Experiments

Metabolomics LC-MS data supporting the publication: Variable glucagon action in diverse mouse models of obesity and type 2 diabetes JOURNAL DETAILS TO BE ADDED Yuqin Wu (1,2), Andrea Y. Chan (1,2), Jana Hauke (3), Okka Htin Aung (1,2), Ashish Foollee (1,2), Maria Almira S. Cleofe (1,2), Helen Stölting (1,2), Mei-Ling Han (4), Katherine Jeppe (5), Christopher K. Barlow (5), Jürgen G. Okun (3), Patricia M. Rusu (1,2), and Adam J. Rose (1,2) Correspondence: adam.rose@monash.edu Affiliations: 1. Nutrient Metabolism & Signalling Laboratory, Metabolism, Diabetes and Obesity Program, Biomedicine Discovery Institute, Monash University, Victoria 3800, Australia.; 2. Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Victoria 3800, Australia.; 3. Division of Inherited Metabolic Diseases, University Children's Hospital, 69120 Heidelberg, Germany.; 4. Infection and Immunity Program, Department of Microbiology, Monash Biomedicine Discovery Institute, Monash University, Clayton, Australia.; 5. Biomedical Proteomics and Metabolomics Facility and the Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia.

• Latest dataset in this experiment:
• Processed Data

Mass spectrometry data supporting "A Clostridioides difficile endolysin modulates toxin secretion without cell lysis", COMMSBIO-23-4840A. Data used to create figure 4C comes from S4_PG_enzyme_R2029.raw Data used to create figure 4D comes from S3_PG_enzyme_M740.raw Data used to create figure S4: S1_PG_M7404_undigested_control.raw S2_PG_R20291_undigested_control.raw S3_PG_enzyme_M7404.raw S4_PG_enzyme_R20291.raw

• Latest dataset in this experiment:
• LC-MS analysis experiment 1
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1 School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia. 2 Cooperative Research Centre for Alertness, Safety and Productivity, Melbourne, Australia 3 Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, USA; Division of Sleep Medicine, Harvard Medical School, Boston, USA 4 Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, Parkville, Australia 5 Centre for Human Brain Health, School of Psychology, University of Birmingham, Edgbaston, UK

• Latest dataset in this experiment:
• Experiment 1 (Sleep deprivation training)

• Latest dataset in this experiment:
• Day_25_Normoxia

Metabolomics LC-MS data supporting the publication: The broccoli-derived antioxidant sulforaphane changes the growth of gastrointestinal microbiota, allowing for the production of anti-inflammatory metabolites. Sarah A. Marshall (1), Remy B. Young (2,3), Jessica M. Lewis (4), Emily L. Rutten (2,3), Jodee Gould (2,3), Christopher K. Barlow (5), Cristina Giogha (2,3), Vanessa R. Marcelino (2,3), Neville Fields (1), Elizabeth L. Hartland (2,3), Nichollas E. Scott (4), Samuel C. Forster (2,3) and Emily L. Gulliver (2,3) Correspondence: Emily.gulliver@hudson.org.au Affiliations: (1) The Ritchie Centre, Department of Obstetrics and Gynaecology, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia (2) Centre of Innate Immunity and Infectious Disease, Hudson Institute of Medical Research, Clayton, Victoria, Australia (3) Department of Molecular and Translational Sciences, Monash University, Clayton, Victoria, 3800, Australia (4) Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Victoria, Australia (5) Monash Proteomics & Metabolomics Facility, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia https://doi.org/10.1016/j.jff.2023.105645

• Latest dataset in this experiment:
• Pooled QC

Instrument: MPMF_Eclipse User folder name: Yunjian.Wu@monash.edu Uploaded from: THERMO-ER2ATNP3:D:\MyData\Yunjian.Wu@monash.edu\S22_0026

• Latest dataset in this experiment:
• Experiment_1
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Instrument: MPMF_Exploris User folder name: Yunjian.Wu@monash.edu Uploaded from: 1W3NB93:D:\MyData\Yunjian.Wu@monash.edu\P22_0531

• Latest dataset in this experiment:
• Experiment 2_CAF_WP
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Metabolomics LC-MS data supporting the publication: An Integrated Multi-Omic Network Analysis Identifies Seizure-Associated Dysregulated Pathways in the GAERS Model of Absence Epilepsy, 2022, International Journal of Molecular Sciences Anna Harutyunyan (1), Debbie Chong (2), Rui Li, Anup D. Shah (3), Zahra Ali (2), Cheng Huang (3), Christopher K. Barlow (3), Piero Perucca (2,4,5,6), Terence J. O’Brien (1,2,4), Nigel C. Jones (1,2,4), Ralf B. Schittenhelm (3), Alison Anderson (1,2,4), Pablo M. Casillas-Espinosa (1,2,4) Correspondence: pablo.casillas-espinosa@monash.edu Samples are sorted into 5 directories: GAERS_Cortex: Samples from the somatosensory cortex in Genetic Absence Epilepsy Rats from Strasbourg (GAERS) rats NEC_Cortex: Samples from the somatosensory cortex in non-epileptic control (NEC) rats GAERS_Thalamus: Samples from thalamus in GAERS rats NEC_Thalamus: Samples from thalamus in NEC rats Pooled_Control: Samples prepared by combining a small amount of the experimental samples and used for quality control during the LC-MS analysis

• Latest dataset in this experiment:
• Pooled Control
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Data for https://doi.org/10.1038/s41598-020-79637-9 Identifying endogenous tissue stem cells remains a key challenge in developmental and regenerative biology. To distinguish and molecularly characterise stem cell populations in large heterogeneous tissues, the combination of cytochemical cell markers with ultrastructural morphology is highly beneficial. Here, we realise this through workflows of multi‐resolution immuno‐correlative light and electron microscopy (iCLEM) methodologies. Taking advantage of the antigenicity preservation of the Tokuyasu technique, we have established robust protocols and workflows and provide a side‐ by‐side comparison of iCLEM used in combination with scanning EM (SEM), scanning TEM (STEM), or transmission EM (TEM). Evaluation of the applications and advantages of each method highlights their practicality for the identification, quantification, and characterization of heterogeneous cell populations in small organisms, organs, or tissues in healthy and diseased states. The iCLEM techniques are broadly applicable and can use either genetically encoded or cytochemical markers on plant, animal and human tissues. We demonstrate how these protocols are particularly suited for investigating neural stem and progenitor cell populations of the vertebrate nervous system.

• Latest dataset in this experiment:
• IF data TEM-CLEM & STEM-CLEM
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Mass spectrometry data supporting the publication: Characterisation of koala (Phascolarctos cinereus) faecal cortisol metabolites using liquid chromatography-mass spectrometry and enzyme immunoassays. Flavia Santamaria et al., Metabolites, 2021, https://www.mdpi.com/2218-1989/11/6/393

• Latest dataset in this experiment:
• pHILIC_Female_Pixie
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Mass spectrometry data supporting the publication: Molecular Basis of Unexpected Specificity of ABC Transporter-Associated Substrate-Binding Protein DppA from Helicobacter pylori, Mohammad M. Rahman et al., Journal of Bacteriology, Vol 201, Issue 20, e00400-19. DOI:10.1128/JB.00400-19 A detailed description of the data processing and location of files may be found in the Read Me.

• Latest dataset in this experiment:
• Untargeted_Analysis_with_MZmine
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Crystallisation & diffraction experiment details available in PDB:6nvb. If you use this data, please cite: Acta Cryst. (2019) F75, https://doi.org/10.1107/S2053230X19009610 Crystal structure of the inhibitor-free form of the serine protease kallikrein-4 B. T. Riley, D. E. Hoke, S. McGowan & A. M. Buckle.

• Latest dataset in this experiment:
• Raw diffraction images of apoKLK4

If you use this data, please cite: Chen, X. et al. Potent, multi-target serine protease inhibition achieved by a simplified β-sheet motif. PLoS One 14, e0210842 (2019). https://dx.doi.org/10.1371/journal.pone.0210842 Processed data available at PDB:6bvh. Crystallisation & diffraction experiment details below: --- Crystallisation: - Protein solution: 20 mg/mL bovine trypsin, 50 mM MES pH 6.0, 50 mM benzamidine, 1mM CaCl2 - Reservoir buffer: 2.3 M (NH4)2SO4 and 0.1M MES pH 6.0 Sitting drops: 4 μL protein solution & 4 μL reservoir buffer, at room temperature Crystal soaking: - Inhibitor exchange buffer: 0.1 M MES, pH 6.0, 2.5 M (NH4)2SO4, 1 mM CaCl2 - Process: 6 hours in inhibitor exchange buffer, 48 hours in fresh inhibitor exchange buffer + saturating SFTI-TCTR(N12,N14) cyclopeptide rinse 3 times in 10 μL fresh inhibitor exchange buffer Cryoprotectant: - 0.1 M MES, pH 6.0, 2.5 M (NH4)2SO4, 1 mM CaCl2, 20 v/v% glycerol - Flash frozen in LN2 Irradiation source: ELLIOTT GX-13 Cu Kα rotating anode, λ=1.542 Å, 45 kV, 30 mA Cryocooling: 100 K N2 vapour stream Capture source: RIGAKU RAXIS IV++ Image Plate, Monash University

• Latest dataset in this experiment:
• Raw diffraction images
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Please cite: https://doi.org/10.1038/s41598-017-16264-x Kitchen, M. J., Buckley, G. A., Gureyev, T. E., Wallace, M. J., Andres-Thio, N., Uesugi, K., Yagi, N., & Hooper, S B. CT dose reduction factors in the thousands using X-ray phase contrast. Scientific Reports 7, 15953. https://doi.org/10.1038/s41598-017-16264-x (2017). Dataset information: ttps://dx.doi.org/10.4225/03/58197dd586bef Uploader: Genevieve_PC User folder name: bapcxi Uploaded from: MU00017665:E:\MyTardis_data\LowDose_CT_data

• Latest dataset in this experiment:
• Experiment_notes

These tables contain second order polynomial coefficients for calculating galaxy absolute magnitudes in the redshift range 0 < z < 1.2 from single observed colors using the method of Beare et al. 2014 (ApJ, 797, 104). These coefficients are used to calculate absolute magnitudes in "The z < 1.2 optical luminosity function for a sample of ~410 000 galaxies in Bootes" (Beare, R.A., Brown, M. J. I., & Pimbblet, K., submitted to ApJ) and in a forthcoming paper by the same authors: "Evolution of the stellar mass function and the infrared luminosity function of galaxies since z = 1.2". The tables assume h = 0.7 and Omega_0 = 0.3. Tables are provided for determining the following absolute magnitudes: Bessell U, B, V, R and I; NEWFIRM J; Johnson K; Sloan g, r and i. Observed colors are derived from the following apparent magnitudes: NDWFS Bw; Bessell R and I; NEWFIRM J and Ks; IRAC [3.6 micron] and [4.5 micron]. The recommended colors for different absolute magnitudes and redshift ranges are as follows: abs U (Bessell) z = 0.0 to 0.8:(Bw − R) z = 0.8 to 1.2: (R − I) abs B (Bessell) z = 0.0 to 0.4:(Bw − R) z = 0.4 to 0.8: (R − I) z = 0.8 to 1.2: (I − J) abs V (Bessell) z = 0.0 to 0.5: (R − I) z = 0.5 to 1.2: (I − J) abs R (Bessell) z = 0.0 to 0.19: (R − I) z = 0.19 to 1.2: (I − J) abs I (Bessell) z = 0.0 to 0.46: (I − J) z = 0.46 to 1.2: (R − J) abs J (NEWFIRM) z = 0.0 to 0.53: (R − I) z = 0.53 to 1.2: (I − J) abs K (Johnson) z = 0.0 to 0.6: (Ks − ch1) where ch1 = [3.6 micron] z = 0.56 to 1.2: (ch1 - ch2) ) where ch1 = [3.6 micron] and ch2 = [4.5 micron] abs u (Sloan u) z = 0.0 to 1.2:(Bw − R) abs gs (Sloan g) z = 0.0 to 0.5:(Bw − R) z = 0.45 to 0.8: (R − I) z = 0.8 to 1.2: (I − J) abs rs (Sloan r) z = 0.0 to 1.2: (R − J) abs is (Sloan i) z = 0.0 to 0.7: (I − J) z = 0.7 to 1.2: (J − Ks) abs zs (Sloan z) z = 0.0 to 1.2: (J − Ks)

• Latest dataset in this experiment:
• Polynomial parameters for calculating absolute magnitudes at z<1.2 from single observed colors together with associated plots.
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This archive contains data in CSV format from Tables 2 to 6 of, "An accurate new method of calculating absolute magnitudes and K-corrections applied to the Sloan filter set", (Beare, R., Brown, M. J. I., & Pimbblet, K. 2014, ApJ, 797, 104). The 10 tables list second order polynomial coefficients for use in determining absolute magnitudes from observed colors, two alternative colors being given for each of the Sloan u, g, r, i, z-bands, as described in the paper. The tables assume h = 0.7 and Omega_0 = 0.3. The recommended colors for different absolute magnitudes and redshift ranges are as follows: abs u z = 0.0 to 0.5: (u − g) preferred, (g − r) alternative abs g z = 0.0 to 0.34:(g − r) z = 0.34 to 0.5: (r − i) abs r z = 0.0 to 0.25 (g − i) z = 0.25 to 0.5 (r − z) abs i z = 0.0 to 0.5: (r − z) preferred, (g − i) alternative abs z z = 0.0 to 0.5: (r − z) preferred, (g − i) alternative ABSTRACT We describe an accurate new method for determining absolute magnitudes, and hence also K-corrections, which is simpler than most previous methods, being based on a quadratic function of just one suitably chosen observed color. The method relies on the extensive and accurate new set of 129 empirical galaxy template SEDs from Brown et al. (2014). A key advantage of our method is that we can reliably estimate random errors in computed absolute magnitudes due to galaxy diversity, photometric error and redshift error. We derive K-corrections for the five Sloan Digital Sky Survey filters and provide parameter tables for use by the astronomical community. Using the New York Value-Added Galaxy Catalog we compare our K-corrections with those from kcorrect. Our K-corrections produce absolute magnitudes that are generally in good agreement with kcorrect. Absolute g, r, i, z-band magnitudes differ by less than 0.02 mag, and those in the u-band by ~0.04 mag. The evolution of rest-frame colors as a function of redshift is better behaved using our method, with relatively few galaxies being assigned anomalously red colors and a tight red sequence being observed across the whole 0.0 < z < 0.5 redshift range.

• Latest dataset in this experiment:
• Polynomial parameters for determining Sloan absolute magnitudes at z<0.5 from single observed colors.
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Full methods and results for the ALE meta-analysis of task-switching fMRI studies presented in Jamadar, Thienel, Karayanidis (2014)

• Latest dataset in this experiment:
• ALE Meta-Analysis Methods and Results

This is the archive for "An Atlas of Galaxy Spectral Energy Distributions From The UV to the Mid-Infrared". The first folder contains the spectral energy distributions and csv tables of galaxy information, photometry and foreground dust extinction values. The folders named after individual galaxies contain the images from which the photometry was measured. The relevant paper was published in the Astrophysical Journal Supplement Series and is available via http://dx.doi.org/10.1088/0067-0049/212/2/18. A brief video introduction to the atlas is available via https://www.youtube.com/watch?v=lhC8ViPGoqU. The beta version of the atlas, which was released when the paper was submitted, is available via http://vera183.its.monash.edu.au/experiment/view/104/. The abstract of the paper follows. We present an atlas of 129 spectral energy distributions for nearby galaxies, with wavelength coverage spanning from the ultraviolet to the mid-infrared. Our atlas spans a broad range of galaxy types, including ellipticals, spirals, merging galaxies, blue compact dwarfs, and luminous infrared galaxies. We have combined ground-based optical drift-scan spectrophotometry with infrared spectroscopy from Spitzer and Akari with gaps in spectral coverage being filled using Multi-wavelength Analysis of Galaxy Physical Properties spectral energy distribution models. The spectroscopy and models were normalized, constrained, and verified with matched-aperture photometry measured from Swift, Galaxy Evolution Explorer, Sloan Digital Sky Survey, Two Micron All Sky Survey, Spitzer, and Wide-field Infrared Space Explorer images. The availability of 26 photometric bands allowed us to identify and mitigate systematic errors present in the data. Comparison of our spectral energy distributions with other template libraries and the observed colors of galaxies indicates that we have smaller systematic errors than existing atlases, while spanning a broader range of galaxy types. Relative to the prior literature, our atlas will provide improved K-corrections, photometric redshifts, and star-formation rate calibrations.

• Latest dataset in this experiment:
• UM_461
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This is the Beta version of "An Atlas of Galaxy Spectral Energy Distributions From The UV to the Mid-Infrared" and will be updated once the paper is accepted for publication. The first folder contains all the SEDs, while the folders for individual galaxies contain the SEDs and images used to constrain and verify the SEDs. We present an atlas of 129 spectral energy distributions for nearby galaxies, with wavelength coverage spanning from the UV to the mid-infrared. Our atlas spans a broad range of galaxy types, including ellipticals, spirals, merging galaxies, blue compact dwarfs and luminous infrared galaxies. We have combined ground-based optical drift-scan spectrophotometry with infrared spectroscopy from Spitzer and Akari, with gaps in spectral coverage being filled using MAGPHYS models. The spectroscopy and models were normalized, constrained and verified using matched aperture photometry measured using imaging from Swift, GALEX, SDSS, 2MASS, Spitzer and WISE. The availability of 26 photometric bands allowed us to identify and mitigate systematic errors present in the data. Comparison of our spectral energy distributions with other template libraries and the observed colors of galaxies indicates that we have smaller systematic errors than existing atlases, while spanning a broader range of galaxy types. Relative to the prior literature, our atlas will provide improved k-corrections, photometric redshifts and star-formation rate calibrations. The preprint is available via http://arxiv.org/abs/1312.3029

• Latest dataset in this experiment:
• UM_461
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Instructions (click 'Toggle Full Description')

1. In the top right-hand corner, there is one dataset available for download. Click the button labelled 'TAR'.
2. The dataset is downloaded as a WinZip (.zip) file. Extract the files to the desired location.
3. Once extracted, there will be 5 files in total. Use your internet browser to open the file named 'index.html' to access the data. This file can be opened in any standard browser, but works best in Mozilla Firefox or Google Chrome.
4. In the left panel is a list of all the proteins identified in the MS analysis. When you click on a particular protein, the corresponding peptograph appears in the centre panel, which shows the peptides detected for each protein (Dix et al., 2008). Peptides detected in the ‘no protease’ control are represented in green, and peptides in the mouse GrB treated sample are represented in red. When you click on a specific peptide, the MS data for that peptide appears in the right panel.

• Latest dataset in this experiment:
• Final peptographs of Protomap analysis (April 2013)

ROIs created from GingerALE (v2.1) meta-analysis of antisaccades and prosaccades reported in: Jamadar, Fielding, Egan (2013). Quantitative meta-analysis of fMRI and PET studies reveals consistent activation in fronto-striatal-parietal regions and cerebellum during antisaccades and prosaccades. Front. Psychol. 4:749. doi: 10.3389/fpsyg.2013.00749 ROIs are given for 2 meta-analyses (antisaccade > fixation, antisaccade > prosaccade) presented in the above paper. The ROIs are given in nii format in MNI space. The naming convention is: contrast_x_y_z_label_cluster# e.g. AS-PS_-2_-56_50_LPCUN_9 is from antisaccade - prosaccade analysis, tal coordinates (-2, -56, 50), left precuneus, cluster #9. Cluster number refers to the number given in Tables 2-4 in the manuscript. Labels are a guide only and were calculated from the peak ALE value within the cluster (Tables 2-4, Jamadar et al.). Please cite the above paper if you use these ROIs in your analysis. Any queries email Sharna Jamadar: sharna.jamadar@monash.edu or sharna.jamadar@gmail.com

• Latest dataset in this experiment:
• Antisaccade > Prosaccade

Mirrored from http://rawdata.chem.uu.nl/ .. Licensed CC-BY, with attribution to: Simon W. M. Tanley, Antoine M. M. Schreurs, John R. Helliwell and Loes M. J. Kroon-Batenburg Journal of Applied Crystallography, 2013, Volume 46, pages 108-119 reprint (PDF file, 1.8 Mb)

• Latest dataset in this experiment:
• 4DDC unwarp

• Latest dataset in this experiment:
• Prx3_VPP
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In order to investigate the biology of nemaline myopathy, a muscle disease characterised by the formation of nemaline (rod-like) aggregates, we created a zebrafish model. To generate the model we expressed a disease causing form of ACTA1 (ACTA1D286G) tagged with enhanced green fluorescent protein within the muscle. This allowed the visualisation of disease onset and progression in the living animal. This led to the discovery of how and where the characteristic aggregates form. In order to confirm that the fluorescent aggregates observed in the fish corresponded to those observed in patients using electron microscopy we carried out correlative light and electron microscopy on the zebrafish disease model allow visualisation of both the fluorescent protein and the electron dense aggregates. The raw data from these experiments are provided.

Related Publications

1. Sztal TE, Zhao M, Williams C, Oorschot V, Parslow AC, Giousoh A, Yuen M, Hall TE, Costin A, Ramm G, Bird PI, Busch-Nentwich EM, Stemple DL, Currie PD, Cooper ST, Laing NG, Nowak KJ, Bryson-Richardson RJ (2015) Zebrafish models for nemaline myopathy reveal a spectrum of nemaline bodies contributing to reduced muscle function. Acta Neuropathol doi: 10.1007/s00401-015-1430-3
2. Oorschot VM, Sztal TE, Bryson-Richardson RJ, Ramm G (2014) Immuno correlative light and electron microscopy on Tokuyasu cryosections. Methods Cell Biol 124:241-57

• Latest dataset in this experiment:
• MCB_CLEM
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