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.
