Metabolomics - Science topic

Only advice I can give is to have a background in chemistry so you can identify what chemical groups make sense and to be able to investigate related chemical families or to predict parent compounds. For example there may very little on a glycoside but if you look up the parent compound as an alcohol or methoxy ether you can often find a trove of relevant literature. Read all the papers you can about the annotation for the compound as there can often be non-enzymatic routes to the formation of compounds (especially when ROS is involved). Look up other synonyms for the compound as these are not standardized, especially in older literature. Use KEGG to help visualize the pathways where possible and always doubt the automatic annotations. I have seen nature papers where their "marker" for a cancer type is actually a tropical plant alkaloid only produced in one family of trees from southeast Asia (the patient samples from that study were not even from that region of the world). Also if you think the metabolite is important you must always do an alternative assay to verify that it is the correct annotation.

Yes, that's correct! After preprocessing your proteomics data (e.g., normalization, missing value imputation, batch effect correction, etc.), you can use the peak intensities of the proteins as features for further multivariate statistical analysis (MVA) using MetaboAnalyst.

To use MetaboAnalyst for proteomics data analysis, you will need to convert your peak intensity data into a suitable format (e.g., comma-separated values (CSV) or tab-separated values (TSV)) and upload it to the platform. You can then choose from a range of statistical and machine learning methods to analyze your data and identify patterns and relationships between the proteins and experimental conditions.

Faheem Akhter, your detailed answer to the question is highly appreciated. Thanks

I would suggest the use of ECMDB (https://ecmdb.ca/). It is a database, specific for metabolomics on E. coli.

You can study the metabolomes of E. coli, with NMR and MS techniques. I would suggest the use of both since they are complementary.

Yes, there are several other metabolome databases available aside from the Human Metabolome Database (HMDB). Here are some examples:

Metlin: A metabolite database that includes both experimental and predicted mass spectrometry data for a wide range of metabolites, including those from human and other species.

KEGG: A comprehensive resource for understanding metabolic pathways in various organisms, including human. It contains information on biochemical pathways, enzymes, and metabolites.

LipidMaps: A database of lipids, including information on their structures, physical properties, and associated enzymes and pathways.

BioCyc: A collection of databases that includes information on metabolic pathways, enzymes, and metabolites for a range of organisms.

ECMDB: The E. coli Metabolome Database, which provides information on the metabolites and metabolic pathways of Escherichia coli.

MetaboLights: A database of metabolomics studies, containing information on experimental designs, protocols, and raw data, as well as curated metadata and metabolite annotations.

These are just a few examples of the many metabolome databases that are available. Each database has its own strengths and limitations, and the choice of database will depend on the specific research question or application.

There are several causes for the presence of outlyers. Impurities, errors during the extraction, the needle that for some reason did not inject the same amount of sample, and samples that are naturally different from the other ones.

You can solve the problem using this checklist.

- check the look of the chromatograms and verify that all of them look more or less the same.

- check IS intensities and verify that their peak area is within an acceptable range for all the samples.

- normalize the data across samples.

- scale the data using UV scaling.

- evaluate the presence of outlyers using RobustPCA.

good luck

The site offers a tutorial. In any case, they provide as well examples of the formats needed.

Dear David Torti ,

It sounds like you may be experiencing a problem with photobleaching of your YFP protein. Photobleaching is a process in which the fluorescence intensity of a dye decreases over time due to exposure to light. It can occur as a result of various mechanisms, such as the conversion of the excited state of the dye to a non-fluorescent state, or the formation of a photoproduct that does not fluoresce.

There are several factors that can contribute to photobleaching, including the intensity and duration of the light source, the temperature of the sample, and the properties of the dye itself. In this case, it is possible that the YFP protein is more susceptible to photobleaching than the GFP protein, due to its unique spectral properties or structural differences.

One potential solution to this problem is to use a filter set that minimizes the exposure of your sample to the excitation light, or to use a lower intensity light source. Additionally, it is possible to use a fluorescence recovery after photobleaching (FRAP) method to measure the recovery of fluorescence of the YFP protein over time.

Another solution to this problem is to use alternative fluorescent proteins such as mCherry, mTurquoise, or mTagBFP, these are all alternatives that are less susceptible to photobleaching, and you can compare the results with YFP.

It's worth consulting with a bioinformatician or a researcher with expertise in this field for more specific suggestions as the cause may be related to some specific details of your experiment such as the buffer and the culture conditions.

I am not sure if could help, but have a look on https://www.omicsnet.ca/.

Hello,

Normally you can check by mass in libraries like chemspider:

This one work pretty good and you can advance search for mass, and it will give you a lot of inputs of the potential candidates.

Regards,

Andreu

the salt (sodium or chloride) as standard for your LC-MS analysis in metabolomics.

Dear Gizem,

you can read this paper about plasma metabolomics by GC-MS:

Molecules 2019, 24, 2367; doi:10.3390/molecules24132367.

If something is not clear, please ask me.

Regards

Nominal mass m/z will give you very little to go on. Accurate Mass at around 1ppm mass accuracy may lead to a small number of possible molecular formulae but each of these could represent millions of different possible chemical structures. A molecular formula might help you search existing databases if you are sure that your metabolite is known. Otherwise, NMR would be my choice. The combination of NMR and accurate mass would be the most likely way to identify the material of interest.

This method developed by Sili Fan and col. is a great option for batch correction.

Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data

DOI: 10.1021/acs.analchem.8b05592

https://www.thermofisher.com/de/de/home/industrial/mass-spectrometry/mass-spectrometry-learning-center/liquid-chromatography-mass-spectrometry-lc-ms-information/lc-ms-sample-preparation.html#:~:text=Total%20LC%2FMS%20sample%20preparation,final%20elution%20from%20the%20column.

How determine the moisture content in fresh leaves?

SGS.

Hamid Reza Farhang

Thank you very much for your answer, I wish you much success,

LIB2NIST can handle a certain amount of spectra to handle at a time. I don't remember the exact number. Please split the spectra something like 10k for each. And use LIB2NIST.

Thank you, Connor Jankowski and Karoly Liliom

Depends on a lot of factors and what exactly you are looking for. Any sample introduced into GCMS, needs to be thermally stable and also volatilized inorder to be separated on the GC column for efficient identification on the MS. If it is volatiles (VOCs) you are looking for, you may not need any further processing of the samples. If you are looking for lipids, fatty acids, esters and likewise compounds, some sort of pre-processing may be required, like derivitization techniques - acylation, alkylation, silylation, among others.

Did you perform the experiment yourself? What kind of digestion? Do you have raw files? How versed are you with LC-MS/MS? Do you have access to any proprietary software e.g. Proteinscape or protein discoverer? Would you like to analyse the data yourself? For proteomics data MaxQuant is a wonderful and user friendly resource and a number of videos are available. Besides, the manual available on its website is quite self explanatory. For metabolomics data, MetaboloAnalyst is the analogous software. However, make yourself versed with the Jargon.

MetaboAnalyst is a comprehensive platform dedicated to metabolomics data analysis via a user-friendly, web-based interface.

The main aim is high-throughput analysis for both targeted and untargeted metabolomics, and to narrow the gap from raw data to biological insights.

heard its uses by one of the co-worker and it seemed to be user friendly

You may start by trying at first to calculate correlation coefficients (R) for both cases you are interested in and then to calculate possible regressions of your data, in order to analyze them. Furthermore, you may try to continue with ANOVA. Please also consult the paper attached which you may find helpfull for your analysis purposes

You can try with other software like MS-DIAL

I think that the LSD mean comparison test is appropriate, but it is point that which test plan did you use for the four levels of drought? Are you considering the factorial experiments? However, it could be considered as an alternative test.

The Metabolomics Innovation Centre (Canada) - www.metabolomicscentre.ca

Over 50 targeted assays (with absolute quantitation) and 6 global/ untargeted assays. Both NMR and MS based services are available, including assays specifically for plants studies. All prices/ assays are available online. Fell free to contact me directly.

Check https://www.mdpi.com/2077-0375/10/10/298/pdf

Perhaps you could add scavenging beads with a thiol surface, and then spin the beads out?

Did you solve your problem?

Kindly visit..

You used metabolomics (LC-MS, I assume) to detect the presence of UDP in your samples, presumably. The same method can be used to quantify the amount of UDP, but you need a standard to correlate the peak size with the analyte concentration. The standard would consist of purchased UDP, prepared in the same matrix as the metabolomics samples at various concentrations to generate a standard curve.

Having done proteomics and metabolomics research for 25 of the last 35 years, I feel your pain. I can only leave you with the following paragraph from Chapter 1 of my biological engineering textbook (hopefully to be published this year).

"For the last three decades, biological science has been dominated by those espousing: 'In ten years time, those with a hypothesis-based approach to science will be equivalent to those who believe in the Flat Earth theory.'[1] To whom I counter with Huxley’s argument that won the Great Evolution Debate, “Six monkeys with typewriters … and an infinite amount of time will collectively produce all the great works of man” (June 30, 1860). Infinite time and resources do not exist in the real world."

[1] Gannon, F. (2000) “Back to Darwin?”, EMBO Rep., 1:373.

To the best of my knowledge, there has been 1 new commercially-validated biomarker used for clincial diagnostic purposes discovered with all the effort in genomics, proteomics, and metabolomics combined, since 1990. Yet, there has been an untold amount of white noise generated in the scientific literature. The conclusion of every paper that I've ever read has been "more study is needed". For every new biomarker one paper 'discovers', I can find another that shows it wasn't significant. Great for government funding agencies. Great for Universities, Great for tenure committees. Great for scientific publishers. But, did all that money and effort really serve society? Steve Naylor (former CSO of Beyond Genomics) once told me, that they spent $22M studying 300,000 functional genomic, proteomic and metabolic features in 40 mice to prove their platform worked. When they went to sell the pharmaceutical companies on their biomarker discovery platform, the consensus was "$22M are you F___ing nuts".

Don't get caught in what economists call "the sunk-cost fallacy". Find something that is causing others real pain in their lives for which you have a better mouse trap and can support yourself doing that. Always remember that government is the funder of last-resort. If what you do was really providing a net benefit people, they would be paying you for it directly. You wouldn't need government to take the money to pay you for it from others by force. I'm retired now, with enough in the bank to live out my life comfortably. I've already been 'cancelled', hence the forced retirement, so I can afford to say it they way I see it. Your decision, however, is yours. Either way, expect that decision is going to get you 'cancelled' by someone. The opinions of others are like arses. Everyone has one and they all stink. You have to do what you can live with.

Isotopic ratio work can be a bit tricky. It is better to use the isotopic vector than just the isotopic ratio of the two isotopic peaks. The math is a bit more complex, but still can be done in a spreadsheet. You get much more reliable results. See link below:

Dear Dr Naga Veera Yerra,

These softwares may actually help you (MetFrag and XCMS).

Here's their respective links:

Best wishes,

Sabri

not exactly, but maybe helpful:

MSCs Conditioned Media and Umbilical Cord Blood Plasma Metabolomics and Composition

Proteomic Analysis for the Assessment of Different Lots of Fetal Bovine Serum as a Raw Material for Cell Culture. Part IV. Application of Proteomics to the Manufacture of Biological Drugs

Characterization of Mammalian Cell Culture Raw Materials by Combining Spectroscopy and Chemometrics

شكرا جزيلا

This paper describes exactly what you are looking for. For related papers, my search was

"effect of storage temperature on feces metabolome"

Thaís Pedrete Yeah, I will take all necessary precautions while transporting samples... Thank you so much for assistance. :)

Cleaning the cells means centrifuging the cells and washing them with a simple buffer system (e.g., phosphate-buffered saline). The less components in the wash buffer the better. The problem with this, however, is that the metabolic composition of the cell changes pretty quickly and you are effectively stressing the cells by doing this. You also want to avoid heat or cold shocks so the buffer should be at the same temp as the growth media. Snap freezing the pellet in liquid N2 or dry ice after the wash helps preserve the metabolites.

The issue with the derivatization will be what in the buffer system also gets derivatized. Simplifying the wash buffer helps with this, but you should be aware if there is any carryover derivatives in the wash buffer. The concept of a control is more related to comparing the metabolome between growth conditions. LC/MS is not quantitative because of ionization differences between individual molecules. Therefore, when you are comparing LC/MS results between two culture conditions, all you get is a ratiometric measurement of what went up and what went down. You don't get an absolute concentration. You can only get to an absolute concentration by spiking samples, preferably with stable isotope versions of the metabolites that you want to track. see:

question 1: very likely isotopes. But as pointed out by Noel, there might be other atoms that influence the mass difference between the two ones. if it was just a C13, it would have been +1.00335 (while yours is + 0.9991). Without the original spectrum, it is difficult to say what it could be, many are possible. how to deal with that: check the spectrum, look for other isotopes, the ratio between the isotopes and their mass distance.

question 2 - 3 options:

1) double peak of the same compound (the compound gets different forms in specific gredients and so it splits during the chromatographic run, giving two close but distinct peaks)

2) isomer of your compound of interest. same formula, and very similar structure except for the position of one atom.

3) unrelated compound (unlikely but still possible). btw if it is an unrelated compound, the isotopic pattern or the fragmentation pattern should be different from your compound of interest.

hope this helps.

Dear Derek

In this case Impact reflects a centrality of the compounds (you have uploaded) in the metabolic pathways they belong to. You can note that when you upload your data, MetaboAnalyst asks you to select several parameters including Topolgy. By default it is relative-betweenness centrality which is a graph-theory concept.

In respect to cut-off, I would say Impact parameter is less important in many cases. So any impact in a "significant" pathway can be accepted. However sometimes, specifically for large data, this parameter can be very useful and interesting to interpret.

VIP represent variables of greatest importance for the model. Generally a threshold of VIP scores greater than 1.0 is used for finding discriminatory variables. We use the average of VIP for every components to rank the variables.

Generally, PLS-DA is carried out when discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). Although as you are saying PCA provides a good separation among different samples, they are not the most appropriate tool for developing models that allow previous designations of specific sets of samples and determine precise thresholds for classification. For this aim, supervised classification multivariate methods are required, such as PLS-DA. PLS-DA provides high rates of sensitivity and specificity. A PLS-DA model can also substantially reduce the number of discriminatory variables by creating significant VIP (variable importance in projection) scores.

So I would suggest incorporating both PCA and PLS-DA.

check out this chapter

Hi Cheng, in the metabolomics data you will get many variables and several samples that come from different classes. Sometimes the number of variables >> samples. And from this data, you want to find some important variables so that your research can be conclusive.

PCA help you to understand the natural separation of the sample class. You can say that PCA is a blind classification, but somehow you can put the class legend in it, to distinguish which point belongs to what class. Basically, PCA reduces your variable dimension by creating PCs. PCs are your "new variables." Usually, using PCA your metabolomics data can be interpreted in a very shallow conclusion. Therefore, you can use PLS-DA to do a more robust classification, because PLS-DA is not a blind algorithm. PLS-DA algorithm is a supervised analysis, it already understands your sample class. Sometimes, you'll get a nice separation by using this model compared to PCA.

Also, one of the advantages of PLS-DA is that you can find Variable Important in Projection (VIP). They are the most influencing variables from your metabolomics data. From the VIP you can get a deeper conclusion about your data.

I suggest this R package for more information http://mixomics.org/

Good luck

Dear Edward,

Escherichia coli strains MG1655 and HMS174 should grow on Galactose.

Best

Michael

Please look at the following below links which may help you in your analysis:

Thanks

HI, you are actually asking two different questions here, as PCA is only a data reduction method while PLS and clusterization aim to interpret the data.

You have 2 groups and only 20 samples all in all, so of course way too many compounds and potential confusions. And I would not rely exclusively on "significantly different" between the 2 groups to select data as, with 4k variables, it is more likely than not that you may have false positive (depending on how stringent you ran the statistical test).

My approach would be to do the PCA and look, is there some spontaneous separation of the 2 groups on one of the early principal compoments? Do your data "cluster" i.e. do you have groups of very correlated data? In which case you can probably simplify these groups to a single variable.

You have only 20 samples so when there are significant differences (72 variables) it might be worthwhile to actually plot and look, is the difference pulled by a few samples or is nicely repeatable in one group.

Anyway you look at it, you are in trouble... too many variables for few samples, this is going to require a lot of brain exercise. This is where statistical tools, however good they are, must leave way to knowledge and human intelligence. Do you have hypotheses as to the related biological mechanisms to help you sift through the data , and the predictive value, as said by Guillermo Quintas, will stay poor. Tentative I'd say, it might be a tool to help the practician but not totally relaible as a diagnostic.

Dear Zhang,

I would recommend metabolon

Metabolon GmbH

Zeppelinstraße 3 85399 Hallbergmoos Germany

+49 89 99017752

all the best,

Sami

Interesting detail, Gert Fricker - but how to recognize these biomarkers' pathogenic/clinical relevance?

The following links are useful.

I am fully agree with the opinion provided by Roberto Molteni.

I think, it is possible using the built in library through screening method. However, for the quantitative method, it is necessary to use external or internal standards. The best option is to communicate with the suppliers of the respective instruments. They may help you better than us.

Mr. Riahi,

Please, consider my comment on a new discussion devoted to quantitative analysis of carbohydrates (below.) In particular, focus your attention on the attached figure, therein.

I hope, that, it is very illustrative, why the current concepts of the so-called 'metabolomics' or, in fact, ''quantitative analysis'' of metabolites, are very far from any quantitative criteria on the 'Analytical Chemistry' and on the Chemistry, in a general context.

[https://www.researchgate.net/post/How_to_develop_HPLC_method_for_D-Fructose]

1) Internal standards (IS) are used to normalize the data for structurlly similar compounds in target or semi-targeted analysis... it is not a good idea to use them to normalize your data in metabolomics, as the structural variability is an important factor influecing the detection.

2) in metabolomics, normalization is performed on QC samples: every 5 samples you should inject a pooled sample (quality control sample = QC) and then you use the data from those injections to normalize your data, using normalization softwares (I suggest NOREVA --> http://idrblab.cn/noreva/)

3) by the way, I suggest to use also IS to check the quality of injection of each sample. If the IS signal is missing or is too intense from any sample, you should remove such sample before proceeding to normalization.

There is no problems with pyridine injection to GC.

Regards

GB

ADMET toxicity tools in general.

A list of tools available on the following link:

To analyze GC-MS data, first of all, you need to prepare the calibration curve using your desire compounds. After that, you have to inject the samples along with the calibration standard, then you need to process the data using the developed method/calibration curve. Then, you have to go to the report portion, to prepare the report.

I would suggest you to contact the supplier of your instrument, the application engineer may help you a lot. Thank you for your interest to work with GCMS.

Hi Arpita,

I back and concurr with Albert Batushansky 's wisdom here too!

Imagine if RNAlater enters the tissues, then the metabolites from fluids also come out of the tissue into RNAlater fluid. And not systematically, but very random process.

Plus, RNAlater processing needs a > Over night 4 C percolation of RNALater into the tissues and then frozen into -20C/-80C. Means, during the 4 C storage of tissues with RNAlater a lot of thawing of tissues/ activation of all sorts of enzymes will take place. Say, all glucose being used up, or proteins degraded into amino acids, dipeptides by proteases, and RNAases released a lot of nucleotides/ nucleosides/ riboses/ other sugars back into the tissue/ solution. Thus, all metabolite data will be painting a false picture.

I am also not very convinced of using paraffin dissected tissues or FPPE stored samples or formalin fixed bulk tissues, as there would be losses into formaldehyde/ such solutions/ matrices. But studies where the phenotype is very strong such as cancer vs non-cancer you may still see a discriminating set of metabollites, say BCAA or lactate, but others worth not reporting! : )

So highly suggest not to use it for metabolomics studies.

Thanks,

Biswa

I m also having Single Quad GC MS data sets from Plant source. Let us work together.

My mail id is vellai1973@gmail.com

Regards

Dear Karin

There are multiple strategies to analyze biological networks, and they strongly depend on a nature of the network (what do vertices and edges represent). Since you mentioned you would like to use partial correlation, this directs us to correlation-based networks. In this case your vertices will be metabolites, and edges will be correlation coefficients (weighted or unweighted). Interpretation of the correlation-based network of metabolites itself is not a trivial task, so what is commonly used in this case - comparison between two or more networks (conditions). In your case these conditions are tissues. So, and please consider this only as suggestion, you can build 7 tissue-specific networks and perform your analysis by comparing them. However, if you decide to go this way, please keep in mind that it still can be difficult, as you likely have different amount of metabolites detected in each tissue, and perhaps different sample size too. In this case I recommend to unify the data-sets beforehand. Doing this you will lose some information, but will minimize artificial differences in the networks parameters. Another potential challenge - I suspect that you have many hundreds of metabolic features. To build a correlation matrix for these large data-sets you may need to use a high computational cluster, as I am not sure what is the size limit of MetaboAnalyst online tool, and you may need to use something local, like R or Python. This also can cause challenges if you have limited experience with them. If this is the case, I would recommend to consult a local bioinformatician or statistician.

My question is do you really need to go in this direction? If you can show qualitative (for example, Venn diagram of the detected metabolites) and quantitative (for example, heat-map) differences in so broad metabolic profiles between the tissues, and general (dis)similarity by PCA or PCoA, I think these will be already great results.

Hello,

In my personal experience, is not a good idea to freeze them, just to avoid rupture. For the isolation, I recommend this manuscript and the corresponding isolation procedure:

PMID: 31434009, DOI: 10.1016/j.jprot.2019.103490

Good luck!

Carlos J.

@Md Saifur Rahman Khan I'm having the same problem..

But instead of saliva, I have cancer tissue sample for gcms studies. I about to do this in a week.... The processing of whole tissue sample for gcms will probably take a day.. so, my problem is "gcms technician guy will not be available after 5pm of working day. So he asks me to do in the next day morning? So if freezing the sample for half night, will it affect my peak? Or suggest me any optional strategy for that..

Any comments, I hope to hear from you...

No, we do not rely on MS signal alone for concentration. So called "abundance" is related to how well the various samples ionized under the specific HPLC method AND the MS parameters you selected (VERY SUBJECTIVE). Change the settings and you get different results. Some samples exist in high concentrations, but ionize poorly. Some in low concentration, but ionize strongly. Some compounds exist, are never seen. Hundreds of variation in signal detection lie between as this is a complex topic, esp for a web forum. 'Areas' or 'ion abundance' counts only have meaning when properly developed methods are used with formal standards. After that, complete calibration tables are created for quantitation (best done using a non-MS detector as MS is poor at quant). Isotopically labeled standards are best, but true "standards" in some form would be needed.

Hi Laura,

we are currently facing the same problem in our lab. At the moment we are using steel needles (Nano-bore emitters from ThermoFisher Scientific). For standard proteomics analysis they work as good as the glas one's from new objectives. Unfortunately you cannot use them in combination with FAIMS or if you want to do Phospho Proteomics, but for a standard proteomics anaylsis they work perfectly fine. I have now expertise on using them for Metabolomics, though.

Best regards,

Britta

Another point to consider is if the two groups are matched for HDL-C and LDL-C levels (these being significantly different or not). If they are matched, I would be much less worried about further normalisations. However, it also depends upon the question you are trying to address.

hello，Bibek。 have you solved this problem? now I have IS and total protein. But I still confused how to normalize thie metabolomics data. should I use IS and protein both or other one of them, could you plesae help me? Thank you.

Hi Pham Quynh Khoa,

I found the following article very useful.

Misra BB. Data normalization strategies in metabolomics: Current challenges, approaches, and tools. European Journal of Mass Spectrometry. 2020;26(3):165-174. doi:10.1177/1469066720918446

Do have a read.

Hope this helps.

jaime

So, in low temp you can store them for sure for few days.

Longer time demands a stability check.

Regards

GB

Agreed. RT is absolutely necessary information to set in the inclusion list in tSIM-ddMS2. tSIM-ddMS2 will normally give better sensitivity compared to FMS-ddMS2, but only if the compounds are not co-eluting. Thus, the compounds in the inclusion list for tSIM-ddMS2 should have different RT ranges. I usually only use tSIM-ddMS2 for a limited number of compounds, up to 10 compounds, depending on their separation. But as Luca says, the loop count number determines how many compounds it will perform MS2 scans of, in the same RT range.

In Full MS the entire mass range is measured and seen in the MS1 scan (e.g. m/z 100-1000), whereas in the tSIM scan, only the targeted compound mass +/- isolation window is measured. In the next tSIM scan it will measure the next mass, according to the inclusion list RT. Thus if two compounds are co-eluting - or in your case 400 compounds are "coleuting" (no RT set), it will switch between perfming MS1 scans of one compound and then the other compound, and also doing MS2 scans when the thresholds and triggers are met. With 400 compounds you will get only one MS1 scan of a few of the compounds and no MS1 scans of the others. And you need at least 10 scans of the compound in order to form a good peak.

vacuum concentrators are compatible with organic solvent evaporation in non-oxidative conditions and can be operated at higher temperatures which is beneficial for lipids. nitrogen positive pressure evap. would also be ok as a second approach in case of the absence of vac. concentrators.