Abstract
Interphase fluorescence in situ hybridization (FISH) is widely used as a diagnostic tool for known genetic abnormalities due to its sensitivity for detection of cryptic aberrations, such as t(4;14)(p16;q32) in multiple myeloma (MM). In many cancers, chromosomal abnormalities are prognostic indicators that also predict response to therapy. Tests to determine the type and extent of these abnormalities are increasingly essential for more informed diagnosis and choice of treatment strategies. However the cost and complexity of the current FISH protocols, and the variability arising from differences in technical approach and subjective evaluation of hybridization patterns has compromised its widespread utilization. To create a standardized platform that will be accurate, robust, cost-effective and easy to use in any clinical setting, we have developed a microfluidic platform that enables simultaneous assessment of 10 chromosomal abnormalities or 10 patients, on a single chip. Microfluidic chips are hybrid polymer/glass microsystems with miniaturized networks of wells and channels, incorporating valves, heaters and fluidic control. The 10 channel microfluidic chip used here is the size of a microscope slide. Each channel requires only 1/10 the amount of probe used in conventional FISH, thus substantially reducing the cost per test. All the probes tested gave comparable results to conventional testing. Three cell lines and three ex-vivo PBMC samples from MM patients were tested against four different chromosomal probe sets, to detect translocation (4:14), any 14q32 translocation, deletion of chromosome 13 or deletion of p53. We used a mixture of patient sample and cell line to test the robustness of our technology and were able to successfully distinguish abnormal patterns with percentages that were comparable to FISH on microscope slides. On-chip FISH was highly reliable with consistent results in multiple test runs. To automate the process of reading slide, a computer vision algorithm was developed to provide a quantitative and objective measure of staining patterns, and to eventually eliminate the requirement for human intervention. This strategy uses artificial intelligence to distinguish probe from background staining, to identify and quantify the number of cells with different chromosomal patterns. This visual processing algorithm has been validated against human interpretation and provides a sensitive and unbiased method to distinguish signal and noise within stained cells. Although reliable and reproducible hybridization occurred in as little as four hours, to further reduce the time required for FISH testing, methods to enhance the hybridization were examined. These included chip designs that implemented mechanical or electrokinetic pumping. Both methods improved the hybridization and are currently being optimized. On-chip FISH appears to be versatile, fast and inexpensive, making fully automated FISH testing a possibility. Compared to conventional methods, these first iterations of on-chip FISH provide a 10-fold higher throughput and a 10 fold reduction in the cost of testing. On-chip FISH technology holds promise for sophisticated and cost-effective screening of cancer patients at every clinic visit in any health care setting, thus facilitating the delivery of personalized cancer care targeted to the genetic characteristics of each individual. Funded by CIHR, NSERC and Western Economic Diversification.
Architecture synthesis methodology of run-time reconfigurable multi-task and multi-mode systems with self-assembling micro-architecture
