Acoustic device and method for analyzing cell or particle properties in flow-through process

Abstract

A method for continuous-flow acoustofluidic analysis of cells may include receiving cells at an inlet position of a channel and displacing flowing cells from the inlet position to an outlet position of the channel, wherein the displacing includes a component transverse to at least a portion of the flowing cells, capturing a trajectory of each cell over time, and determining a property of each cell based on the trajectory.

Description

ACOUSTIC DEVICE AND METHOD FOR ANALYZING CELL OR PARTICLE PROPERTIES IN FLOW-THROUGH PROCESSCROSS-REFERENCE TO RELATED APPLICATIONS[00011 This application claims priority to U.S. provisional application Serial No. 63 / 761,364 filed February 21, 2025, the disclosure of which is hereby incorporated in its entirety by reference herein.TECHNICAL FIELD

[0002] Disclosed herein are aspects related to acoustic devices and methods for analyzing cell or particle properties in a flow-through process.BACKGROUND

[0003] Cell therapies, including those using natural killer (NK) cells, generate heterogeneous populations in which functional quality is difficult to assess rapidly. Conventional flow-cytometry methods rely on biochemical labeling, which can be slow and may not reliably predict therapeutic potential.SUMMARY10004] A method for continuous-flow acoustofluidic analysis of cells may include receiving cells at an inlet position of a channel and displacing flowing cells from the inlet position to an outlet position of the channel, wherein the displacing includes a component transverse to at least a portion of the flowing cells, capturing a trajectory of each cell over time, and determining a property of each cell based on the trajectory.

[0005] A system for continuous-flow acoustofluidic analysis of cells may include a channel having an inlet position for receiving cells and displacing flowing cells from the inlet position to an outlet position of the channel, wherein the displacing includes a component transverse to atleast a portion of the flowing cells, an imaging device configured to capture a trajectory of each cell over time, and a controller programmed to receive each trajectory and determine a property of each cell based on the respective trajectory.BRIEF DESCRIPTION OF THE DRAWINGS[0006 FIG. 1 illustrates an example diagram of system for a microchannel.

[0007] FIG. 2 illustrates an example block diagram of a process for calculating acoustic contrast.

[0008] FIG. 3A illustrates the probability over transverse position of the cell at day zero.

[0009] FIG. 3B illustrates the probability over transverse position of the cell at day five.

[0010] FIG. 3C illustrates the probability over transverse position of the cell at day 8.

[0011] FIG. 4 illustrates an example chart of the frequency over intensity at each of days zero, five and eight.

[0012] FIG. 5 illustrates an example chart of the frequency over time, showing a comparison of different cell categories at day zero, day five, and day eight.

[0013] FIG. 6 illustrates an example diagram microchannel 102 showing sample trajectories, each having a y distribution.

[0014] FIG. 7 illustrates an example chart of distributions of acoustic contrast.DETAILED DESCRIPTION

[0015] As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted aslimiting, but merely as a representative basis forteaching one skilled in the art to variously employ the present invention.

[0016] Cell therapies have achieved remarkable breakthroughs in treating cancers and are poised to expand into treatment of more diseases. However, production of therapeutic cells generates a heterogeneous population, and new tools are needed to identify which among these cells are most efficacious and to qualify them for clinical delivery. Conventional flow cytometry, labeling surface markers, has proven unsatisfactory in predicting therapeutic function. Alternately, label-free measurements that probe physical rather than biochemical cell features offer fast low-cost analysis and could double as sorting criteria.

[0017] Measuring the physical properties of each of a large number of biological cells (i.e. mechanotype) is often valuable for assessing cell state in many laboratory and manufacturing applications. However conventional tools either provide an aggregate measurement of a suspension of cells or are too slow when measuring cells serially one at a time. Physical properties, as opposed to biochemical properties, have the advantage that they can be measured much faster (e.g. thousandths to tenths of a second per cell) than biochemical measurements, which often require secondary “label” or “reporter” molecules to undergo chemical binding or amplification and can take tens of minutes to days depending on the approach.

[0018] This is especially true where rapid measurements of samples containing thousands to millions of cells are desired. For example, for understanding immune response to pathogens or compounds, in assessing infectivity of virus samples in cultured cells, in optimizing engineered cells for specific purposes such as cancer therapy or biomanufacturing. Similarly, there may be uses for measuring synthetic microparticles for consistency or quality control, for example in preparing calibration particles or process particles in biomanufacturing. Other technologies may use electrical impedance as cells flow between sets of electrodes, light scattering forces, or deformability of cells as they flow through constrictions. Among acoustic measurements, there are examples of acoustic “tweezers” or that use acoustic trapping vs sedimentation forces. These methods are not satisfactory.[0019| Disclosed herein is a continuous-flow acoustofluidic mechanotyping of natural killer (NK) cells and its correlation with established markers of NK cell activation. NK cells may act as one of the body’s fastest responders against infection and cancers.[0020J The system includes a rectangular microchannel with two or more inlets and one or more outlets configured to pump a suspension of cells through one inlet, in laminar flow with another co-flowing fluid pumped in the other inlet. An ultrasonic oscillator (piezoelectric) is coupled to the microchannel such that acoustophoretic forces act on the cells. The result is that each cell’s trajectory through the channel undergoes a combination of forces: acoustophoretic forces, drag forces exerted on the cell by the flowing liquid, and in some cases hydrodynamic forces (e.g. “lift” forces near a channel wall). The acoustophoretic forces are typically configured to act primarily perpendicular to the drag forces. The trajectories as the cells pass through a segment of the channel are observed by optical methods and / or imaging devices, for example by a camera mounted to a microscope. Thus, the raw data consists of many spatial trajectories, each one obtained from a single cell passing through the device, along with device parameters such as channel geometry, flow rate, fluid properties, cell concentration, electrical power applied to the acoustic oscillator, imaging resolution and scale, and so forth.[0021[ To extract physical parameters from the trajectories, two approaches may be used. These approaches are non-exclusive and may include aspects of each other. The first is to estimate the forces acting on the cell by calculations, using fluid mechanics and acoustofluidics. Since most of the conditions are known in the device, remaining quantities such as cell acoustic contrast (a combination of cell density and compressibility) can be deduced from accurate measurement of cell trajectory.

[0022] FIG. 1 illustrates an example diagram of system 100 for a microchannel 102. The microchannel 102 may be a rectangular microchannel with two or more inlets and one or more outlets configured to pump a suspension of cells through one inlet, in laminar flow with another co-flowing fluid pumped in the other inlet. The microchannel 102 may receive a cell 104 having a trajectory across the microchannel 102 through a buffer 103. The microchannel 102 may be coupled to an ultrasonic oscillator 106 (piezoelectric) such that acoustophoretic forces act on thecells. The result is that each cell’s trajectory through the channel undergoes a combination of forces: acoustophoretic forces, drag forces exerted on the cell by the flowing liquid, and in some cases hydrodynamic forces (e.g. “lift” forces near a channel wall). The acoustophoretic forces are typically configured to act primarily perpendicular to the drag forces.[0023 J An optical sensor 108 or imaging device may also be coupled to the microchannel 102 and be configured to observe the cell 104 behavior within the microchannel 102. In one example, the optical sensor 108 may be a camera mounted to a microscope. Other examples may include high-speed photodiode or photodiode array detectors, imaging chips such as CMOS or CCD sensor arrays, digital holographic imaging systems, interferometric scattering detectors (iSCAT), to name a few. The raw data acquired by the optical sensor 108 consists of many spatial trajectories, each one obtained from a single cell passing through the device, along with device parameters such as channel geometry, flow rate, fluid properties, cell concentration, electrical power applied to the acoustic oscillator, imaging resolution and scale, and so forth.10024} The system 100 may include a processor 110 or controller configured to control the oscillator 106 and communicate with the optical sensor 108 to acquire the data. The processor 110 may include one or more computer hardware processors coupled to one or more computer storage devices for performing steps of one or more methods as described herein. Although not shown, the system 100 may include a memory. The processor 110 may execute instructions for certain systems and may be maintained in a non-volatile manner using a variety of types of computer-readable storage medium. The computer-readable storage medium (also referred to herein as memory, or storage) includes any non-transitory medium (e.g., a tangible medium) that participates in providing instructions or other data that may be read by the processor 106. Computer-executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and / or technologies, including, without limitation, and either alone or in combination, Java, C, C++, C#, Objective C, Fortran, Pascal, Java Script, Python, Perl, and PL / structured query language (SQL).

[0025] FIG. 2 illustrates an example block diagram of a process 200 for calculating acoustic contrast. This process 200 may include measuring the trajectory of a cell 102 flowing through the acoustic field 103 in the microchannel 100, as referenced above for the first approach.

[0026] At block 205, the process 200 includes receiving image data from the optical sensor 108. The imaging data may convey an acoustophoretic displacement y and a width of a cell trajectory trace a.[00271 At block 210, the process 200 may provide device parameters such as known or calibrated quantities. These may include fluid and acoustic properties of the buffer 103, time duration of the image trajectory, acoustic energy applied to the microchannel 102, and flow rate of the buffer 103 through the microchannel 102.

[0028] At block 215, the process 200 may solve for an acoustic contrast using y and a.<>

[0029] At block 220, the process 200 may determine the acoustic contrast. The acoustic contrast is a combination of cell density and compressibility. The cell density may be the mass of cell per unit volume. The density may affect how strongly a cell reacts or responds to an acoustic radiation force. For example, cells with higher density than the surrounding fluid experience different acoustic contrast. The density may contribute to the acoustic contrast factor which affects the direction and magnitude of the cells lateral displacement in the acoustic field. The compressibility may measure the ability of the cell to change in response to applied pressure. Cells with higher compressibility deform more and experience a difference acoustic force than those with lower compressibility. Compressibility also determines a cell’s acoustic contrast factor, which dictates whether the cell moves toward pressures nodes or antinodes.[00301 Once measured, the acoustic contrast may be used along with size, to predict how different mechanotypes can be fractionated. Contrast can be altered, if desired, for fractionation by tuning buffer properties with common additives.[0031 J FIGs. 3A-CE illustrate example charts using the mechanotyping system to measure activation of natural killer cells, showing similarities between the mechanotype observations and gold standard measurements of activation by flow cytometry over time. For example, FIG. 3 A illustrates the probability over transverse position of the cell at day zero. FIG. 3B illustrates the probability over transverse position of the cell at day five. FIG. 3C illustrates the probability over transverse position of the cell at day 8.[00321 Each of the charts in FIGs. 3A-C may include a set of data points 302 collected from the optical sensor 108. A fit line may be generated based on these data points (not individually labeled). A low-mig line 304 is illustrated representing a low-migration cell population, as well as a high-mig line 306 representing a high-migration cell population.

[0033] FIG. 4 illustrates an example chart of the frequency over intensity at each of days zero, five and eight. The CD44 intensity may refer to how strongly CD44 is expressed on cells. As illustrated, day zero 402 may peak at a lower intensity compared to day five 404 and day eight 406. The distribution rises gradually and reaches a moderate peak before declining.

[0034] Day five illustrates a higher intensity than day zero 402 with a clear single peak. Day eight 406 illustrates a peak at a higher normalized intensity with a sharper and higher frequency compared to day zero 402. As time increases, the distribution moves rightward along the intensity scale.

[0035] FIG. 5 illustrates an example chart of the frequency over time, showing a comparison of different cell categories at day zero, day five, and day eight. At day zero, a high-mig and high diam may each have low frequencies, each increasing as time increases. CD16+ cells may be at their highest at day zero and may decrease over time. CD44 may increase over time. CD 16+ cells include NK cells, neutrophils, macrophages, monocytes, and even some T-cell subsets. These cells may be considered a low IgG receptor and may be a trigger for antibody-dependent cellular cytotoxicity, enabling NK cells and some monocytes to kill infected cells. CD44-high cells may be considered more activated, adhesive and migratory and exhibit enhanced functional properties.

[0036] FIG. 6 illustrates an example diagram microchannel 102 showing sample trajectories, each having a Ay distribution.[0037J FIG. 7 illustrates an example chart of distributions of acoustic contrast among a batch of NK cells, computed by theory-based calculation (also referred to herein as “true labels”) and compared with distributions obtained convolutional neural network machine learning (also referred to herein as “predicted labels”), where the machine learning is can accept as input capture of trajectories as input data. Notably, the table in FIG. 7 illustrates example ranges. It is understood that the values in Table 1 are merely example and that other values may be considered, as well as value outside of the ranges below.[0038J This second approach may use machine learning or artificial intelligence (in this example, a convolutional neural network) to learn to predict the cell properties. In this method, a data set is collected of trajectories observed from particles of known properties, for example, from calibration beads of precise size, density and compressibility. This data is used to train an algorithm such that it may then assess or predict the properties of new cells having similar trajectories experimentally obtained from a similar device.[0039J In some instances, both methods could be combined to evaluate the mechanotype of a sample of cells. When forces acting on the cells are highly controlled, the first method can be advantageous. However, when forces acting on cells are complex or acting from multiple sources, the machine learning approach can be advantageous.

[0040] A further use of the device is to measure the size of the cell along with its other mechanical properties. The size, when defined as a diameter of an effectively equivalent sphere, is often sufficient. The size of a cell will often affect the measurement of the trajectory, so obtaining the cell size independently of the trajectory may be preferred. Because imaging is used to capture the cell trajectories, the size of the cell can be estimated to be the width of the observed trajectory in an image (see FIG. 1). However, this measurement alone can have poor accuracy because the cell may be more or less in focus depending on the optical equipment and the cell’s vertical position as it moves through the field of view. An out-of-focus cell may have a larger apparent width than when it is in focus.

[0041] To refine the size measurement, three considerations may be considered. First, the sharpness of the cell boundary in the image may be an indicator of the focus. A cell with a diffuse boundary (e.g., a gradual change in pixel intensity at its boundary) can be flagged as yielding unreliable size information. A second approach is to incorporate the image sharpness into the dataset and perform corrections to obtain an accurate size measurement. Optical theory can predict the apparent size of objects out of focus if the distance from the focal plane is known, and this theory can be applied to the collected data. Likewise optical theory can predict the sharpness of the boundary. Third, the total brightness (e.g. sum of pixel values in a digital image) of a fluorescing cell can be analyzed to correlate with actual size. For example, independent of focus, if the emitted light is collected from a region containing a cell, it can indicate cell size.[0042| To enrich the size analysis further, the particle trajectory can be used to deduce specifics of its vertical (out of plane) position and therefore its distance from the focal plane. This method takes into account that the drag forces in pressure driven (Poiseuille) flow are not uniform in the vertical direction. Fluid velocity is lower near the channel floor and ceiling than in the channel midplane, and the magnitudes of these velocities are accurately calculated by theory of laminar flow. On this basis, with the approximation that the cell vertical position is constant throughout the segment of channel observed, the displacement over time of the cell in the direction of the fluid streamlines (due to drag forces) gives information about its vertical position. This measurement is independent of the other displacements measured such as those due to acoustic or hydrodynamic effects.[0043J Note that the machine learning analysis can implicitly incorporate the size effects just described without need to perform the theoretical calculations and measurements. Hence it is expected that machine learning can provide not only measurements of cell acoustic contrast, but also of cell size.

[0044] By way of methods for carrying out the measurements, in one embodiment, the rectangular microchannel 102 acts as half- wave resonator and displaces flowing cells, by acoustophoresis, from their inlet position near channel sidewalls toward the channel center stream at outlet. Microscope images capture the trajectory of each cell. Purified human NK cells areactivated in culture and are measured by the acoustic device and by flow cytometry at 0, 5, and 8 days. Separately, calibration beads and a primate cell line are characterized in the acoustic microchannel for calibration. The rate of acoustic displacement observed in the images is quantified in context of the theory of acoustic radiation force to calculate each cell’s acoustic contrast (>100 cells / batch). Additionally or alternately, raw images are input to a convolutional neural network (CNN) model trained using images of previously measured cells to predict calculated acoustic contrast.

[0045] By following the above, in an example system, both acoustic displacement and CD44 expression of NK cells indicated progression from the initial population at day 0 (5%, 15%: displacement high, CD44high respectively), to a bimodal distribution at day 5 (32%, 42%) suggesting partial activation, to near complete activation at day 8 (88%, 80%). Cell size alone may be an insufficient predictor of CD44 expression, suggesting NK activation alters acoustic contrast independently of size. Meanwhile, following training, the CNN model inference of acoustic contrast also distinguished day 0 from day 8 population (p=0.0002), with performance improvements anticipated (via hyperparameter tuning and image preprocessing).[0046| Such results suggest that acoustic mechanotyping can identify NK cell activation, in agreement with gold-standard flow cytometry, and set the stage to test mechanotype versus antitumor function. New implementation of machine learning enables rapid sensitive measurement of populations of cells as needed for cell therapy applications.

[0047] Thus, disclosed herein is a system for:

[0048] 1. Incorporating artificial intelligence (Al) to analyze the image data of cell trajectories for the purposes of predicting cell properties including acoustic contrast factor.[0049J 2. Training Al algorithms using particles previously calibrated by other devices.

[0050] 3. Incorporating Al to obtain measurements where the complexity of the multiple physical forces on the particle make it too cumbersome to calculate particle trajectories deterministically via theory. This may be due to incidental or intentional nonuniformities in channel dimensions, in acoustic force, in hydrodynamic forces, etc.[0051 J 4. Obtaining size measurements of imaged trajectories according to focal plane, image sharpness, or brightness measurements in the vicinity of the trajectory.

[0052] 5. Compensating for size measurement errors by analyzing the particle trajectory to calculate its “vertical” position in the microchannel.[00531 6. Using the device and method:I0054J a. to determine if a cell is infected with a virus, or to determine the extent of infection within a batch of cells;

[0055] b. to determine if a cell is likely to have a desired immune function, such as its ability to destroy a cancerous cell;[0056| c. for quality control or release criteria in manufactured, engineered, or otherwise manipulated therapeutic or prophylactic cells.[0057| Other examples may include:[0058| 1. Combination of acoustic forces as described herein with electrical impedance or light scattering to obtain cell information by multiple modes.[0059J 2. Novel optical configurations rather than conventional cameras, such as laser illumination, linear detectors, or lenses with high depth of field.[0060J 3. Co-flowing fluids with differing fluid-mechanical properties to provide a gradient in fluid properties within the acoustic field.I0061J 4. Intentional perturbations or asymmetries in channel geometry to cause hydrodynamic impingements that with machine learning could enhance sensitivity of the measurement.

[0062] 5. Addition of calibration particles mixed into an input sample containing cells to permit simultaneous calibration and measurement of the cell samples.

[0063] 6. Image preprocessing and hyperparameter tuning to improve performance of the AI-based prediction.

[0064] Aspects of the present embodiments may be embodied as a system, method or computer program product. Accordingly, aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “module” or “system.” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

[0065] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (erasable programmable read-only memory (EPROM) or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0066] Aspects of the present disclosure are described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer,special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / acts specified in the flowchart and / or block diagram block or blocks. Such processors may be, without limitation, general purpose processors, special-purpose processors, application-specific processors, or field-programmable.[0067f The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.[0068| While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the invention. Rather, the words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the invention. Additionally, the features of various implementing embodiments may be combined to form further embodiments of the invention.

Claims

WHAT IS CLAIMED IS:

1. A method for continuous-flow acoustofluidic analysis of cells, comprising: receiving cells at an inlet position of a channel and displacing flowing cells from the inlet position to an outlet position of the channel, wherein the displacing includes a component transverse to at least a portion of the flowing cells;capturing a trajectory of each cell over time; anddetermining a property of each cell based on the trajectory.

2. The method of claim 1, wherein determining the property includes determining an acoustic contrast of one of the cells.

3. The method of claim 2, wherein the acoustic contrast is a combination of cell density and compressibility.

4. The method of claim 1, wherein the trajectory is captured by an optical sensor.

5. The method of claim 4, wherein the optical sensor includes a camera couple to a microscope.

6. The method of claim 1, wherein determining the property includes employing a convolutional neural network (CNN) trained on calibration particles with known density, size, and compressibility.

7. The method of claim 1, wherein the trajectory is determined by analyzing pixel-intensity sharpness, boundary sharpness, or total image brightness.

8. The method of claim 1, further comprising applying acoustophoretic forces on the cells.

9. The method of claim 1, further comprising receiving device parameters and determining the property based at least in part on at least one of the device parameters.

10. The method of claim 9, wherein the device parameters include at least one of fluid or acoustic properties of a buffer of the channel, a time duration for the trajectory, and acoustic energy applied to the channel.

11. A system for continuous-flow acoustofluidic analysis of cells, comprising: a channel having an inlet position for receiving cells and displacing flowing cells from the inlet position to an outlet position of the channel, wherein the displacing includes a component transverse to at least a portion of the flowing cells;an imaging device configured to capture a trajectory of each cell over time; and a controller programmed to receive each trajectory and determine a property of each cell based on the respective trajectory.

12. The system of claim 11, wherein the channel is a rectangular channel includes at least one inlet and one outlet.

13. The system of claim 11, wherein the channel includes a buffer fluid.

14. The system of claim 11, further comprising an ultrasonic oscillator mechanically coupled to the channel and configured to generate an acoustic field transverse to the fluid flow.

15. The system of claim 14, wherein the oscillator is a piezoelectric transducer configured to supply controllable acoustic energy to the channel.

16. The system of claim 11, wherein the imaging device is one of a camera, laser illumination device, linear detector, and lenses.

17. The system of claim 11, wherein the controller is further configured to determine an acoustic contrast of each of the cells.

18. The system of claim 11, wherein the acoustic contrast is determined based on a cell density and compressibility.

19. The system of claim 11, wherein the controller is further configured to run a convolutional neural network (CNN) configured to predict a cell property based on trajectory images captured by the imaging device.

20. The system of claim 11, wherein the controller is further configured to perform image preprocessing and hyperparameter tuning to improve cell property prediction.

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