Daniel J. Peirano

A mobile instrumentation platform to distinguish airway disorders

Asthma and chronic obstructive pulmonary disease (COPD) are distinct but clinically overlapping airway disorders which often create diagnostic and therapeutic dilemmas. Current strategies to discriminate these diseases are limited by insensitivity and poor performance due to biologic variability. We tested the hypothesis that a gas chromatograph/differential mobility spectrometer (GC/DMS) sensor could distinguish between clinically well-defined groups with airway disorders based on the volatile organic compounds (VOCs) obtained from exhaled breath. After comparing VOC profiles obtained from 13 asthma, 5 COPD and 13 healthy control subjects, we found that VOC profiles distinguished asthma from healthy controls and also a subgroup of asthmatics taking the drug omalizumab from healthy controls. The VOC profiles could not distinguish between COPD and any of the other groups. Our results show a potential application of the GC/DMS for non-invasive and bedside diagnostics of asthma and asthma therapy monitoring. Future studies will focus on larger sample sizes and patient cohorts.

Citrus tristeza virus infection in sweet orange trees and a mandarin × tangor cross alters low molecular weight metabolites assessed using gas chromatography mass spectrometry (GC/MS)

Citrus tristeza virus (CTV) (genus Closterovirus) is a plant pathogen which infects economically important citrus crops, resulting in devastating crop losses worldwide. In this study, we analyzed leaf metabolite extracts from six sweet orange varieties and a mandarin × tangor cross infected with CTV collected at the Lindcove Research and Extension Center (LREC; Exeter, CA). In order to analyze low volatility small molecules, the extracts of leaf metabolites were derivatized by N-methyl-N-trimethylsilyl-trifluoracetamide (MSTFA). Chemical analysis was performed with gas chromatography/mass spectrometry (GC/MS) to assess metabolite changes induced by CTV infection. Principal Component Analysis (PCA) and Hotelling’s T2 were used to identify outliers within the set of samples. Partial Least Square Discriminant Analysis (PLS-DA) was applied as a regression method. A cross-validation strategy was repeated 300 times to minimize possible bias in the model selection. Afterwards, a representative model was built with a sensitivity of 0.66 and a specificity of 0.71. The metabolites which had the strongest contribution to differentiate between healthy and CTV-infected were found to be mostly saccharides and their derivatives such as inositol, d-fructose, glucaric and quinic acid. These metabolites are known to be endogenously produced by plants, possess important biological functions and often found to be differentially regulated in disease states, maturation processes, and metabolic responses. Based on the information found in this study, a method may be available that can identify CTV infected plants for removal and halt the spread of the virus.

Proposal of a Citrus translational genomic approach for early and infield detection of Flavescence dorée in Vitis

Flavescence dorée (FD) is one of the most widely known grapevine yellows disease and one of the most unabated worldwide in the viticulture sector. In this paper, we outline a strategy for developing an integrated system of technologies to enable rapid, early disease FD detection and diagnosis. We propose the deployment of a newly developed sensor device, the differential mobility spectrometer (DMS), which has shown positive results with a similar vector-borne disease in Citrus. We have previously demonstrated that the gas chromatograph DMS (GC/DMS) can distinguish various citrus diseases, and the system may also allow detection of volatile organic compound (VOC) signals from a tree of other plant systems of unknown health status. This would be achieved by comparing it with the expected VOC profile analysis of healthy or infected trees for health status determination. We can map regions in the GC/DMS signal to gas chromatography mass spectrometry data, thus allowing for deconvolution of specific GC/DMS signatures. We showed that RNA-seq will allow identifying genes involved in volatile pathways (terpenoids, phenylpropanoids, and mevalonate pathways) and could be used to guide the DMS use for the discovery of new biomarkers.

Supervised semi-automated data analysis software for gas chromatography / differential mobility spectrometry (GC/DMS) metabolomics applications

Modern differential mobility spectrometers (DMS) produce complex and multi-dimensional data streams that allow for near-real-time or post-hoc chemical detection for a variety of applications. An active area of interest for this technology is metabolite monitoring for biological applications, and these data sets regularly have unique technical and data analysis end user requirements. While there are initial publications on how investigators have individually processed and analyzed their DMS metabolomic data, there are no user-ready commercial or open source software packages that are easily used for this purpose. We have created custom software uniquely suited to analyze gas chromatograph / differential mobility spectrometry (GC/DMS) data from biological sources. Here we explain the implementation of the software, describe the user features that are available, and provide an example of how this software functions using a previously-published data set. The software is compatible with many commercial or home-made DMS systems. Because the software is versatile, it can also potentially be used for other similarly structured data sets, such as GC/GC and other IMS modalities.

Coupling a branch enclosure with differential mobility spectrometry to isolate and measure plant volatiles in contained greenhouse settings

Volatile organic compounds (VOCs) are off-gassed from all living organisms and represent end products of metabolic pathways within the system. In agricultural systems, these VOCs can provide important information on plant health and can ordinarily be measured non-invasively without harvesting tissue from the plants. Previously we reported a portable gas chromatography/differential mobility spectrometry (GC/DMS) system that could distinguish VOC profiles of pathogen-infected citrus from healthy trees before visual symptoms of disease were present. These measurements were taken directly from canopies in the field, but the sampling and analysis protocol did not readily transfer to a controlled greenhouse study where the ambient background air was saturated with volatiles contained in the facility. In this study, we describe for the first time a branch enclosure uniquely coupled with GC/DMS to isolate and measure plant volatiles. To test our system, we sought to replicate our field experiment within a contained greenhouse and distinguish the VOC profiles of healthy versus citrus infected with Candidatus Liberibacter asiaticus. We indeed confirm the ability to track infection-related trace biogenic VOCs using our sampling system and method and we now show this difference in Lisbon lemons (Citrus×limon L. Burm. f.), a varietal not previously reported. Furthermore, the system differentiates the volatile profiles of Lisbon lemons from Washington navels [Citrus sinensis (L.) Osbeck] and also from Tango mandarins (Citrus reticulata Blanco). Based on this evidence, we believe this enclosure-GC/DMS system is adaptable to other volatile-based investigations of plant diseases in greenhouses or other contained settings, and this system may be helpful for basic science research studies of infection mechanisms.

Modular and reconfigurable gas chromatography/differential mobility spectrometry (GC/DMS) package for detection of volatile organic compounds (VOCs)

Due to the versatility of present day microcontroller boards and open source development environments, new analytical chemistry devices can now be built outside of large industry and instead within smaller individual groups. While there are a wide range of commercial devices available for detecting and identifying volatile organic compounds (VOCs), most of these devices use their own proprietary software and complex custom electronics, making modifications or reconfiguration of the systems challenging. The development of microprocessors for general use, such as the Arduino prototyping platform, now enables custom chemical analysis instrumentation. We have created an example system using commercially available parts, centered around on differential mobility spectrometer (DMS) device. The Modular Reconfigurable Gas Chromatography - Differential Mobility Spectrometry package (MR-GC-DMS) has swappable components allowing it to be quickly reconfigured for specific application purposes as well as broad, generic use. The MR-GC-DMS has a custom user-friendly graphical user interface (GUI) and precisely tuned proportional-integral-derivative controller (PID) feedback control system managing individual temperature-sensitive components. Accurate temperature control programmed into the microcontroller greatly increases repeatability and system performance. Together, this open-source platform enables researchers to quickly combine DMS devices in customized configurations for new chemical sensing applications.

Automated chemical identification and library building using dispersion plots for differential mobility spectrometry

Differential mobility spectrometry (DMS) based detectors require rapid data analysis capabilities, embedded into the devices to achieve the optimum detection capabiites as portable trace chemical detectors. Automated algorithm-based DMS dispersion plot data analysis method was applied for the first time to pre-process and separate 3-dimentional (3-D) DMS dispersion data. We previously demonstrated our AnalyzeIMS (AIMS) software was capable of analyzing complex gas chromatography differential mobility spectrometry (GC-DMS) data sets. In our present work, the AIMS software was able to easliy separate DMS dispersion data sets of five chemicals that are important in detection of volatile organic compounds (VOCs): 2-butanone, 2-propanone, ethyl acetate, methanol and ethanol. Identification of chemicals from mixtures, separation of chemicals from a mixture and prediction capability of the software were all tested. These automated algorithms may have potential applications in separation of chemicals (or ion peaks) from other 3-D data obtained by hybrid analytical devices such as mass spectrometry (MS). New algorithm developments are included as future considerations to improve the current numerical approaches to fingerprint chemicals (ions) from a significantly complicated dispersion plot. Comprehensive peak identifcation by DMS-MS, variations of the DMS data due to chemical concentration, gas phase ion chemistry, temperature and pressure of the drift gas are considered in future algorithm improvements.