Analytical and statistical strategies for volatilomics

The most commonly used technique for the discovery and identification of volatile molecules is gas chromatography-mass spectrometry (GC-MS). The technical difficulty of using GC-MS to identify low-abundance compounds in complex mixtures is partially responsible for the historical paucity of information on bacterial volatile metabolomes. While a Postdoc, Bean began advancing the use of comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry for bacterial metabolomics, enabling us to separate and identify five to ten-fold more volatiles in each sample than GC-MS. We are now applying GC×GC-TOFMS for breath, sputum, and bronchoalveolar fluid metabolomics analysis, vastly increasing our ability to identify candidate biomarkers of bacterial lung infections. Standardizing GC×GC-TOFMS for breath analysis and biomarker discovery is a primary outcome of the IMPACT-Breath study, facilitated by Bean's continued collaboration with Dr. Jane Hill (U. British Columbia), who is the one of the few other North American breath researchers using this analytical platform. Additionally, through a collaboration with Matt Richardson (U. Leicester), our team has been developing new post-processing methods for untargeted metabolomics data.

Publications

Bilal Ali, Trenton J. Davis, Daniela F. Gutiérrez-Muñoz, Heather D. Bean. Using retention index database matching for compound identification on a non-standard gas chromatography stationary phase. Preprint 

Trenton J. Davis*, Tarek Firzli*, Emily A. Higgins Keppler*, Matthew Richardson, Heather D. Bean. (2022) Addressing missing data in GC×GC metabolomics: Identifying missingness type and evaluating the impact of imputation methods on experimental replication. [*co-first authors] Analytical Chemistry, 94, 10912-10920. Published: Link Preprint: Link 

(editorial) Heather D. Bean (2021). Hot Topics in Gas Chromatography: Using the three Cs of data visualization as a life raft when you’re drowning in multidimensional chromatography peaks. LCGC North America 39 (s6b), 16-18. Link 

Lawrence A. Adutwum, A. Paulina de la Mata, Heather D. Bean, Jane E. Hill, James J. Harynuk. (2017) Estimation of start and stop numbers for cluster resolution feature selection algorithm; An empirical approach using null distribution analysis of fisher ratios. Analytical and Bioanalytical Chemistry. 409, 6699-6708. Link 

Heather D. Bean, Jane E. Hill, Jean-Marie D. Dimandja. (2015) Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of two dimensional gas chromatography-mass spectrometry data. Journal of Chromatography A. 1394, 111-117. Link 

Heather D. Bean, Jiangjiang Zhu, Jane E. Hill. (2011) Characterizing bacterial volatiles using secondary electrospray ionization mass spectrometry. Journal of Visualized Experiments. 52, http://www.jove.com/details.php?id=2664.

Code

We developed an R package called MetabImpute to explore various aspects of metabolomics data sets, especially those with technical or biological replicates. Included in the package are tools to evaluate the mechanisms of data missingness and variable distributions, tools to impute missing values using several methods, and to evaluate the effects of imputation on data reproducibility, measured by intra-class correlation.

https://github.com/BeanLabASU/MetabImpute

Presentations

Using GCxGC-TOFMS and chemometrics for studying microbial evolution in chronic infections
Heather Bean
LECO GCxGC Symposium 2020

Podcasts

Using multi-dimensional gas chromatography to help people breathe easier
Heather Bean
Analytically Speaking, Jan 2024