LaBaer Lab | Technology
technology: Protein Microarray Technology Development
Investigators: Ji Qiu, Ph.D., Alex Mendoza Garcia, Heather Rizzo,and Bharath Takulapalli (CPD and Bioelectronics and Biosensors)
Collaborators: Peter Wiktor, Ph.D. (CBB at Biodesign Institute and Engineering Arts, Inc), Jerry Nepom, Ph.D. (Benaroya Research Institute), and Jane Buckner (Benaroya Research Institute)
We are constantly working to improve our research platform. In collaboration with Dr. Peter Wiktor at the Center of Bioelectronics and Biosensors at the Biodesign Institute and Engineering Arts Inc., two projects are currently on-going.
Currently we are producing arrays at a density of ~2300 genes per standard microscopic slide using a contact-arrayer (pin-spotter). At this density, it requires more than 10 arrays to cover the human proteome which encodes roughly 30,000 proteins. One critical limiting factor for higher densities is the cross-talk among neighboring genes when they are arrayed too close to each other. We are developing arrays of higher densities through advanced piezoelectric-pipetting technology to further increase assay throughput using two complementary approaches, higher- affinity capture chemistry and mechanical separation of arrayed genes.
A protein microarray platform enabling post-translational modifications (PTM)
NAPPA has been successfully applied to many different applications. For most, the components in the standard NAPPA printing mixture (BS3, BSA and plasmid DNA) are inert in downstream applications. However, we have run into problems with these components for some PTM applications. PTMs of proteins play a critical role in a variety of different biological processes. Aberrant disease-specific post-translationally modified proteins often become disease-specific auto-antigens. We are developing next generation NAPPA to separate displayed proteins from the expression mixtures. Just-in-time expressed naked protein arrays will enable on array post-translational modification without the interference of NAPPA printing mixture components for downstream applications. This development will not only benefit research on assessing sero-reactivity against multiple PTMed proteins in parallel but also deliver a universal technology that enables robust functional and interaction studies of post-translationally modified proteins in vitro at the proteome level and promotes our understanding of these modifications in health and disease.
One application for this protein microarray platform enabling PTM is to study antibodies against citrullinated proteins in rheumatoid arthritis in collaboration with Drs. Jerry Nepom and Jane Buckner at the Benaroya Research Institute (Seattle, Washington). Anti-citrulline antibodies are specific and predictive of rheumatoid arthritis (RA). Seropositivity against citrulline is clinically assayed using cyclic citrullinated peptide (CCP) ELISA. Despite being an excellent proxy assay for diagnosis, anti-CCP positivity does not reveal any information about the actual underlying antigens that elicited the immune response. Recent studies demonstrated the value of identifying autoantibodies to particular antigens in the elucidation of RA etiology. Furthermore, the use of specific citrullinated antigens could improve diagnostic performance. Unfortunately, only a few citrullinated antigens have been discovered in the past several decades. Traditional protein immuno-chemistry methods to identify citrullinated antigens suffer drawbacks such as low throughput, poor reproducibility, inadequate quantification and low resolution. Commercial protein arrays are expensive, lack equal representation of candidate antigens and are not compatible with post-translational modifications such as citrullination, which requires harsh conditions. Our overarching goal is to discover additional antigens, when citrullinated, can be recognized by antibodies in RA patients at the proteome level. Identification of these citrullinated antigens will not only help understand the disease pathogenesis but also improve diagnosis and patient stratification.