Biology, optics, and AI for earlier detection.
D-Tails combines living biological sensing, optical readout, and adaptive AI to identify cancer-related olfactory signatures from urine samples.
The most sensitive cancer detector is already in nature.
The nematode C. elegans can natively distinguish cancerous from healthy urine with reported accuracy above 90%. This capability is driven by a vast repertoire of olfactory receptors expressed on specialized neurons. D-Tails translates that biology into a clinical setting — not by using living worms, but by isolating cancer-detection olfactory receptors and re-expressing them on a controlled, scalable biosensor.
Synthetic biology, translated into a measurable signal.
Cancer-detection olfactory receptors are isolated from C. elegans and re-expressed on engineered yeast strains. A bioluminescent BRET² readout converts receptor binding into a precise optical signal, with nanomolar sensitivity.
- 01 Biology
C. elegans receptors
We select specific olfactory receptors that are naturally tuned to sense cancer-related volatile compounds in urine.
- 02 Synthesis
S. cerevisiae engineering
Yeast strains are engineered to express the cancer-sensitive nematode receptors on their cellular membranes.
- 03 Detection
BRET signal
Receptors are coupled to a BRET² construct: binding of a target VOC triggers a conformational change and a measurable light shift with nanomolar sensitivity.
Interpreting biological response signatures.
We use multimodal AI to power an AI-native diagnostic platform that characterizes C. elegans neural-response signatures after exposure to urine samples from people with cancer and healthy controls. Our adaptive AI model is trained on a proprietary neural-response dataset of hundreds of samples, continuously expanded through scalable data collection to improve performance as new biological response data are generated.
- 100s of samples
- Proprietary neural-response dataset, continuously expanded.
- Multimodal
- Joint analysis of biological, optical, and behavioral signals.
- Adaptive
- Model performance improves as new response data is generated.
Peer-reviewed work behind the platform.
A curated set of cancer-detection, olfactory-receptor, microfluidics, and computational neuroscience papers from the D-Tails research archive.
- Nature Scientific Reports2021 Nature Scientific Reports
C. elegans-based chemosensation strategy for the early detection of cancer metabolites in urine samples
Demonstrates how C. elegans chemosensation can detect cancer metabolites in urine, supporting a non-invasive screening approach.
Read paper - Springer Nature2021 Springer Nature
Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors
Presents a computational method to identify C. elegans olfactory receptor binding sites and possible ligand interactions.
Read paper - Wiley Advanced Biology2021 Wiley Advanced Biology
A Shearless Microfluidic Device Detects a Role in Mechanosensitivity for AWCON Neuron in Caenorhabditis elegans
Introduces a shearless microfluidic device for studying mechanosensitivity in C. elegans neurons.
Read paper - IEEE Xplore2021 IEEE Xplore
Microfluidic arena for high-throughput C. elegans calcium imaging experiments with multiple strain confinement
Describes a high-throughput microfluidic platform for C. elegans calcium imaging experiments under confinement.
Read paper - ScienceDirect2021 ScienceDirect
A recurrent neural network model of C. elegans responses to aversive stimuli
Develops a recurrent neural network model to simulate C. elegans responses and support neural computation research.
Read paper - ScienceDirect2019 ScienceDirect
Investigation of the binding between olfactory receptors and odorant molecules in C. elegans organism
Studies molecular interactions between C. elegans olfactory receptors and odorant molecules.
Read paper - PLOS One2019 PLOS One
Biophysical modeling of C. elegans neurons: Single ion currents and whole-cell dynamics of AWCon and RMD
Presents biophysical models of C. elegans neurons, including single ion currents and whole-cell dynamics.
Read paper
