Summary of Study ST003165
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001969. The data can be accessed directly via it's Project DOI: 10.21228/M86Q81 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST003165 |
| Study Title | Spatial Lipidomics Maps Brain Alterations Associated with Mild Traumatic Brain Injury. |
| Study Summary | Traumatic brain injury (TBI) is a global public health problem with 50-60 million incidents per year, most of which are considered mild (mTBI) and many of these repetitive (rmTBI). Despite their massive implications, the pathologies of mTBI and rmTBI are not fully understood, with a paucity of information on brain lipid dysregulation following mild injury event(s). To gain more insight on mTBI and rmTBI pathology, a non-targeted spatial lipidomics workflow utilizing ultrahigh resolution mass spectrometry imaging was developed to map brain region-specific lipid alterations in rats following injury. Discriminant multivariate models were created for regions of interest including the hippocampus, cortex, and corpus callosum to pinpoint lipid species that differentiated between injured and sham animals. A multivariate model focused on the hippocampus region differentiated injured brain tissues with an area under the curve of 0.994 using only four lipid species. Lipid classes that were consistently discriminant included polyunsaturated fatty acid-containing phosphatidylcholines (PC), lysophosphatidylcholines (LPC), LPC-plasmalogens (LPC-P) and PC potassium adducts. Many of the polyunsaturated fatty acid-containing PC and LPC-P selected have never been previously reported as altered in mTBI. The observed lipid alterations indicate that neuroinflammation and , oxidative stress and disrupted sodium-potassium pumps are important pathologies that could serve to explain cognitive deficits associated with rmTBI. Therapeutics which target or attenuate these pathologies may be beneficial to limit persistent damage following a mild brain injury event. |
| Institute | Georgia Institute of Technology |
| Last Name | Leontyev |
| First Name | Dmitry |
| Address | 311 Ferst Dr NW Atlanta GA 30332 |
| dleontyev3@gatech.edu | |
| Phone | 301 538 2301 |
| Submit Date | 2024-04-08 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | imzML |
| Analysis Type Detail | MALDI |
| Release Date | 2024-04-30 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN005193 |
|---|---|
| Analysis type | MS |
| Chromatography type | None (Direct infusion) |
| Chromatography system | Bruker solariX 12T |
| Column | none |
| MS Type | MALDI |
| MS instrument type | FT-ICR |
| MS instrument name | Bruker Solarix FT-ICR-MS |
| Ion Mode | POSITIVE |
| Units | intensity |
MS:
| MS ID: | MS004926 |
| Analysis ID: | AN005193 |
| Instrument Name: | Bruker Solarix FT-ICR-MS |
| Instrument Type: | FT-ICR |
| MS Type: | MALDI |
| MS Comments: | Provided are .imzML/.ibd files that are root mean square normalized when exported from SCiLS Lab. I am providing 1 section from each brain. MALDI imaging data were collected on a solariX 12T FTICR mass spectrometer (Bruker Daltonics, Bremen, Germany) in positive ion mode in the m/z 147-1500 range using 2M transients (~300,000 mass resolution at m/z 314). A 50 µm raster spacing in the x and y directions was used. The laser was set to 100 shots, small focus, 12 % power and 1000 Hz. Real time calibration with a lock mass of m/z 314.152598 from the DAN dimer and m/z 760.585082 from PC(34:1) was used to achieve optimal mass accuracy. Two sections from each of the five sham and six injured brains were examined by FTICR MSI. Serial sections were placed on the same ITO slide at the time of the MSI experiment. Among the sections examined, two replicate images were eliminated from the dataset due to abnormally low ion abundances. A total of twenty images from eleven rats were used for multivariate image analysis with eight of those sections being from sham animals and twelve from injured animals. MS images were uploaded to, and analyzed in SCiLS Lab Version 2022b Pro (Bruker Daltonics, Bremen, Germany). Regarding pre-processing options, no baseline correction or other notable options were used. For segmentation purposes, a feature list was created with the sliding window tool using the average mass spectrum from all brain sections and the lowest possible intensity threshold. This yielded a large peak list with a ± 3 ppm window for each ion. This feature list was used to computationally segment the brain images into molecularly similar regions of interest (ROI). The parameters used for segmentation were the preliminary feature list, root mean square normalization, strong denoising, bisecting k-means and the Manhattan distance metric. Segmentation was performed on individual brain sections or regions. In a few cases, some ROI were not correctly picked out by the automated segmentation approach alone and were thus manually outlined following specific lipid distributions that helped delineate the ROI borders. For each ROI, feature lists were first created in SCiLS Lab with a ± 5 ppm feature tolerance. Receiver operating characteristic (ROC) analysis was then conducted on these ROI-specific feature lists using the mean spectra. All ions with an area under the curve above 0.7 (i.e., those with abundances larger in control brains), or those with an area under the curve (AUC) below 0.3 (more abundant in injured animals) were chosen. The remaining m/z values were filtered out from the input feature list. For ROI involving the gray matter, the white matter, and the corpus callosum, AUC cutoff values were set to a stricter cutoff of 0.8 and 0.2, as the corresponding input feature lists contained a large abundance of ions. Extracted ion images for m/z values in the resulting feature lists were inspected to remove any species originating from the MALDI matrix, the embedding mixture, or ions with poor signal-to-noise ratios. The spectral profile for each feature was inspected to ensure that the interval chosen by SCiLS software was correctly aligned with the apex of each peak, and the feature list interval tolerance then lowered to ± 3 ppm. |
| Ion Mode: | POSITIVE |
