Cell selection

Abstract

This disclosure relates to the use of dielectric spectroscopy for selecting cells, typically cells engineered by recombinant methods, but also to hybridoma cell lines that produce antibodies and express peptides such as antibodies; CAR-T cells; induced pluripotent stem (IPS) cells; and cells derived from IPS cells.

Description

[0001] Invention Field This disclosure relates to the use of dielectric spectroscopy for selecting cells, typically cells engineered by recombinant methods, but also to hybridoma cell lines that produce antibodies and express peptides such as antibodies; CAR-T cells, induced pluripotent stem (IPS) cells and cells derived from IPS cells; and host cells that express viral peptides. Background of the Invention The expression of recombinant proteins, including monoclonal antibodies, provides therapeutic agents such as therapeutic antibodies, pharmaceutical proteins (e.g., protein hormones), and antigens for vaccines. Bacterial systems are often the preferred protein expression host due to their low maintenance costs and ease of handling. However, bacterial expression systems cannot provide accurate post-translational modifications for therapeutic agents, leading to the development of alternative expression systems such as yeast, insect cells, plant, and mammalian expression systems. Currently, approximately 70% of recombinant protein drugs, as well as most proteins used for vaccination, human treatment, or diagnostics, are produced in mammalian cells such as Chinese hamster ovary cells (CHO). However, maintaining mammalian cell cultures for expressing therapeutic proteins, including monoclonal antibodies produced via hybridomas, is significantly more expensive than bacterial expression systems.

[0003] Over the past decade, the demand for the preparation of therapeutic proteins in biological systems has increased dramatically, necessitating systems capable of efficiently facilitating this process. Therapeutic agents (such as therapeutic antibodies), pharmaceutical proteins (such as protein hormones), and antigens for vaccines are obtained through recombinant protein expression in various cell types, such as bacteria or mammalian cells. While bacterial systems are generally preferred due to their low maintenance costs, alternative expression systems such as yeast, insect cells, plant, and mammalian expression systems become crucial when proteins require post-translational modifications.

[0004] The expression of therapeutic proteins is challenging due to their characteristics, such as the need for post-translational modifications and the complex requirements of cell culture. The cell culture process involves small-scale cell line selection and culture medium optimization, and once the desired protein is successfully produced, the system needs to be scaled up to bring the production of therapeutic proteins to an industrial level.

[0005] For industrial-scale protein expression, several optimization steps are required, starting with transfecting cells with a vector encoding the protein to be expressed, followed by culturing these modified cells and selecting cells using selection markers. Once a stable cell line expressing the protein of interest is identified, the scale-up process can begin. Identifying suitable candidates for protein expression at the desired levels can involve screening thousands of cell lines using assays such as ELISA, which is very labor-intensive and costly. Furthermore, even if cells express the desired protein at a small scale, the same cell may not express it adequately at scale-up, and there is no easy way to test which cells perform efficiently under different conditions.

[0006] Analytical methods for measuring cells are known in the art. For example, fluorescence-activated cell sorting (FACS) is one example of such a method. This technique uses flow cytometry combined with a fluorescent dye, typically delivered to cells via an antibody conjugate containing the fluorescent dye, which binds to cellular proteins, such as cell membrane localization receptors (cellularly characteristic). The stained cells are then passed through a flow cell where they are irradiated with a laser beam. As the cells pass through the laser, their fluorescence is detected and analyzed in real time. Based on the fluorescence properties, the cells can be sorted and separated for further analysis.

[0007] An alternative label-free method for cell analysis utilizes impedance or dielectric spectroscopy to measure the characteristics of a cell population. Impedance or dielectric spectroscopy involves applying an electrodynamic field to a solution containing cells and measuring the field changes caused by the presence of cells, such as those caused by the complex permittivity of the cells. When measuring cells using a measuring device, the fluid medium surrounding the cells is also measured. The influence of measuring the surrounding fluid medium is addressed by performing two measurements simultaneously: a first measurement of the cells and the fluid medium containing the cells, and a second measurement of the fluid medium without cells. The difference between the first and second measurements is then determined to provide the cell measurement. Therefore, the measuring device includes a first measuring component for performing the first measurement and a second measuring component for performing the second measurement, the first and second measuring components operating simultaneously.

[0008] In measurements involving the application of a stimulus to a sample and the measurement of the response to that stimulus, such as in impedance or dielectric spectroscopy, the amplitude of the stimulus is typically much larger than the amplitude of the response. If the first and second measurements are not time-aligned, partial stimuli will appear in the difference between the first and second measurements, and the amplitude of these partial stimuli is much larger than the amplitude of the response. Furthermore, these partial stimuli occupy a much larger portion of the available voltage range of the processing electronics compared to the response. Therefore, the response to the stimulus reduces the utilization of the available voltage range of the processing electronics.

[0009] WO2015 / 001355 discloses an example of the use of impedance or dielectric spectroscopy, the contents of which are incorporated herein by reference in their entirety. It describes a spectroscopic system that allows for label-free cell analysis for the detection and analysis of cells. This system enables users to identify cells based on their inherent dielectric properties, providing them with unique identifiers for subsequent downstream analysis and characterization, without the need for cell labeling or lasers.

[0010] This disclosure relates to methods for analyzing cells expressing peptides (typically therapeutic peptides such as antibodies), using impedance or dielectric spectroscopy to aid in the identification of high-titer expression cell lines, such as recombinant cell lines or hybridomas. A problem associated with the production of recombinant peptides and the acquisition of monoclonal antibodies from isolated hybridoma cell lines is the variability in yield obtained from cell lines or hybridomas. This disclosure provides a rapid and simple method for assessing peptide yields in recombinant cell lines or hybridomas to identify high-yielding cell lines compared to low or moderate-yielding cell lines in a label-free environment. This disclosure also relates to the analysis of other cell types, such as CAR-T cells, pluripotent embryonic stem cells, induced pluripotent stem cells, and cells expressing viral proteins, such as adeno-associated virus (AAV) and lentiviral proteins.

[0011] Invention Statement According to an aspect of the present invention, a method is provided for identifying eukaryotic cell clones transfected or transduced with a nucleic acid molecule suitable for expressing at least one recombinant polypeptide or peptide, comprising the following steps: i) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to untransfected eukaryotic cells; ii) Sensing the response in the electrodynamic field through at least one sensing electrode, providing a corresponding electrical response signal to the sensing circuit, and obtaining a first frequency value; iii) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to the eukaryotic cell clone described in i) above, wherein the eukaryotic cell clone is transfected with a nucleic acid molecule suitable for expressing at least one recombinant polypeptide or peptide; iv) Sensing the response in the electrodynamic field through at least one sensing electrode, providing a corresponding electrical response signal to the sensing circuit, and obtaining a second frequency value, and v) Compare the first and second frequency values ​​to obtain a frequency difference, wherein the frequency difference indicates that the eukaryotic cell expresses the polypeptide or peptide.

[0012] In a preferred method of the present invention, the nucleic acid molecule encodes an antibody or antibody fragment.

[0013] In a preferred method of the present invention, the antibody or antibody fragment is a therapeutic antibody or antibody fragment.

[0014] In the alternative preferred method of the present invention, the nucleic acid molecule encodes a pharmacologically active polypeptide or peptide.

[0015] In a preferred method of the present invention, the nucleic acid molecule encodes one or more viral polypeptides.

[0016] In a preferred method of the present invention, the one or more viral polypeptides are selected from the group consisting of adenovirus polypeptides, adeno-associated virus (AAV) polypeptides, and lentivirus polypeptides.

[0017] Preferably, the one or more adeno-associated virus polypeptides are derived from the group consisting of AAVs: AAV2, AAV3, AAV6, AAV13; AAV1, AAV4, AAV5, AAV6, AAV9, and AAVrh10.

[0018] In a preferred method of the present invention, the nucleic acid molecule encodes a chimeric T-cell receptor.

[0019] In a preferred method of the present invention, the nucleic acid molecule encodes one or more pluripotency-related genes.

[0020] Preferably, the nucleic acid molecule encoding one or more pluripotency-related genes is expressed transiently.

[0021] In a preferred method of the present invention, the eukaryotic cell is a mammalian cell.

[0022] Preferably, the mammalian cells are Chinese hamster ovary cells. Alternatively, the mammalian cells are HEK cells.

[0023] In a preferred method of the present invention, the mammalian cell is a somatic cell.

[0024] In a preferred method of the present invention, the eukaryotic cell is a fungal cell.

[0025] In a preferred method of the present invention, the eukaryotic cell is a plant cell.

[0026] In a preferred method of the present invention, the eukaryotic cell is an insect cell.

[0027] According to a further aspect of the invention, impedance or dielectric spectroscopy is provided for the identification and analysis of mammalian cells transfected with nucleic acid molecules encoding recombinant polypeptides or peptides.

[0028] In a preferred embodiment of the present invention, the recombinant polypeptide or peptide is an antibody or antibody fragment expressed at a high antibody titer by mammalian cells.

[0029] High antibody titers refer to antibody yields in the range of 4-10 g / L, more preferably in the range of 5-8 g / L. Furthermore, the identified cells exhibited stable antibody expression.

[0030] In a preferred embodiment of the present invention, the recombinant polypeptide or peptide is a pharmacologically active polypeptide or peptide.

[0031] In a preferred embodiment of the present invention, the recombinant polypeptide is a viral polypeptide.

[0032] In a preferred embodiment of the present invention, the recombinant polypeptide is a chimeric T-cell receptor.

[0033] In a preferred embodiment of the invention, the mammalian cell is a stem cell.

[0034] In a preferred embodiment of the invention, the stem cells are pluripotent stem cells, such as embryonic stem (ES) cells, embryonic germ (EG) cells, or induced pluripotent stem cells.

[0035] In a preferred method of the present invention, the stem cells are lineage-restricted stem cells, such as pluripotent stem cells.

[0036] Preferably, the lineage-restricted stem cells are selected from the group consisting of: hematopoietic stem cells, such as macrophages; neural stem cells; bone stem cells; muscle stem cells; mesenchymal stem cells; trophoblast stem cells; epithelial stem cells (derived from organs such as skin, gastrointestinal mucosa, kidneys, bladder, breast, uterus, prostate, etc., and endocrine glands such as pituitary gland); endoderm stem cells (derived from organs such as liver, pancreas, lungs, and blood vessels); and muscle cells, such as cardiomyocytes.

[0037] In a preferred method of the present invention, the pluripotent stem cells are hematopoietic stem cells.

[0038] In a preferred method of the present invention, the hematopoietic stem cells are monocytes capable of differentiating into macrophages. Preferably, the hematopoietic stem cells are obtained from human subjects.

[0039] In an alternative embodiment of the present invention, the polypeptide or peptide is a pharmacologically active polypeptide or peptide, rather than an antibody.

[0040] In a further alternative embodiment of the invention, the recombinant cell line is adapted to produce antigenic polypeptides or peptides for use in vaccines.

[0041] Cells expressing polypeptides or peptides can be carried in a fluid material. A predetermined electrodynamic field can be applied to the fluid material, such as the fluid material carrying the cells. The electrical stimulation signal and the resulting predetermined electrodynamic field can include a signal packet, with at least one signal packet applied to each cell to be measured.

[0042] The at least one stimulating electrode and the at least one sensing electrode can be arranged for impedance or dielectric spectroscopy. The at least one stimulating electrode and the at least one sensing electrode can be arranged to form a measuring capacitor, wherein the fluid material and any cells therein are situated in an electric field between the electrodes.

[0043] The measurement capacitor can have an effective surface configured to be electrically coupled to a fluid material and any cell, for example, such that the fluid material and any cell form a dielectric of the measurement capacitor. The effective surface of the measurement capacitor can be exposed to the fluid material without an intermediate layer. The effective surface of the measurement capacitor can also conduct into the fluid material.

[0044] The electrodes can be generally cubic, and the effective surface of the electrodes can be square or rectangular. The effective surface of the electrodes can be flat. The at least one stimulation electrode and the at least one sensing electrode can be disposed on the exposed surface of a semiconductor and can be formed in an integrated circuit formed by semiconductor manufacturing processes (e.g., CMOS).

[0045] The electrodes of the measuring capacitor can be positioned on or towards the side of the flow channel, for example, perpendicular to the direction of fluid flow. The electrodes of the measuring capacitor can be arranged side-by-side. This same-side arrangement may be suitable for cases where cell stimulation devices are incorporated into planar semiconductor integrated circuits (e.g., CMOS integrated circuits).

[0046] With the at least one stimulating electrode and the at least one sensing electrode arranged side by side with their effective surfaces in a generally planar manner, fluid material and any cells therein can flow on the effective surfaces of the electrodes.

[0047] The electrodes of the measuring capacitor can be in the form of an electrode array formed on the surface of a semiconductor device. The device may include multiple electrode arrays, i.e., multiple measuring capacitors. The electrodes can be formed from conductive plates (e.g., metal plates) exposed to a flow of fluid material. The fabrication process of the electrode array may omit the polyimide layer deposition step, thus eliminating the need for a polyimide top layer. The electrode array may have a passivation layer (e.g., a silicon nitride layer), but may not have any passivation layer above the electrodes, allowing the electrodes to conduct into the fluid material. The hydrophilicity of the passivation layer provides maximum exposure, and openings in the passivation layer between the electrodes and the fluid material enable conduction between them. The size of the electrodes is selected based on the size of the cells being measured.

[0048] The sensing circuit may include a high-impedance input, thereby providing a significant sensing signal. More specifically, the sensing circuit may include an impedance buffer, such as a field-effect transistor (FET). The FET can provide a capacitive load, such as a sensing electrode for a sensing device, which can be selected to be as small as possible. Alternatively or additionally, the sensing circuit may provide one of a voltage signal and a current signal as an output signal.

[0049] Alternatively or additionally, the sensing circuit may be configured to amplify the input signal. The measuring device may be configured to compare an electrical stimulation signal with an electrical response signal, such as one sensed by the sensing device. Comparing the electrical stimulation signal with the electrical response signal may include cross-correlation of the electrical stimulation signal and the electrical response signal. The measuring device may be used to determine the time delay between the application of the electrical stimulation signal and the response provided by at least one cell in the fluid material. The measuring device may also be used to determine, or at least approximately determine, a transfer function for said at least one cell based on the time delay. The determined or approximately determined transfer function can then provide a characterization of said at least one cell.

[0050] The predetermined electrodynamic field applied to the cell according to the present invention may include at least one corresponding frequency component. A sensing device may be configured to sense in response to the frequency component included in the electrodynamic field, as sensed by a cell sensing device.

[0051] The electrical stimulation signal may include a pseudo-random binary sequence having a length and a data rate, which provides a series of frequencies for a predetermined electrodynamic field. The measuring device may be configured to generate a pseudo-random noise signal. The pseudo-random noise signal may be generated from a pseudo-random binary sequence.

[0052] Pseudo-random binary sequences are generated using deterministic algorithms. However, they exhibit statistical behavior similar to truly random sequences. A pseudo-random binary sequence can be a maximum-length sequence, the so-called "m-sequence," which can be generated by a linear feedback shift register. In the case of a binary sequence generated by a linear feedback shift register, the output will eventually repeat itself. For a given number of shift registers, the m-sequence is the longest possible non-repeating sequence.

[0053] Therefore, pseudo-random noise signals can contain m-sequences. M-sequences exhibit a flat power spectral density over the desired operating bandwidth. Furthermore, m-sequences can be readily provided by standard digital circuitry, and are therefore potentially suitable for implementation in integrated circuits formed using semiconductor manufacturing processes such as CMOS.

[0054] The stimulation circuit can be used to generate stimulation signals in the form of an m-sequence via a linear feedback shift register or in other ways within the conventional design capabilities of those skilled in the art. For example, the stimulation circuit may include a memory storing the m-sequence. Alternatively, the m-sequence may be provided by an external signal generator.

[0055] The length of each m sequence is 2. n – 1, of which n This is the number of registers in the linear feedback shift register. The frequency range provided by the pseudo-random binary sequence can be determined by the length of each pseudo-random binary sequence (determined by the number of bits) and the bit rate of the electrical stimulation signal.

[0056] The frequency range of a pseudo-random binary sequence is determined by the length of the pseudo-random binary sequence and the binary signal achievable by the bit rate of the electrical stimulation signal. The highest frequency in the frequency range can be an alternating sequence of adjacent bits, with a frequency equal to half the bit rate. The lowest frequency can be a constant value over the entire sequence length, with a frequency equal to the bit rate divided by the sequence length.

[0057] The measuring device may include a processing device, which may include stimulation circuitry and / or sensing circuitry. The processing device and / or sensing circuitry may be configured to receive an electrical response signal and convert it into a digital form. Therefore, the processing device and / or sensing circuitry may have analog inputs and digital outputs, such that the output is a digital response signal.

[0058] Therefore, the processing device and / or sensing circuitry may include analog-to-digital converters and any signal conditioning circuitry that may be required, such as amplifiers and anti-aliasing filters. The processing device may be composed of any suitable electronic equipment, such as stand-alone analog-to-digital converter circuitry, stand-alone amplifier circuitry, and stand-alone filter circuitry, or configurable integrated circuits, such as FPGAs, or dedicated integrated circuits that include such circuitry, such as application-specific integrated circuits (ASICs).

[0059] The same clock can be used to generate the electrical stimulation signal and the sampling rate of the processing device. This ensures that the electrical stimulation signal does not drift over time relative to the digital response signal. However, the sampling rate of the processing device can differ from the bit rate of the electrical stimulation signal, as discussed in more detail below. The sampling rate of the processing device and the bit rate of the electrical stimulation signal can also be expressed in terms of the sampling period of the processing device and the bit period of the electrical stimulation signal. When the clock used to generate the electrical stimulation signal and the clock used for sampling are different, the corresponding clocks are preferably derived from the same clock.

[0060] The digital response signal can be stored in memory. The processing device and / or sensing circuitry may include a processor and can be used to decode the received output signal, which may be m-sequence encoded data.

[0061] The impulse response can be calculated by applying the Hadamard transform (e.g., the Fast Hadamard transform). This provides a fast way to calculate the impulse response. The Fast Hadamard transform is given by the following equation:

[0062] Where Ψ' is the estimated output spectrum of the system under test, and m is the sequence order. H It is a Hadamard matrix. η ξ1 and ξ2 are the encoded responses of the measured m-sequence, and are the encoding and decoding matrices used to convert the m-sequence data into the correct order for use with the Hadamard matrix. In one form, ξ1 and ξ2 are equal to each other.

[0063] Alternatively, the analysis device can be configured to decode the received output signal, which can be m-sequence encoded data, for example, through cross-correlation. The analysis device can be constructed from any suitable electronic device, such as a general-purpose computer, like a personal computer (PC).

[0064] The analysis equipment can also be used to perform Fourier transforms on the decoded output signal, such as Fast Fourier Transform (FFT), to provide frequency domain data. This frequency domain data can then be displayed for user interpretation.

[0065] The measuring device is operable and may also be configured to be label-free. Therefore, biosensing devices can operate on fluid materials without any labels, such as fluorescent dyes or microbeads.

[0066] The measuring device is operable for, and may also be configured for, sensing microbial samples. The measuring device can be configured for cell size or a range of cell sizes, relating to the size of at least one of the stimulating and sensing devices (e.g., the size of at least one electrode). More specifically, the size of at least one of the stimulating and sensing devices may correspond to the cell size or a range of cell sizes.

[0067] Semiconductor manufacturing processes can be planar semiconductor manufacturing processes. Alternatively or additionally, semiconductor manufacturing processes can be metal-oxide-semiconductor processes, such as CMOS processes. Alternatively or additionally, semiconductor manufacturing processes can be submicron semiconductor manufacturing processes, such as 0.18-micron CMOS processes, and perhaps also high-voltage 0.18-micron CMOS processes.

[0068] Fluid materials can be essentially liquids, for example, at room temperature. Therefore, fluid materials can contain liquids that carry cells. More specifically, fluid materials can contain charge carriers, such as salt molecules. For example, fluid materials can contain phosphate-buffered saline (PBS).

[0069] The measuring device may include a flow device or be adapted to operate with a flow device that provides flow of fluid material and cells in flow channels. In use, a sample of fluid material (e.g., a cell sample) is introduced into the flow device, which is configured to contain the fluid material and provide flow of the fluid material through the flow channels. For example, the flow device may define an open fluid channel that contains the fluid material and allows flow, such as when flow is generated by a pump. Alternatively or additionally, the flow device may be configured to self-drive the flow. More specifically, the flow device may be configured to draw fluid material into the flow device via capillary action.

[0070] The flow device may define a main channel through which a fluid material flows during use. A sensing device may be positioned relative to the main channel to provide sensing of cells present in the main channel. The sensing device may be positioned on at least one of a first or second opposing side of the fluid material flow. Thus, for example, components of the sensing device (such as sensing electrodes) may be positioned on one side of the fluid material flow. According to another example, components of the cell sensing device may be positioned on both sides of the fluid material flow.

[0071] The flow apparatus may include a sample inlet configured to receive a sample of fluid material to be measured, for example by injection, and the sample inlet is in fluid communication with a main channel. The flow apparatus may include a sample outlet located at the end of the flow apparatus opposite the sample inlet, and the sample outlet is in fluid communication with the main channel. The sample outlet can provide the outflow of fluid material from the main channel.

[0072] The flow device may include at least one additional inlet positioned transversely to the sample inlet. More specifically, the flow device may include first and second additional inlets, wherein the first inlet is transversely positioned to one side of the sample inlet, and the second inlet is transversely positioned to the opposite side of the sample inlet. The at least one additional inlet may be in fluid communication with the main channel. In use, sheath fluid (e.g., phosphate-buffered saline (PBS)) may be received by the at least one additional inlet, thereby providing a sheath fluid flow in the main channel, which is located transversely to the fluid material flow. The sheath fluid flow can provide alignment of the cell-containing fluid material with the cell-sensing device and can also help maintain the integrity of the fluid material flow as it passes through the flow device.

[0073] The flow device may be formed of glass and / or at least partially of a polymer (e.g., poly(methyl methacrylate) (PMMA)). The stimulation and sensing devices may be formed separately from the flow device. The stimulation and sensing devices may be positioned relative to the flow device by attaching them to each other. The stimulation and sensing devices and the flow device may be bonded together, for example by appropriate chemical or physical bonding, or they may be mechanically attached to each other, for example by fastener devices including a releasable silicone gasket layer. Appropriate chemical or physical bonding may include plasma bonding.

[0074] The cell measurement device can be configured for use as a flow cytometer. The measurement device may further include control equipment. The control equipment can consist of any suitable electronic device, such as a microprocessor or configurable electronic circuitry, such as a field-programmable gate array (FPGA).

[0075] The measuring device may include a flow-inducing device, i.e., a pump, which is used to induce the flow of fluid material through the flow device. The flow-inducing device can be controlled, for example, with respect to the flow rate of the fluid material through the flow device, depending on the output of the sensing device. A control device may be used to receive the output from the sensing device and, accordingly, provide an output to the flow-inducing device.

[0076] As described elsewhere in this document, the sensing device can be used to determine the flow rate of a fluid material through a flow device (the flow rate received by a control device). In cases where the sensing device comprises multiple spaced-apart sensing electrodes, each used to sense a cell, the flow rate can be determined based on the known spacing between the sensing electrodes and the time between when different sensing electrodes sense a cell.

[0077] Alternatively or additionally, the characterization of at least one cell, as described elsewhere herein, can be compared to predetermined criteria, and the flow-inducing device can be controlled based on this comparison. For example, the characterization of the at least one cell may include a confidence level value compared to a predetermined value. More specifically, if the confidence level value is lower than the predetermined value, the flow-inducing device may be used to reduce the flow rate of the fluid material, thereby providing improved characterization.

[0078] The processing device can provide an output signal, such as a digital output signal. The output signal can be stored in the memory of the measuring device. The measuring device may include an output for sending the output signal to an analysis device, which may or may not be integrated with the measuring device.

[0079] The analytical device can be configured to determine the cells contained in a fluid material based on at least one output from a cell sensing device. For example, the analytical device can be used to make the determination based on an electric field measurement performed by the cell sensing device after analog-to-digital conversion. Determinations can be made regarding aspects such as the cell density contained in the fluid material, the distinction between one type of cell and another (e.g., related to endogenous or recombinant expression), and cell characteristics (e.g., regarding their endogenous or recombinant expression). The analytical device can be constructed from any suitable electronic device, such as a general-purpose computer (e.g., a personal computer (PC)), an embedded microprocessor, configurable electronic circuitry (e.g., an FPGA), etc. Further embodiments of this aspect of the invention may include one or more additional features of the first aspect of the invention.

[0080] Sensor chips can be configured to be installed within bioreactors, ranging from micro-bioreactors to large-scale bioreactors. Micro-bioreactors are particularly suitable for personalized medicine, where small batches may be required on a per-patient basis.

[0081] According to a further aspect of the present invention, a method for screening differential peptide or polypeptide expression in cell lines is provided, comprising the following steps: i) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to a first cell expressing a polypeptide or peptide; ii) Sensing the response in the electrodynamic field through at least one sensing electrode and providing a corresponding electrical response signal to the sensing circuit; iii) Providing an electrical stimulation signal to at least one stimulating electrode, causing the at least one stimulating electrode to generate a predetermined electrodynamic field, said electrodynamic field being applied to a second, different cell expressing the same polypeptide or peptide; and iv) Sensing the response in the electrodynamic field through at least one sensing electrode and providing a corresponding electrical response signal to the sensing circuit, and v) Compare the responses of the first cell and the second different cell as a measure of the expression of the polypeptide or peptide in each cell.

[0082] According to a further aspect of the present invention, a method for monitoring the expression of recombinant antibodies in mammalian cells during cell culture is provided, comprising the following steps: i) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to cells expressing the recombinant antibody; ii) Sensing the response in the electrodynamic field through at least one sensing electrode, providing a corresponding electrical response signal to the sensing circuit, and obtaining a first frequency value at a first time point; iii) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to the cell expressing the recombinant antibody; iv) Sensing the response in the electrodynamic field through at least one sensing electrode and providing a corresponding electrical response signal to the sensing circuit, and obtaining a second frequency value at a second time point or another time point; and v) Compare the first and second or additional frequency values ​​to obtain a frequency difference, wherein the frequency difference indicates that the mammalian cells express the recombinant antibody as a measure of expression during cell culture.

[0083] In a preferred method of the present invention, the stability of expression is measured during cell culture.

[0084] In a preferred method of the present invention, the mammalian cells are Chinese hamster ovary cells.

[0085] In the preferred alternative method of the present invention, the mammalian cell is a HEK cell. Detailed Implementation

[0086] Recombinant antibodies Antibodies include polyclonal antibodies, monoclonal antibodies, humanized antibodies, and chimeric antibodies, as well as derived fragments containing complementarity-determining regions (CDRs). Chimeric antibodies are recombinant antibodies in which all V regions of a mouse or rat antibody are combined with the C region of a human antibody. Humanized antibodies are recombinant hybrid antibodies that fuse the complementarity-determining region of a rodent antibody's V region with the framework region of a human antibody's V region. The C region of a human antibody is also used. The CDR is a region within the N-terminal domain of the antibody's heavy and light chains, where most variation in the V region is confined. These regions form loops on the surface of the antibody molecule. These loops provide a binding surface between the antibody and the antigen.

[0087] Antibodies derived from non-human animals trigger an immune response to foreign antibodies in the human body and clear them from circulation. Both chimeric and humanized antibodies exhibit reduced antigenicity when injected into human subjects because the amount of rodent (i.e., foreign) antibodies in recombinant hybrid antibodies is reduced, while the human antibody region does not elicit an immune response. This results in a weaker immune response and reduced antibody clearance. This is clearly desirable when using therapeutic antibodies to treat human diseases. Humanized antibodies are engineered to have fewer “foreign” antibody regions and are therefore considered to be less immunogenic than chimeric antibodies.

[0088] Various fragments of antibodies are known in the art. Fab fragments are multimeric proteins composed of immunoglobulin heavy chain variable regions and immunoglobulin light chain variable regions, covalently coupled together and capable of specifically binding antigens. Fab fragments are produced by proteolytic cleavage of intact immunoglobulin molecules (e.g., with papain). Fab2 fragments consist of two linked Fab fragments. When these two fragments are linked by an immunoglobulin hinge region, the F(ab')2 fragment is produced. Fv fragments are multimeric proteins composed of immunoglobulin heavy chain variable regions and immunoglobulin light chain variable regions, covalently coupled together and capable of specifically binding antigens. Fragments can also be single-chain polypeptides containing only one light chain variable region, or fragments containing three CDRs of the light chain variable region without an associated heavy chain variable region, or fragments containing three CDRs of the heavy chain variable region without an associated light chain portion; and multispecific antibodies formed from antibody fragments, as described, for example, in U.S. Patent No. 6,248,516. Fv fragments or single-domain fragments are typically generated by expressing antibody fragments in host cell lines. These and other immunoglobulin or antibody fragments are within the scope of this invention and are found in standard immunology textbooks such as Paul's... Fundamental Immunology Or Janeway and others Immunobiology As described above, molecular biology now allows for the direct synthesis (through expression in cells or chemical means) of these fragments, as well as the synthesis of combinations thereof. Fragments of antibodies or immunoglobulins can also possess the dual-specificity functions described above.

[0089] Pharmacologically active peptides Examples of medicinal proteins include cytokines. Cytokines are involved in a variety of different cellular functions. These include regulating the immune system, regulating energy metabolism, and controlling growth and development. Cytokines mediate their effects through receptors expressed on the cell surface of target cells. Examples of cytokines include interleukins, such as IL1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, and 33. Other examples include growth hormone, leptin, erythropoietin, prolactin, tumor necrosis factor [TNF], granulocyte colony-stimulating factor (GCSF), granulocyte-macrophage colony-stimulating factor (GMCSF), ciliary neurotrophic factor (CNTF), cardiomyocyte nutrient-1 (CT-1), leukemia inhibitory factor (LIF), oncogene M (OSM), interferon α, interferon β, interferon ε, interferon κ, and ω-interferon.

[0090] Examples of pharmacologically active peptides include GLP-1, antidiuretic hormone; oxytocin; gonadotropin-releasing hormone, corticotropin-releasing hormone; calcitonin, glucagon, amylin, natriuretic hormone type A, natriuretic hormone type B, ghrelin, neuropeptide Y, and neuropeptide YY. 3-36 Growth hormone-releasing hormone, somatostatin; or their homologs or analogues.

[0091] The term "chemokine" refers to a group of structure-associated, low-molecular-weight factors secreted by cells that possess mitotic, chemotactic, or inflammatory activities. They are primarily cationic proteins of 70 to 100 amino acid residues, sharing four conserved cysteine ​​residues. These proteins can be divided into two groups based on the spacing between their two N-terminal cysteine ​​residues. In the first group, the two cysteine ​​residues are separated by a single residue (CxC), while in the second group, they are adjacent (CC). Examples of 'Cx-C' chemokine members include, but are not limited to, platelet factor 4 (PF4), platelet basic protein (PBP), interleukin-8 (IL-8), melanoma growth-stimulating protein (MGSA), macrophage inflammatory protein 2 (MIP-2), mouse Mig (m119), chicken 9E3 (or pCEF-4), porcine alveolar macrophage chemokines I and II (AMCF-I and -II), pre-B cell growth-stimulating factor (PBSF), and IP10. Examples of members of the 'C-C' group include, but are not limited to, monocyte chemoattractant protein 1 (MCP-1), monocyte chemoattractant protein 2 (MCP-2), monocyte chemoattractant protein 3 (MCP-3), monocyte chemoattractant protein 4 (MCP-4), macrophage inflammatory protein 1α (MIP-1-α), macrophage inflammatory protein 1β (MIP-1-β), macrophage inflammatory protein 1-γ (MIP-1-γ), macrophage inflammatory protein 3α (MIP-3-α), macrophage inflammatory protein 3β (MIP-3-β), chemokine (ELC), macrophage inflammatory protein-4 (MIP-4), macrophage inflammatory protein-5 (MIP-5), LD78β, RANTES, SIS-ε (p500), thymus and activation-regulated chemokine (TARC), eotaxin, I-309, human protein HCC-1 / NCC-2, and human protein HCC-3.

[0092] Several growth factors that promote / activate endothelial cell angiogenesis have been identified. These include vascular endothelial growth factor (VEGF A); VEGF B, VEGF C, and VEGF D; transforming growth factor (TGFb); acidic and basic fibroblast growth factors (aFGF and bFGF); and platelet-derived growth factor (PDGF). VEGF is an endothelial cell-specific growth factor with a highly specific site of action, promoting endothelial cell proliferation, migration, and differentiation. VEGF is a complex containing two identical 23 kD polypeptides. VEGF can exist in four different polypeptide forms with varying molecular weights, each derived from alternatively spliced ​​mRNA. bFGF is a growth factor that functions to stimulate the proliferation of fibroblasts and endothelial cells. bFGF is a single-chain polypeptide with a molecular weight of 16.5 kDa. Multiple molecular forms of bFGF have been discovered, varying in the length of their N-terminal regions. However, the biological functions of the various molecular forms appear to be the same.

[0093] CAR-T cell therapy Chimeric antigen receptors (CARs) are engineered fusion proteins designed to target antigens (such as antigens on cancer cells) onto T cells (CAR-T cells). This therapy, known as CAR-T therapy, has been effective in treating patients with relapsed B-cell lymphoma, B-cell acute lymphoblastic leukemia, and multiple myeloma. However, several factors directly influence positive outcomes for patients, including the quality and variability of the donor apheresis product or T cells, the CAR-T manufacturing process, product efficacy, and the patient's health condition and disease. To enhance the efficacy of the therapy, researchers are constantly exploring various approaches to optimize the CAR-binding domain for enhanced function and to reduce the immune response to CAR-T therapy by designing CAR-T constructs with low immunogenicity. Furthermore, a better understanding of manufacturing parameters is crucial for ensuring optimal T cell composition (containing less differentiated, primordial T cells, or central memory T cells), optimal process control (the presence of cytokines or inhibitors at key procedural stages), and optimized / shortened culture time to reduce T cell exhaustion and enhance potency. A key step in CAR-T therapy manufacturing is ensuring that the tumor-recognizing genetic material (CAR) is correctly introduced into the T cells. This is known as transduction efficiency and directly affects the functional properties of engineered effector cells, such as proliferation, persistence, antitumor activity, and the safety of CAR-T therapy. Flow cytometry analysis is the standard method for detecting transduction efficiency. However, the time from sampling to obtaining results after measurement can lead to response delays, limiting the ability to make critical process decisions in real time. This study investigates the use of dielectric spectroscopy in the method of this invention to determine transfection efficiency without the need for labeling or labor-intensive assay procedures.

[0094] pluripotent stem cells Pluripotent stem cells have the ability to differentiate into every differentiated or partially differentiated cell type found in an organism. These include embryonic stem cells, embryonic germ cells, lineage-restricted stem cells (also known as pluripotent stem cells), and induced pluripotent stem cells (usually fibroblasts), which have been genetically engineered into a dedifferentiated state and then differentiate into another cell type.

[0095] Typical examples include differentiating pluripotent stem cells or hematopoietic cells into differentiated immune cells, such as macrophages. WO2024 / 038182 (the contents of which are incorporated herein by reference in their entirety) discloses engineered stem cells modified to reduce or exclude the expression of HLA genes (HLA1 and / or HLA2), thereby forming low-immunogenic stem cells that differentiate into low-immunogenic macrophages that can be used to treat inflammatory conditions. The differentiation of stem cells into macrophages is known and is disclosed in WO2021 / 240162 (the contents of which are incorporated herein by reference in their entirety). WO2024 / 074376 (the contents of which are incorporated herein by reference in their entirety) provides another example, disclosing macrophages derived from monocytes engineered to express cytokines such as IL-10 and / or proteases such as matrix metalloproteinase 9 (MMP9), or iPS cells derived from similarly modified cells to express IL-10 and / or MMP9. WO2024 / 068728 (the contents of which are incorporated herein by reference in their entirety) discloses engineered macrophages expressing MMP9 and / or MMP12.

[0096] Viral peptides and gene therapy Various viruses are commonly used as vectors for delivering exogenous genes. Commonly used vectors include recombinantly modified enveloped or non-enveloped DNA and RNA viruses, such as those from the families Baculoviridiae, Parvoviridiae, Picorviridae, Herpesviridae, Poxviridae, Adenoviridae, Picornnaviridiae, or Retroviridae. Chimeric vectors can also be used, utilizing the advantageous elements of each parent vector. These viral vectors can be wild-type or modified using recombinant DNA technology to be replication-deficient, conditionally replicable, or replicable. Conditionally replicable viral vectors are used to achieve selective expression in specific cell types while avoiding undesirable broad-spectrum infection.

[0097] Adeno-associated virus (AAV) vectors are known in the art and offer several advantages over retroviral or lentiviral vectors, such as a milder immune response, the ability to infect a wide range of cells, and the desired DNA not integrating into the genome (leading to potential disruption and knockout of other genes) but being stored in the cell in an extrachromosomal form. An AAV contains a single-stranded DNA genome of approximately 4.8 kilobases (kb) containing three genes whose coding sequences are flanked by inverted repeat sequences required for genome replication and packaging. The use of AAVs and modified AAV vectors is known in the art and is disclosed in WO2019 / 032898, WO2020041498, or WO2019 / 028306.

[0098] In the context of this invention, the term "feature" refers to and is equal to the frequency value obtained using the method of this invention.

[0099] Throughout the description and claims of this specification, the terms “comprising / including” and “containing”, and variations thereof, mean “including but not limited to”, and are not intended to exclude other parts, additives, components, wholes, or steps. “consisting substantially of” means having an essential whole, but includes wholes that do not materially affect the function of the essential whole.

[0100] Throughout the description and claims of this specification, the singular encompasses the plural unless the context requires otherwise. In particular, where the indefinite article is used, this specification should be understood to consider both the plural and the singular unless the context requires otherwise.

[0101] The features, wholes, properties, compounds, chemical parts or groups described in connection with a particular aspect, embodiment or example of the invention should be understood to be applicable to any other aspect, embodiment or example described herein, unless incompatible therewith.

[0102] Embodiments of the present invention will now be described by way of example only with reference to the following figures: Figure 1 This is a block diagram representation of a biometric measurement device; Figure 2 It is a representation of a flow device as part of a biometric measurement device implementation scheme; Figure 3 It is a representation of an electrode array as part of a biometric device implementation scheme; Figure 4 It is the circuit representation of the stimulation device as part of the implementation scheme of the biometric device; Figure 5 It is a circuit representation of a sensing device as part of a biometric measurement device implementation scheme; Figure 6.A. Comparison of frequency responses, showing the mean differences between each cell line and the control line at high frequencies (first peak) and low frequencies (right peak). p-values ​​for statistical comparisons between different samples and the control sample. Bp values ​​less than 5% or 0.05 are used to provide statistical significance; Figure 7A x-axis - high-frequency and low-frequency response time; y-axis - % offset of the test feature from the control feature. Control cells showed largely similar features from day 1 to day 6. B. During this project, parental cells exhibited stable AuraCyt features, as indicated by values ​​greater than 5%, with four exceptions highlighted in red. Figure 8 Clones 3 and 1 showed statistically significant differences from the parental lines (red and green spikes, p < 5%). Clone 7 also showed significant differences from the parental lines (yellow spike - p = 0.026). Clones 6, 9, and 14 showed no significant differences from the parental lines (light blue, green, and purple spikes). x-axis - high-frequency and low-frequency response time; y-axis - % offset of the tested characteristic from the control characteristic. Figure 9. A. Consistent AuraCyt features show the detection of high-yielding clone 1 compared to the control. The features of intermediate-yielding clone 6 and low-yielding clone 14 are indistinguishable from the parental lines. x-axis - high-frequency and low-frequency response times; y-axis - % offset of the tested feature from the control feature. B. Scatter plots show the visible offsets in the scatter plots of parental line features (yellow), high-yielding line features (red), and low-yielding line features (green); Figure 10 The figure shows high-yielding clones 1, 2, 3, and 4. All high-yielding clones were compared with the parental control, and all showed significant differences without exception. x-axis - high-frequency and low-frequency response time; y-axis - % offset of the tested characteristic from the control characteristic; Figure 11. Flow cytometry analysis of CAR-CD34 expression on day 6. (a) Transduced T cell population and (b) Untransduced T cell population; Figure 12 (a) PCA scatter plot generated by AuraCyt™ analysis, showing dimensionality reduction data for untransduced / unactivated T cells (T cells D0) on day 0; untransduced / activated T cells (T cells D6) on day 6; and CAR-CD34 T cells (CAR-T D6) on day 6. (b) PCA scatter plot, showing dimensionality reduction data for untransduced / unactivated T cells (T cells D0) on day 0; untransduced / activated T cells (T cells D8) on day 8; and CAR-CD34 T cells (CAR-T cells D8) on day 8. Figure 13Hotelling's T2 statistical analysis compared each control and test condition. Values ​​were generated to a 95% confidence interval, where p < 0.05 indicated statistical significance. D0 / D4 / D6 / D8 corresponded to days 0, 4, 6, and 8 of the experiment, respectively. D0 NT = untransduced / unactivated cells. D4, D6, and D8 NT = untransduced / activated cells. D4, D6, and D8 CAR-T = CAR-CD34 transduced / activated cells; Figure 14 The line graph shows the relative effect size shift observed throughout the culture and expansion process (days 4, 6, and 8) of the AuraCyt™ signature generated by untransduced / activated (NT) and CAR-T (CAR-CD34) cells compared to the unactivated / untransduced T cell profile at day 0 (to 0.95 confidence interval).

[0103] Figure 15. Dimensionality reduction output of AuraCyt technology evaluated using the standard flow cytometry platform FCS express. Heatmaps were generated for the following cells: (a) unactivated / untransduced T cells on day 0, (b) untransduced / activated T cells on day 4, (c) untransduced / activated T cells on day 6, and (d) untransduced / activated T cells on day 8. (e) CAR CD34-transduced / activated T cells on day 4, (f) CAR CD34-transduced / activated T cells on day 6, and (g) CAR CD34-transduced / activated T cells on day 8. T cell populations were plotted based on the first coefficient (Col5 / Col1) of the phase and amplitude shift of the high-frequency signal received during the AuraCyt™ platform analysis. All plots were gated against the gates generated around the unactivated / untransduced T cell population on day 0; and Figure 16. Dimensionality reduction output of AuraCyt technology evaluated using the standard flow cytometry platform FCS express. Heatmaps were generated for the following cells: (a) untransduced / activated T cells on day 4, (b) untransduced / activated T cells on day 6, and (c) untransduced / activated T cells on day 8. (d) CAR CD34-transduced / activated T cells on day 4, (e) CAR CD34-transduced / activated T cells on day 6, and (f) CAR CD34-transduced / activated T cells on day 8. Figures (a) and (d) show gating for activated untransduced cells on day 4. Figures (b) and (e) show gating for activated untransduced cells on day 6. Figures (c) and (f) show gating for activated untransduced cells on day 8. The T cell population was plotted based on the first coefficient (Col5 / Col1) of the phase and amplitude shift of the high-frequency signal received during the AuraCyt™ platform analysis.

[0104] Materials and Methods Detailed description of the device Figure 1 A block diagram representation of a bioassay device 10 is shown. The bioanalytical device 10 includes a flow device 30, a stimulation device 12, a sensing device 13, a control and processing device 14, and an analytical device 16. The stimulation device 12 and the sensing device 13 receive an analytical stream via the flow device 30, the analytical stream being a fluid material in which cells are suspended.

[0105] The analysis shows that the material flow is guided by the flow device 30 through the stimulation device 12 and the sensing device 13, where stimulation and sensing are performed as detailed below, and then exits from the measuring device 10 at 20.

[0106] The control and processing device 14 controls the stimulation signal applied to the analyte by the stimulation device 12 and processes the signal sensed by the sensing device 13. The processing includes amplifying the sensed signal, converting the sensed signal from analog to digital, and storing the converted sensed signal. Although Figure 1 Not shown, the measuring device 10 also includes a pump for pushing or drawing the analyte through the flow device 30 through the stimulation device 12 and the sensing device 13.

[0107] The analysis device 16 is used to perform at least one analysis based on the stored converted sensing signal. The analysis device 16 is also used to provide supervisory control over the control and processing device 14, for example, regarding changes in the form of control of the biosensing device 12 performed by the control and processing device 14.

[0108] Control and processing device 14 is composed of any suitable electronic equipment, such as a separate analog-to-digital converter circuit, a separate amplifier circuit, and a separate electronic memory circuit, or a configurable integrated circuit (such as a system-on-a-chip (SOC)) including digital circuitry, and an ASIC including analog circuitry. Analysis device 16 is composed of any suitable electronic equipment, such as a general-purpose computer (such as a PC), an embedded microprocessor, configurable electronic circuitry (such as an FPGA), etc. Control and processing device 14 and analysis device 16 are configured separately from each other, for example as separate modules, or together, for example in the same integrated circuit or the same general-purpose computer.

[0109] The flow device 30 receives the analyte 18 and provides an analytical stream, after which the analyte exits the flow device 20. (Reference) Figure 2 The measuring device also includes a two-dimensional electrode array 32, which includes stimulation electrodes of the stimulation device 12 and sensing electrodes of the sensing device 13. The flow device 30 and the electrode array 32 are arranged relative to each other such that the electrode array 32 is located above the main channel of the flow device 30.

[0110] The control and processing device 14 is electrically coupled to the electrode array 32. The control and processing device 14 is used to provide biological cell stimulation, sensing, and actuation via the electrode array 32.

[0111] The electrode array is arranged in Figure 3 More details are shown in the middle.

[0112] The electrode array 32 and the control and processing device 14 are constructed using CMOS technology (e.g., 0.35-micron CMOS technology). Both the electrode array 32 and the control and processing device 14 are included in a CMOS ASIC. Each electrode in the array 32 is 18 microns × 18 microns in size, with a 2-micron gap between the electrodes, resulting in an array spacing of 20 microns. The electrodes are surrounded by a busbar to prevent coupling with silicon capacitors, and the busbar is grounded.

[0113] The thickness and dielectric constant of the standard polyimide top layer in the ASIC are insufficient to provide adequate capacitance for proper bonding between electrode 32 and the analyte. Therefore, the polyimide layer deposition step is omitted in the manufacturing process, resulting in the absence of a polyimide top layer. The hydrophilicity of the silicon nitride layer provides maximum exposure. However, the silicon nitride layer on the electrode can be removed, allowing the electrode to conduct into the analyte. As described above, the ASIC is positioned relative to the flow device 30 such that the electrode array 32 bonds with the analyte flowing through the flow device 30.

[0114] The ASIC's control and processing device 14 includes a binary-to-decimal decoder and memory for row and column addressing of the electrode array 32, global configuration logic, and biasing circuitry for the sensor output signal paths. The global configuration logic provides features such as memory reset and gating of control signals relative to a global reset signal to ensure that all control lines are in a known state when powered on.

[0115] The measuring device 10 also includes a printed circuit board (PCB) that supports the ASIC and provides electrical connections to the circuitry supporting the ASIC. The circuitry included in the PCB includes a System-on-a-Chip (SoC) configured to provide a variety of digital functions, including generating stimulation signals, addressing individual electrodes in the electrode array 32, and communicating with a Universal Serial Bus (USB) module.

[0116] The SOC 44 is used to generate a stimulus signal in the form of an m-sequence, which is stored in memory and output bit by bit. Specifically, in this embodiment, the stimulus signal consists of a higher-frequency m-sequence and a lower-frequency m-sequence. The higher-frequency m-sequence is generated by a linear feedback shift register with 5 registers, and the lower-frequency m-sequence is generated by a linear feedback shift register with 7 registers. Therefore, the higher-frequency m-sequence has a length of 31 bits, and the lower-frequency m-sequence has a length of 127 bits. The bit rate of the higher-frequency m-sequence of the stimulus signal is 950 Mbps, and the bit rate of the lower-frequency m-sequence is 50 Mbps.

[0117] The PCB also includes input signal conditioning circuitry configured to receive stimulation signals from a SOC or an external (not shown) signal generator and provide programmable gain amplification of the voltage swing of the stimulation signals.

[0118] In addition, the PCB includes output signal conditioning circuitry that performs a variety of functions, including fixed-gain, low-distortion amplification of sensed single-ended signals, and then programmable gain amplification or attenuation of such initially amplified signals under the control of the PC.

[0119] The output signal conditioning circuitry also includes an analog-to-digital converter (ADC). The ADC has a sampling rate of 200 MHz. The ADC is configured to sample the higher-frequency m-sequence of the stimulus signal in a four-fold interleaved pass, providing a 124-bit sample length. Therefore, the theoretical bandwidth of this m-sequence is 1.9 GHz – 30.65 MHz. Due to the 4x oversampling rate, the flat region of this bandwidth only extends to 475 MHz, or 1 / 4 of the theoretical bandwidth, but higher frequency regions above this point can be achieved through post-processing.

[0120] The analog-to-digital converter (ADC) is configured to sample the lower frequency m-sequence of the stimulus signal at 200 MHz, which is four times the bit rate of the lower frequency m-sequence of the stimulus signal. However, the ADC is configured to discard samples aligned with the conversion point of the m-sequence and average the remaining three samples to provide a digital output.

[0121] Since both the high-frequency and low-frequency components of the stimulus signal originate from the same source, the low-frequency stimulus signal must be aligned with the sampling method. In this case, each low-frequency sampling period (20 ns) consists of 4 analog-to-digital converter samples (5 ns each) and 19 high-frequency stimulus samples (~1.052 ns each); 19 1.052ns = 20ns.

[0122] To prevent one stimulus pattern from significantly contaminating the next, an interval of at least one m-sequence period is maintained between the two captured portions.

[0123] In this particular implementation, the entire stimulus packet, including both high-frequency and low-frequency signals, is stored in a single memory section. It is then streamed bit-by-bit at a high-frequency data rate (950 MHz in this example). The low-frequency signal is created by padding (7 bits in this example) an M-sequence signal by a factor of 19, thus each bit lasts for 19 high-frequency cycles.

[0124] As described above, the PCB includes a USB module. The USB module provides communication with a PC running software used to execute... Figure 1The analysis device 16 provides functionality. More specifically, the PC is used to configure the ASIC 42 and the circuitry included in the PCB. Furthermore, the PC receives real-time sensing data from the SOC or data blocks that have been acquired and stored locally.

[0125] The PC is used to decode the received m-sequence encoded data via cross-correlation to provide an impulse response. The PC is also used to perform a Fast Fourier Transform (FFT) on the decoded data to provide frequency domain data. The frequency domain data is then displayed for user interpretation.

[0126] The PC is also used to count the biological cells present in the analyte and determine the cell density in the analyte based on the flow rate and the volume of the flow device. The count and density information are displayed to the user.

[0127] Figure 2 The details are shown in the middle. Figure 2 The representation of the mobile device 30 included in the biosensing device. Figure 2 The flow apparatus 30 is made of glass, with a length of approximately 25 mm and a width of approximately 10 mm. The flow apparatus 30 includes a main channel 34 through which the analytical stream flows. An electrode array 32 is disposed above the main channel 34, such that the electrodes 32 engage with the analyte as the analytical stream flows through the main channel.

[0128] As described above, the electrode array 32 is included in the CMOS ASIC. The CMOS ASIC and the flow device 30 are releasably attached to each other by a fastener device including a silicone gasket layer, thereby achieving the correct relative positioning of the electrodes and the main channel.

[0129] The flow apparatus 30 also includes a sample inlet 40 for receiving analytes, for example by injection, and a sample outlet 50 located opposite the sample inlet 40. Both the sample inlet 40 and the sample outlet 50 are in fluid communication with the main channel 34. Furthermore, the flow apparatus includes first and second additional inlets 42 and 44. The first additional inlet 42 is laterally disposed on one side of the sample inlet 40, and the second additional inlet 44 is laterally disposed on the opposite side of the sample inlet. Both the first and second additional inlets 42 and 44 are in fluid communication with the main channel 34.

[0130] In use, the sheath fluid is received by first and second additional inlets 42, 44, thereby providing a sheath fluid flow in the main channel, which is located on the lateral side of the analytical stream received through the sample inlet 40. The sheath fluid flow provides alignment of the analyte containing biological cells with the electrode array 32 and also helps maintain the integrity of the analytical stream as it passes through the main channel.

[0131] Both electric field stimulation and electric field sensing can be performed in a CMOS ASIC of the form described above. More specifically, electrode array 32 is used for both electric field stimulation and electric field sensing, with different electrode groups used for stimulation and sensing.

[0132] Figure 3 The stimulation unit 100 configured for single-end operation is shown. Figure 3 The stimulation unit 100 includes a single electrode 102, which is included in the electrode array 32. A stimulation signal is applied to the stimulation electrode 102.

[0133] The stimulation unit 100 also includes a multiplexer 112 that provides one of two states selected according to the state selection bit 118. The stimulation unit 100 also includes a memory bit 114 that stores the state of the first state selection bit 118. The memory bit 114 is composed of static random access memory (SRAM).

[0134] Figure 3 The multiplexer provides one of two states. To provide one of the two states, the electrode is addressed with the address-sensitive first state selection bit 118, and then the first state selection bit 118 is stored as memory bit 114.

[0135] In the first state, when the state selection bit 118 is zero, electrode 102 is connected to the common ground potential via a switch. In the second state, when the first state selection bit 118 is one, electrode 102 is configured for stimulation, whereby the electrode receives stimulation input from signal bus 124. Signal bus 124 is electrically connected to the portion of control and processing device 14 used to generate stimulation signals, as described above.

[0136] Each stimulation electrode 102 in the electrode array 32 includes Figure 4 The multiplexer and memory circuit shown.

[0137] Figure 5 The sensor unit 200 configured for single-ended operation is shown. Figure 4 The sensing unit 200 includes a single electrode 202, which is included in the electrode array 32. The sensing unit 200 also includes an output buffer 215 and an output pin 230.

[0138] Electrode 202 is configured to sense the electrodynamic response field, and thus electrode 202 is connected to sensor output pin 230 via output buffer 215, which is addressed by second state selection bit 225. Sensor output pin 230 is electrically connected to the portion of control and processing circuitry 34 used for processing the sensed signal, as described above.

[0139] The stimulation electrode 102 and the sensing electrode 202 are arranged in a rectangular electrode array 32, such as Figure 3 As shown schematically.

[0140] The electrode array 32 includes two columns aligned with the analytical flow stream. The first column consists of stimulating electrodes 102, and the second column consists of sensing electrodes 202. The rectangular electrode array 32 may include multiple pairs of two-column electrode arrangements laterally, such as a 32-column, 8-row array, where the rows of stimulating electrodes 102 and sensing electrodes 202 are alternately arranged. Figure 3 As shown.

[0141] exist Figure 3 In one implementation, the width of the main channel of the flow device 30 is sufficient to accommodate five columns of electrodes disposed within the main channel, while the remaining electrodes of the array 32 are located outside the main channel of the flow device. In use, the activated electrodes are selected from columns located approximately at the center of the main channel of the flow device, such as two columns, where laminar flow exists.

[0142] Figure 3 The electrodes used in the configuration are stimulating electrodes 102a and 102b, and sensing electrodes 202a and 202b. Specifically, a stimulation signal is provided to the stimulating electrode 102a, and the same stimulation signal or a complementary signal of opposite polarity is provided to the stimulating electrode 102b. The stimulating electrode 102a forms a capacitor with the adjacent sensing electrode 202a, wherein the fluid material and any cells therein are located within the electric field between these electrodes 102a and 202a. Similarly, the stimulating electrode 102b forms a capacitor with the adjacent sensing electrode 202b, wherein the fluid material and any cells therein are located within the electric field between these electrodes 102b and 202b.

[0143] The output signals from the sensing circuit associated with sensing electrodes 202a and 202b are compared, for example, by subtraction, to perform a differential measurement. Specifically, the cell concentration and flow rate are controlled such that each cell in the flow passes through the first pair of electrodes 102a and 202a, and then through the second pair of electrodes 102b and 202b. The differential measurement will be zero until a cell passes through either the first pair of electrodes 102a and 202a or the second pair of electrodes 102b and 202b, at which point a differential signal will be output.

[0144] The distance between the first pair of electrodes 102a, 202a or the second pair of electrodes 102b, 202b is chosen to be large enough to separate their respective electric fields, so that the cell is not detected by both pairs of electrodes at the same time.

[0145] Figure 1 The pump of the measuring device 10 is controlled according to at least one of the following: the flow rate of the analyte through the measuring device 10; and the confidence level characterized by the analyte flowing through the measuring device 10. Further considering the flow rate of the analyte, the spacing between the electrode pairs in the array is known, and the travel time of the biological cells between the electrode pairs is determined by the control and processing device 14.

[0146] Then, the control and processing device 14 is used to determine the velocity of the biological cells moving through the measuring device 10. The control and processing device 14 then controls the pump according to the determined velocity. For example, if the determined velocity is lower than a predetermined value, the control and processing device 14 increases the flow rate by controlling the pump. Further considering the confidence level of the analyte characterization, the control and processing device 14 characterizes the biological cells and determines the confidence level of the characterization. The control and processing device 14 also compares the determined confidence level with a predetermined level and then controls the pump accordingly. If the determined confidence level is lower than the predetermined level, the control and processing device 14 decreases the flow rate by controlling the pump, thereby providing improved characterization of the cells.

[0147] Cell culture protocols Table 1 presents 15 CHO cell lines (1 parental (control) line and 14 mAb-producing clones, including high, medium, and low mAb producers; see Table 1 for more details). The parental line was untransfected, while all producing clones were transfected with the gene of interest for mAb production. All lines were cultured under standard cell culture procedures.

[0148] Testing using prototype sensors The testing strategy was completed in three phases: Phase 1 - The control line underwent three replicate tests on two different instruments on each passage day. Additionally, a single sample from each clone was tested on two different instruments on each passage day. Characteristic or frequency values ​​(days 1-4) were generated for each test / control (via QC testing).

[0149] Table 1. List of all cell lines tested during the Collaborator 1 project and their respective titers and status classifications. Information provided by collaborator 1.

[0150] Sample Name Final titer (g / L) State classification Comparison na Parental (control) Clones 1 8.3 High-yield individuals Clone 2 7.7 High-yield individuals Clone 3 5.4 High-yield individuals Clone 4 5.0 High-yield individuals Clones 5 3.1 middle class Clones 6 2.9 middle class Clones 7 2.9 middle class Clones 8 2.7 middle class Clones 9 1.8 low-yield workers Clones 10 1.4 low-yield workers Clones 11 1.3 low-yield workers Clones 12 1.2 low-yield workers Clones 13 0.8 low-yield workers Clones 14 0.6 low-yield workers Each test and control generates a characteristic or frequency value. (Days 5-7).

[0151] To test the sensor, cells were washed in buffer P as shown in Appendix 3, and then... 6 150 μL of sample per cell / ml was loaded onto the instrument.

[0152] Buffer P formulation Buffer P is prepared as shown in Table 2 below and should be freshly prepared daily before testing.

[0153] Table 2. List of reagents used in Buffer P formulation

[0154] All reagents are within their expiration date.

[0155] Buffer parameters: pH, conductivity, and osmotic pressure After preparation, the pH, osmotic pressure, and conductivity of buffer P were measured to ensure the buffer was within its optimal parameter range (data not shown). All buffer parameters were within acceptable ranges, with the exception of pH between 6.0 and 6.5. The optimal pH for maintaining cells was 7–7.4; however, equilibration was required to maintain cell viability and conductivity below 125 uS / cm, which corresponds to an ion content of 0.5%. 0.5% PBS Mg+ / Ca+ was identified as crucial for maintaining cell viability and maximizing sensor signal output throughout the assay, allowing for the elimination of the need for further pH buffering in buffer P. Cell count initially decreased after washing with buffer P, but the viable cell concentration remained stable over the subsequent one-hour process.

[0156] Table 3. List of equipment, consumables and reagents used in this study.

[0157]

[0158] All equipment is calibrated or under warranty and in good working order. All reagents are within their expiration date.

[0159] Data collection, quality control, and statistical analysis.

[0160] Use the above technical parameters to generate feature or frequency values.

[0161] The sensor's internal software allows us to check the quality of the data collected for each test. The QC process performs 12 different tests, evaluating different parameters of the data for each sample. Each test has a "pass-fail" result, which has been established based on extensive testing and analysis of parameters that constitute the elements of an "acceptable test" according to regulations. Failure criteria include: pulse drift exceeding 5% and background vs. time exceeding 1000. Sources of variability / test failure may include air bubbles, small clumps, or high ion content due to residual culture medium ions in the sample.

[0162] Following QC evaluation, sensor characteristics are generated for each high, medium, and low-grade clone and parental line. The signals collected by the sensors are processed by algorithms to create unique characteristics. Because each cell is evaluated using multiple frequencies, each cell generates a "point" in multidimensional space. For ease of interpretation, the output is flattened into a two-dimensional scatter plot.

[0163] Sensor features were compared to determine statistical significance using a comparison algorithm to evaluate high-dimensional datasets (an improved T-test for high-dimensional data). Test samples were evaluated relative to controls to generate differential impulse response plots (Figure 6).

[0164] CAR-T cell culture and transduction Donor T cells were seeded into static T-175 flasks in RPMI medium + 10% FBS + 2 mM L-glutamine and cultured for 3 days before the start of the study. Day 0 was the time point before activation. On Day 1 of the study, 30 IU / mL IL-2 was added to the medium to activate the donor T cells. On Day 2, the cultures were separated to accommodate two conditions: a control group and a transduction test group. The control group was not transduced and remained in static T-25 flasks throughout the experiment. The test group was transduced using a CAR-CD34 lentiviral vector in 6-well plates coated with fibronectin and then reseeded into T-175 flasks.

[0165] Test and control populations were preserved in appropriate culture flasks during the pre-amplification phase (days 3 and 4), and then amplified using key cytokines at critical points in the manufacturing process from day 5 to the completion of the experiment (day 8). Flow cytometry analysis was performed on day 6 using a standard flow cytometry procedure to confirm CAR-CD34 surface expression. Cell viability and concentration were analyzed daily using an NC-3000 automated cell counter.

[0166] Testing using AuraCyt™ technology (dielectric spectroscopy) All assays were performed on a Celledonia™ ​​instrument using AuraCyt™ technology. At each assay time point (days 0, 4, 6, and 8), for each condition (test / control), 1 ml of sample was taken from each culture vessel, centrifuged at 400 rcf for 5 min, the supernatant was removed, and the resulting cell pellet was diluted in PBS to 100,000 cells / ml and then loaded onto the Celledonia™ ​​instrument. Samples were run three times on the Celledonia™ ​​instrument. Beads were run at the beginning and end of each experimental day to confirm that the instrument was operating within its optimal parameter range throughout the assay.

[0167] Data collection and statistical analysis were performed using Maestro. AuraCyt™ data were presented as 2D PCA scatter plots using the analysis software's built-in dimensionality reduction algorithm. Multivariate hotelling T2 tests were then performed to determine statistically significant similarities or differences when comparing test and control cohorts throughout the experimental duration. Statistical power analyses were performed to set the power of the tests, ensuring that differences between events of the same cell type were not considered significant. This resulted in data being analyzed in blocks of 200 events. Furthermore, effect sizes and 95% confidence intervals were calculated to quantify the sample-to-sample shift for each test and control condition.

[0168] Data analysis using FCS express Using standard flow cytometry analysis software FCS express The dielectric spectral data were evaluated using AuraCyt™. To do this, the phase and amplitude shifts of the signals received from the cells (as they pass through the sensor) for the generated high-frequency and low-frequency signals were calculated. These shifts were then plotted in AuraCyt™ as four independent third-order polynomial curves: the high-frequency phase shift curve, the high-frequency amplitude shift curve, the low-frequency phase shift curve, and the low-frequency amplitude shift curve.

[0169] Then export the four coefficients of these curves from the AuraCyt™ software and import them. FCS express The software generates heatmaps for two of the selected coefficients for each group. It then plots gates around the baseline spectrum and compares them to samples analyzed subsequently to track changes over time in samples analyzed under different conditions.

[0170] Example 1: QC Analysis of Data All data collected throughout the testing period underwent extensive QC. Only events occurring between 200 and 600 seconds of the test were analyzed. Any test that failed to meet the criteria specified in Section 2.5 was excluded from the analysis. A list of excluded tests is summarized in Table 5: Table 1. Summary of all samples excluded from the analysis due to QC failure

[0171] Example 2: Parental cells exhibited stable sensor characteristics. To demonstrate that a consistent control was generated for comparison between the parental line and the production clone, sensor characteristics of the parental cells were generated from day 1 to day 6. Data from day 7 were not included due to limited data.

[0172] For all comparisons (with only 4 exceptions, such as...) Figure 7B (Highlighted in red) The parental characteristics were significantly similar (p>5% or 0.05), which definitively confirms the generation of robust sensor characteristics.

[0173] The characteristics of the parental control lines remained stable over time, with only four exceptions. This is likely due to inter-instrument and inter-test variability present in PrototypeAlpha. This may be due to the signal-to-noise ratio limitations of the current system, which can be mitigated by using an instrument compensation system.

[0174] This provides confidence that the parent line is an acceptable and stable control, and that all production line sensor characteristics can be tested relative to it.

[0175] Example 3, Phase 1: Sensors are able to detect differences between production clone states. Phase 1 data was collected from two different instruments, one replicate, and four test days (i.e., modules 12 and 14, and replicates on test days 1–4; see Section 2.3 for more details).

[0176] Figure 8 This shows a subset of the parental line compared to two high-yielding individuals (clones 3 and 1), two medium-yielding individuals (clones 7 and 6), and two low-yielding individuals (clones 9 and 14). The complete dataset is shown in Table 2.

[0177] The characteristics or frequencies of the two high-yielding clones were significantly different from those of the parent lines (p<0.05). The medium-yielding clone 7 was also significantly different from the parent lines (p<0.05). However, the characteristics of clones 6, 9, and 14 were similar to those of the control (p>0.05).

[0178] The greatest difference was observed at high frequencies, indicating that cellular complexity, rather than cell membrane factors, is important for distinguishing between control and high-yielding CHO.

[0179] Example 4 Objective 2: Phase 2: The sensor is able to reliably detect the response y between producer clone states. Figure 9A Phase 2 data for clones 1, 6, 14, and the parental control are shown, with three replicates performed to enhance the robustness of the Phase 1 results. Data were pooled from two different instruments, three replicates, and three test days (i.e., replicates on modules 12 and 14, and test days 5, 6, and 7; see Section 2.3 for more details). The figure shows the mean difference for each cell clone relative to the control line.

[0180] The high-yielding clone 1 showed a significant difference from the parental lines (parental, p = 0.00472 – orange line). However, the medium-yielding and low-yielding lines (clones 6 and D11, respectively, p > 0.05 for both comparisons) did not show significant differences compared to the control (green line = medium-yielding / yellow line = low-yielding). This is consistent with the Phase 1 test (see Section 2.3 for more details).

[0181] Figure 9B The visible offsets in the scatter plot show the parental line characteristics (yellow) and high-yielding line characteristics (red, C136).

[0182] Example 5: The sensor was able to reliably identify all high-yield mAb CHO clones tested in this project. All high-yielding individuals were compared with their parental controls. Figure 10 The results showed significant differences (p<0.05).

[0183] High-yielding clones were statistically different from all other medium- and low-yielding clones (p<0.05), data not shown. This definitively confirms that the sensor can reliably identify high-yielding clones.

[0184] Example 6: Direct Correlation between AuraCyt Analysis and Titer A p-value below 5% (<0.05) was associated with high antibody production in 100% of cases (green). One intermediate- and one low-yielding CHO clone (green) showed significant differences from the control. Most intermediate- and low-yielding clones appeared similar to the parental control (p>5%).

[0185] Table 2. Correlation between AuraCyt p-value and final titer for each producer compared to the control. p-values ​​less than 5% indicate... The lines show significant differences from the parent lines (highlighted in green).

[0186] Sample Name Final titer (g / L) Cloned state p value Clones 1 8.3 High-yield individuals 4.542% Clone 2 7.7 High-yield individuals 3.215% Clone 3 5.4 High-yield individuals 0.00547% Clone 4 5.0 High-yield individuals 0.056% Clones 5 3.1 middle class 58.116% Clones 6 2.9 middle class 51.120% Clones 7 2.9 middle class 0.012% Clones 8 2.7 middle class 18.6% Clones 9 1.8 low-yield workers 48.547% Clones 10 1.4 low-yield workers 14.451% Clones 11 1.3 low-yield workers 0.00966% Clones 12 1.2 low-yield workers 30.490% Clones 13 0.8 low-yield workers 66.976% Clones 14 0.6 low-yield workers 53.411% Comparison - N / A control system The parental lines remained stable over time with only four exceptions, likely due to inter-instrument and inter-test variability present in the prototype sensor. This may be due to the signal-to-noise ratio limitations of the current system, which can be mitigated by instrument compensation systems. However, the consistency of the spectrum makes it an ideal control for comparing producer lines.

[0187] Dielectric spectroscopy can detect statistically significant differences in all high-yielding CHO clones compared to parental controls, and these differences are directly correlated with the reported titers.

[0188] The highest observed differences were found at high frequencies, indicating that the differences were due to cellular complexity rather than cell membrane factors. This could be crucial for differentiating between control, low-, medium-, and high-yield CHO cells and for rapidly selecting high-yielding clones, thereby improving the efficiency of cell line development and facilitating the development of lower production costs.

[0189] Typically, medium- and low-yield clones are significantly similar to parental controls, which allows for the rapid exclusion of these clones rather than their integration into extended testing strategies.

[0190] Only one medium-yielding clone and one low-yielding clone showed significant differences from the parental control.

[0191] Example 7 CAR-T transduction using a lentiviral vector was considered successful, as confirmed by flow cytometry detection of CAR (CAR CD34) expression on day 6, with 44% of transduced cells being positive for the CD34 marker. This confirmed the expression of CD34 CAR on the cell surface. As expected, untransduced cells expressed only 0.38% CD34 (Figure 11).

[0192] Example 8 To evaluate whether AuraCyt™ could identify CAR-T CD34 cells by detecting this expression shift based on label-free cell capacitance measurements, test and control cells were tested using AuraCyt™ on days 0, 4, 6, and 8. The resulting PCA plots are shown in Figure 12 (data from day 4 are not shown). The PCA results show a significant shift between inactive cells (T cells D0) and activated cells (T cells D6 or D8), regardless of CAR expression. This may indicate T cell activation and expansion upon the addition of 30 IU / mL IL-2 to the culture medium.

[0193] When comparing untransduced cells (T cells D0 / D6 / D8) with transduced cells (CAR T cells D6 / D8), an even greater shift was observed. This indicates that AuraCyt™ can detect and distinguish between expanded cells and CAR-T CD34-expressing cells.

[0194] Example 9 To demonstrate whether the observed shift was significant, Hotelling's T2 statistic was applied to the data to compare all test and control conditions relative to each other. Figure 13 As shown, all direct conditional comparisons were significantly different from each other (p<0.05). However, this statistic alone cannot measure the magnitude or direction of the differences observed between the test and control groups over time.

[0195] Example 10 To further investigate this, the relative effect sizes (to 0.95% confidence intervals) of untransduced and transduced CAR-TCD34 cells at each time point (days 4, 6, and 8) were calculated, with each condition compared to the day 0 control (untransduced / unactivated). Effect sizes were plotted against the test day. Figure 14 Throughout the experiment, both the untransduced and CAR-T CD34 populations showed significant (p<0.05) and gradually increasing changes relative to the control population. This indicates that the activation of T cells with IL-2 reflects an increase in effect size over time. Furthermore, when using day 0 as a control, the effect size was larger when comparing CAR-T cells with untransduced cells. This demonstrates that CAR-CD34 gene transduction and subsequent CAR expression can be identified and distinguished from cell activation using AuraCyt™ technology.

[0196] In summary, AuraCyt™ analysis has the ability to qualitatively identify cell population progression during manufacturing events using PCA scatter plots (Figure 12). These qualitative results can be analyzed using Hotelling's T-statistics (T-statistics). Figure 13 ) and the measurement of effect size and confidence interval ( Figure 14Quantification and significance determination are performed to identify key manufacturing inflection points that can be used to track the manufacturing of therapeutic cells.

[0197] Real-time cell tracking during CAR-T therapy fabrication has always been challenging for researchers due to the labor-intensive procedures involved in flow cytometry and the variability of donor starting materials. However, combining the simplified output of AuraCyt™ technology with standard flow cytometry platforms... FCS express Combined, it can generate heatmaps that can be gated using identified key manufacturing inflection points, and has the ability to generate systems to track the progress of cells in manufacturing events.

[0198] Example 11 Figure 15 illustrates how activated, untransduced, and activated CAR-CD34 cells were gated against the day 0 control (unactivated / untransduced). The day 0 gate was then applied to the untransduced and CAR-CD34 cell conditions at subsequent time points (days 4, 6, and 8). Both cell conditions shifted away from the control gate over time, with the largest shift observed in CAR-CD34 cells, where <2% of cells remained within the control gate from day 6 onwards. A noticeable shift was also observed in untransfected cells, but to a lesser extent, with approximately 20% of cells remaining within the gate by day 8. Using these known shifts, a well-defined gating strategy can be generated to develop assays for successfully monitoring T cell activation.

[0199] Example 12 To further refine this gating strategy and differentiate between activated untransduced cells and activated CAR-expressing cells, gates were plotted around activated untransduced cells and applied to CAR-CD34 cells (Figure 16). Gates were generated independently for each test day to eliminate the effects of activation. On day 4, >75% of untransduced and transduced cells fell within the same gate. This may indicate that, despite activation, CAR-CD34 cells did not yet exhibit population differences attributable to CAR expression. By day 6, CAR-CD34 cells showed a significant shift from the non-transduced gate (<10% of transduced cells remained within the gate), which may be attributed to CAR expression. This hypothesis is supported by CD34 expression on the surface of CAR-CD34 cells on day 6, as shown in Figure 11 (44% CD34 expression positive). By day 8, the number of CAR-CD34 cells falling within the non-transduced gate increased slightly (37.18%). Further research is needed to investigate this observation, but this increase may be attributed to factors other than CAR expression and may be related to further critical inflection points in the CAR-T manufacturing process, such as depletion or reduced amplification rate.

[0200] Based on the data presented, AuraCyt™ can identify and differentiate transduced cells from controls, demonstrating its potential for monitoring CAR-T manufacturing events. This technology enables cell monitoring at the single-cell level without the need for lasers or tags, allowing for real-time decision-making on process development (e.g., enhancing CAR performance) under optimized growth or manufacturing conditions. Because the donor cell starting material is variable, AuraCyt™ works in conjunction with supporting software platforms (such as… FCS express By combining these methods, subtle cellular changes can be tracked, providing actionable insights for adaptive control within the manufacturing space. This enables researchers to pinpoint critical material properties and key process parameters, which are essential for improving product quality and efficacy.

Claims

1. A method for identifying eukaryotic cell clones transfected or transduced with a nucleic acid molecule suitable for expressing at least one recombinant polypeptide or peptide, comprising the following steps: i) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to untransfected eukaryotic cells; ii) Sensing the response in the electrodynamic field through at least one sensing electrode, providing a corresponding electrical response signal to the sensing circuit, and obtaining a first frequency value; iii) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to a eukaryotic cell clone in i) above, wherein the eukaryotic cell clone is transfected with a nucleic acid molecule suitable for expressing at least one recombinant polypeptide or peptide; iv) Sensing the response in the electrodynamic field through at least one sensing electrode, providing a corresponding electrical response signal to the sensing circuit, and obtaining a second frequency value, and v) Compare the first and second frequency values ​​to obtain a frequency difference, wherein the frequency difference indicates that the eukaryotic cell expresses the polypeptide or peptide.

2. The method according to claim 1, wherein the nucleic acid molecule encodes an antibody or antibody fragment.

3. The method of claim 2, wherein the antibody or antibody fragment is a therapeutic antibody or antibody fragment.

4. The method according to claim 1, wherein the nucleic acid molecule encodes a pharmacologically active polypeptide or peptide.

5. The method according to claim 1, wherein the nucleic acid molecule encodes one or more viral polypeptides.

6. The method of claim 5, wherein the one or more viral polypeptides are selected from the group consisting of adenovirus polypeptides, adeno-associated virus (AAV) polypeptides, and lentivirus polypeptides.

7. The method of claim 6, wherein the one or more adeno-associated virus polypeptides are selected from the group consisting of AAVs: AAV2, AAV3, AAV6, AAV13; AAV1, AAV4, AAV5, AAV6, AAV9, and AAVrh10.

8. The method of claim 1, wherein the nucleic acid molecule encodes a chimeric T-cell receptor.

9. The method of claim 1, wherein the nucleic acid molecule encodes one or more pluripotency-related genes.

10. The method of claim 9, wherein the nucleic acid molecule encoding one or more pluripotency-related genes is transiently expressed.

11. The method according to any one of claims 1 to 10, wherein the eukaryotic cell is a mammalian cell.

12. The method according to claim 11, wherein the mammalian cell is a Chinese hamster ovary cell.

13. The method of claim 11, wherein the mammalian cell is a HEK cell.

14. The method of claim 11, wherein the mammalian cell is a somatic cell.

15. The method according to any one of claims 1 to 3, wherein the eukaryotic cell is a fungal cell.

16. The method according to any one of claims 1 to 3, wherein the eukaryotic cell is a plant cell.

17. The method according to any one of claims 1 to 3, wherein the eukaryotic cell is an insect cell.

18. Impedance or dielectric spectroscopy is used for the identification and analysis of mammalian cells transfected with nucleic acid molecules encoding recombinant polypeptides or peptides.

19. The use according to claim 19, wherein the recombinant polypeptide or peptide is an antibody or antibody fragment expressed at a high antibody titer by mammalian cells.

20. The use according to claim 19, wherein the recombinant polypeptide or peptide is a pharmacologically active polypeptide or peptide.

21. The use according to claim 19, wherein the recombinant polypeptide or peptide is a viral polypeptide.

22. The use according to claim 19, wherein the recombinant polypeptide is a chimeric T-cell receptor.

23. The use according to claim 19, wherein the recombinant polypeptide is a polypeptide that induces pluripotency of the mammalian cells.

24. A method for monitoring the expression of recombinant antibodies in mammalian cells during cell culture, comprising the following steps: i) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to cells expressing the recombinant antibody; ii) Sensing the response in the electrodynamic field through at least one sensing electrode, providing a corresponding electrical response signal to the sensing circuit, and obtaining a first frequency value at a first time point; iii) Providing an electrical stimulation signal to at least one stimulating electrode, such that the at least one stimulating electrode generates a predetermined electrodynamic field, the electrodynamic field being applied to the cell expressing the recombinant antibody; iv) Sensing the response in the electrodynamic field through at least one sensing electrode and providing a corresponding electrical response signal to the sensing circuit, and obtaining a second frequency value at a second time point or another time point; as well as v) Compare the first and second or additional frequency values ​​to obtain a frequency difference, wherein the frequency difference indicates that the mammalian cells express the recombinant antibody as a measure of expression during cell culture.

25. The method of claim 25, wherein the stability of expression is measured during cell culture.

26. The method according to claim 25 or 26, wherein the mammalian cell is a Chinese hamster ovary cell.

27. The method according to claim 25 or 26, wherein the mammalian cell is a HEK cell.

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