amsrpm: Robust Point Matching for Retention Time Aligment of LC/MS Data with R

Proteomics is the study of the abundance, function and dynamics of all proteins present in a living organism, and mass spectrometry (MS) has become its most important tool due to its unmatched sensitivity, resolution and potential for high-throughput experimentation. A frequently used variant of mass spectrometry is coupled with liquid chromatography (LC) and is denoted as "LC/MS". It produces two-dimensional raw data, where significant distortions along one of the dimensions can occur between different runs on the same instrument, and between instruments. A compensation of these distortions is required to allow for comparisons between and inference based on different experiments. This article introduces the amsrpm software package. It implements a variant of the Robust Point Matching (RPM) algorithm that is tailored for the alignment of LC and LC/MS experiments. Problem-specific enhancements include a specialized dissimilarity measure, and means to enforce smoothness and monotonicity of the estimated transformation function. The algorithm does not rely on pre-specified landmarks, it is insensitive towards outliers and capable of modeling nonlinear distortions. Its usefulness is demonstrated using both simulated and experimental data. The software is available as an open source package for the statistical programming language R.

NITPICK: peak identification for mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.</p> <p>Results</p> <p>This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on <it>fractional averagine</it>, a novel extension to Senko's well-known averagine model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra.</p> <p>Conclusion</p> <p>Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from <url>http://hci.iwr.uni-heidelberg.de/mip/proteomics/</url>.</p

amsrpm: Robust Point Matching for Retention Time Aligment of LC/MS Data with R

Proteomics is the study of the abundance, function and dynamics of all proteins present in a living organism, and mass spectrometry (MS) has become its most important tool due to its unmatched sensitivity, resolution and potential for high-throughput experimentation. A frequently used variant of mass spectrometry is coupled with liquid chromatography (LC) and is denoted as "LC/MS". It produces two-dimensional raw data, where significant distortions along one of the dimensions can occur between different runs on the same instrument, and between instruments. A compensation of these distortions is required to allow for comparisons between and inference based on different experiments. This article introduces the amsrpm software package. It implements a variant of the Robust Point Matching (RPM) algorithm that is tailored for the alignment of LC and LC/MS experiments. Problem-specific enhancements include a specialized dissimilarity measure, and means to enforce smoothness and monotonicity of the estimated transformation function. The algorithm does not rely on pre-specified landmarks, it is insensitive towards outliers and capable of modeling nonlinear distortions. Its usefulness is demonstrated using both simulated and experimental data. The software is available as an open source package for the statistical programming language R

Turbulence effect on gas transport in three contrasting forest soils

peer reviewe

Impact of Siponimod on Enteric and Central Nervous System Pathology in Late-Stage Experimental Autoimmune Encephalomyelitis.

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS). Although immune modulation and suppression are effective during relapsing-remitting MS, secondary progressive MS (SPMS) requires neuroregenerative therapeutic options that act on the CNS. The sphingosine-1-phosphate receptor modulator siponimod is the only approved drug for SPMS. In the pivotal trial, siponimod reduced disease progression and brain atrophy compared with placebo. The enteric nervous system (ENS) was recently identified as an additional autoimmune target in MS. We investigated the effects of siponimod on the ENS and CNS in the experimental autoimmune encephalomyelitis model of MS. Mice with late-stage disease were treated with siponimod, fingolimod, or sham. The clinical disease was monitored daily, and treatment success was verified using mass spectrometry and flow cytometry, which revealed peripheral lymphopenia in siponimod- and fingolimod-treated mice. We evaluated the mRNA expression, ultrastructure, and histopathology of the ENS and CNS. Single-cell RNA sequencing revealed an upregulation of proinflammatory genes in spinal cord astrocytes and ependymal cells in siponimod-treated mice. However, differences in CNS and ENS histopathology and ultrastructural pathology between the treatment groups were absent. Thus, our data suggest that siponimod and fingolimod act on the peripheral immune system and do not have pronounced direct neuroprotective effects