JP7158642B2 - Mass spectrometer and mass spectrometry system - Google Patents

Description

The present invention relates to a mass spectrometer, a mass spectrometry method, a mass spectrometry system, and a mass spectrometry analysis program.

In recent years, various methods have been developed to detect circulating biomarkers in order to diagnose disease progression in patients.
For example, circulating tumor cells (CTCs or CTSs) are tumor cells present in the peripheral blood of patients that originate from primary and metastatic lesions and have been detected in patients with various types of cancer. ing. In recent years, the DNA and RNA profiles of CTCs have been elucidated, and it has become clear that CTCs are involved in cancer recurrence/metastasis and treatment resistance. High-precision detection of CTCs is increasingly expected as a clinical test for predicting the prognosis of cancer and the therapeutic effect of chemotherapy.
As a method for detecting CTCs, a method has been proposed in which the cells are treated with an antibody against the cell surface antigen of CTCs and the stained cells are observed under a microscope (see, for example, Patent Document 1 and Non-Patent Documents 1 and 2). ).

Japanese Patent No. 6052756

Ｔ.Ｋａｗａｄａ ｅｔ ａｌ.： “Ｃｉｒｃｕｌａｔｉｎｇ ｔｕｍｏｒ ｃｅｌｌｓ ｉｎ ｐａｔｉｅｎｔｓ ｗｉｔｈ ｈｅａｄ ａｎｄ ｎｅｃｋ ｓｑｕａｍｏｕｓ ｃｅｌｌ ｃａｒｃｉｎｏｍａ： Ｆｅａｓｉｂｉｌｉｔｙ ｏｆ ｄｅｔｅｃｔｉｏｎ ａｎｄ ｑｕａｎｔｉｔａｔｉｏｎ” Ｈｅａｄ Ｎｅｃｋ ３９ （１１） ２１８０－２１８６， ２０１７ Ａｕｇｕｓｔ １７. S Zheng et al.: "3D microfilter device for viable circulating tumor cell (CTC) enrichment from blood" Biomed Microdevices. 13 (1) 203-13, 2011 February.

However, in the CTC detection methods described in Patent Document 1 or Non-Patent Documents 1 and 2, a series of pretreatments related to antibody labeling must be performed, which takes time and effort. Therefore, it has been desired to provide a method for detecting CTCs simply, quickly, and with high accuracy.
In addition, there has been a demand for a method capable of performing simple, rapid, and highly accurate detection not only for CTC but also for a desired test target in blood.

The present invention provides a mass spectrometer, a mass spectrometry method, a mass spectrometry system, and a mass spectrometry analysis for detecting a test target in blood, which can detect the test target in blood simply, quickly, and with high accuracy. Offer a program.

As means for solving the above-described problems, the mass spectrometer of the present invention includes ionization means for ionizing a component of a test target in the blood of a subject;
and analysis means for mass-analyzing the ions generated by the ionization means.
Further, the mass spectrometry system of the present invention comprises the mass spectrometer of the present invention,
At least one of mass spectrum information created based on the mass spectrometry results obtained by the analysis means and mass spectrum analysis result information obtained by learning the mass spectrum information is used to determine whether the existence state of the test object in blood exceeds a predetermined allowable range or whether the mass spectrum exhibits a disease-positive pattern,
If the presence state of the test subject exceeds the predetermined allowable range or the mass spectrum is determined to exhibit a disease-positive pattern, the subject is derived from the presence state of the test subject. and an analysis device having determination means for determining the possibility of corresponding to the disease.

INDUSTRIAL APPLICABILITY According to the present invention, a mass spectrometer, a mass spectrometry method, a mass spectrometry system, and a mass spectrometer for detecting a test target in blood, which can detect the test target in blood simply, quickly, and with high accuracy. An analysis program can be provided.

FIG. 1 is a schematic diagram showing an example of a mass spectrometer according to the first embodiment. FIG. 2A is a schematic diagram showing an example of a mass spectrometer according to the second embodiment. FIG. 2B is an enlarged photograph showing an example of the distal end portion of the first tubular member. FIG. 3 is a photograph showing an example of a schematic configuration diagram showing how the mass spectrometer according to the second embodiment is used. FIG. 4A is a schematic diagram showing how the mass spectrometer according to the second embodiment shown in FIG. 3 automatically collects an object to be examined. FIG. 4B is a schematic diagram showing how the mass spectrometer according to the second embodiment of FIG. 3 automatically collects an object to be examined. FIG. 5 is a schematic diagram showing an example of a mass spectrometer according to the third embodiment. FIG. 6 is a schematic diagram showing another example of the mass spectrometer according to the third embodiment. FIG. 7 is a schematic diagram illustrating the process of separating blood from mononuclear cells. FIG. 8 is a schematic diagram for explaining information derived from mass spectrum information used by the determination means of the analyzer. FIG. 9 is a block diagram showing an example of the analysis device of the present invention. FIG. 10 is a block diagram showing an example of the data structure. FIG. 11 is a flow chart showing an example of the flow of processing in the analysis device of the present invention. FIG. 12 is a flow chart showing another example of the flow of processing in the analysis device of the present invention. FIG. 13 is a diagram showing an example of measurement data (text format). FIG. 14 is a diagram showing an example of expressing the data shown in FIG. 13 in the form of a mass spectrum. FIG. 15 is a diagram showing an example of a chromatogram. FIG. 16 is a diagram showing an example of a representative mass spectrum created from data in the designated time zone of the chromatogram of FIG. FIG. 17 is a diagram showing an example of an input screen (condition setting screen) in automatic (semi-automatic) creation of a representative mass spectrum. FIG. 18 is a diagram showing an example of a chromatogram and a representative mass spectrum displayed in automatic (semi-automatic) creation of a representative mass spectrum. FIG. 19 is a diagram showing an example of an input screen for registering a created representative mass spectrum. FIG. 20 is a diagram showing an example of a label input screen in the labeling of the created representative mass spectrum. FIG. 21 is a diagram showing an example of a setting screen for outputting mass spectrum data for statistical analysis. FIG. 22 is a diagram showing an example of a setting screen for determining an internal indicator. FIG. 23 is a diagram showing an example of an input/output screen for determining an internal standard and mass spectrum evaluation processing using the determined internal standard. FIG. 24 is a diagram showing an example of a setting screen (input screen) for statistical analysis processing. FIG. 25 is a diagram showing an example of a file output by marker search by the test method. FIG. 26 is a diagram showing an example of a result (score plot) that has been reduced to two principal components by the reduction method. FIG. 27 is a diagram showing an example of an errata showing the results of the verification method. FIG. 28 is a diagram showing an example of an output screen collectively showing the results of statistical analysis and verification. FIG. 29 is a diagram showing an example of the results of a detection confirmation test for cancer cells in blood using the mass spectrometry system of the present invention. FIG. 30 is a diagram showing an example of the results of a detection confirmation test for cancer cells in blood using the mass spectrometry system of the present invention.

(mass spectrometry system)
The mass spectrometry system of the present invention has a mass spectrometer and an analysis device, and further has other devices as necessary.
With the mass spectrometer in the mass spectrometry system of the present invention, the profile (analysis result) of the test object in blood can be obtained easily, quickly, and with high accuracy.
In particular, by directly ionizing the test target and performing mass spectrometry on the generated ions, the test target in the blood can be detected without performing the conventional labeling pretreatment of adding an antibody to the test target. profile (analysis results) can be acquired.
Further, by analyzing the mass spectrum obtained as a result of mass spectrometry by the mass spectrometry system of the present invention, it is possible to determine the presence state of the test object in blood with high accuracy. It is possible to determine the possibility of falling under a disease derived from the condition.
In other words, with the mass spectrometry system of the present invention, the profile (analysis result) of the test object itself can be obtained without the pretreatment of adding an antibody (labeling the molecule to be detected). A test target in blood can be detected with high accuracy.
For example, if the object to be examined is CTC, it is possible to directly ionize the CTC without labeling it with an antibody, and use the profile (analysis result) of the CTC cell itself, which is simple, rapid, and highly accurate. CTC detection can be performed at In addition to CTC, for example, when the test object is lipids, the mass spectrometry system of the present invention can detect the presence of lipids in blood, thereby detecting lipids such as hyperlipidemia in the subject. It is possible to determine the possibility of corresponding to a disease whether or not it is a disease derived from. In addition, for example, when the subject to be tested is sugar, the mass spectrometry system of the present invention can be used to detect the presence of sugar in the blood, thereby determining whether the subject has a disease caused by sugar such as diabetes. It is possible to determine the possibility of corresponding to the disease of whether or not there is.

(1. Mass spectrometer and mass spectrometry method)
The mass spectrometer of the present invention has ionization means, analysis means, and other means as necessary.
The mass spectrometry method of the present invention includes an ionization step, an analysis step, and further includes other steps as necessary.

The mass spectrometry method of the present invention can be suitably implemented by the mass spectrometer of the present invention, the ionization step can be performed by ionization means, the analysis step can be performed by analysis means, and other steps can be performed by other methods. can be done by means of

<Ionization Means and Ionization Step>
The ionization step is a step of ionizing the component of the test object in the blood of the subject, and is carried out by ionization means.
Here, the object to be examined is any animal including humans.

<<Inspection object>>
The object of mass spectrometry in the present invention is a test object present in blood.
Here, blood includes blood cell components (cellular components, blood cells), platelets, plasma components (liquid components), and the like.
The blood cell components include red blood cells, white blood cells, platelets, and the like. The leukocytes include neutrophils, eosinophils, basophils, lymphocytes, monocytes and the like. The lymphocytes include NK cells (natural killer cells), B cells (B lymphocytes), T cells (T lymphocytes) and the like.
The plasma components include water, proteins, fats, sugars, inorganic salts, low-molecular-weight metabolites, foreign molecules, and the like.

The subject to be tested is not particularly limited as long as it is a biomarker in blood, and can be appropriately selected according to the purpose. Examples include cells, amino acids, peptides, proteins, nucleic acids, oligonucleotides (oligo DNA), Oligoribonucleotides (oligo RNA), lipids, carbohydrates, vitamins, electrolytes, low-molecular-weight metabolites and the like are included. Here, a biomarker refers to a biomarker, and refers to a substance measured in blood that can recognize the presence and progress of a certain disease.
Examples of the cells to be examined include circulating tumor cells (CTC), erythrocytes, and leukocytes.
Examples of the proteins to be tested include albumin, hemoglobin, γ-globulin, fibrinogen, antithrombin III, transferrin, ceruloplasmin, cytokines including growth factors, and chemokines.
Examples of the lipid to be tested include neutral fat, HDL cholesterol, and LDL cholesterol.
Examples of the carbohydrates to be tested include glucose and 1.5AG (1.5 anhydroglycitol).
Examples of the nucleic acid to be tested include circulating normal DNA, circulating tumor DNA, non-coding RNA (miRNA, transfer RNA, ribosomal RNA, etc.).

The test target may be fructosamine, HbA1c (glycosylated hemoglobin), or the like, which is a combination of protein and carbohydrate.
Moreover, the test target may be an enzyme, a hormone, a tumor marker, or the like. Here, the term "tumor marker" refers to a substance that serves as an indicator of malignant tumors.
Examples of the enzyme include γ-GTP, AST (aspartate aminotransferase), ALT (alanine aminotransferase), amylase and the like.
Examples of the hormone include adrenocorticotropic hormone, luteinizing hormone, follicle-stimulating hormone, prolactone, thyroid hormone, parathyroid hormone, aldosterone, insulin, estrogen, progesterone, and growth hormone.
Examples of the tumor markers include AFP (α-fetoprotein), CA19-9 (serial Lewis A saccharide), CA125 (glycoprotein), CEA (carcinoembryonic antigen), PSA (prostate specific antigen), SSC (squamous epithelium), cancer-associated antigens) and the like.

It is preferable that antibodies (excluding antibodies derived from the same species as the subject) are not added to the test subject. By not performing the pretreatment of adding an antibody, it is possible to easily and quickly detect the object to be tested. For example, when detecting CTCs in human blood, it is preferable to subject CTCs to which antibodies (except human antibodies) have not been added to an ionization step.

As the specimen sample to be subjected to mass spectrometry, the blood collected from the subject may be used as it is, or the collected blood may be subjected to mononuclear cell separation treatment to separate the mononuclear cell layer. may be used.
The treatment method for the mononuclear cell separation treatment is not particularly limited as long as the mononuclear cell layer can be separated, and can be appropriately selected according to the purpose. For example, Ficoll (Ficoll-Paque PLUS, manufactured by GE Healthcare Japan), etc. A general separation method using the lymphocyte separation medium of
When blood is separated by Ficoll, it is separated into layers as shown in FIG. 7, for example. Therefore, it is preferable to use the separated layers as the analyte sample used in the mass spectrometry method of the present invention.
For example, when the object to be examined is CTC, it is preferable to use the mononuclear cell layer among the separated layers.
In FIG. 7, symbol a denotes a separation solution (e.g., lymphocyte separation solution (specific gravity: 1.119)), b blood, c plasma, d mononuclear cells (lymphocytes, monocytes), platelets , e indicates erythrocytes and granulocytes.

When the collected blood is used as it is as the test sample, the time required from the collection of the blood to the ionization is short. However, when mononuclear cell separation treatment is performed, the processing time for mononuclear cell separation is added, but even in this case, the time required from blood collection to ionization is generally within 60 minutes. be. This is a much shorter time than conventionally required for pretreatment, for example, adding an antibody for detection of CTCs.

It is preferable to use the collected blood as it is as the test sample because the treatment is simple and the time is short. On the other hand, it is preferable to separate the mononuclear cell layer from the blood and use the mononuclear cell layer as the specimen sample from the viewpoint of more accurate detection. Therefore, whether or not to perform the mononuclear cell separation process should be appropriately determined in consideration of the type of test object and the specific form of the ionization means (the specific form of the ionization means will be described later). .
For example, when a mononuclear cell separation process is performed and the separated mononuclear cell layer is used as a sample, the mass spectrometer of the present invention may be the following <First Embodiment> or the following <Second Embodiment>. A mass spectrometer having the ionization means described in the section can be used.
In addition, for example, when collected blood is used as a specimen sample without performing mononuclear cell separation processing, the mass spectrometer of the present invention is described in the section <Third Embodiment> below. A mass spectrometer with ionization means can be used.

Examples of the ionization means include means using an ambient ionization method. The ambient ionization method is a method of directly gas-phase ionizing a test sample under a normal environment (for example, normal temperature, atmospheric pressure environment).
In the present invention, an electrospray ionization method, which is one method of ambient ionization, is preferably used. In addition, the line ionization method described below is also preferred.
Specific aspects of the ionization means include the following cases.
(1) Ionization means using a mode having a needle-like member, a power source, and an ion collecting member (hereinafter referred to as probe electrospray ionization (hereinafter sometimes abbreviated as "PESI")) , (2) ionization means using a first tubular member, a second tubular member, a power source, and an ion directing member (hereinafter referred to as Remote Sampling Electrospray Ionization); 3) There is an ionization means using a linear member and an ionization member (hereinafter referred to as a line ionization method).

It should be noted that the above ionization means (1) to (3) require only a very short time from the ionization of the sample to obtaining the determination result of mass spectrometry.
When the mass spectrometer of the present invention is used, for example, once the analyte sample is ionized, the determination result of mass spectrometry can be obtained within about 5 minutes, enabling real-time analysis.

<<Ionization Means Using Probe Electrospray Ionization>>
An ionization means using a probe electrospray ionization (PESI) method has a needle member, a power supply, an ion collection member, and other members as necessary.
In the ionization means using the PESI method, real-time mass spectrometry is possible because ionization is performed immediately after the sample is collected with a needle-like member.

As an ionization means using such a PESI method, for example, one disclosed in International Publication No. 2010/047399 can be used.

The needle-shaped member is used for picking up a sample or an object to be examined contained in the sample by contacting or piercing the tip of the sample.
The power source is used to apply a voltage to the needle-like member in order to generate ions derived from the specimen sample or test object collected from the tip of the needle-like member by electrospray.
The ion collecting member is used to collect the generated ions.

In the ionization means using the PESI method, the needle-shaped member is moved to bring the tip of the needle-shaped member into contact with or pierce the specimen sample, thereby trapping part of the specimen sample at the tip of the needle-shaped member.
If the tip of the needle-shaped member is made very thin, the tip of the needle-shaped member captures a very small area of the sample on the surface of the sample.
When a high voltage is applied to the needle-shaped member with the specimen attached to the tip, a strong electric field acts on the specimen at the tip of the needle-shaped member, and the molecules of the specimen are ionized while being detached by the electrospray method.

The advantages of ionization means using the PESI method are (i) measurement under atmospheric pressure, (ii) a very small amount of sample is sufficient (several picoliters), and (iii) minimal invasiveness. (iv) does not require pretreatment of the test object, (v) is not easily affected by salts and surfactants, and (vi) allows real-time analysis.

-needle-shaped member-
The needle-like member is a member that picks up (captures) a sample by contacting or piercing the tip of the sample.
The shape, structure, size, material, etc. of the needle-like member are not particularly limited and can be appropriately selected according to the purpose.
The shape of the needle-like member can be a tip shape capable of generating an electrospray phenomenon.
The structure of the needle-shaped member includes a solid structure, a single layer structure, a multi-layer structure, and the like.
The size of the needle-like member (including the thickness of the needle, etc.) can be appropriately selected depending on the amount of specimen sample to be captured by the tip and the type of test object.
As a material, it is preferable to use a material that is durable, less contaminated by germs, etc., and can be sterilized. Stainless steel, titanium, tungsten, nickel, aluminum, platinum, gold, silver, or those plated with these are suitable. Furthermore, as these surface treatments, electropolishing, acid treatment, and chemical treatment may be used to adjust hydrophilicity or hydrophobicity.
As the needle-like member, specifically, a needle made of stainless steel for acupuncture and moxibustion can be used.

-electrode-
The electrode is a member that applies a voltage to the needle-shaped member with the sample adhered to the tip of the needle-shaped member.
A standard voltage is 2 kV to 3 kV, and electrospray can be generated by applying a high voltage to the needle-like member.

-Ion collector-
The ion collecting member is provided in the vicinity of the tip of the needle-like member and is a member that collects ions generated from the specimen sample adhering to the needle-like member or the test object.
For example, an ion collecting/transporting tube can be used as the ion collecting member. The inner diameter of the ion collecting and transporting tube is preferably between 1 mm and 5 mm.
The opening at the tip of the ion collecting/transporting tube can be formed in a size and shape that does not interfere with the movement of the needle-like member. It is preferable from the point of
The shape, structure, size, material, etc. of the ion collecting member are not particularly limited, and can be appropriately selected according to the specimen sample.
At this time, by locating the opening of the ion collecting/transporting tube close to the tip of the needle-like member, the generated ions can be efficiently sucked.

-Other parts-
Other members are not particularly limited and can be appropriately selected according to the purpose, and examples thereof include control members.
The control member is not particularly limited as long as it can control the movement of each member, and can be appropriately selected according to the purpose. Examples thereof include devices such as computers.

<<Ionization Means Using Remote Sampling Electrospray Ionization>>
An ionization means using remote sampling electrospray ionization has a first tubular member, a second tubular member, a gas flow generating member, a power supply, an ion directing member, and optionally others has a member of

One end of the first tubular member is brought into contact with the specimen sample, and is used to collect the specimen sample or the test object contained in the specimen sample. Also, the other end is used to generate ions originating from the specimen sample and the test object from which the other end is collected by electrospray.
The second tubular member is used to concentrically insert the first tubular member.
The gas flow generating member is used to cause gas to flow through the second tubular member at high speed.
The power supply is applied to the counter electrode so that a potential difference is generated between the other end of the first tubular member and a counter electrode facing the other end arranged in the region where the electrospray is generated. Used to apply voltage.
The ion guide member is used to guide the generated ions to the mass analysis means.

In the ionization means using the remote sampling electrospray ionization method, one end of the first tubular member is brought into contact with the analyte sample to capture the analyte sample at one end of the first tubular member.
The first tubular member is concentrically inserted within the second tubular member. Gas flows at high speed in the gap between the inside of the second tubular member and the outside of the first tubular member.
A high-speed gas flow is generated by a gas flow generating member.
Due to the venturi effect created by the high velocity gas flow in the second tubular member, the pressure near the other end of the first tubular member is reduced. Due to the pressure difference between one end and the other end of the first tubular member, the specimen sample is aspirated, and the specimen sample collected from one end of the first tubular member flows toward the other end of the first tubular member. Moving.
The high-velocity gas flow in the second tubular member exerts a suction effect on the specimen sample, assists atomization of the charged liquid droplets, and contributes to stable ion generation.

A voltage is applied to the counter electrode such that a potential difference is generated between the other end of the first tubular member and a counter electrode disposed at a position facing the other end in a region where electrospray is generated. Then, a strong electric field acts on the specimen sample at the other end, and the molecules of the specimen sample are ionized while being detached by the electrospray method.
For example, the other end of the first tubular member is electrically held at ground or any potential suitable for the analyte sample. A potential difference is generated between the other end of the first tubular member and the counter electrode, and electrospray is generated when a high potential is applied to the other end. A negative potential of about -3 kV is applied to the counter electrode. Then, it is safe for a person to touch the first tubular member, and electrospray is generated because a high potential is applied to the other end.
The ions produced are transported to the mass spectrometer via the ion directing member.
In a mass spectrometer using the remote sampling electrospray ionization method, a mass spectrometry result is obtained by the mass spectrometer almost immediately after the first tubular member comes into contact with the specimen sample.

Advantages of the ionization means using the remote sampling electrospray ionization method include, in addition to the advantages of the ionization means using the PESI method described above, the ability to continuously measure a large number of samples. If the operation of acquiring the sample and atomizing ions by electrospray is performed only on one side, the step of acquiring the sample is inserted between the ionization steps, and these steps are repeated. On the other hand, when a specimen sample is obtained from one side and ion atomization is immediately performed by electrospray from the other side, the specimen sample can be obtained continuously without being interrupted by the ion atomization. can. In synchronism with acquisition of the specimen, ions are sprayed on the other side and instantaneously subjected to mass spectrometry, so that the specimen can be measured continuously. This allows for faster measurements.

Also, by using a mass spectrometer using the remote sampling electrospray ionization method of the present invention, the ionization process can be automated for a plurality of samples.
For example, a three-axis motorized autosampling device can be constructed as shown in FIG.
The sample stage is driven by two linear actuators on the horizontal (xy) axes. The position of the first tubular member, the sampling probe, is controlled by another linear actuator on the vertical (z) axis.
The sample stage carries a 96-well sample plate containing multiple specimen samples.
As shown in FIG. 4A, when the first tubular member is moved to the lower position and brought into contact with the analyte sample, mass spectrometry results are obtained almost immediately by the mass spectrometer.
Next, as shown in FIG. 4B, the first tubular member is moved to an upper position and away from the analyte sample. Change the position of the 96-well sample plate.
Also, as shown in FIG. 4A, the first tubular member is moved to a lower position and brought into contact with the next analyte sample to obtain the mass spectrometry result of that analyte sample.
By controlling this repeated process with a computer, an automated mass spectrometer for continuous sampling can be constructed.

-First tubular member-
The first tubular member is a member that collects an object to be examined by bringing one end into contact with the object to be examined, and generates ions derived from the specimen sample and the object to be examined that are collected from the other end by electrospray.
The shape, structure, size (length, thickness, etc.), material, etc. of the first tubular member are not particularly limited, and can be appropriately selected in consideration of the amount of sample to be passed and the type of sample. be able to.
However, the length of the first tubular member is preferably short to some extent so that the specimen sample can be easily aspirated and the ions can be easily sprayed by electrostatic action and capillary action. In addition, the thickness of the first tubular member is preferably thick to some extent so as not to cause clogging of the specimen sample.
The tube length of the first tubular member is preferably 1 cm to 5 cm. In addition, the inner diameter of the first tubular member is preferably about 0.1 mm, and the outer diameter is preferably about 0.2 mm.
As the material, it is preferable to use a material that is durable, has little contamination due to germs, etc., and can be sterilized. Stainless steel, titanium, tungsten, nickel, aluminum, platinum, gold, silver, or those plated with these are preferable. Listed in

- Second tubular member -
The second tubular member is a member for concentrically enclosing the first tubular member.
Gas flows at high speed in the gap between the inside of the second tubular member and the outside of the first tubular member.
The shape, structure, size (length, thickness, etc.), material, etc. of the second tubular member are not particularly limited and can be selected as appropriate. Considering the outer diameter of the tubular member, and the amount and speed of the gas to be passed, it is preferable to select it as appropriate.
Moreover, it is preferable that the other end of the first tubular member protrudes slightly from the second tubular member so as not to cause backflow of gas. Therefore, for example, the length of the second tubular member may be appropriately selected so that the other end of the first tubular member protrudes from the second tubular member by about 0.5 mm to 1 mm.
The tube length of the second tubular member is preferably 1 cm to 3 cm. The inner diameter of the second tubular member is preferably 0.3 mm to 0.5 mm, and the outer diameter is preferably about 1.6 mm.
As the material, it is preferable to use a material that is durable, has little contamination due to germs, etc., and can be sterilized. Stainless steel, titanium, tungsten, nickel, aluminum, platinum, gold, silver, or those plated with these are preferable. Listed in

-electrode-
The electrode applies a voltage to the counter electrode such that a potential difference is generated between the other end of the first tubular member and the counter electrode facing the other end disposed in the region where electrospray is generated. It is a member.
For example, when the first tubular member is set to the ground potential, when a negative potential of about 3 kV is applied to the counter electrode, a high voltage is applied to the other end of the first tubular member, causing electrospray. can be made

-Gas flow generating member-
The gas flow generating member is not particularly limited as long as it is a device that compresses gas, increases the pressure, and continuously delivers the gas, and can be appropriately selected according to the purpose. The type of gas to be handled, the compression method, the structure of the apparatus, etc. can be selected as appropriate.
For example, an air compressor can be used as the gas flow generating member. This can, for example, generate a high velocity airflow within the second tubular member. The movement speed of the specimen sample can be adjusted by the thickness of the first tubular member and the flow velocity of the air flowing through the second tubular member. Therefore, the flow rate of the air should be adjusted so that the moving speed of the specimen falls within the desired range, and various conditions of the gas flow generating member should be set so that the flow rate of the air falls within the desired range.
The velocity of airflow in the second tubular member is preferably 2 L/min to 5 L/min, for example.

-Ion induction member-
The ion guide member is a member for guiding the generated ions to the mass analysis means.
An ion transport tube, for example, can be used as the ion guide member.
The shape, structure, size, material, etc. of the ion transport member are not particularly limited, and can be appropriately selected according to the object to be inspected.
As the material for the ion transport member, for example, a flexible polymer tube can be selected.
The length of the ion transport tube is preferably 50 cm to 100 cm. Also, the inner diameter is preferably 1 mm to 5 mm.

-Other parts-
Other members are as described in the above section <<means using probe electrospray ionization method>>.

<<Ionization means using line ionization method>>
The ionization means using the line ionization method has a linear member and an ionization member, and optionally other members.
The linear member uses a sample liquid prepared by a limiting dilution method and is used to transport droplets of the sample liquid. Here, the sample liquid refers to a liquid sample (sample) containing an analyte sample or an inspection target contained in the analyte sample.
The ionization member is used to generate ions originating from the test object from the liquid droplet adhering to the linear member.

In order to attach droplets of the sample liquid to the linear member, a sample liquid supply element is used to drop the sample liquid onto the linear member in units of droplets. Preferably, one drop at a time is dropped onto the linear member. A description of the sample liquid supply element will be given later.
The sample liquid is preferably prepared using a limiting dilution method. This makes it possible to drop one cell/droplet and obtain a profile (here, the result of mass spectrometry) for each cell.
Dropped droplets of the sample liquid are transported to the ionization member by moving the linear member with a driving member (for example, a roller).
Droplets adhering to the linear member are ionized by the ionization member, and the generated ions are immediately subjected to mass spectrometry.

-Linear member-
The material, shape, structure, size, etc. of the linear member are not particularly limited, and can be appropriately selected according to the object to be inspected.
Examples of materials for the linear member include metal wires, natural fibers, and chemical fibers.

Examples of metal wires include stainless steel, titanium, tungsten, piano wires, cobalt-based alloy wires, alloy wires exhibiting pseudoelasticity (including superelastic alloys), steel wires, brass wires, copper wires, and aluminum wires. be done.

Natural fibers include, for example, vegetable fibers such as cotton, hemp, manila hemp, palm and rush, animal fibers such as wool, silk down, and feathers. Natural fiber threads are used.

Chemical fibers include regenerated fibers such as rayon, cupra and casein fibers, semi-synthetic fibers such as acetate, triacetate and Promix, polyamides, polyvinyl alcohol, polyvinyl chloride, polyvinylidene chloride, polyester, and polyacrylonitrile. Synthetic fibers such as polyolefin, polyetherester, polyurethane, etc., are mentioned, and chemical fiber yarns spun from these are used.

-Ionization member-
The ionization member is a member that generates ions from droplets of the sample liquid adhering to the linear member by an ionization method.
The ionization method is not particularly limited and can be appropriately selected depending on the intended purpose. method, DEI (desorption electron ionization) method, DCI (desorption chemical ionization) method, FAB (fast atom bombardment) method, FRIT-FAB (FRIT-fast atom bombardment; frit fast atom bombardment) method, ESI (electrospray ionization) method, MALDI (matrix-assisted laser desorption ionization) method, and the like. Among these, the APCI method and the ESI method are particularly preferred.

-Other parts-
Other members are as described in the above section <<means using probe electrospray ionization method>>.
Moreover, the ionization means using the line ionization method may further include a sample liquid supply element, a driving member, a pretreatment member, and a posttreatment member.

- Sample liquid supply element -
The sample liquid supply element is not particularly limited as long as it can drop the sample liquid drop by drop, and can be appropriately selected according to the purpose. For example, a piezo terminal can be used.

-Driving member-
The driving member is not particularly limited as long as it can move the linear member in a certain direction, and can be appropriately selected according to the purpose. Examples thereof include a conveying roller.
Adjustment of the movement speed of the linear member by the drive member can be performed as appropriate.

-Pretreatment material-
The sample liquid can be pretreated using the time from the sample liquid droplets attached to the linear member by the sample liquid supply element to the ionization. Examples of this pretreatment include protein removal and salt removal.

- Post-treatment material -
The linear member that has undergone the ionization step may be post-treated while moving from the ionization member to the sample liquid supply element. Examples of post-treatment include washing, heating, chemical treatment, and the like for washing the linear member.

<Analytical means and analytical steps>
The analysis step is a step of performing mass analysis on the ions generated by the ionization step, and is performed by analysis means.

The mass spectrometer used for mass spectrometry is not particularly limited and can be appropriately selected according to the purpose. Examples include a heavy pole mass spectrometer, a Fourier transform mass spectrometer, and an analyzer obtained by appropriately combining these mass spectrometers.
In mass spectrometry, an analyte sample is ionized and the ions are separated according to their mass-to-charge ratio (m/z). Therefore, the mass spectrometer outputs as raw data basic data for representing a mass spectrum in which the mass-to-charge ratio is plotted on the horizontal axis and the ion intensity is plotted on the vertical axis.

Here, an embodiment of the mass spectrometer of the present invention will be described in detail with reference to the drawings.
In addition, in each drawing, the same code|symbol may be attached|subjected to the same component part, and the overlapping description may be abbreviate|omitted. Further, the number, positions, shapes, etc. of the following constituent members are not limited to those of the present embodiment, and the number, positions, shapes, etc., can be set to be preferable in carrying out the present invention.

<First Embodiment (Embodiment of Ionization Means Using PESI Method)>
FIG. 1 is a schematic diagram showing an example of a mass spectrometer having ionization means using a probe electrospray ionization method as a first embodiment.
The mass spectrometer 100 of FIG. 1 comprises ionization means 101 and analysis means 102 .

The ionization means 101 has a needle-like member 1 , a power supply 7 , a personal computer (PC), and an ion collecting member 8 . In FIG. 1, 5 denotes a linear actuator and 11 denotes a suction device. In FIG. 1, 2 shows the needle-like member 1 moving up and down, 3 shows the specimen sample (sample), 4 shows ions being generated by electrospray, and 9 shows the generated ions. show.
In this first embodiment, as the needle-like member 1, an acupuncture and moxibustion needle made of stainless steel is used.
The needle-like member 1 can be moved up and down with respect to the specimen by the operation of the linear actuator 5 .
When the needle-like member 1 moves to the lower position (lower end), the sample adheres to the distal end of the needle-like member 1 by contacting or piercing the sample.
When the needle-shaped member 1 moves to the upper position (upper end), a high voltage is applied from the power supply 7 and electrospray is generated from the tip of the needle-shaped member 1 .

As the ion collecting member 8, an ion collecting/transporting tube with an inner diameter of 1 mm to 5 mm (made of flexible resin whose inner surface is smoothed to prevent electrification) is used. Absorbs the ions generated by the spray.
The attracted ions are carried through the ion aperture 10 to the mass spectrometer 12 .
Since a voltage is applied between the tip of the needle-like member 1 and the opening of the ion collecting/transporting tube 8 , the generated ions are attracted by the opening of the ion collecting/transporting tube 8 .

Reference numeral 12 in FIG. 1 denotes a mass spectrometer, which analyzes ions generated by the ionization means 101 in real time. In this first embodiment, any one of a quadrupole mass spectrometer, a Fourier transform mass spectrometer, an ion trap mass spectrometer, and a time-of-flight mass spectrometer is used as the mass spectrometer.

In PESI, the linear actuator 5 and the power source 7 are synchronously controlled by a controller (not shown), but by adopting a configuration in which the voltage is applied from the power source only when sampling is not performed, the use of the controller is omitted. can do. As a result, the device can be simplified, electric shock can be prevented, and safety is improved.

Next, the operation during ionization will be described.
When the position of the needle-like member 1 is lowered, the tip of the needle-like member 1 comes into contact with or penetrates the sample. When the position of the needle-shaped member 1 is raised, a voltage is applied. As a result, ions originating from the sample or test object are generated from the tip of the needle-shaped member 1 . The generated ions are immediately sucked from the opening of the ion collecting/transporting tube as the ion collecting member 8 and transported to the mass spectrometer 12 .

<Second Embodiment (Embodiment of Ionization Means Using Remote Sampling Electrospray Ionization Method)>
FIG. 2A is a schematic diagram showing an example of a mass spectrometer having ionization means using a remote sampling electrospray ionization method as a second embodiment.
The mass spectrometer 200 of FIG. 2A comprises ionization means 201 and analysis means 202 .

The ionization means 201 has a first tubular member 21 , a second tubular member 22 , a gas flow generating member 25 , a power source 20 and an ion guiding member 28 . In FIG. 2A, 18 is a specimen sample (sample), 19 is a positive ion generated, 23 is a tubular member holding member, 24 is the other end of the first tubular member 21 where ions are generated by electrospray, 26 is A counter electrode, 27 an insulating member, and 29 a mass spectrometer.
FIG. 2B shows an enlarged photograph of one end side of the first tubular member 21 of FIG. 2A.
Stainless steel is used as the first tubular member 21 .
The holding member 23 of the tubular member and the counter electrode 26 are insulated by the insulating member 27 . In this second embodiment, polyetheretherketone (PEEK) is used as the insulating member 27 .
The first tubular member 21 is vertically movable with respect to the specimen sample.
The first tubular member 21 is inserted concentrically within the second tubular member 22 .
A high-speed air flow is generated by a gas flow generating member (here, an air compressor is used) in the gap between the inside of the second tubular member and the outside of the first tubular member.
In this second embodiment, the outer diameter of the first tubular member 21 is 0.2 mm, and the inner diameter of the second tubular member 22 is 0.5 mm. The gap between the first tubular member 21 and the second tubular member 22 is 0.15 mm, and air flows through this gap at high speed.

When the first tubular member 21 is moved to the lower position (lower end), one end of the first tubular member 21 comes into contact with the specimen sample, thereby injecting the specimen sample into the first tubular member 21 .
Due to the venturi effect generated by the high-speed air flow in the second tubular member 22, the specimen sample is smoothly aspirated, and the specimen sample collected from one end of the first tubular member 21 flows into the first tubular member. Move to the other end 24 of the member.
A power supply is applied to the counter electrode so that a potential difference is generated between the other end 24 of the first tubular member 21 and a counter electrode 26 disposed at a position facing the other end 24 in a region where electrospray is generated. A voltage is applied from 30 . In this second embodiment, the first tubular member 21 is set at the ground potential, and a voltage of −3 kV is applied to the counter electrode 26 . Electrospray is thereby generated from the other end 24 of the first tubular member 21 .
The generated ions are supplied to the mass spectrometer 29 via the ion guide member 28 along with the air flow. Here, in this second embodiment, a flexible polymer tube with an inner diameter of 3 mm is used as the ion guide member 28 .
When the first tubular member 21 is moved to the upper position (upper end), collection of the specimen sample is stopped.

The mass spectrometer 29 analyzes the ions generated by the ionization means 201 in real time. In this second embodiment, any one of a quadrupole mass spectrometer, a Fourier transform mass spectrometer, an ion trap mass spectrometer, and a time-of-flight mass spectrometer is used as the mass spectrometer.

<Third Embodiment (Embodiment of Ionization Means Using Line Ionization Method)>
FIG. 5 is a schematic diagram showing an example of a mass spectrometer having ionization means using a line ionization method as a third embodiment.
A mass spectrometer 300 in FIG. 5 comprises ionization means 301 and analysis means 302 .

The ionization means 301 includes a sample liquid supply element 31 , a sample liquid droplet 32 that is an analyte sample, a linear member 33 , a drive member 34 , and an ionization member 35 .
Analysis means 302 comprises a mass spectrometer 36 .
The linear member 33 conveys the droplet 32 of the sample liquid.
The droplets of the sample liquid are attached to the linear member by using the sample liquid supply element 31 and dropping the droplets onto the linear member one by one. In this third embodiment, a piezo terminal is used as the sample liquid supply element.
The linear member 33 is moved by the driving roller, which is the driving member 34 , and the droplet of the sample liquid is transported to the ionization member 35 .

An ionization member 35 is used to ionize the droplets 32 adhering to the linear member. In the third embodiment, the ionization member 35 is switched between ESI and APCI depending on the target molecule.
Ions originating from the sample liquid and the test object generated by the ionization member 35 are immediately subjected to mass spectrometry.

The mass spectrometer 36 analyzes the ions generated by the ionization means 301 in real time. In this third embodiment, any one of a quadrupole mass spectrometer, a Fourier transform mass spectrometer, an ion trap mass spectrometer, and a time-of-flight mass spectrometer is used as the mass spectrometer.

(2. Analysis device and analysis method)
The analysis apparatus of the present invention has determination means and, if necessary, other means.
The analysis method of the present invention includes a determination step and, if necessary, other steps.

The analysis method of the present invention can be preferably carried out by the analysis apparatus of the present invention, the determination step can be performed by determination means, and the other steps can be performed by other means.
The processing by the mass spectrometry analysis program is executed using a computer having a control unit that constitutes an analysis device.

The analyzing apparatus of the present invention determines the possibility of whether or not the subject corresponds to a disease derived from the presence state of the test object in the blood based on the results of mass spectrometry obtained by the mass spectrometer of the present invention. can judge.
For example, when the test target is CTC, clinical information such as early detection of cancer, prediction of recurrence, and sensitivity of cancer cells to anticancer drugs can be obtained by detecting CTC. Knowing the number of CTCs per unit of blood may lead to cancer diagnosis or prognosis.
In addition to CTC, for example, when the test target is lipid, if the existence state of lipid in blood is known, the possibility of falling under a disease derived from lipid such as hyperlipidemia can be determined. . Further, for example, when the test target is sugar, if the state of presence of sugar in blood is known, it may be possible to determine the possibility of falling under a disease derived from sugar such as diabetes.

<Determination process and determination means>
The determination step uses at least one of mass spectrum information and mass spectrum analysis result information obtained by learning the mass spectrum information to determine the existence state of the test object in the blood. , determining whether the mass spectrum exceeds a predetermined acceptable range or exhibits a disease-positive pattern,
If the presence state of the test subject exceeds the predetermined allowable range or the mass spectrum is determined to exhibit a disease-positive pattern, the subject is derived from the presence state of the test subject. It is a step of determining the possibility of corresponding to the disease, which is performed by the determination means.
Examples of the disease-positive pattern include a tumor-positive pattern.

The analyzer of the present invention creates an appropriate representative mass spectrum based on the scanned raw data output from the mass spectrometer. There may be multiple representative mass spectra. Using this representative mass spectral data, find the shape of the entire mass spectral data or the marker peaks (for example, the peaks indicated by the arrows in FIG. 8(i)) specific to the inspection object as shown in FIG. 8(i). , the presence state of the test object in the blood can be grasped by determining these peaks.
For example, there is a difference in the shape of the mass spectrum between the mass spectrum of a normal specimen sample that does not contain an object to be inspected and the mass spectrum of an abnormal specimen sample that contains an object to be inspected. By comparing the indicated information with the mass spectrum of the measurement result, it is possible to know the existence state of the test object in the measured object.
For example, the existence state of the test object in blood exceeds a predetermined allowable range. When a measurement result close to the shape of , or a marker peak is obtained, it can be determined that the subject has a high possibility of corresponding to the disease.
In this case, depending on the shape of the mass spectrum and the degree of closeness of the measurement results to the information indicated by the marker peaks, not only the binary determination of whether or not the patient has the disease, but also the likelihood of developing the disease. (probability) can also be determined.

In addition, in the analysis apparatus of the present invention, not only the information of the representative mass spectrum described above is used, but also the analysis result of the mass spectrum obtained by learning the information of the mass spectrum as shown in (ii) of FIG. may be used to grasp the existence state of the test object in the blood.
As shown in FIG. 8, the mass spectrum information shown in (i) and the mass spectrum analysis result information obtained by learning shown in (ii) are used together to grasp the existence state of the test object in blood. You may

In addition, the analysis method of the present invention not only determines whether or not a single measurement sample of blood of a subject corresponds to one disease, but also determines whether or not a plurality of diseases apply at once. can judge. That is, by the analysis method of the present invention, it may be determined whether or not a plurality of diseases are present based on the results of mass spectrometry obtained for one measurement sample of the blood of the subject. In this case, a single analysis can determine the possibility of a plurality of diseases.

A specific embodiment of determining the possibility that a subject has a disease derived from the presence of a test target in blood using the analysis method of the present invention will be described below.
(a) For example, an index for determining the presence of CTCs in blood is prepared by obtaining in advance the results of mass spectrometry of blood from a cancer patient and analyzing the data. Here, the index includes a characteristic marker peak of the mass spectrum, the overall peak shape of the mass spectrum, a criterion for distinguishing between the profile distribution of healthy subjects and the profile distribution of cancer patients, and the like.
By comparing the index prepared in advance with the measurement result of the subject, the possibility of the presence of CTC in the subject can be determined.
Mass spectrometry results of cancer patients prepared in advance are not limited to CTC mass spectrometry results, and mass spectrometry results of primary and metastatic tumor tissues can also be used.
(b) In addition, since there is a difference between the data indicated by the mass spectrum of a healthy subject and the mass spectrum data of a patient with an abnormal test subject, data that serves as an index in advance as in (a) above Even if you do not have , if the measurement result is different from the mass spectrum data shown by a healthy person, it can be suspected that there is a possibility that it corresponds to some disease.
(c) The pre-prepared data stored in the database can be updated sequentially as new measurement results are obtained. By increasing the accumulated data and updating the index information, it is possible to build a database that allows more accurate determination.
(d) Further, the measurement results of the subject himself/herself may be accumulated, and the measurement data of the subject may be compared with the past data of the subject. As a result, it is possible to monitor the progress of the test object in the subject, which in turn leads to early detection of the disease in the subject and selection of the optimum treatment method.

As a method for creating the mass spectrum described above and analyzing the mass spectrum by learning, for example, the method disclosed in Japanese Patent Application Laid-Open No. 2016-200435 can be used.

A mass spectrum analysis method that automatically creates a representative mass spectrum appropriately based on the scanned raw data output from the mass spectrometer, and an analysis method that analyzes the created mass spectrum using each learning method are detailed below. explain.
Although the following description does not describe an example using a test target (for example, CTC) in blood, which is the target of the present invention, as the measurement target, the operation of the analysis system is the same. The operation of the computer system described below can be similarly applied to the analysis apparatus of the present invention.

<Specific analysis system of the analysis device of the present invention>
In the analysis apparatus of the present invention, to create a representative mass spectrum, for example, measurement data input processing, best time zone detection processing, and representative mass spectrum creation processing are performed.
In order to learn and analyze the created mass spectrum, each learning method described below is used.

<<Measurement data input processing and measurement data input unit>>
The measurement data input process is a process of inputting three-dimensional measurement data of mass-to-charge ratio, ion intensity and measurement time, and is performed by the measurement data input unit.
The three-dimensional measurement data is generally referred to as an ion chromatogram obtained by the mass spectrometer (the mass spectrometer of the present invention), and shows the relationship between the mass-to-charge ratio and the ion intensity for each predetermined time (scanning interval). Data (data that can be represented as a spectrum) are obtained. Therefore, the measurement time can be expressed by the number of scans.

The three-dimensional measurement data is generally converted into data (chromatogram) representing the total ion intensity on the time axis (scan frequency axis). The chromatogram includes total ion intensity (TIC), which integrates ion intensity over all mass-to-charge ratios in the measured data; isolated ion intensity (MIC), which integrates ion intensity over a specified range of mass-to-charge ratios; Single Peak Ion Intensity (EIC), which indicates the ion intensity to mass-to-charge ratio, and the like. A specific range of mass-to-charge ratio in MIC and a specific peak in EIC may be designated by the user, or the range or peak showing the maximum value may be automatically determined. Trial and error may be performed by changing these ranges and specific peaks.

<<Best Time Zone Detection Processing and Best Time Zone Detection Unit>>
The best time period detection process is a process of calculating the time period in which the sum of ion intensities is maximized with respect to the designated mass-to-charge ratio of the input measurement data, and is performed by the best time period detection unit.

<<Representative mass spectrum creation processing and representative mass spectrum creation unit>>
The representative mass spectrum creation process is a process of creating a representative mass spectrum based on the detected ion intensity of the input measurement data in the best time period, and is performed by the representative mass spectrum creation unit.

Based on the measurement data of the time period considered to be the most stable and best representing the characteristics of the sample by the best time period detection process and best time period detection unit and the representative mass spectrum creation process and representative mass spectrum creation unit, A representative mass spectrum (generally, the mass-to-charge ratio is taken on the horizontal axis and the sum (or average value) of the ion intensities in the above time period is taken on the vertical axis) is created, so that subsequent appropriate analysis is ensured. be.
When the TIC, MIC, and EIC (chromatograms) calculated in the process of creating a representative mass spectrum and the representative spectra obtained therefrom are displayed on a display device, the user can view the mass-to-charge ratio range ( for MIC), specific peaks (for EIC), the time period above, and other parameters can be changed accordingly.

It is preferable that the analysis device further includes a condition setting unit that sets conditions for defining a data range for detecting the best time period from the input measurement data.

In addition, it is preferable that the analysis device further includes a mass spectrum display unit that displays the detected best time zone and the created representative mass spectrum.
Furthermore, it is preferable that the analysis device further includes a mass spectrum storage unit that adds label information to the created representative mass spectrum and stores it.

The measurement data output from the mass spectrometer (the mass spectrometer of the present invention) is merely added with a date and an identification code (ID). Therefore, by adding abundant label information that is understandable and easy for the user to the created representative spectrum and storing it, later management, editing (grouping, etc.), and reading of the mass spectrum can be easily performed. will be able to The label information may be anything as long as it can be understood by the user, but includes information on the user's organization, plans, and actions, information on the sample, the person who provided the sample, the object, the place, the date and time, the measurement conditions in the mass spectrometer, Information about the measurement environment, etc. is included.

The analyzer has a function of evaluating the created (or already stored) mass spectrum. This evaluation function may be provided in the analysis apparatus having the automatic (semi-automatic) creation function of the representative mass spectrum described above, or may be provided in the analysis apparatus without the automatic (semi-automatic) creation function of the representative spectrum. .

In order to evaluate mass spectra, in addition to the group of mass spectra to be evaluated (including one mass spectrum), it is premised that there is a group of mass spectra for creating an internal standard that serves as a reference for evaluation. is. There is a mass spectrum store that stores these mass spectra. The mass spectrum to be evaluated may be stored in a storage unit (for example, a work area of a computer) separate from the mass spectrum group storage unit for internal standard creation. All of these storage devices are collectively referred to as a mass spectrum storage unit.

The analysis device designates a specific first group of mass spectra for internal standard creation from the mass spectrum storage unit storing a large number of created mass spectra, and the mass spectra stored in the mass spectrum storage unit. 1 or A second designation for designating a second group of mass spectra (including one mass spectrum) to be evaluated from the mass spectra stored in the internal standard candidate creation unit for selecting a plurality of peaks and the mass spectrum storage unit and a pass/fail judgment unit for judging the pass/fail of each mass spectrum in the second group using one or a plurality of peaks among the internal target candidates created by the internal target candidate creation unit as internal indicators. .

The first group of mass spectra is preferably a collection of mass spectra judged good by the user or by the analyzer. When one or more specific peaks are defined as internal standards, if the corresponding peaks in the mass spectrum of the second group are based on the internal standards, and the ion intensity is at least a predetermined value and the variation is within the allowable range, The mass spectrum is judged good. Anything else is bad. A mass spectrum determined to be good can be managed, edited, or used as a target for statistical analysis, which will be described later. It is preferable that those determined to be defective are recreated by returning to the above-described creation of the representative mass spectrum.

Next, the statistical analysis function will be described. For any analyzer equipped with both the representative mass spectrum creation function and the mass spectrum evaluation function described above, an analyzer equipped with either function, or an analyzer equipped with neither function, the following Statistical analysis function can be provided.

Statistical analysis mainly includes significance test, dimensionality reduction, machine learning and verification. There are also many types of significance tests, and similarly, many types of dimensionality reduction, machine learning, and verification have already been developed.

First, an analysis device equipped with a significance test function has a mass spectrum storage unit that stores a large number of mass spectrum data, a plurality of types of significance test methods that can be selected, and the selected significance test method. Data set to which the selected significance test method should be applied (a collection of specific mass spectra ), and a program routine for executing multiple types of significance test methods displayed in the statistical analysis method input section, and the selected significance test method is applied to the designated data set This statistical analysis execution unit selects peaks judged to have significant differences between groups.

Next, the analysis device having a machine learning function has a statistical analysis method input section that selectably displays a plurality of types of machine learning methods, and a program routine that executes the machine learning method displayed in the statistical analysis method input section. , a statistical analysis executor for executing the selected machine learning method on a given data set.

The machine learning method selectably displays a plurality of types of machine learning methods, receives the input of the selected machine learning method, and selects the machine learning method among the program routines for executing the displayed machine learning method. for a given data set.

In this way, the user can select a desired one from among a plurality of machine learning methods to perform learning. The user can select multiple learning methods and compare the results. A verification method, which will be described later, can be used for this comparison.

There are several types of the predetermined data sets that are subject to machine learning.

The first method is to specify and select a data set to which a learning method to be selected is to be applied from a mass spectrum storage section storing a large number of mass spectrum data.

The second method is to apply the learning method to peaks determined to have a significant difference by the above-described significance test method.

The third is to reduce the data set selected from the mass spectrum accumulation unit or the data determined to have a significant difference by the significance test by a predetermined dimensional reduction method, and obtain the reduced score data The learning method is applied to it.

As for the contraction method, it is possible to display a plurality of types of contraction methods and allow the user to select one or more of them. In particular, since the contraction method and machine learning are related, it is preferable to display a plurality of types of contraction methods and a plurality of types of learning methods, and to clearly associate the one selected by the user on the display screen.

Preferably, at least one cross-validation method is selectably displayed, and the learning result of the selected machine learning method is verified by the selected cross-validation method.

A plurality of types of cross-validation methods may also be displayed for the user to select.

Multiple types of significance testing methods, machine learning methods, contraction methods, and cross-validation methods are displayed so that they can be selected. It is also possible to determine whether It is also possible to have the computer run all (or a default selection) combinations and verify which combination is the best by a verification method. The results are displayed. In this way, it is possible to select an objective combination without relying on the user's subjectivity, and it is also possible to present identifiable peaks.

Here, FIG. 9 is a block diagram showing an example of the analysis device of the present invention. The analysis apparatus of FIG. 9 is implemented by a computer system programmed as described below.

The analysis apparatus 320 is functionally divided into the core of the computer system, and a processing unit 321 for creating representative mass spectra, evaluating mass spectra based on internal standards, labeling, various statistical analysis processing, verification processing, and the like; It is composed of an input unit 322 for inputting mass spectrum data obtained by the analysis unit 310, an output unit 323 for outputting processing results such as analysis processing, intermediate progress, etc., and for use as a user interface, and a storage unit 326. . Note that the mass spectrometer 310 corresponds to the mass spectrometer of the present invention.

The input unit 322 includes, in addition to normal input devices such as a keyboard and a mouse, a medium reader for reading data stored in a USB memory, a CD-ROM, etc., and data (including commands) by communication regardless of whether wired or wireless. including a communication device that receives

The output unit 323 may share part of the display screen with the input unit 322 . The output unit 323 displays graphs of various data (including mass spectra) and other data in an easy-to-view form, and also displays a display unit 324 for displaying various input and setting screens as a user interface, and for printing various data and processing results. Although not explicitly illustrated, a medium writer for writing various data to a storage medium, and a communication device for outputting by communication (transmission) (the communication device of the input unit 322 and may also be used).

The storage unit 326 temporarily stores input mass (mass) spectral data, accumulates representative mass spectral data (database shown in FIG. 10), performs various processes (shown in FIGS. 11 and 12, and stores representative mass spectra described later). manual generation, automatic generation of representative mass spectra, editing and management of data, evaluation of mass spectra, statistical analysis processing, verification processing, etc.) and provides a work area. The storage unit 326 is implemented by a semiconductor memory, hard disk, or the like.

The processing unit 321 is a main part of the computer, and executes the processes shown in FIGS. At this time, necessary information (user interface screen) is displayed on the display unit 324 , and data during processing and data of the processing result are stored in the storage unit 326 .

FIG. 10 is a data structure diagram starting from measurement data obtained from the mass spectrometry unit 310 and ending with a database created in the storage unit 326. As shown in FIG.
11 and 12 are flowcharts showing procedures of processing executed by the processing unit 321 according to the programs stored in the storage unit 326. FIG. These drawings are referred to successively in the following description.
The processing unit 321 has means for realizing the functions represented by these flowcharts.

Note that the analysis device 320 can be applied to measurement data obtained from many types of mass spectrometers 310 having scanning functions.

FIG. 13 shows an example of text format raw data (text data) output from the mass spectrometer 310 . This was obtained by repeating scanning from the low-mass side to the high-mass side at regular time intervals (for example, at intervals of 0.05 seconds to 0.5 seconds) using the scanning method for one sample. . The ion intensity (arbitrary scale, the same applies hereinafter) data obtained by each scanning corresponds to the value of the mass-to-charge ratio (m/z) (only the range of 700 to 800 is shown as an example), Scan 1, They are arranged numerically in columns of 2, 3, 4, and so on. This is three-dimensional data (text file) of mass-to-charge ratio (m/z), ion intensity and measurement time (data representing scanning order).

FIG. 14 expresses the data shown in FIG. 13 in the form of a mass spectrum for each scan. The horizontal axis indicates the mass-to-charge ratio (m/z), the vertical axis indicates ion intensity, and the depth direction (indicated by arrows) indicates time or the number of scans.

The input unit 322 of the analysis device 320 acquires such raw data from the mass spectrometry unit 310 (S11 in FIG. 11) (measurement data input unit). Data may be transmitted from the mass spectrometry unit 310 and received by the input unit 322, or data may be stored in a storage medium such as a USB memory in the mass spectrometry unit 310 and read by the input unit 322. good.

FIG. 15 shows the TIC, MIC, or EIC (described next) chromatograms using the raw data described above. The horizontal axis is time (number of scans), and the vertical axis is ion intensity. The ionic strength on the vertical axis takes different values depending on TIC, MIC, and EIC, but it should be understood that FIG. , MIC, and EIC). A chromatogram is a plot of the summation of ion intensities (summed or extracted somehow, as described below) over time (along the time axis, i.e., arranged in scan order). .

TIC is an abbreviation for Total Ion Current, which is the ion intensity (corresponding to all mass-to-charge ratios) of all peaks (represented by the data) contained in the acquired mass spectrum. means the sum of

TICC stands for Chromatogram of TIC (a plot of TIC over time).

MIC is an abbreviation for Merged Ion Current, which is the peak (data represents) (m/ z) means the sum of the ionic strengths. MICC stands for MIC chromatogram.

EIC is an abbreviation for Single (or Extracted) Peak Ion Current (or Current) and represents the ionic intensity of a particular peak (corresponding to m/z). EICC is the chromatogram of EIC.

A mode selection screen (not shown) is displayed on the display unit 324 of the analyzer 320, and the user selects the manual mode or the automatic mode according to the mode selection screen. When the manual mode is selected, the processing unit 321 executes the manual generation processing of the representative mass spectrum (S12 in FIG. 11) described below.

In the manual generation process, one of TIC, MIC, and EIC is selected by the user. In the case of TIC, raw data of the entire acquired m/z range is used, in the case of MIC, the m/z range to be used is specified by the user, and in the case of EIC, a specific peak is specified. be. A chromatogram (as shown in FIG. 15) of the TIC, MIC or EIC specified by the user in this way is created by the processing unit 321 and displayed on the display unit 324 .

The user uses an input device such as a cursor (included in the input unit 322) to input a time range that is considered to best represent the analysis results on this chromatogram. The time range is determined, for example, by specifying a lower limit PL and an upper limit PH. When the time range is determined, the processing unit 321 adds (or averages) each peak having the same m / z value of the mass spectrum of each scan within the time range to create a representative mass spectrum, It is displayed on the display unit 324 . An example of the created representative mass spectrum is shown in FIG. The representative mass spectrum data is text data consisting of two variables, the mass-to-charge ratio (m/z) and the ion intensity, and is stored in the database of the storage unit 326 .

When the user selects AUTO in the mode selection screen for creating a representative mass spectrum, a condition setting screen as shown in FIG. 17 is displayed on the display section 324 (S131 in FIG. 11) (condition setting section). Using this screen, the user can set desired conditions.

On the condition setting screen, the target file is the file storing the raw data (measurement data) acquired from the mass spectrometry unit 310 in S11, and the file name assigned by the mass spectrometry unit 310 is displayed in the box.

The TIC threshold, MIC threshold, and EIC threshold are thresholds for removing noise and the like in TICC, MICC, and EICC, respectively (see FIG. 17), and only data having values exceeding the thresholds are used in the following calculations. The user checks and selects TIC, MIC, and EIC that he or she wishes to use in the calculation (all may be checked, as shown in FIG. 17), and inputs the threshold for the selected items. . If no threshold value is entered, a default value (specified value) is used.

Since the MIC is the sum of the ion intensities in a particular m/z range, an input of the m/z range is required when the MIC is selected. Also, since the EIC represents the ion intensity of a specific peak, when the EIC is selected, it is necessary to input the m/z value (specified spectrum) of the specific peak.

When the above input (that is, condition setting) is completed and the user presses the "execute" button (clicking, the same applies hereinafter), TIC, MIC, and EIC are selected (designated by checking). ) is executed by the processing unit 321 to obtain the following formula (1) for the chromatogram.

The above formula (1) means to obtain the maximum of S(t), and S(t) is given by the following formula (2) in the case of TIC.

In the case of MIC and EIC, MIC and EIC may be used instead of TIC.

τ represents the horizontal axis of TIC, that is, time. The above formula (2) means that τ is the sum of TIC in the time width from t to t + Δt, and the above formula (1) indicates that t (or t + Δt) that maximizes the sum of this chromatogram A time (time of day or sample instant) or time period is determined. Δt may be set in advance (for example, several seconds to several tens of seconds), or may be input by the user on the condition setting screen (see FIG. 17).

The time zone in which the sum of ion intensities in the chromatogram is the highest is obtained by the calculations of the above formulas (1) and (2) (S132 in FIG. 11) (best time zone detection unit). The highest total ion intensity is considered to indicate the most stable mass spectrum and the best characteristic of the sample.

A representative mass spectrum is created for a selected one of TIC, MIC, and EIC using ion intensity data in the time period t to t + Δt that satisfy the above equations (1) and (2) (Fig. 11 S133) (representative mass spectrum creation unit). Then, the screen shown in FIG. 18 is displayed.

On the screen shown in FIG. 18, for each of TIC, MIC, and EIC (assuming that all of these are selected), the chromatogram is displayed on the left side, and in each chromatogram, the above formula (1), A time zone satisfying (2) is indicated by a vertically elongated rectangle with a dashed line. On the right side of the screen in FIG. 18, representative data created by the summation (or average value) for each mass-to-charge ratio m/z of the ion intensity data in the time period that satisfies the above equations (1) and (2) A mass spectrum is shown (mass spectrum display). In addition, in FIG. 18, representative mass spectra are simply described as representative spectra or spectra. Also, the mass spectrum is simply written as spectrum.

Any one of these representative mass spectra is stored in the storage unit 326 as the one that best represents the mass-analyzed sample. This representative mass spectrum is text data consisting of two variables, mass-to-charge ratio and ion intensity. In FIG. 18, the user is requested to select one to be stored in the storage unit 326 as the "adopted spectrum" in the left box of each chromatogram. In FIG. 18, the user selects MIC by checking it. Binary format data is also stored for purposes such as recreating a representative mass spectrum, which will be described later. Check buttons will be described later.

When redoing the creation of the representative mass spectrum, the condition setting shown in FIG. 17 is returned to, and the threshold value, m/z range, specified spectrum, and, if necessary, the value of the time period Δt are input again.

As described above, the representative mass spectrum for one sample created in the processing section 321 is registered in the database of the storage section 326 . At this time, in order to facilitate data management and editing, labels are assigned to measurement condition information and sample information. This label is preferably created so that the collection of data has a hierarchical structure (hierarchical structure). In this example, the highest label is the project name.

Therefore, a project name selection screen as shown in FIG. 19 is displayed on the display screen of the display unit 324 . The user selects and inputs one of the project names displayed in a pull-down system. Here, it is assumed that "human cancer sample" is selected as the project name. Then, the user presses (clicks) a "registration" button.

Then, as shown in FIG. 20, a screen for inputting label information is displayed on the display unit 324 . The project name is already entered. Label information that is easy for the user to understand and that simply represents the origin, attributes, characteristics, and the like of the specimen is preferable. In this embodiment, the label includes the file name, the sex and age of the person who provided the sample, and the name of the disease, stage (progress), and tissue type of the sample. The project name may of course be included in the label information. Also, measurement condition information may be added. When these contents are input as shown in FIG. 20 and the "accumulate" button is pressed, this representative mass spectrum data is stored in the database together with the input label information (S141 in FIG. 11). (mass spectrum accumulation part).

Here, the data structure will be described with reference to FIG.

Measurement data d1 for one sample obtained by measurement (mass spectrometry) in the mass spectrometry unit 310 has a different raw data format depending on the mass spectrometry unit 310. given to An ID (identification code) given by the mass spectrometer 310 or a user is attached to this mass spectrum data.
In the analysis device 320, the text data is converted into binary data d3 in order to display the mass spectrum in the above-described manual generation and automatic generation of the representative mass spectrum (S12, S13 in FIG. 11). . When the representative mass spectrum data (text format) d4 is obtained, label information and the like are input, and the label information is added to the representative mass spectrum data instead of or in addition to the ID given by the mass spectrometry unit 310 (Fig. 11 of S141). Then, the data is converted into a relational database (RDB) construction text file and stored in the database for each project. This database is specifically indicated at 327 in FIG.

The representative mass spectrum data accumulated in the mass spectrum database 327 in this manner is used (utilized) for various purposes. One of them is statistical analysis processing, which will be described later, and the other is internal index search processing, which will be described next. For each of these processes, a group of data (data set) is selected from the accumulated mass spectral data. This is the folder of selected mass spectral datasets shown at 328 in FIG.

As an example, FIG. 21 shows an example of a display screen for selecting mass spectral data belonging to a specific group from the mass spectral data already accumulated in the mass spectral database 327 for statistical analysis. The project name (human cancer sample) and the range of label information that defines the group, that is, gender (no specification), age (50-80), disease (liver cancer), stage (1-3), tissue type ( primary HCC) is entered by the user. When the "Select" button is pressed, the mass spectrum database 327 is searched and mass spectrum data satisfying the above project name and label range are extracted (S142 in FIG. 11). The label information of the extracted data is displayed in the form of a list as shown in the lower part of FIG.
If the "export" button is pressed, it will be exported (S143 in FIG. 11). That is, the extracted mass spectrum data is transferred from the database to a predetermined storage location in the storage unit 326, and is ready for statistical analysis processing (creation of the mass spectrum database 328) (data set designation unit).

By adding label information to all representative mass spectral data in this way, it becomes easier to group, search, and extract (select) data using terms and concepts (labels) that the user can understand.

All of the mass spectra created in the above-described representative mass spectrum creation process (S12, S13 in FIG. 11) (especially in the automatic creation process (S13 in FIG. 11)) are of high quality. Not exclusively. The quality of mass spectra created or already stored in the mass spectrum database 327 is evaluated as follows. This evaluation process is executed by specifying it on a menu screen (not shown), but it is also possible to proceed to this evaluation process by pressing the "check" button on the display screen of FIG.

First, an index for evaluation (called an internal index) is determined (searched, selected). Next, using this internal standard, the pass/fail of a specific mass spectrum is judged (discrimination) (individual spectrum judgment), or the pass/fail of a mass spectrum within a specific group is judged (discrimination) (batch of spectra in a folder). judgment).

Determination of the internal mark is performed using a large number of accumulated existing mass spectral data. It is preferable that the mass spectrum data used for the determination of the internal mark is a group of mass spectra determined to be good mass spectra in past mass spectrum evaluations, or a collection of mass spectra determined by the user's visual inspection to be good mass spectra. .

First, on the display screen shown in FIG. 22, a large number of mass spectral data to be used for determining the internal standard are designated and conditions are set. Here, one of the selected mass spectrum data set folders (reference numeral 328 in FIG. 10) is specified as the target folder (first specifying means). It is also possible to specify a target folder or the like by inputting label information. Also, as conditions, the lower limit of the detection intensity and the upper limit of the coefficient of variation are input. A mass spectrum has multiple peaks corresponding to specific m/z values. An internal standard is determined by selecting one or more of these peaks that appear stably (with little fluctuation). That is, the internal standard is a specific stable peak. The lower limit of detection intensity determines the lower limit of the ion intensity (average intensity) of the peak used as an internal standard. That is, a peak having an average intensity higher than the lower limit can be a candidate for the internal standard. The coefficient of variation is the variance divided by the mean, assuming that the set of peak values (ion intensities) corresponding to a particular m/z value follows a normal distribution. A peak having a coefficient of variation lower than the input upper limit of the coefficient of variation can be a candidate for the internal standard. These two conditions are AND conditions.

When the target file is specified, the conditions for the lower limit of detection intensity and the upper limit of the coefficient of variation are entered, and the "execute" button is pressed, a set of peak values corresponding to each m/z value for all mass spectra in the target folder The coefficient of variation and the average ion intensity are calculated for , and among the obtained results, those that satisfy the above conditions are displayed as internal indicator candidates, arranged in ascending order of the coefficient of variation, as shown in the upper half of FIG. (internal marker candidate creation unit). Displayed are rank order, coefficient of variation, m/z, mean ion intensity. The user selects a suitable internal marker to be used for mass spectrum evaluation from among these internal marker candidates, and checks the corresponding box. In the example of FIG. 23, m/z is chosen as the internal standard for the 208 peak. If not selected by the user, the peak with the smallest coefficient of variation is automatically selected as the internal target (S151 in FIG. 16).

The evaluation of the mass spectrum using this internal standard is based on the following concept. That is, since the internal standard is a peak that stably appears in the mass spectrum as described above, the corresponding peaks (peaks with the same m/z value) also in the mass spectrum to be evaluated have similar ion intensities. can be expected to have Therefore, as an example, an allowable range is set above and below the average ion intensity of the internal standard peak, and if the ion intensity of the corresponding peak of the mass spectrum to be evaluated is within this allowable range, it is judged as a good mass spectrum. If it is out of the allowable range, it is determined as a defective mass spectrum.

In the lower half of the screen of FIG. 23, the user inputs a folder containing mass spectral data files to be evaluated. The mass spectrum data to be evaluated may be designated by the label information described above (referred to as "batch determination of spectrum in folder"). The quality of the data may be judged (individual spectrum judgment) (above, the second designating part).This individual spectrum judgment is typically performed in FIG. Please press here."

When the "execute" button is pressed, it is judged whether the ion intensity of the corresponding peak is within the allowable range above and below the average ion intensity of the internal standard, and if it is within the allowable range, it is good. If the mass spectrum is out of the allowable range, it is judged as a defective mass spectrum (S152 in FIG. 11) (good/bad judgment means). At the bottom of the screen in FIG. 23, file names of the determined good spectra and bad spectra are listed.

The basic mass spectral data group (target folder specified in FIG. 22) used for determining the internal standard and the mass spectral data for quality judgment are obtained from mass spectrometry of the same type of specimen. Samples of the same type include cells with the same disease such as liver cancer, the same organ such as the liver of a specific animal or human, cells of the same site, parts of the same type of living body, etc., and peaks with the same m/z value is expected to appear (which may include peaks with different m/z values). When using the peak determined as the internal standard, if the number of good spectra is smaller than the number of bad spectra, the internal standard may not be correct. A trial can be made by redoing the determination process (changing the basic mass spectrum data, etc.), or by using a peak (m/z value) having a coefficient of variation second or later as an internal index. When a plurality of internal indicators (peaks) are determined, the final judgment result can be obtained by AND logic or OR logic of the pass/fail judgment results based on each internal indicator.

In the individual spectrum determination, when the created mass spectrum is determined to be defective, the process returns to the automatic creation of the representative spectrum (S13 in FIG. 11), and the threshold is changed on the screen in FIG. By selecting TIC or EIC instead of MIC on the screen of FIG. 18, it is possible to recreate (recreate) the representative mass spectrum.

There are various total analysis methods, but here we divide them into four major categories, and explain some representative methods included in each category.

1) Significant difference test Welch t-test (Welch's t-test)
Based on the null hypothesis that "the means of two populations are equal", a two-sided test is performed without assuming equal variances.
・WRST (Wilcoxon rank sum test)
A nonparametric test is performed based on the null hypothesis that "both samples were drawn from the same population."
・ANOVA (Analysis of variance)
A multi-group parametric test is performed based on the null hypothesis that there is no difference in the population means of all groups.

Significance test, in the analyzer, is tested for each mass spectrum peak (m / z value), is useful for searching for peaks with significant differences between mass spectrum groups (groups), marker search and It can be used to elucidate the molecular mechanism. A peak (m/z) determined to have a significant difference between groups can be selected, and the corresponding data can be used in machine learning, which will be described later.

2) Dimensional reduction Dimensional reduction reduces many variables to a small number of variables (scores).
・PCA (principal component analysis)
It is an unsupervised dimensionality reduction method.
・PLS (Partial least squares)
It is a supervised dimensionality reduction method.
・OPLS (Orthogonal Partial Least squares)
An improved version of PLS that separates and analyzes the orthogonal components of the explanatory variables.
・KPLS (kernel partial least squares)
Since the PLS is nonlinearly extended using the kernel method, the separation performance is improved.

Since the analyzer can reduce many peaks (variables) contained in the mass spectrum into a few scores (principal components), the correlation between scores can be determined, and the results can be used for machine learning. The number of scores can be specified by the user.

3) Machine learning method LDA (Linear discriminant analysis)
Construct a discriminant function using a straight line and a hyperplane.
・QDA (Quadratic discriminant analysis)
Construct a discriminant function using curves and hypersurfaces.
・SVM (Support vector machine)
A nonlinear classification method that constructs a margin-maximizing classification surface in the feature space.
・LR (Logistic regression)
A regression model that assumes that the log-likelihood ratios of the posterior probabilities are linear.
・RF (Random forest)
A collective learning algorithm with decision trees as weak learners.

The analyzer can diagnose an unknown mass spectrum using the discriminant functions created by these learning methods. Therefore, it can be used for diagnosis and determination of treatment policy.

4) Verification method Verifies the diagnostic accuracy of the machine learning method. Based on this verification result, it is also possible to automatically select the most suitable machine learning method.

・ k-fold CV (k-fold cross validation)
A verification method in which a sample group is divided into k pieces, one of which is a test sample and the rest are training samples.
・LOOCV (Leave one out cross validation)
A validation method in which only one sample is extracted from a sample group as a test sample and the rest as training samples.

In the storage unit 326 of the analysis device 320, programs (routines) (means) for executing all individual verification methods (Welch t-test, WRST, ANOVA, etc.) included in the significance test method described above, dimension reduction Programs (routines) (means) that execute all individual contraction methods (PCA, PLS, OPLS, KPLS, etc.) included in reduction methods, all individual learning methods (LDA, QDA) included in machine learning methods , SVM, LR, RF, etc.) and programs (routines) (means) for executing all individual verification methods (k-fold CV, LOOCV, etc.) included in the verification method. ) are stored, and the processing unit 321 can execute each statistical analysis method and verification method individually or simultaneously according to these programs.

That is, as shown in FIG. 24, all the statistical analysis methods and verification methods described above are displayed on the display screen of the display unit 324, and the user can select any one of the displayed analysis methods and verification methods. The above can be selected. All analysis and validation methods can also be selected. In addition, one or more of the verification methods, one or more of the contraction methods, one or more of the learning methods, and one or more of the verification methods can be combined and selected. You can also remove one or more from . That is, any combination can be selected (S21 in FIG. 12) (statistical analysis method input section). Then, the processing unit 321 can execute the selected analysis methods and verification methods individually, in parallel, or in combination in order (S30, S40, S50, S60 in FIG. 12) (statistical analysis execution part). The results of these processes (various numerical information obtained as a result of statistical analysis, reference numeral 329 in FIG. 10) are displayed on the screen of the display unit 324, printed by the printer 325, or in the form of data via a communication line. or output to a storage medium and presented (S31, S41, S52, S61 in FIG. 12).

As a result of executing the selected testing method, the data corresponding to the selected peaks are either passed to the selected reduction method and used as data to be processed (reduced), or passed to the selected training method. It can also be used as the processing (learning) target data (S32 in FIG. 12). Similarly, the score data processed by the selected contraction method and output can be passed to the selected learning method and used as the processing (learning) target data (S42 in FIG. 12). In particular, the combination of the selected contraction method and the selected training method is displayed as a line connecting them on the display screen (see FIG. 24). The selected verification method verifies the diagnostic accuracy of the selected learning method (S50, S60 in FIG. 12). The discriminant function determined by the selected learning method can also be used for diagnostic processing of unknown data (S51 in FIG. 12), and the diagnostic results are presented (S52 in FIG. 12). The selected analysis method and verification method are executed in response to pressing (clicking) of the "execute" button (see FIG. 23).

Thus, program routines for performing a wide variety of statistical analysis and validation methods are provided, allowing the user to select any of these general to advanced program routines ( 1 or more) can be performed. It is also possible to install additional program routines for statistical analysis methods and verification methods that are not yet provided (high diffusion). It is possible to set an appropriate combination (combination excluding the test method) from the test method, contraction method, and learning method, and to perform a trial on the target mass spectrum group, and analyze the mass spectrum group. You can choose the best combination for At this time, a verification method can be used to determine whether the set combination was appropriate.
As in the specific examples below, a combination of multiple statistical analysis methods can be used to manually or automatically identify meaningful changes, such as disease-specific molecules (markers), in a group of mass spectra of interest. (including semi-automatic) extraction. The screen (user interface) shown in FIG. 24 is easy to operate, easy to understand, and can shorten the working time.

As shown in FIG. 24, a specific example in which ANOVA is selected as the test method, PCA as the reduction method, LDA as the learning method, and LOOCV as the verification method will be described below.

The folder of the data set used for analysis (the box labeled "Select the folder containing the data set to be used") contains the mass spectrum data of rabbit plasma of 3 groups (in FIG. 11, S142, S143) (data set designation section). The three groups of rabbits are as follows.

10 normal rabbits (served as controls): abbreviated as C0 10 rabbits with cholesterol loading through diet: abbreviated as C16 10 rabbits with genetically abnormal cholesterol metabolism: abbreviated as W

Therefore, 30 mass spectral data of these 30 rabbit plasmas are subject to statistical analysis.

The m/z range defines the range of m/z values used for analysis in the mass spectrum, and the range of 10.0 to 1000.0 is specified here.
Bin size indicates the interval (width) that m/z values can take, and 1 is set here. Therefore, mass spectra having ion intensities corresponding to m/z values that change by 1, such as 10.0, 11.0, 12.0, . If the bin size of the mass spectrum data in the data set is not 1, the average value or added value (when the bin size is small), or interpolation (when the bin size is large) is used to set the bin size to 1. processed to become

As statistical analysis methods ANOVA, PCA, and LDA are selected as described above, and LOOCV is selected as the validation method. These character blocks are connected by lines because the PCA contraction results are used in the learning method LDA. Significance level, multiple testing correction, coefficient of variation range and mean intensity range are for the test method ANOVA and are described in the marker search section below. The number of scores output as a result of contraction in contraction method PCA is set to two. After the above setting (S21 in FIG. 12), when the execution button is pressed, the set statistical analysis and verification are executed.

Data processing is performed prior to execution of the statistical analysis routine and the verification routine (S22 in FIG. 12). The mass spectral data of the data set read from the mass spectral database 328 (see FIG. 10) are processed to have the set m/z range and the set bin size. In addition, ion intensity normalization (northrise) is performed for each group of C0, C16, and W for the 30 mass spectra. That is, normalization is performed by calculating the average intensity of each spectrum and dividing the value of each peak by this average intensity.

The purpose of the marker search is to find marker substances useful for distinguishing the three groups from among m/z=10-1000.

A marker search is performed by narrowing down (searching for) substances (m/z) that satisfy the following conditions (i), (ii), and (iii).

(i) Select m/z with greatly different intensities (ion intensities) between groups. This is accomplished by the test method ANOVA detailed below.
(ii) pick an m/z whose intensity is sufficiently high; The average intensity ranges from 1.0 to inf. (infinite). 1.0 means the average value because it is normalized by the average value. This condition (ii) is to pick m/z corresponding to peaks with an intensity greater than 1 in any group.
(iii) choose m/z with sufficiently small variations in intensity within each group; Variation is determined within the range of coefficient of variation (0.0 to 0.3) set on the screen of FIG. The coefficient of variation is determined by the value of variance when the intensity of each peak is 1 (normalized because the value of variance changes depending on the intensity of the peak). This condition (iii) is to select the m/z corresponding to the peak such that the above variance is less than 0.3 in each group.

An ANOVA method for selecting m/z that satisfies the above condition (i) will be described.

The ANOVA method is based on the null hypothesis that the average intensities of each group are equal (μ1 = μ2 = μ3, where μ1, μ2, and μ3 are the average intensities of each group). Calculate the P value. The P-value indicates the probability that the null hypothesis holds. The larger the P-value, the smaller the intensity difference for the same m/z value between groups (the null hypothesis is correct).

The P-value significance level is set at 0.05 (screen in Figure 24). An m/z whose P-value calculated as above is smaller than this significance level of 0.05 is selected as an m/z (peak) whose intensity differs between groups. BF (Bonferroni) method for multiple test correction in FIG. means.

The m/z values (peaks) selected as satisfying the above conditions (i), (ii), (iii) and their corresponding P values (expressed as −log), coefficient of variation (CV_C0 , CV_C16 and CV_W denoting groups C0, C16 and W respectively) and average intensities (M_C0, M_C16 and M_W denoting groups C0, C16 and W respectively) are shown as output files in FIG.

m/z satisfying the above conditions (i), (ii), and (iii) were selected in the marker search as described above (see FIG. 25). A reduction method reduces the number of variables. This is because the accuracy of learning/diagnosis may be improved by reducing the amount of information using the dimensionality reduction method.

Principal component analysis (PCA) is set for the dimension reduction method, and the number of scores is 2 (see FIG. 24).

FIG. 26 shows the results of contracting the ion intensities of all peaks in the 30 mass spectrum data into two principal components (first and second principal components) by PCA. The ion intensities of all selected m/z peaks shown in FIG. 25 may be contracted.

PC1 and PC2 are the first and second principal components, respectively. jw-0W, JW-16W and WHHL correspond to groups C0, C16 and W, respectively. It can be seen that these groups are highly likely to be separated (determined) by these principal components PC1 and PC2.

All of the original three groups of data (all m /z) can also be used to perform dimensionality reduction.

The object is to perform machine learning using teacher data, and to highly accurately estimate a group to which an unknown spectrum belongs based on the learning result (discriminant function). Most preferably, the statistical test, dimensionality reduction method, and machine learning method described above can be combined to configure a classifier with higher accuracy.

As an example, based on the results obtained by the contraction method shown in FIG. 26, the first and second principal components are used as explanatory variables to configure a discriminator (PCA-LDA) that estimates the objective variable (group). can be done. In this case, even if learning is performed based on the contracted results using the tested (ANOVA) results (Fig. 23), the contracted data using the original three groups of mass spectral data You can either learn based on it or not.

Summarizing the methods for estimating the group to which an unknown spectrum belongs, there are the following four types.

1) Machine learning: learning of teacher data using all m/z as explanatory variables (objective variable is group name).
2) Statistical test→machine learning: Learning is performed on teacher data using m/z, which is considered important for discrimination in the test, as an explanatory variable.
3) Dimensional reduction method→machine learning: All m/z information is reduced to fewer variables (principal components and PLS scores), and learning of teacher data is performed using these as explanatory variables.
4) Statistical test → Dimensional reduction method → Machine learning: m / z considered important for discrimination in the test are reduced to fewer variables (scores), and learning of teacher data is performed using them as explanatory variables. conduct.

Leave-one-out cross-validation (LOOCV) is set as the verification method on the screen shown in FIG. This repeats the process of ``Can you extract one sample for verification from all the samples for verification, perform learning with the remaining samples, and correctly estimate the group to which the verification sample belongs?'' Output rate.

FIG. 27 shows the result (errata table) of LOOCV verification of the rate of correct answers estimated by the discriminator (PCA-LDA). [1] indicates a correct answer, [0] indicates an incorrect answer, and the correct answer rate was 27/30=90%. In addition to the above, the verification results can also be output as performance data such as ROC curves and AUC.

Change the combination of the contraction method and the learning method, and in some cases, change the combination with the testing method, and obtain the accuracy of the estimation based on each learning result from the verification method (the verification method may be changed), and for each combination Based on the results of estimation accuracy, the discrimination method (combination of test method, reduction method, learning method) that allows more accurate data learning/diagnosis is automatically selected (or semi-automatically in which the user selects the combination) ) can also be selected.

FIG. 28 shows the statistical analysis and verification results shown in FIGS. 25, 26, and 27 in one display screen. By displaying such a screen, the user can comprehensively see the results of a series of analyzes and verifications.

(Test example using the mass spectrometry system of the present invention)
Using the mass spectrometry system of the present invention, a confirmation test for detection of cancer cells in blood was performed.
SAS analysis was performed on samples in which 2 mL of human whole blood was spiked with varying concentrations of human squamous cell carcinoma cells (SAS). Sample No. 1 is a sample of whole blood containing no cancer cells. Samples No. 2 to No. 5 varied in SAS concentration.
Sample No. 2: 1×10 ⁵ SAS/2 mL blood
Sample No. 3: 0.5×10 ⁵ SAS/2 mL blood
Sample No. 4: 1×10 ⁴ SAS/2 mL blood
No. 5 sample: 1×10 ³ SAS/2 mL blood
Sample No. 6 is a positive control of cancer cells only.

The test was performed according to the following procedures.
i) Blood was drawn.
ii) mononuclear cell isolation treatment was performed by Ficoll.
iii) Mass spectrometry was performed using the mass spectrometer of the present invention having ionization means using the remote sampling electrospray ionization method described in the second embodiment.
iv) Using the analysis apparatus of the present invention (including the discrimination means and the statistical analysis means including machine learning), the results of mass spectrometry were output.

The test results are shown in FIGS. 29 and 30. FIG.
From the results of FIGS. 29 and 30, it was confirmed that No. 2 to No. 4 exhibited peak shapes similar to No. 6 at 740, 758, and 764 (m/z).
It was confirmed that 5,000 tumor cells mixed in 1 mL of blood could be detected.

Aspects of the present invention are, for example, as follows.
<1> ionization means for ionizing a component of a test target in the blood of a subject;
analysis means for mass-analyzing the ions generated by the ionization means;
A mass spectrometer characterized by having
<2> The mass spectrometer according to <1>, wherein the test object is not added with an antibody (excluding an antibody derived from the same species as the subject).
<3> The mass spectrometer according to any one of <1> to <2>, wherein the test object is any one of cells, proteins, peptides, nucleic acids, lipids, and carbohydrates.
<4> The mass spectrometer according to <3>, wherein the cells are circulating tumor cells (CTCs).
<5> The mass spectrometer according to any one of <1> to <4>, wherein the test object is obtained by separating mononuclear cells from the blood.
<6> The ionization means is
a needle-shaped member that collects the test object by contacting or piercing the tip of the test object;
a power source for applying a voltage to the needle-shaped member in order to electrospray ions originating from the test object from the tip of the needle-shaped member;
an ion collecting member that collects the generated ions;
The mass spectrometer according to any one of <1> to <5>.
<7> The ionization means
a first tubular member, one end of which is used for contacting and sampling a test object and the other end of which is used for electrospraying ions originating from the test object;
a second tubular member concentrically inserted into the first tubular member;
a gas flow generating member for causing gas to flow through the second tubular member;
A voltage is applied to the counter electrode such that a potential difference is generated between the other end of the first tubular member and a counter electrode facing the other end arranged in the region where the electrospray is generated. a power supply;
an ion guiding member that guides the generated ions to the mass spectrometer;
The mass spectrometer according to any one of <1> to <5>.
<8> The ionization means
A linear member that uses the sample liquid to be inspected prepared by a limiting dilution method and conveys droplets of the sample liquid;
an ionization member that generates ions derived from the test object from the droplets adhered to the linear member;
The mass spectrometer according to any one of <1> to <5>.
<9> an ionization step of ionizing a component of the test target in the blood of the subject;
an analysis step of mass-analyzing the ions generated by the ionization step;
A mass spectrometry method characterized by having
<10> The mass spectrometry method according to <9>, wherein the test object is not added with an antibody (excluding an antibody derived from the same species as the subject).
<11> the mass spectrometer according to any one of <1> to <8>;
At least one of mass spectrum information created based on the mass spectrometry results obtained by the analysis means and mass spectrum analysis result information obtained by learning the mass spectrum information is used to determine whether the existence state of the test object in blood exceeds a predetermined allowable range or whether the mass spectrum exhibits a disease-positive pattern,
If the presence state of the test subject exceeds the predetermined allowable range or the mass spectrum is determined to exhibit a disease-positive pattern, the subject is derived from the presence state of the test subject. an analysis device having determination means for determining the possibility of falling under a disease;
A mass spectrometry system characterized by having
<12> The mass spectrometry system according to <11>, wherein no antibody (excluding an antibody derived from the same species as the subject) is added to the test object.
<13> The analysis device determines the possibility of multiple diseases at once based on the results of mass spectrometry obtained for one measurement sample of the blood of the subject, 11> to <12>, which is a mass spectrometry system.
<14> Mass spectrum information created based on the results of mass spectrometry obtained by ionizing the components of the test target in the blood of the subject and subjecting the generated ions to mass spectrometry. , and information on the analysis result of the mass spectrum obtained by learning the information on the mass spectrum, whether the existence state of the test object in the blood exceeds a predetermined allowable range determine whether or not
When it is determined that the presence state of the test target exceeds the predetermined allowable range, determining the possibility that the subject corresponds to a disease derived from the presence state of the test target,
It is a mass spectrometry analysis program characterized by causing a computer to execute processing.
<15> The mass spectrometric analysis program according to <14>, wherein the test object is not added with an antibody (excluding an antibody derived from the same species as the subject).

The mass spectrometer according to any one of <1> to <8> above, the mass spectrometry method according to any one of <9> to <10> above, and any one of <11> to <13> above. and the mass spectrometry analysis program according to any one of the above <14> to <15> solve the above-mentioned problems in the conventional art and achieve the above-mentioned object of the present invention can be done.

REFERENCE SIGNS LIST 1 needle-like member 7 power source 8 ion collecting member 12 mass spectrometer 20 power source 21 first tubular member 22 second tubular member 25 gas flow generating member 28 ion guiding member 29 mass spectrometer 33 linear member 35 ionization member 36 mass Spectrometer 100 mass spectrometer 101 ionization means 102 analysis means 200 mass spectrometer 201 ionization means 202 analysis means 300 mass spectrometer 301 ionization means 302 analysis means 310 mass spectrometer 320 analyzer 321 processing section 322 input section 323 output section 324 display Unit 325 Printer 326 Storage Unit

Claims (8)

ionization means for ionizing a test target in the blood of a subject;
and an analysis means for mass-analyzing the ions generated by the ionization means, wherein
The ionization means is
a first tubular member, one end of which is used to contact and sample the test object and the other end of which is used to electrospray the ions originating from the test object;
a second tubular member concentrically inserted into the first tubular member;
a gas flow generating member for causing gas to flow through the second tubular member;
A voltage is applied to the counter electrode such that a potential difference is generated between the other end of the first tubular member and a counter electrode facing the other end arranged in the region where the electrospray is generated. a power supply;
an ion guide member that guides the generated ions to the analysis means;
A mass spectrometer, wherein both ends of the first tubular member protrude from the second tubular member. The first tubular member has an outer diameter of 0.2 mm,
The inner diameter of the second tubular member is 0.3 to 0.5 mm,
2. The mass spectrometer according to claim 1 , wherein the other end of said first tubular member protrudes from said second tubular member by 0.5 to 1.0 mm. 3. The mass spectrometer according to any one of claims 1 and 2 , wherein the blood, which is the subject, does not contain an antibody for detecting a test target. 4. The mass spectrometer according to any one of claims 1 to 3 , wherein the test object is any one of cells, proteins, peptides, nucleic acids, lipids, and carbohydrates. 5. The mass spectrometer of claim 4 , wherein the cells are circulating tumor cells (CTCs). 6. The mass spectrometer according to any one of claims 1 to 5 , wherein said test object is obtained by separating mononuclear cells from said blood. a mass spectrometer according to any one of claims 1 to 6 ;
At least one of mass spectrum information created based on the mass spectrometry results obtained by the mass spectrometer and mass spectrum analysis result information obtained by learning the mass spectrum information using the information to determine whether the presence state of the test object in blood exceeds a predetermined allowable range or whether the mass spectrum exhibits a disease-positive pattern;
If the presence state of the test subject exceeds the predetermined allowable range or the mass spectrum is determined to exhibit a disease-positive pattern, the subject is derived from the presence state of the test subject. an analysis device having determination means for determining the possibility of falling under a disease;
A mass spectrometry system comprising: 8. The analyzer according to claim 7 , wherein the analyzer determines the possibility of a plurality of diseases at once based on the results of mass spectrometry obtained for one measurement sample of the blood of the subject. mass spectrometry system.

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