Culture and Assay of Large-Scale Mixed-Stage &lt;em&gt;Caenorhabditis elegans&lt;/em&gt; Populations

Shaver, Amanda O.; Gouveia, Goncalo J.; Kirby, Pamela S.; Andersen, Erik C.; Edison, Arthur S.

doi:10.3791/61453

Cited by 9 publications

(17 citation statements)

References 18 publications

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“…Including multiple independent PD1074 samples and pooled PD1074 samples in each batch can mitigate these issues. We continuously seeded and harvested PD1074 every time test samples were seeded or harvested during the large-scale culture plate (LSCP) growth process 43 . These PD1074 samples anchor the three studies and enable inter-study comparisons 37, 39 .…”

Section: Resultsmentioning

confidence: 99%

“…Briefly, a large-scale culture plate (LSCP) was used for each biological sample to generate a large mixed-stage population of worms (four to seven LSCP replicates per test strain). For each LSCP, worms were collected, population size estimated, and subsequently divided into at least 12 identical aliquots of 200,000 worms in ddH 2 O and flash-frozen in liquid nitrogen to quench metabolism and stored at -80°C 43 . As a QC sample, C. elegans IBAT BRM was generated and saved in 200,000 worm aliquots 31 .…”

Section: Methodsmentioning

confidence: 99%

“…Large populations of nematodes were generated for every biological replicate with minimal variability 43 . The stable Escherichia coli IBAT BRM and food source used throughout this experiment was described previously 31 .…”

Section: Elegans Sample Growth and Preparationmentioning

confidence: 99%

“…elegans is a model organism ideally suited to study conserved small molecules in metabolism [40][41][42] . The worm's short life cycle, self-fertilization of homozygous hermaphroditic individuals, ease of cultivation, and ability to propagate large numbers of animals 43 are ideal for large-scale studies 42,[44][45][46] . These traits allow one to (i) develop, test, and validate approaches to identify stable spectral features, (ii) demonstrate the feasibility of large-scale biochemical pathway analyses with genetic mutants, and (iii) focus on spectral features likely to reveal essential components of metabolic pathways by comparing features that vary due to genetic perturbations.…”

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confidence: 99%

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

Shaver¹,

Garcia²,

Gouveia³

et al. 2022

Preprint

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Large-scale untargeted metabolomics studies suffer from individual variation, batch effects and instrument variability, making comparisons of common spectral features across studies difficult. One solution is to compare studies after compound identification. However, compound identification is expensive and time consuming. We successfully identify common spectral features across multiple studies, with a generalizable experimental design approach. First, we included an anchor strain, PD1074, during sample and data collection. Second, we collected data in blocks with multiple controls. These anchors enabled us to successfully integrate three studies of Caenorhabditis elegans for nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) data from five different assays. We found 34% and 14% of features to be significant in LC-MS and NMR, respectively. Between 20-50% of spectral features differ in a mutant and among a set of genetically diverse natural strains, suggesting this reduced set of spectral features are excellent targets for compound identification.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Elegans Sample Growth and Preparationmentioning

confidence: 99%

mentioning

confidence: 99%

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

Shaver¹,

Garcia²,

Gouveia³

et al. 2022

Preprint

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“…We compared the C. elegans RM to 41 independent samples of the same strain (PD1074). 16 These individual samples were prepared in three sets of two extraction blocks. For each set, an equimolar pool was formed from all individual samples for a total of three QC pools.…”

Section: ■ Resultsmentioning

confidence: 99%

Long-Term Metabolomics Reference Material

et al. 2021

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The use of quality control samples in metabolomics ensures data quality, reproducibility, and comparability between studies, analytical platforms, and laboratories. Long-term, stable, and sustainable reference materials (RMs) are a critical component of the quality assurance/quality control (QA/QC) system; however, the limited selection of currently available matrix-matched RMs reduces their applicability for widespread use. To produce an RM in any context, for any matrix that is robust to changes over the course of time, we developed iterative batch averaging method (IBAT). To illustrate this method, we generated 11 independently grown Escherichia coli batches and made an RM over the course of 10 IBAT iterations. We measured the variance of these materials by nuclear magnetic resonance (NMR) and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as a food source to produce a Caenorhabditis elegans RM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individual C. elegans samples of the same genotype, allowed us to estimate the proportion of sample variation in preanalytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

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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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Culture and Assay of Large-Scale Mixed-Stage <em>Caenorhabditis elegans</em> Populations

Cited by 9 publications

References 18 publications

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

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

Long-Term Metabolomics Reference Material

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

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