Normalization in MALDI-TOF imaging datasets of proteins: practical considerations

Normalization is critically important for the proper interpretation of matrix-assisted laser desorption/ionization (MALDI) imaging datasets. The effects of the commonly used normalization techniques based on total ion count (TIC) or vector norm normalization are significant, and they are frequently beneficial. In certain cases, however, these normalization algorithms may produce misleading results and possibly lead to wrong conclusions, e.g. regarding to potential biomarker distributions. This is typical for tissues in which signals of prominent abundance are present in confined areas, such as insulin in the pancreas or β-amyloid peptides in the brain. In this work, we investigated whether normalization can be improved if dominant signals are excluded from the calculation. Because manual interaction with the data (e.g., defining the abundant signals) is not desired for routine analysis, we investigated two alternatives: normalization on the spectra noise level or on the median of signal intensities in the spectrum. Normalization on the median and the noise level was found to be significantly more robust against artifact generation compared to normalization on the TIC. Therefore, we propose to include these normalization methods in the standard “toolbox” of MALDI imaging for reliable results under conditions of automation

MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for investigating the distribution of proteins and small molecules within biological systems through the in situ analysis of tissue sections. MALDI-IMS can determine the distribution of hundreds of unknown compounds in a single measurement and enables the acquisition of cellular expression profiles while maintaining the cellular and molecular integrity. In recent years, a great many advances in the practice of imaging mass spectrometry have taken place, making the technique more sensitive, robust, and ultimately useful. In this review, we focus on the current state of the art of MALDI-IMS, describe basic technological developments for MALDI-IMS of animal and human tissues, and discuss some recent applications in basic research and in clinical settings

Glycosylphosphatidyl Inositol-anchored Proteins and fyn Kinase Assemble in Noncaveolar Plasma Membrane Microdomains Defined by Reggie-1 and -2

Using confocal laser scanning and double immunogold electron microscopy, we demonstrate that reggie-1 and -2 are colocalized in ≤0.1-μm plasma membrane microdomains of neurons and astrocytes. In astrocytes, reggie-1 and -2 do not occur in caveolae but clearly outside these structures. Microscopy and coimmunoprecipitation show that reggie-1 and -2 are associated with fyn kinase and with the glycosylphosphatidyl inositol-anchored proteins Thy-1 and F3 that, when activated by antibody cross-linking, selectively copatch with reggie. Jurkat cells, after cross-linking of Thy-1 or GM1 (with the use of cholera toxin), exhibit substantial colocalization of reggie-1 and -2 with Thy-1, GM1, the T-cell receptor complex and fyn. This, and the accumulation of reggie proteins in detergent-resistant membrane fractions containing F3, Thy-1, and fyn imparts to reggie-1 and -2 properties of raft-associated proteins. It also suggests that reggie-1 and -2 participate in the formation of signal transduction centers. In addition, we find reggie-1 and -2 in endolysosomes. In Jurkat cells, reggie-1 and -2 together with fyn and Thy-1 increase in endolysosomes concurrent with a decrease at the plasma membrane. Thus, reggie-1 and -2 define raft-related microdomain signaling centers in neurons and T cells, and the protein complex involved in signaling becomes subject to degradation

Imagerie MALDI haute définition (HDMI): étude pilote de la spermatogenèse chez le rat.

International audienc

Imagerie MALDI haute définition (HDMI): étude pilote de la spermatogenèse chez le rat.

International audienc

Development of a Class Prediction Model to Discriminate Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor by MALDI Mass Spectrometry Imaging.

PURPOSE To define proteomic differences between pancreatic ductal adenocarcinoma (pDAC) and pancreatic neuroendocrine tumor (pNET) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). EXPERIMENTAL DESIGN Ninety-three pDAC and 126 pNET individual tissues are assembled in tissue microarrays and analyzed by MALDI MSI. The cohort is separated in a training (52 pDAC and 83 pNET) and validation set (41 pDAC and 43 pNET). Subsequently, a linear discriminant analysis (LDA) model based on 46 peptide ions is performed on the training set and evaluated on the validation cohort. Additionally, two liver metastases and a whole slide of pDAC are analyzed by the same LDA algorithm. RESULTS Classification of pDAC and pNET by the LDA model is correct in 95% (39/41) and 100% (43/43) of patients in the validation cohort, respectively. The two liver metastases and the whole slide of pDAC are also correctly classified in agreement with the histopathological diagnosis. CONCLUSION AND CLINICAL RELEVANCE In the present study, a large dataset of pDAC and pNET by MALDI MSI is investigated, a class prediction model that allowed separation of both entities with high accuracy is developed, and differential peptide peaks with potential diagnostic, prognostic, and predictive values are highlighted