A non-invasive, easy to use analyzer for the detection of disease biomarkers in clinical samples

• Rapid, sensitive and selective VOC analyzer
• Untargeted discovery and targeted analysis of disease biomarkers
• Cost-effective and easy to use for both clinicians and researchers

Lonestar Disease Biomarker Detection

Volatile organic compounds (VOCs) are the end product of metabolic processes in the body that are related to diseases such as colon cancer, coeliac disease and tuberculosis. Using FAIMS technology, Lonestar can analyse clinical samples including urine, stool, blood, sweat and breath for the detection of disease biomarkers related to infections, inflammatory diseases and cancers. Lonestar is a portable, self contained unit that can provide rapid and detailed sample analysis, making it ideal for use by researchers and clinicians.

• Highly sensitive

  High sensitivity with part per billion detection levels combined with inlet control for high dynamic range

• Rapid screening

  Ideal for rapid screening of samples for diagnosis

• Highly versatile

  Compatible with various clinical samples - breath, urine, stool, blood, sweat, sputum

• Cost-effective

  Cost-effective and easy to use for both clinicians and researchers

• Targeted Analysis

  Selective screening for target compounds

• Sensors guided stability

  Integrated temperature, flow and humidity sensors for stable, closed-loop operation

• Connectivity

  Network and wireless* connectivity for remote monitoring and operation

• Easy integration

  Easy integration of other sensor data and control of third party systems

• Powerful software

  Powerful custom software for data visualisation, real-time control and offline analysis

HOW IT WORKS

Detecting early signs of disease through unique chemical fingerprints

Different diseases have unique chemical fingerprints that can be detected by the analysis of gas-phase volatile organic compounds (VOCs) produced by clinical samples such as urine, stool, blood, saliva, sweat. Disease related VOCs are also present in and detectable in exhaled breath. The detection of these specific disease biomarkers can provide early diagnosis of cancers, infections and easy monitoring of chronic inflammatory conditions.

DATA ANALYSIS

Open data format and off the shelf multi-variate analysis tools

Owlstone Medical work with new FAIMS users to develop sample and data analysis methodologies. Lonestar uses an open data format, allowing users to also use off the shelf multi-variate analysis tools. Raw FAIMS spectra are produced by Lonestar from the analysis of clinical samples. The plots to the right are FAIMS spectra collected for human a) stool b) urine and c) breath. FAIMS spectra are multi-dimensional and extremely rich in information, so typically techniques such as discrete wavelet transforms or principal component analysis are required to detect the distinguishing features of those patients suffering from specific illnesses vs. spectra from healthy individuals.

A classification algorithm is then developed and applied to the reduced data set. This algorithm tests relevant features of the data, and assigns each sample to the disease or disease-free category. The form of the classifier depends upon the data-set, but random forest, sparse logistic regression and support vector machines have all been successfully deployed. The classifier is then ideally tested on a new set of samples, to externally validate the sensitivity and specificity.

Lonestar Technical Specifications