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><tbody><tr><td>&lt;strong&gt;Reagents and standards&lt;/strong&gt;</td><td></td><td></td><td></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>&lt;strong&gt;Equipment&lt;/strong&gt;</td><td></td><td></td><td></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 &amp;micro;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&amp;trade; Focus Hybrid Quadrupol-Orbitrap&amp;trade;&lt;br/&gt; 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&lt;br/&gt; (100 mm &amp;times; 2.1 mm containing 1.7 &amp;mu;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&lt;br/&gt; (100 mm &amp;times; 2.1 mm containing 1.8 &amp;mu;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>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></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 &quot;data.table&quot;</td><td></td><td></td><td></td></tr><tr><td>R package &quot;fujiplot&quot;</td><td></td><td></td><td>pleiotrpoic map</td></tr><tr><td>R package &quot;genetics&quot;</td><td></td><td></td><td></td></tr><tr><td>R package &quot;Ime4&quot;</td><td></td><td></td><td>BLUPs calculation</td></tr><tr><td>R package &quot;LDheatmap&quot;</td><td></td><td></td><td>LD plots</td></tr><tr><td>R package &quot;MASS&quot;</td><td></td><td></td><td>transformation</td></tr><tr><td>R package &quot;rMVP&quot;</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>

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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

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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

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Complex diseases are often underpinned by multiple common genetic variants that contribute to disease susceptibility. Here, we describe a cost-effective ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Most cellular processes are regulated by transcriptional modulation of specific gene programs. Such modulation is achieved through the combined actions of a wide range of transcription factors (TFs) and cofactors mediating transcriptional activation or repression via changes in chromatin structure. Chromatin immunoprecipitation (ChIP) is a useful molecular biology approach for mapping histone modifications and profiling transcription factors/cofactors binding to DNA, thus providing a snapshot of the dynamic nuclear changes occurring during different biological processes.
To study transcriptional regulation in adipose tissue, samples derived from in vitro cell cultures of immortalized or primary cell lines are often favored in ChIP assays because of the abundance of starting material and reduced biological variability. However, these models represent a limited snapshot of the actual chromatin state in living organisms. Thus, there is a critical need for optimized protocols to perform ChIP on adipose tissue samples derived from animal models.
Here we describe a protocol for efficient ChIP-seq of both histone modifications and non-histone proteins in brown adipose tissue (BAT) isolated from a mouse. The protocol is optimized for investigating genome-wide localization of proteins of interest and epigenetic markers in the BAT, which is a morphologically and physiologically distinct tissue amongst fat depots.

Here we describe a protocol for efficient chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) of brown adipose tissue (BAT) isolated from a mouse. This protocol is suitable for both mapping histone modifications and investigating genome-wide localization of non-histone proteins of interest in vivo.

Most cellular processes are regulated by transcriptional modulation of specific gene programs. Such modulation is achieved through the combined actions of ...

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding ...

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid storage processes contributing to the development of the disease. We demonstrate a complete protocol from selection to epigenetic data analysis of patients with and without obesity. All steps from the protocol were tested and validated in a pilot study. 32 women participated in the study, in which 15 individuals were classified with obesity according to Body Mass Index (BMI) (45.1 &#177; 5.4 kg/m2); and 17 individuals were classified without obesity according to BMI (22.6 &#177; 1.8 kg/m2). In the group with obesity, 564 CpG sites related to fat mass were identified by linear regression analysis. The CpG sites were in the promoter regions. The differential analysis found 470 CpGs hypomethylated and 94 hypermethylated sites in individuals with obesity. The most hypomethylated enriched pathwayswere in the RUNX, WNT signaling, and response to hypoxia. The hypermethylated pathways were related to insulin secretion, glucagon signaling, and Ca2+. We conclude that the protocol effectively identified DNA methylation patterns and trait-related DNA methylation. These patterns could be associated with altered gene expression, affecting adipogenesis and lipid storage. Our results confirmed that an obesogenic lifestyle could promote epigenetic changes in human DNA.

The present study describes the workflow to manage DNA methylation data obtained by microarray technologies. The protocol demonstrates steps from sample preparation to data analysis. All procedures are described in detail, and the video shows the significant steps.

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid ...

ATAC-Seq Optimization for Cancer Epigenetics Research

The assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) probes deoxyribonucleic acid (DNA) accessibility using the hyperactive Tn5 transposase. Tn5 cuts and ligates adapters for high-throughput sequencing within accessible chromatin regions. In eukaryotic cells, genomic DNA is packaged into chromatin, a complex of DNA, histones, and other proteins, which acts as a physical barrier to the transcriptional machinery. In response to extrinsic signals, transcription factors recruit chromatin remodeling complexes to enable access to the transcriptional machinery for gene activation. Therefore, identifying open chromatin regions is useful when monitoring enhancer and gene promoter activities during biological events such as cancer progression. Since this protocol is easy to use and has a low cell input requirement, ATAC-seq has been widely adopted to define open chromatin regions in various cell types, including cancer cells. For successful data acquisition, several parameters need to be considered when preparing ATAC-seq libraries. Among them, the choice of cell lysis buffer, the titration of the Tn5 enzyme, and the starting volume of cells are crucial for ATAC-seq library preparation in cancer cells. Optimization is essential for generating high-quality data. Here, we provide a detailed description of the ATAC-seq optimization methods for epithelial cell types.

ATAC-seq is a DNA sequencing method that uses the hyperactive mutant transposase, Tn5, to map changes in chromatin accessibility mediated by transcription factors. ATAC-seq enables the discovery of the molecular mechanisms underlying phenotypic alterations in cancer cells. This protocol outlines optimization procedures for ATAC-seq in epithelial cell types, including cancer cells.

The assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) probes deoxyribonucleic acid (DNA) accessibility using the ...

Cancer Research

Beginner-Friendly Single-Language Pipeline for CUT&RUN Data Analysis

Introductory Analysis and Validation of CUT&#38;RUN Sequencing Data

The CUT&amp;RUN technique facilitates detection of protein-DNA interactions across the genome. Typical applications of CUT&amp;RUN include profiling changes in histone tail modifications or mapping transcription factor chromatin occupancy. Widespread adoption of CUT&amp;RUN is driven, in part, by technical advantages over conventional ChIP-seq that include lower cell input requirements, lower sequencing depth requirements, and increased sensitivity with reduced background signal due to a lack of cross-linking agents that otherwise mask antibody epitopes. Widespread adoption of CUT&amp;RUN has also been achieved through the generous sharing of reagents by the Henikoff lab and the development of commercial kits to accelerate adoption for beginners. As technical adoption of CUT&amp;RUN increases, CUT&amp;RUN sequencing analysis and validation become critical bottlenecks that must be surmounted to enable complete adoption by predominantly wet lab teams. CUT&amp;RUN analysis typically begins with quality control checks on raw sequencing reads to assess sequencing depth, read quality, and potential biases. Reads are then aligned to a reference genome sequence assembly, and several bioinformatics tools are subsequently employed to annotate genomic regions of protein enrichment, confirm data interpretability, and draw biological conclusions. Although multiple in silico analysis pipelines have been developed to support CUT&amp;RUN data analysis, their complex multi-module structure and usage of multiple programming languages render the platforms difficult for bioinformatics beginners who may lack familiarity with multiple programming languages but wish to understand the CUT&amp;RUN analysis procedure and customize their analysis pipelines. Here, we provide a single-language step-by-step CUT&amp;RUN analysis pipeline protocol designed for users with any level of bioinformatics experience. This protocol includes completing critical quality checks to validate that the sequencing data is suitable for biological interpretation. We expect that following the introductory protocol provided in this article combined with downstream peak annotation will allow users to draw biological insights from their own CUT&amp;RUN datasets.

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

This protocol guides bioinformatics beginners through an introductory CUT&amp;RUN analysis pipeline that enables users to complete an initial analysis and validation of CUT&amp;RUN sequencing data. Completing the analysis steps described here, combined with downstream peak annotation, will allow users to draw mechanistic insights into chromatin regulation.

The CUT&amp;RUN technique facilitates detection of protein-DNA interactions across the genome. Typical applications of CUT&amp;RUN include profiling ...

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using bioinformatics pipelines (&#34;dry bench&#34;). Both elements are essential to produce accurate and reliable results, which are particularly critical for clinical laboratories. Targeted NGS technologies have increasingly found favor in oncology applications to help advance precision medicine objectives, yet the methods often involve disconnected and variable wet and dry bench workflows and uncoordinated reagent sets. In this report, we describe a method for sequencing challenging cancer specimens with a 21-gene panel as an example of a comprehensive targeted NGS system. The system integrates functional DNA quantification and qualification, single-tube multiplexed PCR enrichment, and library purification and normalization using analytically-verified, single-source reagents with a standalone bioinformatics suite. As a result, accurate variant calls from low-quality and low-quantity formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tumor biopsies can be achieved. The method can routinely assess cancer-associated variants from an input of 400 amplifiable DNA copies, and is modular in design to accommodate new gene content. Two different types of analytically-defined controls provide quality assurance and help safeguard call accuracy with clinically-relevant samples. A flexible &#34;tag&#34; PCR step embeds platform-specific adaptors and index codes to allow sample barcoding and compatibility with common benchtop NGS instruments. Importantly, the protocol is streamlined and can produce 24 sequence-ready libraries in a single day. Finally, the approach links wet and dry bench processes by incorporating pre-analytical sample quality control results directly into the variant calling algorithms to improve mutation detection accuracy and differentiate false-negative and indeterminate calls. This targeted NGS method uses advances in both wetware and software to achieve high-depth, multiplexed sequencing and sensitive analysis of heterogeneous cancer samples for diagnostic applications.

An integrated system for targeted next-generation sequencing of oncology specimens is described. This cross-platform system is optimized for low-quality and low-quantity tumor biopsies, accommodates low DNA inputs, includes well-characterized multi-variant controls, and features a novel variant caller that is informed by quantitative pre-analytical quality control measures.

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using ...

Medicine

Infinium Assay for Large-scale SNP Genotyping Applications

Genotyping variants in the human genome has proven to be an efficient method to identify genetic associations with phenotypes. The distribution of variants within families or populations can facilitate identification of the genetic factors of disease. Illumina&#39;s panel of genotyping BeadChips allows investigators to genotype thousands or millions of single nucleotide polymorphisms (SNPs) or to analyze other genomic variants, such as copy number, across a large number of DNA samples. These SNPs can be spread throughout the genome or targeted in specific regions in order to maximize potential discovery. The Infinium assay has been optimized to yield high-quality, accurate results quickly. With proper setup, a single technician can process from a few hundred to over a thousand DNA samples per week, depending on the type of array. This assay guides users through every step, starting with genomic DNA and ending with the scanning of the array. Using propriety reagents, samples are amplified, fragmented, precipitated, resuspended, hybridized to the chip, extended by a single base, stained, and scanned on either an iScan or Hi Scan high-resolution optical imaging system. One overnight step is required to amplify the DNA. The DNA is denatured and isothermally amplified by whole-genome amplification; therefore, no PCR is required. Samples are hybridized to the arrays during a second overnight step. By the third day, the samples are ready to be scanned and analyzed. Amplified DNA may be stockpiled in large quantities, allowing bead arrays to be processed every day of the week, thereby maximizing throughput.

A protocol is described that uses Illumina&#39;s Infinium assays to perform large-scale genotyping. These assays can reliably genotype millions of SNPs across hundreds of individual DNA samples in three days. Once generated, these genotypes can be used to check for associations with a variety of different diseases or phenotypes.