Dataset Information

Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

ABSTRACT: Non-targeted mass spectrometry-based approaches for detecting novel xenobiotics in biological samples are hampered by the occurrence of naturally fluctuating endogenous substances, which are difficult to distinguish from environmental contaminants. Here, we investigate a data reduction strategy for datasets derived from a biological time series. The objective is to flag reoccurring peaks in the time series based on increasing peak intensities, thereby reducing peak lists to only those which may be associated with emerging bioaccumulative contaminants. As a result, compounds with increasing concentrations are flagged while compounds displaying random, decreasing, or steady-state time trends are removed. As an initial proof of concept, we created artificial time trends by fortifying human whole blood samples with isotopically labelled standards. Different scenarios were investigated: eight model compounds had a continuously increasing trend in the last two to nine time points, and four model compounds had a trend that reached steady state after an initial increase. Each time series was investigated at three fortification levels and one unfortified series. Following extraction, analysis by ultra performance liquid chromatography high-resolution mass spectrometry, and data processing, a total of 21,700 aligned peaks were obtained. Peaks displaying an increasing trend were filtered from randomly fluctuating peaks using time trend ratios and Spearman's rank correlation coefficients. The first approach was successful in flagging model compounds spiked at only two to three time points, while the latter approach resulted in all model compounds ranking in the top 11 % of the peak lists. Compared to initial peak lists, a combination of both approaches reduced the size of datasets by 80-85 %. Overall, non-target time trend screening represents a promising data reduction strategy for identifying emerging bioaccumulative contaminants in biological samples. Graphical abstract Using time trends to filter out emerging contaminants from large peak lists.

SUBMITTER: Plassmann MM

PROVIDER: S-EPMC4875932 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

Plassmann Merle M MM Tengstrand Erik E Åberg K Magnus KM Benskin Jonathan P JP

Analytical and bioanalytical chemistry 20160427 16

Non-targeted mass spectrometry-based approaches for detecting novel xenobiotics in biological samples are hampered by the occurrence of naturally fluctuating endogenous substances, which are difficult to distinguish from environmental contaminants. Here, we investigate a data reduction strategy for datasets derived from a biological time series. The objective is to flag reoccurring peaks in the time series based on increasing peak intensities, thereby reducing peak lists to only those which may ...[more]

PMID: 27117254

Similar Datasets

Project description:Sensing of pathogens by innate immune cells is essential for the initiation of appropriate immune responses. Toll-like receptors (TLRs), which are highly sensitive for various structurally and evolutionary conserved molecules derived from microbes have a prominent role in this process. TLR engagement results in the activation of the transcription factor NF-κB, which induces the expression of cytokines and other inflammatory mediators. The exquisite sensitivity of TLR signalling can be exploited for the detection of bacteria and microbial contaminants in tissue cultures and in protein preparations. Here we describe a cellular reporter system for the detection of TLR ligands in biological samples. The well-characterized human monocytic THP-1 cell line was chosen as host for an NF-ᴋB-inducible enhanced green fluorescent protein reporter gene. We studied the sensitivity of the resultant reporter cells for a variety of microbial components and observed a strong reactivity towards TLR1/2 and TLR2/6 ligands. Mycoplasma lipoproteins are potent TLR2/6 agonists and we demonstrate that our reporter cells can be used as reliable and robust detection system for mycoplasma contaminations in cell cultures. In addition, a TLR4-sensitive subline of our reporters was engineered, and probed with recombinant proteins expressed in different host systems. Bacterially expressed but not mammalian expressed proteins induced strong reporter activity. We also tested proteins expressed in an E. coli strain engineered to lack TLR4 agonists. Such preparations also induced reporter activation in THP-1 cells highlighting the importance of testing recombinant protein preparations for microbial contaminations beyond endotoxins. Our results demonstrate the usefulness of monocytic reporter cells for high-throughput screening for microbial contaminations in diverse biological samples, including tissue culture supernatants and recombinant protein preparations. Fluorescent reporter assays can be measured on standard flow cytometers and in contrast to established detection methods, like luciferase-based systems or Limulus Amebocyte Lysate tests, they do not require costly reagents.

Dataset Information

Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

Publications

Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets