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Background: The common inflammatory disease multiple sclerosis (MS) is a disease of the central nervous system. For more than 25 years autologous hematopoietic stem cell transplantation (AHSCT) has been used to treat MS. It has been shown to be highly effective in suppressing inflammatory activity in relapsing-remitting MS (RRMS) patients. This treatment is thought to lead to an immune system reset, inducing a new, more tolerant system; however, the precise mechanism behind the treatment effect in MS patients is unknown. In this study, the effect of AHSCT on the metabolome and lipidome in peripheral blood from RRMS patients was investigated. Methods: Peripheral blood samples were collected from 16 patients with RRMS at ten-time points over the five months course of AHSCT and 16 MS patients not treated with AHSCT. Metabolomics and lipidomics analysis were performed using liquid-chromatography high-resolution mass spectrometry. Mixed linear models, differential expression analysis, and cluster analysis were used to identify differentially expressed features and groups of features that could be of interest. Finally, in-house and in-silico libraries were used for feature identification, and enrichment analysis was performed. Results: Differential expression analysis found 657 features in the lipidomics dataset and 34 in the metabolomics dataset to be differentially expressed throughout AHSCT. The administration of cyclophosphamide during mobilization and conditioning was associated with decreased concentrations in glycerophosphoinositol species. Thymoglobuline administration was associated with an increase in ceramide and glycerophosphoethanolamine species. After the conditioning regimen, a decrease in glycerosphingoidlipids concentration was observed, and following hematopoietic stem cell reinfusion glycerophosphocholine concentrations decreased for a short period of time. Ceramide concentrations were strongly associated with leukocyte levels during the procedure. The

Objective To identify serum proteins associated with MS and affected by interferon beta treatment. Methods Plasma samples from 29 untreated relapsing-remitting MS patients and 15 healthy controls were investigated with a multiplexed panel containing 92 proteins related to inflammation. Follow-up samples were available from 13 patients at 1 and 3 months after initiation of treatment with interferon beta-1a. Results Ten proteins were differentially expressed in MS patients. Five of these were altered by treatment with IFN-β 1a: uPA, CX3CL1, CCL2, TRAIL and IL18. Conclusion CCL2 and TRAIL were confirmed to be modulated with interferon beta treatment in MS. As novel findings, we now report that uPA and CX3CL1 were differentially expressed in MS and increased after IFN-beta-1a treatment. Conflicting results have been reported on how interferon beta affects IL-18., Title in Web of Science: Urokinase, CX3CL1, CCL2, TRAIL and IL-18 induced by interferon-beta treatment

Containers are gaining popularity in life science research as they provide a solution for encompassing dependencies of provisioned tools, simplify software installations for end users and offer a form of isolation between processes. Scientific workflows are ideal for chaining containers into data analysis pipelines to aid in creating reproducible analyses. In this article, we review a number of approaches to using containers as implemented in the workflow tools Nextflow, Galaxy, Pachyderm, Argo, Kubeflow, Luigi and SciPipe, when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.

To increase our understanding of age-related diseases affecting the central nervous system (CNS) it is important to understand the molecular processes of biological ageing. Metabolomics of cerebrospinal fluid (CSF) is a promising methodology to increase our understanding of naturally occurring processes of ageing of the brain and CNS that could be reflected in CSF. In the present study the CSF metabolomes of healthy subjects aged 30-74 years (n = 23) were studied using liquid chromatography high-resolution mass spectrometry (LC-HRMS), and investigated in relation to age. Ten metabolites were identified with high confidence as significantly associated with ageing, eight with increasing levels with ageing: isoleucine, acetylcarnitine, pipecolate, methionine, glutarylcarnitine, 5-hydroxytryptophan, ketoleucine, and hippurate; and two decreasing with ageing: methylthioadenosine and 3-methyladenine. To our knowledge, this is the first time the CSF metabolomes of healthy subjects are assessed in relation to ageing. The present study contributes to the field of ageing metabolomics by presenting a number of metabolites present in CSF with potential relevance for ageing and the results motivate further studies.

We have previously identified selective upregulation of the mevalonate pathway genes upon inhibition of oxidative phosphorylation (OXPHOS) in quiescent cancer cells. Using mass spectrometry-based proteomics, we here investigated whether these responses are corroborated on the protein level and whether proteomics could yield unique insights into context-dependent biology. HCT116 colon carcinoma cells were cultured as monolayer cultures, proliferative multicellular tumor spheroids (P-MCTS), or quiescent (Q:MCTS) multicellular tumor spheroids and exposed to OXPHOS inhibitors: nitazoxanide, FCCP, oligomycin, and salinomycin or the HMG-CoA-reductase inhibitor simvastatin at two different doses for 6 and 24 h. Samples were processed using an in-depth bottom-up proteomics workflow resulting in a total of 9286 identified protein groups. Gene set enrichment analysis showed profound differences between the three cell systems and confirmed differential enrichment of hypoxia, OXPHOS, and cell cycle progression-related protein responses in P-MCTS and QMCTS. Treatment experiments showed that the observed drug-induced alterations in gene expression of metabolically challenged cells are not translated directly to the protein level, but the results reaffirmed OXPHOS as a selective vulnerability of quiescent cancer cells. This work provides rationale for the use of deep proteome profiling to identify context-dependent treatment responses and encourages further studies investigating metabolic processes that could be co-targeted together with OXPHOS to eradicate quiescent cancer cells.

To better understand the pathophysiological differences between secondary progressive multiple sclerosis (SPMS) and relapsing-remitting multiple sclerosis (RRMS), and to identify potential biomarkers of disease progression, we applied high-resolution mass spectrometry (HRMS) to investigate the metabolome of cerebrospinal fluid (CSF). The biochemical differences were determined using partial least squares discriminant analysis (PLS-DA) and connected to biochemical pathways as well as associated to clinical and radiological measures. Tryptophan metabolism was significantly altered, with perturbed levels of kynurenate, 5-hydroxytryptophan, 5-hydroxyindoleacetate, and N-acetylserotonin in SPMS patients compared with RRMS and controls. SPMS patients had altered kynurenine compared with RRMS patients, and altered indole-3-acetate compared with controls. Regarding the pyrimidine metabolism, SPMS patients had altered levels of uridine and deoxyuridine compared with RRMS and controls, and altered thymine and glutamine compared with RRMS patients. Metabolites from the pyrimidine metabolism were significantly associated with disability, disease activity and brain atrophy, making them of particular interest for understanding the disease mechanisms and as markers of disease progression. Overall, these findings are of importance for the characterization of the molecular pathogenesis of SPMS and support the hypothesis that the CSF metabolome may be used to explore changes that occur in the transition between the RRMS and SPMS pathologies.

eSSENCE

Motivation Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. Results We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science., Title in thesis list of papers: Interoperable and scalable metabolomics data analysis with microservices

Fibromyalgia (FM) is a syndrome characterized by widespread muscular pain, fatigue and functional symptoms, which is known to be difficult to diagnose as the various symptoms overlap with many other conditions. Currently, there are no biomarkers for FM, and the diagnosis is made subjectively by the clinicians. We have performed shotgun proteomics on cerebrospinal fluid (CSF) from FM patients and non-pain controls to find potential biomarker candidates for this syndrome. Based on our multivariate and univariate analyses, we found that the relative differences in the CSF proteome between FM patients and controls were moderate. Four proteins, important to discriminate FM patients from non-pain controls, were found: Apolipoprotein C-III, Galectin-3-binding protein, Malate dehydrogenase cytoplasmic and the neuropeptide precursor protein ProSAAS. These proteins are involved in lipoprotein lipase (LPL) activity, inflammatory signaling, energy metabolism and neuropeptide signaling.

Background: Alzheimer’s disease (AD) is diagnosed based on a clinical evaluation as well as analyses of classical biomarkers: Aβ42, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Although the sensitivities and specificities of the classical biomarkers are fairly good for detection of AD, there is still a need to develop novel biochemical markers for early detection of AD. Objective: We explored if integration of novel proteins with classical biomarkers in CSF can better discriminate AD from non-AD subjects. Methods: We applied ELISA, mass spectrometry, and multivariate modeling to investigate classical biomarkers and the CSF proteome in subjects (n = 206) with 76 AD patients, 74 mild cognitive impairment (MCI) patients, 11 frontotemporal dementia (FTD) patients, and 45 non-dementia controls. The MCI patients were followed for 4–9 years and 21 of these converted to AD, whereas 53 remained stable. Results: By combining classical CSF biomarkers with twelve novel markers, the area of the ROC curves (AUROCS) of distinguishing AD and MCI/AD converters from non-AD were 93% and 96%, respectively. The FTDs and non-dementia controls were identified versus all other groups with AUROCS of 96% and 87%, respectively. Conclusions: Integration of new and classical CSF biomarkers in a model-based approach can improve the identification of AD, FTD, and non-dementia control subjects.

Background: Alzheimer's disease (AD) is diagnosed based on a clinical evaluation as well as analyses of classical biomarkers: A beta(42), total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Although the sensitivities and specificities of the classical biomarkers are fairly good for detection of AD, there is still a need to develop novel biochemical markers for early detection of AD. Objective: We explored if integration of novel proteins with classical biomarkers in CSF can better discriminate AD from non-AD subjects. Methods: We applied ELISA, mass spectrometry, and multivariate modeling to investigate classical biomarkers and the CSF proteome in subjects (n = 206) with 76 AD patients, 74 mild cognitive impairment (MCI) patients, 11 frontotemporal dementia (FTD) patients, and 45 non-dementia controls. The MCI patients were followed for 4-9 years and 21 of these converted to AD, whereas 53 remained stable. Results: By combining classical CSF biomarkers with twelve novel markers, the area of the ROC curves (AUROCS) of distinguishing AD and MCl/AD converters from non-AD were 93% and 96%, respectively. The FTDs and non-dementia controls were identified versus all other groups with AUROCS of 96% and 87%, respectively. Conclusions: Integration of new and classical CSF biomarkers in a model-based approach can improve the identification of AD, FTD, and non-dementia control subjects., QC 20190312

In situ sekvensering är en metod som kan användas för att lokalisera differentiellt uttryck av mRNA direkt i vävnadssnitt, vilket kan ge viktiga ledtrådar om många sjukdomstillstånd. Idag förloras många av sekvenserna från in situ sekvensering på grund av det kvalitetsmått man använder för att säkerställa att sekvenser är korrekta. Det finns troligtvis möjlighet att förbättra prestandan av den nuvarande base calling-metoden eftersom att metoden är i ett tidigt utvecklingsskede. Vi har genomfört explorativ dataanalys för att undersöka förekomst av systematiska fel och korrigerat för dessa med hjälp av statistiska metoder. Vi har framförallt undersökt tre metoder för att korrigera för systematiska fel: I) Korrektion av överblödning som sker på grund avöverlappande emissionsspektra mellan fluorescenta prober. II) En sannolikhetsbaserad tolkningav intensitetsdata som resulterar i ett nytt kvalitetsmått och en alternativ klassificerare baseradpå övervakad inlärning. III) En utredning om förekomst av cykelberoende effekter, exempelvisofullständig dehybridisering av fluorescenta prober. Vi föreslår att man gör följande saker: Implementerar och utvärderar det sannolikhetsbaserade kvalitetsmåttet Utvecklar och implementerar den föreslagna klassificeraren Genomför ytterligare experiment för att påvisa eller bestrida förekomst av ofullständigdehybridisering, In situ sequencing is a method that can be used to localize differential expression of mRNA directly in tissue sections, something that can give valuable insights to many statest of disease. Today, many of the registered sequences from in situ sequencing are lost due to a conservative quality measure used to filter out incorrect sequencing reads. There is room for improvement in the performance of the current method for base calling since the technology is in an early stage of development. We have performed exploratory data analysis to investigate occurrence of systematic errors, and corrected for these by using various statistical methods. The primary methods that have been investigated are the following: I) Correction of emission spectra overlap resulting in spillover between channels. II) A probability-based interpretation of intensity data, resulting in a novel quality measure and an alternative classifier based on supervised learning. III) Analysis of occurrence of cycle dependent effects, e.g. incomplete dehybridization of fluorescent probes. We suggest the following: Implementation and evaluation of the probability-based quality measure Development and implementation of the proposed classifier Additional experiments to investigate the possible occurrence of incomplete dehybridization

Currently, very few treatments for patients in the progressive phases of multiple sclerosis (PMS) are available. To enable sensitive evaluation of future treatments, prognostic and predictive markers for therapeutic response are needed, as well as robust markers for early detection of PMS. We have previously demonstrated that a signature of 28 cerebrospinal fluid (CSF) biochemical markers could distinguish PMS from relapsing-remitting multiple sclerosis (RRMS) patients. Herein, we aimed to characterize the 28 previously extracted metabolites by assessing independent differences between 35 PMS and 35 RRMS patients as well as 49 healthy control subjects. Twenty-two of the PMS patients were part of a controlled clinical trial evaluating the effect of intrathecal rituximab for PMS. Using follow-up assessments, we related the metabolites to clinical outcomes of the trial and investigated if they could predict a poor or beneficial treatment response. Finally, we investigated the metabolites’ associations to a panel of six CSF protein biomarkers of axonal, myelin and astrocyte damage as well as T- and B- cell activation and differentiation. The composite signature was predominantly classifying patients as having a poor treatment outcome, achieving an estimated area under the curve (AUC) of 0.63 (sensitivity = 0.90, specificity = 0.38). Univariately, C4H6N6O4 and phenolic phosphate were significantly (p-value<0.05) increased in patients with a poor outcome. We also demonstrated that a majority (n=22) of the metabolites showed PMS distinctive alterations, including increased CSF levels of 4-acetamidobutanoate, 4-hydroxybenzoate and thymine. 4-Acetamidobutanoate did also display significant associations with the results from the symbol digit modalities test (SDMT) and the 9-hole peg test (9-HPT) using the dominant hand, and the protein markers myelin basic protein (MBP), neurofilament light (NFL) and glial fibrillary acidic protein (GFAp), whereas 4-hydroxybenzoate display