AS acknowledges the Academy of Scientific and Innovative Research (AcSIR) for the registration (Registration No. 10BB22A71002). AS also acknowledges Defence Research and Development Organization (DRDO) for the fellowship. We acknowledge the Centre of Biomedical Research (CBMR) for providing the 800 MHz NMR spectrometer facility and funding through the intramural project (CBMR/IMR/0008/2021). We also acknowledge the Department of Critical Care Medicine (CCM), SGPGIMS, for constant support. We acknowledge the help of many nurses as well as, most importantly, the patients enrolled in this study. This study was funded by the intramural project (CBMR/IMR/0008/2021) of the Centre of Biomedical Research (CBMR) and by the extramural project (No. LSRB/01/15001/LSRB-404/PEE&#38;BS/2023) of Defence Research and Development Organization (DRDO).

Ethical approval (IEC code: 2022-71-PhD-126) was obtained from the IEC of Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow. Written and informed consents were taken from the patients or their relatives to perform the study and publish the data for research purpose. Moreover, the research was conducted following the institutional guidelines.
1. Study design and ethical clearance
<ol>
	<li>Determine the sample size for each group. Carefully review and establish the selection criteria for participants. Additionally, if the study design requires the inclusion of human or animal samples, then obtain ethical clearance from the appropriate institutional ethical committee (IEC) before beginning sample collection.</li>
	<li>Select ARDS patients categorized as mild, moderate, and severe, based on the diagnostic criteria of the Berlin 2012 definition based on the partial pressure of oxygen in arterial blood (PaO2) to the fraction of inspired oxygen (FiO2) (P/F) ratio. The ARDS patients with a P/F range of 300-200 are regarded as mild, 200-100 as moderate, and below 100 as severe38. Here, we have focused on two groups: mild and moderate ARDS patients (see Table 1). 
	NOTE: The study involves the use of serum obtained from blood samples to perform NMR-based metabolomics. In this study, the participants included for reference had a mean age of 42 years (&#177; 10.9 years), with a gender distribution of 2 males and 6 females.</li>
</ol>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Category&#160;</td>
			<td>P/F ratio</td>
		</tr>
		<tr>
			<td>Mild</td>
			<td>300-200</td>
		</tr>
		<tr>
			<td>Moderate</td>
			<td>200-100</td>
		</tr>
		<tr>
			<td>Severe</td>
			<td>100-0</td>
		</tr>
	</tbody>
</table>
Table 1: ARDS patients&#39; categorization.
2. Sample selection, collection, and processing
<ol>
	<li>Perform serum isolation by following the steps described below.
	<ol>
		<li>For experimenting, collect 2 mL of blood samples from the arteries using a sterile needle in a plain (with no additives) and sterile vial. Leave the sample to clot for 30 min at room temperature. For this study, 4 patients diagnosed with mild ARDS, and 4 patients having moderate ARDS were selected.</li>
		<li>Centrifuge the sample at 3100 x g for 15 min to separate the serum25. Transfer the serum into sterile microcentrifuge tubes, making aliquots of smaller volume (i.e., 400-500 &#181;L), appropriately label them (Patient name, CR no., day point, etc.), and store them at -80 &#176;C for future analysis. 
		NOTE: At this point, the experiment can be paused and resumed at a convenient time.</li>
	</ol>
	</li>
	<li>Process the serum sample before NMR experiments using the following steps.
	<ol>
		<li>Thaw the sample before performing the NMR experiments. Mix 250 &#181;L of aliquot of the serum with 250 &#181;L of saline phosphate buffer solution (containing 100% D2O, 0.9% NaCl, 50 mM sodium phosphate buffer, pH 7.4) to minimize pH variation.</li>
		<li>Vortex the mixture for a few seconds (~15 s) to ensure it is homogenously mixed. Finally, transfer the prepared sample to a clean NMR tube with a coaxial insert containing trimethylsilyl propionate (TSP) at a concentration of 0.05 mM, which serves as an external standard for calibration&#160;and quantification41. 
		NOTE: For reference, an inert and non-reactive compound that does not interact with the analytes is used. Other commonly used reference compounds include Tetramethylsilane (TMS) and sodium trimethylsilylpropanesulfonate (DSS).</li>
	</ol>
	</li>
</ol>
3. NMR experimentation
NOTE: The focus is on identifying small molecules in the serum samples, therefore, we used the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence, which suppresses macromolecule signals. All the serum samples in this study were recorded using an 800 MHz NMR spectrometer equipped with a cryogenically cooled triple-resonance TCI 5 mm broadband inverse probe-head and shielded z-gradient.
<ol>
	<li>Place the sample into the magnet. Write the command wrpa (mention experiment number) and press enter to set up a proton experiment with the cpmg pulse program. Mention the patient&#39;s name and other details necessary by clicking on the option Title.</li>
	<li>Lock the magnetic field by Writing the command Lock, press enter, and select the option 90% H2O and 10% D2O further. Tune and match the probe manually by&#160;ATMM&#160;to optimize the efficiency of radio frequency pulses and maximize sensitivity. Carry out 1D gradient shimming by clicking on topshim.</li>
	<li>Set the following acquisition parameters, including a relaxation delay of 5 s, a spectral sweep width of 12 parts per million (ppm), and an echo time of 300 &#181;s. All these parameters can be easily modified by selecting the option Acquisition Parameters in the upper panel.</li>
	<li>Apply line broadening of 0.3 Hz using an exponential window function, collecting data into 64,000 points over 128 scans. These parameters can be similarly modified by selecting the option Acquisition Parameters in the upper panel. By clicking on Acquisition Parameters, a list of specifications will appear, and the mentioned attributes, along with other parameters, can be modified during optimization.</li>
	<li>Acquire the NMR spectra using NMR processing and analysis tools. Once the final spectra are obtained, write command apk and absn and press enter to perform phase correction and baseline correction, respectively. Click on the Calibrate Axis option present on the upper panel and then either calibrate the TSP peak to 0 ppm automatically or manually.</li>
	<li>Transfer the acquired data from the system to the workstation, where further processing and analysis are done. 
	NOTE: The experimental phase is now complete and can be paused. Data preprocessing, analysis, and interpretation can be performed conveniently.</li>
</ol>
4. Data preprocessing
<ol>
	<li>Conduct the data preprocessing using NMR processing and analysis tools and NMR metabolite quantification software.
	<ol>
		<li>Sometimes, only automatic correction is not enough, and in that case, manual correction is also necessary. To perform phase correction manually, click on the option Process on the top menu bar of NMR processing and analysis tools. Then click on Adjust Phase, drag the mouse, and observe until the spectra are phase corrected. Once done, click on the Save and Return option (present in the form of an icon) available in the same menu bar.</li>
		<li>To perform manual baseline (Whittaker method) correction, open the spectral file in the processor module of the NMR metabolite quantification software. Select the option Baseline Correction. Then select the option Whittaker Method. Now put the dots at the base of the peaks in the entire spectra (0.0 to 10 ppm usually in the case of serum or as per the availability of the metabolite in the spectra) to adjust the base. After all the spectral files have been corrected baseline, save them in a single folder. Further processing, either using the processor or the profiler module of the NMR metabolite quantification software, can be performed using the same file saved in the specific format.</li>
		<li>Perform binning using the profiler module of the same software. Select the option Tools, then Batch Process, and then Spectral Binning.</li>
	</ol>
	</li>
	<li>To generate the final binning sheet to be used for statistical analysis, select the folder containing all the baseline-corrected spectral files after clicking on Spectral binning.</li>
	<li>Divide the spectra into a set number of equal-sized bins with a defined spectral width, ranging from 0.01 to 0.04 ppm (can be optimized as per the study). This process simplifies and organizes the spectral data, facilitating more consistent analysis. Specify the bucket size along with the start and end ppm values and designate the folder where the output binning sheets will be saved.</li>
	<li>Exclude the chemical shift regions corresponding to water and the solvent TSP (4.8-5.2 and 0.0-0.7 ppm) to prevent spectral interference42,43,44.</li>
	<li>This spreadsheet contains the sample names in one row and the different bin values along with ppm in the other rows. Before using this sheet for statistical analysis, modify it by organizing the samples into groups for analysis and saving it in comma-delimited (CSV) format only. 
	NOTE: For instance, when comparing the mild group with the moderate group of ARDS patients, we arranged the data with mild and moderate labels, added a serial number, and saved the file in CSV format.</li>
</ol>
5. Statistical analysis
NOTE: The binning sheet obtained from the previous step serves as the input file for statistical analysis. In this study, the statistical analysis (one factor) module of metabolomics statistical analysis software was used.
<ol>
	<li>Before statistical analysis, perform the normalization step using sum, log, and Pareto but it can vary across different studies45.</li>
	<li>Perform univariate and multivariate analysis to distinguish the different groups and determine the accuracy of the identified metabolites.
	<ol>
		<li>Perform principal component analysis (PCA), partial least-square discriminant analysis (PLS-DA), and orthogonal partial least-square discriminant analysis (OPLS-DA) to observe discrimination between the groups yielding variable importance of projection (VIP) scores for every metabolite responsible for the difference in the metabolic profile.</li>
		<li>Perform univariate methods, such as the student t-test and ANOVA, to identify the significant metabolites based on the criteria of having a p-value less than 0.05. Both tests yield box whisker plots showing the difference in the relative concentration of the metabolites.</li>
		<li>Perform biomarker analysis, which yields an area under the receiver operating characteristic curve (AUROC) graph. The AUC (area under the curve) value &#62; 0.8 is considered significant46.</li>
		<li>Select the final list of significant metabolites using the criteria VIP score&#62;1, Bonferroni-corrected p-value&#60;0.0544, and AUC &#62; 0.846.</li>
	</ol>
	</li>
	<li>Identify metabolites as described below.
	<ol>
		<li>Binning provides significant metabolites in the form of ppm values rather than names. Identify the metabolites corresponding to specific ppm values using databases like the Biological Magnetic Resonance Bank (BMRB)47and&#160;the Human Metabolome Database (HMDB).</li>
		<li>Other than these, use previously published literature42,44&#160;for identification and validation.</li>
		<li>Use the profiler module of NMR metabolite quantification software to confirm the identified metabolites with their ppm values.</li>
	</ol>
	</li>
</ol>
6. Quantification of metabolites using NMR metabolite quantification software
NOTE: The profiler module of the software is used widely to quantify the identified metabolites.
<ol>
	<li>Before beginning the quantification, calibrate the reference compound selected for the study (tsp used in this study) using the processor module. Click on the option Calibrate CSI (chemical shape indicator). Mention the concentration of the reference compound (0.05 mM for this study). Then, drag the software peak onto the spectral reference peak and adjust its height and width properly. Once the spectral reference peaks fit the software&#39;s peak, click on Accept.</li>
	<li>Open the desired spectra in the profiler module and select the appropriate compound library for study.</li>
	<li>At the bottom of the screen, a list of metabolites is displayed. Select the metabolite of interest from this list. Once selected, the possible ppm of the chosen metabolite will appear in the top corner of the spectra.</li>
	<li>Select the Ppm Value at the top corner and zoom in on the spectra to the specific ppm scale of the metabolite.</li>
	<li>At this point, two peaks will appear on the screen: one representing the recorded data and the other, displayed as a dotted line, representing the software peak. Align both peaks. Drag the software peak and adjust its height and position to match the peak present in the sample.</li>
	<li>Once properly aligned, the concentration of the metabolite can be obtained from the value displayed under the heading concentration in mM for that metabolite (Figure 1).</li>
	<li>Follow the same procedure for all metabolites and samples and export the quantified values in a spreadsheet. To generate the output file, click on the Batch Operations option under the Tools menu. Specify the desired folder for saving the output file.</li>
</ol>
7. Pathway analysis
NOTE: The significant metabolites identified after analysis are utilized to determine the major pathways directly influencing the outcome in disease groups. The pathway analysis module of metabolomics statistical analysis software and the KEGG database are generally employed for this purpose.
<ol>
	<li>Input the obtained list of significant metabolites into the module. A list containing the names of pathways is generated.</li>
	<li>Select the final pathways based on a criterion of having an impact factor greater than 0.1. Significant impact values indicate disease-related pathways30.</li>
	<li>Analyze the resulting pathways thoroughly to elucidate the dysregulation associated with the severity of the disease. One of the demerits of this method is its inefficiency in identifying the dysregulated pathways when the significant metabolites are less than 3.</li>
	<li>To cope with the same, the identified metabolite and its correlation with the disease severity can also be determined by using the previously published literature. Perform a thorough literature survey and observe if the identified metabolite is also following the same trend as per your interest.</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/67319/67319fig01.jpg" /> 
Figure 1:&#160;Fundamental steps in NMR-based metabolomics. The figure presents key steps in NMR-based metabolomics: collecting and preparing samples, performing NMR spectroscopy, preprocessing data, conducting statistical analysis, identifying and quantifying metabolites, and interpreting biological significance. <a href="https://www.jove.com/files/ftp_upload/67319/67319fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Metabolomics Using NMR Spectroscopy to Identify Dysregulated Metabolites

The authors declare no competing financial interest.

Metabolomics efficiently identifies and quantifies metabolites, targeting the metabolic cycles that become deranged during disease. The quality of the results depends on the meticulous execution of each step in the metabolomics approach. Every stage, from sample selection and collection to pathway identification, is critical in accurately identifying the primary factors contributing to the disease. Before performing metabolomics, a thorough review of the literature is essential, and careful attention must be paid at ever...

Despite continuous efforts to provide efficient disease diagnosis worldwide, targeted therapy has still not achieved its true potential. Various approaches, such as transcriptomics, proteomics, etc., have resulted in the identification of several biomarkers, but these did not hold enough clinical utility because of the lack of sensitivity and specificity1,2. Targeted therapy is a big challenge in some multifactorial diseases, eventually leading to higher mortality. There is a need for a better understanding of the underlying mechanism and pathophysiology of complex diseases existing with wide heterogeneity. So, in this regard, the evolvement of metabolomics has revolutionized therapeutic development, which eventually may aid in tailoring the treatment regimens for various critical illnesses such as acute respiratory distress syndrome (ARDS), sepsis, and severe acute pancreatitis (SAP).
Metabolomics is a comprehensive approach aimed at identifying and quantifying small molecular weight molecules (metabolites such as amino acids, lipids, peptides, organic acids, and vitamins) across various biofluids, cells, or tissue extracts. These metabolites, which typically weigh less than 1500 Da, play active roles in biochemical processes, reflecting a progressive outline of the organism&#39;s biological state. They include substrates for key enzymatic processes, intermediates in biological pathways, and by-products of cellular metabolism. Consequently, metabolomics captures a detailed fingerprint of dietary influences, drug interactions, and disease states. Metabolite changes are highly sensitive indicators of metabolism and biological pathways, allowing for correlations with phenotypic expressions and resultant pathophysiological abnormalities3,4. Initial variations in metabolites can serve as early indicators of disease severity, while temporal changes may help in monitoring treatment efficacy, disease progression, and clinical outcomes5,6,7. Metabolomics thus enhances clinical assays and various other omics approaches by redefining diseases through clinical, physiological, and biochemical endpoints8,9,10,11,12,13. The analytical capabilities of metabolomics are employed to monitor and determine disease susceptibility through altered metabolite concentrations14,15.
In this context, both mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have emerged as the primary analytical platforms for metabolite profiling in biological samples. These methods are used for both targeted and untargeted identification and quantification of metabolites16,17,18. Each platform has its advantages and limitations, but the non-destructive nature of NMR makes it preferable in various in vivo studies and for the characterization of the structure of unknown compounds, particularly in the initial stages of metabolomics research. The sample fractionation, derivatization, and ionization required before MS can introduce biases and often result in sample loss, affecting the dynamic features that NMR spectroscopy can capture with minimal or no sample preparation. The primary limitation of NMR is its lower sensitivity compared to MS, which offers a lower limit of detection, making it challenging to detect less abundant metabolites19. However, advancements such as high-resolution superconducting magnets, cryogenically cooled NMR probes, and techniques that enhance sensitivity have mitigated this limitation20,21,22. As a complementary approach to genomics and proteomics, metabolic profiling using NMR spectroscopy is gaining traction as a preferred technique23,24,25. NMR&#39;s minimal sample preparation, reproducibility, and repeatability make it a valuable tool for capturing the inherent dynamic features of metabolites despite its sensitivity challenges26.
Several research groups have performed metabolomics successfully, pinpointing the dysregulated metabolic profile of patients for various diseases27 such as ARDS28,29,30,31,32,33, pneumonia34, sepsis7, gallstones35, and pancreatitis36. NMR-based metabolomics studies of critically ill patients have been instrumental in tracking the progression from systemic inflammatory response syndrome (SIRS) to multiple organ dysfunction syndrome (MODS), which is a leading cause of intensive care unit (ICU) mortality and morbidity37. In a study by Stringer et al., plasma samples were used to examine metabolic alterations in patients with sepsis-induced acute lung injury (ALI) compared to control patients38. Key metabolites found elevated in this pilot study reflected the metabolic pathways involved and their association with clinical scores. This research was extended to serum metabolomics to differentiate sepsis from the early stages of lung injury mechanisms in ARDS32. Additionally, another study in ARDS identified potent serum biomarkers that distinctly differentiate between acute lung injury/ARDS and healthy controls, offering insights into systemic metabolic changes corresponding to the acute onset of lung injury39,40.
NMR spectroscopy is a high-throughput and automated analytical technique that delivers robust and unbiased information about the metabolic fingerprint revealing the underlying pathophysiology38. The clinical application and biological interpretation of NMR data hinge on obtaining high-quality spectra that contain rich information. Therefore, ensuring accurate, uniform, and well-formulated data collection, processing, and analysis is crucial. Therefore, the objective of this study is to leverage the essential steps of NMR-based metabolomics for the identification and quantification of metabolites. This study highlights the key steps of the protocol required for clinical metabolomics study (Figure 1), such as the selection of appropriate samples, collection and storage, sample processing and preparation, data acquisition and analysis, identifying and quantifying the metabolite of interest, and eventually, interpretation of the results in a clinical context to derive relevant insights. Each of these steps is essential for leveraging NMR spectroscopy in metabolomics to uncover significant biological and clinical insights.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Centirfuge</td>
			<td>Sigma aldrich</td>
			<td>3-18KS</td>
			<td></td>
		</tr>
		<tr>
			<td>Chenomx NMR suite&#160;</td>
			<td>NMR Suite, v9, Chenomx Inc., Edmonton, Canada</td>
			<td></td>
			<td>NMR metabolite quantification software</td>
		</tr>
		<tr>
			<td>Co-axial insert</td>
			<td>Sigma aldrich</td>
			<td>Z278513</td>
			<td></td>
		</tr>
		<tr>
			<td>Deuterim oxide</td>
			<td>Sigma aldrich</td>
			<td>151882</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf tubes</td>
			<td>Tarsons</td>
			<td>500020</td>
			<td></td>
		</tr>
		<tr>
			<td>Metaboanalyst</td>
			<td>Wishart Research Group</td>
			<td></td>
			<td>Metabolomics statistical analysis software</td>
		</tr>
		<tr>
			<td>NMR tube</td>
			<td>Wilmad</td>
			<td>Z412007</td>
			<td>5mm diameter</td>
		</tr>
		<tr>
			<td>Pipette</td>
			<td>Eppendorf research plus</td>
			<td>3123000039</td>
			<td>0-100 &#956;l</td>
		</tr>
		<tr>
			<td>Sample collection vials</td>
			<td>Tarsons cryo chill vials</td>
			<td>523194</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium azide</td>
			<td>Sigma aldrich</td>
			<td>S2002</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium chloride crystal</td>
			<td>Sigma aldrich</td>
			<td>S9625</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium phosphate dibasic</td>
			<td>Sigma aldrich</td>
			<td>567550</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium phosphate monobasic</td>
			<td>Sigma aldrich</td>
			<td>S0751</td>
			<td></td>
		</tr>
		<tr>
			<td>Topspin 3.6.4</td>
			<td>Bruker</td>
			<td></td>
			<td>NMR processing and analysis tool</td>
		</tr>
		<tr>
			<td>Tsp salt</td>
			<td>Sigma aldrich</td>
			<td>269913</td>
			<td></td>
		</tr>
	</tbody>
</table>

identification and quantification of deranged metabolites in critically ill patients using nmr-based metabolomics

Metabolomics is emerging as a significant approach to reflect the individual's response to pathophysiological conditions. Nuclear magnetic resonance (NMR) spectroscopy has evolved as a tool to identify metabolic dysregulations in critically ill patients afflicted with conditions like acute respiratory distress syndrome (ARDS), severe acute pancreatitis (SAP), acute kidney injury (AKI), and sepsis. The spectral data from the serum sample of the study and control group are recorded using an 800 MHz NMR spectrometer and processed using NMR processing and analysis tools. Furthermore, a rigorous statistical analysis, such as univariate and multivariate tests, is performed to pinpoint significant metabolites, which are then accurately identified and quantified using NMR metabolite quantification software. Additionally, pathway analysis highlights the deranged biochemical cycles that result in the severity of illness. Through this comprehensive approach, researchers aim to gain deeper insights into the metabolic alterations associated with these critical illnesses, potentially paving the way for a better understanding of the disease and improved diagnostics and treatment strategies.

To conduct a metabolomics study, it is essential to determine the sample size and the specific groups that will be analyzed. Selecting an adequate sample size is essential for obtaining significant results that accurately correlate with disease severity48. However, in this particular work, we used a small sample size to demonstrate the steps involved in the identification and quantification of metabolites using NMR-based metabolomics, which was intended primarily for reference. In this study, we e...

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is used to identify dysregulation in the metabolites in patients with various diseases. This technique allows the quantification of the deranged metabolites, unraveling the pathophysiological insights. Here, we describe the step-by-step procedure of the NMR-based approach for the metabolic characterization of the patients.

Metabolomics is emerging as a significant approach to reflect the individual's response to pathophysiological conditions. Nuclear magnetic resonance (NMR) ...

identification-quantification-deranged-metabolites-critically-ill

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386 Views.  SGPGIMS Campus. Nuclear magnetic resonance (NMR) spectroscopy is used to identify dysregulation in the metabolites in patients with various diseases. This technique allows the quantification of the deranged metabolites, unraveling the pathophysiological insights. Here, we describe the step-by-step procedure of the NMR-based approach for the metabolic characterization of the patients. 

Video: Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Quantification of &gamma;H2AX Foci in Response to Ionising Radiation

DNA double-strand breaks (DSBs), which are induced by either endogenous metabolic processes or by exogenous sources, are one of the most critical DNA lesions with respect to survival and preservation of genomic integrity. An early response to the induction of DSBs is phosphorylation of the H2A histone variant, H2AX, at the serine-139 residue, in the highly conserved C-terminal SQEY motif, forming &gamma;H2AX1. Following induction of DSBs, H2AX is rapidly phosphorylated by the phosphatidyl-inosito 3-kinase (PIKK) family of proteins, ataxia telangiectasia mutated (ATM), DNA-protein kinase catalytic subunit and ATM and RAD3-related (ATR)2. Typically, only a few base-pairs (bp) are implicated in a DSB, however, there is significant signal amplification, given the importance of chromatin modifications in DNA damage signalling and repair. Phosphorylation of H2AX mediated predominantly by ATM spreads to adjacent areas of chromatin, affecting approximately 0.03% of total cellular H2AX per DSB2,3. This corresponds to phosphorylation of approximately 2000 H2AX molecules spanning ~2 Mbp regions of chromatin surrounding the site of the DSB and results in the formation of discrete &gamma;H2AX foci which can be easily visualized and quantitated by immunofluorescence microscopy2. The loss of &gamma;H2AX at DSB reflects repair, however, there is some controversy as to what defines complete repair of DSBs; it has been proposed that rejoining of both strands of DNA is adequate however, it has also been suggested that re-instatement of the original chromatin state of compaction is necessary4-8. The disappearence of &gamma;H2AX involves at least in part, dephosphorylation by phosphatases, phosphatase 2A and phosphatase 4C5,6. Further, removal of &gamma;H2AX by redistribution involving histone exchange with H2A.Z has been implicated7,8. Importantly, the quantitative analysis of &gamma;H2AX foci has led to a wide range of applications in medical and nuclear research. Here, we demonstrate the most commonly used immunofluorescence method for evaluation of initial DNA damage by detection and quantitation of &gamma;H2AX foci in &gamma;-irradiated adherent human keratinocytes9.

Quantification of &#947;H2AX Foci in Response to Ionising Radiation

Quantification of DNA double-strand streaks using &gamma;H2AX formation as a molecular marker has become an invaluable tool in radiation biology. Here we demonstrate the use of an immunofluorescence assay for quantification of &gamma;H2AX foci after exposure of cells to radiation.

DNA double-strand breaks (DSBs), which are induced by either endogenous metabolic processes or by exogenous sources, are one of the most critical DNA ...

One-step Metabolomics: Carbohydrates, Organic and Amino Acids Quantified in a Single Procedure

Every infant born in the US is now screened for up to 42 rare genetic disorders called "inborn errors of metabolism". The screening method is based on tandem mass spectrometry and quantifies acylcarnitines as a screen for organic acidemias and also measures amino acids. All states also perform enzymatic testing for carbohydrate disorders such as galactosemia. Because the results can be non-specific, follow-up testing of positive results is required using a more definitive method. The present report describes the "urease" method of sample preparation for inborn error screening. Crystalline urease enzyme is used to remove urea from body fluids which permits most other water-soluble metabolites to be dehydrated and derivatized for gas chromatography in a single procedure. Dehydration by evaporation in a nitrogen stream is facilitated by adding acetonitrile and methylene chloride. Then, trimethylsilylation takes place in the presence of a unique catalyst, triethylammonium trifluoroacetate. Automated injection and chromatography is followed by macro-driven custom quantification of 192 metabolites and semi-quantification of every major component using specialized libraries of mass spectra of TMS derivatized biological compounds. The analysis may be performed on the widely-used Chemstation platform using the macros and libraries available from the author. In our laboratory, over 16,000 patient samples have been analyzed using the method with a diagnostic yield of about 17%--that is, 17% of the samples results reveal findings that should be acted upon by the ordering physician. Included in these are over 180 confirmed inborn errors, of which about 38% could not have been diagnosed using previous methods.

The urease method of sample preparation for GC/MS analysis of intermediary metabolites is presented by its inventor. The method allows one-step follow-up of newborn screening for inborn errors by tandem mass spectrometry by quantifying carbohydrates, organic and amino acids all in a single process.

Every infant born in the US is now screened for up to 42 rare genetic disorders called "inborn errors of metabolism". The screening method is based on ...

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each other at the biochemical level1. Nuclear magnetic resonance (NMR) spectroscopy is one of several technologies, including gas chromatography&#x2013;mass spectrometry (GC-MS), with considerable promise for such studies. Advantages of NMR are that it is suitable for untargeted analyses, provides structural information and spectra can be queried in quantitative and statistical manners against recently available databases of individual metabolite spectra2,3. In addition, NMR spectral data can be combined with data from other omics levels (e.g. transcriptomics, genomics) to provide a more comprehensive understanding of the physiological responses of taxa to each other and the environment4,5,6. However, NMR is less sensitive than other metabolomic techniques, making it difficult to apply to natural microbial systems where sample populations can be low-density and metabolite concentrations low compared to metabolites from well-defined and readily extractable sources such as whole tissues, biofluids or cell-cultures. Consequently, the few direct environmental metabolomic studies of microbes performed to date have been limited to culture-based or easily defined high-density ecosystems such as host-symbiont systems, constructed co-cultures or manipulations of the gut environment where stable isotope labeling can be additionally used to enhance NMR signals7,8,9,10,11,12. Methods that facilitate the concentration and collection of environmental metabolites at concentrations suitable for NMR are lacking. Since recent attention has been given to the environmental metabolomics of organisms within the aquatic environment, where much of the energy and material flow is mediated by the planktonic community13,14, we have developed a method for the concentration and extraction of whole-community metabolites from planktonic microbial systems by filtration. Commercially available hydrophilic poly-1,1-difluoroethene (PVDF) filters are specially treated to completely remove extractables, which can otherwise appear as contaminants in subsequent analyses. These treated filters are then used to filter environmental or experimental samples of interest. Filters containing the wet sample material are lyophilized and aqueous-soluble metabolites are extracted directly for conventional NMR spectroscopy using a standardized potassium phosphate extraction buffer2. Data derived from these methods can be analyzed statistically to identify meaningful patterns, or integrated with other omics levels for comprehensive understanding of community and ecosystem function.

A method for metabolite extraction from microbial planktonic communities is presented. Whole community sampling is achieved by filtration onto specially prepared filters. After lyophilization, aqueous-soluble metabolites are extracted. This approach allows for application of environmental metabolomics to trans-omics investigations of natural or experimental microbial communities.

Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each ...

Identification and Characterization of Protein Glycosylation using Specific Endo- and Exoglycosidases

Glycosylation, the addition of covalently linked sugars, is a major post-translational modification of proteins that can significantly affect processes such as cell adhesion, molecular trafficking, clearance, and signal transduction1-4. In eukaryotes, the most common glycosylation modifications in the secretory pathway are additions at consensus asparagine residues (N-linked); or at serine or threonine residues (O-linked) (Figure 1). Initiation of N-glycan synthesis is highly conserved in eukaryotes, while the end products can vary greatly among different species, tissues, or proteins. Some glycans remain unmodified ("high mannose N-glycans") or are further processed in the Golgi ("complex N-glycans"). Greater diversity is found for O-glycans, which start with a common N-Acetylgalactosamine (GalNAc) residue in animal cells but differ in lower organisms1.
The detailed analysis of the glycosylation of proteins is a field unto itself and requires extensive resources and expertise to execute properly. However a variety of available enzymes that remove sugars (glycosidases) makes possible to have a general idea of the glycosylation status of a protein in a standard laboratory setting. Here we illustrate the use of glycosidases for the analysis of a model glycoprotein: recombinant human chorionic gonadotropin beta (hCG&beta;), which carries two N-glycans and four O-glycans 5. The technique requires only simple instrumentation and typical consumables, and it can be readily adapted to the analysis of multiple glycoprotein samples.
Several enzymes can be used in parallel to study a glycoprotein. PNGase F is able to remove almost all types of N-linked glycans6,7. For O-glycans, there is no available enzyme that can cleave an intact oligosaccharide from the protein backbone. Instead, O-glycans are trimmed by exoglycosidases to a short core, which is then easily removed by O-Glycosidase. The Protein Deglycosylation Mix contains PNGase F, O-Glycosidase, Neuraminidase (sialidase), &beta;1-4 Galactosidase, and &beta;-N-Acetylglucosaminidase. It is used to simultaneously remove N-glycans and some O-glycans8 . Finally, the Deglycosylation Mix was supplemented with a mixture of other exoglycosidases (&alpha;-N-Acetylgalactosaminidase, &alpha;1-2 Fucosidase, &alpha;1-3,6 Galactosidase, and &beta;1-3 Galactosidase ), which help remove otherwise resistant monosaccharides that could be present in certain O-glycans.
SDS-PAGE/Coomasie blue is used to visualize differences in protein migration before and after glycosidase treatment. In addition, a sugar-specific staining method, ProQ Emerald-300, shows diminished signal as glycans are successively removed. This protocol is designed for the analysis of small amounts of glycoprotein (0.5 to 2 &mu;g), although enzymatic deglycosylation can be scaled up to accommodate larger quantities of protein as needed.

Using specific glycosidases to remove sugars from glycoproteins followed by SDS-PAGE is a valuable method to detect glycan modifications on protein samples and is a good choice for initial glycobiology studies. Changes following deglycosylation can be detected as shifts in gel mobility or by staining with glycan sensitive reagents.

Glycosylation, the addition of covalently linked sugars, is a major post-translational modification of proteins that can significantly affect processes ...

Purification of Transcripts and Metabolites from Drosophila Heads

For the last decade, we have tried to understand the molecular and cellular mechanisms of neuronal degeneration using Drosophila as a model organism. Although fruit flies provide obvious experimental advantages, research on neurodegenerative diseases has mostly relied on traditional techniques, including genetic interaction, histology, immunofluorescence, and protein biochemistry. These techniques are effective for mechanistic, hypothesis-driven studies, which lead to a detailed understanding of the role of single genes in well-defined biological problems. However, neurodegenerative diseases are highly complex and affect multiple cellular organelles and processes over time. The advent of new technologies and the omics age provides a unique opportunity to understand the global cellular perturbations underlying complex diseases. Flexible model organisms such as Drosophila are ideal for adapting these new technologies because of their strong annotation and high tractability. One challenge with these small animals, though, is the purification of enough informational molecules (DNA, mRNA, protein, metabolites) from highly relevant tissues such as fly brains. Other challenges consist of collecting large numbers of flies for experimental replicates (critical for statistical robustness) and developing consistent procedures for the purification of high-quality biological material. Here, we describe the procedures for collecting thousands of fly heads and the extraction of transcripts and metabolites to understand how global changes in gene expression and metabolism contribute to neurodegenerative diseases. These procedures are easily scalable and can be applied to the study of proteomic and epigenomic contributions to disease.

Purification of Transcripts and Metabolites from Drosophila Heads

We describe here the procedures for the extraction and purification of mRNA and metabolites from Drosophila heads. We are applying these techniques to better understand the cellular perturbations underlying neuronal degeneration. These methodologies can be easily scaled and adapted for other "omic" projects.

For the last decade, we have tried to understand the molecular and cellular mechanisms of neuronal degeneration using Drosophila as a model organism. ...

Identification of Kinesin-1 Cargos Using Fluorescence Microscopy

Fluorescence microscopy is employed to identify Kinesin-1 cargos. Recently, the heavy chain of Kinesin-1 (KIF5B) was shown to transport the nuclear transcription factor c-MYC for proteosomal degradation in the cytoplasm. The method described here involves the study of a motorless KIF5B mutant for fluorescence microscopy. The wild-type and motorless KIF5B proteins are tagged with the fluorescent protein tdTomato. The wild-type tdTomato-KIF5B appears homogenously in the cytoplasm, while the motorless tdTomato-KIF5B mutant forms aggregates in the cytoplasm. Aggregation of the motorless KIF5B mutant induces aggregation of its cargo c-MYC in the cytoplasm. Hence, this method provides a visual means to identify the cargos of Kinesin-1. A similar strategy can be utilized to identify cargos of other motor proteins.

Here, a protocol is presented to identify Kinesin-1 cargos. A motorless mutant of the Kinesin-1 heavy chain (KIF5B) aggregates in the cytoplasm and induces aggregation of its cargos. Both aggregates are detected under fluorescence microscopy. A similar strategy can be employed to identify cargos of other motor proteins.

Fluorescence microscopy is employed to identify Kinesin-1 cargos. Recently, the heavy chain of Kinesin-1 (KIF5B) was shown to transport the nuclear ...

Measuring Nitrite and Nitrate, Metabolites in the Nitric Oxide Pathway, in Biological Materials using the Chemiluminescence Method

Nitric oxide (NO) is one of the main regulator molecules in vascular homeostasis and also a neurotransmitter. Enzymatically produced NO is oxidized into nitrite and nitrate by interactions with various oxy-heme proteins and other still not well known pathways. The reverse process, reduction of nitrite and nitrate into NO had been discovered in mammals in the last decade and it is gaining attention as one of the possible pathways to either prevent or ease a whole range of cardiovascular, metabolic and muscular disorders that are thought to be associated with decreased levels of NO. It is therefore important to estimate the amount of NO and its metabolites in different body compartments &#8212; blood, body fluids and the various tissues. Blood, due to its easy accessibility, is the preferred compartment used for estimation of NO metabolites. Due to its short lifetime (few milliseconds) and low sub-nanomolar concentration, direct reliable measurements of blood NO in vivo present great technical difficulties. Thus NO availability is usually estimated based on the amount of its oxidation products, nitrite and nitrate. These two metabolites are always measured separately. There are several well established methods to determine their concentrations in biological fluids and tissues. Here we present a protocol for chemiluminescence method (CL), based on spectrophotometrical detection of NO after nitrite or nitrate reduction by tri-iodide or vanadium(III) chloride solutions, respectively. The sensitivity for nitrite and nitrate detection is in low nanomolar range, which sets CL as the most sensitive method currently available to determine changes in NO metabolic pathways. We explain in detail how to prepare samples from biological fluids and tissues in order to preserve original amounts of nitrite and nitrate present at the time of collection and how to determine their respective amounts in samples. Limitations of the CL technique are also explained.

Nitric oxide (NO) is an important signaling molecule in vascular homeostasis. NO production in vivo is too low for direct measurement. Chemiluminescence provides useful insight into NO cycle via measuring its precursors and oxidation products, nitrite and nitrate. Nitrite / nitrate determination in body tissues and fluids is explained.

Nitric oxide (NO) is one of the main regulator molecules in vascular homeostasis and also a neurotransmitter. Enzymatically produced NO is oxidized into ...

Quantification of Proliferative and Dead Cells in Enteroids

The intestinal epithelium acts as a barrier that prevents luminal contents, such as pathogenic microbiota and toxins, from entering the rest of the body. Epithelial barrier function requires the integrity of intestinal epithelial cells. While epithelial cell proliferation maintains a continuous layer of cells that forms a barrier, epithelial damage leads to barrier dysfunction. As a result, luminal contents can across the intestinal barrier via an unrestricted pathway. Dysfunction of intestinal barrier has been associated with many intestinal diseases, such as inflammatory bowel disease. Isolated mouse intestinal crypts can be cultured and maintained as crypt-villus-like structures, which are termed intestinal organoids or &ldquo;enteroids&rdquo;. Enteroids are ideal to study the proliferation and cell death of intestinal epithelial cells in vitro. In this protocol, we describe a simple method to quantify the number of proliferative and dead cells in cultured enteroids. 5-ethynyl-2&rsquo;-deoxyuridine (EdU) and propidium iodide are used to label proliferating and dead cells in enteroids, and the proportion of proliferating and dead cells are then analyzed by flow cytometry. This is a useful tool to test the effects of drug treatment on intestinal epithelial cell proliferation and cell survival.

The presented protocol uses flow cytometry to quantify the number of proliferating and dead cells in cultured mouse enteroids. This method is helpful to evaluate the effects of drug treatment on organoid proliferation and survival.

The intestinal epithelium acts as a barrier that prevents luminal contents, such as pathogenic microbiota and toxins, from entering the rest of the body. ...

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water deficit or pathogens plays a crucial role. Identifying small molecules that regulate stomatal movement can therefore contribute to understanding the physiological basis by which plants adapt to their environment. Large-scale screening approaches that have been used to identify regulators of stomatal movement have potential limitations: some rely heavily on the abscisic acid (ABA) hormone signaling pathway, therefore excluding ABA-independent mechanisms, while others rely on the observation of indirect, long-term physiological effects such as plant growth and development. The screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging. Since evaporation of water through transpiration results in leaf surface cooling, thermal imaging provides a non-invasive approach to investigate changes in stomatal conductance over time. In this protocol, Helianthus annuus seedlings are grown hydroponically and then treated by root feeding, in which the primary root is cut and dipped into the chemical being tested. Thermal imaging followed by statistical analysis of cotyledonary temperature changes over time allows for the identification of bioactive molecules modulating stomatal aperture. Our proof-of-concept experiments demonstrate that a chemical can be carried from the cut root to the cotyledon of the sunflower seedling within 10 minutes. In addition, when plants are treated with ABA as a positive control, an increase in leaf surface temperature can be detected within minutes. Our method thus allows the efficient and rapid identification of novel molecules regulating stomatal aperture.

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

We provide a method for identifying modulators of foliar transpiration by large-scale screening of a compound library.

Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water ...

In Vivo Quantification of Protein Turnover in Aging C. Elegans using Photoconvertible Dendra2

Proteins are synthesized and degraded constantly within a cell to maintain homeostasis. Being able to monitor the degradation of a protein of interest is key to understanding not only its life cycle, but also to uncover imbalances in the proteostasis network. This method shows how to track the degradation of the disease-causing protein huntingtin. Two versions of huntingtin fused to Dendra2 are expressed in the C. elegans nervous system: a physiological version or one with an expanded and pathogenic stretch of glutamines. Dendra2 is a photoconvertible fluorescent protein; upon a short ultraviolet (UV) irradiation pulse, Dendra2 switches its excitation/emission spectra from green to red. Similar to a pulse-chase experiment, the turnover of the converted red-Dendra2 can be monitored and quantified, regardless of the interference from newly synthesized green-Dendra2. Using confocal-based microscopy and due to the optical transparency of C. elegans, it is possible to monitor and quantify the degradation of huntingtin-Dendra2 in a living, aging organism. Neuronal huntingtin-Dendra2 is partially degraded soon after conversion and cleared further over time. The systems controlling degradation are deficient in the presence of mutant huntingtin and are further impaired with aging. Neuronal subtypes within the same nervous system exhibit different turnover capacities for huntingtin-Dendra2. Overall, monitoring any protein of interest fused to Dendra2 can provide important information not only on its degradation and the players of the proteostasis network involved, but also on its location, trafficking, and transport.

In Vivo Quantification of Protein Turnover in Aging C. Elegans using Photoconvertible Dendra2

Presented here is a protocol to monitor degradation of the protein huntingtin fused to the photoconvertible fluorophore Dendra2.

Proteins are synthesized and degraded constantly within a cell to maintain homeostasis. Being able to monitor the degradation of a protein of interest is ...