Computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer

Abstract

A computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer (220) is disclosed. The method includes: a) at least one imaging step, including imaging at least one reflective target (128) to which a sample containing at least one analyte is applied, using at least one imaging device (235); b) at least one sample recognition step, including locating at least one sample area on the reflective target (128); and c) at least one analyte detection step, including detecting at least one analyte in the sample using surface-assisted laser desorption / ionization mass spectrometry (SALDI-MS) in a laser desorption mass spectrometer (220), wherein laser radiation is applied to the reflective target (128) using at least one laser source (222) of the laser desorption mass spectrometer (220), and the laser radiation is directed to the located sample area using at least one control device (237), such that at least one ion of the at least one analyte is generated, and the ion is detected using at least one of a mass analysis unit (224) of the laser desorption mass spectrometer (220) or an ion mobility spectrometer.

Description

[Technical Field]

[0001] The present invention relates to a computer-implemented method for detecting at least one analyte in a sample using a laser desorption mass spectrometer, a system including at least one laser desorption mass spectrometer, and a computer program. The method and apparatus can be applied to measure the mass of an analyte or the mass of a fragment of an analyte for the purposes of analyte identification and analyte quantification. In particular, the apparatus and method can be applied to the quantitative analysis of biological molecules such as proteins, peptides, oligonucleotides, and small molecule compounds. However, other applications are also possible. [Background technology]

[0002] Matrix-assisted laser desorption / ionization (MALDI) mass spectrometry is an ionization technique that applies a combination of small, radiation-absorbing organic matrix molecules with the respective analyte of interest. Because the ionization process itself tends to be soft, it is nowadays a well-established method for the analysis of biomolecules, especially proteins or peptides.

[0003] A variation of this technique is the surface-assisted laser desorption / ionization (SALDI) process. In contrast to MALDI-MS, no additional organic matrix material is excessively mixed with the analyte of interest. Instead, a functionalized solid surface is utilized for desorption and ionization of the analyte. Based on elemental composition, the majority of functional SALDI materials reported in the literature can generally be classified into three main types: carbon-based, semiconductor-based, and metal-based. G. Eppe et al., Surface-assisted laser desorption / ionization mass spectrometry imaging: A review, Mass Spec Rev. 2022; 41: 373-420 (DOI: 10.1002 / mas.21670), provides a general overview of surface-assisted laser desorption / ionization for mass spectrometry.

[0004] Essentially, the goal of SALDI is to access the mass-to-charge ratio m / z of small molecules, especially those with molar masses less than 1000 Da. Such molecules are typically hidden or suppressed in traditional MALDI techniques due to the presence of interfering signals from the matrix. Furthermore, SALDI is generally advantageous because it avoids specific matrix application processing steps. Typically, the co-crystallization process with the analyte can lead to problems when quantification accuracy is required.

[0005] The classical MALDI approach, which aims at controlled detection and laser irradiation of defect-free matrix-analyte-crystals to generate optimal MALDI-MS spectra, has already been demonstrated. For published works in the field of MALDI-MS, the following references can be made:

[0006] Japanese Patent Publication No. 5504282 describes a MALDI mass spectrometry method that can accurately predict the sweet spot where a large number of ions are generated, thereby efficiently obtaining high-quality MS spectra in a short time. A sweet spot prediction method is described in which a mixed crystal of a matrix and a sample containing a target molecule is separated, and then local analysis of the mixed crystal is performed using an analytical method other than mass spectrometry. Then, MALDI mass spectrometry is performed to identify in advance the analysis results of the sweet spot and / or non-sweet spot portions of the mixed crystal. Next, after separating the mixed crystal of the matrix and the sample containing the target molecule, local analysis of the mixed crystal is performed using the analytical method other than mass spectrometry. The analysis results of the mixed crystal are detected to determine whether the portions are sweet spots and / or non-sweet spots. A MALDI mass spectrometry method using this sweet spot prediction method is also described.

[0007] U.S. Patent No. 7,359,574 describes a mass spectrometer that uses image processing of the output signal of a camera within the mass spectrometer to provide feedback for directing a laser. It describes determining where the sample is actually located on the plate and selecting, based on its structure, several points on each sample to direct the laser during a mass spectrometer cycle. Such feedback information increases the likelihood that the laser will strike the sample and provide useful data.

[0008] International Publication No. WO 2006116166 describes a method and apparatus for performing image analysis of a sample target area on a MALDI sample plate to select a laser impingement location for optimal mass spectral acquisition. A target area image is captured and analyzed to determine the occurrence distribution of pixel values ​​(representing brightness and / or color information). A dynamic threshold can be determined by constructing a virtual histogram and then identifying values ​​at which a local minimum occurs between modes of the bimodal distribution. The threshold is applied to pixels to locate regions in the target area with desired visual characteristics, such as high brightness indicative of crystalline structure. Directing the laser beam to impinge only on regions with these desired visual characteristics can optimize mass spectral acquisition. Combining the image analysis process with automated spectral filtering techniques can further improve mass spectrometer performance by selectively retaining or moving the laser beam in a region of the sample spot based on whether the resulting mass spectrum meets predetermined performance criteria.

[0009] U.S. Patent No. 6,956,208 describes a MALDI mass spectrometer that directs a laser shot at a MALDI sample to generate a sample spectrum, and analyzes the sample spectrum to determine whether it meets predetermined criteria. If the sample spectrum meets the predetermined criteria, a subsequent laser shot is directed to a predetermined location on the MALDI sample. Essentially, if analysis of a previous laser shot indicates that the "sweet spot" of the MALDI sample has been hit, the subsequent laser shot may be directed to an area adjacent to the previous shot, thereby completely sampling the sweet spot. A method of operation of the MALDI mass spectrometer is also described.

[0010] Despite the advantages achieved by the above-described devices, several technical challenges remain.

[0011] Quantitative methods using SALDI have attracted widespread interest due to the possibility of acquiring spectra with ultrafast analysis times for dry spots of analytes on specific SALDI targets. To date, no algorithms are known that direct laser stimulation exclusively to dry spots on the respective SALDI targets. Typically, after spot recognition, dedicated laser stimulation is possible while avoiding stimulation of blank spot areas. Without automatic recognition, there are typically significant acquisition times due to the execution of preprogrammed laser paths. Summary of the Invention [Problem to be solved by the invention]

[0012] It would therefore be desirable to provide a computer-implemented method, a system including at least one laser desorption mass spectrometer, and a computer program for detecting at least one analyte in a sample with a laser desorption mass spectrometer, that at least partially addresses the above-mentioned technical problems. In particular, rapid measurement of analyte spots should be provided. [Means for solving the problem]

[0013] This problem is addressed by a computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer, a system comprising at least one laser desorption mass spectrometer, and a computer program comprising the features of the independent claims. Advantageous embodiments, which may be realized alone or in any combination, are set out in the dependent claims as well as in the specification as a whole.

[0014] When used below, the terms "having," "comprising," or "including," or any grammatical variants thereof, are used in a non-exclusive manner. These terms may therefore refer both to a situation in which, besides the features introduced by these terms, no further features are present in the entity described in this context, and to a situation in which one or more further features are present. For example, the expressions "A has B," "A comprises B," and "A includes B" may refer both to a situation in which no other elements are present in A besides B (i.e., a situation in which A consists exclusively of B), and to a situation in which, besides B, one or more further elements are present in entity A, such as element C, elements C and D, or even further elements.

[0015] Furthermore, it should be noted that the terms "at least one" or "one or more," or similar expressions, indicating that a feature or element may be present more than once, are typically used only once when introducing each feature or element. In the following, in most cases, when referring to each feature or element, the expressions "at least one" or "one or more" will not be repeated, despite the fact that each feature or element may be present more than once.

[0016] Furthermore, when used hereinafter, the terms "preferably," "more preferably," "particularly," "even more particularly," "particularly," "more particularly," or similar terms are used in conjunction with optional features without limiting the possibilities for substitution. Features introduced by these terms are therefore optional features and are not intended to limit the scope of the claims in any way. The present invention may be implemented by using alternative features, as would be understood by one skilled in the art. Similarly, features introduced by "in an embodiment of the invention" or similar phrases are intended to be optional features without any limitations regarding alternative embodiments of the invention, without any limitations regarding the scope of the invention, and without any limitations regarding the possibility of combining a feature introduced in this manner with other optional or non-optional features of the invention.

[0017] In a first aspect of the present invention, a computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer is disclosed.

[0018] The method includes, inter alia, the following steps, which may be performed in the given order. However, it should be noted that a different order is also possible. Furthermore, it is also possible to perform one or more of the method steps once or repeatedly. Furthermore, it is also possible to perform two or more method steps simultaneously or in a time-overlapping manner. The method may include further method steps not listed.

[0019] The method comprises the following steps: a) at least one imaging step comprising imaging at least one reflective target to which a sample containing at least one analyte has been applied by using at least one imaging device; b) at least one sample recognition step including locating at least one sample area on a reflective target; c) at least one analyte detection step, comprising detecting at least one analyte in the sample using surface-assisted laser desorption / ionization mass spectrometry (SALDI-MS) in a laser desorption mass spectrometer; Including, Laser radiation is applied to a reflective target using at least one laser source of the laser desorption mass spectrometer, and the laser radiation is directed to a located sample region using at least one controller, such that at least one ion of the at least one analyte is generated and the ion is detected using at least one of a mass analysis unit of the laser desorption mass spectrometer or an ion mobility spectrometer.

[0020] As used herein, the term "computer-implemented" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to a method involving at least one computer and / or at least one computer network. The computer and / or computer network may include at least one processor configured to perform at least one of the method steps of the method according to the present invention. Preferably, each of the method steps is performed by the computer and / or computer network. The method may be performed fully automatically, e.g., without user interaction. As used herein, the term "automatically" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to a process performed fully by at least one computer and / or computer network and / or machine, particularly without the need for human action and / or user interaction.

[0021] The computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer may be performed at least in part automatically, in particular, at least one of steps a) and c) may be performed automatically.

[0022] The computer-implemented method for detecting at least one analyte in a sample using a laser desorption mass spectrometer may be performed fully automatically, particularly from sample preparation to the analyte detection step. Specifically, at least steps a), b), and c) may be performed automatically. As described in more detail below, the computer-implemented method for detecting at least one analyte may further include at least one sample preparation step. The sample preparation step may also be performed at least partially or fully automatically.

[0023] The method may be performed fully automatically. For example, steps a) to c), as well as further optional method steps, may be performed fully automatically. Steps a) and b), such as image processing and image recognition, may be performed fully automatically. A system, such as the system proposed in further aspects of the present specification, may perform the fully automated method. The system may be configured to perform the fully automated method, starting from sample preparation to analyte detection using SALDI-MS. Performing the method automatically can increase sample throughput.

[0024] As used herein, the term "sample" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to any specific or special meaning. This term may specifically refer to any sample, such as, but not limited to, a biological sample, also referred to as a test sample, a quality control sample, or an internal standard sample. The sample may contain one or more analytes of interest. The sample may specifically be a liquid sample, particularly a liquid sample containing at least one biological substance. Furthermore, the analyte may be provided in a sample, specifically a tissue sample or a processed serum sample. The tissue sample may specifically have a slice thickness of less than 500 μm. For example, the sample may be selected from the group consisting of physiological fluids, including blood, serum, plasma, saliva, ocular lens fluid, cerebrospinal fluid, sweat, urine, milk, ascites fluid, mucus, synovial fluid, peritoneal fluid, amniotic fluid, tissue, cells, etc. The sample may be used directly as obtained from its respective source or may be subjected to pre-processing and / or sample preparation workflows. For example, the sample may be pretreated by adding an internal standard and / or diluting it with another solution and / or mixing it with a reagent. A quality control sample may be a sample that mimics the test sample and has known values ​​for one or more quality control substances. A quality control substance may be identical to the analyte of interest, or may be an analyte that produces an analyte identical to the analyte of interest by reaction or derivatization, and / or may be an analyte of known concentration, and / or may be a substance that mimics the analyte of interest or can otherwise be correlated to a particular analyte of interest. An internal standard sample may be a sample containing at least one internal standard at a known concentration.

[0025] As used herein, the term "analyte" is a broad term and should be given its common and ordinary meaning to those skilled in the art, without being limited to any specific or special meaning. This term may specifically refer to any chemical or biological substance or species, such as, but not limited to, a molecule or chemical compound, to be detected and / or measured. Specifically, the presence, concentration, and / or amount of the analyte in a sample may be detected or measured. Specifically, the analyte may be a biological molecule or macromolecule. The analyte may be selected from the group consisting of steroids, particularly ketosteroids, particularly secosteroids such as vitamin D; therapeutically active substances; detergents; glycosides; peptides; proteins; dyes; ions; nucleic acids; amino acids; metabolites; hormones; fatty acids; lipids; and carbohydrates. Furthermore, the analyte may be a molecule characteristic of a specific modification of another molecule, or a substance internalized by an organism, or a metabolic product of such a substance, or a combination thereof. However, other types of analytes may also be feasible. The steroid may be selected from the group consisting of progesterone, testosterone, estradiol, androstenedione, cortisol, cortisone, and 21-deoxycortisol. However, other steroids may also be feasible. The therapeutically active substance may be selected from the group consisting of digitoxin, mycophenolic acid, theophylline, lidocaine, digoxin, voriconazole, 4-hydroxyalprazolam, and cyclosporin A. However, other therapeutically active substances may also be feasible. The test substance may include a permanently positively or negatively charged molecule. Furthermore, the test substance may have an isotopic pattern. The test substance may have a molar mass of 6 Da to 10,000 Da, preferably 50 Da to 3,000 Da, and most preferably 100 Da to 2,000 Da. However, in principle, any type of test substance that may be applied to a reflective target may be feasible.

[0026] As used herein, the term "mass spectrometer" is a broad term and should be given its common and ordinary meaning to one of ordinary skill in the art, and should not be limited to any specific or particular meaning. The term may specifically refer to, but is not limited to, any analytical device configured to determine or measure the mass-to-charge ratio of ions. The measurement results may specifically be presented as a mass spectrum, e.g., a plot of intensity as a function of mass-to-charge ratio.

[0027] As used herein, the term "laser desorption mass spectrometer" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but is not limited to, any mass spectrometer based on a laser-based ionization technique. Specifically, this term may refer to a mass spectrometer that uses a medium and a laser to desorb and ionize a sample or a portion of a sample from the medium. Specifically, the medium may absorb energy from the laser and then transfer the energy to the sample or a portion of the sample. The ionization technique may also be referred to as a soft ionization technique. A laser desorption mass spectrometer may specifically be configured as a surface-assisted laser desorption / ionization (SALDI) technique. The SALDI technique may include at least three distinct stages. In the first stage, a sample may be applied to a target. In the second stage, laser pulses from a laser may be applied to the target, and the target may absorb the laser energy and transfer the laser energy to the molecules of the sample. In the third step, desorption and ionization can occur, and the generated ions can be accelerated to mass spectrometer by potential difference.Laser desorption mass spectrometry can include the step of analyzing sample using laser desorption mass spectrometry.Detection can specifically refer to the identification of the analyte of sample.Detection can be qualitative and / or quantitative detection.

[0028] As mentioned above, step a) corresponds to at least one imaging step comprising imaging at least one reflective target to which a sample containing at least one test substance has been applied by using at least one imaging device.

[0029] As used herein, the term “imaging step” is a broad term and should be given its common and ordinary meaning to one skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not be limited to, a method step that includes at least one imaging process. As used herein, the term “imaging” is a broad term and should be given its common and ordinary meaning to one skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not be limited to, providing a two-dimensional or three-dimensional representation of a reflective target. Imaging may include one or more of recording or capturing optical information, such as spatially resolved two-dimensional or three-dimensional optical information. Imaging may include generating at least one image of a reflective target. As used herein, the term “image” is a broad term and should be given its common and ordinary meaning to one skilled in the art and should not be limited to a specific or special meaning. The term may specifically refer to data recorded using an imaging device, such as, but not limited to, multiple electronic readings from an imaging device, such as the pixels of a camera chip.

[0030] As used herein, the term "imaging device" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically refer to, but is not limited to, a device having at least one image sensor configured to record or capture spatially resolved one-, two-, or three-dimensional optical information. The imaging device may be at least one device selected from the group consisting of at least one camera, at least one CCD camera, at least one CMOS camera, at least one RGB camera, at least one digital camera, at least one camera of a microscope, and at least one camera of an incident light microscope. The imaging device may generally comprise a one- or two-dimensional array of image sensors, such as pixels. In addition to at least one camera chip or imaging chip, the imaging device may comprise additional elements, such as one or more optical elements, such as one or more lenses. For example, the imaging device may be a fixed-focus camera with at least one lens fixedly adjusted relative to the camera. However, the imaging device may also comprise one or more variable lenses that can be adjusted automatically or manually.

[0031] The method may include loading a reflective target into a laser desorption mass spectrometer. The imaging step may be performed before and / or after loading the reflective target into the laser desorption mass spectrometer.

[0032] As used herein, the term "target" is a broad term and should be given its general and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to any article, device, or element that is or can be exposed to a beam, particularly a laser beam. For example, the target may be configured as a solid target having a predetermined shape, such as a flat target disk or wafer having a flat target surface and a circular, elliptical, or polygonal shape. Specifically, the target may be exposed to or be exposable to a laser beam of a mass spectrometer, particularly a laser desorption mass spectrometer. The target may specifically be a reusable target. As used herein, the term "reusable target" is a broad term and should be given its general and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to any target that can be configured to be used more than once. As described in more detail below, preparing at least one sample for analysis in a laser desorption mass spectrometer may include applying at least one sample to a target. After performing at least one measurement, the target, particularly the surface of the target, may be cleaned, e.g., the sample may be removed. Thereafter, a further sample may be applied to the target, and a further measurement may be performed.

[0033] As used herein, the term "reflective target" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to any target having at least one reflective interface, specifically at least one reflective surface. The reflective interface or surface may be configured to redirect a wavefront between two different media so that the wavefront returns to the original medium. Reflection may specifically refer to the reflection of light. The reflective target may have a specular reflectivity of 45% or greater. Specular reflection may refer to the phenomenon of reflection of parallel light rays incident on a surface at equal angles. Specular reflection generally obeys all three laws of reflection, i.e., the angle of reflection is equal to the angle of incidence, and the normal ray, incident ray, and reflected ray are all in the same plane. The incident ray and reflected ray are on either side of the normal ray.

[0034] The reflective target may specifically have a smooth surface. a It may have a nominal arithmetic roughness of ≦2 μm.

[0035] The reflective target may specifically have a thickness of 0.2 mm to 1 cm, preferably 0.5 mm to 3 mm. Furthermore, the reflective target may have a thickness of less than 1 cm, preferably less than 3 mm. However, other dimensions may also be feasible.

[0036] An example of a reflective target is described below, however, other types of reflective targets may be feasible, such as other types of materials and / or coatings on the reflective target.

[0037] The reflective target may have at least one surface. The surface may be at least partially covered by at least one layer. The layer may be a hydrogen-containing, silicon-incorporated amorphous carbon (aC:H:Si) layer. The aC:H:Si layer may include: 40 atomic % to 80 atomic % carbon, 1 atomic % to 20 atomic % hydrogen, and ·10 atomic % to 40 atomic % silicon.

[0038] The sum of carbon, hydrogen, and silicon may be up to 100%, particularly 100%. However, the aC:H:Si layer may contain additional elements. Therefore, the sum of carbon, hydrogen, and silicon may be less than 100%. Specifically, the sum of carbon, hydrogen, and silicon may be at least 51%, particularly at least 55%, particularly at least 60%, particularly at least 65%, particularly at least 70%, particularly at least 75%, particularly at least 80%, particularly at least 85%, particularly at least 90%, particularly at least 95%, particularly at least 98%.

[0039] As used herein, the term "surface" is a broad term and should be given its general and ordinary meaning to those skilled in the art, without being limited to any specific or special meaning. This term may specifically, but not exclusively, refer to the entire area that separates any object from the outside. Thus, an object may have multiple surfaces. As used herein, the term "layer" is a broad term and should be given its general and ordinary meaning to those skilled in the art, without being limited to any specific or special meaning. This term may specifically, but not exclusively, refer to a quantity of material deposited on the surface of any element. A layer may specifically be a coating. A layer may completely cover an object or may cover only a portion of an object. A layer may specifically have a lateral extent that exceeds its thickness by at least two times, at least five times, at least ten times, or even at least twenty times or more. Specifically, an aC:H:Si layer may have a thickness of 100 nm to 10 μm, preferably 500 nm to 1.5 μm. However, other dimensions may be feasible.

[0040] As used herein, the term "at least partially covered" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to the property of being completely or partially covered with something for any element. Specifically, the surface of any element may be completely or partially covered with something. When a surface is partially covered with something, the covered surface may also be referred to as a surface portion. Further used herein, the term "surface portion" may refer to a portion of a surface, particularly a distinct portion. Illustratively, the term surface portion may refer to at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95% of the surface. However, other embodiments may also be possible. Specifically, the layer may form a continuous layer covering a portion or the entire surface of a reflective target, particularly the substrate of the reflective target.

[0041] As used herein, the term "hydrogen-containing silicon-incorporated amorphous carbon (aC:H:Si) layer" is a broad term and should be given its general and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to an amorphous carbon layer containing hydrogen and silicon. The hydrogen and / or silicon may specifically be embedded or dispersed in the carbon layer without being bonded to the carbon by a covalent chemical bond. The aC:H:Si layer may specifically be an amorphous silicon-containing diamond-like carbon layer. The amorphous silicon-containing diamond-like carbon may have structural, mechanical, electrical, optical, chemical, and / or acoustic properties similar to those of diamond. Specifically, the amorphous silicon-containing diamond-like carbon may be formed by sp 3 It may be a metastable form of amorphous carbon containing hybridized carbon atoms. More specifically, in amorphous silicon-containing diamond-like carbon, the carbon is sp3 , sp 2 , and sp 1 The physical properties of amorphous silicon-containing diamond-like carbon as described above can be attributed to the mixture of carbon bonds. 3 The sp hybridization state may have strong σ bonds, like diamond, which can result in high mechanical hardness and chemical inertness. 2 The hybrid state of sp 2 can have strong intralayer σ bonds and weak interlayer van der Waals bonds, like graphite. 3 sp for bond 2 It may depend on the ratio of binding.

[0042] The aC:H:Si layer may specifically be a hydrogen-containing heteroatom-modified silicon-incorporated amorphous carbon (aC:H:Si:X) layer, where the heteroatom X may be selected from the group consisting of oxygen, nitrogen, fluorine, and boron, and the aC:H:Si:X layer may further comprise: up to 15 atomic % oxygen, Nitrogen up to 10 atomic % up to 10 atomic % boron, and · Maximum 5 atomic % fluorine.

[0043] The sum of oxygen, nitrogen, fluorine, and boron may be at least 1 atomic %, specifically at least 1.5 atomic %, specifically at least 2 atomic %.

[0044] The total of carbon, hydrogen, silicon, oxygen, nitrogen, fluorine, and boron may specifically be 100 atomic %. However, the aC:H:Si:X layer may further contain additional elements. Therefore, the total of carbon, hydrogen, silicon, oxygen, nitrogen, fluorine, and boron may specifically be less than 100 atomic %. Therefore, the total of carbon, hydrogen, silicon, oxygen, nitrogen, fluorine, and boron may specifically be at least 52 atomic %, specifically at least 55 atomic %, specifically at least 60 atomic %, specifically at least 65 atomic %, specifically at least 70 atomic %, specifically at least 75 atomic %, specifically at least 80 atomic %, specifically at least 85 atomic %, specifically at least 90 atomic %, specifically at least 95 atomic %, specifically at least 98 atomic %. Other heteroatoms may also be feasible. The heteroatom may in particular be selected from the group consisting of: metalloids, in particular germanium, in particular antimony, in particular selenium, in particular tellurium; post-transition metals, in particular aluminum; transition metals, in particular titanium, in particular vanadium, in particular niobium, in particular tantalum, in particular chromium, in particular molybdenum, in particular tungsten, in particular iron, in particular cobalt, in particular copper, in particular silver; non-metals, in particular phosphorus, in particular sulfur, in particular chlorine, in particular bromine, in particular iodine.

[0045] As used herein, the term "hydrogen-containing, silicon-incorporated heteroatom-modified amorphous carbon (aC:H:Si:X) layer" is a broad term and should be given its common and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically refer to, but is not limited to, an amorphous carbon layer containing hydrogen and silicon, and further containing one or more heteroatoms in addition to hydrogen and silicon. The hydrogen, silicon, and heteroatoms may specifically be embedded or dispersed in the carbon layer without being bonded to carbon by covalent chemical bonds. The aC:H:Si:X layer may specifically be an amorphous heteroatom-modified silicon-containing diamond-like carbon layer. The term "heteroatom" may refer to any atom other than carbon or hydrogen. As mentioned above, heteroatoms are selected from the group consisting of oxygen, nitrogen, fluorine, and boron. However, other heteroatoms may also be feasible. The heteroatom may in particular be selected from the group consisting of: metalloids, in particular germanium, in particular antimony, in particular selenium, in particular tellurium; post-transition metals, in particular aluminum; transition metals, in particular titanium, in particular vanadium, in particular niobium, in particular tantalum, in particular chromium, in particular molybdenum, in particular tungsten, in particular iron, in particular cobalt, in particular copper, in particular silver; non-metals, in particular phosphorus, in particular sulfur, in particular chlorine, in particular bromine, in particular iodine.

[0046] The expression "atomic %" may specifically refer to an indication of the percentage of atoms in a chemical substance. The percentage of atoms may be calculated by dividing the number of all atoms of an element by the number of all atoms in the chemical substance. The result may then be multiplied by 100.

[0047] The reflective target may specifically include at least one substrate. As used herein, the term "substrate" is a broad term and should be given its common and ordinary meaning to those skilled in the art, without being limited to a specific or special meaning. This term may specifically, but is not limited to, any flat element, for example, a flat element having a lateral extent that exceeds its thickness by at least two times, at least five times, at least ten times, or even at least twenty times or more. The substrate may have any shape. Specifically, the substrate may have a circular, elliptical, or polygonal shape, such as a rectangular or circular shape. Furthermore, as described in more detail below, the substrate may have a strip shape. However, other shapes may also be possible.

[0048] The substrate may be made of at least one conductive material or may comprise at least one layer of at least one conductive material. The conductive material may in particular have a sheet resistance of 100 Ω / sq or less, preferably 60 Ω / sq or less. Thus, exemplarily, the substrate may be made of at least one conductive material and aC:H:Si layer may be deposited on the surface of the substrate.

[0049] Furthermore, illustratively, the substrate may be made of at least one electrically insulating material or at least one conductive material, and at least one layer of the at least one conductive material may be deposited on the substrate. When the reflective target has at least one surface at least partially covered with at least one layer that is an aC:H:Si layer, the aC:H:Si layer may be deposited on the surface of the at least one layer of conductive material. Thus, the aC:H:Si layer may form the outermost layer of the reflective target. The at least one layer of conductive material may form an intermediate layer of the reflective target. Furthermore, the reflective target may have a layer structure having at least one aC:H:Si layer and at least one layer of at least one conductive material. Specifically, the layer structure may include multiple layers of at least one conductive material. The multiple layers of at least one conductive material may form an intermediate layer of the reflective target. Furthermore, the layer structure may include one or more layers of at least one electrically insulating material. The at least one layer of conductive material may also be referred to as a conductive contact layer. The surface of the substrate or the surface of the at least one conductive material layer on which the aC:H:Si layer can be deposited can allow good bonding of the aC:H:Si layer to the surface of the substrate or the surface of the at least one conductive material layer. Exemplarily, the substrate can be made of glass, and the conductive material can be indium tin oxide (ITO). Thus, the glass substrate can be provided with at least one ITO layer, and the aC:H:Si layer can be deposited on the surface of the ITO layer. Furthermore, pretreatment, particularly plasma treatment, of the surface of the substrate and / or intermediate layer can be performed prior to deposition of the aC:H:Si layer, particularly to enhance adhesion of the aC:H:Si layer to the substrate.

[0050] The substrate of the reflective target may be at least partially made of or include at least one material selected from the group consisting of glass; steel, particularly stainless steel; aluminum; silicon; germanium titanium; copper; cobalt; chromium; molybdenum; nickel; tungsten; tantalum; graphite; polymeric materials, particularly polyethylene, particularly polypropylene, particularly polycarbonate, particularly polystyrene, particularly polyacrylate. Furthermore, the polymeric material may be a conductive polymeric material, particularly polyaniline, particularly poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, particularly polypyrrole, particularly polythiophene. Furthermore, other materials may be possible, such as alloys containing at least one of the above-mentioned metals and at least one additional element.

[0051] Substrates having a surface at least partially covered with at least one layer comprising hydrogen-containing, silicon-incorporated amorphous carbon (aC:H:Si) are commercially available, for example, from CeWOTec GmbH (Chemnitzer Werkstoff-und Oberflachentechnik, Chemnitz, Germany). CeWOTec GmbH provides a "Technical Data Sheet for Our XLC-PURA Coating," which contains the information according to Table 1 below. This technical data sheet was created on January 21, 2016, and the information is provided in German. The table below contains a translation of the original text into English. [Table 1]

[0052] As further outlined above, the elemental composition of the aC:H:Si:X layer can be defined as 40 atomic % to 80 atomic % carbon; 1 atomic % to 20 atomic % hydrogen; 10 atomic % to 40 atomic % silicon; up to 15 atomic % oxygen; up to 10 atomic % nitrogen; up to 10 atomic % boron; and up to 5 atomic % fluorine. As outlined above, the sum of oxygen, nitrogen, fluorine, and boron is at least 1 atomic %. Thus, the aC:H:Si:X layer can contain at least 1 atomic % of one of the heteroatoms, or some or all of the heteroatoms. Specifically, the sum of oxygen, nitrogen, fluorine, and boron can be at least 5 atomic %, preferably at least 10 atomic %.

[0053] As outlined above, the aC:H:Si layer may contain up to 15 atomic percent oxygen. Specifically, the oxygen percentage may vary within the aC:H:Si layer. Regions of the aC:H:Si layer closer to the surface of the reflective target may have a lower oxygen percentage. Thus, within the aC:H:Si layer, the oxygen percentage may decrease continuously or discontinuously in a direction perpendicular to the surface of the reflective target, i.e., the surface exposed to the laser radiation. In the bulk region of the aC:H:Si layer, the oxygen percentage may be less than 1 atomic percent, specifically less than 0.1 atomic percent, and more specifically less than 0.01 atomic percent.

[0054] As outlined above, the aC:H:Si layer may be an amorphous layer. As used herein, the term "amorphous layer" is a broad term and should be given its common and ordinary meaning to those skilled in the art, and should not be limited to any specific or special meaning. This term may specifically, but is not limited to, any layer made of at least one material that lacks long-range order. Thus, the atoms of the material may form an irregular pattern and have only short-range order. However, the material may have an internal structure made of interconnected building blocks. The interconnected building blocks may correspond to a crystalline phase of the material.

[0055] Specifically, the aC:H:Si layer may form the outermost layer of the reflective target, which may face the external environment of the reflective target. Furthermore, the aC:H:Si layer may form a continuous layer on the surface of the reflective target. Furthermore, the structural shape of the surface of the aC:H:Si layer may resemble the shape of the surface of the substrate. Specifically, on a smooth substrate, the aC:H:Si layer may also form a continuous and smooth surface with a low number of defect locations.

[0056] Specifically, the aC:H:Si layer may be deposited on the surface of a reflective target by a plasma-assisted surface coating process. Specifically, the plasma-assisted surface coating process may be a plasma-assisted chemical vapor deposition process (PA-CVD). PA-CVD may be performed at a process temperature of 100°C to 200°C. The term "plasma-assisted chemical vapor deposition" may generally refer to a deposition method in which a substrate is exposed to one or more volatile precursors to produce a desired deposit by reaction and / or decomposition of the precursors on the surface of the substrate. However, other deposition methods may also be feasible.

[0057] As mentioned above, step b) corresponds to at least one sample recognition step, which comprises locating at least one sample area on a reflective target.

[0058] As used herein, the term "sample recognition step" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a special or particular meaning. This term may specifically, but is not limited to, a method step including locating a sample area on a reflective target. As used herein, the term "sample area" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a special or particular meaning. This term may specifically, but is not limited to, refer to at least one area on a reflective target that contains a sample. A reflective target may include at least one area where a sample is located, such as an area where a sample was applied. A reflective target may also include at least one other area that does not have a sample. As used herein, the term "locating at least one sample area" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a special or particular meaning. This term may specifically, but is not limited to, refer to identifying a sample area on a reflective target.

[0059] For example, the sample recognition step may include manually locating the sample area on the reflective target, e.g., the locating may include a user determining the sample area on the image of the reflective target captured in step a).

[0060] For example, step b) may be performed automatically, e.g., without user interaction. Using at least one processing device, the sample area may be located on the reflective target using at least one image evaluation algorithm on the image of the reflective target captured in step a). As used herein, the term "processing device" is a broad term and should be given its common and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. The term may specifically, but not exclusively, refer to any logic circuit configured to perform the basic operations of a computer or system, and / or generally to a device configured to perform calculations or logical operations. The processing device may be configured to process basic instructions that drive the computer or system. By way of example, the processing device may include at least one arithmetic logic unit (ALU), at least one floating-point unit (FPU), such as a math coprocessor or coprocessor, multiple registers, specifically registers configured to provide operands to the ALU and store results of operations, and memory, such as L1 and L2 cache memories. The processing device may be a multi-core processor. The processing device may be or comprise a central processing unit (CPU). Additionally or alternatively, the processing device may be or comprise a microprocessor, and thus, in particular, the elements of the processor may be included in one single integrated circuit (IC) chip. Additionally or alternatively, the processing device may be or comprise one or more application-specific integrated circuits (ASICs) and / or one or more field-programmable gate arrays (FPGAs) and / or one or more tensor processing units (TPUs) and / or one or more chips, such as dedicated machine learning optimization chips, etc. The processing device may be configured to perform one or more evaluation operations, such as by software programming. The processing device may be configured to perform specified method steps.Thus, by way of example, a processing device may store software code including several computer instructions, may provide one or more hardware elements for performing one or more of the illustrated operations, and / or may provide one or more processors for running the software to perform one or more of the method steps.

[0061] As used herein, the term "image evaluation algorithm" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically refer to, but is not limited to, an algorithm designed to perform at least one image analysis and / or image processing to identify the location of a sample area. The image evaluation algorithm may include one or more of the following: image alignment via a reference structure element, application of at least one image filter, image inversion, selection of at least one region of interest, background correction, decomposition into color channels, image segmentation such as Watershed transformation or K-means clustering. The region of interest may be determined manually by a user or automatically, such as by recognizing an object in the image. The image evaluation algorithm may include at least one image correction. The image correction may include at least one background subtraction.

[0062] The image assessment algorithm may utilize image recognition, such as software-based automatic image recognition and / or image recognition by machine learning processes, for example. For example, the method may include applying at least one trained model, such as an object classification and / or detection model, to the image. The object classification and / or detection model may comprise at least one machine learning and / or deep learning architecture configured to recognize and / or identify objects in the image and / or classify detected objects, in particular assigning class labels to the detected objects. The object classification and / or detection model may comprise at least one convolutional neural network. The object classification and / or detection model may comprise at least one convolutional neural network selected from the group consisting of: AlexNet; Visual Geometry Group ("VGG") network; residual neural network ("ResNet"); You Only Look Once ("YOLO"); convolutional neural networks ("CNNs") such as convolutional neural networks for spot detection ("detectSpot"), GoogLeNet convolutional neural networks, region-based convolutional neural networks ("R-CNN"), region-based fully convolutional networks ("R-FCN"), single-shot detectors ("SSD"), spatial pyramid pooling (SPP-Net); classical classification machine learning algorithms such as k-nearest neighbors ("KNN"); and support vector machines ("SVMs").

[0063] The image evaluation algorithm may include identifying at least one pattern representative of the sample within the image. As used herein, the term "pattern" is a broad term and should be given its common and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. The term may specifically, but not be limited to, an area of ​​an image that comprises at least one arbitrary characteristic.

[0064] The reflective target may include at least one predetermined reference structural element. The image evaluation algorithm may include considering information about the predetermined reference structural element to identify the location of the sample area within the image. As used herein, the term "predetermined reference structural element" is a broad term and should be given its general and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. Specifically, the term may refer to a predetermined reference structural element having known characteristics, for example, but not limited to, due to the structure of the reflective target. Specifically, an ideal path for laser irradiation may be calculated based on the reference structural element. The predetermined reference structural element may include at least one geometric structure. The geometric structure may include at least one element selected from the group consisting of a circle, a hexagon, a square, a polygon, a reference cross mark, a point, and a line. Furthermore, the predetermined reference structural element may be selected from two-dimensional designs such as a mesh, specifically a polygonal mesh, a grid, etc. However, other elements may also be possible. The predetermined reference structural element may be or include a marking and / or engraving on the reflective target. Specifically, the reflective target may include a plurality of predetermined reference structural elements. Thus, illustratively, the predetermined reference structural elements may be arranged concentrically. However, other arrangements may also be feasible. Specifically, at least one predetermined reference structural element may cover essentially the entire surface of the reflective target. Alternatively, at least one predetermined reference structural element may cover an area or a segment of the surface of the reflective target.

[0065] The image evaluation algorithm may include at least one pattern recognition algorithm that uses information about predetermined reference structural elements to identify patterns that represent samples within the image. As used herein, the term "pattern recognition algorithm" is a broad term and should be given its general and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically, but is not limited to, an object classification and / or detection model designed to identify at least one pattern within an image. As used herein, the term "identifying a pattern that represents a sample within an image" is a broad term and should be given its general and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically, but is not limited to, a process for determining patterns that represent samples within an image.

[0066] Information about a predetermined reference structural element may be used to define a region of interest within an image. As used herein, the term "region of interest" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a special or particular meaning. The term may specifically, but is not limited to, a region or area of ​​any shape in an image. A region of interest may be the entire image or a portion of an image. A region of interest may be a region of an image that contains or is suspected of containing at least one feature. The image may be a pixel image including a plurality of pixels arranged in a pixel array, such as a rectangular array having m rows and n columns, where m and n are each positive integers. A region of interest may be or include a group of pixels including any number of pixels. A region of interest may include multiple subregions, such as multiple pixels. As used herein, the term "subregion" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a special or particular meaning. The term may specifically refer to, but is not limited to, any portion or element of the region of interest, in particular an image element, that includes at least one pixel or group of pixels. A subregion may be a square area of ​​the region of interest. Specifically, the region of interest in an image may correspond to a portion of the image within or around a predetermined reference structural element. First, at least one reference structural element may be recognized. Second, the location of a pattern representing the sample in the image may be identified. Specifically, information about the region of interest may be transferred to a control device. Laser illumination may be directed to an area of ​​the reflective target corresponding to the region of interest using the control device.

[0067] Furthermore, the image of the reflective target captured in step a) may be transferred to a structural element prior to execution of step c). First, at least one image evaluation algorithm may be used on the image of the reflective target. Specifically, a processed image of the reflective target may be obtained. The processed image may illustratively refer to a black-and-white image. Second, particularly as an additional step, the image of the reflective target, specifically the processed image of the reflective target, may be transferred to a structural element. Specifically, the structural element may be selected from the group consisting of a mesh, specifically a polygonal mesh, and a grid. Specifically, in step c), at least one of the path of laser irradiation, the movement path of the reflective target, and the movement path of the holder of the reflective target, and optionally at least one of the laser power and the laser focus, may be calculated based on the structural element. In this way, the directed laser path or the movement of the reflective target may be calculated in combination with a statically positioned laser beam. Specifically, the calculation of the laser power and / or the laser focus may be optional in the case of automatic processing.

[0068] The method may further include at least one fail-safe step. If no sample area is located on the reflective target in step b), the reflective target, specifically the area of ​​interest, may be scanned. The portion or segment of the reflective target to which the sample is applied may be reconstructed based on at least one reference structural element. Thus, essentially only the area of ​​interest may be scanned. As used herein, the term "fail-safe step" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to at least one step that reliably prevents the generation and / or determination and / or display of unreliable or erroneous measurements.

[0069] As described above, step c) corresponds to an at least one analyte detection step comprising detecting at least one analyte in the sample using surface-assisted laser desorption / ionization mass spectrometry (SALDI-MS) in a laser desorption mass spectrometer, wherein laser radiation is applied to a reflective target using at least one laser source of the laser desorption mass spectrometer, and the laser radiation is directed to a located sample region using at least one controller, such that at least one ion of the at least one analyte is generated and the ion is detected using at least one of a mass analysis unit or an ion mobility spectrometer of the laser desorption mass spectrometer.

[0070] As used herein, the term "analyte detection step" is a broad term and should be given its common and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to any method step that involves the quantitative and / or qualitative determination of at least one analyte in any sample. The quantitative and / or qualitative determination of an analyte in a sample may be the result or intermediate result of a detection process that may include at least one measurement step and further steps, such as at least one preparatory step and / or at least one analytical step. As part of the detection process, at least one measurement may be generated, specifically a measurement regarding the presence, absence, relative concentration, or amount of the analyte in the sample.

[0071] As used herein, the term "laser irradiation" is a broad term and should be given its general and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to a process of exposing an object, particularly a surface of an object, to laser light. In particular, a first portion of the object, particularly a first portion of the object's surface, may be irradiated by at least one laser beam, and a second portion of the object, particularly a second portion of the object's surface, may preferably not be irradiated by at least one laser beam. In particular, a reflective target can absorb laser energy and transfer the laser energy to molecules of a sample, causing desorption and ionization.

[0072] As used herein, the term "laser source" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to any device configured to emit light by a light amplification process based on stimulated emission of electromagnetic radiation. Specifically, the laser may be a pulsed laser. The pulse energy may be in the range of less than 60 μJ, specifically less than 35 μJ. Furthermore, the laser may have a laser repetition rate in the range of 500 Hz to 5 kHz, specifically in the range of 1 kHz to 3 kHz. For example, the laser may be configured to generate a laser beam in the UV spectral range. Specifically, the laser wavelength may be in the range of 300 nm to 400 nm. More specifically, the laser may be a neodymium-doped yttrium aluminum garnet laser (Nd:YAG laser) having a wavelength of 355 nm. Other types of laser sources may also be feasible. Additionally, the laser source may comprise one or more further optical elements, such as a deformable mirror, a lens, a polarizer, and / or a shutter.

[0073] As used herein, the term "mass analysis unit" is a broad term and should be given its general and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically, but is not limited to, refer to an apparatus configured to detect incoming ions. The mass analysis unit may be configured to detect charged particles. The mass analysis unit may be or may include at least one electron multiplier. The mass analysis unit may be configured to determine at least one mass spectrum of the detected ions. As used herein, the term "mass spectrum" is a broad term and should be given its general and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically, but is not limited to, refer to a two-dimensional representation of signal intensity versus mass-to-charge ratio m / z, where the signal intensity corresponds to the abundance of each ion. The mass-to-charge ratio may refer to the reciprocal of a specific charge. The mass spectrum may be a pixel image. The signals detected by the mass analysis unit within a specific m / z range may be integrated to determine the resulting intensities of pixels in the mass spectrum. The mass analysis unit may include at least one evaluation device. The at least one evaluation device may identify analytes in the sample. Specifically, the evaluation device may be configured to correlate known masses with identified masses or an entire characteristic fragment pattern.

[0074] As used herein, the term "ion mobility spectrometer" is a broad term and should be given its common and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. The term may specifically, but not exclusively, refer to any analytical technique configured to separate and identify ionized molecules in the gas phase based on their mobility in a carrier buffer gas in the presence of an electric field. Ion mobility spectrometers may be specifically combined with laser desorption mass spectrometers, particularly to achieve multidimensional separation. The ion mobility spectrometer may be configured to detect at least one drift time of ions through an ion mobility spectrometry cell.

[0075] The method may further include at least one sample preparation step. As used herein, the term "sample preparation step" is a broad term and should be given its common and ordinary meaning to those skilled in the art, and should not be limited to a specific or special meaning. This term may specifically refer to, but is not limited to, at least one process of preparing a sample for subsequent measurement. The sample preparation step may include at least one workflow. The workflow may include a single step or multiple steps performed sequentially and / or in parallel. Sample preparation may include sample purification and / or sample dilution and / or sample concentration. The sample may be subjected to one or more pretreatment and / or sample preparation steps. The sample may be pretreated by physical and / or chemical methods, such as centrifugation, filtration, mixing, homogenization, chromatography, purification, precipitation, dilution, concentration, contact with binding agents and / or detection reagents, and / or any other method deemed appropriate by those skilled in the art.

[0076] The sample preparation step may include applying a sample to at least one reflective target, the sample including at least one analyte. Specifically, the sample may be applied to the at least one reflective target as a fluid sample. Optionally, the sample preparation step may include drying the sample on the reflective target. Drying may refer to natural drying of the sample, such as drying in the open air. As part of the drying process, some or all of the liquid in the sample may evaporate.

[0077] Specifically, volumes of 0.01 μl to 10 μl, preferably 0.05 μl to 5 μl, and most preferably 0.75 μl to 2 μl may be applied to the reflective target, although other volumes may also be feasible.

[0078] Specifically, the amount of analyte on one sample spot may be less than 35 nmol, specifically less than 3.5 nmol, specifically less than 350 pmol, specifically less than 35 pmol, specifically less than 3.5 pmol, specifically less than 350 fmol, although other amounts may also be feasible.

[0079] In a further aspect of the present invention, a system is disclosed that includes at least one laser desorption mass spectrometer.

[0080] The laser desorption mass spectrometer includes at least one reflective target. At least one sample can be applied to the reflective target. The sample includes at least one analyte. The laser desorption mass spectrometer is configured to detect the at least one analyte in the sample using surface-assisted laser desorption ionization mass spectrometry (SALDI-MS). The laser desorption mass spectrometer includes at least one laser source. The laser source is configured to apply laser irradiation to the reflective target such that at least one ion of the at least one analyte is generated. Specifically, applying the laser irradiation to the reflective target may include adjusting at least one of a path of the laser irradiation, a movement path of the reflective target, and a movement path of a holder for the reflective target, and optionally adjusting at least one of a laser power and a laser focus. The laser desorption mass spectrometer includes at least one of a mass analysis unit or an ion mobility spectrometer configured to detect the ions. The system includes at least one imaging device configured to image the reflective target. The laser desorption mass spectrometer includes at least one controller configured to direct laser radiation to a localized sample region.

[0081] As used herein, the term "system" is a broad term and should be given its common and ordinary meaning to those skilled in the art, and should not be limited to any specific or special meaning. The term may specifically, but not be limited to, refer to a group of at least two elements that can interact with each other to perform at least one common function. The at least two components may be treated independently or may be coupled, connectable, or integrated to form a common component.

[0082] The system may be configured to execute the computer-implemented methods for detecting at least one analyte in a sample with a laser desorption mass spectrometer, as described above or in more detail below.

[0083] The system may comprise at least one processing unit configured to locate at least one sample area on the reflective target by using at least one image evaluation algorithm on at least one image of the reflective target captured by the imaging device, for further details of which reference is made to the description above.

[0084] The reflective target may be provided as a material strip or a stack of platelets. When the reflective target is provided as a material strip, the reflective target may be provided, specifically, in a coiled form. The material strip may be configured to be unwound before applying one or more samples to the material strip, particularly to the surface of the material strip. The material strip may be configured to pass through a system. Specifically, the material strip may be configured to pass through different stations of the system. The different stations may include a laser desorption mass spectrometer and may further include one or more liquid handling systems and one or more vacuum zones. Further details regarding the liquid handling systems and vacuum zones may be provided in more detail below. The material strip may be made of, specifically, steel or aluminum. However, other materials may also be feasible. The material strip may have a width of 0.5 cm to 10 cm, preferably 1 cm to 3 cm. Furthermore, the material strip may have a thickness of 0.2 mm to 2 mm, preferably 0.5 mm to 1 mm. Furthermore, the material strip may have a length of 5 m to 100 m. However, other dimensions may also be feasible. Specifically, the length of the material strip may not be limited. Specifically, the material strip may be manufactured by performing coating of the substrate during the winding process. Thus, exemplarily, an aC:H:Si layer may be formed on the surface of the substrate during the winding process. This manufacturing process may also exemplarily be referred to as a roll-to-roll PA-CVD coating process.

[0085] Furthermore, as mentioned above, the reflective target may be provided as a stack of platelets. The platelets may specifically have a rectangular shape, such as a square. However, other shapes, such as a circle, may also be feasible. The platelets may specifically have a thickness of 0.2 mm to 1 cm, preferably 0.5 mm to 3 mm. Furthermore, the platelets may have a width in the range of 1 cm to 10 cm, preferably 1 cm to 8 cm, and most preferably 1 cm to 5 cm. Furthermore, the platelets may have a length in the range of 1 cm to 15 cm, preferably 1 cm to 12 cm, and most preferably 1 cm to 7 cm.

[0086] The platelets may be provided stacked on top of one another. Specifically, the system may include at least one platelet holder configured to receive a stack of platelets. The platelet holder may be configured to sequentially discharge the platelets. Specifically, the system may include at least one conveyor belt. The platelet holder may be configured to sequentially discharge the platelets onto the conveyor belt. The conveyor belt may be configured to sequentially pass the platelets through the system. Specifically, the conveyor belt may be configured to sequentially pass the platelets through different stations of the system.

[0087] The system may further include at least one vacuum system. The system may be configured to pass the material strip or platelet through the vacuum system. As used herein, the term "vacuum system" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. This term may specifically, but not exclusively, refer to any device configured to generate a vacuum in a defined space, such as a chamber, for example, a region having a gas pressure lower than atmospheric pressure. To this end, the vacuum system may include at least one vacuum pump. For further details about the vacuum pump, please refer to the description of the vacuum pump above. Specifically, the vacuum system may include at least one vacuum zone, preferably at least two vacuum zones. The term "vacuum zone" may refer to a defined space, such as a chamber, having a gas pressure lower than atmospheric pressure. The at least two vacuum zones may be arranged in series. The material strip or platelet stack may be configured to pass through the vacuum system, specifically through one or more of the vacuum zones, before passing through the laser desorption mass spectrometer. The vacuum zones may be configured to provide low pressures of less than 1500 mbar, such as less than 1000 mbar, 900 mbar, 800 mbar, 700 mbar, 600 mbar, 500 mbar, 400 mbar, 300 mbar, 200 mbar, 100 mbar, 90 mbar, 80 mbar, 70 mbar, 60 mbar, 50 mbar, 40 mbar, 30 mbar, 20 mbar, 10 mbar, 1 mbar, or even lower. Specifically, at least two vacuum zones may be configured to provide different low pressures. Other parameters may also be feasible. The vacuum system, specifically the vacuum zones, may be configured to dry a sample on a reflective target.

[0088] The system may further comprise at least one liquid handling system. The liquid handling system may be configured to apply at least one sample having at least one analyte to a reflective target, specifically one of a material strip or platelet. As used herein, the term "liquid handling system" is a broad term and should be given its common and ordinary meaning to those skilled in the art and should not be limited to a specific or special meaning. The term may specifically, but not exclusively, refer to any device configured to apply a liquid, specifically a defined or desired amount of liquid, to another object. The amount of liquid may be adjustable. The liquid handling system may specifically comprise one or more pipetting units. The pipetting unit may comprise at least one chamber configured to hold or contain at least one liquid. The pipetting unit may be configured to create a partial vacuum above the chamber and selectively release the partial vacuum for drawing up and delivering the liquid. Additionally or alternatively, the liquid handling system may comprise at least one acoustic droplet ejection unit. The acoustic droplet ejection unit may be configured to use pulses of ultrasonic waves to move fluid without physical contact, although other embodiments may be possible.

[0089] The present invention further discloses and proposes a computer program including instructions, which, when executed by the system described above or further described below, cause the system to perform the method described above or further described below. Specifically, the computer program may be stored on a computer-readable data carrier. Specifically, one, two or more, or even all of method steps a), b), and c) of the above method may thus be performed using a computer or a computer network, preferably using a computer program. Specifically, a table of scan parameters may be provided in the instrument software.

[0090] The present invention further discloses and proposes a computer program product having program code means for performing two or more, or even all, of the method steps a), b) and c) of the above-described method according to the present invention in one or more of the embodiments contained herein when the program is run on a computer or a computer network. In particular, the program code means may be stored on a computer-readable data carrier.

[0091] The present invention further discloses and proposes a computer-readable storage medium containing instructions which, when executed by a system as described above or in more detail below, cause the system to perform the method as described above or in more detail below.

[0092] The present invention further discloses and proposes a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform the methods described above or in more detail below.

[0093] Furthermore, the present invention discloses and proposes a data carrier having stored thereon a data structure which, after being loaded into a computer or a computer network, such as for example the working or main memory of the computer or computer network, is capable of performing two or more or all of the method steps a), b) and c) of the above-mentioned method according to one or more of the embodiments disclosed herein.

[0094] The present invention further discloses and proposes a computer program product having program code means stored on a machine-readable carrier for performing two or more, or even all, of method steps a), b), and c) of the above-described method according to one or more of the included embodiments disclosed herein when the program is run on a computer or a computer network. As used herein, a computer program product refers to a program as a tradeable product. The product can generally exist in any format, such as paper format, or on a computer-readable data carrier. In particular, the computer program product may be distributed over a data network.

[0095] Finally, the present invention proposes and discloses a modulated data signal comprising instructions readable by a computer system or computer network for performing two or more, or even all, of the above-described method steps a), b), and c) according to one or more of the embodiments disclosed herein.

[0096] Preferably, with respect to computer-implemented aspects of the present invention, two or more, or even all, of method steps a), b), and c) of the above-described methods according to one or more of the embodiments disclosed herein may be performed using a computer or a computer network. Thus, in general, any of the method steps involving providing and / or manipulating data may be performed using a computer or a computer network. In general, these method steps may include any method steps, except for those that typically require manual intervention, such as providing a sample and / or certain aspects of performing the actual measurement.

[0097] Specifically, the present invention further discloses: a computer or computer network comprising at least one processor, the processor being configured to perform two or more, or even all, of the method steps a), b) and c) of a method according to one of the embodiments described herein, a computer-loadable data structure configured, when executed on a computer, to perform two or more, or even all, of the method steps a), b) and c) of a method according to one of the embodiments described herein; a computer program configured, when running on a computer, to perform two or more, or even all, of the method steps a), b) and c) of a method according to one of the embodiments described herein, a computer program comprising program means for performing two or more, or even all, of the method steps a), b) and c) of a method according to one of the embodiments described herein, when the computer program is run on a computer or on a computer network, a computer program comprising program means according to any of the preceding embodiments, the program means being stored on a computer-readable storage medium; a storage medium storing a data structure, the data structure being configured to execute two or more, or even all, of the method steps a), b) and c) of a method according to one of the embodiments described herein after being loaded into the main and / or working storage of a computer or computer network, and - a computer program product comprising program code means storable or stored on a storage medium and which, when the program code means are executed on a computer or a computer network, perform two or more, or even all, of the method steps a), b) and c) of the method according to one of the embodiments described herein.

[0098] The method and apparatus according to the present invention offers numerous advantages over known methods and apparatus.

[0099] Image-based sample recognition can be combined with a SALDI target, which is used to direct the laser irradiation for the SALDI process only to spots where dried analyte is present. This allows for rapid measurement of analyte spots and supports quantitative analysis, while also reducing the data file size for a given sample area, especially compared to classical measurement methods such as MALDI imaging. Specifically, classical MALDI techniques cannot be combined with this type of idea due to the fact that large amounts of matrix compounds (>10 pg) are typically used to ionize the analyte components and thus cover the entire spot. Therefore, differentiation between matrix and analyte is usually impossible in classical MALDI techniques.

[0100] In summary, without excluding further embodiments, the following embodiments can be envisaged:

[0101] Embodiment 1: A computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer, comprising: a) at least one imaging step comprising imaging at least one reflective target to which a sample containing at least one analyte has been applied by using at least one imaging device; b) at least one sample recognition step including locating at least one sample area on a reflective target; c) at least one analyte detection step, comprising detecting at least one analyte in the sample using surface-assisted laser desorption / ionization mass spectrometry (SALDI-MS) in a laser desorption mass spectrometer; Including, The method includes applying laser radiation to a reflective target using at least one laser source of a laser desorption mass spectrometer, and directing the laser radiation to a located sample region using at least one controller, such that at least one ion of at least one analyte is generated and the ion is detected using at least one of a mass analysis unit of the laser desorption mass spectrometer or an ion mobility spectrometer.

[0102] Embodiment 2: The method according to embodiment 1, further comprising at least one sample preparation step comprising applying a sample comprising at least one analyte to at least one reflective target.

[0103] Embodiment 3: The method according to embodiment 2, wherein the sample preparation step comprises drying the sample on a reflective target.

[0104] Embodiment 4: A method according to any one of embodiments 1 to 3, wherein the sample area is located on the reflective target using at least one image evaluation algorithm on the image of the reflective target captured in step a) by using at least one processing device.

[0105] Embodiment 5: The method according to embodiment 4, wherein the image of the reflective target captured in step a) is further transferred to a structural element prior to performing step c).

[0106] Embodiment 6: A method according to embodiment 5, wherein in step c), at least one of the path of laser irradiation, the movement path of the reflective target, and the movement path of the holder of the reflective target, and optionally at least one of the laser output and the laser focus, are calculated based on the structural element.

[0107] Embodiment 7: The method according to any one of embodiments 4 to 6, wherein the image evaluation algorithm comprises identifying at least one pattern representative of the sample within the image.

[0108] Embodiment 8: A method according to embodiment 7, wherein the reflective target comprises at least one predetermined reference structural element, and the image evaluation algorithm includes taking into account information about the predetermined reference structural element to identify the location of the sample area in the image.

[0109] Embodiment 9: The method according to embodiment 8, wherein the predetermined reference structure element comprises at least one geometric structure, and the geometric structure comprises at least one element selected from the group consisting of a circle, a hexagon, a square, a polygon, a reference cross mark, a point, and a line.

[0110] Embodiment 10: The method of embodiment 8 or 9, wherein the predetermined reference structural element is selected from the group consisting of a mesh, a grid.

[0111] Embodiment 11: A method according to embodiment 9 or 10, wherein the image evaluation algorithm comprises at least one pattern recognition algorithm that uses information about predetermined reference structural elements to identify patterns representative of the sample in the image.

[0112] Embodiment 12: A method according to any one of embodiments 9 to 11, wherein information about predetermined reference structural elements is used to define a region of interest in the image.

[0113] Embodiment 13: The method according to any one of embodiments 1 to 12, wherein the sample recognition step comprises manually locating the sample area on the reflective target.

[0114] Embodiment 14: The method according to any one of embodiments 1 to 13, wherein the computer-implemented method for detecting at least one analyte in a sample by laser desorption mass spectrometry is performed at least in part automatically.

[0115] Embodiment 15: The method according to embodiment 14, wherein at least steps a) and c) are performed automatically.

[0116] Embodiment 16: A method according to any one of embodiments 1 to 15, wherein the computer-implemented method for detecting at least one analyte in a sample by laser desorption mass spectrometry is performed fully automatically, in particular from sample preparation to the analyte detection step.

[0117] Embodiment 17: The method according to embodiment 16, wherein at least steps a), b), and c) are performed automatically.

[0118] Embodiment 18: The method according to any one of embodiments 1 to 17, wherein the reflective target has a specular reflectance of 45% or more.

[0119] Embodiment 19: The reflective target is R a 19. The method according to any one of embodiments 1 to 18, having a nominal arithmetic roughness of ≦2 μm.

[0120] Embodiment 20: A reflective target having at least one surface, the surface being at least partially covered with at least one layer, the layer being a hydrogen-containing silicon-incorporated amorphous carbon (aC:H:Si) layer, the aC:H:Si layer comprising: 40 atomic % to 80 atomic % carbon, 1 atomic % to 20 atomic % hydrogen, and 10 atomic % to 40 atomic % silicon 20. The method according to any one of embodiments 1 to 19, comprising:

[0121] Embodiment 21: The aC:H:Si layer is a hydrogen-containing heteroatom-modified silicon-incorporated amorphous carbon (aC:H:Si:X) layer, where the heteroatom X is selected from the group consisting of oxygen, nitrogen, fluorine, and boron, and the aC:H:Si:X layer is up to 15 atomic % oxygen, Nitrogen up to 10 atomic % up to 10 atomic % boron, and Up to 5 atomic % fluorine further comprising 21. The method according to embodiment 20, wherein the sum of oxygen, nitrogen, fluorine, and boron is at least 1 atomic %.

[0122] Embodiment 22: A method according to embodiment 20 or 21, wherein the aC:H:Si layer is deposited on the surface of the target by a plasma-assisted surface coating process.

[0123] Embodiment 23: A method according to any one of embodiments 1 to 22, wherein the reflective target comprises at least one substrate, the substrate being made of at least one material selected from the group consisting of glass; steel, particularly stainless steel; aluminum; silicon; germanium; titanium; copper; cobalt; chromium; molybdenum; nickel; tungsten; tantalum; graphite; polymeric materials, particularly polyethylene, particularly polypropylene, particularly polycarbonate, particularly polystyrene, particularly polyacrylate, particularly polyaniline, particularly poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, particularly polypyrrole, particularly polythiophene.

[0124] Embodiment 24: A method according to any one of embodiments 1 to 23, wherein the imaging device is at least one device selected from the group consisting of at least one camera, at least one CCD camera, at least one CMOS camera, at least one RGB camera, at least one digital camera, at least one camera of a microscope, and at least one camera of an incident light microscope.

[0125] Embodiment 25: A method according to any one of embodiments 1 to 24, wherein the test substance is at least one test substance selected from the group consisting of steroids, particularly ketosteroids, particularly secosteroids; therapeutically active substances; detergents; glycosides; peptides; proteins; dyes; ions; nucleic acids; amino acids; metabolites; hormones; fatty acids; lipids; and carbohydrates.

[0126] Embodiment 26: The method according to any one of embodiments 1 to 25, wherein the test substance has a molar mass between 6 Da and 10,000 Da, preferably between 50 Da and 3,000 Da.

[0127] Embodiment 27: The method according to any one of embodiments 1 to 26, wherein the test substance comprises a permanently positively charged molecule or a permanently negatively charged molecule.

[0128] Embodiment 28: The method according to any one of embodiments 1 to 27, wherein the amount of the test substance on one sample spot is less than 35 nmol, particularly less than 3.5 nmol, particularly less than 350 pmol, particularly less than 35 pmol, particularly less than 3.5 pmol, particularly less than 350 fmol.

[0129] Embodiment 29: A method according to any one of embodiments 1 to 28, wherein a volume of 0.01 μl to 10 μl, preferably 0.05 μl to 5 μl, most preferably 0.75 μl to 2 μl is applied to the reflective target.

[0130] Embodiment 30: The method according to any one of embodiments 1 to 29, wherein the sample is selected from the group consisting of blood, serum, plasma, saliva, ocular lens fluid, cerebrospinal fluid, sweat, urine, milk, ascites fluid, mucus, synovial fluid, peritoneal fluid, amniotic fluid, tissue, or a physiological fluid containing cells.

[0131] Embodiment 31: A method according to any one of embodiments 1 to 30, comprising loading a reflective target into a laser desorption mass spectrometer, wherein the imaging step is performed before and / or after loading the reflective target into the laser desorption mass spectrometer.

[0132] Embodiment 32: A method according to any one of embodiments 1 to 31, comprising at least one fail-safe step of scanning the reflective target if the sample area is not located on the reflective target in step b).

[0133] Embodiment 33: A system comprising at least one laser desorption mass spectrometer, wherein the laser desorption mass spectrometer comprises at least one reflective target, and wherein at least one sample can be applied onto the reflective target, the sample comprising at least one analyte; a laser desorption mass spectrometer configured to detect at least one analyte in a sample using surface-assisted laser desorption ionization mass spectrometry (SALDI-MS), the laser desorption mass spectrometer comprising at least one laser source configured to apply laser radiation to a reflective target such that at least one ion of the at least one analyte is generated, the laser desorption mass spectrometer comprising at least one of a mass analysis unit or an ion mobility spectrometer configured to detect the ions; the system comprising at least one imaging device configured to image a reflective target; A system in which a laser desorption mass spectrometer comprises at least one controller configured to direct laser radiation to a localized sample region.

[0134] Embodiment 34: A system according to embodiment 33, comprising at least one processing device configured to identify the location of at least one sample area on the reflective target by using at least one image evaluation algorithm on at least one image of the reflective target imaged by the imaging device.

[0135] Embodiment 35: A system according to embodiment 33 or 34, configured to perform a method for detecting at least one analyte according to any one of the preceding claims.

[0136] Embodiment 36: A system according to any one of embodiments 33 to 35, wherein the target is provided as a stack of material strips or platelets, and the system further comprises at least one vacuum system, and the system is configured to pass the material strips or platelets through the vacuum system.

[0137] Embodiment 37: A computer program comprising instructions that, when executed by a system according to any one of embodiments 33 to 36, cause the system to perform a method according to any one of the preceding method-related claims.

[0138] Embodiment 38: A computer-readable storage medium containing instructions, which when executed by a system according to any one of the preceding claims relating to the system, cause the system to perform a method according to any one of embodiments 1 to 32.

[0139] Embodiment 39: A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a method according to any one of embodiments 1 to 32. [Brief explanation of the drawings]

[0140] Further optional features and embodiments are disclosed in more detail in the subsequent description of the embodiments, preferably in conjunction with the dependent claims. Each optional feature therein may be realized in an independent manner as well as in any possible combination, as will be understood by those skilled in the art. The scope of the present invention is not limited by the preferred embodiments. The embodiments are illustrated diagrammatically in the figures, where identical reference numerals in these figures refer to identical or functionally equivalent elements.

[0141] [Figure 1A] 1 is a schematic flow diagram illustrating an exemplary embodiment of a computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer according to the present invention. [Figure 1B] 1 is a schematic flow diagram illustrating an exemplary embodiment of a computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer according to the present invention. [Figure 2A] 1 shows a low-resolution microscope image of a dried sample on a reflective target. [Figure 2B] 1 shows a low-resolution microscope image of a dried sample on a reflective target. [Figure 3A] 1 shows a mass spectrum obtained with a laser desorption mass spectrometer. [Figure 3B] 1 shows a mass spectrum obtained with a laser desorption mass spectrometer. [Figure 4] 10 shows a further low resolution microscope image of the dried sample. [Figure 5] 1 illustrates an exemplary embodiment of a laser desorption mass spectrometer according to the present invention. [Figure 6] 2 shows a further exemplary embodiment of the system according to the invention; DETAILED DESCRIPTION OF THE INVENTION

[0142] 1A and 1B show, in simplified flow diagram form, two exemplary embodiments of a computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer according to the present invention.

[0143] A computer-implemented method for detecting at least one analyte in a sample may include at least one sample preparation step. The sample preparation step may include applying a sample to at least one reflective target. The sample may include at least one analyte. The applied sample may also be referred to as a sample spot. This step is indicated by box 112 in the flow diagram of FIG. 1A.

[0144] Additionally, the sample on the reflective target may be dried. This step is indicated by box 114 in the flow diagram of Figure 1A. Drying the sample on the reflective target may be part of the sample preparation step.

[0145] Additionally, the computer-implemented method for detecting at least one analyte in a sample includes at least one imaging step that includes imaging a reflective target using at least one imaging device, which step is indicated by box 116 in the flow diagram of FIG.

[0146] Furthermore, the computer-implemented method for detecting at least one analyte in a sample includes at least one sample recognition step including locating at least one sample area on a reflective target. This step is shown by box 118 in the flow diagram of FIG. 1A. This step may include data processing and image digitization. Specifically, by using at least one processing device, the sample area may be located on the reflective target using at least one image evaluation algorithm on the image of the reflective target. Further specifically, the image evaluation algorithm may include identifying at least one pattern in the image that represents the sample.

[0147] The reflective target with the sample can then be loaded into a laser desorption mass spectrometer, a step shown in box 120 in the flow diagram of Figure 1A.

[0148] Furthermore, the computer-implemented method for detecting at least one analyte in a sample includes at least one analyte detection step, which includes detecting at least one analyte in the sample using surface-assisted laser desorption / ionization mass spectrometry (SALDI-MS) in a laser desorption mass spectrometer. The detection step includes applying laser irradiation to a reflective target by using at least one laser source of the laser desorption mass spectrometer, such that at least one ion of the at least one analyte is generated. The laser irradiation is directed to the located sample region by using at least one controller. This step is shown in box 122 in the flow diagram of FIG. 1A. Furthermore, the detection step includes detecting at least one ion of the at least one analyte by using at least one of a mass analysis unit of the laser desorption mass spectrometer or an ion mobility spectrometer. This step, sometimes referred to as a sample measurement step, is shown in box 124 in the flow diagram of FIG. 1A.

[0149] The computer-implemented method for detecting at least one analyte in a sample shown in Figure 1B largely corresponds to the computer-implemented method for detecting at least one analyte in a sample shown in Figure 1A, and therefore reference is made to the description of Figure 1A above.

[0150] In contrast to the method shown in Figure 1A, the order of steps is reversed in the method shown in Figure 1B. That is, loading a reflective target with a sample into a laser desorption mass spectrometer, shown in the flow diagram by box 120, may be performed before imaging the reflective target using at least one imaging device and before locating at least one sample region on the reflective target. These steps are shown in the flow diagram by boxes 116 and 118.

[0151] 2A and 2B show low-resolution microscope images of a dried sample 126 on a reflective target 128, respectively.

[0152] 2A shows a low-resolution microscope image 123 of 100 ng of cortisone dried from an 80% MeOH solution. The reflective target 128 may, in particular, comprise a predetermined reference structure element 130. The predetermined reference structure element 130 may, in particular, comprise a plurality of circles 132. In the low-resolution microscope image, a ring 134 can be seen inside one of the circles 132. The ring 134 corresponds to the dried sample 126. The ring 134 may, in particular, represent the dried sample 126 having a typical coffee ring effect.

[0153] Additionally, Figure 2B shows a low-resolution microscope image 123 (black / white image) after binarization of 100 ng of cortisone dried from an 80% MeOH solution. Circle 132 is still visible and can be used to calibrate the laser position. Ring 134 may correspond to the measurement range of the laser. Only the black dots are susceptible to laser stimulation within circle 132. White area 136 corresponds to the area without sample.

[0154] Figures 3A and 3B show mass spectra obtained with a laser desorption mass spectrometer. The mass spectra were obtained from the reflective target 128 shown in Figures 2A and 2B.

[0155] The mass spectrum shows the relative abundance ra (unit: %) based on the mass-to-charge ratio m / z.

[0156] FIG. 3A shows the spectrum from outside the ring 134, corresponding to the dry sample 126; therefore, no analyte is present. FIG. 3B shows the spectrum from inside the ring 134, corresponding to the dry sample 126. The analyte signal of cortisone ([M+H] + and [M+Na] + ) can be clearly seen.

[0157] 4 shows a further low-resolution microscope image of the dried sample 126. The sample 126 corresponds to a mixture of 500 pg of testosterone and 500 pg of progesterone. The reflective target 128 may in particular comprise a predetermined reference structural element 130. The predetermined reference structural element 130 may in particular comprise a plurality of circles 132. In the low-resolution microscope image, a ring 134 can be seen inside one of the circles 132. The ring 134 corresponds to the dried sample 126.

[0158] FIG. 5 illustrates an exemplary embodiment of a system 218 including a laser desorption mass spectrometer 220 according to the present invention.

[0159] The laser desorption mass spectrometer 220 includes at least one reflective target 128. The reflective target 128 may at least partially correspond to the reflective target 128 shown in FIG. 2A . Accordingly, reference is made to the description of FIG. 2A above. Furthermore, the laser desorption mass spectrometer 220 includes at least one laser source 222. The laser source 222 is configured to apply laser radiation to the reflective target 128 such that at least one ion of at least one analyte is generated. Furthermore, the laser desorption mass spectrometer 220 includes at least one mass analysis unit 224. Additionally or alternatively, the laser desorption mass spectrometer 220 may include an ion mobility spectrometer configured to detect the ions. Specifically, the mass analysis unit 224 may be configured to detect at least one mass-to-charge ratio of the at least one ion emitted from the target 118.

[0160] Laser desorption mass spectrometer 220 may include at least one chamber 226. Additionally, laser desorption mass spectrometer 220 may include at least one high vacuum chamber 228, at least one low vacuum chamber 227, and at least one sample loading chamber 229. At least one ion entrance 231 may be disposed between high vacuum chamber 228 and chamber 226.

[0161] Specifically, the laser source 222 may be a pulsed laser 232. The laser source 222 may be configured to generate a laser beam in the UV spectral range. The laser source 222 may be positioned relative to the reflective target 128 such that the laser beam, as indicated generally by arrow 234, strikes the reflective target 128 at an angle of 10° to 90°, preferably 30° to 70°. Specifically, the reflective target 128 may absorb the laser energy and transfer it to molecules of the sample, resulting in desorption and ionization. Furthermore, the system 218 may include at least one imaging device 235. The imaging device 235 may be positioned at an angle of 10° to 90°, preferably 30° to 80°, relative to the reflective target 128, as indicated by arrow 233, for example. Specifically, the imaging device 235 may be positioned at an angle of essentially 90° relative to the reflective target 128.

[0162] Additionally, the laser desorption mass spectrometer 320 may include at least one mass separation module 236. The configuration of the mass separation module 236 may depend on the mass analysis technique applied.

[0163] A mass analysis unit 224 having readout electronics 238 may be received within chamber 226. Furthermore, mass analysis unit 224 may be positioned at a specific distance relative to reflective target 128. Mass analysis unit 224 may be configured to detect or determine at least one mass-to-charge ratio of at least one ion emitted from reflective target 128. Furthermore, laser desorption mass spectrometer 220 includes at least one controller 237 configured to direct laser irradiation to a localized sample region.

[0164] FIG. 6 shows a further exemplary embodiment of a system 218 according to the present invention.

[0165] The system 218 according to Figure 6 comprises at least one laser desorption mass spectrometer 220. The laser desorption mass spectrometer 220 corresponds at least in part to the laser desorption mass spectrometer 220 shown in Figure 5. Reference is therefore made to the description of Figure 5 above.

[0166] 6 illustrates a system 218 in which the reflective target 128 is provided as a stack 164 of platelets 266. Specifically, the platelets 266 may have a rectangular shape, such as a square. The platelets 266 may be provided stacked on top of one another. Specifically, the platelets 266 may be stored in a platelet holder 270. The platelet holder 270 may be configured to sequentially eject the platelets 266. Specifically, the system 218 may include at least one conveyor belt (not shown). The platelet holder 270 may be configured to sequentially eject the platelets 266 onto the conveyor belt. The conveyor belt may be configured to pass the platelets 266 sequentially through various stations 248 of the system 218.

[0167] The various stations 248 may include a laser desorption mass spectrometer 220 and may further include one or more liquid handling systems 250 and / or a vacuum system 252 .

[0168] The liquid handling system 250 may be configured to apply at least one sample 268 having at least one analyte onto the reflective target 128, specifically onto the platelet 266. The liquid handling system 250 may specifically comprise one or more pipetting units 254. Specifically, a conveyor belt may be configured to pass the platelets 166 one after the other through the liquid handling system 150. After collecting the platelets 166 individually from the stack 164, the sample 268 comprising the analyte solution may be loaded onto the platelet 166 by using the pipetting unit 254. The sample 268 may be brought to the surface 272 of the platelet 266 by a pipette.

[0169] Furthermore, the conveyor belt may be configured to pass the platelets 266 sequentially through the vacuum system 252 and the laser desorption mass spectrometer 220. The vacuum system 252 may include one or more vacuum zones 258. The vacuum zones 258 may be arranged consecutively. The platelets 266 may be configured to pass through the vacuum system 252, specifically one or more of the vacuum zones 258, before passing through the laser desorption mass spectrometer 220. Specifically, the platelets 266 may be configured to pass through at least one first vacuum zone 260 and at least one second vacuum zone 262 before passing through the laser desorption mass spectrometer 220. The first vacuum zone 260 may be configured to provide a first low pressure, and the second vacuum zone 262 may be configured to provide a second low pressure. The first low pressure may be higher than the second low pressure, or vice versa. The first low pressure and the second low pressure may be less than 1500 mbar. Vacuum system 252, specifically vacuum zone 258, may be configured to dry the sample on reflective target 128. Furthermore, vacuum system 252 may include at least one third vacuum zone 184 and at least one fourth vacuum zone 186. Specifically, platelet 266 may be configured to pass through at least one third vacuum zone 284 and at least one fourth vacuum zone 286 after passing through laser desorption mass spectrometer 220. Third vacuum zone 284 and fourth vacuum zone 286 may be configured to ensure continuous outward movement while keeping the vacuum in the laser region as low as technically possible, particularly to ensure reliable measurements.

[0170] The laser desorption mass spectrometer 220 illustratively comprises a quadrupole, which may be followed by ion trapping, isobaric separation by ion mobility, fragmentation in a collision cell, followed by quadrupole or time-of-flight (ToF) mass analysis. Other techniques of ion manipulation, such as magnetic sector or ion trapping, and different combinations of corresponding units are also possible. [Explanation of symbols]

[0171] 112 Box 114 Box 116 Box 118 Box 120 Box 122 Box 123 low-resolution microscope images 124 Box 126 dried samples 128 Reflective Target 130 Prescribed reference structural elements 132 yen 134 Ring 136 White Area 218 System 220 Laser Desorption Mass Spectrometer 222 Laser Source 224 Mass Spectroscopy Unit 226 Chamber 227 Low Vacuum Chamber 228 High Vacuum Chamber 229 Sample Loading Chamber 230 Arrow 231 AEON Entrance 232 Pulsed Laser 233 Arrow 234 Arrow 235 Imaging Device 236 Mass Separation Module 237 Control Device 238 Readout Electronic Devices 248 Station 250 Liquid Handling System 252 Vacuum System 254 Pipette Unit 256 areas 258 Vacuum Zone 260 First Vacuum Zone 262 Second Vacuum Zone 264 stack 266 Platelets 268 samples 270 Platelet Holder 272 Surface

Claims

1. 1. A computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer (220), comprising: a) at least one imaging step comprising imaging at least one reflective target (128) to which the sample containing the at least one analyte has been applied by using at least one imaging device (235); b) at least one sample recognition step comprising locating at least one sample area on said reflective target (128); c) at least one analyte detection step, comprising detecting said at least one analyte in said sample using surface-assisted laser desorption / ionization mass spectrometry (SALDI-MS) in said laser desorption mass spectrometer (220); Including, laser radiation is applied to the reflective target (128) using at least one laser source (222) of the laser desorption mass spectrometer (220), and the laser radiation is directed to the sample region located using at least one control device (237), such that at least one ion of the at least one analyte is generated and the ion is detected using at least one of a mass analysis unit (224) of the laser desorption mass spectrometer (220) or an ion mobility spectrometer.

2. The method of claim 1 , further comprising at least one sample preparation step comprising applying the sample containing the at least one analyte to the at least one reflective target (128).

3. 3. The method according to claim 1, wherein the sample area is located on the reflective target (128) using at least one image evaluation algorithm on the image of the reflective target (128) taken in step a) by using at least one processing device.

4. 4. The method of claim 3, wherein the image of the reflective target (128) captured in step a) is transferred to a structural element prior to performing step c).

5. 5. The method of claim 4, wherein in step c), at least one of a path of the laser irradiation, a movement path of the reflective target, a movement path of a holder of the reflective target, a laser output, and a laser focus is calculated based on the structural element.

6. A method according to any one of claims 3 to 5, wherein the image assessment algorithm comprises identifying at least one pattern representative of the sample within the image.

7. 7. The method of claim 6, wherein the reflective target (128) comprises at least one predetermined reference structural element (130), and the image evaluation algorithm includes considering information about the predetermined reference structural element (130) to identify the location of the sample area in the image.

8. 8. The method of claim 7, wherein the image evaluation algorithm comprises at least one pattern recognition algorithm that uses the information about the predetermined reference structural element (130) to identify the pattern representative of the sample in the image.

9. 9. The method of claim 7 or 8, wherein the information about the predetermined reference structural element (130) is used to define a region of interest within the image.

10. The method of any one of claims 1 to 9, wherein the computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer (220) is performed at least in part automatically.

11. The method of any one of claims 1 to 10, wherein the computer-implemented method for detecting at least one analyte in a sample with a laser desorption mass spectrometer (220) is performed fully automatically.

12. The method of any one of claims 1 to 11, wherein the reflective target (128) has a specular reflectivity of 45% or greater.

13. The reflective target (128) is R a 13. The method according to any one of claims 1 to 12, having a nominal arithmetic roughness of ≦2 μm.

14. A system (218) comprising at least one laser desorption mass spectrometer (220), the laser desorption mass spectrometer (220) comprising at least one reflective target (128), at least one sample being applicable onto the reflective target (128), the sample comprising at least one analyte; the laser desorption mass spectrometer (220) is configured to detect the at least one analyte in the sample using surface-assisted laser desorption ionization mass spectrometry (SALDI-MS), the laser desorption mass spectrometer (220) comprising at least one laser source (222) configured to apply laser radiation to the reflective target (128) such that at least one ion of the at least one analyte is generated, and the laser desorption mass spectrometer (220) comprises at least one of a mass analysis unit (224) or an ion mobility spectrometer configured to detect the ions; the system (218) comprising at least one imaging device (235) configured to image the reflective target (128); A system (218) wherein the laser desorption mass spectrometer (220) comprises at least one controller (237) configured to direct the laser radiation to a localized sample region.

15. A computer program comprising instructions which, when executed by a system (218) according to claim 14, cause said system (218) to carry out the method according to any one of claims 1 to 13.

Images