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 μm 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 ∼400 μm or more, including potential neuropeptide markers involved in many diseases such as neurodegenerative diseases, obesity, bulimia, and anorexia nervosa.
We report a method for the unambiguous identification of molecules in biological and materials specimens at high practical lateral resolution using a new TOF-SIMS parallel imaging MS/MS spectrometer. ...The tandem mass spectrometry imaging reported here is based on the precise monoisotopic selection of precursor ions from a TOF-SIMS secondary ion stream followed by the parallel and synchronous collection of the product ion data. Thus, our new method enables simultaneous surface screening of a complex matrix chemistry with TOF-SIMS (MS1) imaging and targeted identification of matrix components with MS/MS (MS2) imaging. This approach takes optimal advantage of all ions produced from a multicomponent sample, compared to classical tandem mass spectrometric methods that discard all ions with the exception of specific ions of interest. We have applied this approach for molecular surface analysis and molecular identification on the nanometer scale. High abundance sensitivity is achieved at low primary ion dose density; therefore, one-of-a-kind samples may be relentlessly probed before ion-beam-induced molecular damage is observed.
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.
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 (CID) 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.
Molecular heterogeneity is a key feature of glioblastoma that impedes patient stratification and leads to large discrepancies in mean patient survival. Here, we analyze a cohort of 96 glioblastoma ...patients with survival ranging from a few months to over 4 years. 46 tumors are analyzed by mass spectrometry-based spatially-resolved proteomics guided by mass spectrometry imaging. Integration of protein expression and clinical information highlights three molecular groups associated with immune, neurogenesis, and tumorigenesis signatures with high intra-tumoral heterogeneity. Furthermore, a set of proteins originating from reference and alternative ORFs is found to be statistically significant based on patient survival times. Among these proteins, a 5-protein signature is associated with survival. The expression of these 5 proteins is validated by immunofluorescence on an additional cohort of 50 patients. Overall, our work characterizes distinct molecular regions within glioblastoma tissues based on protein expression, which may help guide glioblastoma prognosis and improve current glioblastoma classification.
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.
The study aimed to determine the incidence and expression of body asymmetries in dancers of three different dance styles: dancesport (n = 14), hip-hop (n = 21) and ballet (n = 20) and to examine how ...body asymmetries (muscle strength and power, stability and range of motion) are associated with musculoskeletal injuries occurring over the past 12 months.
Cross-sectional and retrospective study.
Maximal isometric voluntary contraction was measured for trunk, hip, knee and ankle movements. Participants performed a single leg stance, unilateral landing, weight bearing symmetry, squat and countermovement jump on force platforms. Passive range of motion was measured for hip, knee and ankle with two-arm goniometer or digital inclinometer (hip flexion, extension and rotations). A retrospective questionnaire was used to collect data on musculoskeletal injuries occurring in the last 12 months.
Different dance styles were associated with different body asymmetries, including strength asymmetries (hip flexion and external rotation), agonist/antagonist asymmetries (trunk flexion/extension, hip abduction/adduction, ankle dorsi/plantar flexion) and hip adduction and internal rotation range of motion asymmetries. Moreover, strength asymmetries of hip flexion, adduction and abduction/adduction as well as stability asymmetries were associated with the total number of musculoskeletal injuries.
The incidence of body asymmetries (> 10%) in dancesport, hip-hop and ballet dancers was confirmed, as well as the association of some asymmetries with self-reported injuries occurring over the last 12 months. The cause-effect relationship should be clarified by further studies.
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.
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