EP3821228A1 - Verfahren zum untersuchen einer flüssigen probe und eine dispensiervorrichtung - Google Patents
Verfahren zum untersuchen einer flüssigen probe und eine dispensiervorrichtungInfo
- Publication number
- EP3821228A1 EP3821228A1 EP19735595.1A EP19735595A EP3821228A1 EP 3821228 A1 EP3821228 A1 EP 3821228A1 EP 19735595 A EP19735595 A EP 19735595A EP 3821228 A1 EP3821228 A1 EP 3821228A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- cell
- algorithm
- sample area
- liquid
- cells
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
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- 238000012549 training Methods 0.000 claims description 87
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- 238000013528 artificial neural network Methods 0.000 claims description 15
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- 238000006073 displacement reaction Methods 0.000 claims description 4
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- 238000003860 storage Methods 0.000 claims description 4
- 238000007599 discharging Methods 0.000 abstract 1
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Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
- G01N15/0205—Investigating particle size or size distribution by optical means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
- B01L3/00—Containers or dishes for laboratory use, e.g. laboratory glassware; Droppers
- B01L3/50—Containers for the purpose of retaining a material to be analysed, e.g. test tubes
- B01L3/502—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures
- B01L3/5027—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip
- B01L3/50273—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip characterised by the means or forces applied to move the fluids
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
- B01L3/00—Containers or dishes for laboratory use, e.g. laboratory glassware; Droppers
- B01L3/56—Labware specially adapted for transferring fluids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N2015/1006—Investigating individual particles for cytology
Definitions
- the invention relates to a method for examining a liquid sample which has a liquid and at least one cell in the liquid and / or at least one particle in the liquid, the method comprising at least one data element which provides information about a sample area contains, is determined. Furthermore, the invention relates to a dispensing device which comprises means for executing the method.
- the invention also relates to a computer program, a data carrier on which the computer program is stored and a data carrier signal which transmits the computer program.
- monoclonal cell lines ⁇ are populations of cells that are all derived from a single mother cell. It is necessary to freeze monoclonal cell lines because this is the only way to ensure that all cells in the population have an approximately identical genome in order to produce the active substances.
- cells are transferred individually to a microtiter plate ⁇ .
- the transferred cells are prepared ⁇ by genetically modifying a host cell line and separating these modified cells.
- Individual cells are deposited in the microtiter plates using, for example, free jet printing methods or pipetting.
- Dispensing devices can be used to deposit the individual cells. Dispensing devices are known from the prior art, by means of which liquid drops can be dispensed into the container. It is known that before the drop of liquid is dispensed, it is examined whether there are no cells or only one or more cells in the liquid drop. Depending on the test result, the liquid drop is dispensed into a container or a reject container. After the dispensing process, the cells dispensed into the containers can multiply in the respective container.
- a disadvantage of the known dispensing devices is that the liquid drops are dispensed into the containers without the quality of the cell being checked. Therefore, dead cells may be broken into the containers. This is disadvantageous because no cell cultivation takes place in the corresponding containers, which is disadvantageous due to the limited number of containers and the limited processing time. An exam the cells can be removed manually before being placed in the container, but this is not economical and is therefore not practiced ⁇ .
- EP 2 042 853 A1 discloses an analysis device in which a biological sample located between two glass plates is analyzed using recorded images.
- the object of the invention is therefore to improve the dispensing process.
- the object is achieved by a method of the type mentioned at the outset, which is characterized in that the data element is fed to a trained algorithm which produces a result which is dependent on the data element ⁇ , and that a dispensing process which involves dispensing at least a part of the liquid sample comprehensive ⁇ , depending on the result ⁇ .
- a dispensing device which has means for carrying out the method ⁇ .
- the object is achieved by a dispensing device which executes a method for examining a liquid sample which has a liquid and at least one cell in the liquid and / or at least one particle in the liquid ⁇ , with an optical detection device Generating at least one data element which contains information on a sample area, characterized in that the data element can be fed to a trained algorithm, in particular stored in a classifier of the dispensing device, which generates a result dependent on the data element ⁇ , and that a dispensing process which dispensing at least a portion of the liquid sample, depending on the result.
- the method according to the invention ⁇ has the advantage that the trained algorithm automatically makes a prediction or determines an estimated value that influences the dispensing process, ie without the involvement of a laboratory employee, ⁇ .
- the trained algorithm can use the data element supplied to predict ⁇ whether the cell dispensed with the liquid will soon die ⁇ or estimate whether the cell to be dispensed is an ⁇ o ⁇ e cell.
- This knowledge is an advantage because it can be used to prevent ⁇ o ⁇ e cells or cells that are highly likely to die from being released into containers. Rather, it can be ensured that such cells are dispensed into a reject container. As a result, the efficiency of the laboratory operation increases due to the trained algorithm.
- the liquid sample dispensed by means of the dispensing device can be a drop, in particular a free-flying one.
- the dispensed liquid sample can be a liquid jet, which after dispensing from a dispenser of the dispensing device disintegrates into individual liquid drops.
- the dispensing device can be a drop generator.
- the liquid drop can have a volume in a range between 1 pl (picoliter) to 50 nl (nanoliter).
- the dispensed liquid sample cannot have a cell and / or a particle.
- the dispensed liquid sample can have a single cell and / or a single particle.
- the dispensed liquid sample may alternatively have more than a single cell and / or more than a single particle.
- the liquid of the liquid sample can have a composition which is conducive to cell growth.
- the particle can be a glass or polymer bead and have substantially the same volume as the cell.
- the cell is a biological cell, in particular the cell is the smallest unit of life that is autonomously capable of reproduction and self-preservation.
- a trained algorithm is an algorithm that can assess data, such as the supplied data elements, on the basis of learned knowledge.
- the algorithm In order to be able to assess the data, the algorithm must first undergo a training ⁇ described in detail below. In the training, the algorithm learns using examples and can generalize these after the end of the learning phase. This means that the algorithm does not memorize the examples ⁇ , but recognizes patterns and / or regularities in the training data ⁇ . This enables the algorithm to assess unknown data, such as the data element.
- an evaluation device of the dispensing device can be used to determine whether a single cell and / or a single particle is arranged in the sample area.
- the evaluation device can be used to determine whether no cell and / or no particle is arranged in the sample area.
- the evaluation device can be used to determine whether more than one cell and / or one particle is arranged in the sample area.
- the number of cells and / or particles arranged in the sample area can be determined by the trained algorithm.
- the Algorithm are checked whether the predetermined number of cells and / or particles is arranged in the sample area.
- the number of cells and / or particles arranged in the sample area can be determined by another trained algorithm.
- the other trained algorithm can check whether the specified number of cells and / or particles is contained in the sample area. The result of the determination and / or testing by the other trained algorithm can be transmitted to the trained algorithm.
- Knowing the number of cells and / or particles located in the sample area also has the advantage that this information is used to decide whether the data element is supplied to the trained algorithm.
- the data element is supplied to the trained algorithm if there is a single cell and / or a single particle in the sample area. This is done because it is advantageous for the further investigation if the container contains only a single cell and / or a single particle. Therefore, only a liquid sample in which only a single cell and / or a single particle is arranged can be supplied to the container.
- the container can be part of a microtiter plate.
- the number of cells and / or particles arranged in the sample area can be determined by an algorithm that cannot be frayed. This offers the advantage that no complex training phase is necessary for the algorithm for determining the number of cells and / or particles.
- the data element cannot be supplied to the trained algorithm or the trained algorithm will terminate the further processing of the data element to determine the result if there is no cell and / or no particle in the sample area or if the predetermined number of cells and / or contains particles.
- This offers the advantage that the computational effort is reduced ⁇ because the trained algorithm is only used and / or the data element is only further processed if a single cell and / or a single particle in the sample area Liquid is included. In this case, the liquid sample can be dispensed into the reject container.
- the data element can contain one or more information relating to the sample area.
- the data element can be a measurement signal or an optical signal or an image signal.
- the data element can contain at least information about a cell property ⁇ of the cell arranged in the liquid of the sample area and / or a particle property ⁇ of the particle arranged in the liquid of the sample area.
- an image can be generated from the image signal.
- the dispensing device can have an optical detection device, such as a camera, which is used to generate an image of the sample area. If several data elements, in particular several image signals, are determined, in particular at different times, several images can be generated.
- the illustration can show a dispenser of the dispensing device receiving the sample area or a part of the dispenser receiving the sample area. In particular, the illustration can show an output channel or a part of the output channel of the dispenser.
- the data element, in particular the image signal contains all the information required to generate the image.
- the dispenser can be used to dispense the liquid sample.
- the sample area can be dispensed or dispensed by the dispenser.
- the image can be a bright field or fluorescence or dark field or phase contrast image. It is possible that several images show the same cell, but from different angles and / or at different times.
- the data element can only be supplied to the trained algorithm if the specified number of cells and / or particles is contained in the sample area.
- the data element can be supplied to the trained algorithm if a single cell and / or a single particle is contained in the sample area. The position of the cell and / or the particle in the sample area and / or the image can be determined.
- the position of the cell and / or of the particle can be determined in a simple manner by evaluating the generated image. After the position of the cell and / or the particle in the image is known, the imaging section described above can be created. The imaging section may completely contain the cell and / or the particle. The imaging section can have a predetermined size.
- the dispensing process can additionally include determining a storage location for the liquid sample to be dispensed, in particular the drop. This makes it easy to ensure that, for example, ⁇ o ⁇ e cells are dispensed into the reject container, while living cells are dispensed into different containers. After the storage location has been determined, the liquid sample can be dispensed into the container or reject container.
- the sample delivery can be carried out according to a drop-on-demand mode of operation.
- the dispensing device ensures discrete and not continuous sample delivery.
- the dispensing device can have an actuating means, which can be, for example, a piezoelectrically operated actuator.
- the dispenser can have a section, in particular a mechanical membrane, which can be actuated by the actuating means. When the actuating means is actuated, the liquid sample, in particular a drop, is expelled from the dispenser.
- the trained algorithm can be part of an artificial neural network and / or an artificial neural network can be part of the trained algorithm. In this way, it can be determined in a particularly simple manner whether the cell and / or the particle should be released into the reject container or the container.
- An artificial neural network is understood to be a collection of individual information processing units, which are called neurons and which are arranged in layers in a network architecture.
- the algorithm can be a folding neural network for classifying images, which can be generated from the data element as described above.
- the neural network is also called convolutional neural network ⁇ .
- the folding neural network consists of at least one folding layer, at least one hidden layer, and at least one fully networked layer.
- another trainable algorithm can also be provided.
- the algorithm can be a support vector machine (e.g. a 2-norm SVM), a linear regression, a boosting network, a probabilistic boosting tree, a linear discriminant analysis, a relevance vector machine, a random forest method, a nearest neighbor Method, or a combination thereof.
- the result of the trained algorithm can depend on a classification of the data element into one of at least two classes.
- the classes can depend on a cell property and / or particle property.
- a prediction for the cell property and / or particle property is made or an estimate for a cell property and / or particle property is determined.
- the estimate may be, for example, a probability ⁇ whether the cell is dead or an estimate of a diameter of the cell and / or particle.
- the result may be a prediction of the cell property ⁇ and / or the particle property ⁇ or the estimate of the cell property ⁇ and / or the particle property ⁇ .
- the cell property ⁇ can be the cell type, cell productivity, a genotype or phenotype, a cell cycle status and / or a state of the cell.
- the classes specify the respective cell properties ⁇ .
- one class can refer to "living cells” and another class to "dead cells”.
- classes are conceivable which depend on whether the cell is stained or not, or whether the cell is intact or not. Classes are also conceivable which depend on whether high or low gene expression or protein production, in particular certain proteins, is possible or available or whether the cells grow or divide rapidly or slowly.
- a classification is based on whether there is a high or low probability that high-quality results will be achieved in a subsequent molecular analysis.
- a molecular analysis can be, for example, the sequencing of the entire genome or parts of the genome and / or the entire transcriptome or parts of the transcriptome of the individual dispensed cells.
- the trained algorithm by applying the learned knowledge to the generated image, can predict cell property and / or particle property and / or estimate cell property and / or particle property. For example, using the trained algorithm, it can be estimated ⁇ whether the cell shown in the figure is dead or alive. This allows the algorithm to be used to maximize the number of living cells that grow into a colony after isolation.
- the classification of the data element into a class can be done by a classifier.
- the classifier can be part of the artificial neural network and / or an artificial neural network is part of the classifier.
- the algorithm can be trained in a training process before the data element is fed to the algorithm.
- the algorithm can be trained using machine learning.
- the training process serves to acquire knowledge that enables an assessment of the supplied data elements. With machine learning, knowledge is generated artificially from experience. As described below, this is done by supplying the algorithm with a large number of training data.
- At least one class can be assigned to the individual training data elements before being fed to the algorithm.
- the assignment of the individual training data elements to the respective classes can be based on measurement data.
- the measurement data can be based on liquid samples which are output from the dispensing device and which have a single cell and / or a single particle.
- first training data elements can each contain at least one piece of information about the sample area.
- the second training data elements can each contain at least one piece of information about cell properties ⁇ and / or particle properties ⁇ .
- At least one second training data item ⁇ can be assigned to a first training data item ⁇ .
- the first training data elements can be determined when the sample area and / or the liquid sample is in the dispenser. Alternatively, the first training data elements can be determined when the dispenser has output the sample area and / or the liquid sample and the sample area and / or the liquid sample is in the container.
- the second training data elements can be determined in time after the first training data elements have been determined.
- the second training data elements are preferably determined after the liquid sample and / or the sample area has been dispensed into the container.
- the determination of the second training data elements in particular the measurement of the cell properties ⁇ and / or the particle properties ⁇ , can be carried out after a predetermined period of time, in particular several days, after the liquid samples have been dispensed into the containers.
- each first training data item ⁇ can have at least one second training data item ⁇ be assigned ⁇ . The assignment can be made automatically, for example via a computer program, or by the laboratory employee.
- a large number of images can be generated from the first training port elements.
- Each of the images can be assigned at least one cell property, such as cell condition, cell type, etc.
- At least two classes can also be formed during the training process.
- the classes can depend on the second training data elements.
- the classes can depend on the cell properties and / or particle properties. It is conceivable that different classes are formed for different cell types and / or that different classes are formed depending on the cell state.
- the classes can be created manually. Alternatively, the classes can also be created automatically. After the classes have been formed, the individual second training data elements can each be assigned to a class.
- the first training data elements, the second training data elements and their assignment to the first training data elements and the classes formed are fed to the algorithm for training the algorithm.
- the aim of the training is that the algorithm recognizes existing patterns and / or regularities between the first training data elements and the second training data elements, and is thus able to classify future data elements that are added.
- the cell property ⁇ and / or particle property ⁇ can be easily predicted or estimated based on the classification of the data element into the corresponding class.
- the trained algorithm can be trained again. On the one hand, this is useful if a cell type is used ⁇ that has different properties, such as a different morphology ⁇ , than the cell types with which the algorithm has been trained up to now. On the other hand, this is useful if, for example, only classes dependent on the cell state were formed in a first training process and a classification according to another cell property ⁇ is desired. So that a classification according to the second cell property ⁇ is possible, a second training process must be carried out, in which, for example, classes dependent on cell types are formed. After performing the two training processes, the algorithm is able to classify data elements according to cell types and cells with different cell growth.
- the dispensing device can have a displacement device by means of which the dispenser and / or the container for receiving the liquid sample and / or the reject container is movable for receiving the liquid sample, a movement process being dependent on the result, in particular on the classification of the data element.
- the process device will move the dispenser in such a way that the liquid sample is dispensed into the reject container if the data element has been classified into a class in which, for example, fof cells are classified.
- the dispenser and / or the container and / or the reject container can be moved in such a way that the liquid sample is dispensed into the reject container when there is no cell and / or no predetermined number of cells and / or in the dispensed liquid sample. or contains particles.
- the dispensed liquid can be dispensed into the container if a single cell and / or a single particle is arranged in the liquid.
- the dispensing device can have a deflection and / or suction device.
- the deflecting device serves to deflect the dispensed liquid sample, in particular the dispensed drop.
- the suction device is used to suck off the dispensed liquid sample, in particular the dispensed drop.
- the dispensed liquid sample can be deflected ⁇ and / or aspirated into the reject container. Alternatively, the dispensed liquid sample can be dispensed into the container, in particular the microtiter plate container.
- the deflection and / or suction can take place before the dispensed liquid sample enters the container, in particular the container of the microtiter plate.
- the dispensed liquid sample can be deflected and / or aspirated if there are no cells and / or particles in the dispensed liquid.
- the dispensed liquid can be deflected and / or aspirated if the number of cells and / or particles arranged in the liquid is greater than a predetermined value, in particular greater than 1.
- the deflection and / or suction can depend on the result of the trained algorithm.
- the deflection and / or suction can depend on the class in which the data element was classified. If the data element is classified into a class in which, for example, dead cells are classified, the deflection and suction device is activated so that the liquid sample that is dispensed is deflected ⁇ and / or suctioned off.
- a data carrier on which the computer program according to the invention is stored is also advantageous.
- a port carrier signal that transmits a computer program according to the invention is advantageous.
- FIG. 1 shows a dispensing device according to the invention
- FIG. 2 shows an enlarged illustration of part of a dispenser of the dispensing device according to the invention
- FIG. 3 shows a sequence in a training process for training an algorithm
- FIG. 4 shows a procedure for examining the liquid sample using the trained algorithm.
- Figure 1 shows a dispensing device 6 according to the invention, which has a dispenser 7 for dispensing a liquid sample 20 ⁇ .
- the liquid sample 20 has a liquid 1 and at least one cell 3 arranged in the liquid 1 and / or at least one particle arranged in the liquid 1.
- the dispensing device 6 has an optical detection device 8 for optically detecting at least part of an output channel 16 of the dispenser 7.
- the dispenser 7 can have a fluid chamber 15 in which the liquid sample 20 is arranged and / or input.
- the fluid chamber 15 is fluidly connected to the discharge channel 16.
- the optical detection device 8 has an imaging device, not shown, such as a camera, for generating an image of the at least part of the output channel 16 and further optical elements, not shown, for guiding light. To generate an image, the at least part of the output channel 16 is illuminated by means of an illuminating light 17 and a detection light 18 then emanating from the at least part of the output channel 16 is detected by the optical detection device 8. The imaging device generates an image of the at least part of the output channel 16 based on the detected detection light 18.
- an imaging device not shown, such as a camera
- the optical detection device 8 is electrically connected to an evaluation device 9 of a computer 12.
- the evaluation device 9 can, based on the generated image, the number of cells 3 contained in the at least part of the output channel 16 and / or determine particles.
- the computer 12 has a classifier 13, which is electrically connected to the ejection device 9.
- the classifier 13 is part of an artificially neural network and / or has an artificially neural network.
- a trained algorithm is stored in the classifier 13, which generates a result after supplying the image generated by the optical detection device 8.
- the computer 12 has a control device 14.
- the control device 14 controls a dispensing operation of the dispenser 7 based on the result of the classifier 13.
- the control device 14 is electrically connected to a displacement device 10.
- the traversing device 10 can move the dispenser 7 and / or a container 4 and / or a reject container 5 such that the liquid sample 20 can be dispensed into the desired storage location.
- control device 14 can control a deflection and / or suction device 11 of the dispensing device 6.
- the control device 14 can control the deflection and / or suction device 1 1 in such a way that the dispensed liquid sample 20 is deflected ⁇ and / or suctioned off if no cell 3 and / or no particle is arranged in the liquid 1 or if there is no cell in the Liquid 1 several cells 3 and / or several particles are arranged.
- the control device 14 can control the displacement device 10 and / or the deflection and / or suction device 1 1 depending on the result of the classifier 13.
- FIG. 1 shows a state in which the dispenser 7 has dispensed the liquid sample 20, in particular a drop, which has a dead cell 3 ⁇ .
- the dispensed liquid sample 20 is dispensed into the reject container 5.
- the dispensing device 6 has an actuating means 19 which is pressed against a section of the dispenser 7 in order to actuate the dispenser 7.
- the liquid sample 20, in particular a drop, is dispensed when the actuating means 19 presses against the section of the dispenser 7 ⁇ .
- the actuating means 19 and the optical detection device 8 face each other with respect to the dispenser 7.
- the dispenser 7 consists at least partially of a transparent material, so that at least part of the output channel 16 can be detected by means of the optical detection device 8.
- FIG. 2 shows an enlarged illustration of part of the dispenser 7.
- FIG. 2 shows an enlarged view of the area A of the output channel 16 shown in dashed lines in FIG. 1.
- the discharge channel 16 is completely filled with liquid 1 of the liquid sample 20. Only the part of the output channel 16 shown in dashed lines in FIG. 2 is considered by means of the optical detection device 8.
- the sample area 2 of the liquid sample 20 is arranged in the part of the output channel 16 of the dispenser 7 shown in dashed lines. In a dispensing process, the liquid sample 20 is dispensed along an application direction R.
- the discharge channel 16 has a nozzle-shaped end at its end remote from the fluid chamber 15.
- the cells 3 arranged in the part of the dispensing channel 16 move due to the weight force in the direction of the nozzle-shaped end facing away from the fluid chamber 15, even if no liquid sample 20 is dispensed from the dispenser 7.
- FIG. 3 shows a sequence in a training process for training an algorithm.
- the algorithm is stored in the classifier 13.
- a first training data item ⁇ is determined.
- the first training data item ⁇ contains information on the sample area 2.
- the first training data element is determined by the optical detection device 8, an image being generated in the optical detection device 8 from the first training data item ⁇ .
- the figure shows at least the part of the output channel 16 that receives the sample area 2 ⁇ .
- the liquid sample 20 is dispensed into the container 4 of the microtiter plate by means of the dispenser 7 if the liquid sample 20 to be dispensed has a single cell 3 and / or a single particle ⁇ .
- Another first training data item ⁇ is then determined again, a further image is generated and the liquid sample 20 is dispensed into another container of the microtiter plate. This process is repeated several times.
- a liquid 1 with a single cell 3 and / or a single particle is arranged in each container 4 of the microtiter plate, it being known in which container 4 which cell 3 is arranged.
- second training data elements are determined in a second training step ⁇ T2.
- at least one cell property ⁇ and / or particle property ⁇ of the cell 3 located in the container 4 is measured.
- it can be measured how quickly the cells 3 grow in the individual containers 4 and thus a conclusion can be drawn about the cell state and / or it can be determined which cell types are contained in the containers 4.
- the second training data elements can be determined a few days after the liquid samples 20 have been dispensed into the containers 4.
- a microscope and / or an automated plate reader can be used to measure cell properties ⁇ and / or particle properties ⁇ .
- a third training step T3 at least two classes are formed.
- the classes depend on the second training data element, in particular the cell property ⁇ and / or the particle property ⁇ .
- the cell property ⁇ can be a cell type, for example, so that the individual classes differ from one another in the cell types. Alternatively, the cell property ⁇ can be the cell state so that the classes differ from each other in whether the cells are dead or alive.
- the second training data elements are each assigned to at least one class.
- the third training step T3 can alternatively be carried out before the first and / or second training step TI, T2.
- each first training data item ⁇ is assigned at least one second training data item ⁇ .
- at least one cell property ⁇ and / or particle property ⁇ is assigned to each image of the sample area 2.
- the first training data element is linked to the second training data item ⁇ . This link is advantageous because the algorithm can thus recognize the relationship between the first training data elements and the second training data elements.
- the cell property ⁇ “living cells” can be assigned to all the first training data elements, in which the measurement carried out in the second training step T2 showed that the cell in the respective container is not dead and thus cell growth takes place.
- a fifth training step T5 the classes formed, the first training data elements, the second training data elements and their assignment to the first training data elements are used to train the classifier by means of machine learning.
- the algorithm recognizes at least one pattern and / or regularities between the first training data elements and the second training data elements.
- a trained algorithm is available. This means that the trained algorithm can apply the learned knowledge to a supplied data element in order to make a prediction or estimate regarding the cell property ⁇ and / or the particle property ⁇ solely on the basis of the data element.
- Figure 4 shows a process flow for Examine the liquid sample 20 using the trained algorithm.
- a data element is determined by means of the optical detection device 8.
- the optical detection device 8 generates an image from the determined data element, which contains the sample area 2.
- a third method step ⁇ S3 the ejection device 9 checks whether the sample area 2 contains a predetermined number of cells 3 and / or particles. This is done using our own algorithm. In particular, the evaluation device 9 checks whether exactly one single cell 3 and / or one single particle is contained in the sample area 2.
- a single cell 3 and / or a single particle is contained in the sample area 2
- the position of the cell 3 and / or the particle in the figure is determined in a fourth method step S4.
- a fifth step S5 is then used to generate an image section which contains the cell 3 and / or the particle completely.
- the image signal containing the image section ⁇ is transmitted to the trained algorithm in a sixth method step ⁇ S6.
- the trained algorithm produces a result in a seventh procedural step ⁇ S7 based on the supplied image section.
- the result depends on a classification of the data element, in particular the mapping, into one of the classes stored in the trained algorithm. Since the classes depend on the cell property and / or paraffin property, the classification of the mapping into one of the classes predicts the cell property and / or paricle property. The figure is classified by the classifier 13 in one of the classes.
- a seventh method step ⁇ S7 the control device 14 controls the moving device 10 and / or the deflection and / or suction device 1 1 depending on the result, in particular on the classification of the data element into a class.
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Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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LU100870A LU100870B1 (de) | 2018-07-09 | 2018-07-09 | Verfahren zum Untersuchen einer flüssigen Probe |
PCT/EP2019/068371 WO2020011773A1 (de) | 2018-07-09 | 2019-07-09 | Verfahren zum untersuchen einer flüssigen probe und eine dispensiervorrichtung |
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EP3821228A1 true EP3821228A1 (de) | 2021-05-19 |
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EP19735595.1A Pending EP3821228A1 (de) | 2018-07-09 | 2019-07-09 | Verfahren zum untersuchen einer flüssigen probe und eine dispensiervorrichtung |
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US (1) | US20210293685A1 (de) |
EP (1) | EP3821228A1 (de) |
JP (1) | JP2021524265A (de) |
KR (1) | KR20210029247A (de) |
CN (1) | CN112400103A (de) |
AU (1) | AU2019301870B2 (de) |
CA (1) | CA3104615A1 (de) |
IL (1) | IL280006A (de) |
LU (1) | LU100870B1 (de) |
SG (1) | SG11202013238TA (de) |
WO (1) | WO2020011773A1 (de) |
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CN110399804A (zh) * | 2019-07-01 | 2019-11-01 | 浙江师范大学 | 一种基于深度学习的食品检测识别方法 |
CN116355725B (zh) * | 2023-03-07 | 2024-04-05 | 广州市艾贝泰生物科技有限公司 | 分配器、分配装置及分配方法 |
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US8486618B2 (en) * | 2002-08-01 | 2013-07-16 | Xy, Llc | Heterogeneous inseminate system |
US20060257854A1 (en) * | 2004-02-27 | 2006-11-16 | Mcdevitt John T | Membrane assay system including preloaded particles |
EP2042853B1 (de) * | 2006-07-12 | 2014-04-23 | Toyobo Co., Ltd. | Analysegerät und dessen Verwendung |
JP5924077B2 (ja) * | 2012-03-30 | 2016-05-25 | ソニー株式会社 | 微小粒子分取装置及び微小粒子分取装置における軌道方向判定方法 |
EP2936116B8 (de) * | 2012-12-19 | 2020-04-01 | Koninklijke Philips N.V. | System und verfahren zur klassifikation von teilchen in einer flüssigprobe |
US9919533B2 (en) * | 2015-10-30 | 2018-03-20 | Ricoh Company, Ltd. | Liquid droplet forming apparatus |
JP6690245B2 (ja) * | 2016-01-12 | 2020-04-28 | 日本精工株式会社 | マニピュレーションシステム及びマニピュレーションシステムの駆動方法 |
WO2017214572A1 (en) * | 2016-06-10 | 2017-12-14 | The Regents Of The University Of California | Image-based cell sorting systems and methods |
JP6646552B2 (ja) * | 2016-09-13 | 2020-02-14 | 株式会社日立ハイテクノロジーズ | 画像診断支援装置、画像診断支援方法及び試料分析システム |
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- 2019-07-09 CN CN201980046152.6A patent/CN112400103A/zh active Pending
- 2019-07-09 AU AU2019301870A patent/AU2019301870B2/en not_active Expired - Fee Related
- 2019-07-09 US US17/258,501 patent/US20210293685A1/en not_active Abandoned
- 2019-07-09 WO PCT/EP2019/068371 patent/WO2020011773A1/de unknown
- 2019-07-09 SG SG11202013238TA patent/SG11202013238TA/en unknown
- 2019-07-09 JP JP2021500252A patent/JP2021524265A/ja active Pending
- 2019-07-09 CA CA3104615A patent/CA3104615A1/en not_active Abandoned
- 2019-07-09 EP EP19735595.1A patent/EP3821228A1/de active Pending
- 2019-07-09 KR KR1020217003904A patent/KR20210029247A/ko not_active Application Discontinuation
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JP2021524265A (ja) | 2021-09-13 |
KR20210029247A (ko) | 2021-03-15 |
LU100870B1 (de) | 2020-01-09 |
AU2019301870A1 (en) | 2021-01-21 |
AU2019301870B2 (en) | 2022-06-23 |
US20210293685A1 (en) | 2021-09-23 |
WO2020011773A1 (de) | 2020-01-16 |
CN112400103A (zh) | 2021-02-23 |
SG11202013238TA (en) | 2021-02-25 |
CA3104615A1 (en) | 2020-01-16 |
IL280006A (en) | 2021-03-01 |
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