Metabolomics Methodology FAQ

How long can the sample extracts be stored? Can freeze-drying extend the preservation period?

They can be stored for one month at -20°C and for three months at -80°C. Theoretically, freeze-drying can extend the preservation period, but no experiments have been conducted to investigate the exact preservation period after freeze-drying, and there is very little relevant literature, which is not available at present. Therefore, we follow a uniform preservation time without freeze-drying.

When we concentrate samples in pre-processing, are the freeze-drying method and nitrogen-blowing method both available? What is the temperature of nitrogen blowing?

Three approaches are available for concentrating samples:1) Freeze-drying: freeze the sample into a solid, achieve freeze-drying by sublimating water and volatile solvents, and the temperature should be below -50℃; 2)Nitrogen blowing: blow away volatile solvents with nitrogen, either at room temperature or with heating (normally at room temperature); 3) Centrifugal drying: evacuate the sample to accelerate the evaporation of water or volatile solvents. This can be done at 4°C, 20°C, or room temperature, and then open the lid and centrifuge the sample.

How to choose the ionization mode for metabolomics?

The non-targeted portion represents the spontaneous ion binding mode of the substance, while the widely targeted part is selected based on the properties of the substance itself and the experience from the development of reference standards. Most of the lipids have their characteristic ions, allowing for selection based on these ions and their responses.

What is our detection sensitivity for metabolomics?

Detection sensitivity in metabolomics refers to the ability of our analytical methods to identify and quantify metabolites at very low concentrations. Ultra-high-performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry allows for the detection of metabolites that might be present in very small amounts, which is crucial for studying subtle metabolic changes in biological samples. The sensitivity of our low-resolution mass spectrometer can reach the picogram level, while our high-resolution mass spectrometer can achieve sensitivity at the femtogram level.

What is our detection limit and quantitation limit?

The detection limit refers to the lowest concentration of a metabolite that can be reliably detected but not necessarily quantified. In metabolomics, this limit is typically in the low nanomolar range, depending on the specific metabolite and the analytical method used. The quantitation limit, on the other hand, is the highest concentration at which a metabolite can be accurately quantified with a known degree of precision. This limit can vary based on factors like the complexity of the sample matrix and the calibration of the analytical method. For instance, in our energy metabolism targeted panel, arginine has a detection limit of 5 ng/mL and a quantitation limit of 10,000 ng/mL.

What is our recovery rate?

"The recovery rate in metabolomics is a measure of how much of a metabolite can be recovered from a sample after extraction and analysis, compared to the amount that was initially present. This is crucial for ensuring that the results accurately reflect the metabolite concentrations in the original sample, making it an important parameter for validating analytical methods in metabolomics studies. This rate can vary significantly depending on the metabolites, extraction methods, and sample types. Typically, recovery rates should ideally be above 70% for a method to be considered reliable; however, many methods may achieve recovery rates between 80% to 120%. For example, in our energy metabolism targeted panel, lysine and ornithine have recovery rates of 93% and 99%, respectively. "

How do we conduct quality control for metabolomics?

Quality control (QC) in metabolomics involves systematic checks to ensure the reliability and accuracy of the data generated. This typically includes the use of QC samples, which are pools of known concentrations of metabolites, run alongside experimental samples to monitor instrument performance and method consistency. These QC samples help identify any variations or anomalies that might arise during the analysis. At MetwareBio, we implement rigorous quality control (QC) measures throughout our entire workflow, from sample receipt to data analysis. Our QC protocols incorporate ten key indicators—including mixed standards, blank samples, and mix samples—to continuously monitor and document process variances, allowing us to promptly address any deviations.

How is the data stability (CV in technical replicates, interday and intraday precision)?

Data stability in metabolomics can be assessed using coefficients of variation (CV) for technical replicates and both interday and intraday precision. Technical replicates involve repeating the same measurement multiple times to evaluate the precision of the method; low CV values (typically below 10%) indicate good stability and reliability of the measurements. Intraday precision refers to the consistency of results obtained from samples analyzed on the same day, while interday precision assesses consistency over multiple days. Ideally, both should show low CV values, indicating that the methodology produces stable results over time. For instance, in our tryptophan-targeted panel, the coefficient of variation (CV) for serotonin demonstrates excellent precision, with intraday precision at 7.17% and interday precision at 1.70%.

How many internal standards and chemical standards and what are their roles?

In metabolomics, the use of internal standards is crucial for accurate quantification of metabolites. Typically, 5-10 internal standards are used, which are compounds structurally similar to the target metabolites but not naturally present in the sample. These standards help to correct for variations in extraction efficiency and instrument response. For example, if you're analyzing amino acids, you might use isotopically labeled amino acids as internal standards to account for losses during extraction and ensure accurate quantification. The number of chemical standards varies depending on the scope of the study, but having a comprehensive library of standards helps in ensuring reliable identification and quantification of metabolites. Together, these standards enhance the robustness of metabolomic analyses by correcting for potential errors and variability in the data. For example, in our bile acid-targeted panel, we utilize 65 chemical standards and 13 isotopic internal standards to ensure accurate and reliable identification and quantification of metabolites in this assay.

How do we process batch effects?

"Batch effects in metabolomics arise when samples are processed at different times or under varying conditions, leading to systematic biases in the data. To mitigate these effects, several strategies can be employed. One common approach is to randomize sample order during analysis, ensuring that samples from different conditions are interspersed. This helps to prevent systematic variations from confounding results. Another effective strategy involves using statistical methods during data analysis, such as ComBat or other normalization techniques that adjust for batch effects. By applying these methods, we can identify and correct for variations associated with different batches, ultimately improving the reliability and interpretability of your results. At MetwareBio, when clients submit samples from multiple batches over several months, we select approximately 10% of representative samples from the first batch to serve as control samples. These control samples are run alongside the subsequent batches, and during data analysis, the results are normalized based on the data from these representative samples across different batches. "

What do your databases consist of?

Databases in metabolomics typically consist of a comprehensive collection of known metabolites, their chemical structures, and associated information such as mass spectra, retention times, and biological functions. These databases are critical for the identification and quantification of metabolites during analysis. For example, databases like METLIN or HMDB (Human Metabolome Database) provide extensive resources that researchers can use to match observed signals in their data to known compounds. In MetwareBio's in-house database, we meticulously document detailed information for each compound analyzed on specific MS instruments, including parameters such as Q1, Q3, retention time (RT), MS2, declustering potential (DP), and collision energy (CE). This thorough documentation ensures the accuracy of compound performance during analysis, minimizing variabilities arising from differences in instrument versions or parameter settings.

What’s the meaning of identification level for metabolomics?

The identification level in metabolomics refers to the confidence with which a metabolite can be identified based on the analytical data obtained. This is often categorized into levels, such as Level 1 (high confidence) where there’s a definitive match with a standard, to Level 2 (medium confidence) where identification is based on mass spectra and retention time comparisons with databases, but without a corresponding standard. For instance, if a metabolite is identified with high confidence (Level 1) based on its unique mass and fragmentation pattern matching a known standard, that’s a solid identification. In contrast, if it matches several similar metabolites in a database but lacks a pure standard for confirmation, it might be classified as Level 2. These levels are important for assessing the reliability of findings and ensuring that conclusions drawn from metabolomic studies are based on sound identification practices.

What mass spectrometry techniques are most suitable for metabolomics?

Several mass spectrometry techniques are commonly used in metabolomics, each with its strengths. Liquid chromatography-mass spectrometry (LC-MS) is widely favored for its ability to analyze polar and non-polar metabolites, making it suitable for a broad range of compounds. Additionally, gas chromatography-mass spectrometry (GC-MS) is excellent for volatile and semi-volatile metabolites, especially those derived from fatty acid metabolism or amino acids. Meanwhile, high-resolution mass spectrometry (HRMS) offers enhanced sensitivity and accuracy, allowing for better identification of metabolites at lower concentrations.

What are the advantages of using gas chromatography-mass spectrometry (GC-MS) in metabolomics?

GC-MS offers several advantages in metabolomics, particularly when analyzing volatile and thermally stable compounds. One significant benefit is its ability to separate complex mixtures of metabolites through gas chromatography, allowing for high-resolution analysis. This separation enhances the sensitivity of the mass spectrometry detection, making it easier to identify and quantify low-abundance metabolites. Another advantage is the robustness and reliability of GC-MS in quantifying metabolites. The technique is well-established and provides reproducible results, which is essential for comparative studies. Additionally, the ability to use derivatization techniques can improve volatility and stability, allowing for better detection of otherwise challenging metabolites.

How can high-resolution mass spectrometry improve metabolite identification?

High-resolution mass spectrometry (HRMS) significantly enhances metabolite identification by providing detailed mass measurements with high accuracy. This precision allows researchers to distinguish between metabolites that may have similar mass-to-charge ratios, which is critical when analyzing complex biological samples. For example, HRMS can differentiate between isomers or metabolites that differ by just a few daltons, improving the reliability of identifications. Additionally, HRMS enables the acquisition of high-resolution mass spectra that can yield more information about the structure of metabolites. This is particularly useful in untargeted metabolomics, where researchers aim to discover new metabolites. By employing HRMS, we can generate comprehensive profiles of metabolites and enhance the ability to match them to databases or elucidate their structures.

How to address matrix effects in mass spectrometry-based metabolomics?

Matrix effects can significantly influence the accuracy and reproducibility of mass spectrometry-based metabolomics. These effects arise from the complex biological matrix, which can interfere with the ionization of metabolites during analysis. One effective approach is the use of internal standards—known compounds added to the sample that compensate for variations in ionization efficiency. By comparing the response of metabolites to that of the internal standards, researchers can better account for matrix effects. Another strategy is sample clean-up, which involves removing or minimizing components of the matrix that could interfere with analysis. Techniques such as solid-phase extraction (SPE) or liquid-liquid extraction can be used to purify samples before mass spectrometric analysis.

How do you ensure reproducibility between different batches of samples?

Ensuring reproducibility between different batches of samples in metabolomics requires careful standardization of protocols and thorough documentation. Key strategies include using consistent sample collection, handling, and processing techniques across batches. For instance, maintaining the same conditions for sample extraction, storage, and analysis can minimize variability. We can use standardized operating procedures (SOPs) to guide every step of the process. Additionally, including quality control (QC) samples in each batch can help monitor and assess reproducibility. QC samples, which are analyzed alongside study samples, can help identify any shifts in instrument performance or method consistency. By comparing the results of QC samples across batches, researchers can detect and address any discrepancies, ultimately enhancing the reliability of the data and conclusions drawn from metabolomic studies.

What is the role of retention time in identifying metabolites using LC-MS?

Retention time is a critical parameter in identifying metabolites using liquid chromatography-mass spectrometry (LC-MS). It refers to the time a metabolite spends in the chromatographic column before being detected by the mass spectrometer. Retention time provides valuable information about the chemical properties of metabolites, helping to establish their identity when compared to known standards or library entries. For example, a metabolite with a specific retention time can be matched to its expected value from a reference database, strengthening the confidence in its identification. Moreover, retention time can aid in distinguishing between closely related compounds, especially in complex mixtures. If two metabolites have similar mass-to-charge ratios but different retention times, this can serve as a differentiating factor. By combining retention time data with mass spectral information, we can enhance the accuracy of metabolite identification and improve the overall reliability of their results.

Can you use a single analytical method to detect both primary and secondary metabolites?

While it is possible to detect both primary and secondary metabolites using a single analytical method, it often depends on the characteristics of the metabolites and the chosen technique. Liquid chromatography-mass spectrometry (LC-MS) is versatile enough to analyze a wide range of metabolites, including both primary metabolites (like amino acids and sugars) and secondary metabolites (like flavonoids and alkaloids). However, the effectiveness of this approach hinges on the specific ionization techniques and chromatographic conditions used. For instance, primary metabolites may require different sample preparation techniques and ionization modes than secondary metabolites. we might need to optimize the method to ensure efficient detection of both classes.

How can you improve the sensitivity of detection for low-abundance metabolites?

One effective strategy is to concentrate the metabolites before analysis, which can be achieved through sample extraction techniques such as solid-phase extraction (SPE) or liquid-liquid extraction. These methods help enrich the target metabolites, enhancing their detection in subsequent analyses. Additionally, employing high-resolution mass spectrometry (HRMS) can greatly improve sensitivity due to its ability to detect and distinguish metabolites at low concentrations with high accuracy. Optimizing ionization conditions, such as using more sensitive ionization techniques like electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI), can also enhance sensitivity.

How can ion suppression affect the accuracy of metabolomics data?

Ion suppression occurs when the presence of one or more components in a sample reduces the ionization efficiency of target metabolites. For instance, if a sample contains a high concentration of proteins or lipids, they may compete for ionization during the mass spectrometry process, leading to underestimation of the concentrations of the metabolites of interest. To mitigate ion suppression, we can employ strategies such as using internal standards or optimizing sample preparation methods. For example, sample clean-up techniques like solid-phase extraction (SPE) can help remove interfering substances before analysis. By minimizing the effects of ion suppression, we can improve the reliability of their quantifications and ensure that their data reflects true biological changes rather than artifacts of the analysis.

How do you validate metabolite identification using spectral libraries?

Validating metabolite identification using spectral libraries involves comparing experimental data with entries in established databases, such as the Human Metabolome Database (HMDB). When a mass spectrum of an unknown metabolite is obtained, researchers look for matches in the library based on the mass-to-charge ratio (m/z) and fragmentation patterns. A high degree of similarity in these parameters between the experimental data and the library entry strengthens the case for identification. In addition to matching the mass spectrum, retention time is often considered. If the retention time of the metabolite in the experimental run closely aligns with that of the library entry, this further confirms the identification. By utilizing multiple criteria—such as m/z values, fragmentation patterns, and retention times—we can confidently validate their findings and increase the credibility of their results in metabolomics studies.

How can chemical derivatization improve metabolite detection?

Chemical derivatization involves modifying metabolites to enhance their detection in mass spectrometry. This process can improve volatility, stability, and ionization efficiency, making it easier to analyze compounds that would otherwise be difficult to detect. For instance, many polar metabolites can be converted into more volatile derivatives, which can then be analyzed using gas chromatography-mass spectrometry (GC-MS). An example is the derivatization of amino acids to form volatile ester or amide derivatives, improving their detectability. Additionally, derivatization can increase the specificity of detection. By creating unique derivatives for specific classes of metabolites, we can enhance separation and reduce interference from other compounds in complex biological matrices. This tailored approach not only improves the sensitivity of detection but also aids in accurately quantifying low-abundance metabolites, making derivatization a valuable tool in metabolomics.

How to combine retention time with MS/MS data to improve metabolite identification?

Retention time provides context regarding when a metabolite elutes from the chromatographic column, while MS/MS data gives detailed information about its fragmentation patterns. When a metabolite has a known retention time and its fragmentation pattern closely matches that of a reference compound in a spectral library, it significantly enhances the confidence in its identification. For instance, if a metabolite elutes at a specific time and generates characteristic fragment ions upon MS/MS analysis, wewe can cross-reference this information with library entries. This multifaceted approach helps to resolve ambiguities that may arise from using mass spectral data alone, particularly in complex samples where isomers or closely related compounds may be present. By integrating both types of data, we can achieve more accurate and reliable metabolite identifications.

How to ensure reproducibility and precision in MS-based quantification?

One key practice is using internal standards, which serve as references to normalize variations in sample preparation and instrument response. By comparing the response of target metabolites to that of the internal standards, we can account for discrepancies and enhance the reliability of their quantifications. Another important aspect is maintaining consistent experimental conditions across runs. This includes using standardized sample preparation techniques, running quality control samples, and calibrating the mass spectrometer regularly.

How to handle ion suppression and enhancement in complex biological samples?

One effective approach is to optimize sample preparation methods, such as using extraction techniques that remove potential interferents. For instance, solid-phase extraction can effectively isolate target metabolites while discarding interfering substances, thereby reducing ion suppression. We can also use internal standards to correct for ion suppression and enhancement. By including known quantities of internal standards that are chemically similar to the target metabolites, we can account for variations in ionization efficiency. This method allows for more accurate quantification and helps ensure that detected levels of metabolites reflect true biological variations rather than artifacts of the analysis.

What are the best practices for maintaining MS system sensitivity over long experiments?

Maintaining mass spectrometry (MS) system sensitivity over long experiments requires a combination of regular maintenance, calibration, and quality control. One best practice is to schedule routine cleaning and servicing of the instrument, as well as regularly checking and replacing critical components like ion sources and detectors. This proactive approach helps prevent degradation of sensitivity due to wear and tear over time. Using quality control samples at regular intervals throughout the experiment can help monitor instrument performance. By assessing the response of these samples, researchers can detect any declines in sensitivity and make adjustments as needed. Regular calibration using known standards is also crucial, as it helps ensure that the instrument remains responsive and accurate, maintaining the reliability of the data collected over long periods.

What are the common substance testing platforms and which one is suitable for me?

NMR: Nuclear magnetic resonance has the advantage of detecting non-damaged samples without complicated sample pre-processing, and thus, the sample is as close to their physiological condition as possible. However, it suffers from lower sensitivity, especially a low abundance of metabolites.

LC-MS: The combined technology of liquid chromatography and mass spectrometry has a relatively high resolution, and high sensitivity, and allows the detection of a wide range of metabolites. This is typically used to detect non-volatile polar compounds that are less than 1000 Da.

GC-MS: Gas chromatography and mass spectrometry are mainly used for identifying volatile substances or metabolites with low polarity. Typically detects molecules with lower molecular weight (< 300Da).

Can I compare compounds from different batches? Can I compare different substances in the same sample?

Mass specs need regular maintenance and calibration, thus quantifications from Widely-Targeted Metabolomics between different batches typically cannot be compared directly. In the mass spec, the degree of ionization of compounds is different and thus you cannot compare different compounds from the same samples.

What is the minimum concentration of metabolites for it to be detected (μmol or even lower).

This depends on the metabolite trait, different metabolite with different LOD. Usually, we can detect at μmol or pg level.

I have a inquiry for Untargeted Metabolomics and need to do HILIC and Reverse Phase LC on mouse plasma. Can you do this?

Yes, We can do HILIC-LC-MS in our Untargeted Metabolomics panel.

Can 4 individual plants be combined into one biological replicate for metabolomics analysis?

Yes, we suggest that the one biological replicate results from a mixture of the same tissue sites from 3 or more individual plants.

Are there difference in sensitivity between Widely-Targeted metabolomics for plants vs Untargeted metabolomics?

Compared to Untargeted metabolomics using QTOF, the Widely-Targeted metabolomics approach uses QQQ for quantification which makes it more sensitive for detecting metabolites.

You have a curated database of 280,000 metabolites and an in house database of 3000+ metabolites for metabolomics analysis. What happens with any metabolites identified that are not found in the in-house database?

First, we identified metabolites according to our self-database by comparing MS1 , MS2 and RT; The rest of ion pairs were identified according to the public database (including Metlin/MS Bank/MONA/Lipid Maps/HMDB/KEGG ), and only retain the metabolites with score>0.5 (by m/z, peak profile and so on, this score from te software); The rest of substances indentified by AI prediction ( MetDNA), which formula are usually simliar to known metabolites, also retain the metabolites with score>0.5.

Does our Widely-Targeted Metabolomics have a threshold for detection? Client is asking when a metabolite is 0, does it mean it is below the detection threshold? If yes, what is this threshold? -but this is widely targeted. Is there a specific concentration threshold? For targeted assays, we know the LLQ , do we know this for widely targeted? -If the client has a specific metabolite that is 0, are we able to check why it is 0? As in can we say it is below xxxx ng?

Yes, when a metabolite is 0, it means it is either below the detection threshold or absent in a specific sample. This detection threshold varies for each metabolite when using LC-MS due to their distinct signal responses. The LC-MS we use has the capability to detect metabolites even at the picogram level. -Nomarlly, no specific concentration threshold has been provided for the widely targeted since we mainly focused on the relative concentration of metabolites in different samples, even that the widely targeted approach is based on the targeted approach. -If necessary, certain metabolites detected through the widely targeted approach can undergo additional validation using another targeted method. Subsequently, the LLQ (Lower Limit of Quantification) for each metabolite can be determined based on the chemical standard curve. I think we can not say so since we have no such data. Well, i might be a little complicate why it is 0. If a primary metabolite like glycine registers as 0, it's undoubtedly an issue on our end regarding detection. However, for other metabolites, the situation varies.

When the table says TM, does it mean only from the 3000 in-house database or does it include the untargeted database?

Only part of the metabolites found in the untargeted based on QTOF have been used and could be repeatedly detected in TM based QQQ. Normally, the metabolites with high levels in samples are more easier to be detected by QQQ. For TM, it also include the untargeted database, but only part of the metabolites from the untargeted have been used for TM which means some have not.

Do you have any experience of Untargeted metabolomics in human plasma/serum, especially in bile acids. How many BAs you can identify using global profiling approach.

We can provide some demo data for human serum and plasma samples in the untargeted metabolomics panels. The average detected bile acid levels are 34.

Do you have any publications working with wheat leaf samples for metabolomics? If there is no publications specifically on wheat leaf, can you provide some publications on leaf samples with other plant species?

We have several publications working with plant leaf samples for metabolomics.

We havesoybean fermentation liquid done by zymogenic bacteria. We want to test the fermentation liquid. What is the best approach? Would it be Widely-Targeted Metabolomics for Plants or for Untargeted. We wish to see metabolites associate with both zymogenic bacteria and soybean.

We suggest the use of the Widely-Targeted Metabolomics for Plants, as both the Widely-Targeted Metabolomics for Plants and untargeted panel are suitable for analyzing the sample. However, the Widely-Targeted Metabolomics for Plants offers superior accuracy in both identification and quantification compared to the untargeted panel.

What measures are in place to ensure consistency across samples for relative quantification, particularly in terms of comparing data such as tissue weights? How are the relative quantifications measured?

We have several methods to ensure the sample consistency used for metabolomics detection. They include BCA protein normalization, volume normalization, quality normalization and count normalization.

I want to know your metabolomics QC parameter for the viability of their samples after snap freeze.

Metabolomics do not need the cells to be viable after thawing. In fact, we use freeze-thaw cycles to lyse the cells and extract the metabolite with our extraction solution. Once we thaw the cells, we will perform the extraction right away. Regarding quality control measures, we implement a total of 10 QC indicators throughout the entire experiment process to guarantee instrument stability and operational consistency.

What is DP and CE for LC-MS/MS?

CE: Collision Energy; DP: Declustering Potential.

I am interested in mouse fecal and serum for Metabolomics. How many peaks do you get typically for Widely Targeted Metabolomics and/or Untargeted Metabolomics for mouse fecal? Are they annotated? How many are level one, level two, etc? Do any of your panels look at dipeptides? Can you send a protocol for how our fecal samples are processed?

We can provide the number of detected peaks (metabolites), as well as the breakdown of metabolites into levels 1 and 2. All of the detected metabolites will be annotated in the final report provided to clients. Numerous dipeptides can be detected in both panels, and they are categorized under "Amino Acid and Its Metabolites". Metabolites are extracted from fecal samples using 70% methanol.

What's the version of HMDB we use for annotation and pathway analysis in your metabolomics services?

Our HMDB annotation information encompasses the entirety of HMDB versions 5.0, 4.0, and 3.0, with a preference for the latest version. Specifically, for metabolites listed in v5.0, we provide annotations from that version. For those absent from v5.0 but present in v4.0, their information is retained from v4.0

We want to send 50 Human Plasma TM Widely Targeted Metabolomics. If we send continuous variables, are we able to control for the variable in these analyses (for example, age)?

Yes, you can specify which groups need to undergo differential analysis once the detection is finished, allowing them to control the variables in these analyses.

For Tryptophan targeted panel: The sample type is dried powdered microbial fermentation with maltodextrin excipient. 1) For the small targeted panel, I assume it will be run with QTRAP instrument as QQQ type of machine with MS/MS mode. Am I correct? 2) What is detection limit or quantification limit for this panel? For the standard curve, what is the linear range? I.e. LLOQ, ULOQ.

For trptophan-targeted panel, we use SCIEX QTRAP 6500+ Mass Spectrometry and the MRM mode to detect metabolites. For dried powder samples, it is preferable to provide the original volume of the samples before lyophilization to enable accurate absolute quantitation. Otherwise, we will proceed based on the weight of the powders used for extraction.

I have a question regarding the setup of your house database for metabolomics. Could you please clarify if the metabolites in our database are processed individually or if they are all run together using LCMS when building the database?

We have been gradually constructing our in-house metabolic database using numerous chemical standards. Each metabolite in our database is individually processed during its creation.

we workwith Untargeted Plus Metabolomics that "I'm told that in "untargeted" analyses, depending on the methods used there might be some "quenching" of compounds with respect to detection. Can you let me know what quenching is and what we should be aware of?" Can you provide any information on this?

In untargeted metabolomics, some compounds present in very low abundance may not be detected due to the sensitivity limitations of the mass spectrometry instrument. The "quenching" you mentioned likely refers to the loss of these low-concentration compounds. This issue is perfectly addressed in our widely-targeted metabolomics approach. This method includes an analysis on QQQ mass spectrometry, which offers very high sensitivity. The quantitation of metabolites is based on the results obtained from the QQQ analysis.

For your tryptophan panel, we want to know if 0.1 ng/ml is the lower detection limit of our mass spectrometer?

The detection range of metabolites is 0.1-1000 ng/ml. Our mass spectrometer can detect concentrations at pg/ml level.

What are the criteria for screening differential metabolites?

Screening criteria for differential metabolites: select metabolites with fold change ≥ 2 and fold change ≤ 0.5. If the difference in metabolites between the control group and the experimental group is more than 2 times or less than 0.5, the difference is considered significant. If there is biological duplication in the sample grouping, on the basis of the above, select the metabolites with VIP≥1. The VIP value represents the influence of the difference between the corresponding metabolites in the classification and discrimination of the samples in each group in the model. It is generally considered that the metabolites with VIP≥1 are significantly different.

The range of substances that can be detected for metabolites?

The detection range of liquid-phase mass spectrometry should be less than 1000Da, and the molecular weight of frequently encountered macromolecular substances such as glycans, peptides, starch, pectin, and lignin are not within the detection range.

What's the difference between global metabolite profiling and targeted metabolomics?

Global metabolite profiling is an unbiased method to detect all metabolites in the sample and provides relative quantification. Targeted metabolomics often only focus on a small number of metabolites and provides absolute quantification due to use of chemical standards.

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