Dried blood spots (DBS) typically consist in the deposition of small volumes of capillary blood onto dedicated paper cards. Comparatively to whole blood or plasma samples, their benefits rely in the fact that sample collection is easier and that logistic aspects related to sample storage and shipment can be relatively limited, respectively, without the need of a refrigerator or dry ice. Originally, this approach has been developed in the sixties to support the analysis of phenylalanine for the detection of phenylketonuria in newborns using bacterial inhibition test. In the nineties tandem mass spectrometry was established as the detection technique for phenylalanine and tyrosine. DBS became rapidly recognized for their clinical value: they were widely implemented in pediatric settings with mass spectrometric detection, and were closely associated to the debut of newborn screening (NBS) programs, as a part of public health policies. Since then, sample collection on paper cards has been explored with various analytical techniques in other areas more or less successfully regarding large‐scale applications. Moreover, in the last 5 years a regain of interest for DBS was observed and originated from the bioanalytical community to support drug development (e.g., PK studies) or therapeutic drug monitoring mainly. Those recent applications were essentially driven by improved sensitivity of triple quadrupole mass spectrometers. This review presents an overall view of all instrumental and methodological developments for DBS analysis with mass spectrometric detection, with and without separation techniques. A general introduction to DBS will describe their advantages and historical aspects of their emergence. A second section will focus on blood collection, with a strong emphasis on specific parameters that can impact quantitative analysis, including chromatographic effects, hematocrit effects, blood effects, and analyte stability. A third part of the review is dedicated to sample preparation and will consider off‐line and on‐line extractions; in particular, instrumental designs that have been developed so far for DBS extraction will be detailed. Flow injection analysis and applications will be discussed in section IV. The application of surface analysis mass spectrometry (DESI, paper spray, DART, APTDCI, MALDI, LDTD‐APCI, and ICP) to DBS is described in section V, while applications based on separation techniques (e.g., liquid or gas chromatography) are presented in section VI. To conclude this review, the current status of DBS analysis is summarized, and future perspectives are provided. © 2014 Wiley Periodicals, Inc. Mass Spec Rev 35:361–438, 2016.

Quantitative and qualitative high-resolution (HR) dependent and independent acquisition schemes on a QqTOF MS (with resolving power 20,000-40,000) were investigated for the analysis of pharmaceutical compounds in biological fluids. High-resolution selected reaction monitoring (HR-SRM) was found to be linear over three orders of magnitude for quantitative analysis of paracetamol in human plasma, offering a real alternative to triple quadrupole LC-SRM/MS. Metabolic stability of talinolol in microsomes was characterized by use of three different acquisition schemes: (i) information-dependent acquisition (IDA) with a TOF MS experiment as survey scan and product-ion scan as dependent scan; (ii) MS ALL by collecting TOF mass spectra with and without fragmentation by alternating the collision energy of the collision cell between a low (i.e., 10 eV) and high setting (i.e., 40 eV); and (iii) a novel independent acquisition mode referred to as "sequential window acquisition of all theoretical fragment-ion spectra" (SWATH) or "global precursor ions scan mode" (GPS) in which sequential precursor ions windows (typically 20 u) are used to collect the same spectrum precursor and fragment ions using a collision energy range. SWATH or GPS was found to be superior to IDA or MS ALL in combination with UHPLC for qualitative analysis but requires a rapidly acquiring mass spectrometer. Finally, the GPS concept was used for QUAL/QUAN analysis (i.e. integration of qualitative and quantitative analysis) of bosentan and its metabolites in urine over a concentration range from 5 to 2,500 ng mL.

No abstract

No abstract

No abstract

No abstract

No abstract

Untargeted metabolomic approaches offer new opportunities for a deeper understanding of the molecular events related to toxic exposure. This study proposes a metabolomic investigation of biochemical alterations occurring in urine as a result of dioxin toxicity. Urine samples were collected from Czech chemical workers submitted to severe dioxin occupational exposure in a herbicide production plant in the late 1960s. Experiments were carried out with ultra-high pressure liquid chromatography (UHPLC) coupled to high-resolution quadrupole time-of-flight (QTOF) mass spectrometry. A chemistry-driven feature selection was applied to focus on steroid-related metabolites. Supervised multivariate data analysis allowed biomarkers, mainly related to bile acids, to be highlighted. These results supported the hypothesis of liver damage and oxidative stress for long-term dioxin toxicity. As a second step of data analysis, the information gained from the urine analysis of Victor Yushchenko after his poisoning was examined. A subset of relevant urinary markers of acute dioxin toxicity from this extreme phenotype, including glucuro- and sulfo-conjugated endogenous steroid metabolites and bile acids, was assessed for its ability to detect long-term effects of exposure. The metabolomic strategy presented in this work allowed the determination of metabolic patterns related to dioxin effects in human and the discovery of highly predictive subsets of biologically meaningful and clinically relevant compounds. These results are expected to provide valuable information for a deeper understanding of the molecular events related to dioxin toxicity. Furthermore, it presents an original methodology of data dimensionality reduction by using extreme phenotype as a guide to select relevant features prior to data modeling (biologically driven data reduction).