Chrombox_D


Contents

Install and startupWindows computersMac computers (OS X)Linux computersStarting Chrombox D from the Matlab desktop (on all systems)Changing settingsUpdatingTutorial 1. Least squares spectral resolution (LSSR)1.1. Starting the program1.2. Loading the data1.3. Basic viewing options1.4. Generating a library of compounds1.5. Generating a mass spectral library1.6. Quantification by LSSR1.7. Chromatographic resolution1.8. Other samples.Tutorial 2. LSSR on high resolution direct infusion data2.1. Importing the data2.2. Setting appropriate conditions for filtering and binning.2.3. Creating the library2.4. Quantifying the data2.5. Using the library as a filter.Tutorial 3. The Lipid Gerator 3.1. Basics3.2. Codes3.3. Main functions of the generator3.4. Generation of compounds and filtering3.5. Editing lists for each class3.6. User defined fatty acid lists3.7. Saving and loading settings and compound lists3.8. Sphingoid basesTutorial 4. The profile analyzer4.1. Initialization4.2. Description of functions4.3. Using the functions4.4. Generating a centroided spectrum and resolving it by LSSR4.5. Further description of the functions in the Analyze profile windowTutorial 5. Least squares resolution of profiles (LSSR-P)5.1. Principle5.2. Low resolution data5.3. High resolution dataTutorial 6. Accurate mass determination6.1. Low resolution data6.2 High resolution dataAppendix 1. List of lipid classes with examples of naming

The following text styling is applied in this document. Commands, paths or filenames are denoted by: command, or path\filename.ext. Buttons in the graphical user interface are shown as [Button]. Keys on the keyboard are denoted by [Key]. A parameter to be set is denoted by parameter, and a value of a parameter or an option in a menu is denoted by option.

Install and startup

Quick start

  • On Windows computers with Matlab you can usually just move the folder DD to your preferred destination and start the program by double click on the Chrombox D.exe file.

  • Follow the instructions below if you install on a Mac or Linux computer, or if you install the program on a network drive.

Windows computers

If installed on a network disk, you may have to use one of the methods described below:

An example of dstart.m is shown below:

You can also create a desktop shortcut by copying the shortcut to Matlab and adding the following to the destination: /automation /r dstart. An example of how it can look is shown below:

C:\MATLAB6p5\bin\win32\matlab.exe /automation /r dstart

Mac computers (OS X)

As an alternative to the above procedure, Chrombox D can be started by the following method:

An example of dstart.m is shown below:

Linux computers

As an alternative to the above procedure you can also start Chrombox D by dstart.m as described for Mac computers above.

Starting Chrombox D from the Matlab desktop (on all systems)

On all operating systems you can use the following procedure to start Chrombox D:

In a minimized Matlab session (running in terminal without Matlab desktop) you can use the cd command to set the working directory and run dd_startscript to start the program.

 

Changing settings

Updating

An example of dd_localsettings.sdv is shown below. The part to edit is between the two semicolons in the first line.

Alternatively, you may select the new code by the following procedure:

Tutorial 1. Least squares spectral resolution (LSSR)

The purpose of this tutorial is to learn basic features of the program and how to quantify samples using least squares spectral resolution (LSSR). The samples are unit-resolution LC-MS data of phosphatidyl cholines (PC) and sphimgomyelins (SM) analyzed by precursor ion scan of m/z 184. Details of the methodology are given in Zeng et al., J. Chromatogr. A 1280 (2013) 23.

1.1. Starting the program

You will need functions for loading data, for creating libraries and for handling spectra.

Fig.1.1
Figure 1.1. Main window at startup

1.2. Loading the data

Fig.1.2
Figure 1.2. Window for importing chromatographic data

1.3. Basic viewing options

In the main window you can you can choose between displaying the total ion currents (TIC) and individual ions by using the radio buttons to the right in the line below the chromatograms. Right-click in an ion-chromatogram will give you additional options. You can navigate in the chromatogram by the [+] and [-] buttons and by the slider next to them.

The region to display can also be changed by right-click on the vertical dotted bars in the chromatogram.

1.4. Generating a library of compounds

The purpose of this exercise is to identify PC species in the four samples. For that you will need a library of compounds and spectra that can be found in the samples. The compounds are generated by the Lipid Generator function that is opened by the [Gen. Lipids] button in the main window. The lipid generator generates possible lipid compounds based on lists of fatty acids, sphingoid bases and the lipid class core formula.

The window of the lipid generator is shown in Figure 1.3. When using the function it is important to consider which fatty acids, sphingoid bases and lipid classes it is possible to have in the analyzed samples – and which that can be detected under the experimental conditions that was applied. Although many of the fatty acids and sphingoid bases in the list are not expected to be abundant in the samples, there is no need to make changes to the default lists in this case. The applied MS conditions (precursor ion scan of m/z 184) means that only choline containing compounds are detected. These must therefore be selected in the lipid class list to the right in the figure.

Fig.1.3
Figure 1.3. The lipid generator window

1.5. Generating a mass spectral library

The next step is to create the mass spectral library from the compound list. Press the [Library] button in the main window. The library window is shown in Figure 1.4. The library window will import and display the library that is currently in the method when opened. When the list is empty, as in figure 1.4, it means that no library is stored in the method.

Before generating the spectra, you must pay attention to the information given by the resolution and offset info. This tells that the data currently in memory has a resolution of 1 and a mass offset of 0.2. The resolution of the data and the generated spectra should usually be identical. In most cases the mass offset should also be identical. The exception is if mass offset is used to compensate for a systematic deviation in the mass accuracy of the instrument.

The next step is to select the spectrum types to be generated. The function can generate several spectra, but in this case all relevant ions have positive H+ adducts.

The spectra are first generated with resolution of 0.001 and thereafter downsampled to the required resolution and mass offset. In the list of generated spectra there are some that are set as not being "active". These are of compounds with very similar spectra to other compounds in the list; the correlation between the spectra is higher than the similarity threshold of 0.9. These will not be applied by the LSSR algorithm. Which of the similar spectra that will be set as active is decided by the weights that are inherited from the compound list. You may change these selections, but only one of the interfering spectra should be set as active at any time. With a higher mass resolution you would have experienced fewer interferents.

Fig.1.4
Figure 1.4. The Library window

1.6. Quantification by LSSR

The next step is to quantify the compounds using LSSR. Select the first chromatogram (CODBRAIN_PC) and ensure that the selected region marked by the vertical dotted bars spans the region of the chromatogram where there are signals (Approx 15-47 min).

Fig.1.5
Figure 1.5. The LSSR window

There are three plots in the window. The main plot is the sum spectrum of the selected region. The horizontal black line is a threshold (in this case set to 2% relative to the most abundant mass). Compounds that do not have a base peak above the threshold are excluded from the calculations. Red masses are masses in the compounds that are included in the calculations. Any green masses (none in this case) are masses that are above the threshold, but that do not match any active compounds in the library. Other masses are blue. The horizontal grey line is a baseline estimate. This level is subtracted from all masses in the regression.

The predicted versus measured plot shows how well the calculated solution explains the spectrum. Any severe deviations (none in this case) show that the masses are not properly explained. Right-click on a deviating mass will show which compounds the mass belongs to. The compounds may not be accurately estimated if there are severe deviations between predicted and measured values of the masses.

The third plot shows the total signal from each compound detected. There will usually be a large number of bars with low levels and also some negative values because of noise and baseline subtraction. It will therefore usually be necessary to do a recalculation after selecting a proper threshold level.

Fig.1.6
Figure 1.6. Abundances

The majority of the compounds belong to the GPC[2] class, which are ordinary prosphatidyl cholines. “[2]” indicates that two fatty acids are bound to the molecule and the numbers that follows “t” indicate total number of carbons and total number of double bonds in the two fatty acids. There are also some compounds belonging to the GPC[2o] class indicating that one of the fatty acids is ether-linked (plasmalogens) and a few minor compounds belonging to the SPCF class (sphingomyelins). It should be emphasized that the identities are the one the program regard as the most likely, based on the weights of fatty acids and compound classes, and that there may be several alternative explanations.

Results can be reported by pressing the [Report] button. The report format can be selected by pressing [Settings] down in the right corner and thereafter [Reports].

1.7. Chromatographic resolution

Since the data is LC-MS data you can perform a chromatographic resolution based on the theoretical spectra of the compounds that is shown in the bar plot.

This should give you a resolved chromatogram similar to the one in Figure 1.7. The numbers in brackets behind the identities are equivalent carbon numbers (ECN). Peaks belonging to the same class and with the same ECN should be grouped together. Severe deviations from this pattern may indicate incorrect identification. Clicking on a peak or on a label will highlight the peaks. You may see that some profiles have double-peaks, which indicates that there are several isomers.

Fig.1.7
Figure 1.7. Chromatographic resolution of the data

1.8. Other samples.

In chromatogram 2 and 3 you can see that several of the major components consist of more than one isomer (e.g. GPC[2] t36:4 at approx 28 min). Some of the peaks late in the chromatogram are sphingomyelins (SPCF).

Chromatogram 4 is a reference mixture of sphingomyelins. All major peaks belong to the SPCF group. Note that there are several isomeric compounds in the SPCF group, so the displayed identities may not be correct. In addition, low resolution mass spectrometry cannot distinguish between compounds such as SPCF[d18:1] 18:0 (C41H83N2O6P) and SPCF[t17:1] 18:1 (C40H79N2O7P). If you have knowledge about which sphingoid bases you can expect in your samples you can avoid many of these conflicts by deactivating or downweighting compounds in the list of sphingoid bases in the Lipid Generator. Four common sphingoid bases are set as active by default. These are d18:1 (Sphingosine), d18:0 (Sphinganine), t18:1 (Dehydrophytosphingosine) and d17:1 (C17 Sphingosine).

Tutorial 2. LSSR on high resolution direct infusion data

The purpose of this tutorial is to learn how to apply LSSR with high resolution direct infusion data, and how you should read in the data with best possible quality.

2.1. Importing the data

Fig.2.1
Figure 2.1. Ion traces with unit resolution

2.2. Setting appropriate conditions for filtering and binning.

The plot in Fig. 2.1 is of unit resolution data. The unit resolution spectrum can be seen by selecting Avg Spectr as the display option. The data were acquired by high resolution MS, and the next step is to find out how good resolution you can use when the data are analysed, by varying the resolution and mass offset. The resolution in AMU can be any value from 0.001 to 5 that will give an integer value when used as a divisor for 1, i.e. 0.5, 0.2, 0.1, 0.05, ..., 0.001). The offset can be set to any number but it should usually have an absolute value smaller than the resolution.

The options for the binning and filtering algorithm is explained in Figure 2.2 and in the text below.

In most MS data files, spectra are stored as pairs of vectors of masses and abundances for each retention time, tr (Fig. 2.2a). These vectors typically vary in number of recorded ions and are without a strictly defined resolution.

The first filter that is applied is the abundance threshold filter that removes low signals that are expected to have insignificant influence on the data. The threshold is given in percent of the largest individual signal in the original spectra. Any signal below this threshold is deleted. The main purpose of this filter is to speed up the binning function and the threshold should normally not be so high that it has significant influence on the final results. The default value for Abund. Thresh is 0.01% of the most intense signal in the data. After application of the filter, the spectra have fewer ions, but the original data structure is kept (Fig. 2.2b).

The next step is the binning function. The purpose of this is to organize the spectra into a matrix of intensities where each entry in the matrix corresponds to the signal from a defined mass and a defined retention time (Fig. 2.2c). This is controlled by the parameters Resolution, Mass offset and Mass win. Resolution is the selected mass resolution in AMU in the final data matrix. Mass offset is a value that is added to the original masses (from Fig. 2.2b) before they are rounded to the required resolution. Mass win is the window size of the binning algorithm. This 100% by default, which means that all signals that pass the Abund. Thresh. filter will contribute to the binned signal matrix. This can be constrained to leave out masses that are between the expected signals. If the window is set to 100% and the resolution is 1, all ions between m-0.5 and m+0.5 are assigned to m, and all ions between m+0.5 and m+1.5 are assigned to m+1. If it is set to 50%, all ions between m-0.25 and m+0.25 are assigned to m and all ions between m+0.75 and m+1.25 are assigned to m+2. This means that signals from ions between m+0.25 and m+0.75 will not be recorded.

Each column in the signal matrix corresponds to an ion trace. After the signal matrix is constructed, vectors containing the maxima, means and the ranges of each column in the matrix are calculated. These values are compared to the maxima of each of these vectors by the max, mean and range filters. The threshold values for these are percents relative to the max value in each of the vectors of corresponding values. Ions that do not pass any of the active filters are deleted. When applying these filters it is important to consider which type of data one are working with. Important ions in chromatographic data can be expected to have a maximum well above baseline and a certain difference between the max and min values, so it makes sense to apply the max and the range filters. The mean filter may be an efficient way to remove spikes because a single spike in the signal will have little influence on the overall mean of the signal. However, this is also the case for small and narrow peaks, so the filter should therefore be used with care with chromatographic data. On direct infusion data, as applied in this tutorial, one should expect intensities to be fairly stable. A range filter therefore makes little sense and the mean filter may work better than the max filter, which is why the mean filter should be active in this case and the two other should be unchecked.

Three more optional filters that work on the ions can be applied. CODA is a method for detecting relevant signals in chromatographic-mass spectrometric data [Windig et al., Analytical Chemistry 68 (1996) 3602-3606] and the parameters that control it are the CODA Thresh. and CODA winsize. You can also constrain the ions to only those that are in the library compounds if there is a library in memory. When applying the library filter it is important that the library has the same resolution as the data, and the mass offset should usually also be the same. You can also set a maximum number of ions to return. If this filter is used, it returns the ions with the largest maxima in the ion traces. By default the filter is active and the maximum number of ions are 5000.

Finally, the data matrix can be resampled in the chromatographic direction by summarizing two or more scans. The final result can be a data matrix (Fig. 2.2d) that is reduced both in the chromatographic and the spectral direction.

Fig.2.2
Figure 2.2. Explanation of the binning and filtering functions. Blue text refers to the settings in the table that controls the function

You can test how different filters and conditions affect the data. Inspect both the ion chromatograms and the average spectra.

In the following sections it is assumed that the data were sampled with a resolution of 0.01, a small negative mass offset of -0.001, Mass win. of 100%, Abund. thresh. of 0.01%, Mean threshold of 0.1%, and all other filters turned off. The ion trace and the average spectrum with these conditions are shown in Figure 2.3. There are still a few negative spikes, but these will not have significant impact on the average spectrum. Apply these settings and thereafter return to the main window by pressing the [Accept] button.

Fig.2.3
Figure 2.3. Ion traces and average spectrum with resolution of 0.01, offset of -0.001 and mean filter of 0.1%.

2.3. Creating the library

2.4. Quantifying the data

The sphingomyelins have masses from approximately 700 to 900 AMU. In this region there are a few minor ions marked red. There are ions that are above the threshold, but that is not accounted for by the library. Their presence indicates that the sample is not pure sphingomyelin, or thst the sphingomyelin contains other sphingoid bases or fatty acids than those in the default lists.

By right-clicking on the bars or the labels in the bar plot you get information about each compound and you can search Lipid Maps or other bases to verify that these are common sphingomyelins, or get alternative identifications, by right-click in the information field.

The predicted versus measured plot shows that most masses are close to the 1:1-line, which indicates good accuracy. But there are a few ions that have a zero measured value and a calculated value above zero. The largest of these are 791.62. If you right-click on the label in the plot you will be told that this ion appears in SPCF[d18:1] 18:0.

Fig.2.4
Figure 2.4. Resolved spectrum (top), quantified solution (middle) and predicted versus measured abundance (bottom)

2.5. Using the library as a filter.

Once a library that fits the data is generated you can also use the library as a filter when the data are imported.

This should give similar results as the previous solution, but with a cleaner spectrum.

Tutorial 3. The Lipid Gerator

The purpose of this tutorial is to give an overview of the lipid generator.

3.1. Basics

The lipid generator generates lipid compounds by combining lists of common lipid classes, common fatty acids, and common sphingoid bases. However, there are lipid compounds that are not covered, either because the entire lipid class is not implemented or because they require fatty acids or sphingoid bases that are not in the default lists.

Because the lipid generator creates every combination of the active classes and compounds it will also generate compounds that does not occur naturally. Which compounds that are generated can to a large extent be controlled by setting weights for the different compounds or by activating or deactivating fatty acids or sphingoid bases for each lipid class.

The molecular formula for the compounds in the lists are given in the fully hydrolyzed form, and the lipid compounds are built by condensation reactions, i.e. for each linkage formed, the molecular formulas of the fragments are added and a water molecule is subtracted. The exception is when ether and vinylether bonds are formed, where O2 and H2O2 are subtracted, respectively.

3.2. Codes

Glycerolipids have the following convention for naming: The letter G denotes that the molecule contains a glycerol, P denotes that it contains a phosphate group. The letters C, E, I and S denote choline, ethanolamine, inositol, and serine, respectively. A number following any of the letters denotes the total number of the group if it is more than one. Numbers in brackets denote how many fatty acids that are found in the molecule. If the number in brackets is followed by o or p it denotes that one of the fatty acids is bound by an ether or vinylether bond, respectively. The bracket is thereafter followed by a specification of the fatty acids, either as total number of carbons and total number of double bonds, denoted by t, or specified for each position, where letters a-c denotes sn-1 to sn-3 position in the glycerol, and x denotes an unknown position. Fatty acids with an additional oxygen (e.g. hydroxy or methoxy fatty acids) are denoted by +O following the number of double bonds.

In this system the compound in Figure 3.1 can be described in several ways with different levels of detail:

  1. GPC[2p] t32:0 – The compound has two fatty acids with a total of 32 carbons and 6 double bonds, and one of the fatty acids is bound by a vinyl ether group.

  2. GPC[2p] x16:0 x22:6 – The two fatty acids are 16:0 and 22:6 but the positions are unknown.

  3. GPC[2p] ap16:0 b22:6 – 16:0 is in sn-1 position and 22:6 is in sn-2 position, and 16:0 is linked by a vinylether bond.

  4. GPC[2p] ap16:0 b22:6(4,7,10,13,16,19) – Same as above but with double bond positions specified.

Alternative 1 is applied by the lipid generator, but if more details about the structure are known, the compounds can be further specified by alternatives 2-4 within the same system.

Fig.3.1
Figure 3.1. Structure of 1-(1Z-hexadecenyl)-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-sn-glycero-3-phosphocholine

Most sphingolipids contain a single fatty acid in addition to the sphingoid base. The naming convention for the sphingolipids is therefore the following:

The letter S denotes that the molecule contains a sphingoid base. The letters C, E, I and S have the same meaning as for the glycerolipids. G and M denotes galactose and mannose units, respectively. Since G is also used for glycerol it is important that is it specified as the first letter in the code if it refers to glycerol. To account for hydrolyzed forms that contain no fatty acid, an F is added as the last letter if a fatty acid is present (in glycerolipids this can be handled by a zero in the bracket)

The bracket in sphingolipids refers to the type of sphingoid base. The numbers in brackets are the total number of carbons and double bonds in the base, and the letter d or t preceding the number denotes two or three hydroxy groups in the base, respectively. The bracket is followed by the number of carbons and double bonds in the fatty acid.

There are some additional classes. Although platelet activation factor can be described as a phosphocholine with an ether bond (GPC[2o]) and an esterified C2 fatty acid, it is defined as a separate group named PAF. Free fatty acids are named F[1]. Cholesterol is not in the current compound list but is denoted by C[0] or as C[1] followed by the fatty acid if esterified.

The complete list of compound classes with structures and examples are given in Appendix 1 at the end of the document.

3.3. Main functions of the generator

The lipid generator window is shown in Figure 3.2. The list of lipid classes that can be generated is shown in the table to the right in the window. The table to the left shows the fatty acid list, fatty acid combinations, sphingoid bases or generated compounds. The fatty acid list is displayed when the window is opened. The list to display is selected in the list selection area. Below the list there are various controls that will change depending on the list shown.

Fig.3.2
Figure 3.2. The lipid generator window

3.4. Generation of compounds and filtering

Assume that you have been analyzing a mixture of phosphatidyl choline (CPC[2]) and phosphatidyl ethanolamine (GPE[2]) and would like to generate a library for these compounds.

You can also see that the molecules have different weights. The weights are inherited from the weight given to the fatty acids in the fatty acid list. All fatty acids with an odd number of carbons have a weight of 0.5 and all fatty acids with an additional oxygen have a weight of 0.4. Other fatty acids have a weight of 1. The molecule GPE[2] t23:0 must contain one odd-numbered fatty acid and one even numbered fatty acid and therefore got the weight 1×0.5=0.5. The compound GPE[2] t24:0+O2 at 611.75 amu has a weight of 0.16 because it contains two oxygenated fatty acids (0.4×0.4). Compounds with an additional fatty acid oxygen and odd number of fatty acid carbons have weights of 0.2 (0.4×0.5). If you scroll down the list to 1000.57 you will see that GPE[2] t54:0 has a weight of 0.25. Even though this compound has an even number of fatty acid carbons, the only combination of the fatty acids in the list that can explain 54:0 is two 27:0 fatty acids.

The weights in the fatty acid list can be edited by the user. The weights can also be edited in the list of fatty acid combinations. This may for instance be necessary to do if you apply internal standards with odd-numbered fatty acids, to ensure that the standard is not deleted. The list of isomers may tell you about possible interferents with the standard. To ensure that a certain compound is always preferred you can set the weight higher than 1.

If you want further information about possible isomers you can search for isomers of the compound selected in the compound list in LipidMaps, EMBL or ChemSpider by using the popup-menu below the list.

3.5. Editing lists for each class

Assume that you have analyzed phosphatidyl cholines and know that you have some plasmalogens with ether or vinyl-ether bound fatty acids. You may also know that usually you will only find 14:0, 16:0, 18:1 and 20:4 fatty acids in plasmalogens in the sample type you are working with. In this case it does not make sense to create all possible plasmalogens from the default fatty acid list. You can solve this in the following way:

The hierarchy of the different lists and functions is illustrated in Figure 3.3. Fatty acid lists for the specific classes are generated from the default fatty acid list, the fatty acid combinations (1 to 4 fatty acids) are thereafter generated from the fatty acid lists. The fatty acids are combined with the core molecule, and a sphingoid base the case of sphingolipids. The generated lists of molecules can be filtered (optionally) and thereafter saved to be used in the library function. Because there is a separate filtering step in the library function, unique spectra can also be generated from unfiltered compound lists.

Fig.3.3
Figure 3.3. Hierarchy of the different lists and functions in the Lipid generator and the Library window.

The fatty acid lists for each class is generated from active compounds in the default fatty acid list the first time the list for a class is displayed or used to generate lipids. The same applies for the combination lists. So if lipid classes are generated one by one, any edits to the default list will affect which compounds that are generated for each class. The controls for the different lists are described below:

3.6. User defined fatty acid lists

The default list of fatty acids may not be suitable for all sample types. User defined lists of fatty acids can be created and are stored in the libraries folder as semicolon delimited csv files with the file names falist_......csv. The number of carbons and double bonds must be specified. Optionally you can also specify additional oxygens, the weight, and whether the fatty acid is set as active or not by true / false or 1 / 0. If nothing is specified the default settings are no additional oxygens, weight 1, and active. An example of a fatty acid list opened in a text editor is shown below.

Assume you are analyzing triacylglycerols in vegetable oil. The G[3] class will generate more than 1000 compounds when used with the default fatty acid list. Many of these will not be naturally present in vegetable oil and can lead to incorrect identifications and poor quantification.

3.7. Saving and loading settings and compound lists

The generated compounds can be saved in two formats. Both these formats can be read by the library function.

You can save the list and all settings by selecting a file name next to the [Save Settings] button. This will save all lists with selections and weights at the time you press [Save Settings]. It is a good option if you have created a compound list based on other than the default settings and want to save all your modifications.

The other option is to save the data as a compound list in csv format that can be edited. These are saved in the libraries folder as cmplist_... .csv. An example of a compound list is shown below:

Code, formula, short name, class and weight must be specified. Other fields are optional. A user defined list does not have to follow conventions for names and classes used by the lipid generator. Note that atoms in the molecular formula should be given in the order H, D (deuterium) ,C, Cl, N, Na, O, P and S. Other atoms are currently not handled.

This will give you a filtered list consisting of both imported and generated compounds. If the imported compounds are covered by the generated compounds the name will be from the imported list and the generated compounds are shown as isomers. If an imported compound has no isomers it means that it is not covered by the generated compounds (such as PC28:6 at 665.85 amu). If the fatty acid composition of your sample is known you can use this procedure to create a fatty acid list and verify the experimental identifications. A compound that cannot be explained by the fatty acid composition of the sample is not correctly identified.

3.8. Sphingoid bases

Because sphingolipids can vary in number of carbons and double bonds both in the fatty acid and in the sphingoid base, activating the full list of sphingoid bases will create a lot of isomers. The common sphingomyelin SPCF[d18:1] 16:0 (C39H79N2O6P at 703.04 amu) can for instance have the following isomers: SPCF[d17:1] 17:0, SPCF[d18:0] 16:1, SPCF[d17:0] 17:1, SPCF[d20:0] 14:1, SPCF[d20:1] 14:0, SPCF[d14:1] 20:0 and SPCF[d16:1] 18:0. To reduce the number of isomers, only the following sphingoid bases are set as active by default:

d17:1 is set with a weight of 0.85, which is higher than the default weight of fatty acids with odd number of carbon atoms (0.5). This means for instance that SPCF[d17:1] 16:0 is preferred over SPCF[d18:1] 15:0. For reasonable identifications based on the molecular mass alone it is therefore important to consider whether one can expect that odd-numbered sphingoid bases are more likely than odd-numbered fatty acids, and adjust the weights if necessary.

d18:1 (Sphingosine) and d18:0 (Sphinganine) are the most common sphingoid bases, and it can be a good approach to test an unknown sample first with only these two activated to check if they adequately explains the data.

Tutorial 4. The profile analyzer

The puropose of this tutorial is to give an introduction to the Profile Analyzer that will be applied also in Tutorials 5 and 6.

4.1. Initialization

This is a low resolution profile spectrum of porcine brain phosphatidyl choline.

The Profile Analyzer resolves the spectrum into individual peaks and is used for the following purposes:

This tutorial will give an introduction to the Profile Analyzer, and you will use it to create a centroided spectrum that can later be resolved by LSSR. The Profile Analyzer window is shown in Figure 4.1.

Fig.4.1
Figure 4.1. The profile analyzer window

4.2. Description of functions

At the bottom left there is a table of check boxes that controls which methods and functions to apply to the spectrum, and the associated parameters for the different functions are given in the field to the right. A summary of the functions are given below:

4.3. Using the functions

This section will teach you how to work with different functions and plot options.

4.4. Generating a centroided spectrum and resolving it by LSSR

The standard LSSR algoritm works only on centroided and binned data. In this section you will use the Profile analyzer to generate a centroided spectrum to be resolved by LSSR.

The library you are going to use in LSSR is already made, but if you want to create it yourself the parameters are: GPC[2], GPC[2p] and SPCF lipid classes with the respective weights 1, 0.5 and 0.75, [+H]+ spectra, resolution 1 and mass offset 0.2.

4.5. Further description of the functions in the Analyze profile window

Baseline estimation

The procedure for finding baseline points is based on correlation between a profile estimated by quadratic functions and the raw data within a window around each m/z value in the profile spectrum. If the point is at baseline, there will be poor correlation because the data contain only noise. The parameter Baseline win size sets the window size around each point. Baseline threshold sets the sensitivity of the method. If the value is set to 10, the points with the 10% lowest correlations will be selected as initial baseline points. An iterative procedure is thereafter applied to remove points with large positive residuals (over 3 standard deviations) when a straight line is fitted through the baseline points. Finally, the baseline equation is calculated by regression on the intensities and m/z values of the remaining baseline points. The parameters that affects the baseline are:

Peak detection

Figure 4.2 shows a normally distributed peak with some noise, the profile of its first derivative, and the profile of the first derivative multiplied with the original profile (all three profiles are normalized to same max). Peak detection is based on the derivative multiplied with the raw profile. This is less affected by noise outside peak regions than the derivative alone.

Peak starts (point a in Figure 4.2) are detected where this profile exceeds a the upper threshold, and peak ends (point b in Figure 4.2) are detected where the profile exceeds the lower threshold. After the initial peak detection there are filters for peak starts with no matching ends and ends with no matching starts. In addition the results are filtered for deviating peak widths.

Peak maxima are determined by fitting a cubic polynomial between points c and d (max/min) in the profile and solving for intensity equal to zero. The final peak widths (LWHH, RWHH) are thereafter determined on the original profile.

The parameters that affects peak detection are:

Fig.4.2
Figure 4.2. Peak detection

Fitting of profiles

Peak profiles will be fitted to the detected peaks if the parameter Fit peaks is selected. There are several types of peak fitting. Each peak will be modelled separately if the method independent is selected. If not, the program applies a common model for the shape and widths of the profiles. There are different types of peak models that can be applied, normal (Gaussian), cauchy (Lorentzian), voigt (Gaussian/Lorentzian), and hybrid models where there are different models for the left and right side of the peak. With normally distributed profiles, the program can model three parameters for each peak in addition to the position, left width at half maximum (LWHM), right width at half maximum (RWHM) and kurtosis. A kurtosis value above one means a platykurtic peak and kurtosis below one means a leptokurtic peak. With Voigt profile, the kurtosis parameter is replaced by the "voigt factor" (vf). A Voigt factor of 1.5 means that the peak is a Cauchy peak, a value of 0.5 means that the peaks is Gaussian, and values between these are mixtures of the two profile types. Normal and Voigt profiles with varying kurtosis and Voigt factors are shown in Figure 4.3. The two blue profiles are identical because both represent the normally distributed peak with no kurtosis.

Fig.4.3
Figure 4.3. Normal and Voigt profiles

The peak profiles are fitted to the raw profile by an iterative method based on the Nelder-Mead algorithm. If the option Autodetect peak widths is checked, the ranges of initial peak widths are based on the estimates from the peak detection function. Otherwise the limits for peak widths are decided by the parameters Min LWHM, Max LWHM, Min RWHM and Max RWHM if the peaks are allowed to be asymmetric (Allow asymmetry checked). The corresponding parameters for symmetric peaks are Min FWHM and Max FWHM. There are also similar settings for Kurtosis/Voigt factor.

The maximum number of iterations are set by the parameter Max iterations. There is also a stop criterion, which is a percent value (1% by default). For each iteration, the lowest residual is stored. If the development in the lowest residual for the last 100 iterations is lower than 1% of the total range of residuals, the iteration procedure is stopped.

There are four procedures for fitting the peaks. If Independent models are selected, each peak is fitted individually. The position of the peak maximum is then one of the factors that are varied in addition to the peak shape parameters. Since the fitting of a peak may be dependent on the position and shapes of overlapping neighbours it is beneficial to repeat the entire peak fitting procedure several rounds. The maximum number of rounds is decided by the Rounds parameter.

If regression or global models are selected, the optimization uses the same peak models on all peaks, but the parameters are allowed to be dependent on mass (Peak with m/z dependence and Kurtosis (vf) m/z dependence). In this case the position of one of the peaks is optimized between each iteration. The number of iterations should therefore be substantially higher than the number of peaks, so that the maximum for each peak is fitted several times.

After the iteration, the statistics for the peaks are calculated. For independent models this is done by regression on all the peaks. For global models the statistics is acquired directly from the model with the lowest residual.

Size estimates

There are different ways of estimating the sizes when profiles are converted to centroids, Area raw, Area prof, Norm area raw, Norm area prof, Height raw and Height prof. The difference between the raw and prof estimates are illustrated in Figure 4.4. The figure shows an estimated peak profile (red) and this profile fitted to raw data acquired with relatively low frequency (blue). Size estimates ending with "raw" are acquired from the fit to the raw profile, and estimates ending with "prof" are estimated from the theoretical distribution. Particularly on the height estimates, there may be substantial differences between the raw and prof estimates, and the prof estimates can be assumed to be most accurate.

For estimates of areas, there are also variants starting with "Norm". This is the area multiplied with the distance between each point on the m/z scale (0.2 in figure 4.4). These areas will be independent of scan frequency.

Fig.4.4
Figure 4.4. Estimated peak profile (red) and this profile fitted to raw data acqured with low frequency (blue)

Tutorial 5. Least squares resolution of profiles (LSSR-P)

In this tutorial you will use least squares spectral resolution (LSSR) to quantify phospholipids acquired by direct infusion mass spectrometry. The LSSR-P algorithm is an extension of LSSR for profile spectra.

5.1. Principle

LSSR was initially developed for centroided spectra, where the intensity of each peak is described by a single number. In profile spectra the peaks must be described by by vectors and the signals from the different peaks may be overlapping.

The differences between LSSR and LSSR-P are illustrated in Figure 5.1, where Figure 5.1a represents information about a single compound (PC 32:0 in this case) that is available from the library. This is the exact masses and the expected intensity of each mass.

In LSSR-P it is necessary to estimate the profiles of the library spectra and match these to the raw spectrum profile (Figure 5.1b). In LSSR for centroided spectra, the binning of both libraries and raw spectra to the same bins ensures that the mass values match, which is illustrated by the grey boxes in Figure 5.1 a and b.

To calculate the library profiles, it is necessary to know the exact masses and the expected intensity of each mass for the different compounds, which is available from the library. In addition, a model for the peak shape is needed, and this is provided by the Profile Analyzer.

The two algorithms are compared in Figure 5.1d. LSSR-P has one additional step. As illustrated in Figure 5.1b, there can be small mass shifts in the raw data compared to the exact masses provided by the library. Finding these mass shifts is handled by iterative optimization in LSSR-P, using the Nelder-Mead algorithm. The calculated shift can be equal for all compounds or they can be linearly dependent on the mass. Because LSSR-P is an iterative procedure, it is slower than LSSR. But in most cases it can be expected to be more accurate, because the centroiding and binning that is necessary for ordinary LSSR lead to loss of information.

Fig.5.1
Figure 5.1. The principles for LSSR and LSSR-P

5.2. Low resolution data

The data to be analyzed are five samples where the first four are egg PC, egg sphingomyelin (SM), bovine brain PC and bovine brain sphingomyelin, and the last sample is a mixture of the first four. The data are acquired in direct infusion mode by a low resolution triple quadrupole instrument.

The data set is stored as mz5 raw data, which must be read by the function for importing chromatograms.

What is important in this case is that you get a good general estimate of the peak shapes. Whether there are some residuals on some of the peaks is less important. There is also no need to detect all peaks since you only need enough peaks to get good general models.

The next thing you have to do is to generate or import a library. As illustrated in Figure 5.1d, LSSR-P does not apply the binned libraries. You can therefore use the same library as in Tutorial-4, even though this is binned to unit resolution with an offset.

For more advanced usage you can step through the different processes illustrated in Figure 5.1 by the [Get Cand] (get candidates), [Filter Cand] (filtering), [Fit] (determination of mass shifts) and [Quantify] (quantification) buttons. In each step you can make changes in the "Candidates", "Filtered" and "Results" tables. You may for instance replace compounds in the filtered table with compounds with similar mass to see if it reduces the residuals.

The results plots for the different samples should look approximately as in Figure 5.2. Verify that the two samples of ordinary PC contain basically PC, and that the two samples of SM contain basically SM (marked SMCi or SPCF). In the mixture (Figure 5.2e) you should find all the major compounds.

Fig.5.2
Figure 5.2. Results plot for the five samples analyzed by the low resolution instrument

5.3. High resolution data

The same samples have also been analysed by direct infusion of a time-of-flight (TOF) instrument. In this case you read the spectra by the [Import Spec] function.

In this case the profiles are not well described by variants of the normal distribution. If you zoom on the largest peaks you will see that the peaks are asymmetric and tailing. The correct peak model to use in this case is the normal/voigt, where the left side of the peak is normally distributed and the right side of the peak has a Voigt distribution.

You can use the same library as for the low resolution data. Load the library as explained in Section 5.2 if it is not already in the memory.

In this case there is a quite large mass shift compared to the width of the peaks.

The difference between the exact mass and the peak maximum is therefore −0.127 (760.585 − 760.712), which is similar to FWHM (approx 0.11). The parameter m/z shift window sets a max limit for m/z shift in percent of the peak width, and the default is 50%. You can increase this value, but it is usually better to manually adjust the m/z scale by the m/z offset parameter.

You can analyze the remaining samples with the same procedure. The mass offset of −0.127 can be used for all samples. The results should be similar to those shown in Figure 5.4. There are small differences between the two instruments even though there is a large degree of overlap in the low resolution spectra. However, in general the high resolution instrument detects a few more samples and it will usually be better in cases where samples are very complex.

Fig.5.3
Figure 5.3. Profile before (a) and after (b) manual adjustment
Fig.5.4
Figure 5.4. Results plot for the five samples analyzed by the high resolution instrument

Tutorial 6. Accurate mass determination

In this tutorial you will apply the peak models from the Profile Analyzer to determine the mass of unknowns. The tutorial has two sections, one for low resolution and one for high resolution data.

6.1. Low resolution data

The data are six replicates of brain phosphocholine analysed by direct infusion on a triple quadrupole instrument. It is the same sample as applied in Tutorial 5. We therefore know that the three most abundant compounds are PC 34:1, PC 36:1 and PC 36:2. However, the sample is now spiked with the saturated PCs 24:0, 28:0, 32:0, 36:0, 40:0, 44:0, and 48:0. These will be used as reference series for the mass calibration.

You will now have to apply the Profile Analyzer on all six data files in the same way as you did in Tutorial 5. The procedure for the first file is:

You should now be in the Profile Analyzer.

Repeat the steps above for the remaining 5 data files.

You should now have a table showing the accurate masses for the library compounds, and there will be vertical grey lines in the spectrum indicating the masses of the two largest peaks from each library compound. The next step is to match these masses from the library to the peaks in the spectrum.

Fig.6.1
Figure 6.1. The spectrum after matching to the library
Fig.6.2
Figure 6.2. Difference between the library masses and the matched peak masses

The next step is to find a regression model that explain the differences . This function will later be used to calculate the accurate masses of the other peaks in the spectrum. The plot shows that the differences are dependent on the mass and that the dependence is close to linear. We should therefore apply a linear regression model.

Two regression lines will now be shown in the plot. The black is the regression model for the current spectrum. The grey is the regression line based on all the other replicates (the open circles).

We know from Tutorial 5 that the three most abundant compounds in the sample are PC 34:1, PC 36:1 and PC 36:2. The base peaks of these are indicated by the red arrows in Figure 6.1. We should now assume that these are unknown compounds and check the predicted masses versus the Lipid Maps database.

Table 6.1. Predictions for the largest peak
ParameterPrediction
Raw m/z, [M+H]+760.8
fitted m/z, [M+H]+760.7626
Predicted m/z, [M+H]+ (internal calibr.)760.5751
Predicted m/z, [M+H]+ (external calibr.)760.5729
Predicted neutral mass (internal calibr.)760.5751
Predicted neutral mass (external calibr.)759.5656
Internal calibr. RMSE0.0095
Internal calibr. median residual0.0066
External calibr. RMSE0.011
External calibr. median residual0.0075
Mean neutral mass (internal calibr.)759.572
Median neutral mass (internal calibr.)759.5739
Number of samples, consensus estimates (internal calibr.)5
Mean neutral mass (external calibr.)759.5752
Median neutral mass (external calibr.)759.5755
Number of samples, consensus estimates (external calibr.)6

There are several estimates for the mass. You can apply the Median neutral mass based on internal calibration that is shown with bold text. This is the median of all six replicates, so it is enough to acquire the statistics from a single sample. The two error estimates show how the predictions vary between the replicates. They indicate that the error may be slightly lower than 0.01 mass units.

In the following web page you can search the Lipid Maps database by mass:

The accurate masses from Lipid Maps and the predictions for the three most abundant compounds are compared in Table 6.2. None of the errors were above 0.01 mass units and none of the compounds have any interference within this mass tolerance. It is worth noting that the errors are much smaller than the digital data resolution of the instrument, which is 0.1 mass units.

You can play around with the mass tolerances to see when interferents (compounds with different gross formula) appear among the alternatives.

Table 6.2. Predictions and errors for the three most abundant compounds
Accurate massPredicted massError
PC 34:1759.5778759.5739-0.0039
PC 36:1787.6091787.6039-0.0052
PC 36:2785.5935785.59590.0024

6.2 High resolution data

The high resolution data are from a time-of-flight instrument. The samples are exactly the same as used with low resolution.

The procedure to analyze the high resolution data is the same as used before.

If you inspect the difference plot (Fig. 6.3) you will see that there is a tendency to curvature in the data, so it may be beneficial to change the model to Polynom ord. 2. If you step through the replicates using the [<] and [>] buttons you will also see that there is a tendency to drift over time.

If you click on the peak numbers and compare predictions to the correct values for the same three compounds analyzed with the low resolution data you should get values similar to those in Table 6.3. The average error is approximately 1/3 of what it was with the low resolution instrument. The FWHM for the high resolution instrument was 0.10, while it was 0.65 for the low resolution instrument, which means that the peak width was more than 6 times higher for the low resolution instrument. It is therefore no direct link between resolution and the expected accuracy of the masses after calibration.

Fig.6.3
Figure 6.3. Difference between the library masses and the matched peak masses for the high resolution data
Table 6.3. Predictions and errors for the three most abundant compounds in the high resolution data
Accurate massPredicted massError
PC 34:1759.5778759.5766-0.0012
PC 36:1787.6091787.6082-0.0009
PC 36:2785.5935785.59480.0013

Appendix 1. List of lipid classes with examples of naming

Code
Core		Explanation / short
Example
F[1]Free fatty acid / FFA
H2Oimg
F[1] 16:0
G[1]Mono-acylglycerol / MAG
C3H8O3img
G[1] 16:0
G[2]Di-acylglycerol / DAG
C3H8O3img
G[2] t32:0
G[3]Tri-acylglycerol / TAG
C3H8O3img
G[3] t48:0
GP[1]Lyso-phosphatidic acid / LPA
C3H9O6Pimg
GP[1] 16:0
GP[2]Phosphatidic acid / PA
C3H9O6Pimg
GP[2] t32:0
GPC[1]Lyso-phosphatidylcholine / LPC
C8H20NO6Pimg
GPC[1] 16:0
GPC[2]Phosphatidylcholine / PC
C8H20NO6Pimg
GPC[2] t32:0
GPC[1o]Plasmanyl-lyso-phosphatidylcholine / LPCo
C8H20NO6Pimg
GPC[1o] 16:0
GPC[2o]Plasmanyl-phosphatidylcholine / PCo
C8H20NO6Pimg
GPC[2o] t32:0
GPC[1p]Plasmenyl-lyso-phosphatidylcholine (LPC plasmalogen) / LPCp
C8H20NO6Pimg
GPC[1p] 16:0
GPC[2p]Plasmenyl-phosphatidylcholine (PC plasmalogen) / PCp
C8H20NO6Pimg
GPC[2p] t32:0
GPE[1]Lyso-phosphatidylethanolamine / LPE
C5H14NO6Pimg
GPE[1] 16:0
GPE[2]Phosphatidylethanolamine / PE
C5H14NO6Pimg
GPE[2] t32:0
GPE[1o]Plasmanyl-lyso-phosphatidylethanolamine / LPEo
C5H14NO6Pimg
GPE[1o] 16:0
GPE[2o]Plasmanyl-phosphatidylethanolamine / PEo
C5H14NO6Pimg
GPE[2o] t32:0
GPE[1p]Plasmenyl-lyso-phosphatidylethanolamine / LPEp
C5H14NO6Pimg
GPE[1p] 16:0
GPE[2p]Plasmenyl-phosphatidylethanolamine (PE plasmalogen) / PEp
C5H14NO6Pimg
GPE[2p] t32:0
GPI[1]Lyso-phosphatidylinositol / LPI
C9H19O11Pimg
GPI[1] 16:0
GPI[2]Phosphatidylinositol / PI
C9H19O11Pimg
GPI[2] t32:0
GPI[1o]Plasmanyl-lyso-phosphatidylinositol / LPIo
C9H19O11Pimg
GPI[1o] 16:0
GPI[2o]Plasmanyl-phosphatidylinositol / PIo
C9H19O11Pimg
GPI[2o] t32:0
GPS[1]Lyso-phosphatidylserine / LPS
C6H14NO8Pimg
GPS[1] 16:0
GPS[2]Phosphatidylserine / PS
C6H14NO8Pimg
GPS[2] t32:0
GPS[1o]Plasmanyl-lyso-phosphatidylserine / LPSo
C6H14NO8Pimg
GPS[1o] 16:0
GPS[2o]Plasmanyl-phosphatidylserine / PSo
C6H14NO8Pimg
GPS[2o] t32:0
G3P2[4]Cardiolipin / CL
C9H22O13P2img
G3P2[4] t72:8
SFCeramide / Cer (Sphingosine base)
H2Oimg
SF[d18:1] 16:0
SFCeramide / Cer (Phytosphingosine base)
H2Oimg
SF[t18:0] 16:0
SPCFPC Ceramide / SMC (Sphingomyelin, sphingosine base)
C5H14NO4Pimg
SPCF[d18:1] 16:0
SPCFPC Ceramide / SMC (Sphingomyelin, phytosphingosine base)
C5H14NO4Pimg
SPCF[d18:0] 16:0
SPEFPE Ceramide / SME
C2H8NO4Pimg
SPEF[d16:1] 18:0
SPIFPI Ceramide (IPC) / SMI (Sphinganine base)
C6H13O9Pimg
SPIF[d18:0] 16:0
SPIFPI Ceramide (IPC) / SMI (Phytosphingosine base)
C6H13O9Pimg
SPIF[t18:0] 16:0
SMIPFMannosylinositol phosphoceramide / MIPC (Sphinganine base)
C12H23O14Pimg
SMIPF[d18:0] 16:0
SMIPFMannosylinositol phosphoceramide / MIPC (N,N-dimethyl-Safingol base)
C12H23O14Pimg
SMIPF[d20:0] 16:0
SMI2P2FInositol phosphomannosylinositol phosphoceramide / MIP2C
C18H34O22P2img
SMI2P2F[d18:0] 16:0
SGFGalactosyl ceramide / GCer (Sphinganine base)
C6H12O6img
SGF[d18:0] 16:0
SGFGalactosyl ceramide / GCer (Sphingosine base)
C6H12O6img
SGF[d18:1] 16:0
PAF[1o]Platelet activation factor / PAF
C10H22NO7Pimg
PAF[1o] 16:0