# US 2023/0266221 A1 · Systems, devices, and methods for quality control of cell production using predictive tagging > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0266221 A1 - Document ID: US20230266221A1 - Status: Published application - Kind: A1 - Application number: 18/041,086 - Filed: 2021-08-11 - Published: 2023-08-24 - Earliest claimed: 2020-08-12 - Assignees: Allen Institute - Applicants: Allen Institute - Inventors: Gregory JOHNSON; Chawin OUNKOMOL; Forrest COLLMAN; Sharmishtaa SESHAMANI; Michael KRANDA - Web page: https://patentgrep.com/patent/US20230266221A1 ## CPC - G01N 15/0227: using imaging; using holography - G01N 15/1429: Signal processing - G01N 15/1433: using image recognition - G01N 2015/0294: {Particle shape} - G01N 2015/1006: {for cytology} - G01N 2015/1445: {Three-dimensional imaging, imaging in different image planes, e.g. under different angles or at different depths, e.g. by a relative motion of sample and detector, for instance by tomography} - G01N 2015/1486: {Counting the particles} - G01N 2015/1493: {Particle size} - G01N 2015/1497: {Particle shape} - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) ## Abstract The present invention provides various methods for easily assessing cell quality of a cell production process, suitably using non-invasive visual methods and neural networks for generating predictive fluorescence images of cells to assess quality attributes. Also provided are systems and methods for carrying out such processes. ## Claims ### Claim 1 A method for assessing cell quality during a cellular production process, the method comprising: a. obtaining a 3D microscopy image that is a transmitted light image of one or more structures of a cell during the cellular production process, wherein no fluorescence labeling is included in the 3D microscopy image; b. generating, with a neural network, a predicted 3D fluorescence image that includes predicted fluorescence labeling of the one or more structures of the cell; c. identifying a quality attribute of the cell based on the predicted 3D fluorescence image; and d. assessing the quality attribute against a comparison set for the cell as a measure of cell quality. ### Claim 2 (depends on 1) The method of claim 1, wherein the transmitted light image of the structures of the cell is obtained in real-time during the cellular production process. ### Claim 3 (depends on 1, 2) The method of claim 1 or claim 2, wherein the transmitted light image of the structures of the cell is obtained without removing the cell from the cellular production process. ### Claim 4 (depends on 1, 2, 3) The method of any one of claims 1-3, wherein the one or more structures include at least one of a cell membrane, a plasma membrane, a nucleus, a pronucleus, a nuclear fragment, a mitochondrion, a microtubule, an endoplasmic reticulum, a vacuole, an endosome, an autophagosome, a Golgi Apparatus, a membrane protein, a plasma membrane bleb, or a lysosome. ### Claim 5 (depends on 1, 2, 3, 4) The method of any one of claims 1-4, wherein the cellular production process is an automated process. ### Claim 6 (depends on 1, 2, 3, 4, 5) The method of any one of claims 1-5, wherein the transmitted light image is obtained prior to a transduction or a transfection step of the cellular production process. ### Claim 7 (depends on 1, 2, 3, 4, 5) The method any one of claims 1-5, wherein the transmitted light image is obtained following a transduction or a transfection step of the cellular production process. ### Claim 8 (depends on 1, 2, 3, 4, 5) The method of any one of claims 1-5, wherein the transmitted light image is obtained following an expansion step of the cellular production process. ### Claim 9 (depends on 1, 2, 3, 4, 5) The method of any one of claims 1-5, wherein the transmitted light image is obtained following a selection step of the cellular production process. ### Claim 10 (depends on 1, 2, 3, 4, 5) The method of any one of claims 1-5, wherein the transmitted light image is obtained following an in vitro fertilization step. ### Claim 11 (depends on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) The method of any one of claims 1-10, wherein the quality attribute is one or more of an average or median cell size, a cell viability, a cell confluency, a cell count, a cell morphology, a cell cycle phase, a cell growth rate, a cell integrity, a membrane integrity, an organelle integrity, a mitochondrial activation level, the presence or absence of a mycoplasmal, bacterial, fungal or viral contaminant, a fertilization event, a differentiation state, a batch to batch variability, or a quantity of transgenic protein produced. ### Claim 12 (depends on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) The method of any one of claims 1-11, wherein the cellular production process produces Chimeric Antigen Receptor (CAR) T-cells, Chinese Hamster Ovary (CHO) cells, Human Embryonic Kidney (HEK) cells, HeLa cells, Jurkat T-cells, fertilized zygotes, fertilized embryos, red blood cells, or stem cells. ### Claim 13 A method for assessing cell quality during a cellular production process, the method comprising: a. obtaining a 3D microscopy image that is a transmitted light image of one or more structures of a cell in real-time during the cellular production process, wherein no fluorescence labeling is included in the 3D microscopy image, and wherein the cell is not removed from the cellular production process during the obtaining of the 3D microscopy image; b. generating, with a neural network, a predicted 3D fluorescence image that includes predicted fluorescence labeling of the one or more structures of the cell; c. identifying a quality attribute of the cell based on the predicted 3D fluorescence images, wherein the quality attribute is one or more of an average or median cell size, a cell viability, a cell confluency, a cell count, a cell morphology, a cell cycle phase, a cell growth rate, a cell integrity, a membrane integrity, an organelle integrity, a mitochondrial activation level, the presence or absence of a mycoplasmal, bacterial, fungal or viral contaminant, a fertilization event, a differentiation state, a batch to batch variability, or a quantity of transgenic protein produced; and d. assessing the quality attribute against a comparison set for the cell as a measure of cell quality. ### Claim 14 (depends on 13) The method of claim 13, wherein the one or more structures include at least one of a cell membrane, a plasma membrane, a nucleus, a pronucleus, a nuclear fragment, a mitochondrion, a microtubule, an endoplasmic reticulum, a vacuole, an endosome, an autophagosome, a Golgi Apparatus, a membrane protein, a plasma membrane bleb, or a lysosome. ### Claim 15 (depends on 13, 14) The method of claim 13 or claim 14, wherein the cellular production process is an automated process. ### Claim 16 (depends on 13, 14, 15) The method of any one of claims 13-15, wherein the transmitted light image is obtained prior to a transduction or a transfection step of the cellular production process. ### Claim 17 (depends on 13, 14, 15) The method any one of claims 13-15, wherein the transmitted light image is obtained following a transduction or a transfection step of the cellular production process. ### Claim 18 (depends on 13, 14, 15) The method of any one of claims 13-15, wherein the transmitted light image is obtained following an expansion step of the cellular production process. ### Claim 19 (depends on 13, 14, 15) The method of any one of claims 13-15, wherein the transmitted light image is obtained following a selection step of the cellular production process. ### Claim 20 (depends on 13, 14, 15) The method of any one of claims 13-15, wherein the transmitted light image is obtained following an in vitro fertilization step. ### Claim 21 (depends on 13, 14, 15, 16, 17, 18, 19, 20) The method of any one of claims 13-20, wherein the cellular production process produces chimeric antigen receptor (CAR) T-cells, Chinese Hamster Ovary (CHO) cells, Human Embryonic Kidney (HEK) cells, HeLa cells, Jurkat T-cells, fertilized zygotes, fertilized embryos, red blood cells, or stem cells. ### Claim 22 A system for assessing cell quality during a cellular production process, comprising: a. a bioreactor for carrying out the cellular production process; b. a microscope configured to obtain a 3D microscopy, transmitted light image of a structure of a cell in the bioreactor; c. a communication interface configured to receive the 3D microscopy, transmitted light image; d. a processor; and e. a non-transitory computer-readable medium communicatively coupled to the communication interface and to the processor, and storing computer-executable instructions that, when executed by the processor, causes the processor to: i. generate, with a neural network, a predicted 3D fluorescence image that includes predicted fluorescence labeling of the one or more structures of the cell; ii. identify a quality attribute of the cell based on the predicted 3D fluorescence image; and iii. assess the quality attribute against a comparison set for the cell as a measure of cell quality. ### Claim 23 (depends on 22) The system of claim 22, wherein the bioreactor is a feed-batch bioreactor. ### Claim 24 (depends on 22) The system of claim 22, wherein the bioreactor is a perfusion bioreactor. ### Claim 25 (depends on 22, 23, 24) The system of any one of claims 22-24, wherein the bioreactor is an automated bioreactor. ### Claim 26 (depends on 22) The system of claim 22, wherein the bioreactor is an in vitro fertilization chamber. ### Claim 27 (depends on 22, 23, 24, 25, 26) The system of any one of claims 22-26, wherein the microscope is configured to obtain the 3D microscopy, transmitted light image without removal of the cell from the bioreactor. ### Claim 28 (depends on 22, 23, 24, 25, 26, 27) The method of any one of claims 22-27, wherein the microscope is directly coupled to the bioreactor.