A surface-assisted laser desorption/ionization method according to an aspect includes: a first process of preparing a sample support (2) having a substrate (21) in which a plurality of through-holes (S) passing from one surface (21a) thereof to the other surface (21b) thereof are provided and a conductive layer (23) that covers at least the one surface (21a); a second process of placing a sample (10) on a sample stage (1) and arranging the sample support (2) on the sample (10) such that the other surface (21b) faces the sample (10); and a third process of applying a laser beam (L) to the one surface (21a) and ionizing the sample (10) moved from the other surface (21b) side to the one surface (21a) side via the through-holes (S) due to a capillary phenomenon.

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 (1) acquires mass spectrometric data by performing an analysis on each of the micro areas on a sample. A region-of-interest setter (32) 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 (33) 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 (34) 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.

A method and apparatus for monitoring and/or controlling the extent of denaturation and/or bond cleavages of proteins on any surface (e.g., biological tissues, biofilms, etc.). In one embodiment, a low power laser (e.g., a 5 mW, 362 nm diode laser) is directed through a biological sample to a photodetector. The sample is heated by a set of radiant heaters to between about 220° C. and about 250° C. in a time period of between 10 seconds to 60 seconds. The baseline transmissivity of the sample is monitored continuously throughout treatment of the biological sample via continuous monitoring of the signal voltage detected at the photodetector. Upon detection of increase in relative transmissivity in the biological sample, the heating treatment is concluded and the biological sample is removed for in situ protein identification as part of an imaging MALDI-MS measurement.

As described herein, one or more parameters of a direct ionization imaging mass spectrometer (IMS) may be set to obtain a desired plasma and deliver it to a mass detector. Depending on the application, certain parameters may be predetermined (e.g., a spot size given a desired resolution) and, as described herein, other parameters can be adjusted to obtain the desired plasma properties. Also included is sample preparation suitable for direct ionization IMS and/or other imaging modalities.

A measurement section (1) performs a mass spectrometric analysis for each micro area within a measurement area on a sample. A dimension reduction processor (23) 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 (24) 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 (25) 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.

A peak-waveform conversion processor detects a peak in a profile spectrum created based on data obtained in each micro area in a measurement area, and acquires a rod-like peak by performing centroid conversion processing on a waveform of the peak in a mountain shape. When receiving a precise m/z value Ma of a target compound and an allowable range ΔM of m/z, an image creator determines whether or not there is a rod-like peak in a range defined by “Ma±ΔM”, for each micro area. When there is a rod-like peak, a height value of the rod-like peak is defined as the signal intensity value of the target compound in the micro area. In contrast, when there is no rod-like peak in the range defined by “Ma±ΔM”, the signal intensity value of the target compound in the micro area is set to zero.

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.

Machine learning approach can combine mass spectral imaging (MSI) techniques, one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The machine learning approach can perform transformations on the spectral image data of the two MSI techniques to reduce dimensionality, and using a correlation technique, find relationships between the transformed spectral image data. The determined relationships can be used to generate MSI spectra of desired resolution.

We describe in this application systems and methods for autofocusing in imaging mass spectrometry. The present application describes improvements over current IMS and IMC apparatus and methods through an autofocus component including a plurality of apertures in the autofocus system, such as a plurality of apertures arranged in 2 dimensions. As a plurality of apertures is used, the autofocus system provides redundancy in the event that measurement of focus on the sample from the illuminating radiation passed through one or more of the apertures fails so as to reduce the number of unsuccessful autofocus attempts.

A mass spectrometry imaging system includes an ionization source located at a first location configured to produce ions from a surface of a sample at the first location; a mass spectrometer located at a second location configured to perform mass spectrometry analysis by analyzing the produced ions based on mass to charge ratio of the ions; and an ion transfer device configured to transfer the ions from the first location to the second location such that the ion transfer device includes a plurality of electrodes, the plurality of electrodes configured to be flexible or flexibly connected to each other, and the ion transfer device is configured to be flexible or re-configurable while transferring the ions.