Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mass spectrometry imaging of phospholipids in mouse urinary bladder (imzML dataset)

ABSTRACT: The spatial distribution of phospholipids in a tissue section of mouse urinary bladder was analyzed by MALDI MS imaging at 10 micrometer pixel size with high mass resolution (using an LTQ Orbitrap mass spectrometer).

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Urinary Bladder

SUBMITTER: Andreas Roempp

LAB HEAD: Bernhard Spengler

PROVIDER: PXD001283 | Pride | 2014-11-19

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	HR2MSImouseurinarybladderS096-opticalimage.tif	Other
	HR2MSImouseurinarybladderS096-results.csv	Csv
	HR2MSImouseurinarybladderS096.ibd	Other
	HR2MSImouseurinarybladderS096.imzML	Other

Items per page:

1 - 4 of 4

Publications

Histology by mass spectrometry: label-free tissue characterization obtained from high-accuracy bioanalytical imaging.

Römpp Andreas A Guenther Sabine S Schober Yvonne Y Schulz Oliver O Takats Zoltan Z Kummer Wolfgang W Spengler Bernhard B

Angewandte Chemie (International ed. in English) 20100501 22

PMID: 20397170

Similar Datasets

Characterization of spatial heterogeneity in metastasized colorectal cancer by MALDI imaging

Project description:About 50% of colorectal cancer patients develop liver metastases. Patients with metastatic colorectal cancer have 5-year survival rates below 20% despite new therapeutic regimens. Tumor heterogeneity has been linked with poor clinical outcome, but was so far mainly studied via bulk genomic analyses. In this study we performed spatial proteomics via MALDI mass spectrometry imaging on six patient matched CRC primary tumor and liver metastases to characterize interpatient, intertumor and intratumor hetereogeneity. We found several peptide features that were enriched in vital tumor areas of primary tumors and liver metastasis and tentatively derived from tumor cell specific proteins such as annexin A4 and prelamin A/C. Liver metastases of colorectal cancer showed higher heterogeneity between patients than primary tumors while within patients both entities show similar intratumor heterogeneity sometimes organized in zonal pattern. Together our findings give new insights into the spatial proteomic heterogeneity of primary CRC and patient matched liver metastases.

2023-02-17 | PXD039409 | Pride

Viral biomarker detection and validation using MALDI Mass Spectrometry Imaging (MSI)

Project description:INTRODUCTION: Mass Spectrometry Imaging (MSI) is a hybrid mass spectrometry technique that integrates aspects of traditional microscopy and mass spectrometry-based omics analysis. The traditional MALDI TOF/TOF instrument still remains the dominant platform for this type of anal-ysis. However, with reduced mass resolution compared to other platforms it is insufficient to rely on mass resolution alone for peptide identification. Here we propose a hybrid method of data analysis that integrates both image-based analysis and a parallel protein identification workflow using peptide mass fingerprinting, and its successful application to the detection and validation of viral biomarkers. METHODS: FFPE samples were imaged as described previously in an UltrafleXtreme MALDI TOF/TOF. Total mass spectra were exported and searched against the mouse FASTA da-tabase, while companion images were exported and processed with image J. RESULTS: Peptide mass fingerprinting (PMF) revealed 14 target peptides that were successfully assigned to viral proteins while a pixel based correlational analysis revealed a very high R2 correlation (>0.81) be-tween those same peptides assigned to the NS1 and VP1 viral proteins. CONCLUSIONS: We successfully identified and validated the presence of viral biomarkers to a high degree of confidence using MALDI MSI.

2022-10-13 | PXD034851 | Pride

Real-time molecular diagnosis of tumors using water-assisted laser desorption/ionization mass spectrometry technology

Project description:This dataset contains MS imaging raw data as well as the SCiLS file used to confirm the regions in dog sarcoma biopsies classified by the SpiderMass system after analysis of the consecutive tissue section. All the tissues were gathered during dog sarcoma surgeries, snap-frozen and analysed later in the lab. The images were acquired on a MALDI LTQ orbitrap XL hybrid instrument.

2019-04-12 | PXD010990 | Pride

MS imaging of tryptic peptides in mouse brain measured at 50 µm pixel size

Project description:A coronal mouse brain section that was measured at 50µm pixel size revealed detailed histological structures such as the ependyma (consisting of one to two cell layers, which was confirmed by HE staining).

2018-08-08 | PXD003172 | Pride

Analysis of Amyloid Plaque Composition in two mouse models of Alzheimer’s disease

Project description:MALDI mass spectrometry imaging (MSI) enables label-free, spatially resolved analysis of a wide range of analytes in tissue sections. Quantitative analysis of MSI datasets is typically performed on single pixels or manually assigned regions of interest (ROI). However, many sparse, small objects such as Alzheimer’s disease (AD) brain deposits of amyloid peptides called plaques are neither single pixels nor ROI. Here, we propose a new approach to facilitate comparative computational evaluation of amyloid plaque-like objects by MSI: a fast PLAQUE PICKER tool that enables statistical evaluation of heterogeneous amyloid peptide composition. Comparing two AD mouse models, APP NL-G-F and APP PS1, we identified distinct heterogeneous plaque populations in the NL-G-F model, but only one class of plaques in the PS1 model. We propose quantitative metrics for the comparison of technical and biological MSI replicates.

2020-10-15 | PXD020824 | Pride

Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging

Project description:Background: Barrett’s esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic haematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE. Methods: Biopsies from 57 patients were considered, divided into non-dysplastic (n=15), LGD non-progressive (n=14), LGD progressive (n=14), and HGD (n=14). MSI experiments were conducted at 50x50 μm spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144,823 tiles for the whole dataset. Results: ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)). Conclusions: In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches.

2021-11-15 | PXD028949 | Pride

Cancer tissue classification using machine learning applied to MALDI MSI and high resolution H&E stained images

Project description:Aim is to identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identification of a molecular pattern that can distinguish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Materials and Methods: Using MALDI MSI data, we developed and validated a machine learning model that can be used for early screening of CRC. Our model yields high sensitivity and specificity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer

2022-02-15 | PXD019662 | Pride

Cancer tissue classication using machine learning applied to MALDI MSI and high resolution H&E stained images

Project description:To identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identication of a molecular pattern that can distinguish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Using MALDI MSI data,we developed and validated a machine learning model that can be used for early screening of CRC. Our model yields high sensitivity and specicity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer.

2022-02-15 | PXD019653 | Pride

Cancer tissue classification using machine learning applied to MALDI MSI and high resolution H&E stained images

Project description:Aim is to identify a panel of m/z markers for the early detection of colorectal cancer (CRC). Identification of a molecular pattern that can distin- guish the primary tumours of colorectal cancer with lymph node metastasis compared to those without. Materials and Methods: Using MALDI MSI data, we developed and validated a machine learning model that can be used for early screening of CRC. Results: Our model yields high sensitivity and speci- ficity in distinguishing normal tissue from the cancerous. Model described here, can be a used in clinical labs for early diagnosis of colorectal cancer

2022-02-15 | PXD019666 | Pride

Cancer tissue classification using supervised machine learning applied to MALDI mass spectrometry imaging

Project description:With the current clinical available technologies, not all cancers are staged accurately. Because of this a large percentage of patients are misclassified before treatment, leading to under or over treatment. Therefore, a classification system based on the molecular feature is required to deter-mine the tumour behavior and metastatic potential. Here, we have shown the diagnostic potential of MALDI MSI using supervised machine learning approach in distinguishing cancerous colorectal tissue from normal with an overall accuracy of 98%. Also, shown is the capability of the technique in predicting the presence of metastasis in endometrial cancer with an overall accuracy of 80%. The development of such a model can help in determining the optimum treatment for cancerous pa-tients, reduce morbidity and better patient outcome.

2022-02-17 | PXD025594 | Pride