Methods and classification systems for single cell analysis

Abstract

Systems and methods for generating a signal map are disclosed. The system includes a memory, one or more processors, and instructions stored in the memory and configured to be executed by the one or more processors. The system generates a signal profile for each block of a plurality of blocks obtained from a tissue sample, each block including a plurality of cells. The system generates a spatial map of the signal from the plurality of signal profiles corresponding to the plurality of blocks. In some embodiments, the system further creates a tree of cell populations from the spatial map. In some embodiments, the system includes a microfluidic device (e.g., a microfluidic sensor chip) configured to detect the impedance of cells, and the signal profile for each block is an impedance profile generated based on the impedance detected from the microfluidic device.

Description

[Technical Field]

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 432,777, entitled "Method and classification system for single cell phenotype analysis," filed December 15, 2022, which is incorporated herein by reference in its entirety.

[0002] Technical Field The present application relates generally to microfluidic analysis, and more particularly to systems, devices, methods, and non-transitory computer-readable storage media for analyzing cells or particles in tissue samples using microfluidic devices, e.g., for cellular analysis. [Background technology]

[0003] Cellular plasticity refers to the ability of cells to change their phenotype in response to changes in their environment without genetic mutations. In some cases, plasticity in diseased cells (e.g., cancer cells) can promote tumor evolution and drug resistance. Summary of the Invention

[0004] Quantifying cellular plasticity at the single-cell level in diseased cells from various tissues has been challenging. Diseased cells exhibit phenotypic changes not seen in normal cells. Subpopulations of diseased cells exhibit heterogeneity or variability that result in more subtle variations in cell morphology. These changes or variations require the measurement of time-lapse samples of surface and subsurface phenotypic characteristics over an extended period of time.

[0005] In some embodiments, the method for quantifying cellular plasticity is performed using cell labeling techniques, in which cells are obtained from a tissue sample, encapsulated, barcoded, tagged with a label (e.g., a fluorescent dye or magnetic label), and sorted using fluorescence-activated cell sorting (FACS) or magnetic cell sorting. While these techniques detect some mutations in diseased cells in some situations, the associated results are inconsistent and highly dependent on sample preparation. Furthermore, in some situations, the tools and techniques are not sensitive enough to detect specific submutations in phenotypes. In one example, cell phenotyping is based on optical methods. Certain diseased cells exhibit weak or no optical signatures, which appear as noise in cell phenotyping, resulting in insufficient differentiation in heterogeneous conditions. Furthermore, microfluidic devices are used for optical detection, requiring complex single-cell flow and guidance based on microscopy and visual detection, which can easily limit the flow or throughput of tissue samples. Optical signals from microfluidic devices are also a poor indicator of signaling entropy because they cannot distinguish between cell subtypes.

[0006] Therefore, there is a need for improved systems, methods, and devices that allow for phenotypic analysis of cells at the single cell level.

[0007] Some embodiments of the present disclosure describe using microfluidic devices to measure signals (eg, impedance or current signals) from unlabeled cells at the single-cell level.

[0008] Impedance probing is a label-free method that allows cells to be examined and classified. Diseased cells exhibit a different impedance fingerprint (e.g., impedance signal or phenotypic impedance signal) than normal cells. The impedance fingerprints differ between different types of diseased cells. The impedance signal is highly correlated with the signaling entropy of cells, and impedance probing is applied to classify heterogeneous mixtures of cells and distinguish between normal and diseased cells.

[0009] As disclosed herein, a tissue sample of cells is divided into blocks (e.g., solid blocks). Each block contains a plurality of cells. Spatial information for each block is recorded. The cells in each block are dissociated from the block to form respective liquid suspensions of the cells, which are then passed through a microfluidic device sensitive to single-cell detection. The microfluidic device includes a sensor chip for detecting signal changes (e.g., real and complex impedance changes or current changes) as each single cell passes under electrodes on the chip. A signal profile of the cells corresponding to each block is recorded and stored. In some embodiments, the signal profile is a time series plot showing impedance values ​​over time, changes in impedance values ​​over time, or changes in current values ​​over time. In some embodiments, each liquid suspension of cells (corresponding to one block) is input into the microfluidic device multiple times, and the signal profiles from the multiple runs are aggregated (e.g., summed or averaged). In some embodiments, a frequency ranking distribution of signal amplitudes is obtained from the signal profile of each block. In some embodiments, a spatial map of the signal is generated from the signal profiles corresponding to the blocks of the tissue sample. In some embodiments, a cell population tree is created from the spatial map of the signal and used to visualize how cancer cells (and cancer cell types) are distributed across many blocks within a bulk tissue sample.

[0010] As disclosed herein, a tissue sample is divided into blocks, each containing a plurality of cells without the need for labeling (i.e., without fluorescent or magnetic labels). In some embodiments, the size of the blocks is selected based on the desired signal-to-noise ratio.

[0011] As disclosed herein, the signaling entropy of a cell is determined at the single cell level based on the impedance of the single cell measured by an impedance sensor in a microfluidic device as the single cell passes through the impedance sensor.

[0012] According to some embodiments, a method for generating a signal map of a mixture of cells includes generating a signal profile for each block of a plurality of blocks obtained from a tissue sample, each block of the plurality of blocks including a plurality of cells. The method further includes generating a signal profile for each block of the plurality of blocks obtained from the tissue sample, each block of the plurality of blocks including a plurality of cells. The method further includes generating a spatial map of signals from the plurality of signal profiles corresponding to the plurality of blocks.

[0013] According to some embodiments, a system includes a memory and one or more processors, the memory storing a plurality of instructions and data configured to be executed by the one or more processors, the plurality of instructions including instructions for performing any of the methods disclosed herein.

[0014] According to some embodiments, a non-transitory computer-readable storage medium stores one or more programs configured to be executed by a computing device having one or more processors and a memory, the one or more programs including instructions for performing any of the methods described herein.

[0015] It should be noted that the various embodiments described above can be combined with any other embodiment described herein. The features and advantages described herein are not exhaustive, and in particular, many additional features and advantages will be apparent to those skilled in the art upon consideration of the drawings, specification, and claims. Furthermore, it should be noted that the language used herein has been chosen primarily for readability and descriptive purposes, and may not be chosen to limit or restrict the subject matter of the present invention. [Brief explanation of the drawings]

[0016] For a better understanding of the various embodiments described, reference should be made to the following detailed description in conjunction with the following drawings, in which like reference numerals refer to corresponding parts throughout: [Figure 1A] 1 illustrates a microfluidic device for flow control of cells or particles in a microfluidic channel, according to some embodiments. [Figure 1B] 1 illustrates a microfluidic device for flow control of cells or particles in a microfluidic channel, according to some embodiments. [Figure 2] FIG. 1 is a block diagram illustrating electrical components for flow control of cells or particles in a microfluidic channel, according to some embodiments. [Figure 3] 3 shows an exemplary tissue section 302 according to some embodiments. [Figure 4] 3 illustrates an exemplary process in which a tissue section is divided for microfluidic measurements in block 304, according to some embodiments. [Figure 5] 1 illustrates three exemplary signal profiles according to some embodiments. [Figure 6] 1 shows a histogram of cell populations of an exemplary block of cells, according to some embodiments. [Figure 7] 1 illustrates an exemplary signal profile (eg, impedance profile) measured for a block of tissue sample in the frequency domain, according to some embodiments. [Figure 8] 1 illustrates a spatial map (eg, a spatial map of a signal) according to some embodiments. [Figure 9A] 1 shows a tree of cell populations in a three-dimensional perspective view, according to some embodiments. [Figure 9B] 9B illustrates a two-dimensional projection of the tree of FIG. 9A, according to some embodiments. [Figure 10] FIG. 1 is a block diagram of a system configured to perform single-cell phenotyping, according to some embodiments. [Figure 11A] 1 illustrates a flowchart diagram of a method for generating a signal map, according to some embodiments. [Figure 11B] 1 illustrates a flowchart diagram of a method for generating a signal map, according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0017] Reference will now be made to embodiments, examples of which are illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments being described. However, it will be apparent to those skilled in the art that the various embodiments described may be practiced without these specific details. In other instances, methods, procedures, components, circuits, and networks well known to those skilled in the art have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0018] 1A shows a microfluidic device 100, according to some embodiments. The device 100 includes a fluidic channel 102 (e.g., a microfluidic channel) formed on a substrate. In some embodiments, the fluidic channel 102 may be formed by bonding a first substrate having a depression, recess, or notch to a second substrate such that the fluidic channel 102 is provided between the first and second substrates.

[0019] Fluidic channel 102 has an inlet 103 and an outlet 104, both of which are shown by dashed lines in FIG. 1A . The locations of inlet 103 and outlet 104 shown for fluidic channel 102 in FIG. 1A are merely examples. Inlet 103 and outlet 104 may be located at any other location along the length of fluidic channel 102 or device 100. In some embodiments, length L of fluidic channel 102 (e.g., measured from inlet 103 to outlet 104) is within a range of 1 mm to 50 mm. In some embodiments, width W of fluidic channel 102 (e.g., a representative portion such as 102-A, which may be the narrowest portion) can be configured based on the size of particles to be analyzed. For example, for cellular measurements, width W of fluidic channel 102 is configured according to the size of cells so that only single cells are detected at a time. In some embodiments, width W of fluidic channel 102 is within a range of 10 μm to 100 μm (e.g., 50 μm). In some embodiments, fluidic channel 102 includes one or more portions having a width different from width W. 1A, fluidic channel 102 may include portions 102-B and 102-C having a (protruding) shape, such that the widths of portions 102-B and 102-C are greater than width W. Similarly, fluidic channel 102 may include one or more portions having a width less than width W. In some embodiments, the wider the fluidic channel 102, the slower the velocity of particles flowing through the corresponding portion of fluidic channel 102 (e.g., if fluidic channel 102 has a uniform height). Thus, for example, wider portion 102-C may be used to reduce the velocity of particles (e.g., immobilize particles), thereby allowing more time for analyzing the particles.

[0020] Device 100 includes an input region 105 for receiving a sample fluid having particles (e.g., cells) at inlet port 106 as an input to device 100 and for supplying the sample fluid from inlet port 106 to fluidic channel 102 via inlet 103. Device 100 further includes an output region 107 for collecting at least a portion of the sample fluid from fluidic channel 102 via outlet 104 and for ejecting or delivering a portion of the sample fluid via outlet port 108 (e.g., a nozzle) for further processing or analysis. In some embodiments, outlet port 108 has a diameter in the range of 60 microns to 120 microns. In some embodiments, fluidic channel 102 is configured such that inlet port 106 is the inlet 103 of fluidic channel 102 and outlet port 108 is the outlet 104 of fluidic channel 102.

[0021] In some embodiments, the output region 107 includes a first array of piezoelectric actuators 109 disposed adjacent to the outlet 104 to eject a portion of the fluid in the fluid channel 102. In some embodiments, the first array of piezoelectric actuators 109 includes one or more piezoelectric actuators (e.g., piezoelectric micro-electro-mechanical system (MEMS) actuators). In some embodiments, the first array of piezoelectric actuators 109 includes two or more piezoelectric actuators (e.g., piezoelectric actuators 109-1 and 109-2). In some embodiments, each of the first array of piezoelectric actuators 109 is a piezoelectric element. The piezoelectric elements may have a length equal to 1 mm and a width equal to 0.5 mm. In some embodiments, the device 100 includes an actuation circuit (e.g., actuation circuit 230 described with reference to FIG. 2 ) electrically connected to the first array of piezoelectric actuators 109. In some embodiments, upon application of an electrical signal from an actuation circuit, the first array of piezoelectric actuators 109 generates vibrations that generate displacements and acoustic waves, thereby controlling the local inertial motion of particles within the fluid channel 102 in the three-dimensional x, y, and z planes with sub-micron level control. In some embodiments, the first array of piezoelectric actuators 109 induces laminar flow from the inlet 103 toward the outlet 104.

[0022] In some embodiments, device 100 includes one or more pairs (e.g., a pair of electrodes) of electrodes 110. The one or more pairs of electrodes 110 can be used to charge particles flowing through fluid channel 102 so that they can be manipulated using an electric field. In some embodiments, the distance between a pair of electrodes 110 is configured so that only a single cell at a time is manipulated using an electric field.

[0023] In some embodiments, one or more pairs of electrodes 110 are also referred to as electromagnetic field generators. For example, in some embodiments, one or more pairs of electrodes 110 can be configured to apply a preset frequency to input cells. In some embodiments, the preset frequency corresponds to a particular type of cellular abnormality. In some embodiments, the preset frequency is within a range of 1 kHz to 10 GHz.

[0024] In some embodiments, device 100 includes a microfluidic sensor chip 208. In some embodiments, the device includes one or more sensors 210 (e.g., coupled onto the sensor chip 208 described with respect to FIG. 2 ). The one or more sensors 210 are configured to detect signal changes, such as changes in impedance or current (e.g., single-ended or double-ended differential), as a single cell passes through fluidic channel 102 (e.g., through or under one or more pairs of electrodes 110).

[0025] In some embodiments, device 100 can measure a signal profile (e.g., an impedance profile or a current profile) of a cell with single-cell sensitivity. For example, in some embodiments, device 100 includes a transimpedance amplifier 212 (e.g., coupled to sensor chip 208) that can generate a signal profile of a single cell as it passes through fluidic channel 102 (e.g., through or under one or more pairs of electrodes 110). In some embodiments, generating the signal profile includes measuring one or more capacitance values ​​for the cell (e.g., because diseased cells have higher capacitance values ​​than normal (non-diseased) cells, and therefore, diseased cells have higher impedance values ​​than normal cells).

[0026] In some embodiments, device 100 is configured to measure the impedance of an input cell. In some embodiments, device 100 is configured to measure the impedance of an input cell in response to a preset frequency (e.g., applied by one or more pairs of electrodes 110). In some embodiments, each measurement of the input cell impedance is performed using a different set of parameters. For example, in some embodiments, the set of parameters includes an applied frequency (or lock-in frequency), a flow rate, and a sample fluid mixture. In some embodiments, the processing time for each cell sample is within a time range of 0.5 seconds to 10 seconds.

[0027] In some embodiments, device 100 includes a driver circuit (e.g., driver circuit 240 described with respect to FIG. 2) electrically connected to one or more pairs of electrodes 110. In some embodiments, the driver circuit is configured to generate electrical signals in the MHz and GHz frequency range. In some embodiments, the frequency of the electrical signals provided to one or more pairs of electrodes 110 depends on one or more types of particles to be analyzed using device 100.

[0028] In some embodiments, the output region 107 is divided into multiple output subregions (e.g., subregions 107-1 to 107-3), as shown in FIG. 1B. In some embodiments, each output subregion has one outlet port and at least one of the first array of piezoelectric actuators 109. In this embodiment, each different portion of the sample fluid from the outlet 104 (e.g., each portion corresponding to a particular cell or cell type) is deflected toward a corresponding output subregion of the output region 107. Thus, each different portion of the sample fluid is collected at and ejected from a corresponding output subregion. Deflection of the different portions of the sample fluid may be achieved, for example, by vibration and displacement caused by activation of the first array of piezoelectric actuators 109 (and / or other piezoelectric actuators implemented within or operatively associated with the device 100).

[0029] FIG. 2 is a block diagram illustrating electrical components for controlling the flow of particles in a fluidic channel, according to some embodiments. In some embodiments, device 100 includes one or more processors 202 and memory 204. In some embodiments, device 100 includes a microfluidic sensor chip 208. The device includes one or more sensors 210 (e.g., coupled to sensor chip 208) configured to detect changes in impedance or current as a single cell passes through fluidic channel 102. In some embodiments, device 100 includes a transimpedance amplifier 212 configured to generate a signal profile of the cell. In some embodiments, memory 204 includes instructions for execution by one or more processors 202. In some embodiments, the stored instructions include instructions for providing actuation signals to a first array of piezoelectric actuators 109 (and / or other arrays of actuators). In some embodiments, the actuation signals to the arrays of piezoelectric actuators can be configured to cause each array of piezoelectric actuators to generate vibrations at a frequency different from the vibration frequency of another array of piezoelectric actuators. For example, in some embodiments, the first array of piezoelectric actuators 109 can operate at a frequency ranging from 0.5 kHz to 100 kHz based on the desired flow rate. In some embodiments, the stored instructions include instructions for providing an actuation signal to the electrode 110 to charge particles flowing through the fluid channel 102, thereby allowing the particles to be manipulated using an electric field.

[0030] In some embodiments, the device also includes an electrical interface 206 coupled to the one or more processors 202 and the memory 204 .

[0031] In some embodiments, the device further includes an actuation circuit 230 connected to one or more piezoelectric actuators, such as the first array of piezoelectric actuators 109. The actuation circuit 230 sends electrical signals to the one or more arrays of piezoelectric actuators 109 to initiate actuation of the one or more arrays of piezoelectric actuators.

[0032] In some embodiments, the device further includes a driver circuit 240 connected to one or more electrodes, such as electrode 110. The driver circuit 240 sends an electrical signal to the one or more electrodes 110 to generate an electric field using the one or more electrodes to charge particles flowing through the fluidic channel 102.

[0033] In some embodiments, the device further includes readout circuitry 250 coupled to one or more electrodes, such as electrode 110. Readout circuitry 250 receives electrical signals from one or more electrodes 110 and provides the electrical signals (with or without processing) to one or more processors 202 via electrical interface 206. In some embodiments, readout circuitry 250 is coupled to one or more sensors, such as sensor 210. Readout circuitry 250 receives signals (e.g., impedance signals or current signals) from one or more sensors 210 and provides the signals (with or without processing) to one or more processors 202 via electrical interface 206.

[0034] Methods and systems for phenotyping of single cells Some embodiments of the present disclosure are directed to methods, devices, and systems for single-cell phenotyping.

[0035] Some embodiments of the present disclosure involve obtaining a three-dimensional tissue sample (e.g., from a biopsy) for phenotypic analysis. In some embodiments, the tissue sample is approximately 5 mm 3 1cm from 3 In some embodiments, the tissue sample has a volume of about 10 mm 2 from 200mm 2The tissue sample comprises cells, each of which has a respective cell type. In some embodiments, the tissue sample comprises cells belonging to multiple cell types. In some embodiments, the tissue sample comprises cells that all belong to a single (i.e., the same) cell type. In some embodiments, the tissue sample comprises cancer cells. In some embodiments, the tissue sample comprises stem cells.

[0036] In some embodiments, the tissue sample is divided (e.g., cut) into tissue sections. Figure 3 shows an exemplary tissue section 302, according to some embodiments. In some embodiments, the tissue section has a thickness of about 0.2 mm to 5 mm.

[0037] In some embodiments, the tissue section 302 (or tissue sample) is further divided into a plurality of blocks. In the example of FIG. 3, the tissue section 302 is divided into a matrix (e.g., a grid or array) with m rows and n columns. Each unit in the matrix is ​​identified by its respective coordinates (x, y) and is also referred to as a block 304 (e.g., tissue block) (e.g., block 304-1 and block 304-2). For example, a tissue section divided into a matrix of 10 rows and 10 columns has 100 blocks. In some embodiments, each block is approximately 1 to 5 mm 2 In some embodiments, each of the blocks of tissue sections has the same size. In some embodiments, the tissue section is divided into a plurality of blocks having at least two different sizes. In some embodiments, the size of the blocks is selected based on a desired signal-to-noise ratio (e.g., of the signal profile to be collected).

[0038] In some embodiments, a method of single cell phenotyping includes using a microfluidic device (e.g., device 100) to collect (e.g., generate or obtain) a signal profile (e.g., an impedance profile or a current profile) for each block 304 of cells (or for each block of cells) of a tissue sample.

[0039] 4 shows an exemplary process 400 in which a tissue section is divided into blocks 304 for microfluidic measurements, according to some embodiments. In some embodiments, spatial information of the blocks 304 (e.g., relative to other blocks within the same tissue section and / or relative to other tissue sections of the tissue sample) is recorded when the blocks are prepared for microfluidic measurements. Typically, the preparation process involves dissociating the blocks 304 into a liquid suspension of cells (e.g., by adding a buffer to the blocks and centrifuging the mixture).

[0040] For microfluidic measurements, the liquid suspension is input into a microfluidic device that includes one or more sensors (e.g., sensors 210) that can detect changes in impedance or current (e.g., single-ended or double-ended differential) as each individual cell moves through its respective channel 102 (and passes over or between its respective pair of electrodes 110). In some embodiments, the electrodes 110 can apply a preset frequency in the range of approximately 1 kHz to 10 GHz to capture the real (resistance) and imaginary (reactance) parts of the impedance signal. In some embodiments, the impedance measurements are performed using a transimpedance amplifier. Exemplary methods and systems for analyzing cellular samples using impedance spectroscopy are disclosed in U.S. Patent Application No. 17 / 488,374, entitled "Apparatus, methods and computer programs for analyzing cellular samples," which is incorporated herein by reference in its entirety.

[0041] FIG. 5 shows three exemplary signal profiles 502 (e.g., time series plots) according to some embodiments. In some embodiments, each signal profile 502 is a profile of noise from a system (e.g., microfluidic device 100). In some embodiments, each signal profile 502 corresponds to a respective block 304 of cells. For example, in FIG. 5, signal profile 502-1 corresponds to exemplary system noise that can later be removed by digital signal processing. Signal profile 502-2 corresponds to block 304-4 (FIG. 4), and signal profile 502-3 corresponds to block 304-5 (FIG. 4). Each of the data points 504 in signal profiles 502-2 and 502-3 (e.g., data points 504-1 and 504-2 in signal profile 502-2, and data points 504-3 and 504-4 in signal profile 502-3) corresponds to the measurement of a single cell. In some embodiments, the signal profile comprises an impedance profile, which is a time series plot of impedance values ​​(on the vertical axis) against time (on the horizontal axis). In some embodiments, the signal profile comprises a current profile, which is a time series plot of current values ​​(on the vertical axis) against time (on the horizontal axis).

[0042] In some embodiments, the signal profile 502 is stored on the device 100 or on a computer system communicatively connected to the device 100 .

[0043] In some embodiments, the microfluidic device 100 is configured to acquire signals from the same block of cells multiple times (e.g., by repeating measurements on the same block of cells or by running the same block multiple times on the microfluidic device 100). In some embodiments, each run takes between about 0.5 seconds and 10 seconds.

[0044] In some embodiments, the microfluidic device 100 is configured to acquire signals of known cell lines to establish baseline or "ground truth" measurements for these cell lines.

[0045] In some embodiments, the microfluidic device 100 is configured to acquire a signal of only the buffer in which each block of cells is suspended (i.e., without any cells) to establish the "noise floor" of the device.

[0046] In some embodiments, each cell type has a respective predetermined signal value (e.g., impedance value or current value) within an acceptable error margin (e.g., ±0.1%, ±0.5%, ±1.0%, or ±2.0%). Thus, in some embodiments, the signal values ​​(corresponding to data marks 504) of signal profile 502 are further analyzed to identify and / or quantify cell populations for each block 304 of cells.

[0047] 6 illustrates a histogram 602 of a cell population of an exemplary block 304 of cells, according to some embodiments. In the example of FIG. 6, each bar 604 (e.g., bars 604-1, 604-2, 604-3, and 604-4) corresponds to a cell type (e.g., cell type A, B, C, or D) present in the block. The height of the bar represents the respective count (e.g., frequency) of cells corresponding to the cell type. For example, FIG. 6 illustrates that the majority of cells in the exemplary block are type C cells, followed by type A cells, type D cells, and type B cells.

[0048] 7 shows an exemplary signal profile 700 (e.g., an impedance profile) measured for a block of tissue sample 304 in the frequency domain, according to some embodiments. In some embodiments, the signal profile 700 represents a profile of impedance values ​​measured for the block of tissue sample 304. In some embodiments, the signaling entropy is proportional to the impedance values ​​measured for the block of tissue sample 304, and the signal profile 700 represents a profile of the signal entropy 702.

[0049] FIG. 8 illustrates a spatial map 802 (e.g., a spatial map of signals) according to some embodiments. In some embodiments, the signal profile 502 ( FIG. 5 ) and spatial information of the blocks 304 are used to generate a spatial map 802 that shows how cells are distributed across a tissue sample. Referring to FIG. 8 , in some embodiments, the spatial map 802 includes map slices 804 (e.g., map slices 804-1 to 804-3). Each map slice 804 is composed of units 806 (e.g., unit 806-1 and unit 806-2). In some embodiments, each map slice 804 corresponds to a respective tissue section 302. In some embodiments, each unit 806 in a map slice 804 corresponds to a respective block 304 of the tissue section 302. In some embodiments, the units 806 are coded (e.g., color-coded or text-coded) with information about the cell populations present in the corresponding block 304 (e.g., cell types and their corresponding counts). In some embodiments, the spatial map provides a visualization of how cells are distributed across many blocks within a three-dimensional mass of tissue.

[0050] FIG. 9A shows a cell population tree 910 in a three-dimensional perspective view, according to some embodiments. FIG. 9B shows a two-dimensional projection 920 of the tree 910 of FIG. 9A, according to some embodiments. In some embodiments, the tree 910 includes nodes 914, which may be connected by connectors 912 (e.g., branches). Each of the nodes 914 represents a respective cell population (e.g., cell type). The size of the node 914 is proportional to the cell population. In some embodiments, the cell population tree 910 is constructed by ranking cells based on their frequency of occurrence in each block (or each tissue section), as determined by the impedance profile. The cell population tree 910 visually shows the most frequently occurring cell types, followed by subpopulations of cells that are less frequently occurring than the more frequently occurring cells, followed by further sub-branches with higher frequency and lower amplitude impedance scores.

[0051] In some embodiments, a score is calculated for each block of cells by determining the distribution of cells for each type within each block and calculating the mean and standard deviation for each distribution. An exemplary score representation is the mean of the distribution. One example of a calculation is the Shannon index (described below with respect to FIG. 11).

[0052] The tree 910 (and its projection 920) shows the relationships between different cell populations (e.g., how cell type A is connected to cell type B, B to C, C to D, etc.). The x-axis in Figures 9A and 9B shows the number of times a block has been processed by the microfluidic device 100. In some instances, the relationships between different cell populations become more clearly apparent as the number of runs increases.

[0053] 10 is a block diagram of a system 1000 configured to perform single-cell phenotyping, according to some embodiments. System 1000 typically includes one or more processors (e.g., processing units or CPUs) 1002, memory 1004, and one or more communication buses 1006 for interconnecting these components (sometimes referred to as a chipset). In some embodiments, system 1000 is communicatively coupled to a microfluidic device (e.g., device 100). In some embodiments, system 1000 is communicatively coupled to one or more components of device 100, such as piezoelectric actuator 109, sensor 210, and / or one or more electrode pairs 110, and is configured to select parameters for controlling sensor 210 and / or electrode pair 110.

[0054] In some embodiments, the system 1000 includes one or more input devices 1008 to facilitate user input, such as a keyboard, a mouse, a voice command input unit or microphone, a touchscreen display, a touch-sensitive input pad, a gesture capture camera, or other input buttons or controls. In some embodiments, the system 1000 includes one or more cameras or scanners for capturing data. The system 1000 also includes one or more output devices 1010 that enable the presentation of a user interface and display content, including one or more speakers and / or one or more visual displays. For example, in some embodiments, the system is configured to display (or cause to be displayed) signal profile(s) 502, cell population histogram(s) 602, spatial map(s) 802 showing how cells are distributed across the tissue sample(s), and / or cell population tree(s) 910.

[0055] In some implementations, memory 1004 includes high-speed random-access memory such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices, and optionally includes non-volatile memory such as one or more magnetic disk storage devices, one or more optical disk storage devices, one or more flash memory devices, or one or more other non-volatile solid-state storage devices. In some implementations, memory 1004 includes one or more storage devices located remotely from one or more processing units 1002. Memory 1004, or alternatively, the non-volatile memory device(s) within memory 1004, includes a non-transitory computer-readable storage medium. In some implementations, memory 1004 or the computer-readable storage medium of memory 1004 stores the following programs, modules, and data structures, or a subset or superset thereof: Operating system 1016, which contains instructions for handling various basic system services and performing hardware-dependent tasks a user interface module 1018 for enabling presentation of information (e.g., graphical user interfaces for application(s) 1020, widgets, websites and their web pages, and / or games, audio and / or video content, text, etc.) in the system 1000 via one or more output devices 1010 (e.g., displays, speakers, etc.); One or more user applications 1020 for execution by the system 1000 (e.g., web-based or non-web-based applications for controlling another electronic device or for reviewing data captured by such a device) A signal processor 1022 for signals (e.g., impedance or current) captured by one or more sensors 210 A controller module 1024 for selecting parameters for controlling the pairs of sensors 210 and / or electrodes 110. A data processing module 1028 for recording and processing content data and generating signal profiles 502, histograms 602, spatial maps 802, and / or trees of cell populations (e.g., trees 910 and their 2D projections 920). · Data 1030 including: ○ Block spatial position information of each tissue sample 1032 ○ Signal profile 1034 including timestamp 1035 ○Spatial Map 1036 Tree(s) of cell population(s) 1038 Phenome data from different patients (e.g., databases)

[0056] Each of the above-identified elements may be stored in one or more of the aforementioned memory devices and corresponds to a set of instructions for performing the functions described above. The above-identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures, modules, or data structures; thus, various subsets of these modules may be combined or otherwise rearranged in various embodiments. In some embodiments, memory 1004 optionally stores a subset of the above-identified modules and data structures. Additionally, memory 1004 is configured to store additional modules and data structures not described above.

[0057] In some embodiments, memory 1004 (e.g., programs, modules, and data structures stored thereon) includes instructions programmed to perform the operations described with respect to Figures 1 through 9B, 11A, and 11B.

[0058] In some embodiments, the memory 1004 is configured to execute the instructions automatically (e.g., without user intervention). In some implementations, the memory 1004 is configured to execute the instructions according to (e.g., in response to) user interactions received via one or more input devices 1008.

[0059] 11A and 11B show a flowchart diagram of a method 1100 for generating a signal map, according to some embodiments. In some embodiments, the method 1100 is performed in a system (e.g., system 1000) including one or more processors (e.g., CPU(s) 1002) and a memory (e.g., memory 1004). The memory stores instructions configured to be executed by the one or more processors. In some embodiments, the system 1000 is a computer system (e.g., a server, a standalone computer, a workstation, a smartphone, a tablet device, a medical system) in communication with a microfluidic device (e.g., device 100). In some embodiments, the system 1000 is a microfluidic device.

[0060] The method includes generating (1102) a signal profile (e.g., signal profile 502, impedance profile, or current profile) for each block (e.g., a block of cells) (e.g., block 304) of a plurality of blocks (e.g., blocks 304-1 and 304-2) acquired from a tissue sample.

[0061] In some embodiments, the tissue sample is 5 mm 2 from 500mm 2 It has an area within the range of

[0062] In some embodiments, the blocks are obtained from the tissue sample by separating the tissue sample into layers (e.g., sections) and dividing each of the layers into separate blocks, e.g., each layer having a thickness in the range of 0.5 mm to 10 mm.

[0063] In some embodiments, the plurality of blocks comprises a grid of blocks. In some embodiments, dividing the tissue sample into the plurality of blocks comprises dividing the tissue sample into a two-dimensional matrix (e.g., a grid) of m rows and n columns, where the plurality of blocks is m by n (e.g., as shown in FIG. 3).

[0064] In some embodiments, each block of the plurality of blocks has a length and / or width in the range of 0.5 mm to 10 mm. In some embodiments, each block of the plurality of blocks has the same size. For example, in some embodiments, each block is about 1 mm. 2 from 5mm 2 In some embodiments, each block of the plurality of blocks has a size selected based on a desired signal-to-noise ratio (e.g., of signal profile 502).

[0065] In some embodiments, the signal profile for each block is generated using a transimpedance amplifier (eg, transimpedance amplifier 212).

[0066] 11A , in some embodiments, generating a signal profile for each block of the plurality of blocks includes generating an impedance profile for each block of the plurality of blocks (1104). In some embodiments, the impedance profile is a time series plot showing the impedance values ​​of individual cells in each block over time as they pass through a microfluidic flow channel (e.g., channel 102) of the microfluidic device. For example, in FIG. 5 , each data mark (e.g., data point) 504 on the signal profile 502 (e.g., impedance profile) corresponds to a respective impedance value of one cell (e.g., a single cell) as the cell passes under an electrode of the microfluidic chip.

[0067] In some embodiments, generating an impedance profile for each block includes determining 1106 a real impedance value and an imaginary impedance value for a cell (e.g., for each of the cells) within each block of the plurality of blocks.

[0068] In some embodiments, the impedance profile for each block is generated label-free, i.e., without applying any markers (such as optical or magnetic markers) to the cells in the blocks.

[0069] In some embodiments, generating a signal profile for each block of the plurality of blocks includes inputting (1108) cells of each block of cells into a microfluidic device (e.g., device 100).

[0070] For example, in some embodiments, cells in each block (e.g., solid block) are dissociated from the block to form a cell suspension in liquid medium, which is then input into an inlet (e.g., inlet port 106) of the microfluidic device.

[0071] In some embodiments, before each block of cells is input into the microfluidic device, a "ground truth" baseline is established by inputting cells of a known cell line(s) into the microfluidic device and obtaining the impedance profile of the known cell line(s).

[0072] In some embodiments, prior to inputting each block of cells into the microfluidic device, a "noise floor" baseline is established by inputting the buffer(s) used to create the liquid suspension of cells into the microfluidic device and obtaining a baseline profile of the buffer(s).

[0073] In some embodiments, the signal profile (e.g., impedance profile) for each block includes a series of peaks (e.g., data marks 504 or data points in FIG. 5 ). Each peak corresponds to the impedance value of a single cell. In some embodiments, the cell type corresponding to each peak is identified by mapping the impedance value to a value (within a threshold number, percentage, etc.) of a known cell type.

[0074] In some embodiments, the signal profile for each block of the plurality of blocks includes an impedance profile. Generating the signal profile for each block of the plurality of blocks includes measuring 1110 the impedance of input cells multiple times via the microfluidic device 100 and aggregating (e.g., summing or averaging) the measured impedances. For example, in some embodiments, the method includes repeating impedance measurements for the same block of cells or by running the same block multiple times on the microfluidic device 100.

[0075] In some embodiments, generating a signal profile for each block of the plurality of blocks includes measuring (1111) one or more capacitance values ​​for the input cell via the microfluidic device.

[0076] The method includes generating 1112 a spatial map of signals (e.g., spatial map 802) from multiple signal profiles corresponding to multiple blocks (e.g., by concatenating, combining, or aggregating respective signal profiles of a grid of blocks).

[0077] In some embodiments, generating a spatial map of signals from a plurality of signal profiles includes generating 1114 a spatial map of impedance from a plurality of impedance profiles corresponding to a plurality of blocks.

[0078] In some embodiments, the method includes determining 1116 the plasticity of cells in the tissue sample based on the signal profile. For example, the Shannon index (also known as the Shannon diversity index or Shannon-Wiener index) is a method for measuring cellular diversity in a population of cells. In some embodiments, the method includes calculating the Shannon index for each block of cells or for each tissue section to determine the entropy / probability of emergence of subpopulations of single-cell plasticity for cells in the tissue sample. The Shannon index is defined as:

[0079]

number

[0080] In equation (1), p i is the probability (or proportional abundance) of the i-th cell type. A higher probability means a higher frequency of high impedance values, and a lower probability means a lower frequency of lower amplitude impedance values.

[0081] In some embodiments, the method includes recording 1118 coordinates of each block of the plurality of blocks, and a spatial map of the signal is generated based on the recorded coordinates.

[0082] In some embodiments, the method includes recording 1120 a timestamp when each block of cells is input into the microfluidic device, and a spatial map of signals from the plurality of signal profiles corresponding to the plurality of blocks is generated based on the timestamp and position data of each block (e.g., position data of each block relative to other blocks).

[0083] In some embodiments, the method includes creating 1122 a tree of cell populations (e.g., tree 910) from the spatial map of the signal.

[0084] In some embodiments, creating a tree of cell populations from the spatial map of the signal includes identifying (1124) abnormal cell populations (e.g., cell types) in the tissue sample and determining spatial relationships (e.g., branches) between the abnormal cell populations.

[0085] In some embodiments, creating a tree of cell populations from the spatial map of signals includes obtaining impedance data for a plurality of cell types 1126. Creating a tree of cell populations from the spatial map of signals includes comparing the spatial map to the impedance data to identify the cell types.

[0086] In some embodiments, the plurality of cell types comprises a plurality of diseased cell types.

[0087] In some embodiments, the signal profile for each block includes an impedance profile. The method includes determining 1128 a histogram of impedance gradient boundaries for the plurality of blocks from the plurality of impedance profiles corresponding to the plurality of blocks, and creating a tree of cell populations based on the histogram of impedance gradient boundaries.

[0088] In some embodiments, creating the tree of cell populations includes determining 1130 the probability of occurrence of the cell populations and ranking the cell populations according to the determined probability.

[0089] With continued reference to FIG. 11B, in some embodiments, the method includes identifying 1132 cellular abnormalities in the tissue sample based on the cell population tree.

[0090] In some embodiments, the tissue sample is from a patient. The method includes identifying 1134 a location on the patient for a subsequent tissue sample according to the cell population tree.

[0091] In some embodiments, the tissue sample is from a patient. The method includes identifying 1136 a treatment for the patient according to the cell population tree.

[0092] In some embodiments, the tissue sample is from a patient. The method includes storing 1138 the spatial map of the signal in a phenom database (e.g., phenom data 1040) that includes spatial maps of other signals from other patients. For example, in some embodiments, each entry in the phenom database corresponds to a respective patient and includes a respective impedance spatial map (e.g., including impedance values ​​(real and imaginary components)), a spatial location cell type, and a respective signaling rate matrix. The signaling rate is calculated as the product of the probability of occurrence of a cell type and its spatial location. Each signaling rate matrix is ​​a collection of such measurements. In some embodiments, the phenom database is cross-referenced with a genome tree. In some embodiments, the phenom database is applied to develop personalized medicine.

[0093] In some embodiments, the method includes identifying a treatment based on the tree of cell populations (1140), applying the treatment to at least a portion of the tissue sample, generating an updated spatial map of the signals after applying the treatment, and determining the effectiveness of the treatment based on the updated spatial map of the signals.

[0094] In some embodiments, when the blocks of cells are dissociated in an appropriate buffer, the cells continue to grow in the buffer. Thus, in some embodiments, the method includes generating an updated impedance profile for each block of the plurality of blocks of cells after a predetermined time interval (e.g., after 1 day, 3 days, 5 days, or 7 days), and generating an updated spatial map of the signal from the plurality of updated impedance profiles corresponding to the plurality of blocks to determine whether a characteristic of the cells changes over time or how a particular disease is progressing.

[0095] The microfluidic devices described herein enable electrical and / or optical sensing of one or more cells (or other particles). The microfluidic aspects of the devices enable precise flow control (e.g., using electrodes, MEMS sensors, and / or piezoelectric components). In addition, the piezoelectric layer enables additional integrated functions such as cell sorting (e.g., after a cell signature has been captured). In some embodiments where the substrate is constructed from silicon (e.g., a second substrate portion), electrodes are deposited in close proximity to one another (e.g., with various aspect ratios) (e.g., allowing for handling of various sample types and sample heterogeneity). Piezoelectric components (e.g., MEMS piezoelectric layers) with exit ports (e.g., nozzles) enable direct ejection (e.g., jetting) of cells (e.g., after they have been processed).

[0096] Some embodiments may be described with reference to the following clauses.

[0097] Clause 1. A method for generating a signal map, the method comprising: generating a signal profile for each block of a plurality of blocks obtained from a tissue sample, each block comprising a plurality of cells; and generating a spatial map of signals from the plurality of signal profiles corresponding to the plurality of blocks.

[0098] Clause 2. The method of clause 1, wherein generating a signal profile for each block of the plurality of blocks includes generating an impedance profile for each block of the plurality of blocks.

[0099] Clause 3. The method of clause 2, wherein generating a spatial map of signals from a plurality of signal profiles includes generating a spatial map of impedance from a plurality of impedance profiles corresponding to a plurality of blocks.

[0100] Clause 4. The method of clause 2 or clause 3, wherein generating an impedance profile for each block includes determining real impedance values ​​and imaginary impedance values ​​for cells within each block of the plurality of blocks.

[0101] Clause 5. The method of any one of clauses 2 to 4, wherein the impedance profile of each block is generated without applying optical or magnetic markers to cells in the multiple blocks.

[0102] Clause 6. The method of any one of clauses 1 to 5, further comprising creating a tree of cell populations based on occurrence scores from the spatial map of signals.

[0103] Clause 7. The method of clause 6, wherein creating a tree of cell populations from the spatial map of signals includes identifying abnormal cell populations in the tissue sample and determining spatial relationships between the abnormal cell populations.

[0104] Clause 8. The method of clause 6 or clause 7, further comprising obtaining signal data for a plurality of cell types, and creating a tree of cell populations based on occurrence scores from the spatial map of signals comprises comparing the spatial map with the signal data to identify the cell types.

[0105] Clause 9. The method of Clause 8, wherein the plurality of cell types comprises a plurality of diseased cell types.

[0106] Clause 10. The method of any one of clauses 6 to 9, wherein the signal profile of each block comprises an impedance profile, and the method comprises determining a histogram of impedance gradient boundaries in the plurality of blocks from a plurality of impedance profiles corresponding to the plurality of blocks, and the tree of cell populations is created based on the histogram of impedance gradient boundaries.

[0107] Clause 11. The method of any one of clauses 6 to 10, wherein creating a tree of cell populations comprises determining the probability of occurrence of the cell populations and ranking the cell populations according to the determined probability.

[0108] Clause 12. The method of any one of clauses 6 to 11, further comprising identifying cellular abnormalities in the tissue sample based on the cell population tree.

[0109] Clause 13. The method of any one of clauses 6 to 12, wherein the tissue sample is derived from a patient, and the method further comprises identifying a location on the patient for a subsequent tissue sample according to the cell population tree.

[0110] Clause 14. The method of any one of clauses 6 to 13, wherein the tissue sample is from a patient and the method further comprises identifying a treatment for the patient according to the cell population tree.

[0111] Clause 15. The method of any one of clauses 6 to 14, wherein the tissue sample is from a patient and the method further comprises storing the spatial map of the signal in a phenome database containing spatial maps of other signals from other patients.

[0112] Clause 16. The method of any one of clauses 6 to 15, further comprising identifying a treatment based on the cell population tree, applying the treatment to at least a portion of the tissue sample, generating an updated spatial map of the signals after applying the treatment, and determining the effectiveness of the treatment based on the updated spatial map of the signals.

[0113] Clause 17. The method of any one of clauses 1 to 16, further comprising determining the plasticity of cells in the tissue sample based on the signal profile.

[0114] Clause 18. The method of any one of clauses 1 to 17, wherein the plurality of blocks comprises a grid of blocks.

[0115] Clause 19. The method of any one of clauses 1 to 18, wherein the plurality of blocks is obtained from the tissue sample by separating the tissue sample into a plurality of layers and dividing each of the plurality of layers into separate blocks.

[0116] Clause 20. The method of clause 19, wherein each layer has a thickness in the range of 0.5 mm to 10 mm.

[0117] Clause 21. The method of any one of clauses 1 to 20, wherein each block of the plurality of blocks has the same size.

[0118] Clause 22. The method of any one of clauses 1 to 21, further comprising recording coordinates of each block of the plurality of blocks, wherein a spatial map of the signal is generated based on the recorded coordinates.

[0119] Clause 23. The method of any one of clauses 1 to 22, wherein generating a signal profile for each block of the plurality of blocks comprises inputting cells of each block of cells into a microfluidic device.

[0120] Clause 24. The method of clause 23, wherein the microfluidic device comprises a microfluidic sensor chip.

[0121] Clause 25. The method of clause 23 or clause 24, wherein the microfluidic device comprises one or more electromagnetic field generators configured to apply a preset frequency to the input cells.

[0122] Clause 26. The method of clause 25, wherein the microfluidic device is configured to measure the impedance of the input cell in response to a preset frequency.

[0123] Clause 27. The method of clause 25 or clause 26, wherein the predetermined frequency corresponds to a particular type of cellular abnormality.

[0124] Clause 28. The method of any one of clauses 25 to 27, wherein the preset frequency is in the range of 1 kHz to 10 GHz.

[0125] Clause 29. The method of any one of clauses 25 to 28, wherein a preset frequency is applied to the input cells using one or more electrodes.

[0126] Clause 30. The method of any one of clauses 23 to 29, wherein the signal profile for each block of the plurality of blocks comprises an impedance profile, and generating the signal profile for each block of the plurality of blocks comprises measuring the impedance of an input cell multiple times and aggregating the measured impedances.

[0127] Clause 31. The method of clause 30, wherein each measurement of input cell impedance is performed using a different set of parameters.

[0128] Clause 32. The method of clause 31, wherein the set of parameters includes an applied frequency, a flow rate, and a sample fluid mixture.

[0129] Clause 33. The method of any one of clauses 23 to 32, wherein each input cell is processed through the microfluidic device within a time range of 0.5 seconds to 10 seconds.

[0130] Clause 34. The method of any one of clauses 23 to 33, wherein generating a signal profile for each block of the plurality of blocks comprises measuring one or more capacitance values ​​for the input cells.

[0131] Clause 35. The method of any one of clauses 23 to 34, wherein each block of cells is input into the microfluidic device sequentially.

[0132] Clause 36. The method of any one of clauses 23 to 35, further comprising recording a timestamp when each block of cells is input into the microfluidic device, and wherein a spatial map of signals from a plurality of signal profiles corresponding to the plurality of blocks is generated based on the timestamp and the position data of each block.

[0133] Clause 37. The method of any one of clauses 1 to 36, wherein each block of the plurality of blocks has a length and / or width in the range of 0.5 mm to 10 mm.

[0134] Clause 38. The method of any one of clauses 1 to 37, wherein the signal profile of each block is generated using a transimpedance amplifier.

[0135] Article 39. The tissue sample is 5 mm 2 from 500mm 2 39. The method of any one of clauses 1 to 38, having an area in the range of

[0136] Clause 40. The method of any one of clauses 1 to 39, wherein each block of the plurality of blocks has a size selected based on a desired signal-to-noise ratio of the signal profile.

[0137] Clause 41. A system comprising one or more processors and a memory storing instructions, the instructions, when executed by the one or more processors, causing the system to perform a method according to any one of clauses 1 to 40.

[0138] Clause 42. The system of clause 41, comprising a microfluidic device configured to detect cellular impedance and a memory containing instructions, which when executed by one or more processors cause the system to generate an impedance profile for each block of the plurality of blocks based on the detected impedance from the microfluidic device.

[0139] Clause 43. The system of clause 42, wherein the microfluidic device comprises a substrate having a microfluidic channel with at least one outlet, and an array of piezoelectric actuators positioned adjacent to the at least one outlet for ejecting a portion of the fluid in the microfluidic channel.

[0140] Clause 44. A system described in clause 42 or clause 43, wherein the microfluidic device comprises one or more sensors positioned adjacent to the first region of the microfluidic channel for sensing each particle flowing through the microfluidic channel.

[0141] Clause 45. A system described in any one of clauses 42 to 44, wherein the microfluidic device comprises a first piezoelectric actuator positioned adjacent to a second region of the microfluidic channel downstream from the first region to deflect each particle flowing through the microfluidic channel to a respective one of the two or more output channels based on signals from the one or more sensors.

[0142] Clause 46. A non-transitory computer-readable storage medium having stored thereon program code instructions that, when executed by a processor, cause the processor to perform a method according to any one of clauses 1 to 40.

[0143] The terminology used in the description of embodiments herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the claims. In the description and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will also be understood that the term "and / or" herein refers to and includes any and all possible combinations of one or more of the associated listed items. It will also be understood that the terms "comprises" and / or "comprising" herein specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0144] As used herein, the term "if" is optionally interpreted to mean "when," "upon," "in response to determining," "in response to detecting," or "in accordance with a determination that," depending on the context. Similarly, the phrase "when determined" or "when [stated condition or event] is detected" is optionally interpreted to mean "upon determining," "in response to determining," "upon detecting [stated condition or event]," "in response to detecting [stated condition or event]," or "in accordance with a determination that [stated condition or event] is detected," depending on the context.

[0145] The foregoing description has been described with reference to specific embodiments for purposes of explanation. However, the illustrative description above is not intended to be exhaustive or to limit the scope of the claims to the precise form disclosed. Many modifications and variations are possible in light of the above teachings. The embodiments have been chosen and described to best explain the principles and their practical applications of the various described embodiments, so as to enable others skilled in the art to best utilize the principles and various described embodiments, with various modifications suited to the particular use contemplated.

Claims

1. 1. A system for generating a signal map, comprising: Memory and one or more processors; a plurality of instructions and data stored in the memory and configured to be executed by the one or more processors; wherein the plurality of instructions comprises: generating a signal profile for each block of a plurality of blocks obtained from a tissue sample, each block comprising a plurality of cells; generating a spatial map of signals from a plurality of signal profiles corresponding to the plurality of blocks; Includes instructions for system.

2. the instructions for generating the signal profile for each block of the plurality of blocks include: instructions for generating an impedance profile for each block of the plurality of blocks; The system of claim 1 .

3. the plurality of instructions: further comprising instructions for creating a tree of cell populations based on occurrence scores from the spatial map. The system of claim 1 .

4. the instructions for creating the tree of cell populations include: identifying an abnormal cell population in said tissue sample; determining the spatial relationship between said abnormal cell populations; further comprising instructions for: The system of claim 3.

5. the plurality of instructions: and instructions for acquiring signal data for a plurality of cell types, wherein the instructions for creating the tree of cell populations based on occurrence scores from the spatial map include instructions for comparing the spatial map to the signal data to identify cell types. The system of claim 3.

6. The signal profile of each block includes an impedance profile, and the instructions include: and instructions for determining a histogram of impedance gradient boundaries in the plurality of blocks from a plurality of impedance profiles corresponding to the plurality of blocks, wherein the tree of cell populations is created based on the histogram of impedance gradient boundaries. The system of claim 3.

7. the instructions for creating the tree of cell populations include: determining the probability of occurrence of the cell population; ranking the cell populations according to the determined probabilities; Includes instructions for The system of claim 3.

8. the plurality of instructions: and instructions for recording coordinates of each block of the plurality of blocks, wherein the spatial map of the signal is generated based on a plurality of recorded coordinates corresponding to the plurality of blocks. The system of claim 1 .

9. The system of claim 1 , wherein the instructions for generating the signal profile for each block include instructions for inputting a respective plurality of cells for each block into a microfluidic device.

10. The system of claim 9 , wherein the microfluidic device comprises a microfluidic sensor chip.

11. 10. The system of claim 9, wherein the microfluidic device comprises one or more electromagnetic field generators configured to apply a preset frequency to the respective plurality of cells of each block input into the microfluidic device.

12. The instructions for generating the signal profile for each block include, for each block: measuring the impedance of each of the plurality of cells a plurality of times; aggregating the multiple measured impedances; Includes instructions for The system of claim 9.

13. 13. The system of claim 12, wherein for each of the plurality of times, the impedance of the respective plurality of cells is measured using a different set of parameters.

14. The system of claim 13 , wherein the different sets of parameters include applied frequency, flow rate, and sample fluid mixture.

15. 10. The system of claim 9, wherein each input cell is processed through the microfluidic device within a time range of 0.5 seconds to 10 seconds.

16. a microfluidic device configured to detect the impedance of the cell; the signal profile of each block is an impedance profile generated based on detected impedance from the microfluidic device; The system of claim 2 .

17. the microfluidic device comprises: a substrate having a microfluidic channel with at least one outlet; an array of piezoelectric actuators positioned adjacent to the at least one outlet for ejecting a portion of fluid within the microfluidic channel; Equipped with 17. The system of claim 16.

18. 1. A method for generating a signal map, comprising: generating a signal profile for each block of a plurality of blocks obtained from a tissue sample, each block comprising a plurality of cells; generating a spatial map of signals from a plurality of signal profiles corresponding to the plurality of blocks; A method comprising:

19. generating the signal profile for each block of the plurality of blocks; generating an impedance profile for each block of the plurality of blocks; 20. The method of claim 18.

20. 1. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a system, cause the system to: generating a signal profile for each block of a plurality of blocks obtained from a tissue sample, each block comprising a plurality of cells; generating a spatial map of signals from a plurality of signal profiles corresponding to the plurality of blocks; performing an action including A non-transitory computer-readable storage medium.

Images