Limits...
A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language.

Herzog R, Schwudke D, Schuhmann K, Sampaio JL, Bornstein SR, Schroeder M, Shevchenko A - Genome Biol. (2011)

Bottom Line: Shotgun lipidome profiling relies on direct mass spectrometric analysis of total lipid extracts from cells, tissues or organisms and is a powerful tool to elucidate the molecular composition of lipidomes.We present a novel informatics concept of the molecular fragmentation query language implemented within the LipidXplorer open source software kit that supports accurate quantification of individual species of any ionizable lipid class in shotgun spectra acquired on any mass spectrometry platform.

View Article: PubMed Central - HTML - PubMed

Affiliation: Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307 Dresden, Germany.

ABSTRACT
Shotgun lipidome profiling relies on direct mass spectrometric analysis of total lipid extracts from cells, tissues or organisms and is a powerful tool to elucidate the molecular composition of lipidomes. We present a novel informatics concept of the molecular fragmentation query language implemented within the LipidXplorer open source software kit that supports accurate quantification of individual species of any ionizable lipid class in shotgun spectra acquired on any mass spectrometry platform.

Show MeSH

Related in: MedlinePlus

Scan averaging algorithm. (a) Related individual scans (here as an example we only show four scans) imported as a complete *.mzXML file are recognized. (b) Peaks are combined into a single peak list and sorted. (c) The full mass range is divided into bins of [m; m+] size, starting from the lowest reported mass. The bold dots stand for the lowest mass of each bin, while the arrow length reflects the bin size . Within each bin, masses are weight averaged by peak intensities and stored. The procedure (steps (c) and (d)) is repeated two more times on the binned spectrum (not shown). (d) In this way, a single representative average spectrum (d) is produced from several individual scans (a).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3091306&req=5

Figure 3: Scan averaging algorithm. (a) Related individual scans (here as an example we only show four scans) imported as a complete *.mzXML file are recognized. (b) Peaks are combined into a single peak list and sorted. (c) The full mass range is divided into bins of [m; m+] size, starting from the lowest reported mass. The bold dots stand for the lowest mass of each bin, while the arrow length reflects the bin size . Within each bin, masses are weight averaged by peak intensities and stored. The procedure (steps (c) and (d)) is repeated two more times on the binned spectrum (not shown). (d) In this way, a single representative average spectrum (d) is produced from several individual scans (a).

Mentions: While acquiring mass spectra, m/z and intensities of peaks might slightly vary within each scan (further, solely for presentation clarity, we will use the mass of a precursor ion m instead of its m/z). Therefore, averaging individual scans into a single representative spectrum improves the ion statistics and, hence, the accuracy of both measured masses and abundances of corresponding peaks and is commonly applied in proteomics [21,22]. Here we describe a simple linear time algorithm for aligning MS and MS/MS spectra of small molecules (particularly lipids) acquired in large series of shotgun experiments. It assumes that masses pertinent to the same peak are Gaussian distributed within individual scans. The algorithm recognizes related peaks in each individual scan and averages their masses and intensities (Additional file 2). First, the algorithm considers all pertinent scans within the acquisition and combines all reported masses into a single peak list (Figure 3). This list is then sorted by masses in ascending order and averaging proceeds in steps, starting from the lowest detected mass. In every step the algorithm considers mass m and checks whether other masses fall into a bin of [m; m+ ] width, where R(m) is the mass resolution at the mass m. R(m) is assumed to change linearly within the full mass range; its slope (mass resolution gradient) and intercept (resolution at the lowest mass of the full mass range) are instrument-dependent features pre-calculated by the user from some reference spectra. All masses within the bin are average weighted by peak intensities according to Equation 1:


A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language.

Herzog R, Schwudke D, Schuhmann K, Sampaio JL, Bornstein SR, Schroeder M, Shevchenko A - Genome Biol. (2011)

Scan averaging algorithm. (a) Related individual scans (here as an example we only show four scans) imported as a complete *.mzXML file are recognized. (b) Peaks are combined into a single peak list and sorted. (c) The full mass range is divided into bins of [m; m+] size, starting from the lowest reported mass. The bold dots stand for the lowest mass of each bin, while the arrow length reflects the bin size . Within each bin, masses are weight averaged by peak intensities and stored. The procedure (steps (c) and (d)) is repeated two more times on the binned spectrum (not shown). (d) In this way, a single representative average spectrum (d) is produced from several individual scans (a).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3091306&req=5

Figure 3: Scan averaging algorithm. (a) Related individual scans (here as an example we only show four scans) imported as a complete *.mzXML file are recognized. (b) Peaks are combined into a single peak list and sorted. (c) The full mass range is divided into bins of [m; m+] size, starting from the lowest reported mass. The bold dots stand for the lowest mass of each bin, while the arrow length reflects the bin size . Within each bin, masses are weight averaged by peak intensities and stored. The procedure (steps (c) and (d)) is repeated two more times on the binned spectrum (not shown). (d) In this way, a single representative average spectrum (d) is produced from several individual scans (a).
Mentions: While acquiring mass spectra, m/z and intensities of peaks might slightly vary within each scan (further, solely for presentation clarity, we will use the mass of a precursor ion m instead of its m/z). Therefore, averaging individual scans into a single representative spectrum improves the ion statistics and, hence, the accuracy of both measured masses and abundances of corresponding peaks and is commonly applied in proteomics [21,22]. Here we describe a simple linear time algorithm for aligning MS and MS/MS spectra of small molecules (particularly lipids) acquired in large series of shotgun experiments. It assumes that masses pertinent to the same peak are Gaussian distributed within individual scans. The algorithm recognizes related peaks in each individual scan and averages their masses and intensities (Additional file 2). First, the algorithm considers all pertinent scans within the acquisition and combines all reported masses into a single peak list (Figure 3). This list is then sorted by masses in ascending order and averaging proceeds in steps, starting from the lowest detected mass. In every step the algorithm considers mass m and checks whether other masses fall into a bin of [m; m+ ] width, where R(m) is the mass resolution at the mass m. R(m) is assumed to change linearly within the full mass range; its slope (mass resolution gradient) and intercept (resolution at the lowest mass of the full mass range) are instrument-dependent features pre-calculated by the user from some reference spectra. All masses within the bin are average weighted by peak intensities according to Equation 1:

Bottom Line: Shotgun lipidome profiling relies on direct mass spectrometric analysis of total lipid extracts from cells, tissues or organisms and is a powerful tool to elucidate the molecular composition of lipidomes.We present a novel informatics concept of the molecular fragmentation query language implemented within the LipidXplorer open source software kit that supports accurate quantification of individual species of any ionizable lipid class in shotgun spectra acquired on any mass spectrometry platform.

View Article: PubMed Central - HTML - PubMed

Affiliation: Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307 Dresden, Germany.

ABSTRACT
Shotgun lipidome profiling relies on direct mass spectrometric analysis of total lipid extracts from cells, tissues or organisms and is a powerful tool to elucidate the molecular composition of lipidomes. We present a novel informatics concept of the molecular fragmentation query language implemented within the LipidXplorer open source software kit that supports accurate quantification of individual species of any ionizable lipid class in shotgun spectra acquired on any mass spectrometry platform.

Show MeSH
Related in: MedlinePlus