Shimadzu Corporation

Murata Manufacturing Co., Ltd.

Osaka University

Matsushita Electric Industrial Co., Ltd.

Kwansei Gakuin Educational Foundation

Shinichi Yamaguchi

Shoichiro Suzuki

Michisato Toyoda

Morio Ishihara

Akitaka Doi

Daisuke Okumura

Masaru Nishiguchi

Eizo Kawato

Yusuke Inohana

Masanori Nakamura

Yoshihiro Ueno

Osamu Furuhashi

Kiyoshi Ogawa

Hiroaki Kozawa

Masahiro Ikegami

Yoshikatsu Umemura

Toshikazu Takeda

Hideaki Izumi

Ryoichi Shimatani

Takushi Yamamoto

Koji Tanaka

Kazuaki Nakanishi

Hisao Ohishi

Nobuhiko Yamazaki

Akihiro Yamaguchi

Kazuko Hasegawa

Yuzuki Morita

Ikuko Yao

In a data-analyzing method in which an analysis based on a plurality of data groups each obtained by an instrumental analysis on each of a plurality of samples, with each data group including signal values having an n-dimensional array structure (where n≥2), is performed by using a computer, to obtain desired information concerning a difference between the samples, the method includes: a computational processing step for performing, in each data group, a persistent-homology processing on data including signal values having an m-dimensional array structure (where 2≤m≤n) obtained from one data group, to create a persistence diagram (PD); and an analytical processing step for comparing PDs respectively obtained from the data groups, to obtain the desired information based on a difference in the dispersion state of plots across the entire PDs being compared and/or based on information concerning a plot having no positional correspondence determined on the PDs being compared.

Data-analyzing method, data-analyzing device, and sample analyzer

An imaging mass spectrometer includes an analysis executing section that executes MSanalysis for a target component on each of a plurality of micro regions set within a two-dimensional measurement region on a sample to collect data; an ion selecting section that selects a plurality of kinds of product ions derived from, or assumed to be derived from, the target component based on at least a part of the data collected by the analysis executing section; and a distribution image creating section that determines, using signal strength of each of the kinds of product ions in each micro region within the measurement region. a small region in which all of the kinds of product ions are detected or a small region assumed to have high reliability that all of the kinds of product ions are derived from the target component, and creates a distribution image visualizing the small region.

Imaging mass spectrometer

An imaging analysis device according to the present invention includes: an analysis execution unit () configured to perform analysis by a predetermined analysis method for each of a plurality of measurement points set in a measurement area on a sample and collect imaging data; a reference image acquiring unit () configured to acquire a reference image for the measurement area; a regression analysis executer () configured to, with respect to the imaging data and the reference image obtained for the same sample, perform predetermined regression analysis calculation with the imaging data as an explanatory variable and data constituting the reference image as a target variable and acquire a regression model; and a predicted image creator () configured to apply, to the regression model, imaging data obtained by the analysis execution unit using a sample different from the sample used in the regression analysis executer, and create a predicted image based on a pseudo regression analysis result.

Imaging analysis device and imaging data analysis method

An analysis device collects data by performing a predetermined analysis on each specimen and processes the data. The analysis device includes an analysis processing unit () configured to execute multivariate analysis processing based on collected data for analysis of a difference between a plurality of measurement targets or classification of the plurality of measurement targets, a feature extraction unit () configured to extract a characteristic parameter or element estimated to be mainly related to a difference or classification from a multivariate analysis result according to a predetermined criterion, an image creation unit () configured to create an image of a predetermined two-dimensional range corresponding to the parameter or element extracted by the feature extraction unit, and a display processing unit () configured to assign a same visual aspect to the characteristic parameter or element extracted on the multivariate analysis result and the image created correspondingly and display the multivariate analysis result and the image on a display unit ().

Analysis device

An imaging mass spectrometry unit () executes predetermined analysis on each of a plurality of micro areas set in a two-dimensional measurement region on a sample or a three-dimensional measurement region in a sample to acquire spectrum data. A clustering execution section () classifies spectrum data for a plurality of measurement points obtained for a reference sample into any of a plurality of clusters. A clustering model information storage section () stores a clustering model obtained by clustering processing. A segmentation execution section () classifies respective spectral data for a plurality of measurement points obtained for a sample other than a reference sample using a clustering model, and a spatial distribution image creation section () creates a segmentation image obtained by partitioning a two-dimensional or three-dimensional image into a plurality of small regions on the basis of a result of the classification.

Imaging analyzer

An imaging mass spectrometer according to one aspect of the present invention includes an analysis executing section () configured to collect data by executing predetermined analysis on each of a plurality of micro regions set in a two-dimensional measurement region () on a sample () or a three-dimensional measurement region in the sample; a first image creating section () that uses the data obtained by the analysis executing section () to create one or a plurality of first distribution images each reflecting a distribution of one or a plurality of specific components included in the sample (); a formula storage section () that stores, as a formula, a chemical reaction formula including at least the one or a plurality of specific components as elements, or a calculation formula including an amount of the specific component as element; a signal value calculating section () that calculates different signal values from the signal values in the micro regions constituting the one or the plurality of first distribution images by using the formula acquired from the formula storage section () in response to a user's instruction; and a second image creating section () that creates a second distribution image based on a calculation result.

Imaging analysis device

At the time of superimposing and aligning a stained image and a mass spectrometric (MS) image obtained for the same sample, an image display processor displays, on the superimposed images, grid lines () at the spacing corresponding to an operation of a grid spacing adjustment slider (). When an operator depresses an image deformation range 'SET' button (), specifies an arbitrary area on the superimposed images with a mouse, and then depresses a “SELECT” button (), an image deformation range specification receiving section determines an image deformation range. When the operator selects an intersection (grid point) of the grid lines () within the image deformation range and performs an operation of moving the intersection point to an arbitrary position, an image deformation processor deforms an image included in the image deformation range in accordance with the operation. The image deformation range can be set irrespective of the grid-line spacing, so that it is possible to perform accurate image deformation in accordance with the amount and range of deformation for each site on the image, and improve work efficiency.

Imaging data processing device

An imaging mass spectrometer includes: a storage configured to acquire and store data constituting a first imaging graphic indicating an ion intensity distribution in a specific one or plurality of m/z or m/z ranges based on data obtained by mass spectrometry for a sample; a Raman imaging data acquisition unit configured to acquire and store data constituting one or plurality of second imaging graphics obtained by Raman analysis that is a type different from mass spectrometry for the sample; a signal intensity normalization processor configured to perform data conversion processing of normalizing signal intensity in one or plurality of first and second imaging graphics; an adjustment processor configured to perform data processing of aligning spatial resolutions of the one or plurality of first and second imaging graphics; and a statistical analysis processor configured to execute statistical analysis processing on images and to classify the first and second imaging graphics.

An analysis operator checks an optical microscopic image obtained with an imaging mass microscope and indicates a color characteristic of an area which the analysis operator is focusing on. An optical microscopic image feature extractor calculates luminance distribution data in the indicated color. An image position adjustment processor performs a position adjustment process on a luminance distribution image derived from the optical microscopic image and an MS imaging graphic, while a resolution adjuster equalizes their spatial resolutions. A statistical analysis processor calculates a coefficient of spatial correlation between the luminance distribution image and the MS imaging graphic for each mass-to-charge ratio. Based on the calculated correlation coefficients, an analysis result display processor extracts a mass-to-charge ratio which shows an ion intensity distribution similar to the luminance distribution image. and displays it on a display unit.

Data processing device

When a user designates a region of interest for a plurality of groups targeted for difference analysis in a microscopic observation image of a sample, an m/z candidate search unit searches for candidates for m/z presumed to differ, based on collected mass spectral data. An intensity histogram creation unit processing unit creates and displays a graph showing a frequency distribution of peak intensities at measurement points included in the ROI of the groups for each of the m/z candidates. If this graph exhibits multimodality, the data distribution is not suitable for a statistical hypothesis test. An intensity range determination unit limits an intensity range in accordance with a user's instruction. Then, ROI correction unit corrects the ROI so as to include only measurement points with peak intensities within the limited intensity range. A test processing unit performs a statistical hypothesis test using the data corresponding to the corrected ROI.

Imaging data analyzer

A measurement section () performs a mass spectrometric analysis for each micro area within a measurement area on a sample. A dimension reduction processor () performs data processing by non-linear dimension reduction using manifold learning on mass spectrometric data for each micro area, to obtain, for each micro area, a set of data reduced to three dimensions from the dimensions corresponding to the number of mass-to-charge-ratio values. A display color determiner () determines a color for each of the points corresponding to the data of the micro areas after the dimension reduction, by arranging those points within a three-dimensional space having three axes representing the three dimensions, with three primary colors respectively assigned to the three axes. A segmentation image creator () creates a segmentation image corresponding to the measurement area or a partial area in the measurement area, by arranging, on two dimensions, pixels which respectively correspond to the points within the three-dimensional space, where each pixel has a color given to the point corresponding to the pixel and is located according to the position within the measurement area of the micro area corresponding to the point.

Imaging mass spectrometer and method for imaging mass spectrometry

Imaging mass spectrometry section () performs a mass spectrometric analysis at each of the micro areas set within a measurement area on a target sample, and acquires a graphical image showing a signal-intensity distribution at a specific mass-to-charge ratio or mass-to-charge-ratio range. Quantitative analysis section () determines a quantitative value using an analysis result obtained by performing an analysis on the sample collected from each predetermined site within the measurement area of the target sample, using a predetermined analytical technique exhibiting a higher level of quantitative determination performance than the mass spectrometric analysis. Processing section () determines the relationship between signal intensity and quantitative value, based on quantitative values determined for the sample at predetermined sites and signal intensities at positions corresponding to the predetermined sites in the signal-intensity distribution, and estimates the quantitative value at an arbitrary position within the signal-intensity distribution, using the relationship.

Imaging mass spectrometry system and analytical method using imaging mass spectrometry

In an imaging mass spectrometer for analyzing the same kind of samples using results of imaging mass spectrometric analysis performed on those samples, a measurement section () acquires mass spectrometric data by performing an analysis on each of the micro areas on a sample. A region-of-interest setter () sets an ROI on each sample, and divides each ROI into the same number of subregions each including the micro areas so that the subregions correspond to each other on the samples respectively covering roughly identical sites on the samples. An individual-index-value calculator () calculates an individual index value for each subregion, using mass spectrometric data acquired at the micro areas in the subregion, the individual index value reflecting a similarity or difference among the samples in terms of a degree of expression of each m/z value. A general-index-value calculator () calculates a general index value for each m/z value among the ROIs of the samples, using the individual index values calculated for the ink values for each subregion included in each ROI.

Method for imaging mass spectrometry and imaging mass spectrometer

A user enters structures of a plurality of metabolite candidates contained in a sample. A dissociation pattern predictor predicts a dissociation pattern for each metabolite candidate. An MS/MS spectrum estimator estimates an MS/MS spectrum and stores it in a teaching data storage. An imaging mass spectrometry unit acquires measured MS/MS spectra for each measurement point within a measurement range on a sample by performing an MS/MS analysis in which a precursor ion based on mass information of each metabolite candidate is used as an analysis target. A multivariate analysis processor performs a multivariate analysis in which the peak information based on the MS/MS spectra stored in the teaching data storage is used as teaching data, to classify measured MS/MS spectra at each measurement point into a plurality of metabolite candidates. Based on the classification result, a spatial distribution creator creates an image showing a spatial distribution for each metabolite candidate.

A reference image data input section reads, from a Raman spectroscopic analyzer, a set of data constituting a Raman spectroscopic imaging graphic for a target sample. An ROI specification processor) displays a Raman spectroscopic imaging graphic based on those data on a display unit. An operator viewing the image operates an input unit to set a plurality of ROIs. Then, the ROI specification processor determines position information of the ROIs. An analysis processor extracts the data of measurement points corresponding to the set ROIs from MS imaging data acquired by an analysis performed by an imaging mass spectrometry unit for the same target sample. The processor also calculates an average mass spectrum from the data of a large number of measurement points for each ROI, and performs a multivariate analysis on the plurality of average mass spectrum data to compare the ROIs with each other or divide them into groups.

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Kyoto, Japan

Shinichi Yamaguchi