricoin at al. Cancer Res. 2007,
|Multiple sclerosis||Purified protein microarray||51 autoantibodies showed differential responses to 8 subtypes of multiple sclerosis||Ayoglu et al. Mol. Cell. Proteomics 2013, 12,
|(bead-based assay used for verification)|
|Systemic Lupus erythematosus||Purified protein microarray||Confirmed known autoantibodies, new autoantibodies discovered||Price et al. J. Clin. Invest. 2013, 123, 5135–5145.||Antibody reactivity correlates with severity of disease|
|Type 1 diabetes||NAPPA||10 potential autoantibody biomarkers identified||Mierch et al.J. Proteomics 2013, 94, 486–496.|
|Viral infections associated with Type 1 diabetes||NAPPA||Epstein-Barr virus was closely associated with type 1 diabetes||Bian et al. Proteomics 2015, 15, 2136–2145.
Bian et al. Diabetes 2015, 65, 285–296.
|SARS coronavirus||Purified protein microarray||Good correlation in predicting SARS infections 147 patients||Zhu et al. Proc. Natl. Acad. Sci. USA 2006, 103, 4011–4016.|
|Mycobacterium tuberculosis (MTb)||Unpurified protein microarrays||About 10% of MTb is recognized by the body’s immune system of patients with TB, with the majority of proteins being membrane and extracellular proteins.||Kunnath-Velayudhan et al. Proc. Natl. Acad. Sci. USA 2010, 107, 14703–14708.|
|MbT||Purified protein microarray||14 antibodies against MtB antigens discriminated between active from recovered patients||Deng et al. Cell Rep. 2014, 9, 2317–2329.|
|MbT -vaccine potential of the Mtb membrane vesicle (MV)||NAPPA||About a dozen humoral antibodies specifically induced in the mouse immunized with MV.||Prados-Rosales et al. mBio 2014, 5, e01921–e01914.||Mouse model-vaccine development|
|Plasmodium Falciparum||unpurified||A three antibody signature was revealed that accurately classify individuals 30, 90 or 365 days post infection.||Helb et al. Proc. Natl. Acad. Sci. USA 2015, 112, E4438–E4447.||Identification of potential vaccine targets|
One of the most popular technologies employed appears to be NAPPA (nucleic acid programmable microarray), which relies on `cell-free’ protein expression to create an ‘in-situ protein library’ directly on the array surface.
NAPPA overcomes some of the most important limitations of purified protein arrays: the need to express and purify a large number of proteins or to undergo time-consuming chromatographic fractionation steps, which often results in reduced sensitivity for low abundant proteins and could easily lead to protein denaturation and loss of activity.
It is encouraging to see how protein microarrays are systematically delivering promising new data and consolidating previous knowledge. Nonetheless, the challenge for the next generation of protein microarrays is to achieve very high reproducibility coupled with high sensitivity and broad dynamic range, so that small variations across a large number of samples can be accurately detected. Automation of all the processes involved, from sample handling, to signal readout is also going to greatly contribute to reducing errors, increasing reproducibility and processing time.
The full article is available here: