SIMS and MALDI MS imaging of the spinal cord

Abstract

The application of mass spectrometry to imaging, or MS imaging (MSI), allows for the direct investigation of tissue sections to identify biological compounds and determine their spatial distribution. We present an approach to MSI that combines secondary ion mass spectrometry (SIMS) and MALDI MS for the imaging and analysis of rat spinal cord sections, thereby enhancing the chemical coverage obtained from an MSI experiment. The spinal cord is organized into discrete, anatomically defined areas that include motor and sensory networks composed of chemically diverse cells. The MSI data presented here reveal the spatial distribution of multiple phospholipids, proteins, and neuropeptides obtained within single, 20-μm sections of rat spinal cord. Analyte identities are initially determined by primary mass match and confirmed in follow-up experiments using LC MS/ MS from extracts of adjacent spinal cord sections. Additionally, a regional analysis of differentially localized signals serves to rapidly screen compounds of varying intensities across multiple spinal regions. These MSI analyses reveal new insights into the chemical architecture of the spinal cord and set the stage for future imaging studies of the chemical changes induced by pain, anesthesia, and drug tolerance.

Time-of-Flight Secondary Ion Mass Spectrometry-Based Molecular Distribution Distinguishing Healthy and Osteoarthritic Human Cartilage

“…The FA detected and here described as masses specific to the OA condition, could be part of these lipids. Future parallel studies using MALDI-imaging for instance, could discriminate these intact lipids as other authors have shown (33)(34)(35).…”

OpenMSI: A High-Performance Web-Based Platform for Mass Spectrometry Imaging

“…E.g., a selection of [1,4,5] usually results in the retrieval of a single scalar corresponding to the item with index (1,4,5).…”

“…That is all elements in the selection specified for x are combined with the selection specified for y. x=1:4 and y=1:3, hence, results in the retrieval of the elements [(1,1), (1,2), (2,1), (2,2) (3,1), (4,2)]. All-to-all selection, hence, always result in the retrieval of of a single or multiple rectangular regions.…”

“…This means if multiple lists are specified, then the lists must be of equal length and the lists are merged to define specific indexpairs to be selected. E.g, x= [1,2] and y= [4,5] results in the retrieval of the elements [ (1,4), (2,4) ] compared to an all-to-all matching, which would retrieve [ (1,4), (1,5), (2,4), (2,5) ]. This scheme allows for selection of arbitrary regions of interest.…”

“…All tests are performed using a 100×100×100,000 test dataset. We, evaluate the performance of k×k×l layouts with k= [1,2,4,8,16, 32] and l= [128,256,512,1024,2048,4096,8192]. We omitted 32×32×128, 32×32×256, 32×32×512 due to the poor spectrum-at-a-time write performance of these data layouts.…”