Gain-independent flow cytometry data

Abstract

This invention provides a system and method for providing gain-independent flow cytometry data. [Solution] Regardless of the detector gain setting, the median sample fluorescence intensity (MFI) can be maintained. In some such embodiments, the numerical scale used to represent the MFI may correspond to the true intensity of the input signal. As a result, quality control (QC) procedures can instead focus on maintaining the resolution performance of the flow cytometer. For example, QC procedures can instead focus on maximizing the signal-to-noise ratio (SNR) of one or more photodetectors. In some such embodiments, the frequency in which such QC procedures are performed can be reduced (e.g., from daily to weekly or monthly). In some such embodiments, repeated execution of QC procedures may eliminate the need to ensure a consistent sample MFI over time.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims priority and benefits of U.S. Provisional Patent Application No. 63 / 749,034, filed on 24 January 2025, which is incorporated herein by reference.

[0002] This technology relates to a system and method for providing gain-independent flow cytometry data. [Background technology]

[0003] Characterization of analytes in biological fluids is a crucial part of biological research, medical diagnosis, and the assessment of a patient's overall health and well-being. Detecting analytes in biological fluids such as human blood or blood products can provide results that can be useful in determining treatment protocols for patients with diverse medical conditions.

[0004] Flow cytometry is a technique used to characterize and, optionally, separate, biomolecules such as cells in blood samples or particles of interest in other types of biological or chemical samples. A flow cytometer typically comprises a sample reservoir for receiving a fluid sample, such as a blood sample, and a sheath reservoir containing sheath fluid. In a flow cytometer, particles in a biological sample (e.g., cells) are transported as a flow stream to a flow cell, while the sheath fluid is also guided to the flow cell. The flow stream is illuminated with light to characterize its components. Variations in the substances in the flow stream, such as morphology or the presence or absence of fluorescent labeling, can cause variations in the observed light, and these variations can enable characterization and separation. To characterize the components of the flow stream, light can be collided with the flow stream and collected. The light source of a flow cytometer is diverse and may include one or more broad-spectrum lamps, light-emitting diodes, or lasers. The light source is aligned with the flow stream, and the optical response from the illuminated particles is collected and quantified.

[0005] A flow cytometer comprises an optical tuning component for detecting optical signals and converting these signals into corresponding electrical signals, a detector, and an electronic photodetection system. Parameters are obtained through the processing of the electronic signals, which the user can then use to perform the desired analysis. Flow cytometers can be equipped with various types of photodetectors for detecting signals. When an optical signal (e.g., from a sample being analyzed in the flow cytometer) is incident on a photodetector, an electrical signal proportional to the incident optical signal is generated at its output. The gain of the photodetector can be determined by the ratio of the output signal to the input signal. The operating range of detection by the photodetector can be controlled by using the photodetector's gain to ensure that the fluorescence of the sample appears with high reliability within the photodetector's operating range, and / or to improve the resolution between positive and negative signals to facilitate the distinction between such signals. Typically, the gain of a photodetector is positively correlated with voltage, and therefore can be controlled by modulating the voltage applied to the photodetector. However, this correlation is complicated by many parameters, including the type of photodetector, the wavelength of the incident light, and the temperature.

[0006] Parameters measured using a flow cytometer typically include light of the excitation wavelength scattered by the particle at a narrow angle approximately along the forward direction (sometimes referred to as forward scattering (FSC)), excitation light scattered by the particle in a direction perpendicular to the excitation laser (sometimes referred to as side scattering (SSC)), and light emitted from fluorescent molecules in one or more detectors that measure signals across a wide spectral wavelength range, or light emitted from fluorescent dyes primarily detected in that particular detector or detector array. Identification of various cell types is possible through the light scattering properties and fluorescence emission resulting from labeling various cellular proteins or other components with fluorescent dye-labeled antibodies or other fluorescent probes.

[0007] A flow cytometer may further comprise a memory for recording measurement data and one or more processors for analyzing the data. For example, data storage and analysis may be performed using a computer connected to the detection electronic equipment. In some cases, the data may be stored in a tabular format, with each row corresponding to data for a given particle and each column corresponding to each measured feature. Furthermore, the use of a standard file format, such as the "FCS" file format, for storing data from the particle analyzer may facilitate data analysis by separate programs and / or machines. The data may be displayed in one-dimensional histograms and / or two-dimensional (2D) plots to facilitate visualization.

[0008] A common goal of flow cytometry analysis is to classify different populations of flow cytometer data as being associated with one or more different parameters. This classification is influenced by the degree to which different populations of flow cytometer data are separated. The degree of separation between two populations is determined by the difference in their mean or median values, as well as their within-population variance (spread). Although the difference in mean values ​​between populations of flow cytometer data received from stained samples is already defined, users can improve the separation between different populations by adjusting the gain of each detector, thereby improving the resolution of the populations and / or suppressing the spread of the populations.

[0009] Existing flow cytometers provide photodetector measurement data scaled to arbitrary units. The position of a given input optical signal on this arbitrary unit scale can be increased or decreased by adjusting (e.g., increasing or decreasing) the gain of one or more photodetectors. The ability of a flow cytometer to operate consistently on a daily basis (e.g., producing nearly the same output signal over time for the same input sample) depends on many factors, such as temperature and optical-mechanical alignment, which can fluctuate randomly over time. To maintain performance, manufacturers have developed routine quality control (QC) procedures for adjusting the photodetector gain of flow cytometers. These procedures often use target measurement signal values ​​relative to a reference input light source, such as QC beads (e.g., broad-spectrum fluorescent beads). After measuring the median fluorescence intensity (MFI) of the QC beads over various voltages (e.g., by a voltage titration procedure), the MFI is maintained at a target value for a particular detector channel associated with one or more photodetectors by calculating a resolution adjustment coefficient. This adjustment coefficient is then applied to the gain of one or more photodetectors to maintain the consistency of the MFI.

[0010] However, standardizing photodetector data in this way requires users to be able to reach detector gain settings that maintain consistent MFI, even if it means compromising resolution, without having to manually change each detector setting. In other words, users can ensure consistent sample MFI over time by optimizing or adjusting each detector setting, but they cannot achieve both simultaneously. [Overview of the project]

[0011] A system and method for providing gain-independent flow cytometry data are disclosed. In some embodiments, the median sample fluorescence intensity (MFI) can be maintained regardless of the detector gain setting. In some such embodiments, the numerical scale used to represent the MFI may correspond to the true intensity of the input signal. As a result, quality control (QC) procedures can instead focus on maintaining the resolution performance of the flow cytometer. For example, QC procedures can instead focus on maximizing the signal-to-noise ratio (SNR) of one or more photodetectors. In some such embodiments, the frequency in which such QC procedures are performed can be reduced (e.g., from daily to weekly or monthly). In some such embodiments, repeated execution of QC procedures may eliminate the need to ensure a consistent sample MFI over time.

[0012] One aspect of the present disclosure relates to a system comprising a detector, memory, and one or more processors. The detector may be configured to measure particle-modulated light emitted by particles in a flow stream in raw space. The memory may store (a) a lookup table associating each of a plurality of user-selectable parameters for adjusting the detector's gain with each of a plurality of linear gains, or (b) a predetermined function associating a provided user-selectable parameter for adjusting the detector's gain with the corresponding linear gain. One or more processors may be configured to (a) receive raw measurements of particle-modulated light from the detector, (b) retrieve user-selectable parameters for adjusting the detector's gain, (c) retrieve linear gains associated with the retrieved user-selectable parameters by accessing the lookup table or using the predetermined function, and (d) convert the raw measurements into gain-independent values ​​by dividing the raw measurements by the retrieved linear gains.

[0013] In some embodiments, the memory stores a lookup table, and the linear gain associated with the acquired user-selected parameter is obtained by accessing the lookup table. In some embodiments, the memory stores a predetermined function, and the linear gain associated with the acquired user-selected parameter is obtained by using the predetermined function. In some embodiments, the user-selectable parameter is a voltage that adjusts the gain of the detector when applied to the detector.

[0014] In some embodiments, one or more processors are further configured to (a) apply a scaling factor to a gain-independent value to obtain a scaled gain-independent value, and (b) decompose the scaled gain-independent value to obtain a scaled decomposed value. In some embodiments, one or more processors are further configured to (a) decompose a gain-independent value to obtain a decomposed value, and (b) apply a scaling factor to the decomposed value to obtain a scaled decomposed value. In some embodiments, one or more processors are further configured to obtain a target gain for the detector, and the scaling factor is based on the target gain to be obtained. In some embodiments, the scaling factor takes into account the drift of the detector's sensitivity over time, the fluorophore properties of the particles, or the labeling properties of the particles.

[0015] In some embodiments, one or more processors are further configured to calculate the optimal gain of the detector. In some such embodiments, the signal-to-noise ratio (SNR) of the detector is optimized by the optimal gain, and one or more processors are further configured to change the gain of the detector to the optimal gain. In some embodiments, one or more processors are further configured to (a) gradually increase the gain of the detector so that the detector collects light from the flow stream at each of a plurality of successively increasing gains; (b) obtain a baseline noise level from the detector at each of the plurality of successively increasing gains; (c) calculate the limit of detection (LoD) for each of the plurality of successively increasing gains; and (d) determine the optimal gain based on the calculated LoD.

[0016] In some embodiments, one or more processors are further configured to separate multiple particles based on a threshold defined using gain-independent values. In some embodiments, one or more processors are further configured to generate a graphical user interface for display, including a plot of gain-independent values. In some embodiments, the graphical user interface further includes graphical elements for enabling or disabling the conversion of raw measurement results received from the detector to gain-independent values. In some embodiments, the graphical user interface further includes graphical elements for adjusting the detector gain.

[0017] Another aspect of the present disclosure is: (a) receiving, by one or more processors, raw measurement results of particle-modulated light emitted by particles in a flow stream from a detector; (b) obtaining, by one or more processors, user-selectable parameters for adjusting the gain of the detector; (c) obtaining, by one or more processors, a linear gain associated with the obtained user-selectable parameters by accessing a look-up table or using a predetermined function, wherein the look-up table associates each of a plurality of user-selectable parameters for adjusting the gain of the detector with a respective plurality of linear gains, and the predetermined function associates a provided user-selectable parameter for adjusting the gain of the detector with a corresponding linear gain; and (d) converting the raw measurement results to gain-independent values by dividing the raw measurement results by the obtained linear gain.

[0018] Yet another aspect of the present disclosure relates to a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to: (a) receive, from a detector, raw measurement results of particle-modulated light emitted by particles in a flow stream; (b) obtain user-selectable parameters for adjusting the gain of the detector; (c) obtain a linear gain associated with the obtained user-selectable parameters by accessing a look-up table or using a predetermined function, wherein the look-up table associates each of a plurality of user-selectable parameters for adjusting the gain of the detector with a respective plurality of linear gains, and the predetermined function associates a provided user-selectable parameter for adjusting the gain of the detector with a corresponding linear gain; and (d) convert the raw measurement results to gain-independent values by dividing the raw measurement results by the obtained linear gain.

[0019] Yet another aspect of the present disclosure relates to a method comprising: (a) obtaining, by one or more processors, decomposition measurement results of particle-modulated light emitted by particles in a flow stream; (b) obtaining, by one or more processors, initial user-selected parameters for adjusting the gain of a detector; (c) obtaining, by one or more processors, updated user-selected parameters for adjusting the gain of the detector; (d) deriving, by one or more processors, a scaling factor based on a ratio of the updated user-selected parameters and the initial user-selected parameters; and (e) obtaining a gain-independent value by applying the scaling factor to the decomposition measurement results by one or more processors. In some embodiments, the method further comprises obtaining, by one or more processors, a re-normalized gain-independent value by applying a re-scaling factor to the gain-independent value.

[0020] Yet another aspect of the present disclosure relates to a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to: (a) obtain decomposition measurement results of particle-modulated light emitted by particles in a flow stream; (b) obtain initial user-selected parameters for adjusting the gain of a detector; (c) obtain updated user-selected parameters for adjusting the gain of the detector; (d) derive a scaling factor based on a ratio of the updated user-selected parameters and the initial user-selected parameters; and (e) obtain a gain-independent value by applying the scaling factor to the decomposition measurement results. BRIEF DESCRIPTION OF THE DRAWINGS <正确的英文内容>

[0021] [Figure 1] It is a functional block diagram of a system for analyzing and displaying biological events. [Figure 2A] It is a diagram showing a particle separation system. [Figure 2B] It is a diagram showing an embodiment of the particle separation system of FIG. 2A including a deflection plate. 注:原文中处内容缺失,我按照格式要求保留原样并标注了<正确的英文内容>,请根据实际情况补充完整。 [Figure 3] This is a functional block diagram of the particle analysis system. [Figure 4] This is a diagram showing a system for flow cytometry. [Figure 5A-1] This is a diagram showing a FIRE (radiofrequency tagged emission) particle sorting system. [Figure 5A-2] These are the lateral leaves shown in Figure 5A-1. [Figure 5B] This figure shows image-based particle sorting data processing. [Figure 6A] This graph visually represents the aspects of the gain calibration process in a data normalization method. [Figure 6B] This graph visually represents the resolution adjustment process of a data normalization method. [Figure 6C] This graph visually represents the aspects of the gain normalization process in a data normalization method. [Figure 7A] This plot shows the effect of the user changing the gain when the corresponding measurement result is a gain-dependent value. [Figure 7B] This plot shows the effect of the user changing the gain when the corresponding measurement result is a scaled, gain-independent value. [Figure 8A] This diagram shows how to operate a flow cytometer. [Figure 8B] This diagram shows how to operate a flow cytometer. [Figure 8C] This diagram shows how to operate a flow cytometer. [Figure 9] This is a flowchart illustrating the operation of a flow cytometer. [Figure 10] This is a block diagram of a particle analysis and / or sorting system. [Figure 11A] This diagram shows the graphical user interface of a flow cytometer with the option to display gain-dependent data selected. [Figure 11B]Figure 11A shows the graphical user interface when the option to display scaled, gain-independent data is selected. [Figure 11C] This is a plot in the graphical user interface shown in Figure 11A. [Figure 11D] This is a plot in the graphical user interface shown in Figure 11B. [Modes for carrying out the invention]

[0022] Embodiments of the Disclosure will be described in detail with reference to the drawings, where the same reference numerals in the drawings identify similar or identical elements. It should be understood that the embodiments of the Disclosure are merely examples of the various forms in which the Disclosure may be embodied. To avoid obscuring the Disclosure by providing more detail than necessary, well-known functions or configurations will not be described in detail. Accordingly, the specific structural and functional details of the Disclosure herein should not be construed as limiting in any way, but rather as representative grounds to teach those skilled in the art how to employ the Disclosure in various ways, based on the claims and in substantially and appropriately detailed structures.

[0023] Figure 1 is a functional block diagram of a system 100 for analyzing and displaying biological events. As shown, the system 100 comprises a biological instrument 102, a storage device 104, a display device 106, a keyboard 108, a mouse 110, and a controller 190. In some embodiments, at least some of these components are I 2The components can communicate with each other via wired connections using standard or custom communication protocols such as C (Inter-Integrated Circuit), Serial Peripheral Interface (SPI), Controller Area Network (CAN), Universal Asynchronous Receive and Transmit (UART), Ethernet, or Universal Serial Bus (USB). In some embodiments, at least some of these components can wirelessly communicate with each other using standard or custom communication protocols such as Bluetooth, WiFi, ZigBee, Z Wave, NED Infrared (IR), Code Division Multiple Access (CDMA), Pan-European Digital Mobile Telephone System (GSM), or Long-Term Evolution (LTE).

[0024] The biomedical device 102 may be configured to acquire biological event data. For example, in some embodiments, the biomedical device 102 may be a flow cytometer configured to acquire flow cytometry event data. Suitable flow cytometry systems include, for example, BD FACSCanto® flow cytometer, BD FACSCanto® II cytometer, BD Accuri® flow cytometer, BD Accuri® C6 Plus flow cytometer, BD FACSCelesta® flow cytometer, BD FACSLyric® flow cytometer, BD FACSVerse® flow cytometer, BD FACSymphony® flow cytometer, BD LSRFortessa® flow cytometer, BD LSRFortessa® X-20 flow cytometer, BD FACSPresto® flow cytometer, BD FACSVia® flow cytometer, BD FACSCalibur® cell sorter, BD FACSCount® cell sorter, BD FACSLyric® cell sorter, BD Via® cell sorter, BD Influx® cell sorter, BD Jazz® cell sorter, BD Aria® cell sorter, and BD This may include flow cytometers manufactured by Becton, Dickinson and Company (Franklin Lakes, NJ), such as the FACSAria® II cell sorter, BD FACSAria® III cell sorter, BD FACSAria® Fusion cell sorter, BD FACSMelody® cell sorter, BD FACSymphony® S6 cell sorter, or BD FACSDiscover® S8 cell sorter, or other similar flow cytometers.

[0025] In some of these embodiments, the bioinstrument 102 may be configured to separate one or more components of a biological sample identified, for example, based on an estimate of the relevant fluorophore. As used herein, the term “sorting” may mean separating components of a biological sample (e.g., non-cellular particles such as cells or biomolecules), and in some cases, delivering the separated components to one or more sample collection containers. For example, in some embodiments, the bioinstrument 102 may be configured to sort a biological sample by separating one of the components from a particular biological sample having two or more components and delivering it to a sample collection container.

[0026] As used herein, the term “biological sample” may refer to a whole organism, plant, or fungus, or a subset of animal tissue, cells, or components, which in certain cases are found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic umbilical cord blood, urine, vaginal fluid, or semen. Therefore, “biological sample” refers to a subset of a naturally occurring organism or its tissue, as well as homogenates, lysates, or extracts prepared from a subset of an organism or its tissue, including, but not limited to, plasma, serum, cerebrospinal fluid, lymph, skin, respiratory, gastrointestinal, cardiovascular, and urogenital sections, tears, saliva, milk, blood cells, tumors, or organs. A biological sample may be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In some embodiments, the biological sample is a liquid sample such as blood or its derivatives (e.g., plasma, tears, urine, semen, etc.), and in some cases, it is a blood sample (including whole blood) such as blood obtained by venipuncture or fingertip puncture (for example, the blood in this case may or may not be combined with any reagents prior to the assay, such as preservatives or anticoagulants).

[0027] In some embodiments, the biological sample source is a “mammal” or “mammalian,” and these terms are used broadly to refer to organisms belonging to the class Mammalia, which includes carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). For example, in some cases, the subject is human. The techniques described herein may be applied to samples obtained from subjects of both sexes and / or any developmental stage (e.g., neonates, infants, juveniles, adolescents, and adults). The techniques described herein may also be performed on samples from other test animals (e.g., “non-human subjects”), including, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0028] In some embodiments, the biological device 102 is published in U.S. Patent Publication No. 2021 / 0239530A1, U.S. Patent Publication No. 2021 / 0333192A1, U.S. Patent Publication No. 2021 / 0349005A1, U.S. Patent Publication No. 2022 / 0091017A1, U.S. Patent Publication No. 2022 / 0108774A1, U.S. Patent Publication No. 2022 / 0136956A1, U.S. Patent Publication No. 2023 / 0014629A1, U.S. Patent Publication No. 2023 / 0062339A1, and U.S. Patent Publication No. 202 This may encompass one or more aspects of the systems disclosed in U.S. Patent Publication No. 3 / 0243735A1, U.S. Patent Publication No. 2023 / 0296493A1, U.S. Patent Publication No. 2023 / 0393049A1, U.S. Patent Publication No. 2024 / 0133791A1, U.S. Patent Publication No. 2024 / 0192122A1, U.S. Patent Publication No. 2024 / 0280465A1, U.S. Patent Publication No. 2024 / 0344983A1, and / or International Publication No. WO2024 / 173053A1, all of which are incorporated herein by reference.

[0029] The storage device 104 includes a memory medium capable of storing information, such as a hard drive, memory card, ROM, RAM, DVD, CD-ROM, writable memory, and / or read-only memory. The storage device 104 may be configured to receive and store biological event data (e.g., flow cytometry event data) from the controller 190. The storage device 104 may also be configured to allow the controller 190 to read such biological event data.

[0030] The display device 106 may be a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphical interface. In some embodiments, the display device 106 may be configured to receive display data (for example, from the controller 190). The display data may include plots of biological event data and / or gates that outline parts of the plots. The display data may also include particle parameters and / or saturation detector data. The display device 106 may also be configured to modify the presented information according to signals received from the biological instrument 102, the storage device 104, the keyboard 108, the mouse 110, and / or the controller 190.

[0031] The controller 190 may comprise one or more processors, one or more application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and / or other similar components. The controller 190 may also comprise memory media capable of storing information, such as hard drives, memory cards, ROMs, RAMs, DVDs, CD-ROMs, writable memory, and / or read-only memory. As shown in the figure, the controller 190 is implemented as a single controller. However, in some embodiments, the controller 190 may be replaced by multiple controllers that communicate with each other and distribute the load of the controller 190. As shown in the figure, the controller 190 is communication-coupled to a biomedical device 102, a storage device 104, a display device 106, a keyboard 108, and a mouse 110, each capable of sending and / or receiving information to and from the controller 190.

[0032] In some embodiments, the controller 190 may be configured to receive bioevent data (e.g., flow cytometry event data) from the biomedical device 102. The controller 190 may also be configured to provide the display device 106 with a graphic display that may include a first plot of the bioevent data. In some such embodiments, the controller 190 may be configured to render regions of interest as gates around the bioevent data set shown by the display device 106 (e.g., superimposed on the first plot). In some embodiments, the gates may be logical combinations of one or more graphic regions of interest drawn on a single parameter histogram or bivariate plot. In some embodiments, the controller 190 may be configured to display the bioevent data within the gates on the display device 106 in a different color from other events in the bioevent data outside the gates. For example, the controller 190 may be configured to render the colors of the bioevent data contained within the gates in a different color from the bioevent data outside the gates.

[0033] In some embodiments, the controller 190 may generate a user interface that accepts exemplary events to be sorted. The controller 190 may also be configured to provide and display the user interface on the display device 106. In some embodiments, the user interface may include controls for accepting exemplary events, images, and / or gates. The exemplary events, images, and / or gates may be provided prior to the collection of event data relating to the biological sample, or based on an initial set of events relating to a portion of the biological sample.

[0034] In some embodiments, the controller 190 may be configured to receive input signals from input devices such as a keyboard 108 and / or a mouse 110. For example, the keyboard 108 and / or mouse 110 may provide the controller 190 with input signals that identify gates on the display device 106 or gates on which an operation is performed via the display device 106 (for example, by clicking on or within a desired gate on which the cursor is placed). In some embodiments, the keyboard 108 and / or mouse 110 can be replaced with a touchscreen, a stylus, an optical detector, a voice recognition system, and / or another type of input device. In some embodiments, the input device may include multiple input functions. For example, as shown in Figure 1, the mouse 110 may have a right mouse button and a left mouse button, each capable of generating an input signal.

[0035] In some embodiments, the controller 190 may initiate the execution of one or more processes upon receiving input signals (for example, from input devices such as a keyboard 108 and / or a mouse 110). For example, upon receiving input signals, the controller 190 may initiate input for other processing, such as changing the way in which the bioevent data is displayed, changing the portion of the bioevent data displayed on the display device 106, and / or selecting a population of interest for particle segregation. In another example, upon receiving input signals, the controller 190 may facilitate the gating process described above by initiating automatic modification of the plot visualization. This modification may be based on a specific distribution of the bioevent data received by the controller 190.

[0036] Figure 2A shows the particle separation system 200. In some embodiments, the biomedical device 102 may comprise the particle separation system 200 and / or other similar systems. As used herein, the term “particle” may refer to any particulate matter that can be transported in a flowstream. Particles of interest may include, but are not limited to, cells, beads, polypeptides, polynucleotides, and combinations thereof. Particles may be of any preferred size. For example, in some embodiments, particles may have a diameter in the range of 1 μm to 20 μm. In some embodiments, the particle separation system 200 is a cell separation system.

[0037] The system 200 comprises a droplet-forming transducer 202 (e.g., a piezoelectric oscillator) coupled to a fluid conduit 201 having an orifice 203. Within the fluid conduit 201, a sheath liquid 204 hydrodynamically focuses a sample fluid 206 containing particles 209 into a flow stream 208. Within the flow stream 208, the particles 209 are arranged in a line across the investigation area 211 illuminated by a light source 212. Vibration of the droplet-forming transducer 202 causes the flow stream 208 to split into multiple droplets 210, some of which contain particles 209.

[0038] During operation, a detection station 214 (e.g., an event detector) may identify a particle of interest as it crosses the investigation area 211. The detection station 214 becomes the input to the timing circuit 228, which in turn becomes the input to the flash charging circuit 230. At the droplet splitting point, given by the timing-specified droplet delay (Δt), a flash charge is applied to the flow stream 208, causing the droplet of interest to become charged. The droplet of interest may contain one or more particles. Charged droplets can then be separated by deflecting them into a container such as a collection tube, multiwell sample plate, or microwell sample plate through the operation of a deflection plate (see Figure 2B). In some embodiments, each well or microwell may be associated with a specific droplet of interest. As shown in Figure 2A, droplets can be collected in a drain receptacle 238.

[0039] The detection system 216 (e.g., a droplet boundary detector) may be configured to automatically determine the phase of the droplet driving signal as the particle of interest passes through the investigation region 211. In some embodiments, the detection system 216 may enable the system 200 to accurately calculate the position of each detected particle in the droplet. The detection system 216 may also generate an amplitude signal 220 and / or a phase signal 218, which are then supplied (e.g., via an amplifier 222) to an amplitude control circuit 226 and / or a frequency control circuit 224. The amplitude control circuit 226 and / or frequency control circuit 224 then control the droplet formation transducer 202.

[0040] In some embodiments, the sorting electronics (e.g., a detection station 214, a detection system 216, and a processor 240) may be coupled to a memory (not shown) configured to store detected biological events and sorting decision information based thereon. The sorting decision information may be included in the particle event data. In some embodiments, the detection station 214 and the detection system 216 may be implemented as a single detection unit or be communication-coupled so that event measurement results collected by the detection station 214 or the detection system 216 can be provided to another component of the system 200. In some embodiments, the sorting electronics, the amplitude control circuit 226, and / or the frequency control circuit 224 may be included in a larger control system comprising one or more controllers (e.g., controller 190).

[0041] The fluid conduit 201 may be implemented in a variety of sizes and shapes. For example, as shown in Figure 2A, the fluid conduit 201 may be implemented as a nozzle and the orifice 203 may be implemented as a nozzle orifice. In some such embodiments, the fluid conduit 201 may include a proximal cylindrical portion defining a longitudinal axis and a distal frustoconical portion terminating in a flat surface having a nozzle orifice (e.g., orifice 203) that crosses the longitudinal axis. In some embodiments, the fluid conduit 201 may include one of these portions but not the other. In some embodiments, the length of the proximal cylindrical portion (for example, measured along the longitudinal axis) may be in the range of 1 mm to 15 mm. In some embodiments, the length of the distal frustoconical portion (for example, measured along the longitudinal axis) may be in the range of 1 mm to 10 mm. In some embodiments, the diameter of the internal chamber of the fluid conduit 201 may be in the range of 1 mm to 10 mm.

[0042] The orifice 203 may be realized in a variety of sizes and shapes. For example, the orifice 203 may have a linear cross-sectional shape (e.g., square, rectangle, trapezoid, triangle, hexagon, etc.), a curved cross-sectional shape (e.g., circle, oval, etc.), and / or an irregular cross-sectional shape (e.g., a shape in which a parabolic base is joined to a flat apex). For example, in some embodiments, the orifice 203 may be a circular orifice. In some embodiments, the width of the orifice 203 may be in the range of 1 μm to 20,000 μm.

[0043] The sample fluid 206 may contain particles (e.g., particles 209) from a biological sample (e.g., a liquid sample). In some embodiments, the biological sample may have multiple fluorophores. In some embodiments, the fluorescence spectrum of each fluorophore in the sample overlaps with the fluorescence spectrum of at least one other fluorophore. In some embodiments, particle-modulated light may be emitted by particles 209 after irradiation with light from a light source 212. In some cases, the particle-modulated light is fluorescence. Fluorescence may be emitted, for example, by particles having a fluorescent dye, after the fluorescent dye is irradiated with excitation wavelength light. In other cases, the particle-modulated light is side-scattered light (e.g., light refracted and reflected by the surface and internal structure of the particles). In yet another case, the particle-modulated light includes both fluorescence and side-scattered light. In some embodiments, the particle-modulated light includes forward-scattered light (e.g., light passing through or around the particles substantially in the forward direction).

[0044] In some embodiments, the fluid conduit 201 comprises a sample injection port (not shown) configured to provide a flow of a biological sample (e.g., a liquid sample) to an internal chamber of the fluid conduit 201. Depending on the desired characteristics of the flowstream 208, the velocity of the sample delivered to the internal chamber of the fluid conduit 201 by the sample injection port may be in the range of, for example, 1 μL / s to 500 μL / s. In some embodiments, the sample injection port may be an orifice located in the wall of the fluid conduit 201 or located at the proximal end of the fluid conduit 201 (e.g., on the line of the orifice 203). In some embodiments, the sample injection port may have a linear cross-sectional shape, a curved cross-sectional shape, and / or an irregular cross-sectional shape. For example, in some embodiments, the sample injection port may have a circular cross-sectional shape. In some embodiments, the width of the sample injection port may be in the range of 0.1 mm to 5.0 mm.

[0045] In some embodiments, the fluid conduit 201 comprises a sheath fluid injection port (not shown) configured to supply a flow of sheath fluid (e.g., sheath fluid 204) to an internal chamber of the fluid conduit 201. In some embodiments, the sheath fluid may be supplied together with a biological sample to create a layered flowstream of sheath fluid surrounding a flowstream of the biological sample (e.g., sample fluid 206). Depending on the desired characteristics of the flowstream 208, the velocity of the sheath fluid delivered to the internal chamber of the fluid conduit 201 by the sheath fluid injection port may be in the range of, for example, 1 μL / s to 2,500 μL / s. In some embodiments, the sheath fluid injection port may be an orifice located in the wall of the fluid conduit 201. In some embodiments, the sheath fluid injection port may have a linear cross-sectional shape, a curved cross-sectional shape, and / or an irregular cross-sectional shape. For example, in some embodiments, the sheath fluid injection port may have a circular cross-sectional shape. In some embodiments, the width of the sheath fluid injection port may be in the range of 0.1 mm to 5.0 mm.

[0046] In some embodiments, the diameter of the flowstream 208 can be adjusted in proportion to the pressure applied to the particles 209 when injected into the sheath fluid 204. In some embodiments, the flow rate of the sheath fluid 204 may remain constant. Thus, the injection of particles 209 into the sheath fluid 204 and hydrodynamic focusing generates a laminar flow, allowing the particles 209 to travel along the same axis at approximately the same velocity. In some embodiments, the flowstream 208 may contain a liquid sample injected from a sample tube. In some embodiments, the flowstream 208 contains a buffer such as water. In some embodiments, the flowstream 208 contains a rapidly flowing narrow liquid stream, which is configured such that linearly separated particles (e.g., particles 209) being transported inside are separated from each other in a single line.

[0047] As described above, the flowstream 208 is illuminated by the light source 212 in the investigation area 211. The size of the investigation area 211 may vary depending on the properties of the fluid conduit 201, such as the size and / or shape of the internal chamber, orifice 203, sample injection port, and / or sheath fluid injection port of the fluid conduit 201. In some embodiments, the width of the investigation area 211 may be in the range of 0.01 mm to 5 mm, and the length of the investigation area 211 may be in the range of 0.01 mm to 50 mm. In some embodiments, the investigation area 211 may be configured to facilitate illumination of a planar cross section of the flowstream 208 by a diffusion field of a predetermined length (e.g., by a diffusion laser or lamp). In some embodiments, the investigation area 211 includes a transparent window (not shown) to facilitate illumination of the flowstream 208 over a predetermined length (e.g., in the range of 1 mm to 10 mm). In some embodiments, the investigation area 211 may be configured to allow light in the range of 100 nm to 1500 nm to pass through. For example, in some embodiments, the investigation area 211 may include a transparent material such as glass, quartz, sapphire, or plastic.

[0048] In some embodiments, a cuvette (not shown) may be placed in the investigation area 211. In some embodiments, the cuvette may be configured to allow light in the range of 100 nm to 1500 nm to pass through. For example, in some embodiments, the cuvette may include a transparent material such as glass, quartz, sapphire, or plastic. In some embodiments, the cuvette may have a passage extending through its interior. As used herein, the term “flow cell” may refer to a component such as a cuvette that includes a flow channel having a liquid flow stream (e.g., flow stream 208) for transporting particles (e.g., particles 209) in a sheath fluid (e.g., sheath fluid 204). Any convenient flow cell that delivers the liquid flow stream to the investigation area (e.g., investigation area 211) may be employed as the flow cell described herein. In some embodiments, the flow cell is a cylindrical flow cell, a frustoconical flow cell, or a flow cell comprising a proximal cylindrical portion defining a longitudinal axis and a distal frustoconical portion terminating in a flat surface having an orifice transverse the longitudinal axis. In some embodiments, the flow cell is a stream-in-air flow cell in which the optical investigation of particles in a liquid flow stream is performed in free space.

[0049] The light source 212 may be configured to emit light with wavelengths in the range of 200 nm to 1500 nm. In some embodiments, the light source 212 may be a broadband light source configured to emit light with a wide range of wavelengths (for example, extending to 500 nm and above). In some embodiments, the light source 212 may be a narrowband light source configured to emit light with a narrow range of wavelengths (for example, extending to 50 nm and below). In some embodiments, the light source 212 may be positioned 0.001 mm to 100 mm from the flowstream 208. In some embodiments, the light source 212 may be configured to irradiate the flowstream 208 at an angle in the range of 10° to 90° (for example, with respect to the vertical axis of the flowstream 208).

[0050] The light source 212 may be implemented as a single light source or as multiple discrete light sources (for example, a combination of different or similar types of light sources). When two or more light sources are used, the flowstream 208 may be irradiated simultaneously, sequentially, or in combination thereof by the light sources. When two or more light sources are used to sequentially irradiate the flowstream 208, the time each light source irradiates the flowstream 208 may be in the range of 0.001 μs to 60 μs. In some embodiments, the corresponding irradiation duration may be the same or different for each light source. In some embodiments, the time between each sequential irradiation may be in the range of 0.001 μs to 60 μs. In some embodiments, these times may be the same or different.

[0051] In some embodiments, the light source 212 is a laser. For example, the light source 212 may be a gas laser such as a helium-neon laser, argon laser, krypton laser, xenon laser, nitrogen laser, CO2 laser, CO laser, argon-fluorine (ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenon-chlorine (XeCl) excimer laser, xenon-fluorine (XeF) excimer laser, or a combination thereof. As another example, the light source 212 may be a dye laser such as a stilbene laser, coumarin laser, rhodamine laser, or a combination thereof. As yet another example, the light source 212 may be a metal vapor laser such as a helium-cadmium (HeCd) laser, helium-mercury (HeHg) laser, helium-selenium (HeSe) laser, helium-silver (HeAg) laser, strontium laser, neon-copper (NeCu) laser, copper laser, gold laser, or a combination thereof. As yet another example, the light source 212 may be a solid-state laser such as a ruby ​​laser, Nd:YAG laser, NdCrYAG laser, Er:YAG laser, Nd:YLF laser, Nd:YVO4 laser, Nd:yCa4O(BO3)3 laser, Nd:YCOB laser, titanium sapphire laser, thulium YAG laser, ytterbium YAG laser, ytterbium 2O3 laser, cerium-doped laser, or a combination thereof. As yet another example, the light source 212 may be a semiconductor diode laser, an optically excited semiconductor laser (OPSL), a doubled or tripled frequency implementation of any of the aforementioned lasers, or a combination thereof.

[0052] In some embodiments, the light source 212 is a light source other than a laser. For example, the light source 212 may be a lamp such as a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, or a combination thereof. As another example, the light source 212 may be a light-emitting diode ("LED") such as a continuous-spectrum broadband LED, a superluminescent light-emitting diode, a semiconductor LED, a wide-spectrum LED, a narrow-wavelength LED, or a combination thereof.

[0053] The light source 212 may be configured to irradiate the flowstream 208 continuously or at discrete intervals. For example, the light source 212 may be configured to irradiate the flowstream 208 continuously by a continuous-wave laser or the like that irradiates the flowstream 208 continuously in the investigation area 211. As another example, the light source 212 may be configured to irradiate the flowstream 208 at discrete intervals such as every 0.001 ms, every 0.1 ms, every 1 ms, every 10 ms, or every 1000 ms. In some such embodiments, the system 200 may include one or more additional components that result in intermittent irradiation of the flowstream 208. For example, the system 200 may include one or more laser beam choppers (e.g., manual or computer-controlled beam stops for blocking the flowstream 208 and exposing it to the light source 212).

[0054] In some embodiments, the light source 212 may be configured to generate two or more frequency-shifted rays. In some such embodiments, the light source 212 comprises a laser, a high-frequency generator, and an acousto-optical device configured to generate two or more angle-deflected laser beams. In some embodiments, the laser may be a pulsed laser or a continuous-wave laser. In some embodiments, the high-frequency generator may be a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator. In some embodiments, the high-frequency generator may be configured to generate two or more high-frequency drive signals. In some embodiments, the high-frequency drive signals may have an amplitude in the range of about 0.001 V to about 500 V. In some embodiments, the high-frequency drive signals may have a frequency in the range of about 0.001 MHz to about 500 MHz. In some embodiments, the acousto-optical device may be an acousto-optical deflector. In some embodiments, the acousto-optical device may be configured to generate an angle-deflected laser beam using light from the laser and high-frequency drive signals from the high-frequency generator.

[0055] In some embodiments, the light source 212 may be configured to generate two or more angularly deflected laser beams having a desired intensity profile. For example, the light source 212 may be configured to generate two or more angularly deflected laser beams having the same and / or different intensities. In some embodiments, the light source 212 may be configured to generate an output laser beam whose intensity decreases from the edge to the center along the horizontal axis. For example, the intensity of the angularly deflected laser beam at the center of the output beam may be in the range of 0.1% to about 99% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. Similarly, in some embodiments, the light source 212 may be configured to generate an output laser beam whose intensity increases from the edge to the center along the horizontal axis. For example, the intensity of the angularly deflected laser beam at the edge of the output beam may be in the range of 0.1% to about 99% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In some embodiments, the light source 212 may be configured to generate an output laser beam having a Gaussian intensity profile or a top-hat intensity profile along the horizontal axis.

[0056] In some embodiments, the light source 212 may be configured to generate two or more spatially separated angular-bend laser beams. For example, the angular-bend laser beams may be separated by a distance in the range of 0.001 μm to 5,000 μm. In some embodiments, the light source 212 may be configured to generate two or more angular-bend laser beams that overlap (for example, adjacent angular-bend laser beams along the horizontal axis). In some embodiments, the overlap of adjacent angular-bend laser beams (for example, the overlap of beam spots) may be in the range of 0.001 μm to 100 μm.

[0057] In some embodiments, the detection station 214 and / or the detection system 216 may comprise a photodetector array of multiple photodetectors. In some embodiments, the photodetector array may have a length in the range of 0.01 mm to 100 mm, a width in the range of 0.01 mm to 100 mm, and / or 0.1 mm 2 ~10,000mm 2 The total area of ​​the range may be such that. In some embodiments, the detection station 214 and / or the detection system 216 may be configured to measure light continuously or at discrete intervals. For example, in some embodiments, the detection station 214 and / or the detection system 216 may be configured to continuously acquire measurement results of collected light. As another example, in some embodiments, the detection station 214 and / or the detection system 216 may be configured to acquire measurement results at discrete intervals such as every 0.001 ms, every 0.1 ms, every 1 ms, every 10 ms, or every 1000 ms.

[0058] In some embodiments, at least a portion of the photodetectors in the detection station 214 and / or detection system 216 may be configured to measure collected light of one or more wavelengths. For example, in some embodiments, at least a portion of the photodetectors may be configured to measure collected light over a wavelength range (e.g., 200 nm to 1000 nm). In some embodiments, at least a portion of the photodetectors may be image and / or optical sensors such as active pixel sensors (APS), avalanche photodiodes (APDs), complementary metal-oxide-semiconductor (CMOS) image sensors, N-type metal-oxide-semiconductor (NMOS) image sensors, charge-coupled devices (CCDs), high-sensitivity charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors, or combinations thereof. The photodetectors may be arranged in a variety of shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, circular, or irregular configurations. In some embodiments, at least some of the photodetectors may be oriented toward each other (for example, in the XZ plane) at angles ranging from 10° to 180°. In some embodiments, each photodetector may have a width ranging from 5 μm to 250 μm, a length ranging from 5 μm to 250 μm, and / or 25 μm. 2 ~10,000 μm 2 It may have an effective surface having a total area within the range. In some embodiments, the gain of at least a portion of the photodetectors of the detection station 214 and / or the detection system 216 can be adjusted (for example, by the processor 240 or another control system).

[0059] Figure 2B shows one embodiment of the system 200, including deflection plates 252 and 254. During operation, an electric charge may be applied via a stream-charging wire in the valve. This forms a droplet stream 210 containing particles 209. As described above, the particles 209 may be illuminated by one or more light sources (e.g., light source 212) to generate light scattering and fluorescence information. Information about the particles may be analyzed, for example, by sorting electronic equipment (e.g., detection station 214, detection system 216, and processor 240) and / or another detection system. The deflection plates 252 and 254 may be independently controlled to guide the charged droplets to a desired collection receptacle (e.g., one of receptacles 272, 274, 276, or 278) by attracting or repelling the droplets. For example, as shown in Figure 2B, deflection plates 252 and 254 can be controlled to guide particles along a first path 262 to receptacle 274, or along a second path 268 to receptacle 278. If the particles are not of interest (e.g., do not exhibit scattering or emission information within a specified separation range), deflection plates 252 and 254 can be controlled to allow the particles to continue along the channel 264. Such uncharged droplets can enter the waste receptacle (e.g., via the aspirator 270).

[0060] Figure 3 is a functional block diagram of the particle analysis system 300. System 300 can be used for particle analysis and / or characterization with or without physical separation of particles into a collection container (for example, as described above with respect to the particle separation system 200 in Figures 2A and 2B). As shown, system 300 comprises a fluid engineering system 302, a detection system 304, a control system 306, and a detection station 308. Fluid engineering system 302 comprises a sample tube 305 and a flow stream in the sample tube through which sample particles 303 travel along a common sample path 309. Fluid engineering system 302 can be comparable to many of the components of system 200. For this reason, fluid engineering system 302 may be configured according to any of the embodiments described above with respect to, for example, the fluid conduit 201, droplet-forming transducer 202, orifice 203, flow stream 208, and / or drain receptacle 238.

[0061] The detection system 304 may be configured to collect one or more signals from each particle 303 as it passes along a common sample path 309 to one or more detection stations (e.g., detection station 308). As shown in the figure, detection station 308 monitors a survey area 307 of the common sample path 309. In some embodiments, detection may include the detection of light or one or more other properties of the particle 303 as it passes through the survey area 307. In some such embodiments, the gain of at least a portion of the photodetector of detection station 308 may be adjusted (e.g., by detection system 304, control system 306, or another control system). As shown in the figure, system 300 comprises one detection station and one survey area. However, in some embodiments, system 300 may comprise multiple detection stations and multiple corresponding survey areas. Furthermore, in some embodiments, at least a portion of the detection stations may monitor two or more survey areas.

[0062] Each signal collected by the detection system 304 may be assigned a signal value that constitutes a data point for each particle. As described above, this data may also be referred to as event data. The data points may be multidimensional data points containing values ​​for each property measured for the particle. In some embodiments, the detection system 304 may be configured to collect a series of such data points at a certain time interval.

[0063] The control system 306 may comprise one or more processors, amplitude control circuits, and / or frequency control circuits (for example, as described above with respect to system 200). In some embodiments, the control system 306 may be configured to generate a calculated signal frequency for at least a portion of the time interval based on the Poisson distribution and / or number of data points collected by the detection system 304 in the time interval. In some such embodiments, the control system 306 may be further configured to generate an experimental signal frequency based on the number of data points in that portion of the time interval. In some embodiments, the control system 306 may additionally compare the experimental signal frequency with a calculated signal frequency or a predetermined signal frequency. In some embodiments, the detection system 304, the control system 306, and / or the detection station 308 may be included in a larger control system comprising one or more controllers (for example, controller 190).

[0064] In some embodiments, a particle 303 may be associated with one or more fluorophores (e.g., chemically (e.g., covalently, ionically) or physically), and the detection station 308 may be configured to collect particle-modulated light emitted by the particle 303 (e.g., after irradiation by light from a light source such as light source 212). In some such embodiments, the detection system 304 and / or control system 306 may be configured to calculate the amount of fluorophores associated with the particle from the particle-modulated light. For example, in some embodiments, the relative or absolute amount of each fluorophore associated with the particle is calculated from the particle-modulated light. If the spectra of fluorophores associated with particle 303 overlap, the analysis may further include spectrally decomposing the particle-modulated light (e.g., by calculating a spectral decomposition matrix).

[0065] In some embodiments, particles can be identified or classified by the detection system 304 and / or control system 306 based on the relative amount of each fluorophore determined to be associated with the particle. In some such embodiments, particles can be identified or classified by comparing the relative or absolute amount of each fluorophore associated with the particle to a control sample having known particles. In some embodiments, particles can be identified or classified by performing spectroscopic analysis and / or other assay analysis of a population of particles having calculated relative or calculated absolute amounts of associated fluorophores. In some embodiments, the detection system 304 and / or control system 306 may be configured to separate particles 303 (e.g., by one or more deflection plates) based on estimates of the fluorophore associated with each particle 303 by operating a fluid engineering system 302.

[0066] Figure 4 shows a system 400 for flow cytometry. As shown, the system 400 comprises a flow cytometer 410, a controller / processor 490, and memory 495. The flow cytometer 410 comprises one or more excitation lasers 415a-415c, a focusing lens 420, a flow cell 425, a forward scatter detector 430, a side scatter detector 435, a fluorescence collection lens 440, one or more beam splitters 445a-445g, one or more bandpass filters 450a-450e, one or more long-pass ("LP") filters 455a and 455b, and one or more fluorescence detectors 460a-460f.

[0067] Many of the components of System 400 can be equivalent to the components of the systems described above (for example, Systems 100, 200, and 300). For example, (a) excitation lasers 415a-415c, (b) flow cell 425, and (c) fluorescence detectors 460a-460f can be equivalent to (a) light source 212, (b) investigation areas 211 and 307, and (c) detection station 214, detection system 216, and detection station 308, respectively. Thus, any of the components of System 400 can be configured according to any of the corresponding embodiments described above. Furthermore, in some embodiments, an aspect of System 400 may be incorporated into one of the systems described above. For example, an equivalent optical adjustment component may be incorporated into any of the systems described above.

[0068] During operation, the excitation lasers 415a–415c emit light in the form of laser beams. As shown in the figure, the wavelengths of the laser beams emitted from the excitation lasers 415a–415c are 488 nm, 633 nm, and 325 nm, respectively. However, in other embodiments, different wavelengths and / or different ranges of wavelengths may be used. The laser beams are first guided through beam splitters 445a and 445b. Beam splitter 445a transmits 488 nm light and reflects 633 nm light. Beam splitter 445b transmits ultraviolet (UV) light (e.g., light with wavelengths in the range of 10 nm to 400 nm) and reflects 488 nm and 633 nm light.

[0069] The laser beam is then guided to a focusing lens 420, which focuses the beam onto a portion of the flow stream (e.g., flow stream 208) where particles of the biological sample may be placed within the flow cell 425 (e.g., see investigation areas 211 and 307). In some embodiments, the focusing lens 420 may be configured to reduce the diameter of the beam. In some embodiments, the focusing lens 420 may have a magnification in the range of 0.1 to 0.95. In some embodiments, the focal length of the focusing lens 420 is in the range of 5 mm to 20 mm. In some embodiments, the flow cell 425 may be part of a fluid engineering system (e.g., fluid engineering system 302) that guides particles in the stream (e.g., one at a time) to the focused laser beam emitted from the focusing lens 420 for investigation. For example, the flow cell 425 may be a flow cell in a benchtop cytometer or a nozzle tip in a stream-in-air cytometer.

[0070] The laser beam interacts with the biological sample particles through diffraction, refraction, reflection, scattering, and / or absorption, with re-emission at various different wavelengths, depending on the characteristics of the particles (their size, internal structure, and the presence of one or more fluorescent molecules due to adhesion to or natural presence on or within the particles). In addition to fluorescence emission, the diffracted, refracted, reflected, and scattered light can be routed through one or more of the beam splitters 445a-445g, bandpass filters 450a-450e, longpass filters 455a and 455b, and / or fluorescence collection lenses 440 to one or more of the forward scatter detector 430, side scatter detector 435, and one or more of the fluorescence detectors 460a-460f.

[0071] The fluorescence collection lens 440 collects light emitted by particle-laser beam interaction (e.g., particle-modulated light) and routes the light toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a to 450e, allow the passage of a narrow range of wavelengths. For example, bandpass filter 450a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number gives the range of the spectral band. Thus, the 510 / 20 filter extends 10 nm on both sides of the center of the spectral band, i.e., from 500 nm to 520 nm. Short-pass filters transmit light with wavelengths below a specified wavelength. Long-pass filters, such as long-pass filters 455a and 455b, transmit light with wavelengths above a specified wavelength. For example, long-pass filter 455a is a 670 nm long-pass filter and transmits light above 670 nm. Filters are often selected to optimize the detector's specificity for a particular fluorescent dye. The filter may be configured such that the spectral band of the light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0072] A beam splitter directs light of different wavelengths in different directions. Beam splitters can be characterized by filtering properties such as short-pass and long-pass. For example, beam splitter 445g is a 620SP beam splitter, meaning that beam splitter 445g transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in different directions. In some embodiments, one or more of the beam splitters 445a to 445g may include optical mirrors such as dichroic mirrors.

[0073] The forward scatter detector 430 is positioned slightly off-axis from the direct beam passing through the flow cell 425 and is configured to detect diffracted light (e.g., excitation light passing through or around the particle in a nearly forward direction). The intensity of the light detected by the forward scatter detector 430 is determined by the overall size of the particle. The forward scatter detector 430 may include a photodiode. The side scatter detector 435 is configured to detect refracted and reflected light from the surface and internal structure of the particle, the light of which tends to be stronger as the particle structure becomes more complex. Fluorescence emission from fluorescent molecules associated with the particle may be detected by one or more of the fluorescence detectors 460a-460f. The side scatter detector 435 and / or fluorescence detectors 460a-460f may include photomultiplier tubes. The signals detected by the forward scatter detector 430, the side scatter detector 435, and / or fluorescence detectors 460a-460f can be converted into electronic signals (e.g., voltages) by the detectors. This data may provide information about the biological sample. In some such embodiments, the gain of at least one of the forward scatter detector 430, the side scatter detector 435, and / or the fluorescence detectors 460a-460f may be adjusted (for example, by the controller / processor 490 or another control system).

[0074] Various modifications are possible to the flow cytometer 410. For example, the flow cytometer 410 may have any number of lasers, beam splitters, filters, and / or detectors of various wavelengths and various different configurations. For example, the flow cytometer 410 may have one or more additional optical adjustment components, each configured to increase the dimension of light, focus light, split light, and / or parallelize light. For example, the flow cytometer 410 may have a magnifying lens configured to increase the dimension of light. As another example, the flow cytometer 410 may have a collimator. As used herein, the term “collimate” may mean optically adjusting the collinearity of light propagation or suppressing the divergence of light from a common axis of propagation. Optionally, parallelization may include narrowing the spatial cross-section of the light rays. In some embodiments, the collimator may comprise one or more mirrors, one or more curved lenses, or a combination thereof.

[0075] As shown in Figure 4, the flow cytometer 410 is controlled by a controller / processor 490. Furthermore, measurement data from the detector is stored in memory 495 and can be processed by the controller / processor 490. Although not explicitly shown, the controller / processor 490 is coupled to the detector and receives output signals from them. It may also be coupled to the electrical and electromechanical components of the flow cytometer 410 to control the laser, fluid flow parameters, etc. The system 400 may also be provided with an input / output (I / O) mechanism 497. In some embodiments, the memory 495, controller / processor 490, and I / O 497 may be provided as an integrated part of the flow cytometer 410. In some embodiments, a display may constitute part of the I / O mechanism 497 to present experimental data to the user of the system 400. Alternatively, the memory 495, controller / processor 490, and / or some or all of the I / O mechanism may be included in a larger control system comprising one or more controllers (e.g., controller 190). In some embodiments, some or all of the memory 495 and / or the controller / processor 490 may be in wireless or wired communication with the flow cytometer 410. In some embodiments, the controller / processor 490 may be configured together with the memory 495 and I / O 497 to perform various functions related to the preparation and analysis of flow cytometer experiments.

[0076] As shown in the figure, system 400 comprises six different detectors that detect fluorescence in six different wavelength bands (each of which may be referred to herein as a “filter window”), as defined by the configuration of filters and / or splitters in the beam path from flow cell 425 to each detector. Different fluorescent molecules used in flow cytometry experiments will each emit light in a wavelength band specific to their respective type. The specific fluorescent labels used in the experiment and their associated fluorescence emission bands can generally be selected to match the filter windows of the detectors. However, if more detectors are provided and more labels are used, a perfect correspondence between the filter windows and the fluorescence emission spectra may not be possible. As a general fact, while the peak of the emission spectrum of a particular fluorescent molecule may lie within the filter window of a particular detector, a portion of the emission spectrum of that label will overlap with the filter windows of one or more other detectors. This is sometimes referred to as spillover.

[0077] In some embodiments, I / O497 may be configured to accept data relating to a flow cytometer experiment having a fluorescently labeled panel and multiple cell populations having multiple markers, each cell population having a subset of multiple markers. Also in some embodiments, I / O497 may be configured to accept one or more cell populations, marker density data, emission spectral data, data assigning labels to one or more markers, and biological data assigning one or more markers to cytometer configuration data. Flow cytometer experiment data, such as labeled spectral characteristics and flow cytometer configuration data, may also be stored in memory 495. In some embodiments, the controller / processor 490 may be configured to evaluate the assignment of one or more labels to markers.

[0078] Figures 5A-1 and 5A-2 show the FIRE (radiofrequency tagged emission) particle sorting system 500. As shown, the system 500 comprises a light irradiation component 500a, a photodetection system 500b, a sorting component 500c, a flow cell 507, an irradiation area 510, processors 550 and 551, and a sorting trigger 552. The light irradiation component 500a comprises a light source 501, beam splitters 502 and 505, acousto-optical devices (AODs) 503 and 504, and optical components 506. The photodetection system 500b comprises photodetectors 511-517, a beam splitter 520, and bandpass optical components 521, 522, 523, and 524. The sorting component 500c comprises a deflection plate 531 and a sample container 532.

[0079] Many of the components of System 500 can be equivalent to the components of the systems described above (for example, Systems 100, 200, 300, and 400). For example, (a) the light irradiation component 500a, (b) the irradiation area 510, (c) the photodetection system 500b, and (d) the deflection plate 531 can be equivalent to (a) the light source 212 and excitation lasers 415a-415c, (b) the investigation areas 211 and 307 and the flow cell 425, (c) the detection station 214, the detection system 216, the detection station 308, and the fluorescence detectors 460a-460f, and (d) the deflection plates 252 and 254, respectively. Thus, any of the components of System 500 can be configured according to any of the corresponding embodiments described above. Furthermore, in some embodiments, an aspect of System 500 may be incorporated into one of the systems described above. For example, an equivalent optical adjustment component and / or AOD may be incorporated into one of the systems described above.

[0080] As shown in the figure, a light source 501 (e.g., a 488 nm laser) generates an output ray 501a, which is then split into beams 502a and 502b by a beam splitter 502. When ray 502a propagates through an AOD 503 (e.g., an acousto-optic deflector), an output beam 503a is generated having one or more angularly deflected rays. In some embodiments, the output beam 503a includes a local oscillation beam and multiple high-frequency comb beams. When ray 502b propagates through an AOD 504 (e.g., an acousto-optic deflector), an output beam 504a is generated having one or more angularly deflected rays. In some embodiments, the output beam 504a includes a local oscillation beam and multiple high-frequency comb beams. The output beams 503a and 504a generated from AOD503 and 504, respectively, are combined by a beam splitter 505 to produce an output beam 505a, which is then transported through an optical component 506 (e.g., an objective lens) to irradiate particles in the flow cell 507. In some embodiments, AOD503 splits a single laser beam into an array of beamlets, each having a different optical frequency and angle. AOD504 adjusts the optical frequency of a reference beam, which is then superimposed with the array of beamlets by the beam splitter 505.

[0081] The output beam 505a irradiates sample particles 508 propagating through the flow cell 507 (for example, together with the sheath fluid 509) in the irradiation area 510. As shown in the figure, in the irradiation area 510, multiple beams (for example, angularly deflected high-frequency shift rays shown as dots across the irradiation area 510) overlap with the reference local oscillation beam (shown as shaded lines across the irradiation area 510). The overlapping beams exhibit beat behavior because they each have different optical frequencies, so each beamlet has a different frequency f 1-n It receives sinusoidal modulation.

[0082] Particle-modulated light from the irradiated sample particles 508 is transported to the photodetector system 500b. A forward scatter detector 511 generates a forward scatter image 511a from the particle-modulated light. A side scatter detector 512 generates a side scatter image 512a from the particle-modulated light. A bright-field photodetector 513 generates an optical loss image 513a from the particle-modulated light. In some embodiments, the forward scatter detector 511 and the side scatter detector 512 are photodiodes (e.g., APDs). In some cases, the bright-field photodetector 513 is a photomultiplier tube (PMT). Fluorescence from the irradiated sample particles 508 is also detected by fluorescence detectors 514-517. In some embodiments, the fluorescence detectors 514-517 are photomultiplier tubes. In some such embodiments, the gains of the photodetectors 511-517 can be adjusted (e.g., by processors 550 and / or 551 or another control system).

[0083] Particle-modulated light from the irradiated sample particles 508 is guided through the beam splitter 520 to the side-scatter light detector 512 and the fluorescence light detectors 514-517. Bandpass optics 521, 522, 523, and 524 (e.g., dichroic mirrors) propagate light of a predetermined wavelength to the fluorescence light detectors 514-517. In some embodiments, optical component 521 is a 534 nm / 40 nm bandpass. In some embodiments, optical component 522 is a 586 nm / 42 nm bandpass. In some embodiments, optical component 523 is a 700 nm / 54 nm bandpass. In some embodiments, optical component 524 is a 783 nm / 56 nm bandpass. The first number represents the center of the spectral band. The second number gives the range of the spectral band. Thus, the 510 / 20 filter extends 10 nm on both sides of the center of the spectral band, i.e., from 500 nm to 520 nm.

[0084] Data signals generated in response to light received by photodetectors 511-517 are analyzed by processors 550 and 551. Based on the data signals generated in processors 550 and 551, images 511a-517a can be generated in each photodetector channel. Each photodetector channel may correspond to one of the photodetectors 511-517. Image-aware sorting can be performed in response to a sorting signal generated in sorting trigger 552. For example, in response to a sorting signal, sorting component 500c can deflect particles 508 to a sample container 532 or waste stream 533 by operating a deflection plate 531. In some embodiments, processors 550 and 551 and / or sorting trigger 552 may be included in a larger control system comprising one or more controllers (e.g., controller 190).

[0085] Figure 5B illustrates image-based particle sorting data processing. In some cases, image-based particle sorting data processing is a low-latency data processing pipeline. As shown, multiple photodetectors each encode an image and generate high-frequency modulated pulses (see "Waveform" in Figure 5B). Fourier analysis is performed to reconstruct the image from the modulated pulses. The image processing pipeline generates a set of image features (see "Image Analysis" in Figure 5B), which are combined with features from the pulse processing pipeline (see "Event Packet" in Figure 5B). Then, real-time sorting and classification electronics classify particles based on the image features and generate sorting decision information used for selective charging of droplets.

[0086] In any of the systems described above, one or more processors (e.g., one or more processors included in the biomedical device 102, controller 190, detection station 214, detection system 216, processor 240, detection system 304, control system 306, detection station 308, controller / processor 490, processor 550, processor 551, and / or sorting trigger 552) may be configured to adjust the gain of one or more detectors (e.g., one or more detectors included in detection station 214, detection system 216, detection station 308, fluorescence detectors 460a-460f, and / or photodetectors 511-517). The one or more detectors may be photodetectors and / or image sensors. In some embodiments, the gain of a detector may be determined by the ratio of the output signal to the input signal. In some such embodiments, the gain of a detector may be controlled by modulating the voltage applied to the detector, since the gain of a detector is positively correlated with voltage. As used herein, the term “detector voltage” may refer to the voltage applied to a detector to adjust its gain. In some embodiments, it is convenient that one or more processors may be configured to provide gain-independent values ​​by correcting the measurement results collected by one or more detectors. As a result, in some such embodiments, the median sample fluorescence intensity (MFI) can be maintained regardless of the gain of one or more detectors. Furthermore, in some such embodiments, one or more processors may be configured to ensure consistency of the sensitivity, resolution, and / or dynamic range of one or more detectors by optimizing their gains. For example, in some embodiments, one or more processors may be configured to maximize the signal-to-noise ratio (SNR) of one or more detectors by adjusting the gain of one or more detectors.

[0087] To provide gain-independent values, one or more processors may be configured to perform a data normalization method. The data normalization method may include the step of calculating a linear gain as a function of a user-selectable parameter (e.g., detector voltage). In the use herein, the term “gain calibration” may be used to describe this process. In some embodiments, the gain calibration process may derive a lookup table of linear changes (e.g., increases or decreases) in particle MFI with respect to detector voltage by acquiring measurement results (e.g., generated by at least one of the detectors) of a reference light source (e.g., an LED pulse generator) at various detector voltages. According to the resulting lookup table, the user can adjust the detector gain (rather than the voltage), which has a more intuitive impact on data scaling between settings. In some embodiments, the lookup table derived from the gain calibration process may be used to guarantee linear MFI as the detector voltage changes. In some embodiments, the values ​​stored in the lookup table may derive a function (e.g., continuous time) for calculating the linear gain as a function of a user-selectable parameter (e.g., detector voltage). Furthermore, in some embodiments, the gain calibration process may provide instrument performance criteria such as characterization of system background noise in the calibration unit and / or resolution of each detector in the calibration unit. Figure 6A is a graph visually representing the values ​​derived by the gain calibration process and stored in an exemplary lookup table. For simplicity, Figure 6A shows data for a single detector. However, the gain calibration process may generate lookup tables (or corresponding functions) for multiple detectors in the instrument (for example, multiple detectors, each present in a flow cytometer).

[0088] The data normalization method may further include one or more steps to determine and / or set a target gain required to achieve the target MFI for each of the multiple detectors (e.g., associated with quality control (QC) beads such as multispectral beads (MSBs)). In its use herein, the term “resolution adjustment” may be used to describe this process. Figure 6b is a graph visually representing the measured MFI of the MSB at multiple detector voltages. This graph shows the target MFI (in this particular example, 8 × 10⁻⁶). 4 The diagram further includes a dashed line in the diagram and an arrow pointing to the corresponding target gain (29 dB in this particular example). In other embodiments, the target MFI and target gain may be set to different values. For example, these values ​​may need to be changed to accommodate different types of QC beads. In some embodiments, the target MFI is 1 to 2 64 This may also be the case. Similarly, in some embodiments, the target gain may be 0 to 100 dB. For simplicity, Figure 6B shows data for a single detector. However, in the resolution adjustment process, the target gain may be determined and / or set for multiple detectors in the instrument (for example, each of multiple detectors present in the flow cytometer).

[0089] The data normalization method may further include applying one or more gain scaling factors to the measurement results from the detector to make them gain-independent. For the purposes of use herein, the term "gain normalization" may be used to represent this process. In some embodiments, in this process, the measurement results from the detector may be divided by the linear gain associated with the user-selected gain. The linear gain may be determined by a look-up table or function resulting from the gain calibration process described above. In some embodiments, the result of the gain normalization process naturally results in gain-independent decomposition data values, for example, the same decomposition MFI is achieved for a given sample regardless of the gain setting, which is convenient. FIG. 6C is a graph showing the effect of dividing the measured MFI of FIG. 6B by the linear gain associated with the user-selected gain. As shown, regardless of the gain selected by the user, the measured gain-independent MFI is 3×10 3 which is less than the target MFI of 8×10 4 . In other embodiments, the measured MFI may be greater than or equal to the target MFI. For simplicity, FIG. 6C shows the data of a single detector. However, the gain normalization process may be performed for multiple detectors in the instrument (for example, each of the multiple detectors is present in a flow cytometer).

[0090] According to the gain normalization process, it is convenient because inter-instrument analysis can be made possible by the same instrument acquisition and data analysis template. Also, downstream platform and cross-sectional analysis can be improved. For example, in multicolor flow cytometry experiments that require spectral decomposition and / or compensation, the gain normalization process can facilitate the use of single-stain control data that can be obtained across different gain settings and / or daily QC conditions. More specifically, in some embodiments, the single-stain data can be directly reused for compensation and / or decomposition in combination with the gain settings and / or instrument drift changes that have already been considered.

[0091] To generate meaningful data for standard units (e.g., statistical photoelectrons, receptor quantity, receptor detector, fluorophore quantity, fluorophore density, particle size, particle refractive index, or particle surface area), the data normalization method may further include the step of applying one or more assay scaling factors to the raw gain-independent measurement results obtained by the gain normalization process (e.g., gain-independent MFI measurement results in Figure 6C) or data derived from such measurement results. For example, the use of an assay scaling factor may convert the gain-independent measurement results for any detection unit into the number of fluorescent molecules that generated the optical signal, or to calculate the number of proteins on a particle by taking into account the number of fluorophores per label. As used herein, the term “assay normalization” may be used to describe this process. In some embodiments, the gain-independent measurement results obtained by the gain normalization process may reside in a “raw space” or “detector space” having a number of dimensions equal to the number of detectors. In some embodiments, data derived from such measurement results may reside in a “compensation space” or “decomposition space” having a number of dimensions equal to the number of fluorescent dyes in the sample. Compensation or decomposition data can be generated by mathematical processes of fluorescence compensation or spectral decomposition (e.g., in a full-spectrum cytometer). Spectral decomposition methods include, for example, weighted least squares (WLS) based spectral decomposition and ordinary least squares (OLS) based spectral decomposition. In some embodiments, assay scaling factors may be provided for each of multiple detectors and / or fluorescent dyes.

[0092] In some embodiments, a target value may be used to calculate the assay scaling factor. In some embodiments, the target value is the target MFI (8 × 10 shown in Figures 6B and 6C). 4 The target MFI may be (e.g., 3 × 10⁻¹⁰ as shown in Figure 6C). In some such embodiments, the corresponding assay scaling factor is the measured gain-independent MFI (e.g., 3 × 10⁻¹⁰ as shown in Figure 6C).3 This may be equal to the difference between the measured gain-independent MFI and the target MFI. In the background of Figure 6C, multiplying the gain-independent measurement result by such a value results in a measurement result of 8 × 10 4 This shifts to a target MFI. In some embodiments, the target value may be the number of fluorophores, antibodies, or proteins detected on the particle. For example, if the QC beads have the equivalent of 100,000 fluorescein isothiocyanate (FITC) molecules and approximately 5 FITC fluorophores are measured per antibody, the corresponding assay scaling factor is considered to be equal to 20,000 (i.e., 100,000 / 5). The number of proteins can be detected by multiplying the gain-independent measurement result by this value.

[0093] In some embodiments, one or more assay scaling factors may be used to compensate for one or more properties of an instrument (e.g., a flow cytometer) (e.g., tolerances such as particle velocity and / or laser intensity). In some such embodiments, one or more assay scaling factors may be used both to convert gain-independent data (e.g., raw gain-independent measurement results obtained by a gain normalization process or decomposed data derived from such measurement results) from any detection unit to a more significant unit (e.g., the number of fluorescent molecules that generated the optical signal) and to compensate for one or more properties of the instrument. For example, the assay scaling factor may be modified to take into account the tolerances of the instrument by incorporating a variable based on the measured fluorescent dye sample. In some such embodiments, the assay scaling factor may take into account the sensitivity drift of the instrument, the fluorophore properties of the biological sample, and / or the labeling properties of the biological sample.

[0094] As described above, one or more assay scaling factors may be applied to raw, gain-independent measurement results obtained by a gain-normalization process or to decomposed data derived from such measurement results. Applying one or more assay scaling factors to decomposed data (rather than measurement results in original space) is advantageous because it can promote performance improvements (for example, by reducing the required computing resources). For example, in some embodiments, one or more assay scaling factors may be applied to the axes of a graph displaying the decomposed data, rather than to the transformation of the entire underlying raw data. Applying one or more assay scaling factors to decomposed data can reduce the computing resources required by the instrument hardware (e.g., FPGA).

[0095] In the embodiments described above with respect to Figures 6A to 6C, the gain normalization process is performed on the raw data (e.g., detector measurement results in the "original space" or "detector space" having a number of dimensions equal to the number of detectors). However, in some embodiments, it is considered advantageous to maintain gain-dependent scaling of the raw data (e.g., due to constraints of the computing hardware for real-time calculations for cell separation) while performing gain-independent scaling of the resolved data (e.g., detector measurement results in the "compensated space" or "resolved space" having a number of dimensions equal to the number of fluorescent dyes in the sample). To achieve this, the gain normalization process uses, for example, the following equation to set the initial gain G 0 The initial spectral matrix M recorded 0 By modifying the settings, the measurement results from the detector may be decomposed using a new gain setting G.

[0096]

number

[0097] In Equation 1, the matrix on the left contains the gain scaling factor for each detector. For example, in the case of detector m, the gain scaling factor is ratio

number

number

[0098] The central matrix M in Equation 1 0 This is the initial spectral matrix M 0 This includes the spectral matrix coefficients. For example, in the case of detector m, the measurement result

number

[0099] The right-hand matrix of Equation 1 includes a rescaling factor for renormalizing each column n of the intermediate matrix generated by multiplying the left-hand and middle matrices. In some embodiments, these rescaling factors cause the values ​​of each column to be between 0 and 1. Multiplying the left-hand matrix by the middle matrix generates an intermediate matrix containing spectral matrix coefficients rescaled to match the new gain setting. Then, by multiplying this intermediate matrix by the right-hand matrix, these values ​​are rescaled so that the gain-independent spectral matrix coefficients are normalized. Subsequently, the use of the new matrix M(G) can generate resolved data from raw detector data recorded with linear gain G, which is matrix M 0 and gain G 0 The decomposed data generated from the raw data obtained will have the same scaling (for example, the same sample measured under both conditions will have the same decomposed MFI for each decomposition parameter).

[0100] In some embodiments, each rescaling factor in the right-hand matrix of Equation 1 may be determined by the following equation:

[0101]

number

[0102] In some embodiments, the matrix M(G) in Equation 1 may be multiplied by another matrix containing assay scaling factors. As described above, the use of these assay scaling factors may be used to convert gain-independent data of any detection unit to a more significant unit (e.g., the number of fluorescent molecules that generated the optical signal) and / or to compensate for one or more properties of the instrument (e.g., a flow cytometer) (e.g., tolerances such as particle velocity and / or laser intensity).

[0103] In some embodiments, it may be convenient to use gain-dependent measurement results (e.g., initial uncorrected data from one or more detectors), unscaled gain-independent measurement results (e.g., obtained by a gain-normalization process), and / or scaled gain-independent measurement results (e.g., obtained by a gain-and-assay normalization process). For example, in some embodiments, gain-dependent measurement results may be used for the segregation of one or more particles in a flow stream. Furthermore, in some such embodiments, scaled gain-independent measurement results may be presented to the user (e.g., via a display such as the display device 106). Gain-independent measurement results may make it easier for the user to define one or more thresholds for segregating particles. In some embodiments, gain-independent measurement results may enable the user to define more precise thresholds (e.g., gates) for segregating one or more particles (e.g., cells).

[0104] As described above, the data normalization process may include one or more steps for gain calibration, resolution adjustment, gain normalization (e.g., application of one or more gain scaling factors), and / or assay normalization (e.g., application of one or more assay scaling factors). In some embodiments, this process may be conveniently combined with a process for optimizing the gain settings of an instrument (e.g., a flow cytometer). For example, in some embodiments, optimizing the gain settings of the flow cytometer may ensure consistency in sensitivity, resolution, and / or dynamic range. As a result, resolution and MFI may be standardized simultaneously across the entire instrument, for example. This may facilitate the reuse of acquisition templates across platforms over time. It is also considered beneficial in terms of consistency of data quality with spectral decomposition.

[0105] In some embodiments, the data normalization process may be specifically combined with a process for adjusting the gain of detectors to maximize the signal-to-noise ratio (SNR) of the detectors. An example of a process for adjusting the gain of one or more detectors to maximize the SNR of one or more detectors is disclosed in U.S. Patent Application Publication 2023 / 0296493A1, which is incorporated herein by reference. For example, the gain normalization process described above may be combined with a gain optimization method which includes illuminating a flow stream with light from a light source, gradually increasing the gain of one or more detectors configured to collect light from the flow stream, obtaining a baseline noise level from one or more detectors at each gain, calculating the detection limit (LoD) for each gain, and determining the optimal gain by evaluating the calculated LoD. However, it will be recognized by those skilled in the art that the gain can be optimized in a variety of ways.

[0106] As used herein, the term “baseline noise” refers to the baseline electronic signal from a detector (e.g., an electronic signal originating from the operating electronic components of the detector or the optical components of a photodetector). In some embodiments, baseline noise includes electronic signals present in the system, such as electronic signals generated by the light source or other electronic subcomponents of a photodetector. In some embodiments, baseline noise includes electronic signals resulting from vibrations or thermal effects from components of the system. In some embodiments, baseline noise includes optical signals, such as light from an illumination source in the system (e.g., from one or more lasers present in a flow cytometer). In some embodiments, baseline noise arises from a combination of zero-mean electronic thermal noise (e.g., electrical background) and photon shot noise (e.g., optical background) resulting from, for example, ambient light and / or elastically scattered light. In some embodiments, baseline noise is associated with sample fluid properties (e.g., the amount of dye in the sample). In some embodiments, baseline noise can be determined by calculating the mean square error of the detector signal in the absence of particle-based signals or events. In some embodiments, obtaining baseline noise involves calculating the moving mean square error of the signal generated by the detector.

[0107] As discussed herein, the term “LoD” represents the smallest measurable particle-modulated optical signal that can be observed with sufficient confidence or statistical significance. In some embodiments, the LoD represents the smallest measurable particle-modulated optical signal corresponding to a noisy standard deviation. In some embodiments, the LoD is calculated based on gain and baseline noise level. For example, in some embodiments, calculating the LoD involves generating a ratio between gain and baseline noise level. If the baseline noise is a moving mean square error, calculating the LoD may involve finding the reciprocal of the ratio between gain and baseline noise level. In some embodiments, the LoD can be calculated in real time (for example, by monitoring the baseline variation at a given gain).

[0108] In some embodiments, evaluating the calculated LoD involves generating an LoD curve containing the calculated LoDs for each gain in a plurality of successively increasing gains. In other words, the LoDs calculated for each gain are plotted as a function of the gains. In some embodiments, after the calculation of individual LoDs, the LoDs are added as data points on the LoD curve. In some embodiments, the LoD curve can be evaluated for the presence or absence of inflection points (e.g., points where the concavity of the curve changes). In some embodiments, the optimized gain may be one of the plurality of successively increasing gains that is associated with the LoD at such inflection points.

[0109] Figure 7A shows the effect of changing the gain using a gain-dependent scaling value when analyzing a sample of beads with six different fluorescence intensities. Figure 7B shows the effect of changing the gain using a gain-independent scaling value when analyzing a sample of beads with six different fluorescence intensities. Comparing Figures 7A and 7B reveals several notable differences. First, in Figure 7A, although the emission intensity of the samples does not change, the median fluorescence intensity per population increases with the gain. On the other hand, in Figure 7B, the relative MFI of each population remains constant regardless of the gain. This indicates that the MFI data between samples in Figure 7B can be consistent regardless of the detector settings, whereas in Figure 7A, the differences between samples can be attributed to changes in the detector settings. Second, in Figure 7A, due to the spread of the population and the changes in MFI, it is difficult to quantify the change in SNR with the naked eye, and therefore it may be difficult to derive the selection of the optimal setting. In Figure 7B, since the MFI is consistent regardless of the setting, the optimal SNR can be clearly identified with the naked eye.

[0110] Figure 8A shows method 610 for operating a flow cytometer. In step 611, one or more user-defined settings of the flow cytometer are adjusted. For example, the gain of one or more detectors may be adjusted to ensure consistency in the sensitivity, resolution, and / or dynamic range of one or more detectors. In step 612, a resolution adjustment process is performed. In step 613, gain-dependent measurement results are acquired. In step 614, the gain-dependent measurement results are decomposed (for example, by WLS-based spectral decomposition or OLS-based spectral decomposition). In step 615, raw decomposed data is acquired. Although detector settings can be optimized or adjusted to ensure consistent sample MFI over time by using a method such as method 610, this adjustment cannot achieve both objectives simultaneously.

[0111] Figure 8B shows method 620 for operating the flow cytometer, which is similar to method 610 in Figure 8A, except that method 620 incorporates gain and assay normalization processes. In step 621, one or more user-defined settings of the flow cytometer are adjusted. In step 622, a resolution adjustment process is performed. In step 623, a gain normalization process is performed. In step 624, unscaled gain-independent measurement results are obtained from the gain normalization process. In step 625, an assay normalization process is performed on the unscaled gain-independent measurement results to obtain scaled gain-independent measurement results. In step 626, the scaled gain-independent measurement results are decomposed. In step 627, scaled decomposed data is obtained. As described above, the results of the gain normalization process naturally become gain-independent decomposed data values. Therefore, the same decomposition parameters can be used in steps 614 and 626. Using a method like method 620 is convenient because detector settings can be optimized and adjusted to ensure consistent sample MFI over time.

[0112] Figure 8C shows method 630 for operating the flow cytometer, which is similar to method 620 in Figure 8B. However, in method 630, the assay scaling factor is applied (e.g., in the assay normalization process) to the resolved data rather than the measurement results in the original space. In step 631, one or more user-defined settings of the flow cytometer are adjusted. In step 632, the resolution adjustment process is performed. In step 633, the gain normalization process is performed. In step 634, the gain normalization process obtains the unscaled gain-independent measurement results. In step 635, the unscaled gain-independent measurement results are decomposed. In step 636, the unscaled resolved data (e.g., in arbitrary units) is obtained. In step 637, the assay normalization process is performed on the unscaled resolved data. In step 638, the scaled resolved data is obtained by the assay normalization process. Using a method like method 630 is convenient because the detector settings can be optimized and adjusted to ensure consistent sample MFI over time. Furthermore, applying one or more assay scaling factors to decomposed data (rather than measurement results in original space) can improve performance (for example, by reducing the required computing resources). For example, in some embodiments, one or more assay scaling factors may be applied to the axes of a graph displaying the decomposed data, rather than transforming the entire underlying raw data.

[0113] Some or all of the steps of methods 610, 620, and / or 630 may be configured to be performed by one or more processors (for example, one or more processors included in the biomedical device 102, controller 190, detection station 214, detection system 216, processor 240, detection system 304, control system 306, detection station 308, controller / processor 490, processor 550, processor 551, and / or sorting trigger 552). Furthermore, various improvements are possible to methods 610, 620, and / or 630. For example, in some embodiments, one or more of the steps may be deleted, combined, and / or modified. For example, in some embodiments, steps 611, 621, and / or 631 may be deleted from methods 610, 620, and / or 630, respectively. Furthermore, in some embodiments, one or more steps may be added to methods 610, 620, and / or 630. For example, in some embodiments, methods 610, 620, and / or 630 may include a step in which a gain calibration process is performed.

[0114] Figure 9 is a flowchart 700 of the operation of a flow cytometer utilizing gain-independent measurement results. As shown, the flowchart 700 includes a detector calibration step 710, a QC step 720, and an assay calibration step 730. Some or all of these steps may be configured to be performed by one or more processors (e.g., one or more processors included in the biomedical instrument 102, controller 190, detection station 214, detection system 216, processor 240, detection system 304, control system 306, detection station 308, controller / processor 490, processor 550, processor 551, and / or differential trigger 552). As shown, the output from the detector calibration step 710 may be supplied to part of the QC step 720. Similarly, the output from the QC step 720 may be supplied to part of the detector calibration step 710. For example, if one or more variations in performance measured in QC step 720 (e.g., per-instrument variation, per-day variation, or per-sample variation) exceed one or more predetermined thresholds, one or more of the detector calibration steps 710 (e.g., steps 712 and / or 713) may be repeated. Furthermore, the outputs from both the detector calibration step 710 and the QC step 720 may be supplied to part of the assay calibration step 730. For example, the raw measurement results or degraded data obtained in the detector calibration step 710 and / or QC step 720 may be corrected by the assay calibration step 730.

[0115] The detector calibration step 710 includes step 711 for controlling the light source, step 712 for performing a gain calibration process (for example, as part of a data normalization method), step 713 for quantifying the noise level (for example, as part of a gain optimization method), and step 714 for calculating one or more gain scaling factors (for example, as part of a data normalization method), and Q SPE Step 715 to perform the calibration process and one or more Q SPEThe process includes step 716 for calculating the scaling factor. In its use herein, the term "SPE" represents a statistical photoelectron unit. In its use herein, the term "Q" represents a statistical photoelectron unit. SPE " represents the inverse coefficient of linear noise. In some embodiments, Q SPE This can be used to estimate photon shot noise. In some embodiments, in steps 711 and 712, a reference light source (e.g., an LED pulse generator) may be controlled, and by acquiring measurement results (e.g., generated by one or more detectors) at various user-selectable parameters (e.g., detector voltage), a lookup table or function may be derived to correlate a linear change (e.g., increase or decrease) in particle MFI with respect to detector voltage. According to the resulting lookup table or function, the user can adjust the detector gain (rather than voltage), which has a more intuitive impact on data scaling between settings. In some embodiments, the gain scaling factor acquired by step 714 and / or Q acquired by step 716 SPE A scaling factor may be applied downstream (e.g., QC step 720 and / or assay calibration step 730) for data normalization. In some embodiments, detector calibration step 710 provides instrument performance criteria such as characterization of system background noise in the calibration unit and / or resolution of each detector in the calibration unit (e.g., by step 713).

[0116] QC step 720 includes step 721 for selecting a target light source, step 722 for verifying the calibration of one or more detectors, and step 723 for performing a resolution adjustment process (for example, as part of a data normalization method). In some embodiments, QC step 720 includes a process for verifying that system performance is consistent (for example, by analyzing per-instrument variations, daily changes, and / or per-sample variations) using a target light source (for example, a reference material such as an LED or beads), and / or verifying that the scaling factor and / or calibration are still valid (for example, with or without a comprehensive gain calibration procedure). In some embodiments, QC step 720 includes a process for adjusting default system and / or user settings (for example, to address variations in system performance).

[0117] Assay calibration step 730 includes step 731 for calculating one or more reagent scaling factors and step 732 for calculating one or more assay scaling factors (e.g., as part of a data normalization method). In some embodiments, one or more reagent scaling factors may be used to compensate for one or more properties of the additional reagents added to the biological sample and / or one or more properties of the biological sample itself. The signal from a biological sample stained with a reagent may have multiple components, including the intrinsic fluorescence properties of the reagent itself and the sample. Cells contain molecules with intrinsic fluorescence properties (similar to reagents), which may be taken into account in one or more reagent scaling factors. In some embodiments, one or more assay scaling factors may be used to convert gain-independent data (e.g., raw gain-independent measurement results obtained by a gain normalization process or degraded data derived from such measurement results) from any detection unit to a more significant unit (e.g., the number of fluorescent molecules that produced the optical signal) and / or to compensate for one or more properties of the instrument.

[0118] Flowchart 700 is subject to various modifications. For example, in some embodiments, one or more steps may be deleted, combined, and / or modified. For example, in some embodiments, steps 713, 715, and / or 716 may be removed from the detector calibration step 710. Furthermore, in some embodiments, one or more steps may be added to flowchart 700. For example, in some embodiments, flowchart 700 may include one or more steps for applying one of the scaling factors described above to, for example, raw measurement results and / or resolved data.

[0119] Figure 10 is a block diagram of a particle analysis and / or sorting system. As shown, the system comprises a sorting module 810 and a user interface module 820. Modules 810 and 820 may implement one or more processors, one or more ASICs, one or more FPGAs, and / or other similar components. Modules 810 and 820 may also implement memory media capable of storing information, such as hard drives, memory cards, ROMs, RAMs, DVDs, CD-ROMs, writable memory, and / or read-only memory. As shown in Figure 10, the system may utilize both unscaled gain-dependent measurement results (e.g., unscaled decomposed data) and scaled gain-independent measurement results (e.g., scaled decomposed data). As shown, the sorting module 810 utilizes unscaled gain-dependent measurement results, and the user interface module 820 utilizes scaled gain-independent measurement results. Gain-independent measurement results can, for example, make it easier for a user to define one or more thresholds (e.g., gate coordinates) for sorting one or more particles.

[0120] As shown in Figure 10, the separation module 810 comprises a separation block 811 which can be configured to receive measurement result data from one or more detectors in original space (e.g., one or more detectors included in detection station 214, detection system 216, detection station 308, fluorescence detectors 460a-460f, and / or photodetectors 511-517) and to decompose the received measurement result data (e.g., by WLS-based spectral decomposition or OLS-based spectral decomposition). The separation module 810 also comprises a separation block 812 which is configured to receive decomposed measurement result data from the separation block 811 and gate coordinates from the descaling block 826 of the user interface module 820. The separation block 812 may be configured to operate a separation device (e.g., deflection plates 252, 254, or 531) to separate one or more particles (e.g., cells) in the flow stream based on the decomposed measurement result data and gate coordinates.

[0121] The user interface module 820 comprises a scaling block 821, a plotting block 822, a gating block 823, an analysis block 824, an export block 825, and a descaling block 826. The scaling block 821 is configured to receive decomposed measurement result data from the decomposition block 811 of the separation module 810 and convert it into scaled gain-independent measurement result data (for example, by equation 1 and / or equation 2). The plotting block 822 is configured to generate one or more plots, graphs, and / or other visual representations of the scaled gain-independent measurement result data for display to the user (for example, via a display such as the display device 106). The gating block 823 is configured to render regions of interest as one or more gates around a set of bio-event data shown on the display (for example, overlaid on a plot generated by the plotting block 822), and / or to accept one or more gates from the user (for example, via an input device such as the keyboard 108 and / or mouse 110). The analysis block 824 is configured to process scaled gain-independent measurement result data. These processes may include obtaining statistics of the population within the gate (e.g., proportion of the population or subpopulation), changes in median fluorescence intensity, changes in standard deviation or coefficient of variation, changes in absolute numbers or numbers per unit volume, dimensionality reduction methods, or generating new parameters by combining features from multiple recorded parameters. The export block 825 is configured to export the scaled gain-independent measurement result data and / or additional information generated by the analysis block 824 to another device. For example, any information in module 820 can be transferred or exported from one instrument to another. The descaling block 826 is configured to receive gate values ​​from the gating block 823 and convert these values ​​to gain-dependent values.The gain-dependent value can be used by the separation block 812 of the separation module 810 to separate one or more particles in the flow stream.

[0122] The particle analysis and / or separation system in Figure 10 is subject to various modifications. For example, in some embodiments, one or more blocks and / or modules can be removed, combined, or functionally modified. For example, in some embodiments, the plotting block 822 may be modified to display a different type of visual representation of scaled gain-independent measurement result data. As another example, in some embodiments, the analysis block 824 and / or export block 825 may be removed from the system. As yet another example, in some embodiments, the separation module 810 and the user interface module 820 may be combined as a single module. Furthermore, in some embodiments, one or more modules and / or blocks may be added to the system in Figure 10. Furthermore, in some embodiments, the scaling block 821 may be configured to receive measurement result data directly from one or more detectors as an alternative to or addition to receiving resolved measurement result data from the resolution block 811. In some such embodiments, the scaling block 821 may convert resolved measurement result data from one or more detectors into scaled gain-independent values.

[0123] Figure 11A shows the graphical user interface of a flow cytometer with the "No Rescaling" display mode selected from the dropdown menu. Due to this selection, the values ​​in the plot of the decomposition data are given as gain-dependent values. Figure 11B shows the same graphical user interface as in Figure 11A, but with the "Gain Rescaling" display mode selected from the dropdown menu. Due to this selection, the values ​​in the plot of the decomposition data are given as scaled gain-independent values ​​(e.g., by the gain and / or assay normalization process). Both plots show the relative intensities of two different dyes (e.g., fluorescein isothiocyanate (FITC) and phycoerythrin (PE)) detected in each of the six different detector channels. Also, as shown, the graphical user interfaces in Figures 11A and 11B include buttons for switching between WLS-based and OLS-based spectral decomposition, checkboxes for enabling or disabling the display of the stain index (SI), six sliders for adjusting the gain of each of the six detection channels, each potentially corresponding to one or more detectors, and a chart of the raw measurement results.

[0124] In some embodiments, one or more graphic elements of the graphical user interface in Figures 11A and 11B may be modified or removed. For example, a dropdown menu may be replaced with checkboxes to enable or disable the conversion of gain-dependent values ​​to scaled gain-independent values. Similarly, one or more sliders may be replaced with text boxes for receiving gain values ​​from the user. As another example, the positions of one or more graphic elements may be reversed. For example, a plot may be displayed above the chart instead of below it. As yet another example, the number of sliders may be increased or decreased. For example, depending on the number of detection channels in the corresponding flow cytometer, it may be convenient to increase or decrease the number of sliders so that there is a slider for each detection channel.

[0125] Figure 11C is a plot of the decomposed data shown in the graphical user interface of Figure 11A. Figure 11D is a plot of the decomposed data shown in the graphical user interface of Figure 11B. A comparison between these two plots demonstrates some of the advantages of using scaled gain-independent values. As shown, each plot includes “initial” data and “adjusted” data. The “initial” data corresponds to what is displayed when all detector channels are set to some first set of selected detector gains. The “adjusted” data corresponds to what is displayed when the detector channels are set to some modified set of detector gains different from the first set of detector gains. In this example, one detector gain is increased by 15 dB and another gain is increased by 5 dB. In some embodiments, only the “adjusted” data is displayed. However, it is useful to have both datasets for comparison purposes. As seen in Figure 11C, the displayed values ​​shift and spread to a specific area depending on the gain setting, making visual interpretation of the signal-to-noise ratio difficult. In contrast, in Figure 11D, when scaled gain-independent values ​​are used, no shift occurs due to the gain setting. Instead, the effect of the detector gain setting on the signal-to-noise ratio is directly revealed by the consistent concentration of the displayed values ​​in a specific area of ​​the plot (for example, covering a smaller area). In both Figure 11C and Figure 11D, the stain index criterion for the signal-to-noise ratio is shown as an overlay on each plot for each decomposition parameter in the adjusted data.

[0126] From the foregoing, it will be apparent to those skilled in the art that, by referring to the various drawings, certain improvements to the disclosure are possible without departing from the scope of the disclosure. While the drawings illustrate several embodiments of the disclosure, the disclosure is not limited thereto and extends to the extent permitted in the art, and the same interpretation is intended for this specification. Therefore, the above description is not limiting and should be construed as illustrative of specific embodiments only. Those skilled in the art will likely conceive of other improvements contained in the appended claims and ideas.

Claims

1. A detector configured to measure particle-modulated light emitted by flowstream particles in the original space, It is memory, A lookup table that associates each of a plurality of user-selectable parameters for adjusting the gain of the detector with each of a plurality of linear gains, and A predetermined function that associates a provided user-selectable parameter for adjusting the gain of the detector with a corresponding linear gain, A memory to store at least one of the following, One or more processors, The detector receives the unprocessed measurement results of the particle-modulated light, To obtain a user-selectable parameter from among the plurality of user-selectable parameters for adjusting the gain of the detector, Obtaining the linear gain associated with the acquired user-selected parameter by at least one of accessing the lookup table or using the predetermined function, The raw measurement result is converted into a gain-independent value by dividing it by the acquired linear gain. One or more processors configured to perform the following: A system equipped with these features.

2. The system according to claim 1, wherein the memory stores the lookup table, and the linear gain associated with the acquired user-selected parameter is obtained by accessing the lookup table.

3. The system according to claim 1, wherein the memory stores the predetermined function, and the linear gain associated with the acquired user-selected parameter is acquired by using the predetermined function.

4. The one or more processors Applying a scaling factor to the gain-independent value to obtain a scaled gain-independent value, The scaled gain-independent value is decomposed to obtain the scaled decomposed value, The system according to any one of claims 1 to 3, further configured to perform the following:

5. The one or more processors Decomposing the aforementioned gain-independent value and obtaining the decomposed value, Applying a scaling factor to the aforementioned decomposition value to obtain a scaled decomposition value, The system according to any one of claims 1 to 3, further configured to perform the following:

6. The system according to claim 4 or 5, wherein the one or more processors are further configured to acquire a target gain of the detector, and the scaling factor is based on the acquired target gain.

7. The system according to any one of claims 4 to 6, wherein the scaling factor takes into account the drift of the detector's sensitivity over time, the fluorophore properties of the particles, or the labeling properties of the particles.

8. The system according to any one of claims 1 to 7, wherein one or more processors are further configured to calculate the optimization gain of the detector.

9. The system according to claim 8, wherein the signal-to-noise ratio (SNR) of the detector is optimized by the optimization gain, and one or more processors are further configured to change the gain of the detector to the optimization gain.

10. The one or more processors The gain of the detector is gradually increased so that the detector collects light from the flow stream with each of a plurality of continuously increasing gains. In each of the multiple successively increasing gains, the baseline noise level is obtained from the detector. For each of the aforementioned successively increasing gains, the detection limit (LoD) is calculated. Based on the calculated LoD, the optimization gain is determined, The system according to any one of claims 1 to 9, further configured to perform the following:

11. The system according to any one of claims 1 to 10, wherein the user-selectable parameter is a voltage that adjusts the gain of the detector when applied to the detector.

12. The system according to any one of claims 1 to 11, wherein one or more processors are further configured to separate a plurality of particles based on a threshold defined using the gain-independent value.

13. The system according to any one of claims 1 to 12, wherein one or more processors are further configured to generate a graphical user interface for display, including a plot of the gain-independent values.

14. The system according to claim 13, wherein the graphical user interface further includes graphic elements for enabling or disabling the conversion of raw measurement results received from the detector to gain-independent values.

15. The system according to claim 13 or 14, wherein the graphical user interface further includes a graphic element for adjusting the gain of the detector.

16. One or more processors receive the unprocessed measurement results of particle-modulated light emitted by flowstream particles from the detector, The one or more processors acquire user-selected parameters for adjusting the gain of the detector, The process involves one or more processors obtaining linear gains associated with the acquired user-selectable parameters by accessing a lookup table or using a predetermined function, wherein the lookup table associates each of a plurality of user-selectable parameters for adjusting the gain of the detector with each of a plurality of linear gains, and the predetermined function associates the provided user-selectable parameters for adjusting the gain of the detector with the corresponding linear gains. The one or more processors convert the raw measurement result into a gain-independent value by dividing the raw measurement result by the acquired linear gain, Methods that include...

17. A non-temporary computer-readable storage medium that stores instructions causing one or more processors to perform the method according to claim 16 when executed by one or more processors.

18. One or more processors are used to obtain the resolved measurement results of particle-modulated light emitted by particles in a flowstream, The one or more processors acquire initial user-selected parameters for adjusting the detector gain, The one or more processors obtain updated user-selected parameters for adjusting the gain of the detector, The one or more processors derive a scaling factor based on the ratio of the updated user selection parameter and the initial user selection parameter, The gain-independent value is obtained by applying the scaling factor to the decomposed measurement result using one or more of the aforementioned processors. Methods that include...

19. The method according to claim 18, further comprising obtaining a renormalized gain-independent value by applying a rescaling factor to the gain-independent value using one or more processors.

20. A non-temporary computer-readable storage medium that stores instructions causing one or more processors to perform the method according to claim 18 or 19 when executed by one or more processors.

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