Lipid Data Analyzer (LDA)

Extendable chromatographic-based DDA data processing tool for untargeted mass spectrometry supporting the detection of novel lipid species

Description

LDA provides high‑throughput accurate lipid quantification and identification for LC-MS data, as well as specific structural annotation of lipid species using MSn spectra acquired in data-dependent mode. Specificity for MS based identification is increased by automatically removing isotopic peaks, and by taking the isotopic intensity pattern into account. MS/MS based identification relies on decision rule sets, which are a reflection of an MSn interpretation process by a trained expert. LDA is MS platform independent and easily extendable to additional lipid subclasses and adducts. For quantification, the high accuracy of chromatographic peak integration is achieved by an algorithm that detects peak borders in both the m/z and retention time dimensions. Depending on the resolution of the MS platform, this algorithm can differentiate closely eluting isobaric species and even isotopologues in tracing experiments.

Technical Information

License:

GPL (academic and commercial)

GUI:

Yes

CLI:

Yes

Desktop client:

Yes

Web platform:

Input formats:

.raw(Waters),

.raw(Thermo),

.wiff.scan(SCIEX),

.wiff(SCIEX),

.d(Bruker),

.d(Agilent),

mzXML,

mzML

Output formats:

rdb,

mzTab-M,

Excel,

TSV

Platforms:

MacOS,

Linux,

Windows

Programming languages:

Java

Source code repository:

Yes

Download / Web-service link:

Yes

Documentation and user guide:

Yes

Training datasets:

Yes

Publications:

PMID:21169379,

PMID:32578539,

PMID:33003696,

PMID:29058722,

PMID 34600364

Tasks

4.2) Data dependent acquisition (DDA)

Shotgun MS/MS:

LC-MS and LC-MS/MS:

Yes

Spectra matching:

Decision rules:

Yes

Requirements:

Customizable Excel mass lists and rule sets consisting of rules for the definition of fragments, their intensity relations, and rules for differentiating between potential isomeric/isobaric candidates; rule sets can be used for lipid class verification (head group), FA/LCB chain annotation and sn-position assignment

Algorithms:

MS1 accuracy and isotopic pattern check, MSn rule-based assignment

Scores:

No scoring: unambiguous assignments are indicated in green, and ambiguous assignments (which may be co-eluting isobaric/isomeric species of other subclasses) are indicated in yellow or orange

Spectra annotation:

Yes

Support multiple adducts:

Yes

Correction for in source fragments:

Batch processing:

Yes

Other features:

3D spectra visualization

4.1) Full MS (HRAM LC-MS)

Shotgun MS/MS:

LC-MS and LC-MS/MS:

LC-MS and LC-MS/MS, PRM

Algorithms:

Peak border localization in m/z and retention time dimensions, followed by an isotopic intensity pattern check. Matching to customizable mass lists in Excel (example mass lists for common lipid classes are provided)

Support multiple adducts:

Yes

Correction for in source fragments:

Lipid identification:

Matching to customizable mass lists in Excel (Example mass lists for common lipid classes are provided)

Batch processing:

Yes

Other features:

For RP chromatography, a class‑specific filter is available for removal of features at unlikely retention time by retention time prediction based on number of carbon atoms and double bonds; quantitation can be carried out in parallel, and runs on CUDA compatible GPUs,

3D chromatogram visualization.

4.5) Identification of oxidized lipids

Lipid coverage:

oxo-, keto-, epoxy-, furan-, hydroxyl-, epidioxy-, carboxy-, hydroperoxy-, cyclopentane-, bromo-, chloro-, fluoro-, iodo-, and nitro- for 4 out of 8 LIPID MAPS categories

Spectra matching:

Decision rules:

Yes

Requirements:

Oxidation levels defined in mass list (provided and customizable)

Algorithms:

On the fly mass calculation

Scores:

No scoring: validation through rule sets and closest theoretical m/z

Spectra annotation:

Yes

Other features:

Validation of ambiguous hits using closest theoretical m/z

5) Lipid quantification from untargeted lipidomics datasets (HRAM MS, DDA, DIA)

Peak processings:

Deconvolution,

Peak alignment,

Peak picking

Adjustable feature detection:

Smoothing in time and m/z dimension; border detection; delimitation of overlapping signals.

Quantified using:

Peak volume.

Normalization:

Yes

Relative quantification:

Yes

Semi absolute quantification using internal standards:

Yes

Calibration curve:

6) Analysis and visualization of lipidomics data

Data pre-treatments:

Normalization (single/multi standard, normalization factor, samples sum)

Missing data handling:

Yes

Univariate statical analysis:

Multivariate statical analysis(unsupervised):

Multivariate statical analysis(supervised):

Feature identification:

Clustering and correlation analysis:

Classification and features selection:

Direct connection to identification results: