Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
39
datasets available to search
ShareScore release 0.7.1
Dataset results
39 results for “mass spectrometry imaging”
Oral Squamous Cell Carcinoma - Mass Spectrometry Imaging
<p>The dataset was first featured in <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201500458">Widlak, Piotr, et al. "Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium–application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data." <em>Proteomics</em> 16.11-12 (2016): 1613-1621</a>. For the tissue sample's biochemical preparation details, please refer to the original publication.</p> <p>The biological material was collected from five patients who underwent surgery due to Oral Squamous Cell Carcinoma (OSCC). Tissue samples contained both tumor and surrounding healthy tissue.</p> <p>Each specimen was cut into 10 µm sections in a cryostat. During the sample preparation for the MS imaging, a high-resolution optical scan of each section was captured.</p> <p>Tissue sections were subjected to peptide imaging with the use of a MALDI ToF mass spectrometer. Spectra were recorded within <em>m/z</em> range of 800-4,000. A raster width of 100 µm was applied, and 400 shots were collected from each ablation point. The obtained dataset consisted of 45,738 raw spectra with 109,568 mass channels.</p> <p>An experienced pathologist analyzed the optical scan obtained during the data acquisition process, and tissue regions were annotated. For the highest confidence of the results obtained in this work, we will focus on the two tissue samples out of the entire dataset (8,005 and 11,869 spectra), which have the highest confidence labels, as explained by the pathologist.</p> <p>The preprocessing of the spectra was conducted in MATLAB. Standard preprocessing steps were applied to the spectra. Spectra were resampled to unify the <em>m/z</em> axis across the dataset. The baseline was removed with MATLAB procedure <em>msbackadj()</em> from the Bioinformatics Toolbox. Peaks were aligned using Fast Fourier Transform-based spectral alignment. The TIC normalization ensured a similar intensity level for all spectra. Finally, a GMM approach was used to model the spectra. GMM locates the peak but also estimates the peak area instead of a raw magnitude provided by most methods. Note that the peaks in MSI spectra are right-skewed, so the neighboring GMM components resulting from that phenomenon were identified and merged to better correspond to actual chemical compounds. The resulting dataset is characterized by 3,714 GMM components corresponding to MSI spectrum peaks.</p>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of tobacco seedlings
<p>Mass spectrometry imaging (MSI) data set in imzML format, of complete tobacco (<em>Nicotiana tabacum</em>) seedling using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to nicotine shows accumulation in the roots and at the borders of leaves.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of San Pedro cactus
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from San Pedro cactus (<em>Echinopsis pachanoi</em>) cross-section using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to mescaline shows a star-like distribution of this interesting alkaloid.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</p> <p> </p>
Mass spectrometry Imaging dataset for the study on fungicide application to tomato leaves - I
<p>The dataset uploaded here is in association to a manuscript in press by Ajith et al. titled, "Visualizing active fungicide formulation mobility in tomato leaves with Desorption Electrospray Ionisation Mass Spectrometry Imaging". This dataset contains .imzML format files of Mass Spectrometry Imaging data along with the zipped .ibd files for a fungicide application study with a commerical Azoxystrobin formulation. The files were generated with a DESI Imprint imaging method for a commercial pesticide formulation applied young tomato leaves after 2 hours, 24 hours, 56 hours and a week after application.</p> <table> <tbody> <tr> <td>File Name</td> <td>Time point</td> </tr> <tr> <td>DTIM_2h</td> <td>2h Adaxial Imprint</td> </tr> <tr> <td>DTIM_24h_1</td> <td>24h Adaxial Imprint</td> </tr> <tr> <td>DTIM_24h_2</td> <td>24h Adaxial Imprint</td> </tr> <tr> <td>DTIM_24h_3</td> <td>24h Adaxial Imprint</td> </tr> <tr> <td>DTIM_56h_1</td> <td>56h Adaxial Imprint</td> </tr> <tr> <td>DTIM_56h_2</td> <td>56h Adaxial Imprint</td> </tr> <tr> <td>DTIM_56h_3</td> <td>56h Adaxial Imprint</td> </tr> <tr> <td>DTIM_1week_1</td> <td>1 week Adaxial Imprint</td> </tr> <tr> <td>DTIM_1week_2</td> <td>1 week Adaxial Imprint</td> </tr> <tr> <td>DTIM_1week_3</td> <td>1 week Adaxial Imprint</td> </tr> <tr> <td>DTIM_48h_Abaxial</td> <td>48h Abaxial imprint</td> </tr> <tr> <td>DTIM_48h_Adaxial</td> <td>48h Adaxial Imprint</td> </tr> </tbody> </table> <p> </p>
Multiplexed Staining Dataset - OMAP 5 - Liver-Lanthanides-conjugated antibodies and C60-secondary ion mass spectrometry imaging
<p>This dataset contains images of multiplexed antibody panel on a human pediatric liver section including the nuclear marker and antibodies conjugated with lanthanides tags. The dataset is one example of serial experiments of multiplexed antibody staining and imaging. The antibody panel targets the major cell types and tissue structures in the liver tissue. Data acquisition was performed using single multiplexing imaging by C60-secondary ion mass spectrometry.</p> <p> </p>
Laser Ablation Electrospray Ionization Mass Spectrometry Imaging (LAESI MSI) of Arabidopsis thaliana leaf
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from the 5th leaf of an Arabidopsis thaliana wildtype plant using Laser Ablation Electrospray Ionization. Laser ablation took place with 20 pulses per pixel at an energy of 58.4 µJ/pulse. The ROI measures 9 mm by 5 mm and was sampled with a step size of 200 µm.</p>
Low-temperature plasma mass spectrometry imaging (LTP-MSI) of Chili pepper
<p>Low-temperature plasma mass spectrometry imaging of a chili pepper. The data set contains raw data and the scripts to prepare the imzML file. The final imzML/ ibd files are also included.</p> <p>The creation of the data is described in: Maldonado-Torres, Mauricio, José Fabricio López-Hernández, Pedro Jiménez-Sandoval, and Robert Winkler. 2014. “‘Plug and Play’ Assembly of a Low-Temperature Plasma Ionization Mass Spectrometry Imaging (LTP-MSI) System.” <em>Journal of Proteomics</em> 102C (March): 60–65. doi:10.1016/j.jprot.2014.03.003.</p> <p>The data set was re-analyzed using R scripts, as reported in: Gamboa-Becerra, Roberto, Enrique Ramírez-Chávez, Jorge Molina-Torres, Robert Winkler, Enrique Ramírez-Chávez, Jorge Molina-Torres, and Robert Winkler. 2015. “MSI.R Scripts Reveal Volatile and Semi-Volatile Features in Low-Temperature Plasma Mass Spectrometry Imaging (LTP-MSI) of Chilli (Capsicum Annuum).” <em>Analytical and Bioanalytical Chemistry</em> 407 (19): 5673–84. doi:10.1007/s00216-015-8744-9.</p>
Imaging of Organic Samples with Megaelectron Volt Time-of-Flight Secondary Ion Mass Spectrometry Capillary Microprobe
<p>Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) with MeV primary ions offers a fine balance between secondary ion yield for molecules in the mass range from 100 to 1000 Da and beam spot size, both of which are critical for imaging applications of organic samples. Using conically shaped glass capillaries with an exit diameter of a few micrometers, a high energy heavy primary beam can be collimated to less than 10 μm. In this work, imaging capabilities of such a setup are presented for some organic samples (leucine-evaporated mesh, fly wing section, ink deposited on paper). Lateral resolution measurement and molecular distributions of selected mass peaks are shown. The negative influence of the beam halo, an unavoidable characteristic of primary beam collimation with a conical capillary, is also discussed. A new start trigger for TOF measurements based on the detection of secondary electrons released by the primary ion is presented. This method is applicable for a continuous primary ion beam, and for thick targets that are not transparent to the primary ion beam. The solution preserves the good mass resolution of the thin target setup, where the detection of primary ions with a PIN diode is used for a start trigger, reduces the background, and enables a wide range of samples to be analyzed.</p>
Data and code for publication: A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging
<p>Data & Code release for publication:</p> <p>Rebecca Buchholz, Sebastian Krossa, Maria K Andersen, Michael Holtkamp, Michael Sperling, Uwe Karst, May-Britt Tessem, A simple preparation protocol for shipping and storage of tissue sections for laser ablation-inductively coupled plasma-mass spectrometry imaging, <em>Metallomics</em>, Volume 14, Issue 3, March 2022, mfac013, <a href="https://doi.org/10.1093/mtomcs/mfac013">https://doi.org/10.1093/mtomcs/mfac013</a></p> <p>Python code for LA ICP MS imaging data segmentation</p> <p>Code & Data also on <a href="https://github.com/sekro/la-icp-msi_segmentation">github</a></p> <p>Thresholding based segmentation of LA-ICP-MS imaging data</p> <p>Description</p> <p><a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/main.py">src/main.py</a> - run this to process LA ICP MS data in data folder - generates matplotlib.figures - project specific setup <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/laicpms_data_handler.py">src/laicpms_data_handler.py</a> - contains object to import, handle and segment (shimadzu) raw data</p> <p>Dependencies</p> <p>Python 3.8.1 or newer</p> <p>For packages see <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/requirements.txt">requirements.txt</a></p> <p>Data</p> <p>LA-ICP-MS imaging data of human prostate tissue of the elements Zn, Fe & P. Details on data generation & collection in <a href="https://doi.org/10.1093/mtomcs/mfac013">publication</a>. LA-ICP-MS imaging data as plain text files (comma-separated values)</p> <ul> <li>Condition 1 = fresh frozen (FF)</li> <li>Condition 2 = room temperature vacuum dried and sealed (RTV)</li> <li>Condition 3 = formalin fixed (FFix)</li> <li>Condition 4 = formalin fixed, paraffin sealed (FFPS)</li> </ul> <p>3 replicate sectioning sets named A, B, C</p> <p>File-naming: LA_Data_CISN1.csv, where I = [1, 2, 3, 4] is indicating the condition used and N = [A, B, C] is indicating the replicate set</p> <p>License</p> <p>Data</p> <p>CC-BY 4.0 - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/data/LICENSE">LICENSE</a> file in data folder</p> <p>Source code</p> <p>MIT - respective <a href="https://github.com/sekro/la-icp-msi_segmentation/blob/master/src/LICENSE">LICENSE</a> file in src folder</p>
Mass Spectrometry Imaging Raw Data Files
<p>The enclosed ZIP file contains raw data generated using the Waters MALDI SYNAPT G2-Si High-Definition MS System. Imaging experiments were performed on fresh-frozen human carotid plaque sections and fresh-frozen rabbit aorta sections. Data were collected in both positive and negative modes for each type of tissue.</p>
Thermal evaporation as sample preparation for silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues
<p><strong>Thermal evaporation as sample preparation </strong><strong>for </strong><strong>silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues</strong></p> <p>MSI datasets in SCiLS Lab SL File (*.sl) or as flexImaging sequence (*.mis)</p>
Data from: SLICE-MSI: A machine learning interface for system suitability testing of mass spectrometry imaging platforms
Open the record for dataset details and reuse information.
Protocols for mass spectrometry imaging
<p>Protocols for metabolomic profiling using GC-MS, semi-thin cryosectioning of tissues for MALDI-IMS, and the application of a matrix on semi-thin cryosections for MALDI-IMS.</p>
Mass spectrometry imaging of metabolites in symbiont containing tissues of Bathymodiolus sp. mussels from a hydrothermal vent
<p>Molecules in <em>Bathymodiolus </em>sp. tissue. Distribution of five lipid metabolites in symbiont containing gill tissues was visualized using MALDI mass spectrometry imaging (red: high amounts, blue: low amounts of lipids).</p>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation - ESI
<p>ESI data for the paper 'Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation'.</p>
Mass spectrometry Imaging dataset for a study on fungicide application to tomato leaves - II
<p>The dataset uploaded here is in association to a manuscript in press by Ajith et al., titled, "Visualizing active fungicide formulation mobility in tomato leaves with Desorption Electrospray Ionisation Mass Spectrometry Imaging". This dataset contains .imzML files along with zipped .ibd files of mass spectrometry imaging data and .mzML format LC-MS data for a fungicide applicaion study on tomato leaves. DESI Imprint imaging data here is for tomato leaves applied with Azoxystrobin standard after 24 hours, 56 hours and a week after formulation application along with a blank leaf data with no Azoxystrobin application.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Time point</strong></td> </tr> <tr> <td>DTIM_Std_24h</td> <td>24h</td> </tr> <tr> <td>DTIM_Std_56h</td> <td>56h</td> </tr> <tr> <td>DTIM_Std_1week</td> <td>1 week</td> </tr> <tr> <td>DTIM_Blank</td> <td> No application</td> </tr> </tbody> </table> <p>This data upload contains a zipped LC-MS data folder (LC_mzML.7z). The details of the files, the time point of sampling and part of leaf is in the excel sheet uploaded along with the data (LC-MS_datanames.xls).</p> <p> </p>
Dataset related to article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging"
<p><em>The .zip file contains raw data related to the article "Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging", available from <a href="https://www.ijbs.com/v16p1363.htm">https://www.ijbs.com/v16p1363.htm</a>.</em></p> <ul> <li><em>The folder "<strong>fig 1 2 3 NIRA</strong>" contains raw data related to the experiments with niraparib presented in figures 1, 2 and 3 </em></li> <li><em>The folder "<strong>fig 1 2 3 OLA</strong>" contains raw data related to the experiments with olaparib presented in figures 1, 2 and 3 </em></li> <li><em>The folders "<strong>fig 4</strong>" and "<strong>fig 6</strong>" contain raw data related to figures 4 and 6, respectively.</em></li> </ul> <p><strong>For any additional information on how to read and reuse the dataset please contact Dr. Ubezio at paolo.ubezio@marionegri.it.</strong></p> <p> </p> <p><strong>ABSTRACT OF THE MANUSCRIPT:</strong></p> <p><strong>Rationale</strong>: Optimal intratumor distribution of an anticancer drug is fundamental to reach an active concentration in neoplastic cells, ensuring the therapeutic effect. Determination of drug concentration in tumor homogenates by LC-MS/MS gives important information about this issue but the spatial information gets lost. Targeted mass spectrometry imaging (MSI) has great potential to visualize drug distribution in the different areas of tumor sections, with good spatial resolution and superior specificity. MSI is rapidly evolving as a quantitative technique to measure the absolute drug concentration in each single pixel.</p> <p><strong>Methods</strong>: Different inorganic nanoparticles were tested as matrices to visualize the PARP inhibitors (PARPi) niraparib and olaparib. Normalization by deuterated internal standard and a custom preprocessing pipeline were applied to achieve a reliable single pixel quantification of the two drugs in human ovarian tumors from treated mice.</p> <p><strong>Results</strong>: A quantitative method to visualize niraparib and olaparib in tumor tissue of treated mice was set up and validated regarding precision, accuracy, linearity, repeatability and limit of detection. The different tumor penetration of the two drugs was visualized by MSI and confirmed by LC-MS/MS, indicating the homogeneous distribution and higher tumor exposure reached by niraparib compared to olaparib. On the other hand, niraparib distribution was heterogeneous in an ovarian tumor model overexpressing the multidrug resistance protein P-gp, a possible cause of resistance to PARPi.</p> <p><strong>Conclusions</strong>: The current work highlights for the first time quantitative distribution of PAPRi in tumor tissue. The different tumor distribution of niraparib and olaparib could have important clinical implications. These data confirm the validity of MSI for spatial quantitative measurement of drug distribution providing fundamental information for pharmacokinetic studies, drug discovery and the study of resistance mechanisms.</p>
Data from: In situ lipidomics of Staphylococcus aureus osteomyelitis using imaging mass spectrometry
Open the record for dataset details and reuse information.
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of jimsonweed
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from jimsonweed (Datura stramonium) fruits and seeds using Laser Desorption Low-Temperature Plasma ionization. The data indicate the non-uniform distribution of tropane alkaloids.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry 2019 91 (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.