Dataset Information

Highly sensitive feature detection for high resolution LC/MS.

ABSTRACT:

Background

Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.

Results

We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity.

Conclusion

The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (http://www.bioconductor.org/).

SUBMITTER: Tautenhahn R

PROVIDER: S-EPMC2639432 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Publications

Highly sensitive feature detection for high resolution LC/MS.

Tautenhahn Ralf R Böttcher Christoph C Neumann Steffen S

BMC bioinformatics 20081128

<h4>Background</h4>Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.<h4>Results</h4>We ...[more]

PMID: 19040729

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Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.

Dataset Information

Highly sensitive feature detection for high resolution LC/MS.

Background

Results

Conclusion

Publications

Highly sensitive feature detection for high resolution LC/MS.

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