Project description:Muscle invasive bladder cancer (MIBC) is a heterogeneous disease with a high recurrence rate and poor clinical outcomes. Molecular subtype provides a new framework for the study of MIBC heterogeneity. Clinically, MIBC can be classified as basal and luminal subtypes, they display different clinical and pathological characteristics, but the molecular mechanism is still unclear. Lipidomic and metabolomic molecules have recently been considered to play an important role in the genesis and development of tumors, especially as potential biomarkers. Their different expression profiles in basal and luminal subtypes provide clues for the molecular mechanism of basal and luminal subtypes and the discovery of new biomarkers. Herein, we stratified MIBC patients into basal and luminal subtype using a MIBC classifier based on transcriptome expression profiles. We qualitatively and quantitatively analyzed the lipids and metabolites of basal and luminal MIBC subtypes, and identified differential lipid and metabolite profiles of them. Our results suggest that free fatty acids (FFA) and sulfatides (SL), which are closely associated with immune and stromal cell types, can contribute to the diagnosis of basal and luminal subtypes of MIBC. Moreover, we showe that glycerophosphocholine (GCP)/imidazoles and nucleosides/imidazoles ratios can accurately distinguish the basal and luminal tumors. Overall, by integrating transcriptomic, lipidomic, and metabolomic data, our study reveals specific biomarkers to differentially diagnose basal and luminal MIBC subtypes and may provide a basis for precision therapy of MIBC.
Project description:Provided is the annotated raw data for N-glycan mass spectrometry imaging (MSI) annotations in thin cross-sections of formalin-fixed and paraffin-embedded murine kidney. Relevant meta-data have been provided in this brief and the raw MSI data can be accessed using ProteomeXchange with the PRoteomics IDEntifications (PRIDE) identifier PXD009808. This brief is the first in a set of submissions from our group which will make raw data publicly accessible for existing and future MSI studies.
Project description:SummaryIn recent years, SWATH-MS has become the proteomic method of choice for data-independent-acquisition, as it enables high proteome coverage, accuracy and reproducibility. However, data analysis is convoluted and requires prior information and expert curation. Furthermore, as quantification is limited to a small set of peptides, potentially important biological information may be discarded. Here we demonstrate that deep learning can be used to learn discriminative features directly from raw MS data, eliminating hence the need of elaborate data processing pipelines. Using transfer learning to overcome sample sparsity, we exploit a collection of publicly available deep learning models already trained for the task of natural image classification. These models are used to produce feature vectors from each mass spectrometry (MS) raw image, which are later used as input for a classifier trained to distinguish tumor from normal prostate biopsies. Although the deep learning models were originally trained for a completely different classification task and no additional fine-tuning is performed on them, we achieve a highly remarkable classification performance of 0.876 AUC. We investigate different types of image preprocessing and encoding. We also investigate whether the inclusion of the secondary MS2 spectra improves the classification performance. Throughout all tested models, we use standard protein expression vectors as gold standards. Even with our naïve implementation, our results suggest that the application of deep learning and transfer learning techniques might pave the way to the broader usage of raw mass spectrometry data in real-time diagnosis.Availability and implementationThe open source code used to generate the results from MS images is available on GitHub: https://ibm.biz/mstransc. The raw MS data underlying this article cannot be shared publicly for the privacy of individuals that participated in the study. Processed data including the MS images, their encodings, classification labels and results can be accessed at the following link: https://ibm.box.com/v/mstc-supplementary.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:Integrative Transcriptomic, Lipidomic, and Metabolomic Analysis Reveals Potential Biomarkers of Basal and Luminal Muscle Invasive Bladder Cancer Subtypes
Project description:Central and peripheral nervous systems are lipid rich tissues. Lipids, in the context of lipid-protein complexes, surround neurons and provide electrical insulation for transmission of signals allowing neurons to remain embedded within a conducting environment. Lipids play a key role in vesicle formation and fusion in synapses. They provide means of rapid signaling, cell motility and migration for astrocytes and other cell types that surround and play supporting roles neurons. Unlike many other signaling molecules, lipids are capable of multiple signaling events based on the different fragments generated from a single precursor during each event. Lipidomics, until recently suffered from two major disadvantages: (1) level of expertise required an overwhelming amount of chemical detail to correctly identify a vast number of different lipids which could be close in their chemical reactivity; and (2) high amount of purified compounds needed by analytical techniques to determine their structures. Advances in mass spectrometry have enabled overcoming these two limitations. Mass spectrometry offers a great degree of simplicity in identification and quantification of lipids directly extracted from complex biological mixtures. Mass spectrometers can be regarded to as mass analyzers. There are those that separate and analyze the product ion fragments in space (spatial) and those which separate product ions in time in the same space (temporal). Databases and standardized instrument parameters have further aided the capabilities of the spatial instruments while recent advances in bioinformatics have made the identification and quantification possible using temporal instruments.
Project description:MotivationAlthough R packages exist for the pre-processing of metabolomic data, they currently do not incorporate additional analysis steps of summarization, filtering and normalization of aligned data. We developed the MSPrep R package to complement other packages by providing these additional steps, implementing a selection of popular normalization algorithms and generating diagnostics to help guide investigators in their analyses.Availabilityhttp://www.sourceforge.net/projects/msprep
Project description:Chansu, which is prepared from the skin secretions of toad (Bufo bufo gargarizans Cantor), is widely used in traditional Chinese medicine (TCM). Being the principal bioactive constituents of ChanSu, bufalin (BFL) and cinobufagin (CBF) have been shown to possess anticancer properties. TCM confer bioactivities through the synergistic effect between potential active ingredients, so as to interfere with the development of the disease, and ultimately achieve the therapeutic effect. We found that the anticancer effect was significantly potentiated by co-treatment of BFL and CBF as compared to mono-treatment, suggesting their synergistic interaction. To reveal their synergistic mechanisms, metabolomic and lipidomic profiling based on liquid chromatography-mass spectrometry (LC���������MS) were utilized to delineate the responses in HepG2 cells after treatment with BFL and CBF individually or in combination. Metabolic pathways including methionine metabolism, energy metabolism, lipid metabolism and amino acid metabolism were modulated and subsequently lead to apoptosis and cell cycle arrest of HepG2 cells. In particular, the discrepant regulation of methionine metabolism between mono-treatment and co-treatment of BFL and CBF may account for their synergistic effect. Our study provided novel insights into the mechanistic links between cellular metabolism and synergistic effect, which may ultimately lead to better treatments for hepatoma.
Project description:BackgroundThe majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.ResultsIn this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.ConclusionThree evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.