Shigeki Kajihara

Hierarchical Bayesian estimation for MEG inverse problem.

Source current estimation from MEG measurement is an ill-posed problem that requires prior assumptions about brain activity and an efficient estimation algorithm. In this article, we propose a new hierarchical Bayesian method introducing a hierarchical prior that can effectively incorporate both structural and functional MRI data. In our method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed MEG data and prior information by using the Variational Bayesian method. The fMRI information can be imposed as prior information on the variance distribution rather than the variance itself so that it gives a soft constraint on the variance. A spatial smoothness constraint, that the neural activity within a few millimeter radius tends to be similar due to the neural connections, can also be implemented as a hierarchical prior. The proposed method provides a unified theory to deal with the following three situations: (1) MEG with no other data, (2) MEG with structural MRI data on cortical surfaces, and (3) MEG with both structural MRI and fMRI data. We investigated the performance of our method and conventional linear inverse methods under these three conditions. Simulation results indicate that our method has better accuracy and spatial resolution than the conventional linear inverse methods under all three conditions. It is also shown that accuracy of our method improves as MRI and fMRI information becomes available. Simulation results demonstrate that our method appropriately resolves the inverse problem even if fMRI data convey inaccurate information, while the Wiener filter method is seriously deteriorated by inaccurate fMRI information.

Visualization of the cell-selective distribution of PUFA-containing phosphatidylcholines in mouse brain by imaging mass spectrometry.

Previous studies have shown that MALDI-imaging mass spectrometry (IMS) can be used to visualize the distribution of various biomolecules, especially lipids, in the cells and tissues. In this study, we report the cell-selective distribution of PUFA-containing glycerophospholipids (GPLs) in the mouse brain. We established a practical experimental procedure for the IMS of GPLs. We demonstrated that optimization of the composition of the matrix solution and spectrum normalization to the total ion current (TIC) is critical. Using our procedure, we simultaneously differentiated and visualized the localizations of specific molecular species of GPLs in mouse brain sections. The results showed that PUFA-containing phosphatidylcholines (PCs) were distributed in a cell-selective manner: arachidonic acid- and docosahexaenoic acid-containing PCs were seen in the hippocampal neurons and cerebellar Purkinje cells, respectively. Furthermore, these characteristic localizations of PUFA-PCs were formed during neuronal maturation. The phenomenon of brain cell-selective production of specific PUFA-GPLs will help elucidate the potential physiological functions of PUFAs in specific brain regions.

Semi-quantitative analyses of metabolic systems of human colon cancer metastatic xenografts in livers of superimmunodeficient NOG mice

Analyses of energy metabolism in human cancer have been difficult because of rapid turnover of the metabolites and difficulties in reducing time for collecting clinical samples under surgical procedures. Utilization of xenograft transplantation of human-derived colon cancer HCT116 cells in spleens of superimmunodeficient NOD/SCID/IL-2Rγnull (NOG) mice led us to establish an experimental model of hepatic micrometastasis of the solid tumor, whereby analyses of the tissue sections collected by snap-frozen procedures through newly developed microscopic imaging mass spectrometry (MIMS) revealed distinct spatial distribution of a variety of metabolites. To perform intergroup comparison of the signal intensities of metabolites among different tissue sections collected from mice in fed states, we combined matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry (MALDI–TOF-IMS) and capillary electrophoresis–mass spectrometry (CE–MS), to determine the apparent contents of individual metabolites in serial tissue sections. The results indicated significant elevation of ATP and energy charge in both metastases and the parenchyma of the tumor-bearing livers. To note were significant increases in UDP-N-acetyl hexosamines, and reduced and oxidized forms of glutathione in the metastatic foci versus the liver parenchyma. These findings thus provided a potentially important method for characterizing the properties of metabolic systems of human-derived cancer and the host tissues in vivo.

Integrated mass spectrometry–based analysis of plasma glycoproteins and their glycan modifications

We present a protocol for the identification of glycosylated proteins in plasma followed by elucidation of their individual glycan compositions. The study of glycoproteins by mass spectrometry is usually based on cleavage of glycans followed by separate analysis of glycans and deglycosylated proteins, which limits the ability to derive glycan compositions for individual glycoproteins. The methodology described here consists of 2D HPLC fractionation of intact proteins and liquid chromatography–multistage tandem mass spectrometry (LC-MS/MSn) analysis of digested protein fractions. Protein samples are separated by 1D anion-exchange chromatography (AEX) with an eight-step salt elution. Protein fractions from each of the eight AEX elution steps are transferred onto the 2D reversed-phase column to further separate proteins. A digital ion trap mass spectrometer with a wide mass range is then used for LC-MS/MSn analysis of intact glycopeptides from the 2D HPLC fractions. Both peptide and oligosaccharide compositions are revealed by analysis of the ion fragmentation patterns of glycopeptides with an intact glycopeptide analysis pipeline.

Salamander retina phospholipids and their localization by MALDI imaging mass spectrometry at cellular size resolution

Salamander large cells facilitated identification and localization of lipids by MALDI imaging mass spectrometry. Salamander retina lipid extract showed similarity with rodent retina lipid extract in phospholipid content and composition. Like rodent retina section, distinct layer distributions of phospholipids were observed in the salamander retina section. Phosphatidylcholines (PCs) composing saturated and monounsaturated fatty acids (PC 32:0, PC 32:1, and PC 34:1) were detected mainly in the outer and inner plexiform layers (OPL and IPL), whereas PCs containing polyunsaturated fatty acids (PC 36:4, PC 38:6, and PC 40:6) composed the inner segment (IS) and outer segment (OS). The presence of PCs containing polyunsaturated fatty acids in the OS layer implied that these phospholipids form flexible lipid bilayers, which facilitate phototransduction process occurring in the rhodopsin rich OS layer. Distinct distributions and relative signal intensities of phospholipids also indicated their relative abundance in a particular cell or a cell part. Using salamander large cells, a single cell level localization and identification of biomolecules could be achieved by MALDI imaging mass spectrometry.

Development of imaging mass spectrometry (IMS) dataset extractor software, IMS convolution

Imaging mass spectrometry (IMS) is a powerful tool for detecting and visualizing biomolecules in tissue sections. The technology has been applied to several fields, and many researchers have started to apply it to pathological samples. However, it is very difficult for inexperienced users to extract meaningful signals from enormous IMS datasets, and the procedure is time-consuming. We have developed software, called IMS Convolution with regions of interest (ROI), to automatically extract meaningful signals from IMS datasets. The processing is based on the detection of common peaks within the ordered area in the IMS dataset. In this study, the IMS dataset from a mouse eyeball section was acquired by a mass microscope that we recently developed, and the peaks extracted by manual and automatic procedures were compared. The manual procedure extracted 16 peaks with higher intensity in mass spectra averaged in whole measurement points. On the other hand, the automatic procedure using IMS Convolution easily and equally extracted peaks without any effort. Moreover, the use of ROIs with IMS Convolution enabled us to extract the peak on each ROI area, and all of the 16 ion images on mouse eyeball tissue were from phosphatidylcholine species. Therefore, we believe that IMS Convolution with ROIs could automatically extract the meaningful peaks from large-volume IMS datasets for inexperienced users as well as for researchers who have performed the analysis.

Wiener filter-magnetoencephalography of visual cortical activity.

This paper reports a revised Wiener filter to resolve the inverse problem for magnetoencephalograms (MEGs) according to the structural and functional constraints based on magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI). Wiener filter-MEG imaging for half field stimulation with the chromatic stimulus resolved fast, slow and late responses in V1, V4 and the inferotemporal cortex, respectively. The time courses of these responses were roughly comparable with those reported by unit recording studies of the corresponding monkey visual cortical areas. Wiener filter-MEG imaging had comparable spatial resolution and better signal to noise ratio than fMRI. The background noise was robust in fMRI responses, but became virtually eliminated in Wiener filter responses. Wiener filter-MEG imaging with upper and lower quadrant field stimulation demonstrated V1 responses differentially distributed respectively in the lower and upper banks of the calcarine sulcus. These results demonstrate that responses in two cortical areas facing close to each other can be resolved by Wiener filter-MEG. The present method provides a way to image brain activities with millisecond- and millimeter-order spatiotemporal resolution.

The C-Terminal Fragment of Prostate-Specific Antigen, a 2331 Da Peptide, as a New Urinary Pathognomonic Biomarker Candidate for Diagnosing Prostate Cancer

Background and Objectives Prostate cancer (PCa) is one of the most common cancers and leading cause of cancer-related deaths in men. Mass screening has been carried out since the 1990s using prostate-specific antigen (PSA) levels in the serum as a PCa biomarker. However, although PSA is an excellent organ-specific marker, it is not a cancer-specific marker. Therefore, the aim of this study was to discover new biomarkers for the diagnosis of PCa. Materials and Methods We focused on urine samples voided following prostate massage (digital rectal examination [DRE]) and conducted a peptidomic analysis of these samples using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MSn). Urinary biomaterials were concentrated and desalted using CM-Sepharose prior to the following analyses being performed by MALDI-TOF/MSn: 1) differential analyses of mass spectra; 2) determination of amino acid sequences; and 3) quantitative analyses using a stable isotope-labeled internal standard. Results Multivariate analysis of the MALDI-TOF/MS mass spectra of urinary extracts revealed a 2331 Da peptide in urine samples following DRE. This peptide was identified as a C-terminal PSA fragment composed of 19 amino acid residues. Moreover, quantitative analysis of the relationship between isotope-labeled synthetic and intact peptides using MALDI-TOF/MS revealed that this peptide may be a new pathognomonic biomarker candidate that can differentiate PCa patients from non-cancer subjects. Conclusion The results of the present study indicate that the 2331 Da peptide fragment of PSA may become a new pathognomonic biomarker for the diagnosis of PCa. A further large-scale investigation is currently underway to assess the possibility of using this peptide in the early detection of PCa.

Influence of head model in biomagnetic source localization

SummaryWe evaluated the influence of the head model on biomagnetic source localization by utilizing a computer simulation. We localized the source of a magnetic field that was calculated using a realistic head model, and then evaluated the localization errors. It was seen that the sphere model adequately localized the dipole in cases near the sensor, but not in cases where the dipole was deeply situated.

Serum immunoglobulin G Fc region N-glycosylation profiling by matrix-assisted laser desorption/ionization mass spectrometry can distinguish breast cancer patients from cancer-free controls

Herein, we report that breast cancer (BC) patients can be distinguished from cancer-free (NC) controls by serum immunoglobulin G (IgG) crystallizable fragment (Fc) region N-glycosylation profiling using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Recently, there has been much progress in the field of tumor immunology. However, to date, the role and biomarker potential of IgG Fc region N-glycosylation, which affects the function of antibodies, have not been examined in BC. In the present study, we profiled serum IgG Fc region N-glycans in BC patients (N = 90) and NC controls (N = 54) using MALDI-MS. An IgG Fc region N-glycan-based multiple logistic regression model was produced which could distinguish BC patients from NC controls (area under the receiver operative characteristic curve = 0.874). Furthermore, stage 0 patients could also be distinguished using this model. These results suggest that an unknown humoral factor or soluble mediator affects IgGs from the earliest stage of breast cancer, and also suggests that IgG Fc region N-glycosylation may play a role in tumor biology. Although further investigation is required, our findings are the evidence that IgG N-glycan profiling has the potential to be used as a breast cancer biomarker and may provide the insights into tumor immunology.