Characterization and Identification of Clinically Relevant Microorganisms Using Rapid Evaporative Ionization Mass Spectrometry

Rapid Evaporative Ionisation Mass Spectrometry (REIMS) Provides Accurate Direct from Culture Species Identification within the Genus Candida

Members of the genus Candida, such as C. albicans and C. parapsilosis, are important human pathogens. Other members of this genus, previously believed to carry minimal disease risk, are increasingly recognised as important human pathogens, particularly because of variations in susceptibilities to widely used anti-fungal agents. Thus, rapid and accurate identification of clinical Candida isolates is fundamental in ensuring timely and effective treatments are delivered. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has previously been shown to provide a high-throughput platform for the rapid and accurate identification of bacterial and fungal isolates. In comparison to commercially available matrix assisted laser desorption ionisation time of flight mass spectrometry (MALDI-ToF), REIMS based methods require no preparative steps nor time-consuming cell extractions. Here, we report on the ability of REIMS-based analysis to rapidly and accurately identify 153 clinical Candida isolates to species level. Both handheld bipolar REIMS and high-throughput REIMS platforms showed high levels of species classification accuracy, with 96% and 100% of isolates classified correctly to species level respectively. In addition, significantly different (FDR corrected P value < 0.05) lipids within the 600 to 1000 m/z mass range were identified, which could act as species-specific biomarkers in complex microbial communities.

Shotgun Lipidomic Profiling of the NCI60 Cell Line Panel Using Rapid Evaporative Ionization Mass Spectrometry

Rapid evaporative ionization mass spectrometry (REIMS) was used for the rapid mass spectrometric profiling of cancer cell lines. Spectral reproducibility was assessed for three different cell lines, and the extent of interclass differences and intraclass variance was found to allow the identification of these cell lines based on the REIMS data. Subsequently, the NCI60 cell line panel was subjected to REIMS analysis, and the resulting data set was investigated for its distinction of individual cell lines and different tissue types of origin. Information content of REIMS spectral profiles of cell lines were found to be similar to those obtained from mammalian tissues although pronounced differences in relative lipid intensity were observed. Ultimately, REIMS was shown to detect changes in lipid content of cell lines due to mycoplasma infection. The data show that REIMS is an attractive means to study cell lines involving minimal sample preparation and analysis times in the range of seconds.

Identification of the Species of Origin for Meat Products by Rapid Evaporative Ionization Mass Spectrometry

Increasingly abundant food fraud cases have brought food authenticity and safety into major focus. This study presents a fast and effective way to identify meat products using rapid evaporative ionization mass spectrometry (REIMS). The experimental setup was demonstrated to be able to record a mass spectrometric profile of meat specimens in a time frame of <5 s. A multivariate statistical algorithm was developed and successfully tested for the identification of animal tissue with different anatomical origin, breed, and species with 100% accuracy at species and 97% accuracy at breed level. Detection of the presence of meat originating from a different species (horse, cattle, and venison) has also been demonstrated with high accuracy using mixed patties with a 5% detection limit. REIMS technology was found to be a promising tool in food safety applications providing a reliable and simple method for the rapid characterization of food products.

In vivo Real-Time Mass Spectrometry for Guided Surgery Application

Here we describe a new instrument (SpiderMass) designed for in vivo and real-time analysis. In this instrument ion production is performed remotely from the MS instrument and the generated ions are transported in real-time to the MS analyzer. Ion production is promoted by Resonant Infrared Laser Ablation (RIR-LA) based on the highly effective excitation of O-H bonds in water molecules naturally present in most biological samples. The retrieved molecular patterns are specific to the cell phenotypes and benign versus cancer regions of patient biopsies can be easily differentiated. We also demonstrate by analysis of human skin that SpiderMass can be used under in vivo conditions with minimal damage and pain. Furthermore SpiderMass can also be used for real-time drug metabolism and pharmacokinetic (DMPK) analysis or food safety topics. SpiderMass is thus the first MS based system designed for in vivo real-time analysis under minimally invasive conditions.

Pseudomonas aeruginosa infection in cystic fibrosis: pathophysiological mechanisms and therapeutic approaches

Pseudomonas aeruginosa is a remarkably versatile environmental bacterium with an extraordinary capacity to infect the cystic fibrosis (CF) lung. Infection with P. aeruginosa occurs early, and although eradication can be achieved following early detection, chronic infection occurs in over 60% of adults with CF. Chronic infection is associated with accelerated disease progression and increased mortality. Extensive research has revealed complex mechanisms by which P. aeruginosa adapts to and persists within the CF airway. Yet knowledge gaps remain, and prevention and treatment strategies are limited by the lack of sensitive detection methods and by a narrow armoury of antibiotics. Further developments in this field are urgently needed in order to improve morbidity and mortality in people with CF. Here, we summarize current knowledge of pathophysiological mechanisms underlying P. aeruginosa infection in CF. Established treatments are discussed, and an overview is offered of novel detection methods and therapeutic strategies in development.

Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance

Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidate microbes, one needs to go beyond apparent morphology or simple 'fingerprinting'; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptidome profiles of microbes to better separate them and by designing an analysis method that yields accurate statistical significance. Here, we present an analysis pipeline that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms. The proposed analysis pipeline has been implemented in MiCId, a freely available software for Microorganism Classification and Identification. MiCId is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

Metabolome analyses in exposome studies: Profiling methods for a vast chemical space

Metabolic profiling (metabonomics/metabolomics) is now used routinely as a tool to provide information-rich datasets for biomarker discovery, prompting and augmenting detailed mechanistic studies. The experimental design and focus of any individual study will be reflected in the types of biomarkers that can be detected; toxicological studies will likely focus on markers of response to insult, whereas clinical case-control studies may yield diagnostic markers of disease. Population studies can make use of omics analyses, including metabonomics, to provide mechanistically-relevant markers that link environmental exposures to chronic disease endpoints. In this article, examples of how metabolic profiling has played a key role in molecular epidemiological analyses of chronic disease are presented, and how these reflect different aspects of the causal pathway. A commentary on the nature of metabolome analysis as a complex mixture problem as opposed to a coded, sequence or template problem is provided, alongside an overview of current and future analytical platforms that are being applied to meet this analytical challenge. Epidemiological studies are an important nexus for integrating various measures of the human exposome, and the ubiquity, diversity and functions of small molecule metabolites, represent an important way to link individual exposures, genetics and phenotype.

In vivo endoscopic tissue identification by rapid evaporative ionization mass spectrometry (REIMS)

Gastrointestinal cancers are a leading cause of mortality, accounting for 23 % of cancer-related deaths worldwide. In order to improve outcomes from these cancers, novel tissue characterization methods are needed to facilitate accurate diagnosis. Rapid evaporative ionization mass spectrometry (REIMS) is a technique developed for the in vivo classification of human tissue through mass spectrometric analysis of aerosols released during electrosurgical dissection. This ionization technique was further developed by utilizing surface induced dissociation and was integrated with an endoscopic polypectomy snare to allow in vivo analysis of the gastrointestinal tract. We tested the classification performance of this novel endoscopic REIMS method in vivo. It was shown to be capable of differentiating between healthy layers of the intestinal wall, cancer, and adenomatous polyps based on the REIMS fingerprint of each tissue type in vivo.

Real-time tissue differentiation based on optical emission spectroscopy for guided electrosurgical tumor resection

Complete surgical removal of cancer tissue with effective preservation of healthy tissue is one of the most important challenges in modern oncology. We present a method for real-time, in situ differentiation of tissue based on optical emission spectroscopy (OES) performed during electrosurgery not requiring any biomarkers, additional light sources or other excitation processes. The analysis of the optical emission spectra, enables the differentiation of healthy and tumorous tissue. By using multi-class support vector machine (SVM) algorithms, distinguishing between tumor types also seems to be possible. Due to its fast reaction time (0.05s) the method can be used for real-time navigation helping the surgeon achieve complete resection. The system's easy realization has been proven by successful integration in a commercial electro surgical unit (ESU). In a first step the method was verified by using ex vivo tissue samples. The histological analysis confirmed, 95% of correctly classified tissue types.

Rapid evaporative ionization mass spectrometry imaging platform for direct mapping from bulk tissue and bacterial growth media

Rapid evaporative ionization mass spectrometry (REIMS) technology allows real time intraoperative tissue classification and the characterization and identification of microorganisms. In order to create spectral libraries for training the classification models, reference data need to be acquired in large quantities as classification accuracy generally improves as a function of number of training samples. In this study, we present an automated high-throughput method for collecting REIMS data from heterogeneous organic tissue. The underlying instrumentation consists of a 2D stage with an additional high-precision z-axis actuator that is equipped with an electrosurgical diathermy-based sampling probe. The approach was validated using samples of human liver with metastases and bacterial strains, cultured on solid medium, belonging to the species P. aeruginosa, B. subtilis, and S. aureus. For both sample types, spatially resolved spectral information was obtained that resulted in clearly distinguishable multivariate clustering between the healthy/cancerous liver tissues and between the bacterial species.

XMS: cross-platform normalization method for multimodal mass spectrometric tissue profiling

Here we present a proof of concept cross-platform normalization approach to convert raw mass spectra acquired by distinct desorption ionization methods and/or instrumental setups to cross-platform normalized analyte profiles. The initial step of the workflow is database driven peak annotation followed by summarization of peak intensities of different ions from the same molecule. The resulting compound-intensity spectra are adjusted to a method-independent intensity scale by using predetermined, compound-specific normalization factors. The method is based on the assumption that distinct MS-based platforms capture a similar set of chemical species in a biological sample, though these species may exhibit platform-specific molecular ion intensity distribution patterns. The method was validated on two sample sets of (1) porcine tissue analyzed by laser desorption ionization (LDI), desorption electrospray ionization (DESI), and rapid evaporative ionization mass spectrometric (REIMS) in combination with Fourier transformation-based mass spectrometry; and (2) healthy/cancerous colorectal tissue analyzed by DESI and REIMS with the latter being combined with time-of-flight mass spectrometry. We demonstrate the capacity of our method to reduce MS-platform specific variation resulting in (1) high inter-platform concordance coefficients of analyte intensities; (2) clear principal component based clustering of analyte profiles according to histological tissue types, irrespective of the used desorption ionization technique or mass spectrometer; and (3) accurate "blind" classification of histologic tissue types using cross-platform normalized analyte profiles.

Visualizing life with ambient mass spectrometry

Since the development of desorption electrospray ionization (DESI), many other ionization methods for ambient and atmospheric pressure mass spectrometry have been developed. Ambient ionization mass spectrometry has now been used for a wide variety of biological applications, including plant science, microbiology, neuroscience, and cancer pathology. Multimodal integration of atmospheric ionization sources with the other biotechnologies, as well as high performance computational methods for mass spectrometry data processing is one of the major emerging area's for ambient mass spectrometry. In this opinion article, we will highlight some of the most influential technological advances of ambient mass spectrometry in recent years and their applications to the life sciences.