Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP.
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ABSTRACT:
SUBMITTER: Guo G
PROVIDER: S-EPMC8163805 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNon-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, together with dedicated compound identification tools, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated fragment formula annotation for unknown compounds measured by GC-EI-HRMS.ResultsBased on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method does not require the presence of the molecular ion, which is absent in [Formula: see text]40% of EI spectra. Our method has been trained and validated on >50 halocarbons and hydrocarbons, with 3-20 atoms and molar masses of 30-330 g mol[Formula: see text], measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the annotated fragment formulae are correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound or the lack of isotopic constraint (no minor isotopocule detected).ConclusionsOur method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples. The L-GPL licenced Python code is released under the name ALPINAC for ALgorithmic Process for Identification of Non-targeted Atmospheric Compounds.
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Project description:Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.
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Project description:UnlabelledAs a common post-translational modification, protein glycosylation plays an important role in many biological processes, and it is known to be associated with human diseases. Mass spectrometry (MS)-based glycomic profiling techniques have been developed to measure the abundances of glycans in complex biological samples and applied to the discovery of putative glycan biomarkers. To automate the annotation of glycomic profiles in the liquid chromatography-MS (LC-MS) data, we present here a user-friendly software tool, MultiGlycan, implemented in C# on Windows systems. We tested MultiGlycan by using several glycomic profiling datasets acquired using LC-MS under different preparations and show that MultiGlycan executes fast and generates robust and reliable results.AvailabilityMultiGlycan can be freely downloaded at http://darwin.informatics.indiana.edu/MultiGlycan/.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BACKGROUND:Retinoblastoma is an ocular neoplastic cancer caused primarily due to the mutation/deletion of RB1 gene. Due to the rarity of the disease very limited information is available on molecular changes in primary retinoblastoma. High throughput analysis of retinoblastoma transcriptome is available however the proteomic landscape of retinoblastoma remains unexplored. In the present study we used high resolution mass spectrometry-based quantitative proteomics to identify proteins associated with pathogenesis of retinoblastoma. METHODS:We used five pooled normal retina and five pooled retinoblastoma tissues to prepare tissue lysates. Equivalent amount of proteins from each group was trypsin digested and labeled with iTRAQ tags. The samples were analyzed on Orbitrap Velos mass spectrometer. We further validated few of the differentially expressed proteins by immunohistochemistry on primary tumors. RESULTS:We identified and quantified a total of 3587 proteins in retinoblastoma when compared with normal adult retina. In total, we identified 899 proteins that were differentially expressed in retinoblastoma with a fold change of ?2 of which 402 proteins were upregulated and 497 were down regulated. Insulin growth factor 2 mRNA binding protein 1 (IGF2BP1), chromogranin A, fetuin A (ASHG), Rac GTPase-activating protein 1 and midkine that were found to be overexpressed in retinoblastoma were further confirmed by immunohistochemistry by staining 15 independent retinoblastoma tissue sections. We further verified the effect of IGF2BP1 on cell proliferation and migration capability of a retinoblastoma cell line using knockdown studies. CONCLUSIONS:In the present study mass spectrometry-based quantitative proteomic approach was applied to identify proteins differentially expressed in retinoblastoma tumor. This study identified the mitochondrial dysfunction and lipid metabolism pathways as the major pathways to be deregulated in retinoblastoma. Further knockdown studies of IGF2BP1 in retinoblastoma cell lines revealed it as a prospective therapeutic target for retinoblastoma.
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Project description:Introduction: Serratia marcescens, an opportunistic human pathogen, is reported as an important cause of nosocomial infection and outbreaks. Although the genome of S. marcescens (ATCC 13880) was completely sequenced by 2014, there are no studies on the proteomic profile of the organism. The objective of the present study is to analyze the protein profile of S. marcescens (ATCC 13880) using a high resolution mass spectrometry (MS). Methods: Serratia marcescens ATCC 13880 strain was grown in Luria-Bertani broth and the protein extracted was subjected to trypsin digestion, followed by basic reverse phase liquid chromatography fractionation. The peptide fractions were then analysed using Orbitrap Fusion Mass Spectrometry and the raw MS data were processed in Proteome Discoverer software. Results: The proteomic analysis identified 15?009 unique peptides mapping to 2541 unique protein groups, which corresponds to approximately 54% of the computationally predicted protein-coding genes. Bioinformatic analysis of these identified proteins showed their involvement in biological processes such as cell wall organization, chaperone-mediated protein folding and ATP binding. Pathway analysis revealed that some of these proteins are associated with bacterial chemotaxis and beta-lactam resistance pathway. Conclusion: To the best of our knowledge, this is the first high-throughput proteomics study of S. marcescens (ATCC 13880). These novel observations provide a crucial baseline molecular profile of the S. marcescens proteome which will prove to be helpful for the future research in understanding the host-pathogen interactions during infection, elucidating the mechanism of multidrug resistance, and developing novel diagnostic markers or vaccine for the disease.
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Project description:Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.
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Project description:High spatial resolution in mass spectrometry imaging (MSI) is crucial to understanding the biology dictated by molecular distributions in complex tissue systems. Here, we present MSI using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) at 50 ?m resolution. An adjustable iris, beam expander, and an aspherical focusing lens were used to reduce tissue ablation diameters for MSI at high resolution. The laser beam caustic was modeled using laser ablation paper to calculate relevant laser beam characteristics. The minimum laser spot diameter on the tissue was determined using tissue staining and microscopy. Finally, the newly constructed optical system was used to image hen ovarian tissue with and without oversampling, detailing tissue features at 50 ?m resolution. Graphical Abstract ?.
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Project description:Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H]+ candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H]+ identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H]+ candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance. Graphical abstract.
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Project description:We describe an easy and inexpensive way to provide a highly defined Gaussian shaped laser spot on target of 5 ?m diameter for imaging mass spectrometry using a commercial MALDI TOF instrument that is designed to produce a 20 ?m diameter laser beam on target at its lowest setting. A 25 ?m pinhole filter on a swivel arm was installed in the laser beam optics outside the vacuum ion source chamber so it is easily flipped into or out of the beam as desired by the operator. The resulting ion images at 5 ?m spatial resolution are sharp since the satellite secondary laser beam maxima have been removed by the filter. Ion images are shown to demonstrate the performance and are compared with the method of oversampling to achieve higher spatial resolution when only a larger laser beam spot on target is available.
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Project description:Bone and bone marrow are vital to mammalian structure, movement, and immunity. These tissues are also commonly subjected to molecular alterations giving rise to debilitating diseases like rheumatoid arthritis and osteomyelitis. Technologies such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) facilitate the discovery of spatially resolved chemical information in biological tissue samples to help elucidate the complex molecular processes underlying pathology. Traditionally, preparation of osseous tissue for MALDI IMS has been difficult due to its mineralized composition and heterogeneous morphology, and compensation for these challenges with decalcification and fixation protocols can remove or delocalize molecular species. Here, sample preparation methods were advanced to enable multimodal MALDI IMS of undecalcified, fresh-frozen murine femurs, allowing the distribution of endogenous lipids to be linked to tissue structures and cell types. Adhesive-bound bone sections were mounted onto conductive glass slides with microscopy-compatible glue and freeze-dried to minimize artificial bone marrow damage. High spatial resolution (10 μm) MALDI IMS was employed to characterize lipid distributions, and use of complementary microscopy modalities aided tissue and cell assignments. For example, various phosphatidylcholines localize to the bone marrow, adipose tissue, marrow adipose tissue, and muscle. Further, sphingomyelin(42:1) was abundant in megakaryocytes, whereas sphingomyelin(42:2) was diminished in this cell type. These data reflect the vast molecular and cellular heterogeneity indicative of the bone marrow and the soft tissue surrounding the femur. Multimodal MALDI IMS has the potential to advance bone-related biomedical research by offering deep molecular coverage with spatial relevance in a preserved native bone microenvironment.
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