Noninvasive and early detection of endometriosis using a biological neural circuit-based novel gas sensor

PROJECT SUMMARY Endometriosis is a chronic inflammatory estrogen-dependent disease, and it affects approximately 10-15% of women of reproductive age. Definitive diagnosis of endometriosis is often delayed an average of 7-10 years after the onset of symptoms due to heterogeneity of symptoms. The current gold standard for diagnosis is invasive laparoscopic surgery coupled with histological verification which is associated with high surgical risk. Therefore, the capability to detect endometriosis early and reliably by employing a safe and noninvasive exhaled breath-based screening approach represents a major breakthrough in diagnosis and treatment of endometriosis. Exhaled breath-based technologies can also achieve early detection of endometriosis since the metabolic changes are reflected in exhaled breath during the initial stages of the disease. However, one of the major obstacles in applying exhaled breath-based disease detection in clinical settings is that current VOC sensors are not adequately sensitive and reliable. While gas chromatography-mass spectrometry (GC-MS) based component-wise gas mixture classification technique is sensitive, the performance of this technology is severely impacted by very low concentrations of several target VOCs (parts per billion-ppb to parts per trillion- ppt) associated with different diseases. Portable engineered gas sensors or ‘electronic noses’ are not as sensitive or reliable as their biological counterparts (e.g., dog’s nose). The innovation of this study comes from the forward bioengineering approach of directly harnessing the power of an entire biological olfactory system and addition of biological neural computations for data analysis. This proposal builds on and extends our latest published work where we demonstrated that the brain-based sensing technology can detect human oral cancer from emitted VOC mixtures of cell cultures (in vitro). In Aim 1, we will detect and distinguish between endometriosis vs. healthy cells by analyzing the cell culture (both 2-D and 3-D) headspace VOC mixtures and compare the detection performance with the gold standard of gas sensing technology (e.g., GC-MS). In Aim 2, we will quantify the brain-based sensor’s performance for detection of urinary VOC profiles from a healthy vs. endometriosis murine model during the course of the disease progression. Our long-term goal is to transition this ‘disease sniffing neuron’ technology towards a sensitive, reliable, real time, and portable exhaled breath-based endometriosis screening device.