METHOD FOR EXAMINING A LIQUID SAMPLE AND A DISPENSING APPARATUS

Abstract

The invention relates to a method for examining a liquid sample that has a liquid and at least one cell located in the liquid and/or at least one particle located in the liquid, wherein at least one data element containing information about a sample region is determined with the method. The method is characterised in that the data element is supplied to a trained algorithm that generates a result dependent on the data element, and in that a dispensing process comprising the discharging of at least part of the liquid sample depends on the result.

Claims

1. A method for examining a liquid sample (20) which has a liquid (1) and at least one cell (3) located in the liquid (1) and/or at least one particle located in the liquid (1), wherein at least one data element that contains information on a sample region (2) is determined with the method, wherein the data element is supplied to a trained algorithm that generates a result dependent on the data element, and wherein a dispensing process comprising the discharging of at least a part of the liquid sample (20) depends on the result, wherein the result is a prediction of a cell property and/or a particle property or an estimated value for a cell property and/or a particle property.

2. The method according to claim 1, wherein the method comprises checking whether a predetermined number of cells (3) and/or particles are arranged in the sample region (2).

3. The method according to claim 2, wherein a. the data element is supplied to the trained algorithm when the predetermined number of cells (3) and/or particles is arranged in the sample region (2) and/or b. the data element is not supplied to the trained algorithm if the predetermined number of cells (3) and/or particles is not arranged in the sample region (2) and/or c. the number of cells and/or particles arranged in the sample region is determined by the trained algorithm or another trained algorithm, or d. the number of cells and/or particles arranged in the sample region is determined by the trained algorithm or another trained algorithm and it is checked whether the predetermined number of cells (3) and/or particles is arranged in the sample region or e. the number of cells and/or particles arranged in the sample region is determined by an algorithm that cannot be trained and it is checked whether the predetermined number of cells (3) and/or particles is arranged in the sample region.

4. (canceled)

5. The method according to claim 1, wherein a. the data element is a measurement signal or an image signal and/or b. only a part of the data element is supplied to the trained algorithm.

6. (canceled)

7. The method according to claim 5, wherein an image is generated from the image signal.

8. The method according to claim 7, wherein a. the position of the cell (3) and/or of the particle in the image is determined or an image section is determined that has the cell (3) and/or the particle and only that part of the image signal containing the image section is supplied to the trained algorithm and/or b. the image shows a dispenser (7) receiving the sample region (2) or a part of the dispenser (7) receiving the sample region (2).

9. (canceled)

10. The method according to claim 1, wherein a. the dispensing process comprises determining a storage location for the liquid sample (20) to be dispensed and/or b. the fluid discharge is carried out according to a drop-on-demand mode of operation and/or c. the trained algorithm is part of an artificial neural network and/or contains at least one artificial neural network and/or d. the result depends on a classification of the data element into one of at least two classes.

11. (canceled)

12. (canceled)

13. (canceled)

14. The method according to claim 1, wherein the algorithm is trained before the data element is supplied to the algorithm.

15. The method according to claim 14, wherein a. a class is assigned to at least one training data element or b. a class is assigned to at least one training data element and the class assignment of the training data element depends on measurement data based on a liquid sample that is dispensed.

16. (canceled)

17. The method according to claim 14, wherein the algorithm is trained by means of machine learning.

18. The method according to claim 14, wherein a plurality of first training data elements is determined and a plurality of second training data elements is determined.

19. The method according to claim 18, wherein a. at least one second training data element is assigned to each first training data element and/or b. at least two classes are formed depending on the second training data elements and/or c. the classes and/or the first training data elements and/or the second training data elements are transmitted to the algorithm.

20. (canceled)

21. (canceled)

22. The method according to claim 1, wherein a. the trained algorithm is retrained and/or b. the data element contains information on a cell property of the cell arranged in the sample region and/or information on a particle property of the particle arranged in the sample region.

23. (canceled)

24. A dispensing apparatus (6) comprising means for carrying out the method according to claim 1.

25. The dispensing apparatus according to claim 24, comprising a. a dispenser (7) for discharging the liquid sample (20) or a dispenser (7) for discharging the liquid sample (20) wherein the sample region (2) is arranged in the dispenser (7) and/or can be discharged by the dispenser (7) and/or b. an optical detection device (8) for generating an image of the sample region (2) and/or c. an evaluation device (9) for evaluating whether a predetermined number of cells (3) and/or particles are arranged in the sample region (2).

26. (canceled)

27. (canceled)

28. The dispensing apparatus (6) according to claim 24, comprising a. a classifier (13) for classifying the data elements into a class or b. a classifier (13) for classifying the data elements into a class wherein the classifier (13) is part of an artificial neural network and/or contains at least one artificial neural network.

29. (canceled)

30. The dispensing apparatus (6) according to claim 24, comprising a. a displacement device (10) by means of which the dispenser (7) and/or a container (4) for receiving the liquid sample (20) and/or a reject container (5) can be displaced for receiving the liquid sample (20), wherein a displacement process depends on the result and/or b. a deflection device for deflecting the discharged liquid sample (20) and/or a suction device for suctioning off the discharged liquid sample (20), wherein a deflection process and/or suction process depends on the result.

31. (canceled)

32. A non-transient computer readable storage medium comprising a computer program comprising instructions that, when the computer program is executed by a computer (12), cause the computer to carry out the method according to claim 1.

33. (canceled)

34. (canceled)

Description

BRIEF DESCRIPTION OF THE DRAWING VIEWS

[0055] The subject matter of the disclosure is shown schematically in the figures, wherein elements that are the same or have the same effect are mostly provided with the same reference symbols. In the figures:

[0056] FIG. 1 shows a dispensing apparatus according to the disclosure,

[0057] FIG. 2 shows an enlarged illustration of part of a dispenser of the dispensing apparatus according to the disclosure,

[0058] FIG. 3 shows a sequence in a training process for training an algorithm, and

[0059] FIG. 4 shows a method sequence for examining the liquid sample by means of the trained algorithm.

DETAILED DESCRIPTION

[0060] FIG. 1 shows a dispensing apparatus 6 according to the disclosure that has a dispenser 7 for discharging 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. In addition, the dispensing apparatus 6 has an optical detection device 8 for the optical detection of at least part of a discharge 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 is introduced. The liquid chamber 15 is fluidically connected to the discharge channel 16.

[0061] The optical detection device 8 has an imaging device (not shown), such as a camera, for generating an image of the at least one part of the discharge channel 16 and further optical elements (not shown) for the guiding of light. To generate an image, the at least one part of the discharge channel 16 is illuminated by means of an illumination light 17 and a detection light 18 emanating from the at least one part of the discharge channel 16 is detected by the optical detection device 8. The imaging device generates an image of the at least one part of the discharge channel 16 based on the detected detection light 18.

[0062] The optical detection device 8 is electrically connected to an evaluation device 9 of a computer 12. The evaluation device 9 can determine the number of cells 3 and/or particles contained in the at least one part of the discharge channel 16 based on the generated image.

[0063] The computer 12 has a classifier 13 that is electrically connected to the evaluation device 9. The classifier 13 is part of an artificial neural network and/or has an artificial neural network. In the classifier 13 is stored an algorithm that generates a result after the image generated by the optical detection device 8 has been generated.

[0064] In addition, the computer 12 has a control apparatus 14. Based on the result from the classifier 13, the control apparatus 14 controls a dispensing process of the dispenser 7. The control apparatus 14 is electrically connected to a displacement device 10. The displacement device 10 can displace the dispenser 7 and/or a container 4 and/or a reject container 5 in such a way that the liquid sample 20 can be discharged into the desired storage location.

[0065] In addition, the control apparatus 14 can control a deflection and/or suction device 11 of the dispensing apparatus 6. The control apparatus 14 can control the deflection and/or suction device 11 in such a way that the dispensed liquid sample 20 is deflected and/or suctioned off if no cells 3 and/or no particles are arranged in the liquid 1 or if a plurality of cells 3 and/or a plurality of particles is arranged in the liquid 1.

[0066] In this case, the control apparatus 14 can control the displacement device 10 and/or the deflection and/or suction device 11 depending on the result of the classifier 13.

[0067] FIG. 1 shows a state in which the dispenser 7 has discharged the liquid sample 20, in particular a droplet, which includes a dead cell 3. The discharged liquid sample 20 is discharged into the reject container 5.

[0068] The dispensing apparatus 6 has an actuating means 19, which is pressed against a section of the dispenser 7 to actuate the dispenser 7. The liquid sample 20, in particular a droplet, is discharged when the actuating means 19 presses against the section of the dispenser 7. The actuating means 19 and the optical detection device 8 lie opposite one another with respect to the dispenser 7. The dispenser 7 consists at least partially of a transparent material, so that at least part of the discharge channel 16 can be detected by means of the optical detection device 8.

[0069] FIG. 2 shows an enlarged illustration of part of the dispenser 7. In particular, FIG. 2 shows an enlarged illustration of the region A of the discharge channel 16 shown in dashed lines in FIG. 1.

[0070] The discharge channel 16 is completely filled with liquid 1 of the liquid sample 20. In this case, only that part of the discharge channel 16 shown in dashed lines in FIG. 2 is viewed by means of the optical detection device 8. The sample region 2 of the liquid sample 20 is arranged in the part of the discharge channel 16 of the dispenser 7 shown in dashed lines. During a dispensing process, the liquid sample 20 is discharged along a deploying direction R. The discharge channel 16 has a nozzle-shaped end at the end thereof remote from that of the fluid chamber 15.

[0071] The cells 3 arranged in the part of the discharge channel 16 move due to the weight in the direction of the nozzle-shaped end facing away from the fluid chamber 15, even if no liquid sample 20 is discharged from the dispenser 7.

[0072] 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 element is determined in a first training step T1. The first training data element contains information on the sample region 2. The first training data element is determined by the optical detection device 8, wherein an image is generated from the first training data element in the optical detection device 8. The figure shows at least that part of the discharge channel 16 that receives the sample region 2.

[0073] After the first training data element has been determined, the liquid sample 20 is discharged into the container 4 of the microtitre 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 element is then determined again, a further image is generated and the liquid sample 20 is discharged into a further container of the microtitre plate. This process is repeated several times. At the end of the first training step T1, a liquid 1 with a single cell 3 and/or a single particle is arranged in each container 4 of the microtitre plate, it being known which cell 3 is arranged in which container 4.

[0074] After the first training data elements have been determined, second training data elements are determined in a second training step T2. For this purpose, at least one cell property and/or particle property of the cell 3 located in the container 4 is measured. In particular, it can be measured how fast the cells 3 grow in the individual containers 4 and thus a conclusion can be drawn about the cell condition and/or it can be determined which cell types are contained in the containers 4. This is repeated for all containers in which a liquid sample 20 and thus a cell 3 is contained. The second training data elements can be determined a few days after the liquid samples 20 have been discharged into the containers 4. A microscope and/or an automated plate reader can be used to measure the cell property and/or the particle property.

[0075] In a third training step T3, at least two classes are formed. The classes depend on the second training data elements, in particular on the cell property and/or the particle property. The cell property can be a cell type, for example, so that the individual classes in the cell types differ from one another. Alternatively, the cell property can be the cell state so that the classes differ from one another in whether the cells are dead or alive. After the classes have been formed, 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 T1, T2.

[0076] In a fourth training step T4, at least one second training data element is assigned to each first training data element. In particular, at least one cell property and/or particle property is assigned to each image of the sample region 2. Thus, in the fourth training step T4, the first training data element is linked to the second training data element. This link is advantageous because the algorithm can thus recognise the relationship between the first training data elements and the second training data elements. For example, the cell property “living cells” can be assigned to all first training data elements for which the measurement carried out in the second training step T2 has shown that the cell in the respective container is not dead and that cell growth is therefore taking place.

[0077] In a fifth training step T5, the classes are formed, and the first training data elements, the second training data elements and the assignment thereof to the first training data elements are used to train the classifier by means of machine learning. The algorithm uses the transmitted information to recognise at least one pattern and/or regularities between the first training data elements and the second training data elements. After the training process has been completed, a trained algorithm is available. This means that the trained algorithm can apply the knowledge it has learned to a supplied data element to use the data element alone to make a prediction or estimate of the cell property and/or the particle property.

[0078] This is explained in more detail with reference to FIG. 4. FIG. 4 shows a method sequence for examining the liquid sample 20 by means of the trained algorithm. In a first method step S1, a data element is determined by means of the optical detection device 8. In addition, in the first method step S1, the optical detection device 8 generates an image from the determined data element that contains the sample region 2.

[0079] In a second method step S2, imperfections are removed from the image.

[0080] In a third method step S3, the evaluation device 9 checks whether the sample region 2 contains a predetermined number of cells 3 and/or particles. This is done using its own algorithm. In particular, the evaluation device 9 checks whether the sample region 2 contains exactly one single cell 3 and/or one single particle.

[0081] If it is determined that the sample region 2 contains a single cell 3 and/or a single particle, the position of the cell 3 and/or the particle in the image is determined in a fourth method step S4. Subsequently, in a fifth method step S5, an image section is generated which completely contains the cell 3 and/or the particle.

[0082] The image signal containing the image section is transmitted to the trained algorithm in a sixth method step S6. In a seventh method step S7, the trained algorithm generates a result based on the supplied image section. The result depends on a classification of the data element, in particular the image, into one of the classes stored in the trained algorithm. Since the classes depend on the cell property and/or particle property, a prediction of the cell property and/or particle property is made through the classification of the image into one of the classes. The image is classified into one of the classes by the classifier 13.

[0083] In a seventh method step S7, the control apparatus 14 controls the displacement device 10 and/or the deflection and/or suction apparatus 11 according to the result, in particular to the classification of the data element into a class.

[0084] If it was determined in the third method step S3 that there are no cells 3 and/or no particles in the liquid sample and/or the number of cells 3 and/or particles is greater than 1, the method steps S3 to S6 are skipped and the liquid sample 20 is discharged into the reject container 5 in the seventh method step S7.

LIST OF REFERENCE SYMBOLS

[0085] 1 Liquid [0086] 2 Sample region [0087] 3 Cell [0088] 4 Container [0089] 5 Reject container [0090] 6 Dispensing apparatus [0091] 7 Dispenser [0092] 8 Optical detection device [0093] 9 Evaluation device [0094] 10 Displacement device [0095] 11 Deflection and/or suction apparatus [0096] 12 Computer [0097] 13 Classifier [0098] 14 Control apparatus [0099] 15 Fluid chamber [0100] 16 Discharge channel [0101] 17 Illumination light [0102] 18 Detection light [0103] 19 Actuating means [0104] 20 Liquid sample [0105] R Deploying direction [0106] T1-T5 First to fifth training step [0107] S1-S8 First to eighth method step