Chapter 1 Introduction

Biological information flows across multiple scales, from individuals to populations, communities, and ecosystems. Metabolomics, the profiling and quantification of metabolites, is a relatively new member of the omics family. Compared with other omics fields, metabolomics focuses on small molecules, typically with molecular weights below 1500 Da. Figure 1.1 illustrates the position of metabolomics within the broader omics landscape[@b.dunn2011].

Figure 1.1: The complex interactions of functional levels in biological systems.

Metabolomics studies commonly employ GC-MS(Theodoridis et al. 2012; Beale et al. 2018), GC*GC-MS(Tian et al. 2016), LC-MS(Gika et al. 2014), LC-MS/MS(Begou et al. 2017), IM-MS(Levy et al. 2019), infrared ion spectroscopy(Martens et al. 2017), or NMR[@b.dunn2011] to measure metabolites. For broader discussion of analytical methods, see(Zhang et al. 2012), and for an overview of technical progress during 2012-2018, see(Miggiels et al. 2019). This book focuses on mass spectrometry-based metabolomics, especially XC-MS workflows.

Exposomics extends metabolomics toward the comprehensive measurement of environmental, dietary, lifestyle, and xenobiotic exposures together with their biological responses. Because untargeted mass spectrometry is widely used in both fields, exposomics shares many analytical foundations with metabolomics, including sample preparation, feature detection, annotation, normalization, and statistical modeling. However, exposomics also introduces broader questions in exposure science, epidemiology, ethics, and causal interpretation. Therefore, this book focuses on metabolomics workflows, while exposomics is acknowledged as an important related direction beyond the present scope.

Despite its power, metabolomics has several important limitations. No single analytical platform can comprehensively measure the entire metabolome because metabolites differ widely in polarity, abundance, volatility, stability, and ionization behavior. Untargeted studies also face major challenges in confident annotation and identification, while signal intensities can be strongly affected by matrix effects, batch effects, and pre-analytical variation during sample collection, storage, and extraction. Compared with other omics fields, metabolomics usually has lower analytical throughput and higher per-sample experimental complexity, which often limits practical sample size in real studies. As a result, many metabolomics datasets are not large enough to provide stable effect size estimation or strong independent validation, making reproducibility, cross-study comparison, and causal interpretation more difficult.

1.1 History

1.1.1 History of Mass Spectrometry

For a broader historical commentary on mass spectrometry, see(Yates Iii 2011). A brief summary is given below:

• 1913, Sir Joseph John Thomson “Rays of Positive Electricity and Their Application to Chemical Analyses.”

<img src=“images/mshistory.jpg” alt=“Sir Joseph John Thomson”Rays of Positive Electricity and Their Application to Chemical Analyses.”” width=“90%” />

Figure 1.2: Sir Joseph John Thomson “Rays of Positive Electricity and Their Application to Chemical Analyses.”

• Petroleum industry bring mass spectrometry from physics to chemistry

• The first commercial mass spectrometer is from Consolidated Engineering Corp to analysis simple gas mixtures from petroleum

• In World War II, U.S. use mass spectrometer to separate and enrich isotopes of uranium in Manhattan Project

• U.S. also use mass spectrometer for organic compounds during wartime and extend the application of mass spectrometer

• 1946, TOF, William E. Stephens

• 1970s, quadrupole mass analyzer

• 1970s, R. Graham Cooks developed mass-analyzed ion kinetic energy spectrometry, or MIKES to make MRM analysis for multi-stage mass spectrometry

• 1980s, MALDI rescue TOF and mass spectrometry move into biological application

• 1990s, Orbitrap mass spectrometry

• 2010s, Aperture Coding mass spectrometry

1.1.2 History of Metabolomics

You could check this report(Baker 2011). According to this book section(Kusonmano et al. 2016):

Figure 1.3: Metabolomics timeline during pre- and post-metabolomics era

• 2000-1500 BC some traditional Chinese doctors who began to evaluate the glucose level in urine of diabetic patients using ants

• 300 BC ancient Egypt and Greece that traditionally determine the urine taste to diagnose human diseases

• 1913 Joseph John Thomson and Francis William Aston mass spectrometry

• 1946 Felix Bloch and Edward Purcell Nuclear magnetic resonance

• late 1960s chromatographic separation technique

• 1971 Pauling’s research team “Quantitative Analysis of Urine Vapor and Breath by Gas–Liquid Partition Chromatography”

• Willmitzer and his research team pioneer group in metabolomics which suggested the promotion of the metabolomics field and its potential applications from agriculture to medicine and other related areas in the biological sciences

• 2007 Human Metabolome Project consists of databases of approximately 2500 metabolites, 1200 drugs, and 3500 food components

• post-metabolomics era high-throughput analytical techniques

1.1.3 Definition

Metabolomics is actually a comprehensive analysis with identification and quantification of both known and unknown compounds in an unbiased way. Metabolic fingerprinting is working on fast classification of samples based on metabolite data without quantifying or identification of the metabolites. Metabolite profiling always need a pre-defined metabolites list to be quantification(Madsen et al. 2010).

Meanwhile, targeted and untargeted metabolomics are also used in publications. For targeted metabolomics, the majority of the molecules within a biological pathway or a defined group of related metabolites are determined. Sometimes broad collection of known metabolites could also be referred as targeted analysis. Untargeted analysis detect all of possible metabolites unbiased in the samples of interest. A similar concept called non-targeted analysis/screen is actually describe the similar studies or workflow.

1.2 Reviews and tutorials

Some nice reviews and tutorials related to this workflow could be found in those papers or directly online:

1.2.1 Workflow

Those papers are recommended(González-Riano et al. 2020; Pezzatti et al. 2020; Liu et al. 2019; Barnes et al. 2016a; Cajka and Fiehn 2016b; Gika et al. 2014; Theodoridis et al. 2012; Lu and Xu 2008; Fiehn 2002) for general metabolomics related topics.

• For targeted metabolomics, you could check those reviews(Griffiths et al. 2010; Lu et al. 2008; Weljie et al. 2006; Yuan et al. 2012; Zhou and Yin 2016; Begou et al. 2017).

1.2.2 Data analysis

You could firstly read those papers(Barnes et al. 2016b; Kusonmano et al. 2016; Madsen et al. 2010; Uppal et al. 2016; Alonso et al. 2015) to get the concepts and issues for data analysis in metabolomics. Then this paper(Gromski et al. 2015) could be treated as a step-by-step tutorial. For GC-MS based metabolomics, check this paper(Rey-Stolle et al. 2022).

• A guide could be used choose an informatics software and tools for lipidomics(Ni et al. 2022).

• For annotation, this paper(Domingo-Almenara, Montenegro-Burke, Benton, et al. 2018) is a well organized review.

• For database used in metabolomics, you could check this review(Vinaixa et al. 2016).

• For metabolomics software, check this series of reviews for each year(Misra and van der Hooft 2016; Misra et al. 2017; Misra 2018).

• For open sourced software, those reviews(Chang et al. 2021; R. Spicer et al. 2017; Dryden et al. 2017) could be a good start.

• For DIA or DDA metabolomics, check those papers(Fenaille et al. 2017; Bilbao et al. 2015).

Here is the slides for metabolomics data analysis workshop and I have made presentations twice in UWaterloo and UC Irvine.

• Introduction

• Statistical Analysis

• Batch Correction

• Annotation

1.2.3 Application

• For environmental research related metabolomics or exposome, check those papers(Matich et al. 2019; Tang et al. 2020; Warth et al. 2017; Bundy et al. 2009).

• For toxicology, check this paper(Viant et al. 2019).

• Check this piece(Wishart 2016) for drug discovery and precision medicine.

• For food chemistry, check this paper(Castro-Puyana et al. 2017), this paper for livestock(Goldansaz et al. 2017) and those papers for nutrition(Allam-Ndoul et al. 2016; Jones et al. 2012; Müller and Bosy-Westphal 2020).

• For disease related metabolomics such as oncology(Spratlin et al. 2009), Cardiovascular(Cheng et al. 2017) . This paper(Kennedy et al. 2018) cover the metabolomics related clinical research.

• For plant science, check those paper(Sumner et al. 2003; Jorge et al. 2016; Hansen and Lee 2018).

• For single cell metabolomics analysis, check here(Fessenden 2016; Zenobi 2013; Ali et al. 2019; Hansen and Lee 2018).

• For gut microbiota, check here(Smirnov et al. 2016).

1.2.4 Challenge

General challenge for metabolomics studies could be found here (Schymanski and Williams 2017; Uppal et al. 2016; Schrimpe-Rutledge et al. 2016; Wolfender et al. 2015).

• For reproducible research, check those papers (Du et al. 2022; Place et al. 2021; Verhoeven et al. 2020; Mangul et al. 2019; Wallach et al. 2018; Hites and Jobst 2018; Considine et al. 2017; Sarpe and Schriemer 2017). To match data from different LC system, M2S could be used(Climaco Pinto et al. 2022).

• Quantitative Metabolomics related issues could be found here(Kapoore and Vaidyanathan 2016; Jorge et al. 2016; Lv et al. 2022; Vitale et al. 2022).

• For quality control issues, check here(Dudzik et al. 2018; Siskos et al. 2017; Sumner et al. 2007; Place et al. 2021; Broeckling et al. 2023; González-Domínguez et al. 2024). You might also try postcolumn infusion as a quality control tool(González et al. 2022).

1.3 Trends in Metabolomics

library(rentrez)
papers_by_year <- function(years, search_term){
    return(sapply(years, function(y) entrez_search(db="pubmed",term=search_term, mindate=y, maxdate=y, retmax=0)$count))
}
years <- 2002:2022
total_papers <- papers_by_year(years, "")
omics <- c("genomics", "epigenomics",  "metagenomic", "proteomics", "transcriptomics","metabolomics","exposomics")
trend_data <- sapply(omics, function(t) papers_by_year(years, t))
trend_props <- trend_data/total_papers
library(reshape)
library(ggplot2)
trend_df <- melt(data.frame(years, trend_data), id.vars="years")
p <- ggplot(trend_df, aes(years, value, colour=variable))
p + geom_line(size=1) + scale_y_log10("number of papers") + theme_bw()

1.4 Glossary

The following abbreviations and terms are used throughout this book:

Abbreviation	Full Name
ANOVA	Analysis of Variance
APCI	Atmospheric Pressure Chemical Ionization
CID	Collision-Induced Dissociation
DBS	Dried Blood Spots
DDA	Data-Dependent Acquisition
DIA	Data-Independent Acquisition
DoE	Design of Experiments
EIC	Extracted Ion Chromatogram
ESI	Electrospray Ionization
ExWAS	Exposome-Wide Association Study
FDR	False Discovery Rate
GC-MS	Gas Chromatography-Mass Spectrometry
GNPS	Global Natural Products Social Molecular Networking
HILIC	Hydrophilic Interaction Liquid Chromatography
HMDB	Human Metabolome Database
HRMS	High-Resolution Mass Spectrometry
ISF	In-Source Fragmentation
LC-MS	Liquid Chromatography-Mass Spectrometry
ML/DL	Machine Learning / Deep Learning
MR	Mendelian Randomization
MRM	Multiple Reaction Monitoring
MS/MS (MS2)	Tandem Mass Spectrometry
MSI	Metabolomics Standards Initiative
NMR	Nuclear Magnetic Resonance
PCA	Principal Component Analysis
PLS-DA	Partial Least Squares Discriminant Analysis
PMD	Paired Mass Distance
QC	Quality Control
QSPR	Quantitative Structure-Property Relationship
ROI	Region of Interest
RSD	Relative Standard Deviation
SIL-IS	Stable Isotope-Labeled Internal Standard
SPME	Solid-Phase Microextraction
TOF	Time-of-Flight
UPLC	Ultra-Performance Liquid Chromatography
VAMS	Volumetric Absorptive Microsampling

1.5 Workflow

References

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Pezzatti, Julian, Julien Boccard, Santiago Codesido, et al. 2020. “Implementation of Liquid Chromatography–High Resolution Mass Spectrometry Methods for Untargeted Metabolomic Analyses of Biological Samples: A Tutorial.” Analytica Chimica Acta 1105 (April): 28–44. https://doi.org/10.1016/j.aca.2019.12.062.

Place, Benjamin J., Elin M. Ulrich, Jonathan K. Challis, et al. 2021. “An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group.” Analytical Chemistry 93 (49): 16289–96. https://doi.org/10.1021/acs.analchem.1c02660.

Rey-Stolle, Fernanda, Danuta Dudzik, Carolina Gonzalez-Riano, et al. 2022. “Low and High Resolution Gas Chromatography-Mass Spectrometry for Untargeted Metabolomics: A Tutorial.” Analytica Chimica Acta 1210 (June): 339043. https://doi.org/10.1016/j.aca.2021.339043.

Sarpe, Vladimir, and David C Schriemer. 2017. “Supporting Metabolomics with Adaptable Software: Design Architectures for the End-User.” Current Opinion in Biotechnology, Analytical biotechnology, vol. 43 (February): 110–17. https://doi.org/10.1016/j.copbio.2016.11.001.

Schrimpe-Rutledge, Alexandra C., Simona G. Codreanu, Stacy D. Sherrod, and John A. McLean. 2016. “Untargeted Metabolomics Strategies—Challenges and Emerging Directions.” Journal of The American Society for Mass Spectrometry 27 (12): 1897–905. https://doi.org/10.1007/s13361-016-1469-y.

Schymanski, Emma L., and Antony J. Williams. 2017. “Open Science for Identifying ‘Known Unknown’ Chemicals.” Environmental Science & Technology 51 (10): 5357–59. https://doi.org/10.1021/acs.est.7b01908.

Siskos, Alexandros P., Pooja Jain, Werner Römisch-Margl, et al. 2017. “Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma.” Analytical Chemistry 89 (1): 656–65. https://doi.org/10.1021/acs.analchem.6b02930.

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