An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Shaver, Amanda O.; Garcia, Brianna M.; Gouveia, Goncalo J.; Morse, Alison M.; Liu, Zihao; Borges, Ricardo Moreira; Asef, Carter K.; Leach, Franklin E.; Andersen, Erik C.; Amster, I. Jonathan; Fernández, Facundo M.; Edison, Arthur S.; McIntyre, Lauren M.

doi:10.1101/2022.03.25.485859

Cited by 3 publications

(3 citation statements)

References 94 publications

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“…Two strains of C. elegans (Caenorhabditis Genetics Center strains VC1265 and RB2347 with alterations to pyk -1 and idh -2, respectively) were selected for their mutations to central metabolism pathways and grown in large scale culture plates alongside the PD1074 reference strain samples as previously described. , Six replicate samples for both test strains were prepared with paired reference samples. Mixed-stage worm populations were harvested, diluted to 200,000 worm aliquots, flash-frozen in liquid nitrogen, and stored at −80 °C.…”

Section: Methodsmentioning

confidence: 99%

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry

et al. 2023

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Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM−MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM−MS to focus and provide insights into areas requiring further improvement.

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Section: Methodsmentioning

confidence: 99%

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry

et al. 2023

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“…Sample Growth. Two strains of C. elegans (Caenorhabditis Genetics Center strains VC1265 and RB2347 with alterations to pyk-1 and idh-2 respectively) were selected for their mutations to central metabolism pathways and grown in large scale culture plates alongside the PD1074 reference strain samples as previously described 18,19 . Six replicate samples for both test strains were prepared with paired reference samples.…”

Section: Methodsmentioning

confidence: 99%

Ion Mobility for Unknown Metabolite Identification: Hope or Hype?

Asef

Rainey

Garcia³

et al. 2022

Preprint

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Ion mobility (IM) spectrometry provides semi-orthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics datasets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of +/-3%, an average 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in nontargeted metabolomics using IM-MS to focus and provide insight on areas requiring further improvement.

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“…Alternatively, when comparing a set of consistently detected features, comparisons between the normalized intensity of each feature can provide insights into the effect of different parameters. 27 There have been major recent efforts to detect biologically relevant features without the need for upfront compound identification 32 or sacrificing the coverage of unknowns for exploratory analysis. 33,34 Traditional experimental optimization methods such as the one-factor-at-time (OFAT) approach remain a popular choice for conducting such comparisons.…”

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confidence: 99%

A Taguchi Design of Experiments Approach for Untargeted Metabolomics Sample Preparation Optimization

Garcia¹,

Gouveia²,

Shaver³

et al. 2022

Preprint

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Metabolomics commonly uses analytical techniques such as nuclear magnetic resonance (NMR) and liquid chromatography coupled to mass spectrometry (LC-MS) to quantify and identify metabolites associated with biological variation. Metabolome coverage from untargeted LC-MS studies relies heavily on the pre-analytical protocols used (e.g., homogenization and extraction). Chosen protocols impact which metabolites are successfully measured, which in turn impacts biological conclusions. Furthermore, different homogenization and extraction parameters produce significant variability in metabolome coverage, sample reproducibility, and extraction efficiency. There is a need for an efficient and systematic approach to optimize matrix-specific sample preparation parameters. Herein we describe a Taguchi design of experiments (DOE) approach for matrix-specific sample preparation optimization using model organism Caenorhabditis elegans. To demonstrate this methodology we optimized: i) extraction solvent, ii) volume, iii) extraction time, and iv) LC reconstitution solvent for a sequential non-polar and polar extraction, and confirmed our optimized results using NMR spectroscopy. DOE is rarely used in metabolomics, yet it provides a systematic path forward for optimizing multiple sample preparation parameters while keeping the number of experiments, labor, and costs necessarily low. Altogether, the Taguchi DOE method is an adaptable and scalable method well-fit for the diversity of current and future hypotheses studied using untargeted metabolomics.

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An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Cited by 3 publications

References 94 publications

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry

Ion Mobility for Unknown Metabolite Identification: Hope or Hype?

A Taguchi Design of Experiments Approach for Untargeted Metabolomics Sample Preparation Optimization

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