Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization (TrIQ)

Abstract

Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient discovery of ‘regions of interest’ (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values (‘outliers’) and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.

Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia

“…Therefore, we developed the "Threshold Intensity Quantization" (TrIQ) algorithm that reduces noise, optimizes the contrast, and maintains the true signal intensities. 36 We implemented the TrIQ data set into the Julia library, and bitmap figures of m/z intensity distributions can be created with two code lines:…”

“…The visualization of MSI data is often hampered by noise signals that obscure local differences. Therefore, we developed the “ Threshold Intensity Quantization ” (TrIQ) algorithm that reduces noise, optimizes the contrast, and maintains the true signal intensities . We implemented the TrIQ data set into the Julia library, and bitmap figures of m / z intensity distributions can be created with two code lines: # Extract image slice data slice = GetSlice­(spectra, 885.55, 0.005) # Save image, using the TrIQ algorithm and the Viridis color map SaveBitmap­(“TrIQ.bmp”, TrIQ­(slice, 256, 0.95), ViridisPalette) …”

Identification of Plant Compounds from Mass Spectrometry Imaging (MSI)

“…Many vendors' software can now read imzML files (MassLynx, FlexImaging, Imaging For Windows) or provide an option to export imzML files (http://ms-imaging.org/imzml/software-tools/). In addition, several open-source MSI programs provide data conversion [63] and support for imzML files [37,[64][65][66][67].…”

The profiling and imaging of sterol molecules in the human brain

“…There are many imaging techniques that utilise several ion sources for MSI including desorption electrospray ionisation (DESI), easy ambient sonic spray ionisation (EASI) and secondary ion mass spectrometry (SIMS), however, the most popular ionisation technique is matrix assisted laser desorption/ ionisation (MALDI) 269 due to its capability of analysing a broad range of molecules including many lipid species.…”