Technology insight: microarrays--research and clinical applications

The DendrisCHIP® Technology as a New, Rapid and Reliable Molecular Method for the Diagnosis of Osteoarticular Infections

Osteoarticular infections are major disabling diseases that can occur after orthopedic implant surgery in patients. The management of these infections is very complex and painful, requiring surgical intervention in combination with long-term antibiotic treatment. Therefore, early and accurate diagnosis of the causal pathogens is essential before formulating chemotherapeutic regimens. Although culture-based microbiology remains the most common diagnosis of osteoarticular infections, its regular failure to identify the causative pathogen as well as its long-term modus operandi motivates the development of rapid, accurate, and sufficiently comprehensive bacterial species-specific diagnostics that must be easy to use by routine clinical laboratories. Based on these criteria, we reported on the feasibility of our DendrisCHIP® technology using DendrisCHIP®OA as an innovative molecular diagnostic method to diagnose pathogen bacteria implicated in osteoarticular infections. This technology is based on the principle of microarrays in which the hybridization signals between oligoprobes and complementary labeled DNA fragments from isolates queries a database of hybridization signatures corresponding to a list of pre-established bacteria implicated in osteoarticular infections by a decision algorithm based on machine learning methods. In this way, this technology combines the advantages of a PCR-based method and next-generation sequencing (NGS) while reducing the limitations and constraints of the two latter technologies. On the one hand, DendrisCHIP®OA is more comprehensive than multiplex PCR tests as it is able to detect many more germs on a single sample. On the other hand, this method is not affected by the large number of nonclinically relevant bacteria or false positives that characterize NGS, as our DendrisCHIP®OA has been designed to date to target only a subset of 20 bacteria potentially responsible for osteoarticular infections. DendrisCHIP®OA has been compared with microbial culture on more than 300 isolates and a 40% discrepancy between the two methods was found, which could be due in part but not solely to the absence or poor identification of germs detected by microbial culture. We also demonstrated the reliability of our technology in correctly identifying bacteria in isolates by showing a convergence (i.e., same bacteria identified) with NGS superior to 55% while this convergence was only 32% between NGS and microbial culture data. Finally, we showed that our technology can provide a diagnostic result in less than one day (technically, 5 h), which is comparatively faster and less labor intensive than microbial cultures and NGS.

Innovative DendrisChips® Technology for a Syndromic Approach of In Vitro Diagnosis: Application to the Respiratory Infectious Diseases

Clinical microbiology is experiencing the emergence of the syndromic approach of diagnosis. This paradigm shift will require innovative technologies to detect rapidly, and in a single sample, multiple pathogens associated with an infectious disease. Here, we report on a multiplex technology based on DNA-microarray that allows detecting and discriminating 11 bacteria implicated in respiratory tract infection. The process requires a PCR amplification of bacterial 16S rDNA, a 30 min hybridization step on species-specific oligoprobes covalently linked on dendrimers coated glass slides (DendriChips®) and a reading of the slides by a dedicated laser scanner. A diagnostic result is delivered in about 4 h as a predictive value of presence/absence of pathogens using a decision algorithm based on machine-learning method, which was constructed from hybridization profiles of known bacterial and clinical isolated samples and which can be regularly enriched with hybridization profiles from clinical samples. We demonstrated that our technology converged in more than 95% of cases with the microbiological culture for bacteria detection and identification.

Sensing lectin-glycan interactions using lectin super-microarrays and glycans labeled with dye-doped silica nanoparticles

A new microarray platform, based on lectin super-microarrays and glycans labeled with dye-doped nanoparticles, has been developed to study glycan-lectin interactions. Glycan ligands were conjugated onto fluorescein-doped silica nanoparticles (FSNPs) using a general photocoupling chemistry to afford FSNP-labeled glycan probes. Lectins were printed on epoxy slides in duplicate sets to generate lectin super-microarrays where multiple assays could be carried out simultaneously in each lectin microarray. Thus, the lectin super-microarray was treated with FSNP-labeled glycans to screen for specific binding pairs. Furthermore, a series of ligand competition assays were carried out on a single lectin super-microarray to generate the dose-response curve for each glycan-lectin pair, from which the apparent affinity constants were obtained. Results showed 4-7 orders of magnitude increase in affinity over the free glycans with the corresponding lectins. Thus, the glycan epitope structures having weaker affinity than the parent glycans could be readily identified and analyzed from the lectin super-microarrays.

Molecular markers for cancer prognosis and treatment: have we struck gold?

The last decade has witnessed an emerging role for molecular or biochemical markers indicating a specific cellular mechanism or tissue function, often called 'biomarkers'. Biomarkers such as altered DNA, proteins and inflammatory cytokines are critical in cancer research and strategizing treatment in the clinic. In this review we look at the application of biological indicators to cancer research and highlight their roles in cancer detection and treatment. With technological advances in gene expression, genomic and proteomic analysis, biomarker discovery is expanding fast. We focus on some of the predominantly used markers in different types of malignancies, their advantages, and their limitations. Finally we conclude by looking at the future of biomarkers, their utility in the tumorigenic studies, and the progress towards personalized treatment strategies.