Longitudinal Bottom-Up Proteomics of Serum, Serum Extracellular Vesicles, and Cerebrospinal Fluid Reveals Candidate Biomarkers for Early Detection of Glioblastoma in a Murine Model

Glioblastoma Multiforme (GBM) is a brain tumor with a poor prognosis and low survival rates. GBM is diagnosed at an advanced stage, so little information is available on the early stage of the disease and few improvements have been made for earlier diagnosis. Longitudinal murine models are a promising platform for biomarker discovery as they allow access to the early stages of the disease. Nevertheless, their use in proteomics has been limited owing to the low sample amount that can be collected at each longitudinal time point. Here we used optimized microproteomics workflows to investigate longitudinal changes in the protein profile of serum, serum small extracellular vesicles (sEVs), and cerebrospinal fluid (CSF) in a GBM murine model. Baseline, pre-symptomatic, and symptomatic tumor stages were determined using non-invasive motor tests. Forty-four proteins displayed significant differences in signal intensities during GBM progression. Dysregulated proteins are involved in cell motility, cell growth, and angiogenesis. Most of the dysregulated proteins already exhibited a difference from baseline at the pre-symptomatic stage of the disease, suggesting that early effects of GBM might be detectable before symptom onset

Pro-inflammatory pattern of IgG1 Fc glycosylation in multiple sclerosis cerebrospinal fluid.

Background Immunoglobulin G (IgG) effector functions are regulated by the composition of glycans attached to a conserved N-glycosylation site in the Fc part. Intrathecal production of IgG, especially IgG1, is a hallmark of multiple sclerosis (MS), but nothing is known about IgG Fc glycosylation in MS and in cerebrospinal fluid (CSF) in general. Methods We applied mass spectrometry of tryptic Fc glycopeptides to analyze IgG Fc glycosylation (sialylation, galactosylation, fucosylation, and bisecting N-acetylglucosamine (GlcNAc)) in 48 paired CSF and serum samples from adult patients with MS or a first demyelinating event highly suggestive of MS (designated as MS cases), and from healthy volunteers and patients with other non-inflammatory diseases (control group). p values were adjusted for multiple testing. Results Our experiments revealed four main results. First, IgG1 glycosylation patterns were different in CSF vs. serum, in the MS group and even in control donors without intrathecal IgG synthesis. Second, in MS patients vs. controls, IgG1 glycosylation patterns were altered in CSF, but not in serum. Specifically, in CSF from the MS group, bisecting GlcNAc were elevated, and afucosylation and galactosylation were reduced. Elevated bisecting GlcNAc and reduced galactosylation are known to enhance IgG effector functions. Third, hypothesis-free regression analysis revealed that alterations of afucosylation and bisecting GlcNAc in CSF from MS cases peaked 2–3 months after the last relapse. Fourth, CSF IgG1 glycosylation correlated with the degree of intrathecal IgG synthesis and CSF cell count. Conclusions The CNS compartment as well as the inflammatory milieu in MS affect IgG1 Fc glycosylation. In MS, the CSF IgG1 glycosylation has features that enhance Fc effector functions

Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution

Technological advances in mass spectrometry imaging (MSI) have contributed to growing interest in 3D MSI. However, the large size of 3D MSI data sets has made their efficient analysis and visualization and the identification of informative molecular patterns computationally challenging. Hierarchical stochastic neighbor embedding (HSNE), a nonlinear dimensionality reduction technique that aims at finding hierarchical and multiscale representations of large data sets, is a recent development that enables the analysis of millions of data points, with manageable time and memory complexities. We demonstrate that HSNE can be used to analyze large 3D MSI data sets at full mass spectral and spatial resolution. To benchmark the technique as well as demonstrate its broad applicability, we have analyzed a number of publicly available 3D MSI data sets, recorded from various biological systems and spanning different mass-spectrometry ionization techniques. We demonstrate that HSNE is able to rapidly identify regions of interest within these large high-dimensionality data sets as well as aid the identification of molecular ions that characterize these regions of interest; furthermore, through clearly separating measurement artifacts, the HSNE analysis exhibits a degree of robustness to measurement batch effects, spatially correlated noise, and mass spectral misalignment

Multiple Statistical Analysis Techniques Corroborate Intratumor Heterogeneity in Imaging Mass Spectrometry Datasets of Myxofibrosarcoma

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information

ANKRd44 gene silencing: a putative role in trastuzumab resistance in HER2-like breast cancer

Trastuzumab is an effective therapeutic treatment for Her2-like breast cancer; despite this most of these tumors develop resistance to therapy due to specific gene mutations or alterations in gene expression. Understanding the mechanisms of resistance to Trastuzumab could be a useful tool in order to identify combinations of drugs that elude resistance and allow a better response for the treated patients. Twelve primary biopsies of Her2+/hormone receptor negative (ER-/PgR-) breast cancer patients were selected based on the specific response to neoadjuvant therapy with Trastuzumab and their whole exome was sequenced leading to the identification of 18 informative gene mutations that discriminate patients selectively based on response to treatment. Among these genes, we focused on the study of the ANKRD44 gene to understand its role in the mechanism of resistance to Trastuzumab. The ANKRD44 gene was silenced in Her2-like breast cancer cell line (BT474), obtaining a partially Trastuzumab-resistant breast cancer cell line that constitutively activates the NF-kb protein via the TAK1/AKT pathway. Following this activation an increase in the level of glycolysis in resistant cells is promoted, also confirmed by the up-regulation of the LDHB protein and by an increased TROP2 protein expression, found generally associated with aggressive tumors. These results allow us to consider the ANKRD44 gene as a potential gene involved in Trastuzumab resistance

Characterizing metastatic uveal melanoma patients who develop symptomatic brain metastases.

Metastatic uveal melanoma (mUM) is an advanced ocular malignancy characterized by a hepatotropic pattern of spread. As the incidence of brain metastases (BM) in mUM patients has been thought to be low, routine CNS surveillance has not been recommended. Notably, no formal assessment of BM incidence in mUM has to date been published to support this clinical practice. We aimed to determine the true rate of BM in mUM and to clarify the clinical and genomic risk factors associated with BM patients through a collaborative multicenter, retrospective research effort. Data collected from 1,845 mUM patients in databases across four NCI-designated comprehensive cancer centers from 2006-2021 were retrospectively analyzed to identify patients with BM. Brain imaging in most cases were performed due to onset of neurological symptoms and not for routine surveillance. An analysis of demographics, therapies, gene expression profile, tumor next generation sequencing (NGS) data, time to metastasis (brain or other), and survival in the BM cohort was completed. 116/1,845 (6.3%) mUM patients were identified with BM. The median age at time of UM diagnosis was 54 years old (range: 18-77). The median time to any metastasis was 4.2 years (range: 0-30.8). The most common initial metastatic site was the liver (75.9%). 15/116 (12.9%) BM patients presented with BM at the time of initial metastatic diagnosis. Median survival after a diagnosis of BM was 7.6 months (range: 0.4-73.9). The median number of organs involved at time of BM diagnosis was 3 (range: 1-9). DecisionDX-UM profiling was completed on 13 patients: 10-Class 2, 2-Class 1B, and 1-Class 1A. NGS and cytogenetic data were available for 34 and 21 patients, respectively. BM was identified in 6.3% of mUM cases and was associated with high disease burden and a median survival of under 8 months once diagnosed. Since most patients in this cohort were symptomatic, the incidence of asymptomatic BM remains unknown. These data suggest the use of routine brain imaging in all mUM patients at risk for developing BM for early detection

Localization Subsystem Simulation for Mobile Robot

Tato práce řeší implementaci lokalizačního algoritmu pro lokalizační subsystém mobilního robotu. Výstupem práce je simulační program v jazyce C\#, který simuluje chování senzorů, pohyb a lokalizační algoritmus založený na pravděpodobnosti. Konkrétně se jedná o algoritmus Monte Carlo. Simulační software obsahuje možnost výběru mapy, grafické zobrazení průběhu simulace, krokování lokalizačního algoritmu Monte Carlo, LiDAR pro měření vzdálenosti, nastavení šumu a nastavení hustoty částic pro MCL.The thesis deals with an implementation of a localization algorithm for a localization subsystem of a mobile robot. The outcome is a simulation program in C\# language which simulates sensor behaviour, motion and localization algorithm based on probability. Specifically, it is Monte Carlo algorithm. The simulation software contains a possibility to choose a map, graphic projection of a simulation development, debuging of the Monte Carlo localization algorithm, LiDAR for distance measuring, noise level setting and density of particles for MCL setting.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Mass spectrometry imaging of metabolites

Mass spectrometry imaging (MSI) is a technique which is gaining increasing interest in biomedical research due to its capacity to visualize molecules in tissues. First applied to the field of clinical proteomics, its potential for metabolite imaging in biomedical studies is now being recognized. Here we describe how to set up experiments for mass spectrometry imaging of metabolites in clinical tissues and how to tackle most of the obstacles in the subsequent analysis of the data