M.B. is supported by the IMPRS-PMPG &#39;Primary Metabolism and Plant Growth&#39;. A.R.F. and S.A. acknowledge the financial support of the EU Horizon 2020 Research and Innovation Programme, project PlantaSYST (SGA-CSA No. 739582 under FPA No. 664620), and project INCREASE (GA 862862).

The authors have no conflicts of interest to declare.

Both GC-MS and LC-MS are widely used tools for profiling complex mixtures of various metabolite classes. Handling large datasets with these tools is inherently associated with a non-biological variation, e.g., analytical variation, which interferes and biases the interpretation of the results. This protocol presents a robust and high-throughput extraction pipeline for comprehensive metabolic&#160;profiling to eliminate variation of non-biological origin and conduct large-scale &#34;omics&#34; studies. The volumes and concentrations used in this protocol were adjusted for legume species in different tissues. However, these&#160;parameters can be slightly modified and used for large-scale metabolic samples from other plant species as well.
The previously15&#160;described MTBE-based extractions can be used to analyze derivatized metabolites, semi-polar metabolites, and lipids. This can be expanded for protein and plant hormone extractions39, which were out of the scope of this protocol. Other extraction protocols rely on dichloromethane:ethanol mixtures40,41. Of these extraction protocols, the MTBE:methanol extraction protocol provides a favorable and less hazardous alternative to the existing chloroform-based extraction protocols42&#160;and does not result in a protein pellet as an interphase between the polar and lipid phases. Furthermore, MTBE methods have already been used in several studies for various biological samples43,44,45.
This protocol discusses several crucial steps that might lead to potential variation while handling a&#160;large number of samples,&#160;e.g., during harvesting12,13, extraction14, as well as randomization46. Furthermore, there are additional issues that have not been discussed in this protocol that must be considered to ensure high-quality metabolomic data, e.g., matrix effect and ion suppression14.
The power of QC-based normalization methods inherently depends on the number of QC samples in each batch. As mentioned earlier, although increasing the number would increase the power, the intra-batch variation of the QCs is relatively marginal compared to inter-batch variation in these analytical systems, as illustrated in Figure 3. Overall, there are other QC-based normalization methods, such as systemic error removal using random forest (SERRF), which have been shown to outperform most of the other normalization methods such as batch-wise-ratio, normalization using an optimal selection of multiple internal standards (NOMIS), and probabilistic quotient normalization (PQN)47. However, SERRF relies on multiple QC samples in each batch, e.g., every tenth sample, which is not feasible while handling large numbers of samples. The main advantage of QC-based normalization over other data-driven or internal standard-based methods is that it retains the essential biological variation while accommodating unwanted technical variation28. Readers may refer to this review on the handling of variation28.
One main issue in GWAS is the rate of false positives, which originate mostly due to the linkage of causal and non-causal sites48,49. Second, the conservative statistical correction approaches, e.g., Bonferroni and FDR, correct for the number of independent tests, which is not equal to the number of assayed SNPs in GWAS due to the linkage between proximate SNPs50,51&#160;Therefore, the actual number of independent tests is often lower. Another way to reduce the conservative statistical threshold would be to reduce the number of tested SNPs used for GWAS based on linkage decay over defined genomic regions52. The GWAS-integrated high-throughput metabolomics platform described in this protocol has a wide range of applications. In particular, it will facilitate improvements in crop breeding by changing the metabolite/lipid composition for industrially and nutritionally desired levels. Overall, metabolomics has provided an in-depth insight into the genetic architecture of a plethora of metabolites and metabolic diversification that occurred during crop domestication over the last decades, indicating the vast potential of metabolomics-associated breeding53. The molecular biological approaches for downstream QTL validation include the generation of CRISPR/Cas9 mutant lines54, T-DNA insertion lines55, stable and/or transient overexpression lines56, VIGS, ex vivo metabolomics approaches57&#160;next to the conventional approach in generating cross F2 populations as well as cross validation in different populations.
By performing the necessary correction for the analytical variations as described above, several integrated approaches can be performed in addition to GWAS, such as metabolite-metabolite, metabolite-lipid correlation analysis, correlation analysis to phenomic data to shed light on more complex traits, and/or co-expression analysis to further unravel the basis of biological systems58.

Large-scale &#34;omics&#34; approaches have enabled the analysis of complex biological systems1,2,3&#160;and further understanding of the link between genotypes and the resulting phenotypes4. Metabolomics using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) and GC-MS enabled the detection of a plethora of metabolite features, of which only some are annotated to a certain degree, resulting in a high proportion of unknown metabolites. Complex interactions can be explored by combining large-scale metabolomics with the underlying genotypic variation of a diverse population5. However, handling large sample sets is inherently associated with analytical variations, distorting the evaluation of metabolic variance for further downstream processes. Specifically, major issues leading to analytical variations are based on machine performance and instrumental drift over time6. The integration of batch-to-batch variation is challenging and especially problematic when analyzing large-scale structured plant populations. Multiple normalization procedures were suggested to correct for non-biological variations, e.g., the usage of internal, external, and isotope-labeled internal standards to correct for analytical errors, of which each is inherently associated with known problems and pitfalls7,8,9,10.
In addition to analytical variation, the choice of extraction protocols generally varies depending on the analytical method. Ultimately, it is desired to reduce material and labor costs as well as the necessity of using several aliquots of the same sample for various analytical processes by performing phase separation-based extraction methods. These methods were first introduced using chloroform: methanol/water solvents to fractionate polar and hydrophobic compounds11.
This protocol describes a fast high-throughput pipeline for a multi-omics platform to profile both polar metabolites and lipids in legume species. Further, it shows how those datasets can be appropriately corrected for analytical variation and normalized before integrating genotypic information to detect metabolite quantitative trait loci (QTL) by performing GWAS.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="4">Reagents and standards</td>
		</tr>
		<tr>
			<td>1,2-diheptadecanoyl-sn-glycero-3- phosphocholine (17:0 PC)</td>
			<td>Avanti Polar Lipids</td>
			<td>850360P</td>
			<td>Internal standard for lipids</td>
		</tr>
		<tr>
			<td>Chloroform</td>
			<td>Supleco</td>
			<td>67-66-3</td>
			<td>FAME solvent</td>
		</tr>
		<tr>
			<td>Isovitexin</td>
			<td>Sigma Aldrich</td>
			<td>38953-85-4</td>
			<td>Internal standard for metabolites</td>
		</tr>
		<tr>
			<td>Lignoceric Acid Methylester</td>
			<td>Sigma Aldrich</td>
			<td>2442-49-1</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methanol (MeOH)</td>
			<td>Biosolve Chemicals</td>
			<td>13684102</td>
			<td>ULC-MS grade</td>
		</tr>
		<tr>
			<td>Methoxyamin -hydrochlorid</td>
			<td>Sigma Aldrich</td>
			<td>593-56-6</td>
			<td>Metabolite deriviatization</td>
		</tr>
		<tr>
			<td>Methyl laurate</td>
			<td>Sigma Aldrich</td>
			<td>111-82-0</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyl myristate</td>
			<td>Sigma Aldrich</td>
			<td>124-10-7</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyl palmitate</td>
			<td>Sigma Aldrich</td>
			<td>112-39-0</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyl stearate</td>
			<td>Sigma Aldrich</td>
			<td>112-61-8</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyl tert-butyl ether (MTBE)</td>
			<td>Biosolve Chemicals</td>
			<td>13890602</td>
			<td>HPLC grade</td>
		</tr>
		<tr>
			<td>Methyl-caprat</td>
			<td>Sigma Aldrich</td>
			<td>110-42-9</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methylcaprylat</td>
			<td>Sigma Aldrich</td>
			<td>111-11-5</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyldocosanoat</td>
			<td>Sigma Aldrich</td>
			<td>929-77-1</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyleicosanoat</td>
			<td>Sigma Aldrich</td>
			<td>1120-28-1</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyl-hexacosanoat</td>
			<td>Sigma Aldrich</td>
			<td>5802-82-4</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyl-octacosanoat</td>
			<td>Sigma Aldrich</td>
			<td>55682-92-3</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Methyl-pelargonate</td>
			<td>Sigma Aldrich</td>
			<td>1731-84-6</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>N-Methyl-N-(trimethylsilyl)trifluoracetamid (MSTFA)</td>
			<td>Macherey-Nagel</td>
			<td>24589-78-4</td>
			<td>Metabolite deriviatization</td>
		</tr>
		<tr>
			<td>Pyridine</td>
			<td>Supleco</td>
			<td>110-86-1</td>
			<td>Metabolite deriviatization</td>
		</tr>
		<tr>
			<td>Ribitol</td>
			<td>Supleco</td>
			<td>22566-17-2</td>
			<td>Internal standard for derivatized metabolites</td>
		</tr>
		<tr>
			<td>Triacontanoic Acid Methyl Ester</td>
			<td>TCI Chemicals</td>
			<td>629-83-4</td>
			<td>FAME</td>
		</tr>
		<tr>
			<td>Water</td>
			<td>Biosolve Chemicals</td>
			<td>23214102</td>
			<td>ULC-MS grade</td>
		</tr>
		<tr>
			<td colspan="4">Equipment</td>
		</tr>
		<tr>
			<td>1.5 mL Safe-lock microcentrifuge tubes</td>
			<td>Eppendorf</td>
			<td>3120086</td>
			<td></td>
		</tr>
		<tr>
			<td>2 mL Safe-lock microcentrifuge tubes</td>
			<td>Eppendorf</td>
			<td>3120094</td>
			<td></td>
		</tr>
		<tr>
			<td>Balance</td>
			<td>Sartorius Corporation</td>
			<td>14 557 572</td>
			<td></td>
		</tr>
		<tr>
			<td>DB-35ms, 30 m, 0,25 mm, 0,25 &#181;m</td>
			<td>Aglient</td>
			<td>123-3832</td>
			<td>Analysis of derivatized metabolites</td>
		</tr>
		<tr>
			<td>GC-MS system</td>
			<td>Leco Pegasus HT TOF-MS (LECO Corporation)</td>
			<td></td>
			<td>Analysis of derivatized metabolites</td>
		</tr>
		<tr>
			<td>Grinding Balls, Stainless Steel</td>
			<td>OPS DIAGNOSTICS</td>
			<td>GBSS 196-2500-10</td>
			<td></td>
		</tr>
		<tr>
			<td>MS system</td>
			<td>Exactive, Orbitrap-type, MS (Exactive, Thermo Fisher Scientific)</td>
			<td></td>
			<td>Analysis of lipids</td>
		</tr>
		<tr>
			<td>MS system</td>
			<td>Q Exactive Focus (Q Exactive&#8482; Focus Hybrid Quadrupol-Orbitrap&#8482; 
			Massenspektrometer, Thermo Fisher Scientific)</td>
			<td></td>
			<td>Analysis of metabolites</td>
		</tr>
		<tr>
			<td>Refrigerated microcentrifuge</td>
			<td>Eppendorf, model 5427R</td>
			<td>22620701</td>
			<td></td>
		</tr>
		<tr>
			<td>Reversed Phase (RP) Bridged Ethyl Hybrid (BEH) C8 column 
			(100 mm &#215; 2.1 mm containing 1.7 &#956;m diameter particles)</td>
			<td>Waters</td>
			<td>186002878</td>
			<td>Analysis of lipids</td>
		</tr>
		<tr>
			<td>RP High Strength Silica (HSS) T3 column 
			(100 mm &#215; 2.1 mm containing 1.8 &#956;m diameter particles)</td>
			<td>Waters</td>
			<td>186003539</td>
			<td>Analysis of metabolites</td>
		</tr>
		<tr>
			<td>Shaker</td>
			<td>Eppendorf Thermomixer 5436</td>
			<td>2050-100-05</td>
			<td></td>
		</tr>
		<tr>
			<td>Sonicator</td>
			<td>USC 300 TH</td>
			<td>142-0084</td>
			<td></td>
		</tr>
		<tr>
			<td>Tissue grinding mixer mill</td>
			<td>Retsch, Mixer Mill MM 300</td>
			<td>20.746.0001</td>
			<td></td>
		</tr>
		<tr>
			<td>UPLC system</td>
			<td>Waters Acquity UPLC system (Waters)</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Vacuum concentrator</td>
			<td>Scan Speed Maxi Vac Alpha Evaporators</td>
			<td>7.008.500.002</td>
			<td></td>
		</tr>
		<tr>
			<td>Vortex mixer</td>
			<td>Vortex-Genie 2, Model G560</td>
			<td>SI-0236</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Software</td>
		</tr>
		<tr>
			<td>MetAlign</td>
			<td></td>
			<td></td>
			<td>Chromatogram processing</td>
		</tr>
		<tr>
			<td>MzMine</td>
			<td></td>
			<td></td>
			<td>Chromatogram processing</td>
		</tr>
		<tr>
			<td>R package &#34;data.table&#34;</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>R package &#34;fujiplot&#34;</td>
			<td></td>
			<td></td>
			<td>pleiotrpoic map</td>
		</tr>
		<tr>
			<td>R package &#34;genetics&#34;</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>R package &#34;Ime4&#34;</td>
			<td></td>
			<td></td>
			<td>BLUPs calculation</td>
		</tr>
		<tr>
			<td>R package &#34;LDheatmap&#34;</td>
			<td></td>
			<td></td>
			<td>LD plots</td>
		</tr>
		<tr>
			<td>R package &#34;MASS&#34;</td>
			<td></td>
			<td></td>
			<td>transformation</td>
		</tr>
		<tr>
			<td>R package &#34;rMVP&#34;</td>
			<td></td>
			<td></td>
			<td>GWAS</td>
		</tr>
		<tr>
			<td>R version 4.0.4</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>RefinerMS</td>
			<td></td>
			<td></td>
			<td>Chromatogram processing</td>
		</tr>
		<tr>
			<td>RefinerMS Genedata</td>
			<td>Expressionist</td>
			<td></td>
			<td>Chromatogram processing</td>
		</tr>
		<tr>
			<td>Tassel 5</td>
			<td></td>
			<td></td>
			<td>Genotype filtering</td>
		</tr>
		<tr>
			<td>Xcalibur</td>
			<td>Thermo Fisher Scientific</td>
			<td>OPTON-30965</td>
			<td>Chromatogram processing</td>
		</tr>
	</tbody>
</table>

large-scale multi-omics genome-wide association studies (mo-gwas): guidelines for sample preparation and normalization

Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are widely used metabolomics approaches to detect and quantify hundreds of thousands of metabolite features. However, the application of these techniques to a large number of samples is subject to more complex interactions, particularly for genome-wide association studies (GWAS). This protocol describes an optimized metabolic workflow, which combines an efficient and fast sample preparation with the analysis of a large number of samples for legume crop species. This slightly modified extraction method was initially developed for the analysis of plant and animal tissues and is based on extraction in methyl tert-butyl ether: methanol solvent to allow the capture of polar and lipid metabolites. In addition, we provide a step-by-step guide for reducing analytical variations, which are essential for the high-throughput evaluation of metabolic variance in GWAS.

Successful metabolomics GWAS experiments should begin with a proper experimental design, followed by sample collection, extraction, data acquisition, and processing, as illustrated in Figure 1. In this protocol, the MTBE method15&#160;was used to extract and analyze hundreds of metabolites belonging to several compound classes.&#160;Chromatography depends highly on the properties of the utilized column as well as elution buffer mixtures.&#160;Figure 2&#160;shows chromatograms of QC samples, indicating the elution pattern of some major lipid classes in this analytical system. The applied gradients for each platform are given in Table 1. Strong emphasis was placed on handling systemic errors in large-scale experiments. Performing large-scale metabolomics is inherently associated with systemic errors. For demonstration, we analyzed lipidomic data across several common bean species. Supplemental Table 1&#160;provides the extracted raw lipidomic&#160;data obtained after chromatogram processing using the software indicated in the Table of Materials. Following this protocol enabled us to circumvent&#160;major issues in dealing with omics data, especially while handling large sample sets.&#160;The normalization procedure yields in accurate correction of batch-wise analytical errors, as demonstrated in Figure 3. Although increasing the numbers of QC samples would increase the power of the normalization, this is not always feasible due to cost and time constraints. For high-throughput metabolomics GWAS with non-targeted metabolic features, it is essential to illustrate higher numbers of trait-marker association appropriately. A pleiotropic map38&#160;combining multiple GWAS results could be used to highlight the genomic regions to which several traits are linked (Figure 4).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62732/62732fig01.jpg" /> 
Figure 1: Flowchart of the metabolomics-based GWAS in plants. Several steps starting from the experimental design up to the detection of QTL are shown in the left panel. In the right panel, multiple figures are shown to support several steps mentioned in the left panel. Starting from the right top, (1) a suggested sequence of samples is shown for LC-MS, (2) pre- and post-normalized score plots of PCA, including a representative feature distribution pre- and post-processing, with red indicating QC sample intensities, and (3) a Manhattan plot with significant associations to which LD and haplotype distributions were generated. Abbreviations: GWAS = genome-wide association studies; QTL = quantitative trait loci; PCA = principal component analysis; QC = quality control; LD = linkage disequilibrium; MS = mass spectrometry; LC-MS = liquid chromatography-mass spectrometry; GC-MS = gas chromatography-mass spectrometry; LOESS = locally estimated scatterplot smoothing; MLM/MLMM = mixed linear model/multi-locus mixed-model. <a href="https://www.jove.com/files/ftp_upload/62732/62732fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62732/62732fig02.jpg" /> 
Figure 2: Chromatogram processing. Two QC chromatograms (base peak; lipid data) from different batches demonstrate the batch-wise variation for certain lipid classes in the pooled QC samples. Four major lipid classes are indicated with their respective elution windows in the in-house LC-MS system. The chromatograms were exported from MzMine21. Abbreviations: QC = quality control; LC-MS = liquid chromatography-mass spectrometry. <a href="https://www.jove.com/files/ftp_upload/62732/62732fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62732/62732fig03.jpg" /> 
Figure 3: Correction of systematic error. Principal component analysis of acquired lipidomic data, pre- (left, raw data) and post-correction for systemic errors (right, batch loess). The lower panels illustrate the feature (Cluster_00005) distribution over the samples (n=650) and batches (n=10) pre- (left) and post (right)-correction for analytical variation. Abbreviations: PCA = principal component analysis; QC = quality control; LOESS = locally estimated scatterplot smoothing. <a href="https://www.jove.com/files/ftp_upload/62732/62732fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62732/62732fig04.jpg" /> 
Figure 4: Pleiotropic map illustrating the combined GWAS results. The pleiotropic map highlights regions in the whole genome that are associated with several traits. The numbers on the outer rings indicate the corresponding chromosomes. Each circlet represents an individual trait with its significantly associated SNPs. The colors represent different compound classes (grey = compound class 1; green = compound class 2; purple = compound class 3; yellow = compound class 4). In the case of inter-compound class associations with the same genomic region, genes are highlighted. The inner grey circle shows the sum of all significant SNPs associated with a specific genomic position. The associations shown in this figure are artificially generated only for illustration. Abbreviations: GWAS = genome-wide association studies; SNPs = single-nucleotide polymorphisms. <a href="https://www.jove.com/files/ftp_upload/62732/62732fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="5">UHPLC-MS settings for lipids</td>
		</tr>
		<tr>
			<td>Time [min]</td>
			<td>Eluent A to B [%]*</td>
			<td colspan="3">Information</td>
		</tr>
		<tr>
			<td>0 - 1.00</td>
			<td>45% A</td>
			<td colspan="3">Eluent A: 1% 1M NH4-Acetate, 0.1% acetic acid in water (UHPLC grade)</td>
		</tr>
		<tr>
			<td>1.00 - 4.00</td>
			<td>lg 45% - 25% A</td>
			<td colspan="3">Eluent B: 1% 1M NH4-Acetate, 0.1% acetic acid in acetonitrile/2-propanol 7:3 (UHPLC grade)</td>
		</tr>
		<tr>
			<td>4.00 - 12.00</td>
			<td>lg 25% - 11% A</td>
			<td colspan="3">Flow rate: 400 &#181;L/min</td>
		</tr>
		<tr>
			<td>12.00 - 15.00</td>
			<td>lg 11% - 0% A</td>
			<td colspan="3">Injection volume: 2 &#181;L</td>
		</tr>
		<tr>
			<td>15.00 - 19.50</td>
			<td>cw 0% A</td>
			<td colspan="3"></td>
		</tr>
		<tr>
			<td>19.50-19.51</td>
			<td>0% - 45% A</td>
			<td colspan="3"></td>
		</tr>
		<tr>
			<td>19.51-24.00</td>
			<td>eq 45%</td>
			<td colspan="3"></td>
		</tr>
		<tr>
			<td colspan="5">UHPLC-MS/MS settings for polar and semi-polar metabolites</td>
		</tr>
		<tr>
			<td>Time [min]</td>
			<td>Eluent A and B [%]*</td>
			<td colspan="3">Information</td>
		</tr>
		<tr>
			<td>0 - 1.00</td>
			<td>99% A</td>
			<td colspan="3">Eluent A: 0.1% formic acid in water (UHPLC grade)</td>
		</tr>
		<tr>
			<td>1.00 - 11.00</td>
			<td>lg 99% -60% A</td>
			<td colspan="3">Eluent B: 0.1% formic acid in acetonitrile (UHPLC grade)</td>
		</tr>
		<tr>
			<td>11.00 - 13.00</td>
			<td>lg 60% - 30% A</td>
			<td colspan="3">Flow rate: 400 &#181;L/min</td>
		</tr>
		<tr>
			<td>13.00 - 15.00</td>
			<td>lg 30% - 1% A</td>
			<td colspan="3">Injection volume: 3 &#181;L</td>
		</tr>
		<tr>
			<td>15.00 - 16.00</td>
			<td>cw 1% A</td>
			<td colspan="3"></td>
		</tr>
		<tr>
			<td>16.00 - 17.00</td>
			<td>lg 1% - 99% A</td>
			<td colspan="3"></td>
		</tr>
		<tr>
			<td>17.00 - 20.00</td>
			<td>eq 99% A</td>
			<td colspan="3"></td>
		</tr>
		<tr>
			<td colspan="5">GC-MS settings for derivatized metabolites</td>
		</tr>
		<tr>
			<td>Time [min]</td>
			<td>Temperature [&#176;C]</td>
			<td colspan="3">Information</td>
		</tr>
		<tr>
			<td>0 - 2.00</td>
			<td>85</td>
			<td colspan="3">Carrier gas: Helium</td>
		</tr>
		<tr>
			<td>2.00 - 18.66</td>
			<td>lg 80 - 330</td>
			<td colspan="3">Flow rate: 2 mL/min</td>
		</tr>
		<tr>
			<td>18.66 - 24.66</td>
			<td>cw 330</td>
			<td colspan="3">Temperature gradient: 15 &#176;C/min</td>
		</tr>
		<tr>
			<td>24.66</td>
			<td>rapid cooling</td>
			<td colspan="3">Injection volume: 1 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 1: Gradient settings for each of the analytical platforms7. Abbreviations:&#160;lg = linear gradient; cw = column washing; eq = equilibrate; UHPLC-MS = ultra-high-performance liquid chromatography-mass spectrometry; UHPLC-MS/MS = ultra-high-performance liquid chromatography-tandem mass spectrometry; GC-MS = gas chromatography-mass spectrometry. * = percentage value corresponds to eluent A; remaining percentage value corresponds to eluent B.
Supplemental Table 1: Raw lipidomics data. Indicates the peak intensities for each of the detected clusters over each sample. <a href="https://www.jove.com/files/ftp_upload/62732/Supplementary Table 1 .xlsx" target="_blank">Please click here to download this Table.</a>

Watch this Scientific Journal Video about Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization at JoVE.com

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

In this protocol, we present an optimized workflow, which combines an efficient and fast sample preparation of many samples. In addition, we provide a step-by-step guide to reduce analytical variations for high-throughput evaluation of metabolic GWAS studies.

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are widely used metabolomics approaches to detect ...

large-scale-multi-omics-genome-wide-association-studies-mo-gwas

Research

JoVE Journal

Biochemistry

3.5K Views.  Max-Planck-Institute of Molecular Plant Physiology. In this protocol, we present an optimized workflow, which combines an efficient and fast sample preparation of many samples. In addition, we provide a step-by-step guide to reduce analytical variations for high-throughput evaluation of metabolic GWAS studies.

Video: Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Noncoding RNAs play important roles in several nuclear processes, including regulating gene expression, chromatin structure, and DNA repair. In most cases, the action of noncoding RNAs is mediated by proteins whose functions are in turn regulated by these interactions with noncoding RNAs. Consistent with this, a growing number of proteins involved in nuclear functions have been reported to bind RNA and in a few cases the RNA-binding regions of these proteins have been mapped, often through laborious, candidate-based methods.
Here, we report a detailed protocol to perform a high-throughput, proteome-wide unbiased identification of RNA-binding proteins and their RNA-binding regions. The methodology relies on the incorporation of a photoreactive uridine analog in the cellular RNA, followed by UV-mediated protein-RNA crosslinking, and mass spectrometry analyses to reveal RNA-crosslinked peptides within the proteome. Although we describe the procedure for mouse embryonic stem cells, the protocol should be easily adapted to a variety of cultured cells.

We describe a protocol to identify RNA-binding proteins and map their RNA-binding regions in live cells using UV-mediated photocrosslinking and mass spectrometry.

Noncoding RNAs play important roles in several nuclear processes, including regulating gene expression, chromatin structure, and DNA repair. In most ...

Genome-wide Quantification of Translation in Budding Yeast by Ribosome Profiling

Translation of mRNA into proteins is a complex process involving several layers of regulation. It is often assumed that changes in mRNA transcription reflect changes in protein synthesis, but many exceptions have been observed. Recently, a technique called ribosome profiling (or Ribo-Seq) has emerged as a powerful method that allows identification, with high accuracy, which regions of mRNA are translated into proteins and quantification of translation at the genome-wide level. Here, we present a generalized protocol for genome-wide quantification of translation using Ribo-Seq in budding yeast. In addition, combining Ribo-Seq data with mRNA abundance measurements allows us to simultaneously quantify translation efficiency of thousands of mRNA transcripts in the same sample and compare changes in these parameters in response to experimental manipulations or in different physiological states. We describe a detailed protocol for generation of ribosome footprints using nuclease digestion, isolation of intact ribosome-footprint complexes via sucrose gradient fractionation, and preparation of DNA libraries for deep sequencing along with appropriate quality controls necessary to ensure accurate analysis of in vivo translation.

Translational regulation plays an important role in the control of protein abundance. Here, we describe a high-throughput method for quantitative analysis of translation in the budding yeast Saccharomyces cerevisiae.

Translation of mRNA into proteins is a complex process involving several layers of regulation. It is often assumed that changes in mRNA transcription ...

Plate-based Large-scale Cultivation of Caenorhabditis elegans: Sample Preparation for the Study of Metabolic Alterations in Diabetes

Culturing Caenorhabditis elegans (C. elegans) in a large-scale manner on agar plates can be time-consuming and difficult. This protocol describes a simple and inexpensive method to obtain a large number of animals for the isolation of proteins to proceed with a western blot, mass spectrometry, or further proteomics analyses. Furthermore, an increase of nematode numbers for immunostainings and the integration of multiple analyses under the same culturing conditions can easily be achieved. Additionally, a transfer between plates with different experimental conditions is facilitated. Common techniques in plate culture involve the transfer of a single C. elegans using a platinum wire and the transfer of populated agar chunks using a scalpel. However, with increasing nematode numbers, these techniques become overly time-consuming. This protocol describes the large-scale culture of C. elegans including numerous steps to minimize the impact of the sample preparation on the physiology of the worm. Fluid and shear stress can alter the lifespan of and metabolic processes in C. elegans, thus requiring a detailed description of the critical steps in order to retrieve reliable and reproducible results. C. elegans is a model organism, consisting of neuronal cells for up to one-third, but lacking blood vessels, thus providing the possibility to investigate solely neuronal alterations independent of vascular control. Recently, early neurodegeneration in diabetic retinopathy was found prior to vascular alterations. Thus, C. elegans is of special interest for studying general mechanisms of diabetic complications. For example, an increased formation of advanced glycation end products (AGEs) and reactive oxygen species (ROS) is observed, which are reproducibly found in C. elegans. Protocols to handle samples of adequate size for a broader spectrum of investigations are presented here, exemplified by the study of diabetes-induced biochemical alterations. In general, this protocol can be useful for studies requiring large C. elegans numbers and in which liquid culture is not suitable.

Plate-based Large-scale Cultivation of Caenorhabditis elegans: Sample Preparation for the Study of Metabolic Alterations in Diabetes

This protocol describes a method for the large-scale cultivation of Caenorhabditis elegans on solid media. As an alternative to liquid culture, this protocol allows obtaining parameters of different scales under plate-based cultivation. This increases the comparability of results by omitting the morphological and metabolic differences between liquid and solid media culture.

Culturing Caenorhabditis elegans (C. elegans) in a large-scale manner on agar plates can be time-consuming and difficult. This protocol describes a simple ...

An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering

Macromolecular X-ray crystallography (MX) is the most prominent method to obtain high-resolution three-dimensional knowledge of biological macromolecules. A prerequisite for the method is that highly ordered crystalline specimen need to be grown from the macromolecule to be studied, which then need to be prepared for the diffraction experiment. This preparation procedure typically involves removal of the crystal from the solution, in which it was grown, soaking of the crystal in ligand solution or cryo-protectant solution and then immobilizing the crystal on a mount suitable for the experiment. A serious problem for this procedure is that macromolecular crystals are often mechanically unstable and rather fragile. Consequently, the handling of such fragile crystals can easily become a bottleneck in a structure determination attempt. Any mechanical force applied to such delicate crystals may disturb the regular packing of the molecules and may lead to a loss of diffraction power of the crystals. Here, we present a novel all-in-one sample holder, which has been developed in order to minimize the handling steps of crystals and hence to maximize the success rate of the structure determination experiment. The sample holder supports the setup of crystal drops by replacing the commonly used microscope cover slips. Further, it allows in-place crystal manipulation such as ligand soaking, cryo-protection and complex formation without any opening of the crystallization cavity and without crystal handling. Finally, the sample holder has been designed in order to enable the collection of in situ X-ray diffraction data at both, ambient and cryogenic temperature. By using this sample holder, the chances to damage the crystal on its way from crystallization to diffraction data collection are considerably reduced since direct crystal handling is no longer required.

A novel sample holder for macromolecular X-ray crystallography along with a suitable handling protocol is presented. The system allows crystal growth, crystal soaking and in situ diffraction data collection at both, ambient and cryogenic temperature without the need of any crystal manipulation or mounting.

Macromolecular X-ray crystallography (MX) is the most prominent method to obtain high-resolution three-dimensional knowledge of biological macromolecules. ...

Sample Preparation for Probe Electrospray Ionization Mass Spectrometry

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ratios (m/z), which are useful to deduce molecular weights and structures. While it is essentially a destructive analytical method, recent advancements in the ambient ionization technique have enabled us to acquire data while leaving tissue in a relatively intact state in terms of integrity. Probe electrospray ionization (PESI) is a so-called direct method because it does not require complex and time-consuming pretreatment of samples. A fine needle serves as a sample picker, as well as an ionization emitter. Based on the very sharp and fine property of the probe tip, destruction of the samples is minimal, allowing us to acquire the real-time molecular information from living things in situ. Herein, we introduce three applications of PESI-MS technique that will be useful for biomedical research and development. One involves the application to solid tissue, which is the basic application of this technique for the medical diagnosis. As this technique requires only 10 mg of the sample, it may be very useful in the routine clinical settings. The second application is for in vitro medical diagnostics where human blood serum is measured. The ability to measure fluid samples is also valuable in various biological experiments where a sufficient volume of sample for conventional analytical techniques cannot be provided. The third application leans toward the direct application of probe needles in living animals, where we can obtain real-time dynamics of metabolites or drugs in specific organs. In each application, we can infer the molecules that have been detected by MS or use artificial intelligence to obtain a medical diagnosis.

This article introduces sample preparation methods for a unique real-time analytical method based on the ambient mass spectrometry. This method lets us perform real-time analysis of the biological molecules in vivo without any special pretreatments.

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ...

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This publication presents a complete interaction proteomics workflow designed for identifying low abundance protein-protein interactions from the nucleus that could also be applied to other subcellular compartments. This workflow includes subcellular fractionation, immunoprecipitation, sample preparation, offline cleanup, single-shot label-free mass spectrometry, and downstream computational analysis and data visualization. Our protocol is optimized for detecting compartmentalized, low abundance interactions that are difficult to identify from whole cell lysates (e.g., transcription factor interactions in the nucleus) by immunoprecipitation of endogenous proteins from fractionated subcellular compartments. The sample preparation pipeline outlined here provides detailed instructions for the preparation of HeLa cell nuclear extract, immunoaffinity purification of endogenous bait protein, and quantitative mass spectrometry analysis. We also discuss methodological considerations for performing large-scale immunoprecipitation in mass spectrometry-based interaction profiling experiments and provide guidelines for evaluating data quality to distinguish true positive protein interactions from nonspecific interactions. This approach is demonstrated here by investigating the nuclear interactome of the CMGC kinase, DYRK1A, a low abundance protein kinase with poorly defined interactions within the nucleus.

Described is a proteomics workflow for identifying protein interaction partners from a nuclear subcellular fraction using immunoaffinity enrichment of a given protein of interest and label-free mass spectrometry. The workflow includes subcellular fractionation, immunoprecipitation, filter aided sample preparation, offline cleanup, mass spectrometry, and a downstream bioinformatics pipeline.

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This ...

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view of the impact of a disease, treatment, or other condition on the proteome as a whole. This report provides a detailed protocol for the extraction, quantification, precipitation, digestion, labeling, and subsequent data analysis of protein samples. Our optimized TMT labeling protocol requires a lower tag-label concentration and achieves consistently reliable data. We have used this protocol to evaluate protein expression profiles in a variety of mouse tissues (i.e., heart, skeletal muscle, and brain) as well as cells cultured in vitro. In addition, we demonstrate how to evaluate thousands of proteins from the resulting dataset.

We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view ...

Plant Sample Preparation for Nucleoside/Nucleotide Content Measurement with An HPLC-MS/MS

Nucleosides/nucleotides are building blocks of nucleic acids, parts of cosubstrates and coenzymes, cell signaling molecules, and energy carriers, which are involved in many cell activities. Here, we describe a rapid and reliable method for the absolute qualification of nucleoside/nucleotide contents in plants. Briefly, 100 mg of homogenized plant material was extracted with 1 mL of extraction buffer (methanol, acetonitrile, and water at a ratio of 2:2:1). Later, the sample was concentrated five times in a freeze dryer and then injected into an HPLC-MS/MS. Nucleotides were separated on a porous graphitic carbon (PGC) column and nucleosides were separated on a C18 column. The mass transitions of each nucleoside and nucleotide were monitored by mass spectrometry. The contents of the nucleosides and nucleotides were quantified against their external standards (ESTDs). Using this method, therefore, researchers can easily quantify nucleosides/nucleotides in different plants.

A precise and reproducible method for in vivo nucleosides/nucleotides quantification in plants is described here. This method employs an HPLC-MS/MS.

Nucleosides/nucleotides are building blocks of nucleic acids, parts of cosubstrates and coenzymes, cell signaling molecules, and energy carriers, which ...

A Sample Preparation Pipeline for Microcrystals at the VMXm Beamline

The mounting of microcrystals (&#60;10 &#181;m) for single crystal cryo-crystallography presents a non-trivial challenge. Improvements in data quality have been seen for microcrystals with the development of beamline optics, beam stability and variable beam size focusing from submicron to microns, such as at the VMXm beamline at Diamond Light Source1. Further improvements in data quality will be gained through improvements in sample environment and sample preparation. Microcrystals inherently generate weaker diffraction, therefore improving the signal-to-noise is key to collecting quality X-ray diffraction data and will predominantly come from reductions in background noise. Major sources of X-ray background noise in a diffraction experiment are from their interaction with the air path before and after the sample, excess crystallization solution surrounding the sample, the presence of crystalline ice and scatter from any other beamline instrumentation or X-ray windows. The VMXm beamline comprises instrumentation and a sample preparation protocol to reduce all these sources of noise.
Firstly, an in-vacuum sample environment at VMXm removes the air path between X-ray source and sample. Next, sample preparation protocols for macromolecular crystallography at VMXm utilize a number of processes and tools adapted from cryoTEM. These include copper grids with holey carbon support films, automated blotting and plunge cooling robotics making use of liquid ethane. These tools enable the preparation of hundreds of microcrystals on a single cryoTEM grid with minimal surrounding liquid on a low-noise support. They also minimize the formation of crystalline ice from any remaining liquid surrounding the crystals. 
We present the process for preparing and assessing the quality of soluble protein microcrystals using visible light and scanning electron microscopy before mounting the samples on the VMXm beamline for X-ray diffraction experiments. We will also provide examples of good quality samples as well as those which require further optimization and strategies to do so.

The signal-to-noise ratio of data is one of the most important considerations in performing X-ray diffraction measurements from microcrystals. The VMXm beamline provides a low-noise environment and microbeam for such experiments. Here, we describe sample preparation methods for mounting and cooling microcrystals for VMXm and other microfocus macromolecular crystallography beamlines.

The mounting of microcrystals (&#60;10 &#181;m) for single crystal cryo-crystallography presents a non-trivial challenge. Improvements in data quality ...

Preparation of High-Temperature Sample Grids for Cryo-EM

The sample grids for cryo-electron microscopy (cryo-EM) experiments are usually prepared at a temperature optimal for the storage of biological samples, mostly at 4 &#176;C and occasionally at room temperature. Recently, we discovered that the protein structure solved at low temperature may not be functionally relevant, particularly for proteins from thermophilic archaea. A procedure was developed to prepare protein samples at higher temperatures (up to 70 &#176;C) for cryo-EM analysis. We showed that the structures from samples prepared at higher temperatures are functionally relevant and temperature dependent. Here we describe a detailed protocol for preparing sample grids at high temperature, using 55 &#176;C as an example. The experiment made use of a vitrification apparatus modified using an additional centrifuge tube, and samples were incubated at 55 &#176;C. The detailed procedures were fine-tuned to minimize vapor condensation and obtain a thin layer of ice on the grid. Examples of successful and unsuccessful experiments are provided.

This paper provides a detailed protocol for preparing sample grids at temperatures as high as 70 &#176;C, prior to plunge freezing for cryo-EM experiments.

The sample grids for cryo-electron microscopy (cryo-EM) experiments are usually prepared at a temperature optimal for the storage of biological samples, ...