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Background Formalin-fixed paraffin-embedded (FFPE) tissue has been the gold standard for routine pathology for general and cancer postoperative diagnostics. Despite robust histopathology, immunohistochemistry, and molecular methods, accurate diagnosis remains difficult for certain cases. Overall, the entire process can be time consuming, labor intensive, and does not reach over 90% diagnostic sensitivity and specificity. There is a growing need in onco-pathology for adjunct novel rapid, accurate, reliable, diagnostically sensitive, and specific methods for high-throughput biomolecular identification. Lipids have long been considered only as building blocks of cell membranes or signaling molecules, but have recently been introduced as central players in cancer. Due to sample processing, which limits their detection, lipid analysis directly from unprocessed FFPE tissues has never been reported. Methods We present a proof-of-concept with direct analysis of tissue-lipidomic signatures from FFPE tissues without dewaxing and minimal sample preparation using water-assisted laser desorption ionization mass spectrometry and deep-learning. Results On a cohort of difficult canine and human sarcoma cases, classification for canine sarcoma subtyping was possible with 99.1% accuracy using “5-fold” and 98.5% using “leave-one-patient out,” and 91.2% accuracy for human sarcoma using 5-fold and 73.8% using leave-one-patient out. The developed classification model enabled stratification of blind samples in 95% probability for discriminating 2 human sarcoma blind samples. Conclusion It is possible to create a rapid diagnostic platform to screen clinical FFPE tissues with minimal sample preparation for molecular pathology.

Molecular heterogeneity is a key feature of glioblastoma pathology impeding patient’s stratification and leading to high discrepancies between patients mean survivals. We performed a spatial proteomics analysis on a cohort of 96 glioblastoma patients with survival varying from few months to more than 4 years. 46 tumors were analyzed by spatially-resolved high resolution mass spectrometry proteomics. Integrative analysis of protein expression and clinical information allowed us to identify three molecular regions associated with immune, neurogenesis and tumorigenesis signatures. Several of these molecular signatures can be enriched within the same tumor sample leading to high intra-tumoral heterogeneity. Nevertheless, a set of proteins was found statistically significant based on patient’s survival times, 10 of which stem from alternative AltORF or non-coding RNA. From these proteins, 5 were selected as survival markers. Classification of patients based on the expression of these 5 proteins leads to a clear difference in survival. The expression of these 5 proteins was validated by immunofluorescence on an external cohort of 50 glioblastoma patients, with a similar correlation with their survival. Taken together, our work has enabled the characterization of new molecular regions within glioblastoma tissues based on protein expression which can help to guide glioblastoma prognosis and to improve the current glioblastoma classification.

Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available., Convolutional Neural Networks are powerful tools for clinical diagnosis but their effectiveness decreases when the number of available samples is small. Here, the authors develop a cumulative learning method by training the same model through several classification tasks over various small Mass Spectrometry datasets.

In the recent years, Cannabis and hemp-based products have become increasingly popular for various applications ranging from recreational use, edibles, beverages to health care products and medicines. The rapid detection and differentiation of phytocannabinoids is, therefore, essential to assess the potency, therapeutic and nutritional values of cannabis cultivars. Here, we implemented the SpiderMass technology for the in vivo detection of cannabidiol acid (CBDA) and tetrahydrocannabinol acid (THCA) and other endogenous organic plant compounds to access distribution gradients within the plants and differentiate cultivars. The SpiderMass system is composed of an IR- laser handheld microsampling probe connected to the mass spectrometer through a transfer tube. The analysis was performed in situ on different plant organs from freshly cultivated Cannabis plants in only a few seconds. SpiderMass analysis easily discriminated the two acid phytocannabinoid isomers by MS/MS and the built statistical models differentiated between four Cannabis cultivars. Different abundancies of acid phytocannabinoids were also found along the plant as well as between different cultivars. All together, these results introduce the direct analysis by SpiderMass as a compelling analytical alternative for forensic and hemp industrial analysis.

Mass spectrometry imaging (MSI) has shown to bring invaluable information for biological and clinical applications. However, conventional MSI is generally performed

Mass Spectrometry Imaging (MSI) has shown to bring invaluable information for biological and clinical applications. However, conventional MSI is generally performed ex vivo from tissue sections. Here, we develop a novel MS-based method for in vivo mass spectrometry imaging. By coupling the SpiderMass technology - that provides in vivo minimally invasive analysis – to a robotic arm of high accuracy, we demonstrate that images can be acquired from any surface by moving the laser probe above the surface. By equipping the robotic arm with a sensor, we are also able to both get the topography image of the sample surface and the molecular distribution, and then and plot back the molecular data, directly to the 3D topographical image without the need for image fusion. This is shown for the first time with the 3D topographic MS-Based whole-body imaging of a mouse. Enabling fast in vivo MSI bridged to topography pave the way for surgical applications to excision margins.

Human hair absorbs numerous biomolecules from the body during its growth. This can act as a fingerprint to determine substance intake of an individual, which can be useful in forensic studies. The cocaine concentration profile along the growth axis of hair indicates the time evolution of the metabolic incorporation of cocaine usage. It could be either assessed by chemical extraction and further analysis of hair bundels, or by direct single hair fibre analysis with mass spectroscopy imaging (MSI). Within this work, we analyzed the cocaine distribution in individual hair samples using MeV-SIMS. Unlike conventional surface analysis methods, we demonstrate high yields of nonfragmented molecular ions from the surface of biological materials, resulting in high chemical sensitivity and non-destructive characterisation. Hair samples were prepared by longitudinally cutting along the axis of growth, leaving half-cylindrical shape to access the interior structure of the hair by the probing ion beam, and attached to the silicon wafer. A focused 5.8 MeV 35Cl6+ beam was scanned across the intact, chemically pristine hair structure. A non-fragmented protonated [M+ H]+ cocaine molecular peak at m/z = 304 was detected and localized along the cross-section of the hair. Its intensity exhibits strong fluctuations along the direction of the hair’s growth, with pronounced peaks as narrow as 50 micrometres, corresponding to a metabolic incorporation time of approx. three hours.

Inhibition of the initial stages of amyloid-β peptide self-assembly is a key approach in drug development for Alzheimer’s disease, in which soluble and highly neurotoxic low molecular weight oligomers are produced and aggregate in the brain over time. Here we report a high-throughput method based on ion mobility mass spectrometry and multivariate statistical analysis to rapidly select statistically significant early-stage species of amyloid-β1-40 whose formation is inhibited by a candidate theranostic agent. Using this method, we have confirmed the inhibition of a Zn-porphyrin-peptide conjugate in the early self-assembly of Aβ40 peptide. The MS/MS fragmentation patterns of the species detected in the samples containing the Zn-porphyrin-peptide conjugate suggested a porphyrin-catalyzed oxidation at Met-35(O) of Aβ40. We introduce ion mobility MS combined with multivariate statistics as a systematic approach to perform data analytics in drug discovery/amyloid research that aims at the evaluation of the inhibitory effect on the Aβ early assembly in vitro models at very low concentration levels of Aβ peptides. Electronic supplementary material The online version of this article (10.1007/s00216-019-02030-7) contains supplementary material, which is available to authorized users.

International audience; Secondary ion mass spectrometry (SIMS), based on primary ions within the MeV energy domain, also known as MeV-SIMS, is a subject of increasing scientific interest. The main drive for the interest in the development of MeV-SIMS is the ability to desorb high yields of large non-fragmented organic molecular ions from the sample surface. This makes MeV-SIMS particulary useful in imaging of biological tissues.Imaging methods based on scanning a focused primary ion beam are associated with demanding focusing of the heavy energetic ions. As an alternative, stigmatic imaging mode has been studied here, applying point-to-point imaging characteristics of secondary ions in the linear Time-Of-Flight mass spectrometer. In stigmatic imaging approaches, spatial resolution is independent of the focussed spot size of the ionising primary ion beam, but instead dependant on the ability of the ion optics to project an image of the ion distributions removed from the surface onto a position sensitive ion detector.

Background :Formalin-fixation and paraffin-embedding (FFPE) is the worldwide gold standard for tissue preservation and routine pathology for general and cancer diagnostics. Despite robust histopathology methods, accurate diagnostic remains difficult for certain cases. Overall, the entire process is time-consuming, labor-intensive and doesn’t reach the right sensitivity and specificity. Lipids are central players in cancer, but they are not routinely used in diagnostics because of the sample processing which limits their detection, and lipid analysis directly from unprocessed FFPE tissues has never been reported. Methods :We present a new method for cancer diagnostics based on tissue-lipidomic signatures through rapid screening of FFPE without dewaxing using Water-Assisted Laser Desorption Ionization Mass Spectrometry (WALDI-MS) and deep-learning. Results :We show on a cohort of canine and human sarcoma tissues that classification for sarcoma sub-typing is possible and enables diagnosing blind samples in less than five minutes, with over 95% probability to discriminate specific subtypes. Conclusion: We have revealed the possibility to create a rapid diagnostic platform to screen clinical FFPE tissues with minimal sample preparation for molecular pathology.

In vivo cancer margin delineation during surgery remains a major challenge. Despite the availability of several image guidance techniques and intraoperative assessment, clear surgical margins and debulking efficiency remain scarce. For this reason, there is particular interest in developing rapid intraoperative tools with high sensitivity and specificity to help guide cancer surgery in vivo. Recently, several emerging technologies including intraoperative mass spectrometry have paved the way for molecular guidance in a clinical setting. We evaluate these techniques and assess their relevance for intraoperative surgical guidance and how they can transform the future of molecular cancer surgery, diagnostics, patient management and care.

SUMMARYMolecular heterogeneities are a key feature of glioblastoma (GBM) pathology impeding patient’s stratification and leading to high discrepancies between patients mean survivals. Here, we established a molecular classification of GBM tumors using a pan-proteomic analysis. Then, we identified, from our proteomic data, 2 clusters of biomarkers associated with good or bad patient survival from 46 IDH wild-type GBMs. Three molecular groups have been identified and associated with systemic biology analyses. Group A tumors exhibit neurogenesis characteristics and tumorigenesis. Group B shows a strong immune cell signature and express poor prognosis markers while group C tumors are characterized by an anti-viral signature and tumor growth proteins. 124 proteins were found statistically different based on patient’s survival times, of which 10 are issued from alternative AltORF or non-coding RNA. After statistical analysis, a panel of markers associated to higher survival (PPP1R12A, RPS14, HSPD1 and LASP1) and another panel associated to lower survival (ALCAM, ANXA11, MAOB, IP_652563 and IGHM) has been validated by immunofluorescence. Taken together, our data will guide GBM prognosis and help to improve the current GBM classification by stratifying the patients and may open new opportunities for therapeutic development.SignificanceGlioblastoma are very heterogeneous tumors with median survivals usually inferior to 20 months. We conducted a pan-proteomics analysis of glioblastoma (GBM) in order to stratify GBM based on the molecular contained. Forty-six GBM cases were classified into three groups where proteins are involved in specific pathwaysi.e.the first group has a neurogenesis signature and is associated with a better prognosis while the second group of patients has an immune profile with a bad prognosis. The third group is more associated to tumorigenesis. We correlated these results with the TCGA data. Finally, we have identified 28 new prognostic markers of GBM and from these 28, a panel of 4 higher and 5 lower survival markers were validated. With these 9 markers in hand, now pathologist can stratify GBM patients and can guide the therapeutic decision.HighlightsA novel stratification of glioblastoma based on mass spectrometry was established.Three groups with different molecular features and survival were identified.This new classification could improve prognostication and may help therapeutic options.8 prognosis markers for oncologist therapeutic decision have been validated.

Rapid and accurate clinical diagnosis of pathological conditions remains highly challenging. A very important component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some popular Machine Learning (ML) approaches have been investigated for this purpose but these ML models require time-consuming preprocessing steps such as baseline correction, denoising, and spectrum alignment to remove non-sample-related data artifacts. They also depend on the tedious extraction of handcrafted features, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn efficient representations from raw data without the need for costly preprocessing. However, their effectiveness drastically decreases when the number of available training samples is small, which is a common situation in medical applications. Transfer learning strategies extend an accurate representation model learnt usually on a large dataset containing many categories, to a smaller dataset with far fewer categories. In this study, we first investigate transfer learning on a 1D-CNN we have designed to classify MS data, then we develop a new representation learning method when transfer learning is not powerful enough, as in cases of low-resolution or data heterogeneity. What we propose is to train the same model through several classification tasks over various small datasets in order to accumulate generic knowledge of what MS data are, in the resulting representation. By using rat brain data as the initial training dataset, a representation learning approach can have a classification accuracy exceeding 98% for canine sarcoma cancer cells, human ovarian cancer serums, and pathogenic microorganism biotypes in 1D clinical datasets. We show for the first time the use of cumulative representation learning using datasets generated in different biological contexts, on different organisms, in different mass ranges, with different MS ionization sources, and acquired by different instruments at different resolutions. Our approach thus proposes a promising strategy for improving MS data classification accuracy when only small numbers of samples are available as a prospective cohort. The principles demonstrated in this work could even be beneficial to other domains (astronomy, archaeology…) where training samples are scarce.

Formalin-fixation and paraffin-embedding (FFPE) is the worldwide gold standard for tissue preservation and routine material for general diagnostics and cancer in particular. Despite the fact that histology is complemented by other modalities such as in situ hybridization (ISH) or immunohistochemistry (IHC), the pathology can fail to provide the right diagnosis in a certain number of cases. Overall, the standard process is time-consuming, labor intensive and doesn’t reach the right sensitivity and specificity. Lipids are central players in cancer. Unfortunately, to this date lipids aren’t used for diagnostics and lipid analysis from FFPE has never been reported due to tissue processing which limits their detection. Here, we report a new method for cancer diagnostics based on their unique tissue-lipidomic signatures through rapid molecular screening of FFPE sections without dewaxing using Water-Assisted Laser Desorption Ionization Mass Spectrometry (WALDI-MS) - SpiderMass. Advantageously, the method does not require human intervention. The annotated tissues are first analyzed to train the system and then blind samples can be diagnosed in less than a few minutes. The platform was demonstrated for the diagnostics of canine and human sarcoma, including cases which are known to be problematic in the gold standard workflow. Interrogation of blind samples against the built models revealed over 95% probability values to discriminate specific subtypes

Direct analysis by mass spectrometry (imaging) has become increasingly deployed in preclinical and clinical research due to its rapid and accurate readouts. However, when it comes to biomarker discovery or histopathological diagnostics, more sensitive and in-depth profiling from localized areas is required. We developed a comprehensive, fully automated online platform for high-resolution liquid extraction surface analysis (HR-LESA) followed by micro-liquid chromatography (LC) separation and a data-independent acquisition strategy for untargeted and low abundant analyte identification directly from tissue sections. Applied to tissue sections of rat pituitary, the platform demonstrated improved spatial resolution, allowing sample areas as small as 400 mu m to be studied, a major advantage over conventional LESA. The platform integrates an online buffer exchange and washing step for removal of salts and other endogenous contamination that originates from local tissue extraction. Our carry over free platform showed high reproducibility, with an interextraction variability below 30%. Another strength of the platform is the additional selectivity provided by a postsampling gas-phase ion mobility separation. This allowed distinguishing coeluted isobaric compounds without requiring additional separation time. Furthermore, we identified untargeted and low-abundance analytes, including neuropeptides deriving from the pro-opiomelanocortin precursor protein and localized a specific area of the pituitary gland (i.e., adenohypophysis) known to secrete neuropeptides and other small metabolites related to development, growth, and metabolism. This platform can thus be applied for the in-depth study of small samples of complex tissues with histologic features of similar to 400 mu m or more, including potential neuropeptide markers involved in many diseases such as neurodegenerative diseases, obesity, bulimia, and anorexia nervosa.

Matrix-enhanced secondary ion mass spectrometry (ME-SIMS) has overcome one of the biggest disadvantages of SIMS analysis by providing the ability to detect intact biomolecules at high spatial resolution. By increasing ionization efficiency and minimizing primary ion beam induced fragmentation of analytes, ME-SIMS has proven useful for detection of numerous biorelevant species, now including peptides. We report here the first demonstration of tandem ME-SIMS for de novo sequencing of endogenous neuropeptides from tissue in situ (i.e., rat pituitary gland). The peptide ions were isolated for tandem MS analysis using a 1 Da mass isolation window, followed by collision-induced dissociation (CM) at 1.5 keV in a collision cell filled with argon gas, for confident identification of the detected peptide. Using this method, neuropeptides up to m/z 2000 were detected and sequenced from the posterior lobe of the rat pituitary gland. These results demonstrate the potential for ME-SIMS tandem MS development in bottom-up proteomics imaging at high-spatial resolution.

Imaging Mass Spectroscopy (IMS) is a unique research tool providing localization and identification of a wide range of biomolecules as essential data to understand biochemical processes in living organisms. Secondary Ion Mass Spectrometry with high-energy heavy ions (MeV-SIMS) is emerging as a promising IMS technique for chemical imaging of biological tissue.We measured the molecular mass spatial distributions in leaves of khat (Catha edulis). Khat is a natural drug plant, native to eastern Africa and the Arabian Peninsula. In these countries, fresh leaves are being chewed by significant part of population. It was reported that 80% of the adult population in Yemen chew the khat leaves. The main stimulating effects of khat are induced by a monoamine alkaloid called cathinone. During leaf ageing, cathinone is further metabolised to cathine and norephedrine. Earlier studies identified the alkaloids in khat, however little is known on their spatial distribution, reflecting the biosynthesis and accumulation in the tissue.Chemical mapping of alkaloids on cross-sections of khat leaves by MeV-SIMS was done at JSI by a pulsed 5.8 MeV Cl-35(6+) beam, focused to a diameter of 15 mu m, using a linear time-of-flight (TOF) spectrometer with a mass resolution of 500. In addition, measurements of MeV-SIMS mass spectra were performed at Kyoto University by a continuous broad beam of 6 MeV Cu-63(4+) ions at an orthogonal TOF spectrometer with a high mass resolution of 11,000. Sections of leaves were analysed and mass spectra obtained at both MeV-SIMS setups were compared. Tissue-level distributions of detected alkaloids are presented and discussed. (C) 2017 Elsevier B.V. All rights reserved.

Mass spectrometry imaging (MSI) can uniquely detect thousands of compounds allowing both their identification and localization within biological tissue samples. MSI is an interdisciplinary science that crosses the borders of physics, chemistry and biology, and enables local molecular analysis at a broad range of length scales: From the subcellular level to whole body tissue sections. The spatial resolution of some mass spectrometers now allows nano-scale research, crucial for studies in nanomedicine. Recent developments in MSI have enabled the optimization and localization of drug delivery with nanoparticles within the body and in specific organs such as kidney, liver and brain. Combining MSI with nanomedicine has vast potential, specifically in the treatment of neurological disorders, where effective drug delivery has been hampered by the blood-brain barrier. This review provides an introduction to MSI and its different technologies, with the application of MSI to nanomedicine and the different possibilities that MSI offers to study molecular signals in the brain. Finally, we provide an outlook for the future and exciting potential of MSI in nanoparticle-related research.

International audience; Rapid, sensitive, precise and accurate analysis of samples in their native in vivo environment is critical to better decipher physiological and physiopathological mechanisms. SpiderMass is an ambient mass spectrometry (MS) system designed for mobile in vivo and real-time surface analyses of biological tissues. The system uses a fibered laser, which is tuned to excite the most intense vibrational band of water, resulting in a process termed water-assisted laser desorption/ionization (WALDI). The water molecules act as an endogenous matrix in a matrix-assisted laser desorption ionization (MALDI)-like scenario, leading to the desorption/ionization of biomolecules (lipids, metabolites and proteins). The ejected material is transferred to the mass spectrometer through an atmospheric interface and a transfer line that is several meters long. Here, we formulate a three-stage procedure that includes (i) a laser system setup coupled to a Waters Q-TOF or Thermo Fisher Q Exactive mass analyzer, (ii) analysis of specimens and (iii) data processing. We also describe the optimal setup for the analysis of cell cultures, fresh-frozen tissue sections and in vivo experiments on skin. With proper optimization, the system can be used for a variety of different targets and applications. The entire procedure takes 1-2 d for complex samples.

Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images are created through a grid-wise interrogation of individual spots by mass spectrometry across a surface. Classical statistical tests for within-sample comparisons fail as close-by measurement spots violate the assumption of independence of these tests, which can lead to an increased false-discovery rate. For spatial data this effect is referred to as spatial autocorrelation. In this study we investigated spatial autocorrelation in three different matrix-assisted laser desorption/ionization MSI datasets. These datasets cover different molecular classes (metabolites/drugs, lipids, and proteins) and different spatial resolutions ranging from 20 µm to 100 µm. Significant spatial autocorrelation was detected in all three datasets and found to increase with decreasing pixel size. To enable statistical testing for differences in mass signal intensities between regions of interest within MSI datasets, we propose the use of Conditional Autoregressive (CAR) models. We show that by accounting for spatial autocorrelation, discovery rates (i.e. the ratio between the features identified and the total number of features) could be reduced between 21% and 69%. The reliability of this approach was validated by control mass signals based on prior knowledge. In light of the advent of larger MSI datasets based on either an increased spatial resolution or 3D datasets, accounting for effects due to spatial autocorrelation becomes even more indispensable. Here we propose a generic and easily applicable workflow to enable within-sample statistical comparisons.

To broaden our analytical capabilities with molecular imaging in addition to the existing elemental imaging with micro-PIXE, a linear Time-Of-Flight mass spectrometer for MeV Secondary Ion Mass Spectrometry (MeV-SIMS) was constructed and added to the existing nuclear microprobe at the Jožef Stefan Institute. We measured absolute molecular yields and damage cross-section of reference materials, without significant alteration of the fragile biological samples during the duration of measurements in the mapping mode. We explored the analytical capability of the MeV-SIMS technique for chemical mapping of the plant tissue of medicinal cannabis leaves. A series of hand-cut plant tissue slices were prepared by standard shock-freezing and freeze-drying protocol and deposited on the Si wafer. We show the measured MeV-SIMS spectra showing a series of peaks in the mass area of cannabinoids, as well as their corresponding maps. The indicated molecular distributions at masses of 345.5 u and 359.4 u may be attributed to the protonated THCA and THCA-C4 acids, and show enhancement in the areas with opened trichome morphology.

The high ion signals produced by many lipids in mass spectrometry imaging (MSI) make them an ideal molecular class to study compositional changes throughout tissue sections and their relationship with disease. However, the large extent of structural diversity observed in the lipidome means optimal ion signal for different lipid classes is often obtained in opposite polarities. In this work we demonstrate how new high speed MALDI-MSI technologies combined with precise laser position control enables the acquisition of positive and negative ion mode lipid data from the same tissue section much faster than is possible with other MSI instruments. Critically, using this approach we explicitly demonstrate how such dual polarity acquisitions provide more information regarding molecular composition and spatial distributions throughout biological tissues. For example, in applying this approach to the zebra finch songbird brain we reveal the high abundance of DHA containing phospholipids (PC in positive mode and PE, PS in negative ion mode) in the nuclei that control song learning behaviour. To make the most of dual polarity data from single tissues we have also developed a pLSA-based multivariate analysis technique that includes both positive and negative ion data in the classification approach. In doing so the correlation amongst different lipid classes that ionise best in opposite polarities and contribute to certain spatial patterns within the tissue can be directly revealed. To demonstrate we apply this approach to studying the lipidomic changes throughout the tumor microenvironment within xenografts from a lung cancer model.

Rationale In mass spectrometry imaging (MSI) it is often desirable to analyse the same sample in both polarities to extract the most information. However, many matrices that produce high-quality spectra in matrix-assisted laser desorption/ionization (MALDI) are volatile, greatly limiting their use in long imaging experiments. We demonstrate that using a new high speed MALDI-MSI instrument, volatile matrices, including those that produce intense lipid signals in both positive and negative ion mode, can now be effectively used in MSI. Methods A prototype Bruker rapifleX MALDI Tissuetyper™ time-of-flight (TOF) instrument was used for high-speed imaging. This allows acquisition rates up to 50 pixels/s made possible by use of a 10 kHz laser and two rotating mirrors that allow the laser beam to be moved over, and synchronised with, the rapidly moving sample. MSI experiments were performed on mouse brain sections using non-vacuum stable dithranol and 2,6-dihydroxyacetophenone (DHA) matrices with pixel sizes ranging from 10 × 10 µm2 to 50 × 50 µm2. Results Both DHA and dithranol produced rich, complementary lipid spectra in both positive and negative ion modes. Due to the rapid acquisition speed of the instrument, both matrices could be effectively used for MSI despite their volatility. For example, an entire mouse brain could be imaged consecutively in both positive and negative ion mode with 50 × 50 µm2 pixels in ~35 min. We demonstrate that these speeds make possible both faster and higher resolution imaging of biological tissues on practical timescales. Conclusions These high acquisition speeds now make possible whole new classes of matrices that are unstable under high vacuum for MALDI-MSI studies. This provides researchers with far greater range and flexibility in choosing the best matrix for the given sample and analytes that they wish to detect. In addition, such instruments allow MSI to be performed at higher resolution across larger areas on practical time scales. Copyright © 2015 John Wiley & Sons, Ltd.

Jožef Stefan Institute recently commissioned a high brightness H − ion beam injection system for its existing tandem accelerator facility. Custom developed by High Voltage Engineering Europa, the multicusp ion source has been tuned to deliver at the entrance of the Tandetron™ accelerator H − ion beams with a measured brightness of 17.1 A m −2 rad −2 eV −1 at 170 μA, equivalent to an energy normalized beam emittance of 0.767 π mm mrad MeV 1/2 . Upgrading the accelerator facility with the new injection system provides two main advantages. First, the high brightness of the new ion source enables the reduction of object slit aperture and the reduction of acceptance angle at the nuclear microprobe, resulting in a reduced beam size at selected beam intensity, which significantly improves the probe resolution for micro-PIXE applications. Secondly, the upgrade strongly enhances the accelerator up-time since H and He beams are produced by independent ion sources, introducing a constant availability of 3 He beam for fusion-related research with NRA. The ion beam particle losses and ion beam emittance growth imply that the aforementioned beam brightness is reduced by transport through the ion optical system. To obtain quantitative information on the available brightness at the high-energy side of the accelerator, the proton beam brightness is determined in the nuclear microprobe beamline. Based on the experience obtained during the first months of operation for micro-PIXE applications, further necessary steps are indicated to obtain optimal coupling of the new ion source with the accelerator to increase the normalized high-energy proton beam brightness at the JSI microprobe, currently at 14 A m −2 rad −2 eV −1 , with the output current at 18% of its available maximum.

Elemental concentrations in micro-PIXE (Particle Induced X-ray Emission) maps of elements in biological tissue slices have been determined using auxiliary information on the sample matrix composition from EBS (Elastic Backscattering Spectroscopy) and STIM (Scanning Transmission Ion Microscopy). The thin sample approximation may be used for evaluating micro-PIXE data in cases, where X-ray absorption in the sample can be neglected and the mass of elements in a selected area can be estimated. The resulting sensitivity amounts to an impressive 10−12 g of the selected elements. Two cases are presented as examples. In the first, we determined the total mass of gold nanoparticles internalized by human monocyte-derived dendritic cells (MDDC). In the second, an inventory of the mass of elements in the micro-particulate material adsorbed at the wall of the lorica of the microzooplankton species Tintinnopsis radix has been created.

Recently we completed a construction of a cryostat at Jožef Stefan Institute (JSI) nuclear microprobe enabling us to analyze various types of biological samples in frozen hydrated state using micro-PIXE/STIM/RBS. Sample load-lock system was added to our existing setup to enable us to quickly insert a sample holder with frozen hydrated tissue samples onto a cold goniometer head cooled with liquid nitrogen inside the measuring chamber. Cryotome-cut slices of frozen hydrated plant samples were mounted between two thin silicon nitride foils and then attached to the sample holder. Sufficient thermal contact between silicon nitride foils and sample holder must be achieved, as well as between the sample holder and the cold goniometer head inside the measuring chamber to prevent melting of the samples. Matrix composition of frozen hydrated tissue is consisted mostly of ice. Thinning of the sample as well as water evaporation during high vacuum and proton beam exposure was inspected by the measurements with RBS and STIM method simultaneously with micro-PIXE. For first measuring attempts a standard micro-PIXE configuration for tissue mapping was used with proton beam cross section of 1.2 × 1.2 μm 2 and a beam current of 100 pA. The temperature of the cold goniometer head was kept below 130 K throughout the entire proton beam exposure. First measurements of thin plant tissue samples in frozen hydrated state show minute sample degradation during the 10 h period of micro-PIXE measurements.

With a model invertebrate animal, we have assessed the fate of magnetic nanoparticles in biologically relevant media, i.e., digestive juices. The toxic potential and the internalization of such nanoparticles by nontarget cells were also examined. The aim of this study was to provide experimental evidence on the formation of Co2+, Fe2+, and Fe3+ ions from CoFe2O4 nanoparticles in the digestive juices of a model organism. Standard toxicological parameters were assessed. Cell membrane stability was tested with a modified method for measurement of its quality. Proton-induced X-ray emission and low energy synchrotron radiation X-ray fluorescence were used to study internalization and distribution of Co and Fe. Co2+ ions were found to be more toxic than nanoparticles. We confirmed that Co2+ ions accumulate in the hepatopancreas, but Fen+ ions or CoFe2O4 nanoparticles are not retained in vivo. A model biological system with a terrestrial isopod is suited to studies of the potential dissolution of ions and other ...

In mass spectrometry imaging (MSI) it is often desirable to analyse the same sample in both polarities to extract the most information. However, many matrices that produce high-quality spectra in matrix-assisted laser desorption/ionization (MALDI) are volatile, greatly limiting their use in long imaging experiments. We demonstrate that using a new high speed MALDI-MSI instrument, volatile matrices, including those that produce intense lipid signals in both positive and negative ion mode, can now be effectively used in MSI.A prototype Bruker rapifleX MALDI Tissuetyper? time-of-flight (TOF) instrument was used for high-speed imaging. This allows acquisition rates up to 50 pixels/s made possible by use of a 10 kHz laser and two rotating mirrors that allow the laser beam to be moved over, and synchronised with, the rapidly moving sample. MSI experiments were performed on mouse brain sections using non-vacuum stable dithranol and 2,6-dihydroxyacetophenone (DHA) matrices with pixel sizes ranging from 10?10?m(2) to 50?50?m(2).Both DHA and dithranol produced rich, complementary lipid spectra in both positive and negative ion modes. Due to the rapid acquisition speed of the instrument, both matrices could be effectively used for MSI despite their volatility. For example, an entire mouse brain could be imaged consecutively in both positive and negative ion mode with 50?50?m(2) pixels in ~35?min. We demonstrate that these speeds make possible both faster and higher resolution imaging of biological tissues on practical timescales.These high acquisition speeds now make possible whole new classes of matrices that are unstable under high vacuum for MALDI-MSI studies. This provides researchers with far greater range and flexibility in choosing the best matrix for the given sample and analytes that they wish to detect. In addition, such instruments allow MSI to be performed at higher resolution across larger areas on practical time scales.

In mass spectrometry imaging (MSI) it is often desirable to analyse the same sample in both polarities to extract the most information. However, many matrices that produce high-quality spectra in matrix-assisted laser desorption/ionization (MALDI) are volatile, greatly limiting their use in long imaging experiments. We demonstrate that using a new high speed MALDI-MSI instrument, volatile matrices, including those that produce intense lipid signals in both positive and negative ion mode, can now be effectively used in MSI.A prototype Bruker rapifleX MALDI Tissuetyper™ time-of-flight (TOF) instrument was used for high-speed imaging. This allows acquisition rates up to 50 pixels/s made possible by use of a 10 kHz laser and two rotating mirrors that allow the laser beam to be moved over, and synchronised with, the rapidly moving sample. MSI experiments were performed on mouse brain sections using non-vacuum stable dithranol and 2,6-dihydroxyacetophenone (DHA) matrices with pixel sizes ranging from 10 × 10 µm(2) to 50 × 50 µm(2).Both DHA and dithranol produced rich, complementary lipid spectra in both positive and negative ion modes. Due to the rapid acquisition speed of the instrument, both matrices could be effectively used for MSI despite their volatility. For example, an entire mouse brain could be imaged consecutively in both positive and negative ion mode with 50 × 50 µm(2) pixels in ~35 min. We demonstrate that these speeds make possible both faster and higher resolution imaging of biological tissues on practical timescales.These high acquisition speeds now make possible whole new classes of matrices that are unstable under high vacuum for MALDI-MSI studies. This provides researchers with far greater range and flexibility in choosing the best matrix for the given sample and analytes that they wish to detect. In addition, such instruments allow MSI to be performed at higher resolution across larger areas on practical time scales.

Mass spectrometry imaging (MSI) is a label free technique capable of providing simultaneous identification and localization of biomolecules. A multimodal approach is required that allows for the study of the complexity of biological tissue samples to overcome the limitations of a single MSI technique. Secondary ion mass spectrometry (SIMS) allows for high spatial resolution imaging while matrix-assisted laser desorption (MALDI) offers a significantly wider mass range. The combination of coregistered SIMS and MALDI images results in detailed and unique biomolecular information. In this Technical Note, we describe how gold sputtered/implanted fiducial markers (FM) are created and can be used to ensure a proper overlay and coregistration of the two-dimensional images provided by the two MSI modalities.

Gold nanoparticles (GNPs) are claimed as outstanding biomedical tools for cancer diagnostics and photo-thermal therapy, but without enough evidence on their potentially adverse immunological effects. Using a model of human dendritic cells (DCs), we showed that 10 nm- and 50 nm-sized GNPs (GNP10 and GNP50, respectively) were internalized predominantly via dynamin-dependent mechanisms, and they both impaired LPS-induced maturation and allostimulatory capacity of DCs, although the effect of GNP10 was more prominent. However, GNP10 inhibited LPS-induced production of IL-12p70 by DCs, and potentiated their Th2 polarization capacity, while GNP50 promoted Th17 polarization. Such effects of GNP10 correlated with a stronger inhibition of LPS-induced changes in Ca2+ oscillations, their higher number per DC, and more frequent extra-endosomal localization, as judged by live-cell imaging, proton, and electron microscopy, respectively. Even when released from heat-killed necrotic HEp-2 cells, GNP10 inhibited the necrotic tumor cell-induced maturation and functions of DCs, potentiated their Th2/Th17 polarization capacity, and thus, impaired the DCs' capacity to induce T cell-mediated anti-tumor cytotoxicity in vitro. Therefore, GNP10 could potentially induce more adverse DC-mediated immunological effects, compared to GNP50.

Particle induced X-ray emission (PIXE) at microprobe of Jožef Stefan Institute is used to measure two-dimensional quantitative elemental maps of biological tissue. To improve chemical and biological understanding of the processes in vivo, supplementary information about chemical bonding and/or molecular distributions could be obtained by heavy-ion induced molecular desorption and a corresponding mass spectroscopy with Time-Of-Flight (TOF) mass spectrometer. As the method combines the use of heavy focused ions in MeV energy range and TOF Secondary Ion Mass Spectrometry, it is denoted as MeV SIMS. At Jožef Stefan Institute, we constructed a linear TOF spectrometer and mount it to our multipurpose nuclear microprobe. A beam of 8 MeV 35Cl7+ could be focused to a diameter of better than 3 μm × 3 μm and pulsed by electrostatic deflection at the high-energy side of accelerator. TOF mass spectrometer incorporates an 1 m long drift tube and a double stack microchannel plate (MCP) as a stop detector positioned at the end of the drift path. Secondary ions are focused at MCP using electrostatic cylindrical einzel lens. Time of flight spectra are currently acquired with a single-hit time-to-digital converter. Pulsed ion beam produces a shower of secondary ions that are ejected from positively biased target and accelerated towards MCP. We start our time measurement simultaneously with the start of the beam pulse. Signal of the first ion hitting MCP is used to stop the time measurement. Standard pulses proportional to the time of flight are produced with time to analog converter (TAC) and fed into analog-to-digital converter to obtain a time histogram. To enable efficient detection of desorbed fragments with higher molecular masses, which are of particular interest, we recently implemented a state-of art Field Programmable Gate Array (FPGA)-based multi-hit TOF acquisition. To test the system we used focused 8 MeV 35Cl7+ ion beam with pulse length of 180 ns. Mass resolution of measured SIMS spectra, dominantly determined by the duration of the beam pulse, is in good agreement with resolution estimated from pulse length. With improved high-voltage switching ability that will enable beam pulses with duration of 50 ns, a mass resolution of better than 500 is anticipated.

With a model invertebrate animal, we have assessed the fate of magnetic nanoparticles in biologically relevant media, i.e., digestive juices. The toxic potential and the internalization of such nanoparticles by nontarget cells were also examined. The aim of this study was to provide experimental evidence on the formation of Co(2+), Fe(2+), and Fe(3+) ions from CoFe₂O₄ nanoparticles in the digestive juices of a model organism. Standard toxicological parameters were assessed. Cell membrane stability was tested with a modified method for measurement of its quality. Proton-induced X-ray emission and low energy synchrotron radiation X-ray fluorescence were used to study internalization and distribution of Co and Fe. Co(2+) ions were found to be more toxic than nanoparticles. We confirmed that Co(2+) ions accumulate in the hepatopancreas, but Fe(n+) ions or CoFe₂O₄ nanoparticles are not retained in vivo. A model biological system with a terrestrial isopod is suited to studies of the potential dissolution of ions and other products from metal-containing nanoparticles in biologically complex media.