Method and system for continuous measurement of baseline noise in a flow cytometer

Abstract

Aspects of the present disclosure include methods for determining the baseline noise of a photodetector (e.g., in an optical detection system of a particle analyzer). The method, according to certain embodiments, includes illuminating a sample containing particles in a flow stream, detecting light from the illuminated flow stream with a photodetector, generating a data signal from the detected light, and calculating a running mean square error of the generated data signal to determine the baseline of the photodetector. Systems (e.g., particle analyzers) are also described that include a light source and an optical detection system having a photodetector for implementing the subject methods. Integrated circuits and non-transitory computer-readable storage media are also provided.

Description

[Background technology]

[0001] Light detection is often used to characterize components of a sample (e.g., a biological sample), for example, when the sample is used in diagnosing a disease or condition. When a sample is illuminated, light can be scattered by the sample, transmitted through the sample, and emitted by the sample (e.g., by fluorescence). Variations in sample components, such as morphology, absorbance, and the presence of fluorescent labels, can cause variations in the light scattered, transmitted, or emitted by the sample. These variations can be used to characterize and identify the presence of components in the sample. To quantify these variations, light is collected and directed toward a detector surface.

[0002] One technique that utilizes optical detection to characterize components in a sample is flow cytometry. Data generated from the detected light can be used to record the distribution of components and sort desired materials. Flow cytometers typically include a sample reservoir for receiving a fluid sample, such as a blood sample, and a sheath reservoir containing a sheath fluid.

[0003] A flow cytometer transports particles (including cells) in a fluid sample as a cell stream into a flow cell while directing a sheath fluid toward the flow cell. Within the flow cell, a liquid sheath forms around the cell stream, imparting a substantially uniform velocity to the cell stream. The flow cell hydrodynamically focuses the cells in the stream through a central light source within the flow cell. Light from the light source can be detected as scattered or transmitted spectroscopy, or it can be absorbed by one or more components in the sample and re-emitted as luminescence. Summary of the Invention

[0004] Aspects of the present disclosure include methods for determining the baseline noise of a photodetector (e.g., in an optical detection system of a particle analyzer). According to certain embodiments, the method includes illuminating a sample containing particles in a flow stream, detecting light from the illuminated flow stream with a photodetector, generating a data signal from the detected light, and calculating a running mean square error of the generated data signal to determine the baseline noise of the photodetector. Systems (e.g., particle analyzers) are also described that include a light source and an optical detection system having a photodetector for implementing the subject methods. Integrated circuits and non-transitory computer-readable storage media are also provided.

[0005] In practicing the subject methods, a particle-containing sample is illuminated in a flowstream and light from the flowstream is detected. The light detected from the flowstream can be scattered light (e.g., side-scattered light or forward-scattered light) or emission light. In some embodiments, the light detected according to the subject methods is emission light. In certain embodiments, the sample contains one or more fluorophores and the light detected by the photodetector is fluorescence. A data signal is generated by the photodetector from the detected light, and a running average squared error is calculated to determine the baseline noise of the photodetector. In some embodiments, light is detected from particle-free components of the flowstream, such as light emitted between particles flowing in the flowstream. In some embodiments, the running average of the squared error of the generated data signals is calculated over a time interval of 10 ms or more, e.g., 50 ms or more, e.g., 100 ms or more, e.g., 250 ms or more, including calculating a running average of the squared error of the generated data signals over a time interval of 500 ms or more.

[0006] In some embodiments, calculating the moving mean squared error of the generated data signal includes measuring the squared difference between the generated data signal and a calculated baseline data signal. In particular cases, calculating the moving mean squared error of the generated data signal includes measuring the squared difference between multiple generated data signals and the calculated baseline data signal over a predetermined sampling period to generate multiple baseline noise signals, summing the baseline noise signals over the sampling period, and dividing the summed baseline noise signals by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is between 0.001 μs and 100 μs in duration. In other cases, the predetermined sampling period is between 1 μs and 10 μs in duration. In particular embodiments, the subject method includes calculating the moving mean squared error of the generated data signal at a predetermined time interval. For example, the moving mean squared error of the generated data signal is calculated once every 1 ms or more, e.g., once every 5 ms or more, e.g., once every 10 ms or more, e.g., once every 25 ms or more, e.g., once every 50 ms or more, e.g., once every 100 ms or more, and once every 500 ms or more. In some embodiments, the moving mean squared error of the generated data signal is calculated between once every second and once every 60 seconds. In other embodiments, the moving mean squared error of the generated data signal is calculated between once every minute and once every 60 minutes. In certain embodiments, the method includes continuously calculating the moving mean squared error of the generated data signal.

[0007] In some embodiments, the sample contains multiple fluorophores, such as when the fluorophores have overlapping fluorescence spectra. In some cases, the fluorophores are functionally associated with particles of the sample. In certain cases, the flow stream contains one or more free fluorophores (e.g., unbound fluorophores in the flow stream) that are not functionally associated with particles of the sample. In certain embodiments, the method includes detecting light from the free fluorophores in the sample with a photodetector, generating data signals from the detected light, and calculating a running mean square error of the data signals generated from the light emitted from the free fluorophores in the sample. In some cases, the method further includes spectrally resolving the light from each type of fluorophore in the sample, such as by calculating a spectral unmixing matrix for the fluorescence spectrum of each type of fluorophore in the sample. In certain cases, the spectral unmixing matrix is ​​calculated using a weighted least squares algorithm. In some embodiments, the data signals generated from the light from the free fluorophores in the sample are weighted based on the determined baseline noise of the photodetector. In certain embodiments, the baseline noise of the photodetector is determined using an integrated circuit, such as a field-programmable gate array. In another embodiment, the spectral separation matrix is ​​calculated on an integrated circuit using a weighted least squares algorithm.

[0008] Aspects of the present disclosure also include systems (e.g., particle analyzers) for implementing the subject methods, where the systems of interest comprise a light detection system including a light source and a light detector. In some embodiments, the light detection system includes one or more light detectors for detecting light from the illuminated flow stream, e.g., two or more light detectors, e.g., five or more light detectors, e.g., ten or more light detectors, e.g., twenty-five or more light detectors, e.g., fifty or more light detectors, e.g., one hundred or more light detectors, and one thousand or more light detectors. In some embodiments, the system includes a processor having a memory operably coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to generate data signals from light from particles in the illuminated flow stream and calculate a running mean squared error of the generated data signals to determine baseline noise of the photodetectors. In some embodiments, the memory includes instructions that, when executed by the processor, cause the processor to calculate the running mean squared error of the generated data signals by measuring the squared difference between the generated data signals and a calculated baseline data signal. In certain embodiments, the memory includes instructions for measuring the squared difference between a plurality of generated data signals and a calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals, summing the baseline noise signals over the sampling period, and dividing the summed baseline noise signal by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is between 0.001 μs and 100 μs in duration. In other cases, the predetermined sampling period is between 1 μs and 10 μs in duration.

[0009] In some embodiments, the memory includes instructions that, when executed by the processor, cause the processor to calculate a moving mean squared error of the generated data signal at predetermined time intervals. In other embodiments, the memory includes instructions that, when executed by the processor, cause the processor to calculate a moving mean squared error of the generated data signal at a frequency between once every millisecond and once every 1000 milliseconds. For example, the memory includes instructions for calculating the moving mean squared error of the generated data signal once every 1 ms or more, e.g., once every 5 ms or more, e.g., once every 10 ms or more, e.g., once every 25 ms or more, e.g., once every 50 ms or more, e.g., once every 100 ms, and once every 500 ms or more. In some embodiments, the memory includes instructions for calculating the moving mean squared error of the generated data signal at a frequency between once every second and once every 60 seconds. In other embodiments, the memory includes instructions for calculating the moving mean squared error of the generated data signal at a frequency between once every minute and once every 60 minutes. In a particular embodiment, the memory includes instructions for continuously calculating a moving mean squared error of the generated data signal.

[0010] In certain embodiments, the system includes a processor having a memory operably coupled to the processor, the memory including instructions stored therein that, when executed by the processor, cause the processor to detect light from free fluorophores in the sample with a photodetector, generate data signals from the detected light, and calculate a running mean square error of the data signals generated from the light emitted from the free fluorophores in the sample. In some cases, the memory includes instructions for spectrally resolving the light from each type of fluorophore in the sample. In certain cases, the memory includes instructions for resolving the light from each type of fluorophore by calculating a spectral separation matrix for the fluorescence spectrum of each type of fluorophore in the sample. In certain cases, the memory includes instructions for calculating the spectral separation matrix using a weighted least squares algorithm. In some embodiments, the data signals generated from the light from free fluorophores in the sample are weighted based on the determined baseline noise of the photodetector. In certain embodiments, the system includes an integrated circuit such as a field-programmable gate array.

[0011] Aspects of the present disclosure also include an integrated circuit programmed to calculate a running mean square error of a data signal generated from light detected from illuminated particles of a sample in a flow stream. In some cases, the integrated circuit is a field programmable gate array. In other cases, the integrated circuit is an application specific integrated circuit. In yet other cases, the integrated circuit is a complex programmable logic device.

[0012] In some embodiments, the integrated circuit is programmed to calculate a moving average squared error of the generated data signal by measuring the squared difference between the generated data signal and a calculated baseline data signal. In particular embodiments, the integrated circuit is programmed to measure the squared difference between multiple generated data signals and the calculated baseline data signal over a predetermined sampling period to generate multiple baseline noise signals, sum the baseline noise signals over the sampling period, and divide the summed baseline noise signal by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is between 0.001 μs and 100 μs in duration. In other cases, the predetermined sampling period is between 1 μs and 10 μs in duration.

[0013] In some embodiments, an integrated circuit of the present disclosure is programmed to calculate the moving mean squared error of the generated data signal at predetermined time intervals. In some cases, the integrated circuit is programmed to calculate the moving mean squared error of the generated data signal at a frequency of between once every millisecond and once every 1000 milliseconds. For example, the integrated circuit may be programmed to calculate the moving mean squared error of the generated data signal once every 1 ms or more, e.g., once every 5 ms or more, e.g., once every 10 ms or more, e.g., once every 25 ms or more, e.g., once every 50 ms or more, e.g., once every 100 ms or more, and once every 500 ms or more. In other embodiments, the integrated circuit is programmed to calculate the moving mean squared error of the generated data signal at a frequency of between once every second and once every 60 seconds. In yet other embodiments, the integrated circuit is programmed to calculate the moving mean squared error of the generated data signal at a frequency of between once every minute and once every 60 minutes. In certain embodiments, the integrated circuit is programmed to continuously calculate the moving mean squared error of the generated data signal.

[0014] In certain embodiments, the integrated circuit is programmed to detect light from free fluorophores in the sample with a photodetector, generate data signals from the detected light, and calculate a running mean square error of the data signals generated from the light emitted from the free fluorophores in the sample. In some cases, the integrated circuit is programmed to spectrally resolve the light from each type of fluorophore in the sample. In certain cases, the integrated circuit is programmed to resolve the light from each type of fluorophore by calculating a spectral separation matrix for the fluorescence spectrum of each type of fluorophore in the sample. In certain cases, the integrated circuit is programmed to calculate the spectral separation matrix using a weighted least squares algorithm. In some embodiments, the integrated circuit is programmed to weight the data signals generated from the light from free fluorophores in the sample based on the determined baseline noise of the photodetector.

[0015] Aspects of the present disclosure also include a non-transitory computer-readable storage medium having instructions stored thereon for determining the baseline noise of a photodetector of an optical detection system of a particle analyzer. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a running mean squared error of a data signal generated from light detected from illuminated particles of a sample in a flow stream. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a running mean squared error of a generated data signal by measuring the squared difference between the generated data signal and a calculated baseline data signal. In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for measuring the squared difference between the multiple generated data signals and the calculated baseline data signal over a predetermined sampling period to generate multiple baseline noise signals, and an algorithm for summing the baseline noise signals over the sampling period and dividing the summed baseline noise signal by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is between 0.001 μs and 100 μs in duration. In other cases, the predetermined sampling period is between 1 μs and 10 μs in duration.

[0016] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating the moving mean squared error of the generated data signal at predetermined time intervals. In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating the moving mean squared error of the generated data signal at a frequency of between once every millisecond and once every 1000 milliseconds. For example, the non-transitory computer-readable storage medium may include an algorithm for calculating the moving mean squared error of the generated data signal at a frequency of once every 1 ms or more, e.g., once every 5 ms or more, e.g., once every 10 ms or more, e.g., once every 25 ms or more, e.g., once every 50 ms or more, e.g., once every 100 ms or more, and once every 500 ms or more. In other embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating the moving mean squared error of the generated data signal at a frequency of between once every second and once every 60 seconds. In yet other embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a moving mean squared error of the generated data signal at a frequency between once every minute and once every 60 minutes. In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for continuously calculating a moving mean squared error of the generated data signal.

[0017] In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for detecting light from free fluorophores in the sample with a photodetector, an algorithm for generating a data signal from the detected light, and an algorithm for calculating a running mean square error of the data signal generated from the light emitted from the free fluorophores in the sample. In some cases, the non-transitory computer-readable storage medium includes an algorithm for spectrally resolving the light from each type of fluorophore in the sample. In certain cases, the non-transitory computer-readable storage medium includes an algorithm for resolving the light from each type of fluorophore by calculating a spectral separation matrix for the fluorescence spectrum of each type of fluorophore in the sample. In certain cases, the non-transitory computer-readable storage medium includes an algorithm for calculating the spectral separation matrix by using a weighted least squares algorithm. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for weighting the data signal generated from the light from free fluorophores in the sample based on the determined baseline noise of the photodetector. [Brief explanation of the drawings]

[0018] The invention can be best understood from the following detailed description when read in conjunction with the accompanying drawings, in which:

[0019] [Figure 1] FIG. 1 illustrates a flow diagram for measuring the baseline noise of a photodetector in accordance with certain embodiments. [Figure 2A] 1 shows a diagram of baseline noise detected from an illuminated sample containing particles in a flow stream according to certain embodiments. [Figure 2B] 1 shows the baseline noise determined from a sample with free fluorophore, according to certain embodiments. [Figure 2C] 1 shows the baseline noise determined from samples with fluorophores titrated to different concentrations, according to certain embodiments. [Figure 3A] FIG. 1 shows a block diagram of a system for spectrally resolving fluorescence from a sample containing illuminated particles in a flow stream, according to certain embodiments. [Figure 3B] 10 shows examples of simulated spectral separation uncertainties from spectrally resolving fluorescence using ordinary least squares and weighted least squares algorithms according to certain embodiments. [Figure 4A] FIG. 1 illustrates a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization according to certain embodiments. [Figure 4B] 1 illustrates a flow cytometer according to certain embodiments. [Figure 5] FIG. 1 illustrates a functional block diagram of an example particle analyzer control system according to certain embodiments. [Figure 6] FIG. 1 illustrates a block diagram of a computing system according to certain embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0020] Aspects of the present disclosure include methods for determining the baseline noise of a photodetector (e.g., in an optical detection system of a particle analyzer). According to certain embodiments, the method includes illuminating a sample containing particles in a flow stream, detecting light from the illuminated flow stream with a photodetector, generating a data signal from the detected light, and calculating a running mean square error of the generated data signal to determine the baseline of the photodetector. Systems (e.g., particle analyzers) are also described that include a light source and an optical detection system having a photodetector for implementing the subject methods. Integrated circuits and non-transitory computer-readable storage media are also provided.

[0021] Before describing the present invention in more detail, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0022] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limits of that range, and any otherwise stated or intervening value in that stated range, is included in the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also included in the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both limits, ranges excluding either or both of those included limits are also included in the invention.

[0023] In this specification, certain ranges are set forth, and the numerical values ​​are preceded by the term "about." The term "about" is used herein to provide literal support for the exact number it precedes, as well as a number that is close to or approximately the number it precedes. In determining whether a number is close to or approximately equal to a specifically stated number, the unstated number that is close or approximate may be a number that, in the context in which it is presented, results in a substantial equivalence to the specifically stated number.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary methods and materials are described herein.

[0025] All publications and patents cited in this specification are incorporated herein by reference as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated herein by reference to describe and describe the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the publication dates provided may be different from the actual publication dates which may need to be independently confirmed.

[0026] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude any optional element. Accordingly, this statement is intended to serve as a prior basis for using exclusive terminology such as "solely," "only," and the like, or for using "negative" limitations in connection with the recitation of claim elements.

[0027] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has distinct components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0028] Although apparatus and methods have been or will be described for grammatical fluidity with functional descriptions, unless expressly formulated under 35 U.S.C. § 112, the claims should not be construed as necessarily limited in any way by means- or step-limitation constructions, but should be given the full scope of the meaning and equivalents of the definitions provided by the claims under the doctrine of equivalents, and if the claims are expressly formulated under 35 U.S.C. § 112, they should be expressly understood to be entitled to the full legal equivalents under 35 U.S.C. § 112.

[0029] As summarized above, the present disclosure provides methods for determining the baseline noise of a photodetector (e.g., in an optical detection system of a particle analyzer). To further describe embodiments of the present disclosure, a method for determining the baseline noise by illuminating a particle-containing sample in a flow stream, detecting light from the illuminated flow stream with a photodetector, generating a data signal from the detected light, and calculating a running mean square error of the generated data signal is first described in more detail. A system is then described that includes a light source and an optical detection system having a photodetector for implementing the subject method. Integrated circuits and non-transitory computer-readable storage media are also provided.

[0030] Method for measuring baseline noise of a photodetector in an optical detection system Aspects of the present disclosure include methods for determining the baseline noise of a photodetector in an optical detection system (e.g., a particle analyzer in a flow cytometer). In some embodiments, the methods for determining the baseline noise of a photodetector provide a real-time measurement of the baseline noise, such as during illumination of a sample in a flow stream. As described in more detail below, the subject methods provide a sample-specific measurement of the baseline noise. Obtaining a sample-specific measurement of the baseline noise in real time according to embodiments of the present disclosure provides for determining the contribution to the background noise of each individual photodetector in the optical detection system, for example, contributions from time-varying parameters including, but not limited to, laser focus drift, laser alignment drift, time-dependent changes in the flow rate and flow profile of the flow stream, and increases in electronic noise due to thermal drift of detector components such as transimpedance amplifiers. In certain embodiments, the subject methods provide an increase in the signal-to-noise ratio of the data signal from the photodetector, such as when the signal-to-noise ratio of the photodetector increases by 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, e.g., 75% or more, e.g., 90% or more, and 99% or more. In certain cases, the subject methods increase the signal-to-noise ratio of a photodetector by 2x or more, such as 3x or more, such as 4x or more, such as 5x or more, and 10x or more.

[0031] In practicing the subject methods, baseline noise from a photodetector is calculated. The term "baseline noise" is used herein in its conventional sense to refer to a baseline electronic signal from a photodetector (e.g., an electronic signal originating from the photodetector's operating electronics or the optical components of the photodetector system). In certain cases, baseline noise includes electronic signals present in the photodetector system, such as those generated by a light source or other electronic subcomponents of the system. In other embodiments, baseline noise includes electronic signals resulting from vibration or thermal effects from components of the system. In still other 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).

[0032] In carrying out the subject method, a flowstream is illuminated with a light source, and light from the flowstream is detected with a light detection system having one or more photodetectors. In some embodiments, a sample containing particles is illuminated within the flowstream. In certain embodiments, the sample is a biological sample. In embodiments, a flowstream (e.g., with the sample having particles flowing therethrough) is illuminated with light from the light source. In some embodiments, the light source is a broadband light source, emitting light having a wide range of wavelengths, e.g., 50 nm or greater, e.g., 100 nm or greater, e.g., 150 nm or greater, e.g., 200 nm or greater, e.g., 250 nm or greater, e.g., 300 nm or greater, e.g., 350 nm or greater, e.g., 400 nm or greater, and 500 nm or greater. For example, one suitable broadband light source emits light having a wavelength from 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength from 400 nm to 1000 nm. Where the method includes irradiation with a broadband light source, broadband light source protocols of interest may include, but are not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a broadband LED white light source, a multi-LED integrated white light source, other broadband light sources, or any combination thereof.

[0033] In other embodiments, the method comprises irradiating with a narrow-band light source that emits a specific wavelength or a narrow range of wavelengths, for example, a light source that emits light at a narrow range of wavelengths, such as 50 nm or less, for example, 40 nm or less, for example, 30 nm or less, for example, 25 nm or less, for example, 20 nm or less, for example, 15 nm or less, for example, 10 nm or less, for example, 5 nm or less, for example, 2 nm or less, and a light source that emits light of a specific wavelength (e.g., monochromatic light).When the method comprises irradiating with a narrow-band light source, the narrow-band light source protocol of interest can include, but is not limited to, a narrow-wavelength LED, a laser diode, or a broad-band light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0034] In certain embodiments, the method includes irradiating the flowstream with one or more lasers. As previously mentioned, the type and number of lasers will vary depending on the sample and the desired light to be collected, and can be gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other cases, the method includes irradiating the flowstream with a dye laser, such as a stilbene, coumarin, or rhodamine laser. In still other cases, the method includes irradiating the flowstream with a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In still other cases, the method includes irradiating the flowstream with a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, a ytterbium YAG laser, a Y2O3 laser, or a cerium-doped laser, and combinations thereof.

[0035] A flow stream (e.g., with a sample having flowing particles) can be illuminated with one or more of the above light sources, such as two or more light sources, such as three or more light sources, such as four or more light sources, such as five or more light sources, and ten or more light sources. The light sources can include any combination of light source types. For example, in some embodiments, the method includes illuminating the flow stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0036] The flow stream can be illuminated with wavelengths ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and 400 nm to 800 nm. For example, if the light source is a broadband light source, the sample can be illuminated with wavelengths ranging from 200 nm to 900 nm. In other cases, the light source can include multiple narrowband light sources, and the sample can be illuminated with specific wavelengths within the 200 nm to 900 nm range. For example, the light source can be multiple narrowband LEDs (1 nm to 25 nm), each independently emitting light having a wavelength range between 200 nm and 900 nm. In other embodiments, the narrowband light source can include one or more lasers (e.g., a laser array), and the sample can be illuminated with specific wavelengths ranging from 200 nm to 700 nm, such as a laser array including gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers, as described above.

[0037] When more than one light source is used, the flow stream can be illuminated by the light sources simultaneously, sequentially, or a combination thereof. For example, the flow stream can be illuminated by each of the light sources simultaneously. In other embodiments, the flow stream is illuminated sequentially by each of the light sources. When more than one light source is employed to illuminate the sample sequentially, the time for which each light source illuminates the sample can independently be 0.001 microseconds or more, for example, 0.01 microseconds or more, for example, 0.1 microseconds or more, for example, 1 microsecond or more, for example, 5 microseconds or more, for example, 10 microseconds or more, for example, 30 microseconds or more, and 60 microseconds or more. For example, the method can include irradiating the sample with a light source (e.g., a laser) for a duration ranging from 0.001 microseconds to 100 microseconds, for example, 0.01 microseconds to 75 microseconds, for example, 0.1 microseconds to 50 microseconds, for example, 1 microsecond to 25 microseconds, and 5 microseconds to 10 microseconds. In embodiments in which the flow stream is illuminated sequentially with two or more light sources, the duration for which the sample is illuminated by each light source can be the same or different.

[0038] The time interval between illumination by each light source can also vary as desired, being independently separated by a delay of 0.001 microseconds or more, for example, 0.01 microseconds or more, for example, 0.1 microseconds or more, for example, 1 microsecond or more, for example, 5 microseconds or more, for example, 10 microseconds or more, for example, 15 microseconds or more, for example, 30 microseconds or more, and 60 microseconds or more. For example, the time interval between illumination by each light source can range from 0.001 microseconds to 60 microseconds, for example, 0.01 microseconds to 50 microseconds, for example, 0.1 microseconds to 35 microseconds, for example, 1 microsecond to 25 microseconds, and 5 microseconds to 10 microseconds. In certain embodiments, the time interval between illumination by each light source is 10 microseconds. In embodiments in which the flow stream is illuminated sequentially by more than two (i.e., three or more) light sources, the delay between illumination by each light source can be the same or different.

[0039] The flow stream can be illuminated continuously or at discrete intervals. In some cases, the method involves continuously illuminating the flow stream (e.g., with a sample having particles) with a light source. In other cases, the flow stream is illuminated with a light source at discrete intervals, such as every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or some other interval.

[0040] Depending on the light source, the flow stream may be illuminated from a variety of distances, such as 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and 50 mm or more. The angle of illumination may also vary, ranging from 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, and 30° to 60°, for example 90°.

[0041] In certain embodiments, the method includes irradiating a sample having particles in a flowstream with two or more beams of frequency-shifted light. As described above, a light beam generator component including a laser and an acousto-optical device for frequency-shifting the laser light can be used. In these embodiments, the method includes irradiating the acousto-optical device with a laser. Depending on the desired wavelength of light generated in the output laser beam (e.g., for use in irradiating a sample in a flowstream), the laser can have a specific wavelength ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and 400 nm to 800 nm. The acousto-optical device can be illuminated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, and ten or more lasers. The lasers can include any combination of laser types. For example, in some embodiments, the method includes irradiating the acousto-optical device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0042] When more than one laser is employed, the acousto-optical device can be irradiated with the lasers simultaneously, sequentially, or a combination thereof. For example, the acousto-optical device can be irradiated with each of the lasers simultaneously. In other embodiments, the acousto-optical device is irradiated with each of the lasers sequentially. When more than one laser is used to sequentially irradiate the acousto-optical device, the time for which each laser irradiates the acousto-optical device can independently be 0.001 microseconds or more, for example, 0.01 microseconds or more, for example, 0.1 microseconds or more, for example, 1 microsecond, for example, 5 microseconds or more, for example, 10 microseconds or more, for example, 30 microseconds or more, and 60 microseconds or more. For example, the method can include irradiating the acousto-optical device with the laser for a duration ranging from 0.001 microseconds to 100 microseconds, for example, 0.01 microseconds to 75 microseconds, for example, 0.1 microseconds to 50 microseconds, for example, 1 microsecond to 25 microseconds, and 5 microseconds to 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially with two or more lasers, the duration for which the acousto-optic device is illuminated by each laser may be the same or different.

[0043] The time interval between irradiation by each laser can also vary as desired, being independently separated by a delay of 0.001 microseconds or more, for example, 0.01 microseconds or more, for example, 0.1 microseconds or more, for example, 1 microsecond or more, for example, 5 microseconds or more, for example, 10 microseconds or more, for example, 15 microseconds or more, for example, 30 microseconds or more, and 60 microseconds or more. For example, the time interval between irradiation by each light source can range from 0.001 microseconds to 60 microseconds, for example, 0.01 microseconds to 50 microseconds, for example, 0.1 microseconds to 35 microseconds, for example, 1 microsecond to 25 microseconds, and 5 microseconds to 10 microseconds. In certain embodiments, the time interval between irradiation by each laser is 10 microseconds. In embodiments in which the acousto-optic device is sequentially illuminated by more than two (i.e., three or more) lasers, the delay between irradiation by each laser can be the same or different.

[0044] The acousto-optic device can be illuminated continuously or at discrete intervals. In some cases, the method includes illuminating the acousto-optic device with a laser continuously. In other cases, the acousto-optic device is illuminated with a laser at discrete intervals, such as every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or some other interval.

[0045] Depending on the laser, the acousto-optic device may be illuminated from a distance that varies, for example, 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and 50 mm or more, and the angle of illumination may vary from 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, and 30° to 60°, for example an angle of 90°.

[0046] In an embodiment, a method includes applying radio frequency drive signals to an acousto-optic device to generate angularly deflected laser beams. Two or more radio frequency drive signals, for example, three or more radio frequency drive signals, for example, four or more radio frequency drive signals, for example, five or more radio frequency drive signals, for example, six or more radio frequency drive signals, for example, seven or more radio frequency drive signals, for example, eight or more radio frequency drive signals, for example, nine or more radio frequency drive signals, for example, ten or more radio frequency drive signals, for example, fifteen or more radio frequency drive signals, for example, twenty-five or more radio frequency drive signals, for example, fifty or more radio frequency drive signals, and one hundred or more radio frequency drive signals may be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams.

[0047] Each angularly deflected laser beam generated by the radio frequency drive signal has an intensity based on the amplitude of the applied radio frequency drive signal. In some embodiments, the method includes applying a radio frequency drive signal having an amplitude sufficient to generate an angularly deflected laser beam at a desired intensity. In some cases, each applied radio frequency drive signal independently has an amplitude of about 0.001 V to about 500 V, e.g., about 0.005 V to about 400 V, e.g., about 0.01 V to about 300 V, e.g., about 0.05 V to about 200 V, e.g., about 0.1 V to about 100 V, e.g., about 0.5 V to about 75 V, e.g., about 1 V to 50 V, e.g., 2 V to 40 V, e.g., about 3 V to about 30 V, and about 5 V to about 25 V. Each applied radio frequency drive signal in some embodiments has a frequency of about 0.001 MHz to about 500 MHz, for example about 0.005 MHz to about 400 MHz, for example about 0.01 MHz to about 300 MHz, for example about 0.05 to about 200 MHz, for example about 0.1 MHz to about 100 MHz, for example about 0.5 MHz to about 90 MHz, for example about 1 MHz to about 75 MHz, for example about 2 MHz to about 70 MHz, for example about 3 MHz to about 65 MHz, for example about 4 MHz to about 60 MHz, and about 5 MHz to about 50 MHz.

[0048] In these embodiments, the angularly deflected laser beams within the output laser beam are spatially separated. Depending on the applied radio frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams can be separated by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 1000 μm or more, and 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap, for example, with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (such as overlap of beam spots) can be 0.001 μm or more, for example 0.005 μm or more, for example 0.01 μm or more, for example 0.05 μm or more, for example 0.1 μm or more, for example 0.5 μm or more, for example 1 μm or more, for example 5 μm or more, for example 10 μm or more, and for example 100 μm or more.

[0049] In certain cases, the flow stream is illuminated with multiple beams of frequency-shifted light, and cells in the flow stream are irradiated with light beams as described in Diebold et al., Nature Photonics Vol. 7(10); 806-810 (2013), the disclosures of which are incorporated herein by reference, as well as U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10 ,620,111; and U.S. Patent Publication Nos. 2017 / 0133857; 2017 / 0328826; 2017 / 0350803; 2018 / 0275042; 2019 / 0376895 and 2019 / 0376894, and are imaged by fluorescence imaging using radio frequency emission (FIRE) to generate frequency-encoded images.

[0050] As described above, in embodiments, light from the illuminated sample is conveyed to a light detection system (described in more detail below) and measured by one or more photodetectors. In some embodiments, the method includes measuring the collected light over a wavelength range (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more wavelengths in the range of 200 nm to 1000 nm. In still other embodiments, the method includes measuring the collected light at one or more specific wavelengths. For example, the collected light may be measured at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the method comprises measuring a wavelength of light corresponding to the fluorescence peak wavelength of the fluorophore, hi some embodiments, the method comprises measuring light collected across the fluorescence spectrum of each fluorophore in the sample.

[0051] The collected light can be measured continuously or at discrete intervals. In some cases, the method includes measuring the light continuously. In other cases, the light is measured at discrete intervals, including measuring the light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, every 1000 milliseconds, or some other interval.

[0052] Measurements of collected light can be made one or more times during the subject method, e.g., two or more times, e.g., three or more times, e.g., five or more times, and ten or more times. In certain embodiments, light propagation is measured two or more times, and in certain cases the data is averaged.

[0053] In embodiments, the data signal from the photodetector is generated in response to light from the flowstream. In some embodiments, the light is detected from particle-free components of the illuminated flowstream. Light from a "particle-free" component refers to light from the flowstream that does not originate from illuminated particles (e.g., of a sample), such as scattered light or emitted light, e.g., fluorescence from one or more fluorophores conjugated to or physically associated with particles. In some embodiments, the light emitted from particle-free components of the flowstream is fluorescence from free fluorophores in the sample. The term "free fluorophore" refers to a fluorophore that is not associated with a particle in the flowstream, such as a fluorophore that is conjugated (i.e., covalently bonded) to a particle or physically associated (e.g., hydrogen bonding, ionic interactions) with a particle. Where the method comprises detecting light from particle-free components of the illuminated flow stream, the data signal may be generated from a sampling period having a duration of 0.001 μs to 100 μs, for example, 0.005 μs to 95 μs, for example, 0.01 μs to 90 μs, for example, 0.05 μs to 85 μs, for example, 0.1 μs to 80 μs, for example, 0.5 μs to 75 μs, for example, 1 μs to 70 μs, for example, 2 μs to 65 μs, for example, 3 μs to 60 μs, for example, 4 μs to 55 μs, and 5 μs to 50 μs. In certain cases, the data signal is generated from light detected from particle-free components of the illuminated flow stream over a sampling period having a duration of 1 μs to 10 μs.

[0054] In performing the subject method, a moving mean squared error of the generated data signal is calculated. In some embodiments, the moving mean squared error is calculated by measuring the squared difference between the generated data signal and a calculated baseline data signal. In particular cases, calculating the moving mean squared error of the generated data signal includes measuring the squared difference between multiple generated data signals and the calculated baseline data signal over a predetermined sampling period to generate multiple baseline noise signals, summing the baseline noise signals over the sampling period, and dividing the summed baseline noise signals by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is from 0.001 μs to 100 μs, such as from 0.005 μs to 95 μs, for example from 0.01 μs to 90 μs, for example from 0.05 μs to 85 μs, for example from 0.1 μs to 80 μs, for example from 0.5 μs to 75 μs, for example from 1 μs to 70 μs, for example from 2 μs to 65 μs, for example from 3 μs to 60 μs, for example from 4 μs to 55 μs, and from 5 μs to 50 μs in duration.

[0055] In certain embodiments, the subject method comprises calculating a moving mean squared error of the generated data signal at predetermined time intervals, for example, the moving mean squared error of the generated data signal is calculated once every 0.0001 ms or more, such as once every 0.0005 ms or more, for example, once every 0.001 ms, for example, once every 0.005 ms or more, such as once every 0.01 ms or more, for example, once every 0.05 ms or more, for example, once every 0.1 ms or more, for example, once every 0.5 ms or more, for example, once every 1 ms or more, for example, once every 1 ms or more, for example, once every 2 ms or more, for example, once every 3 ms or more, for example, once every 4 ms or more, for example, once every 5 ms or more, for example, once every 10 ms, for example, once every 25 ms or more, for example, once every 50 ms or more, for example, once every 100 ms or more, and once every 500 ms or more. In some embodiments, the moving mean squared error of the generated data signal is calculated once per second, for example, once per 2 seconds, for example, once per 3 seconds, for example, once per 4 seconds, for example, once per 5 seconds, for example, once per 10 seconds, for example, once per 15 seconds, for example, once per 30 seconds, and once per 60 seconds. In other embodiments, the moving mean squared error of the generated data signal is calculated once per minute, for example, once per 2 minutes, for example, once per 3 minutes, for example, once per 4 minutes, for example, once per 5 minutes, once per 10 minutes, for example, once per 15 minutes, for example, once per 30 minutes, for example, once per 60 minutes. In certain embodiments, the method comprises continuously calculating the moving mean squared error of the generated data signal.

[0056] In certain embodiments, the method includes continuously calculating and updating the mean square error of the baseline noise signal over a sampling window. For example, the duration of the sampling window can be 1 μs or more, such as 10 μs or more, for example 25 μs or more, for example 50 μs or more, for example 100 μs or more, for example 500 μs or more, for example 1 ms or more, for example 10 ms or more, for example 25 ms or more, for example 50 ms or more, for example 100 ms or more, for example 500 ms or more, for example 1 sec or more, for example 5 sec or more, for example 10 sec or more, for example 25 sec or more, for example 50 sec or more, for example 100 sec or more, including over a sampling window duration of 500 seconds or more. In these embodiments, the mean squared error may be calculated over all or a portion of the sampling window duration, for example over 5% or more, for example 10% or more, for example 15% or more, for example 25% or more, for example 50% or more, for example 75% or more, for example 90% or more, for example 95% or more, for example 97% or more, and for example 99% or more of the sampling window duration. In certain embodiments, the mean squared error is calculated continuously over the entire sampling window duration (100%).

[0057] In some embodiments, the baseline noise for each photodetector is measured as the square of the difference between the current sample value and the calculated baseline. In some cases, the baseline noise in these embodiments is sampled and accumulated every 2^baseline sample interval clocks over 2^baseline window size clocks. The sum of the baseline noise samples is then divided by the number of noise samples accumulated to obtain the mean squared baseline noise measurement. In certain embodiments, an approximate average of this value is used for each sample, and for example, for each sample, the sum of squared noise is continuously updated according to embodiments of the present disclosure as follows: (current sum) - (current mean) + (new baseline noise squared sample). In certain embodiments, the baseline sampling is updated periodically over the course of data acquisition, such as once every 1 μs or more, for example once every 10 μs or more, for example once every 25 μs or more, for example once every 50 μs or more, for example once every 100 μs or more, for example once every 500 μs or more, for example once every 1 ms or more, for example once every 10 ms or more, for example once every 25 ms or more, for example once every 50 ms or more, for example once every 100 ms or more, for example once every 500 ms or more, for example once every 1 second or more, for example once every 5 seconds or more, for example once every 10 seconds or more, for example once every 25 seconds or more, for example once every 50 seconds or more, for example once every 100 seconds or more, and once every 500 seconds or more.

[0058] In certain embodiments, the baseline noise of each photodetector is updated (i.e., calculated according to the subject method) at a predetermined time before light is detected from particles in the sample (e.g., by determining the baseline noise of the photodetector immediately before the particles in the sample are illuminated with light). For example, in some cases, the baseline noise of each photodetector is updated immediately before generating a data signal from the light detected from particles in the sample. In other cases, the baseline noise of each photodetector is updated 0.0001 μs to 500 μs, for example 0.0005 μs to 450 μs, for example 0.001 μs to 400 μs, for example 0.005 μs to 350 μs, for example 0.01 μs to 300 μs, for example 0.05 μs to 250 μs, for example 0.1 μs to 200 μs, for example 0.5 μs to 150 μs, before generating a data signal from light detected from particles in the sample, including cases where the baseline noise of each photodetector is updated 1 μs to 100 μs before generating a data signal from light detected from particles in the sample.

[0059] FIG. 1 shows a flow diagram for measuring the baseline noise of photodetectors according to certain embodiments. In step 101, a sample containing particles is illuminated in a flow stream. In step 102, light from the illuminated flow stream is detected by photodetectors, and in step 103, data signals are generated in response to the light detected by each photodetector. The baseline noise of each photodetector is determined by calculating the running mean squared error of the data signals generated in step 104. In some embodiments, light from fluorophores in the sample is spectrally resolved in steps 106 and 106a by calculating a spectral unmixing matrix using a least-squares algorithm. In some cases, the spectral unmixing matrix is ​​calculated by weighting the least-squares algorithm with the calculated baseline noise of each photodetector. In certain cases, the bandwidth of the baseline noise is adjusted in step 105 to match the bandwidth of the data signals generated by illuminating particles in the sample.

[0060] In certain embodiments, the method includes adjusting the bandwidth of the calculated baseline noise for each photodetector. In some cases, adjusting the bandwidth of the calculated baseline noise includes increasing the bandwidth by, for example, 5% or more, such as 10% or more, such as 15% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, and increasing the bandwidth of the calculated baseline noise by 99% or more. For example, the bandwidth of the calculated baseline noise may be increased by 0.0001 μs or more, such as 0.0005 μs or more, for example 0.001 μs or more, for example 0.005 μs or more, such as 0.01 μs or more, for example 0.05 μs or more, such as 0.1 μs or more, for example 0.5 μs or more, such as 1 μs or more, for example 2 μs or more, for example 3 μs or more, such as 4 μs or more, for example 5 μs or more, such as 10 μs or more, for example 25 μs or more, for example 50 μs or more, including increasing the bandwidth of the calculated baseline noise by 100 μs or more. In other cases, adjusting the bandwidth of the calculated baseline noise includes decreasing the bandwidth by, for example, 5% or more, such as 10% or more, for example 15% or more, such as 25% or more, for example 50% or more, such as 75% or more, for example 90% or more, and decreasing the bandwidth of the calculated baseline noise by 99% or more. For example, the bandwidth of the calculated baseline noise may be reduced by 0.0001 μs or more, such as 0.0005 μs or more, for example 0.001 μs or more, such as 0.005 μs or more, for example 0.01 μs or more, such as 0.05 μs or more, for example 0.1 μs or more, such as 0.5 μs or more, for example 1 μs or more, such as 2 μs or more, for example 3 μs or more, such as 4 μs or more, for example 5 μs or more, such as 10 μs or more, for example 25 μs or more, such as 50 μs or more, and include reducing the bandwidth of the calculated baseline noise by 100 μs or more. In certain embodiments, the method comprises matching the bandwidth of the calculated baseline noise to the bandwidth of a data signal generated from particles in the sample.For example, the bandwidth of the calculated baseline noise can be adjusted to be 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more of the bandwidth of the data signals generated from the particles in the sample, including when the bandwidth of the calculated baseline noise is adjusted to be 99.9% or more of the bandwidth of the data signals generated from the particles in the sample. In certain embodiments, the bandwidth of the calculated baseline noise matches (100%) the bandwidth of the data signals generated from the particles in the sample.

[0061] In some embodiments, the sample contains multiple fluorophores, and one or more of the fluorophores have overlapping fluorescence spectra. In some cases, the method further comprises spectrally resolving light from each type of fluorophore in the sample, such as by calculating a spectral separation matrix for the fluorescence spectrum of each type of fluorophore in the sample. In certain embodiments, the method comprises determining the spectral overlap of light from the flow stream and calculating each contribution to the overlapping detected light spectrum. In certain embodiments, the method comprises calculating a spectral separation matrix to estimate the abundance of each contribution to the light signal detected by the photodetector. In certain cases, the spectral separation matrix is ​​calculated using a weighted least squares algorithm. In some embodiments, the data signal generated from light from free fluorophores in the sample is weighted based on the calculated baseline noise of the photodetector.

[0062] In certain embodiments, the method comprises spectrally decomposing light detected by a plurality of photodetectors (e.g., weighted using the calculated baseline noise of each photodetector), e.g., as described in International Patent Application No. PCT / US2019 / 068395, filed December 23, 2019, U.S. Provisional Patent Application No. 62 / 971,840, filed February 7, 2020, and U.S. Provisional Patent Application No. 63 / 010,890, filed April 16, 2020, the disclosures of which are incorporated herein by reference in their entireties. For example, spectrally decomposing the light detected by the multiple photodetectors may include solving the spectral separation matrix using one or more of: 1) a weighted least squares algorithm; 2) a Sherman-Morrison iterative inverse updater; 3) an LU matrix decomposition, such that the matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix; 4) a modified Cholesky decomposition; 5) a weighted least squares algorithm calculation via QR decomposition; and 6) a singular value decomposition calculation.

[0063] FIG. 2A shows a diagram of baseline noise detected from an illuminated sample containing particles in a flow stream, according to certain embodiments. Panel A of FIG. 2A shows a data signal from an illuminated particle in the absence of baseline noise (e.g., electronic noise due to laser focus drift, laser alignment drift, time-dependent changes in the flow rate and flow profile of the flow stream, and thermal drift of detector components such as a transimpedance amplifier). Panel B of FIG. 2A shows a data signal from an illuminated particle in the presence of photonic shot noise without optical background noise. Panel C of FIG. 2A shows a data signal from an illuminated particle in the presence of photonic shot noise with optical background noise. Panel C also shows a data signal from an illuminated particle after baseline recovery of the data signal. Panel D of FIG. 2A shows a data signal from an illuminated particle in the presence of photonic shot noise and electronic noise, with the baseline recovered.

[0064] Figure 2B shows baseline noise determined from samples with free fluorophores according to certain embodiments. Two samples (color panel 1 and color panel 2) stained with bone marrow cells were centrifuged to remove the cells, leaving only the supernatant containing the free fluorophores. The samples contain 28 different fluorophores. The background baseline noise from the free fluorophores in each sample was measured and plotted against a control sample containing only water. Figure 2C shows baseline noise determined from samples with different titrated fluorophores according to certain embodiments. Sample panels stained with 100%, 75%, and 50% antibody titrations were illuminated to determine the baseline noise level for each sample panel. As shown in Figure 2C, the baseline noise level depends on the level of free fluorophores in each sample.

[0065] In embodiments, the sample in the flow stream contains particles. In some embodiments, the sample is a biological sample. The term "biological sample" is used in its conventional sense to refer to a whole organism, a subset of plant, fungus, or animal tissue, a cell, or, in certain cases, components that can be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, umbilical cord blood, urine, vaginal fluid, and semen. Thus, "biological sample" refers to both a natural organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including, but not limited to, plasma, serum, spinal fluid, lymph, skin, respiratory, gastrointestinal, cardiovascular, and genitourinary sections, tears, saliva, milk, blood cells, tumors, and organs. A biological sample can be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is blood or a derivative thereof, e.g., plasma, or other biological fluid sample, e.g., a liquid sample such as tears, urine, semen, etc., and in some cases the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger prick (which may or may not be mixed with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0066] In certain embodiments, the source of the sample is a "mammal" or "mammalian," terms used broadly to refer to organisms belonging to the class Mammalia, including Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The methods may be applied to samples obtained from human subjects of any gender and any developmental stage (i.e., newborn, infant, juvenile, adolescent, or adult); in certain embodiments, the human subject is a juvenile, adolescent, or adult. While embodiments of the present disclosure may be applied to samples from human subjects, it should be understood that the methods may also be performed on samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0067] In certain embodiments, a biological sample comprises cells. Cells that may be present in a sample include eukaryotic cells (e.g., mammalian cells) and / or prokaryotic cells (e.g., bacterial or archaeal cells). Samples may be obtained from in vitro sources (e.g., cell suspensions from laboratory cells grown in culture) or from in vivo sources (e.g., mammalian subjects, human subjects, etc.). In some embodiments, cell samples are obtained from in vitro sources. In vitro sources include, but are not limited to, prokaryotic (e.g., bacterial, archaeal) cell cultures, environmental samples containing prokaryotic and / or eukaryotic (e.g., mammalian, proteus, fungal, etc.) cells, eukaryotic cell cultures (e.g., cultures of established cell lines, cultures of known or purchased cell lines, cultures of immortalized cell lines, cultures of primary cells, cultures of laboratory yeast, etc.), tissue cultures, etc.

[0068] When the biological sample includes cells, the disclosed methods can include characterizing components of the cells, such as cell fragments, fragmented cell membranes, organelles, dead cells, or lysed cells. In some embodiments, the methods can include characterizing extracellular vesicles of the cells. Characterizing extracellular vesicles of the cells can include identifying the type of extracellular vesicles within the cells or determining the size of the extracellular vesicles within the cells.

[0069] In some embodiments, the method further comprises sorting one or more particles (e.g., cells) of the sample. The term "sorting" is used herein in its conventional sense to refer to separating components of a sample (e.g., cells, non-cellular particles such as biopolymers), and in some cases, delivering the separated components to one or more sample collection vessels. For example, the method may comprise sorting a sample having two or more components, e.g., three or more components, e.g., four or more components, e.g., five or more components, e.g., ten or more components, e.g., fifteen or more components, including sorting a sample having twenty-five or more components. One or more of the sample components, e.g., two or more sample components, e.g., three or more sample components, e.g., four or more sample components, e.g., five or more sample components, e.g., ten or more sample components, may be separated from the sample and delivered to a sample collection vessel, including separating fifteen or more sample components from the sample and delivered to a sample collection vessel.

[0070] In some embodiments, a method of sorting components of a sample comprises sorting particles (e.g., cells in a biological sample) as described in U.S. Patent Nos. 3,960,449; 4,347,935; 4,667,830; 5,245,318; 5,464,581; 5,483,469; 5,602,039; 5,643,796; 5,700,692; 6,372,506 and 6,809,804, the disclosures of which are incorporated herein by reference. In some embodiments, the method comprises sorting components of the sample with a particle sorting module, such as those described in U.S. Patent Nos. 9,551,643 and 10,324,019, U.S. Patent Publication No. 2017 / 0299493, and International Patent Publication No. WO / 2017 / 040151, the disclosures of which are incorporated herein by reference. In certain embodiments, cells of the sample are sorted using a sorting decision module having multiple sorting decision units, such as those described in U.S. Patent Application No. 16 / 725,756, filed December 23, 2019, the disclosure of which is incorporated herein by reference.

[0071] System for measuring baseline noise of photodetectors Aspects of the present disclosure also include systems (e.g., particle analyzers) for implementing the subject methods, where the system of interest comprises a light source and a light detection system including a light detector. In embodiments, the system comprises a processor having a memory operably coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to generate data signals from light detected by the light detector and calculate a running mean square error of the generated data signals to determine the baseline noise of the light detector. In some embodiments, the subject systems are configured to determine real-time measurements of the baseline noise of the light detector, such as during illumination of a sample in a flow stream. In some embodiments, the system is configured to obtain sample-specific measurements of the baseline noise in real time. In certain embodiments, the system includes a memory having instructions stored thereon that, when executed by the processor, cause the processor to determine the contribution to the background noise of each individual light detector in the light detection system, for example, from time-varying parameters including, but not limited to, laser focus drift, laser alignment drift, time-dependent changes in the flow rate and flow profile of the flow stream, and increased electronic noise due to thermal drift of detector components such as transimpedance amplifiers. As previously mentioned, the term baseline noise refers to baseline electronic signals from a photodetector (e.g., electronic signals originating from the operating electronics of the photodetector or the optical components of the photodetector system). In certain cases, baseline noise includes electronic signals present in the photodetector system, such as those generated by a light source or other electronic subcomponents of the system. In other embodiments, baseline noise includes electronic signals resulting from vibration or thermal effects from components of the system. In still other 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).

[0072] In embodiments, the system includes a light source for illuminating a flow stream (e.g., a flow stream carrying a particle-bearing fluid sample composition). The light source may be any convenient light source, including laser and non-laser light sources. In certain embodiments, the light source is a non-laser light source, such as a narrowband light source that emits a specific wavelength or a narrow range of wavelengths. In some cases, the narrowband light source emits light having a narrow range of wavelengths, e.g., 50 nm or less, e.g., 40 nm or less, e.g., 30 nm or less, e.g., 25 nm or less, e.g., 20 nm or less, e.g., 15 nm or less, e.g., 10 nm or less, e.g., 5 nm or less, e.g., 2 nm or less, including light sources that emit light of a specific wavelength (i.e., monochromatic light). Any convenient narrowband light source protocol may be employed, such as a narrow-wavelength LED.

[0073] In other embodiments, the light source is a broadband light source, such as a broadband light source coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof. In some cases, the broadband light source emits light having a wide range of wavelengths, such as, for example, 50 nm or more, for example, 100 nm or more, for example, 150 nm or more, for example, 200 nm or more, for example, 250 nm or more, for example, 300 nm or more, for example, 350 nm or more, for example, 400 nm or more, and 500 nm or more. For example, one suitable broadband light source emits light having a wavelength from 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength from 400 nm to 1000 nm. Any convenient broadband light source protocol may be employed, such as a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a broad spectrum LED white light source, a multi-LED integrated white light source, other broadband light sources, or any combination thereof. In certain embodiments, the light source comprises an array of infrared LEDs.

[0074] In certain embodiments, the light source is a laser, such as a continuous wave laser. For example, the laser can be a diode laser, such as an ultraviolet diode laser, a visible diode laser, and a near-infrared diode laser. In other embodiments, the laser can be a helium-neon (HeNe) laser. In some cases, the laser is a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other cases, the subject systems include a dye laser, such as a stilbene, coumarin, or rhodamine laser. In still other cases, lasers of interest include metal vapor lasers such as helium-cadmium (HeCd), helium-mercury (HeHg), helium-selenium (HeSe), helium-silver (HeAg), strontium, neon-copper (NeCu), copper, or gold lasers, and combinations thereof. In still other cases, the subject systems include solid-state lasers such as ruby, Nd:YAG, NdCrYAG, Er:YAG, Nd:YLF, Nd:YVO, Nd:YCaO(BO), Nd:YCOB, titanium sapphire, thorium YAG, ytterbium YAG, Y2O, or cerium-doped lasers, and combinations thereof.

[0075] The system may include one or more of the light sources described above, such as two or more light sources, such as three or more light sources, such as four or more light sources, such as five or more light sources, and ten or more light sources. The light source may include any combination of light source types. For example, in some embodiments, the system includes an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0076] In some embodiments, the light source is a narrow bandwidth light source. In some cases, the light source is a light source that outputs a specific wavelength of 200 nm to 1500 nm, for example, 250 nm to 1250 nm, for example, 300 nm to 1000 nm, for example, 350 nm to 900 nm and 400 nm to 800 nm. In certain embodiments, the continuous wave light source emits light having a wavelength of 365 nm, 385 nm, 405 nm, 460 nm, 490 nm, 525 nm, 550 nm, 580 nm, 635 nm, 660 nm, 740 nm, 770 nm or 850 nm.

[0077] The light source may be positioned at any suitable distance from the flow stream, such as 0.001 mm or more, for example 0.005 mm or more, for example 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 5 mm or more, for example 10 mm or more, for example 25 mm or more, and for example 100 mm or more. Furthermore, the light source may be positioned at any suitable angle relative to the photodetector, such as between 10° and 90°, for example 15° and 85°, for example 20° and 80°, for example 25° and 75°, and 30° and 60°, for example at a 90° angle.

[0078] In certain embodiments, the light source is a continuous wave light source. In some embodiments, the continuous wave light source emits non-pulsed or non-stroboscopic illumination. In certain embodiments, the continuous wave light source provides a substantially constant emitted light intensity. For example, the continuous wave light source can provide a light emission intensity during the time interval of illumination that varies by 10% or less, such as 9% or less, for example 8% or less, such as 7% or less, for example 6% or less, such as 5% or less, for example 4% or less, for example 3% or less, such as 2% or less, for example 1% or less, such as 0.5% or less, for example 0.1% or less, for example 0.01% or less, such as 0.001% or less, for example 0.0001% or less, for example 0.00001% or less, including cases where the emitted light intensity during the time interval of illumination varies by 0.000001% or less. The intensity of the light output can be measured with any convenient protocol, including, but not limited to, a scanning slit profiler, a charge-coupled device (CCD such as an intensified charge-coupled device ICCD), a positioning sensor, a power sensor (e.g., a thermopile power sensor), an optical power sensor, an energy meter, a digital laser photometer, a laser diode detector, or any other type of light detector.

[0079] In certain embodiments, the light source is an optical beam generator configured to generate two or more beams of frequency-shifted light. In some cases, the optical beam generator includes a laser, a radio frequency generator configured to apply a radio frequency drive signal to an acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous wave laser. For example, optical beam generator lasers of interest include gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof; dye lasers such as stilbene, coumarin, or rhodamine lasers; helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-cerium (Ce) lasers, and combinations thereof. The laser may be a metal vapor laser, such as a helium-silver (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser or a gold laser, and combinations thereof; or a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thorium YAG laser, an ytterbium YAG laser, a Y2O3 laser or a cerium-doped laser, and combinations thereof.

[0080] The acousto-optic device can be any convenient acousto-optic protocol configured to frequency-shift laser light using applied acoustic waves. In certain embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device of the subject system is configured to generate an angularly deflected laser beam from light from a laser and an applied radio frequency drive signal. The radio frequency drive signal can be applied to the acousto-optic device using any suitable radio frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0081] In an embodiment, the controller is configured to apply radio frequency drive signals to the acousto-optic device to generate a desired number of angularly deflected laser beams in the output laser beam, including being configured to apply three or more radio frequency drive signals, such as four or more radio frequency drive signals, for example five or more radio frequency drive signals, for example six or more radio frequency drive signals, for example seven or more radio frequency drive signals, for example eight or more radio frequency drive signals, for example nine or more radio frequency drive signals, for example ten or more radio frequency drive signals, for example fifteen or more radio frequency drive signals, for example twenty-five or more radio frequency drive signals, for example fifty or more radio frequency drive signals, including being configured to apply one hundred or more radio frequency drive signals.

[0082] In some cases, to generate an angularly deflected laser beam intensity profile in the output laser beam, the controller is configured to apply a radio frequency drive signal having an amplitude that varies from about 0.001V to about 500V, for example, from about 0.005V to about 400V, for example, from about 0.01V to about 300V, for example, from about 0.05V to about 200V, for example, from about 0.1V to about 100V, for example, from about 0.5V to about 75V, for example, from about 1V to 50V, for example, from about 2V to 40V, for example, from 3V to about 30V, and from about 5V to about 25V. Each applied radio frequency drive signal in some embodiments has a frequency of from about 0.001 MHz to about 500 MHz, for example from about 0.005 MHz to about 400 MHz, for example from about 0.01 MHz to about 300 MHz, for example from about 0.05 MHz to about 200 MHz, for example from about 0.1 MHz to about 100 MHz, for example from about 0.5 MHz to about 90 MHz, for example from about 1 MHz to about 75 MHz, for example from about 2 MHz to about 70 MHz, for example from about 3 MHz to about 65 MHz, for example from about 4 MHz to about 60 MHz, and from about 5 MHz to about 50 MHz.

[0083] In certain embodiments, the system includes a processor having a memory operatively coupled to the processor, the memory including instructions stored therein that, when executed by the processor, cause the processor to generate an output laser beam with an angularly deflected laser beam having a desired intensity profile. For example, the memory may include instructions for generating two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more, angularly deflected laser beams with the same intensity, including where the memory may include instructions for generating 100 or more angularly deflected laser beams with the same intensity. In other embodiments, the memory may include instructions for generating two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more, angularly deflected laser beams with different intensities, including where the memory may include instructions for generating 100 or more angularly deflected laser beams with different intensities.

[0084] In certain embodiments, the system comprises a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon, which, when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from the edge of the output laser beam to the center along a horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the center of the output beam can be in the range of 0.1% to about 99%, such as 0.5% to about 95%, such as 1% to about 90%, such as about 2% to about 85%, such as about 3% to about 80%, such as about 4% to about 75%, such as about 5% to about 70%, such as about 6% to about 65%, such as about 7% to about 60%, such as about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. In other embodiments, the system comprises a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon, which, when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the edge of the output beam can be in the range of 0.1% to about 99%, such as 0.5% to about 95%, such as 1% to about 90%, such as about 2% to about 85%, such as about 3% to about 80%, such as about 4% to about 75%, such as about 5% to about 70%, such as about 6% to about 65%, such as about 7% to about 60%, such as about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In yet another embodiment, a system includes a processor having a memory operatively coupled to the processor such that the memory includes instructions stored therein, the instructions, when executed by the processor, causing the processor to generate an output laser beam having an intensity profile with a Gaussian distribution along a horizontal axis.In yet another embodiment, a system includes a processor having a memory operatively coupled to the processor such that the memory includes instructions stored therein, the instructions, when executed by the processor, causing the processor to generate an output laser beam having a top-hat intensity profile along a horizontal axis.

[0085] In embodiments, the optical beam generator of interest can be configured to generate angularly polarized laser beams in a spatially separated output laser beam. Depending on the applied radio frequency drive signal and the desired illumination profile of the output laser beam, the angularly polarized laser beams can be separated by 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1000 μm or more, and 5000 μm or more. In some embodiments, the system is configured to generate overlapping angularly polarized laser beams in the output laser beam, such as adjacent angularly polarized laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (such as overlap of beam spots) can be 0.001 μm or more, for example 0.005 μm or more, for example 0.01 μm or more, for example 0.05 μm or more, for example 0.1 μm or more, for example 0.5 μm or more, for example 1 μm or more, for example 5 μm or more, for example 10 μm or more, and for example 100 μm or more.

[0086] In certain cases, optical beam generators configured to generate two or more beams of frequency-shifted light are described in Diebold et al., Nature Photonics Vol. 7(10); 806-810 (2013), the disclosures of which are incorporated herein by reference, and in U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,7 58; 10,451,538; 10,620,111; and U.S. Patent Publication Nos. 2017 / 0133857; 2017 / 0328826; 2017 / 0350803; 2018 / 0275042; 2019 / 0376895 and 2019 / 0376894.

[0087] In embodiments, the system comprises a photodetection system having one or more photodetectors, for example, two or more photodetectors, for example, three or more photodetectors, for example, four or more photodetectors, for example, five or more photodetectors, for example, ten or more photodetectors, for example, twenty-five or more photodetectors, for example, fifty or more photodetectors, for example, one hundred or more photodetectors, for example, two hundred or more photodetectors, and five hundred or more photodetectors. In some embodiments, the photodetectors are avalanche photodiodes. In certain embodiments, the photodetection system comprises an array of photodetectors. In these embodiments, the photodetector array may comprise four or more photodetectors, for example, ten or more photodetectors, for example, twenty-five or more photodetectors, for example, fifty or more photodetectors, for example, one hundred or more photodetectors, for example, two hundred or more photodetectors, for example, five hundred or more photodetectors, for example, seven hundred or more photodetectors, and one thousand or more photodetectors.

[0088] The photodetectors can be arranged in any geometric configuration as desired, and configurations of interest include, but are not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, elliptical, and irregular pattern configurations. The photodetectors within a photodetector array can be oriented relative to one another at angles (as referenced in the XZ plane) ranging from 10° to 180°, e.g., 15° to 170°, e.g., 20° to 160°, e.g., 25° to 150°, e.g., 30° to 120°, and 45° to 90°. The photodiode array can be of any suitable shape, including rectilinear shapes such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curvilinear shapes such as circles, ellipses, and irregular shapes such as a parabolic base connected to a flat top. In certain embodiments, the photodetector array has a rectangular active surface.

[0089] Each photodetector in the array may have an active surface with a width in the range of 5 μm to 250 μm, for example 10 μm to 225 μm, for example 15 μm to 200 μm, such as 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, and 50 μm to 100 μm, and a length in the range of 5 μm to 250 μm, for example 10 μm to 225 μm, for example 15 μm to 200 μm, such as 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, and 50 μm to 100 μm, and 2 to 10,000 μm 2 , e.g., 50 μm 2 to 9000 μm 2 , e.g., 75 μm 2 to 8000 μm 2 , e.g., 100 μm 2 to 7000 μm 2 , e.g., 150 to μm 2 to 6000 μm 2 , and 200 μm 2 to 5000 μm 2 The range is.

[0090] The size of the photodetector array can vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and can have a length ranging from 0.01 mm to 100 mm, for example, 0.05 mm to 90 mm, for example, 0.1 mm to 80 mm, for example, 0.5 mm to 70 mm, for example, 1 mm to 60 mm, for example, 2 mm to 50 mm, for example, 3 mm to 40 mm, for example, 4 mm to 30 mm, and 5 mm to 25 mm. The width of the photodiode array can also vary, and can range from 0.01 mm to 100 mm, for example, 0.05 mm to 90 mm, for example, 0.1 mm to 80 mm, for example, 0.5 mm to 70 mm, for example, 1 mm to 60 mm, for example, 2 mm to 50 mm, for example, 3 mm to 40 mm, for example, 4 mm to 30 mm, and 5 mm to 25 mm. Thus, the active surface of the photodiode array can be 0.1 mm 2 From 10,000 mm 2 , e.g. 0.5 mm 2 to 5000mm 2 , e.g. 1 mm 2 to 1000mm 2 , e.g. 5mm 2 from 500mm 2 , and 10mm 2 from 100mm 2 The range may be:

[0091] The photodetectors of interest are configured to measure collected light at one or more wavelengths, for example two or more wavelengths, for example five or more different wavelengths, for example ten or more different wavelengths, for example fifteen or more, for example twenty-five or more, for example fifty or more, such as fifty or more, for example hundred or more, for example two or more different wavelengths, such as ten or more different wavelengths, for example fifteen or more, for example twenty-five or more, for example fifty or more, such as fifty or more, for example one hundred or more, for example two or more different wavelengths, such as fifteen or more, for example twenty-five or more, for example thirty-five or more, for example four hundred or more, for example five hundred or more, for example one thousand or more, for example fifteen or more, such as fifteen or more, for example five hundred or more, for example five hundred or more, for example five hundred or more, for example six hundred or more, for example seven hundred or more, for example eight hundred or more, for example nine hundred or more, for example five hundred or more, and for example five hundred or more different wavelengths of light. For example, the photodiode may be configured to measure light in the range of 200 nm to 1500 nm, such as 400 nm to 1100 nm.

[0092] The light detection system is configured to measure light continuously or at discrete intervals. In some cases, the light detector of interest is configured to measure collected light continuously. In other cases, the light detection system is configured to make measurements at discrete intervals, such as measuring light every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, 1000 milliseconds, or other intervals.

[0093] In certain embodiments, the light detection system also includes an amplifier component. In embodiments, the amplifier component is configured to amplify an output signal from the light detector in response to the detected light. In some embodiments, the amplifier component comprises a current-to-voltage converter, such as a transimpedance amplifier. In other embodiments, the amplifier component comprises an operational amplifier circuit, such as a summing amplifier. In embodiments, the output current from the light detector is converted to a voltage, in certain cases combined with a summing amplifier, and propagated to a processor to output a data signal.

[0094] The system is configured to generate a data signal from a photodetector in response to light from the flowstream. In some embodiments, the photodetection system is configured to detect light from particle-free components of the illuminated flowstream. In these embodiments, the system may include a memory having instructions stored thereon that, when executed by a processor, cause the processor to generate a data signal from the light detected from the particle-free components of the illuminated flowstream over a sampling period having a duration of 0.001 μs to 100 μs, e.g., 0.005 μs to 95 μs, e.g., 0.01 μs to 90 μs, e.g., 0.05 μs to 85 μs, e.g., 0.1 μs to 80 μs, e.g., 0.5 μs to 75 μs, e.g., 1 μs to 70 μs, e.g., 2 μs to 65 μs, e.g., 3 μs to 60 μs, e.g., 4 μs to 55 μs, and 5 μs to 50 μs. In certain cases, the memory includes instructions for generating a data signal from light detected from particle-free components of the illuminated flow stream over a sampling period having a duration of 1 μs to 10 μs.

[0095] In embodiments, the system includes a processor having a memory operatively coupled to the processor, the memory having instructions stored thereon that, when executed by the processor, cause the processor to calculate a moving mean squared error of a generated data signal. In some embodiments, the memory has instructions for calculating the moving mean squared error by measuring the squared difference between the generated data signal and a calculated baseline data signal. In particular, the memory has instructions for calculating the moving mean squared error of the generated data signal by measuring the squared difference between a plurality of generated data signals and the calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals, summing the baseline noise signals over the sampling period, and dividing the summed baseline noise signal by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is from 0.001 μs to 100 μs, such as from 0.005 μs to 95 μs, for example from 0.01 μs to 90 μs, for example from 0.05 μs to 85 μs, for example from 0.1 μs to 80 μs, for example from 0.5 μs to 75 μs, for example from 1 μs to 70 μs, for example from 2 μs to 65 μs, for example from 3 μs to 60 μs, for example from 4 μs to 55 μs, and from 5 μs to 50 μs in duration.

[0096] In certain embodiments, the system includes a memory having instructions stored thereon that, when executed by the processor, cause the processor to calculate a moving mean squared error of the generated data signal at predetermined time intervals. For example, the memory may have instructions for calculating a moving mean squared error of the generated data signal once every 0.0001 ms or more, such as once every 0.0005 ms or more, such as once every 0.001 ms or more, such as once every 0.005 ms or more, such as once every 0.01 ms or more, such as once every 0.05 ms or more, such as once every 0.1 ms or more, such as once every 0.5 ms or more, such as once every 1 ms or more, such as once every 2 ms or more, such as once every 3 ms or more, such as once every 4 ms or more, such as once every 5 ms or more, such as once every 10 ms or more, such as once every 25 ms or more, such as once every 50 ms or more, such as once every 100 ms or more, and once every 500 ms or more. In some embodiments, the memory has instructions for calculating the moving mean squared error of the generated data signal once per second, for example, once per 2 seconds, for example, once per 3 seconds, for example, once per 4 seconds, for example, once per 5 seconds, for example, once per 10 seconds, for example, once per 15 seconds, for example, once per 30 seconds, and once per 60 seconds. In other embodiments, the memory has instructions for calculating the moving mean squared error of the generated data signal once per minute, for example, once per 2 minutes, for example, once per 3 minutes, for example, once per 4 minutes, for example, once per 5 minutes, for example, once per 10 minutes, for example, once per 15 minutes, for example, once per 30 minutes, and once per 60 minutes. In particular embodiments, the memory has instructions for continuously calculating the moving mean squared error of the generated data signal.

[0097] In certain embodiments, the system comprises a processor having a memory operatively coupled to the processor, the memory having instructions stored thereon that, when executed by the processor, cause the processor to continuously calculate and update a mean squared error of a baseline noise signal over a sampling window. For example, the duration of the sampling window may be 1 μs or more, such as 10 μs or more, for example 25 μs or more, for example 50 μs or more, for example 100 μs or more, for example 500 μs or more, for example 1 ms or more, for example 10 ms or more, for example 25 ms or more, for example 50 ms or more, for example 100 ms or more, for example 500 ms or more, for example 1 sec or more, for example 5 sec or more, for example 10 sec or more, for example 25 sec or more, for example 50 sec or more, for example 100 sec or more, including over a sampling window duration of 500 seconds or more. In these embodiments, the system may be configured to calculate the mean squared error over all or a portion of the duration of the sampling window, for example, 5% or more, for example 10% or more, for example 15% or more, for example 25% or more, for example 50% or more, for example 75% or more, for example 90% or more, 95% or more, for example 97% or more, and 99% or more of the sampling window duration. In certain embodiments, the system is configured to continuously calculate the mean squared error over the entire sampling window duration (100%).

[0098] In some embodiments, the subject system comprises a memory having instructions for measuring the baseline noise of each photodetector as the square of the difference between the current sample value and the calculated baseline. In some cases, the memory has instructions for sampling the baseline noise every 2^baseline sample interval clocks over 2^baseline window size clocks. The memory may include instructions for dividing the sum of the baseline noise samples by the number of accumulated noise samples to obtain a mean-squared baseline noise measurement. In certain embodiments, an approximate average of this value is used for each sample; for example, for each sample, the sum of squared noise is continuously updated according to embodiments of the present disclosure as follows: (current sum) - (current mean) + (new baseline noise squared sample). In certain embodiments, the memory comprises instructions for updating the baseline sampling periodically during the course of data acquisition, for example once every 1 μs or more, for example once every 10 μs or more, for example once every 25 μs, for example once every 50 μs or more, for example once every 100 μs or more, for example once every 500 μs or more, for example once every 1 ms or more, for example once every 10 ms or more, for example once every 25 ms or more, for example once every 50 ms or more, for example once every 100 ms or more, for example once every 500 ms or more, for example once every 1 second or more, for example once every 5 seconds or more, for example once every 10 seconds or more, for example once every 25 seconds or more, for example once every 50 seconds or more, for example once every 100 seconds or more, including where the memory comprises instructions for updating the baseline sampling once every 500 seconds or more.

[0099] In certain embodiments, the memory has instructions stored thereon that, when executed by the processor, cause the processor to update the baseline noise of each photodetector a predetermined period of time before light is detected from a particle in the sample. For example, in some cases, the system includes a memory having instructions for updating the baseline noise of each photodetector immediately before generating a data signal from light detected from a particle in the sample. In other cases, the memory has instructions for updating the baseline noise of each photodetector from 0.0001 μs to 500 μs, for example 0.0005 μs to 450 μs, for example 0.001 μs to 400 μs, for example 0.005 μs to 350 μs, for example 0.01 μs to 300 μs, for example 0.05 μs to 250 μs, for example 0.1 μs to 200 μs, for example 0.5 μs to 150 μs, before generating a data signal from light detected from particles in the sample, and also includes instructions for updating the baseline noise of each photodetector from 1 μs to 100 μs before generating a data signal from light detected from particles in the sample.

[0100] In certain embodiments, the system includes a processor having a memory operatively coupled to the processor, the memory having instructions stored thereon that, when executed by the processor, cause the processor to adjust a calculated baseline noise bandwidth for each photodetector. In some cases, the memory has instructions for adjusting the bandwidth, e.g., by increasing the calculated baseline noise bandwidth by 5% or more, such as 10% or more, such as 15% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, and by increasing the calculated baseline noise bandwidth by 99% or more. For example, the memory may have instructions to increase the bandwidth of the calculated baseline noise by 0.0001 μs or more, such as 0.0005 μs or more, for example 0.001 μs or more, such as 0.005 μs or more, for example 0.01 μs or more, such as 0.05 μs or more, for example 0.1 μs or more, such as 0.5 μs or more, for example 1 μs or more, such as 2 μs or more, for example 3 μs or more, such as 4 μs or more, for example 5 μs or more, such as 10 μs or more, for example 25 μs or more, such as 50 μs or more, including increasing the bandwidth of the calculated baseline noise by 100 μs or more. In other cases, the memory has instructions for adjusting the bandwidth by reducing the bandwidth of the calculated baseline noise by, for example, 5% or more, for example 10% or more, for example 15% or more, for example 25% or more, for example 50% or more, for example 75% or more, for example 90% or more, including reducing the bandwidth of the calculated baseline noise by 99% or more. For example, the memory may have instructions to reduce the bandwidth of the calculated baseline noise by 0.0001 μs or more, such as 0.0005 μs or more, for example 0.001 μs or more, for example 0.005 μs or more, such as 0.01 μs or more, for example 0.05 μs or more, such as 0.1 μs or more, for example 0.5 μs or more, such as 1 μs or more, for example 2 μs or more, such as 3 μs or more, for example 4 μs or more, such as 5 μs or more, for example 10 μs or more, such as 25 μs or more, for example 50 μs or more, including reducing the bandwidth of the calculated baseline noise by 100 μs or more.In certain embodiments, the memory has instructions for matching the bandwidth of the calculated baseline noise to the bandwidth of the data signal generated from the particles in the sample. For example, the memory can have instructions for adjusting the bandwidth of the calculated baseline noise to be 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more of the bandwidth of the data signal generated from the particles in the sample, including adjusting the bandwidth of the calculated baseline noise to be 99.9% or more of the bandwidth of the data signal generated from the particles in the sample. In certain embodiments, the memory has instructions for matching the bandwidth of the calculated baseline noise to the bandwidth of the data signal generated from the particles in the sample (100%).

[0101] In some embodiments, the sample includes multiple fluorophores, with one or more of the fluorophores having overlapping fluorescence spectra. In some cases, the system includes a processor having a memory operably coupled to the processor, the memory having instructions stored thereon that, when executed by the processor, cause the processor to spectrally resolve light from each type of fluorophore in the sample, such as by calculating a spectral separation matrix for the fluorescence spectrum of each type of fluorophore in the sample. In certain embodiments, the memory has instructions for determining the spectral overlap of light from the flowstream and calculating each contribution to the overlapping detected light spectrum. In certain embodiments, the memory has instructions for calculating a spectral separation matrix to estimate the abundance of each contribution to the light signal detected by the photodetector. In certain cases, the spectral separation matrix is ​​calculated using a weighted least-squares algorithm. In some embodiments, the memory has instructions for weighting data signals generated from light from free fluorophores in the sample based on the calculated baseline noise of the photodetector.

[0102] In certain embodiments, the system is configured to spectrally resolve light detected by multiple photodetectors (e.g., weighted using the calculated baseline noise of each photodetector), as described, for example, in International Patent Application No. PCT / US2019 / 068395, filed December 23, 2019, U.S. Provisional Patent Application No. 62 / 971,840, filed February 7, 2020, and U.S. Provisional Patent Application No. 63 / 010,890, filed April 16, 2020, the disclosures of which are incorporated herein by reference in their entireties. For example, the system may comprise a memory having instructions for spectrally decomposing light detected by a plurality of photodetectors by solving a spectral separation matrix using one or more of the following calculations: 1) a weighted least squares algorithm; 2) a Sherman-Morrison iterative inverse updater; 3) an LU matrix decomposition, such that a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix; 4) a modified Cholesky decomposition; 5) a weighted least squares algorithm via QR decomposition; and 6) a singular value decomposition.

[0103] 3A shows a block diagram of a system for spectrally resolving fluorescence from a sample containing illuminated particles in a flow stream, according to certain embodiments. Light from the illuminated sample is detected by an optical detection system including a detector, an amplifier, an analog baseline restoration component, and an analog-to-digital (ADC) converter. The optical detection system is operably coupled to an integrated circuit (e.g., an FPGA, as described in more detail below) that receives a baseline noise determination based on a calculated baseline noise vector. The baseline noise vector is used by a computer program (e.g., stored in the memory of a processor) in a weighted least-squares algorithm to calculate a spectral separation matrix. The spectral separation matrix solved by the weighted least-squares algorithm can be fed back to the integrated circuit for sorting particles in the sample.

[0104] Figure 3B shows examples of simulated spectral separation uncertainties from spectrally decomposing fluorescence using ordinary least squares (OLS) and weighted least squares (WLS) algorithms according to certain embodiments. Simulated separation uncertainties for a random expression pattern of 32 fluorophores were calculated using OLS or WLS with different baseline noise weights. All simulations were performed with a baseline noise representative of a fully stained sample. As shown in Figure 3B, "WLS, correct weights" indicates the spectral separation uncertainty using the true baseline noise vector, while "WLS, incorrect weights" indicates the separation uncertainty when the baseline noise vector is measured in the absence of sample solution, which underestimates the baseline noise. In certain cases, the spectral separation calculated using the incorrect weights, as calculated using the OLS algorithm, exhibits even greater uncertainty than when calculated without a weighting factor.

[0105] In certain embodiments, a light detection system comprising one or more of the light detectors described above is part of or located within a particle analyzer, such as a particle sorter. In certain embodiments, the subject system is a flow cytometry system comprising a photodiode and amplifier components as part of the light detection system for detecting light emitted by a sample in a flow stream. Suitable flow cytometry systems include those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd Edition, Wiley-Liss (1995); Virgo et al., (2012) Ann Clin Biochem. January; 49(pt1):17-28; Linden et al., Semin Thromb Hemost. October 2004; 30(5):502-11; Alison et al., J Pathol. December 2010; 222(4):335-344; and Herbig et al., (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255.In particular cases, flow cytometry systems of interest are the BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortess™ X-20 flow cytometer, and BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, and BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, and BD Biosciences Includes FACSMelody™ cell sorter and the like.

[0106] In some embodiments, the subject particle analyzer systems may be configured in accordance with U.S. Patent Nos. 10,006,852; 9,952,076; 9,933,341; 9,784,661; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,095,494; 9,092,034; 8,975,595; 8,753,596; 73; 8,233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039.

[0107] In certain embodiments, the subject system is a flow cytometry system having an excitation module that uses radio frequency multiplexing excitation to generate multiple frequency-shifted light beams.In certain cases, the subject system is described in Diebold et al., Nature Photonics Vol.7(10);806-810(2013), and in U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451, 538; 10,620,111; and U.S. Patent Publication Nos. 2017 / 0133857; 2017 / 0328826; 2017 / 0350803; 2018 / 0275042; 2019 / 0376895 and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0108] In some embodiments, the subject system is a particle sorting system configured to sort particles in an enclosed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting decision module having multiple sorting decision units, such as that described in U.S. Patent Application No. 16 / 725,756, filed December 23, 2019, the disclosure of which is incorporated herein by reference. In some embodiments, the subject particle sorting systems may be modified in accordance with U.S. Patent Nos. 10,663,476; 10,620,111; 10,613,017; 10,605,713; 10,585,031; 10,578,542; 10,578,469; 10,481,074; 10,302,545; 10,145,793; 10,113,967; 10,006,852; 9,952,076; 9,933,341; 9,726,527; 9,453,789; 9,200,334; No. 9,097,640; No. 9,095,494; No. 9,092,034; No. 8,975,595; No. 8,753,573; No. 8,233,146 No. 8,140,300; No. 7,544,326; No. 7,201,875; No. 7,129,505; No. 6,821,740; No. 6,813,0 17; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; 4,987,086; 4,498,766.

[0109] In some embodiments, the system is a particle analyzer, and particle analysis system 401 (FIG. 4A) can be used to analyze and characterize particles with or without physical sorting of the particles into collection containers. FIG. 4A shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, particle analysis system 401 is a flow system. Particle analysis system 401 shown in FIG. 4A can be configured to perform all or part of such methods described herein. Particle analysis system 401 includes a fluidics system 402. Fluidics system 402 can comprise or be coupled to a sample tube 405 and a moving fluid column within the sample tube through which particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0110] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. The detection stations 408 generally refer to monitoring regions 407 of the common sample path. Detection, in some implementations, can include detecting light or one or more other properties of the particle 403 as it passes through the monitoring region 407. FIG. 4A shows one detection station 408 with one monitoring region 407. Some implementations of the particle analysis system 401 can include multiple detection stations. Additionally, some detection stations can monitor more than one region.

[0111] Each signal is assigned a signal value to form a data point for each particle. As previously mentioned, this data may be referred to as event data. The data points may be multidimensional data points that include values ​​for each property measured for the particle. The detection system 404 is configured to collect a series of such data points over a first time interval.

[0112] The particle analysis system 401 may also include a control system 406. The control system 406 may have one or more processors, amplitude control circuitry, and / or frequency control circuitry. The illustrated control system may be operatively associated with the fluidics system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time interval. The control system 406 may further be configured to generate an experimental signal frequency based on the number of data points in the portion of the first time interval. The control system 406 may further compare the experimental signal frequency to the calculated signal frequency or a predetermined signal frequency.

[0113] 4B shows a system 400 for flow cytometry according to an exemplary embodiment of the invention. System 400 includes a flow cytometer 410, a controller / processor 490, and memory 495. Flow cytometer 410 includes one or more excitation lasers 415a-415c, a focusing lens 420, a flow chamber 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 longpass ("LP") filters 455a-455b, and one or more fluorescence detectors 460a-460f.

[0114] Pump lasers 415a-415c emit light in the form of laser beams. In the exemplary system of FIG. 4B, the wavelengths of the laser beams emitted from pump lasers 415a-415c are 488 nm, 633 nm, and 325 nm, respectively. The laser beams are first directed to pass through one or more of beam splitters 445a and 445b. Beam splitter 445a transmits 488 nm light and reflects 633 nm light. Beam splitter 445b transmits UV light (light having wavelengths in the range of 10 to 400 nm) and reflects 488 nm and 633 nm light.

[0115] The laser beam is directed onto a focusing lens 420, which focuses the beam onto the portion of the fluid stream where the sample particles are located in a flow chamber 425. The flow chamber is part of a fluidics system that directs particles in the stream, typically one at a time, towards the focused laser beam for investigation. The flow chamber can comprise the flow cell of a benchtop cytometer or the nozzle tip of a stream-in air cytometer.

[0116] Light from the laser beam(s) interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption, and is re-emitted at a variety of different wavelengths depending on the particle's characteristics, such as its size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally occurring on or within the particle. Fluorescence emissions, as well as diffracted, refracted, reflected, and scattered light, can be routed through one or more of beam splitters 445a-445g, bandpass filters 450a-450e, longpass filters 455a-455b, and fluorescence collection lens 440 to one or more of forward scatter detector 430, side scatter detector 435, and one or more fluorescence detectors 460a-460f.

[0117] The fluorescence collection lens 440 collects light emitted from the particle-laser beam interaction and routes the light to one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 450a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on either side of the center of the spectral band, from 500 nm to 520 nm. Shortpass filters transmit light with wavelengths below a specific wavelength. Longpass filters, such as longpass filters 455a-455b, transmit light with wavelengths above a specified wavelength. For example, longpass filter 455a is a 670 nm longpass filter, transmitting light above 670 nm. Filters are often selected to optimize the detector's specificity for a particular fluorochrome. The filter can be configured so that the spectral band of light transmitted to the detector approaches the emission peak of the fluorescent dye.

[0118] Beam splitters direct different wavelengths of light in different directions. Beam splitters can be characterized by filter properties such as short-pass and long-pass. For example, beam splitter 445g is a 620SP beam splitter, meaning that beam splitter 445g transmits wavelengths of light less than or equal to 620 nm and reflects wavelengths of light longer than 620 nm in different directions. In one embodiment, beam splitters 445a-445g can comprise optical mirrors, such as dichroic mirrors.

[0119] The forward scatter detector 430 is positioned slightly off-axis from the direct beam passing through the flow cell and is configured to detect diffracted light, or excitation light traveling mostly forward through or around the particle. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 435 is configured to detect refracted and reflected light from the particle's surface and internal structure, which tends to increase as the particle's structural complexity increases. Fluorescence emissions from fluorescent molecules associated with the particle can be detected by one or more fluorescence detectors 460a-460f. The side scatter detector 435 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 430, side scatter detector 435, and fluorescence detector can be converted to electronic signals (voltage) by the detectors. This data can provide information about the sample.

[0120] Those skilled in the art will recognize that flow cytometers according to embodiments of the present invention are not limited to the flow cytometer shown in Figure 4B, but can include any flow cytometer known in the art. For example, a flow cytometer can have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0121] During operation, the operation of the cytometer is controlled by the controller / processor 490, and measurement data from the detectors can be stored in memory 495 and processed by the controller / processor 490. While not explicitly shown, the controller / processor 490 is coupled to the detectors to receive output signals therefrom and may also be coupled to electrical and electromechanical components of the flow cytometer 400 to control lasers, fluid flow parameters, etc. Input / output (I / O) functionality 497 may also be provided within the system. The memory 495, controller / processor 490, and I / O 497 may be provided entirely as an integral part of the flow cytometer 410. In such embodiments, a display may also form part of the I / O functionality 497 for presenting experimental data to a user of the cytometer 400. Alternatively, some or all of the memory 495, controller / processor 490, and I / O functionality may be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of the memory 495 and controller / processor 490 may be in wireless or wired communication with the cytometer 410. The controller / processor 490, together with the memory 495 and I / O 497, can be configured to perform a variety of functions associated with the preparation and analysis of flow cytometer experiments.

[0122] The system shown in FIG. 4B includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to herein as "filter windows" for a given detector), 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 a flow cytometer experiment emit in their respective characteristic wavelength bands. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands may be selected to approximately match the filter windows of the detectors. However, as more detectors are provided and more labels are utilized, perfect correspondence between filter windows and fluorescence emission spectra is no longer possible. Generally, while the peak of the emission spectrum of a particular fluorescent molecule may fall within the filter window of one particular detector, it is true that a portion of that label's emission spectrum will also overlap the filter windows of one or more other detectors. This may be referred to as spillover. I / O 497 can be configured to receive data regarding a flow cytometer experiment having a panel of fluorescent labels and multiple cell populations with multiple markers, each cell population having a subset of the multiple markers. I / O 497 can also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data, such as label spectral properties and flow cytometer configuration data, can also be stored in memory 495. Controller / processor 490 can be configured to evaluate one or more assignments of labels to markers.

[0123] 5 shows a functional block diagram of an example particle analyzer control system for analyzing and displaying biological events, such as an analysis controller 500. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0124] The particle analyzer 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 502 can be configured to provide the biological event data to the analysis controller 500. A data communication channel can be included between the particle analyzer 502 and the analysis controller 500. The biological event data can be provided to the analysis controller 500 via the data communication channel.

[0125] The analysis controller 500 can be configured to receive biological event data from the particle analyzer 502. The biological event data received from the particle analyzer 502 can include flow cytometry event data. The analysis controller 500 can be configured to provide a graphical display including a first plot of the biological event data on the display device 506. The analysis controller 500 can be further configured to render a region of interest as a gate around a population of the biological event data shown by the display device 506, e.g., overlaid on the first plot. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest depicted on a single-parameter histogram or a bivariate plot. In some embodiments, the display can be used to display particle parameters or saturation detector data.

[0126] Analysis controller 500 can be further configured to display the in-gated biological event data separately from other events in the out-gated biological event data on display device 506. For example, analysis controller 500 can be configured to render the color of the biological event data contained within the gate to distinguish it from the color of the out-gated biological event data. Display device 506 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0127] The analysis controller 500 can be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device can be embodied as a mouse 510. The mouse 510 can initiate a gate selection signal to the analysis controller 500 that identifies a gate to be displayed on or manipulated via the display device 506 (e.g., by clicking on or within the desired gate while a cursor is positioned there). In some implementations, the first device can be implemented as a keyboard 508 or other means for providing input signals to the analysis controller 500, such as a touchscreen, a stylus, an optical detector, or a voice recognition system. Some input devices can include multiple input functions. In such implementations, each input function can be considered an input device. For example, as shown in FIG. 5, the mouse 510 can include a right mouse button and a left mouse button, each of which can generate a trigger event.

[0128] The trigger event can cause the analysis controller 500 to change how the data is displayed, what portions of the data are actually displayed on the display device 506, and / or provide input to further processing, such as selecting a population of interest for particle sorting.

[0129] In some embodiments, the analysis controller 500 can be configured to detect when gate selection is initiated by the mouse 510. The analysis controller 500 can be further configured to automatically modify the visualization of the plot to facilitate the gating process. The modification can be based on a particular distribution of the biological event data received by the analysis controller 500.

[0130] The analysis controller 500 can be connected to a storage device 504. The storage device 504 can also be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 can further be configured to enable retrieval of biological event data, such as flow cytometry event data, by the analysis controller 500.

[0131] The display device 506 can be configured to receive display data from the analysis controller 500. The display data can comprise plots of the biological event data and gates outlining sections of the plot. The display device 506 can be further configured to modify the information presented according to input received from the analysis controller 500, along with input from the particle analyzer 502, the storage device 504, the keyboard 508, and / or the mouse 510.

[0132] In some implementations, the analysis controller 500 can generate a user interface for receiving example events for sorting. For example, the user interface can include controls for receiving example events or example images. The example events or images or example gates can be provided prior to collection of event data for the sample or based on an initial set of events for a subset of the sample.

[0133] Computer Control System Aspects of the present disclosure further include a computer control system, the system further comprising one or more computers for full or partial automation. In some embodiments, the system comprises a computer having a computer-readable storage medium having stored thereon a computer program, the computer program having, when loaded into the computer, instructions for illuminating a sample containing particles in a flow stream, instructions for detecting light from the illuminated flow stream with a photodetector, instructions for generating a data signal from the detected light, and instructions for calculating a running mean square error of the generated data signal to determine the baseline noise of the photodetector.

[0134] In some embodiments, the computer program has instructions for generating a data signal in response to light detected from the particle-free component of the illuminated flow stream. In these embodiments, the computer program has instructions for generating a data signal from the light detected from the particle-free component of the illuminated flow stream over a sampling period having a duration of 0.001 μs to 100 μs, e.g., 0.005 μs to 95 μs, e.g., 0.01 μs to 90 μs, e.g., 0.05 μs to 85 μs, e.g., 0.1 μs to 80 μs, e.g., 0.5 μs to 75 μs, e.g., 1 μs to 70 μs, e.g., 2 μs to 65 μs, e.g., 3 μs to 60 μs, e.g., 4 μs to 55 μs, and 5 μs to 50 μs. In particular cases, the computer program has instructions for generating a data signal from the light detected from the particle-free component of the illuminated flow stream over a sampling period having a duration of 1 μs to 10 μs.

[0135] In embodiments, the computer program has instructions for calculating a moving mean squared error of the generated data signal. In some embodiments, the computer program has instructions for calculating the moving mean squared error by measuring the squared difference between the generated data signal and a calculated baseline data signal. In particular cases, the computer program has instructions for calculating the moving mean squared error of the generated data signal by measuring the squared difference between a plurality of generated data signals and the calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals, summing the baseline noise signals over the sampling period, and dividing the summed baseline noise signal by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is from 0.001 μs to 100 μs, such as from 0.005 μs to 95 μs, for example from 0.01 μs to 90 μs, for example from 0.05 μs to 85 μs, for example from 0.1 μs to 80 μs, for example from 0.5 μs to 75 μs, for example from 1 μs to 70 μs, for example from 2 μs to 65 μs, for example from 3 μs to 60 μs, for example from 4 μs to 55 μs, and from 5 μs to 50 μs in duration.

[0136] In certain embodiments, the computer program has instructions for calculating the moving mean square error of the generated data signal at predetermined time intervals. For example, the computer program may have instructions for calculating the moving mean square error of the generated data signal at a frequency of once every 0.0001 ms or more, for example, once every 0.0005 ms or more, for example, once every 0.001 ms or more, for example, once every 0.005 ms or more, for example, once every 0.01 ms or more, for example, once every 0.05 ms or more, for example, once every 0.1 ms or more, for example, once every 0.5 ms or more, for example, once every 1 ms or more, for example, once every 1 ms or more, for example, once every 2 ms or more, for example, once every 3 ms or more, for example, once every 4 ms or more, for example, once every 5 ms or more, for example, once every 10 ms or more, for example, once every 25 ms or more, for example, once every 50 ms or more, for example, once every 100 ms or more, and once every 500 ms or more. In some embodiments, the computer program has instructions for calculating the moving mean squared error of the generated data signal once per second, for example, once per 2 seconds, for example, once per 3 seconds, for example, once per 4 seconds, for example, once per 5 seconds, for example, once per 10 seconds, for example, once per 15 seconds, for example, once per 30 seconds, and once per 60 seconds. In other embodiments, the computer program has instructions for calculating the moving mean squared error of the generated data signal once per minute, for example, once per 2 minutes, for example, once per 3 minutes, for example, once per 4 minutes, for example, once per 5 minutes, for example, once per 10 minutes, for example, once per 15 minutes, for example, once per 30 minutes, and once per 60 minutes. In certain embodiments, the computer program has instructions for continuously calculating the moving mean squared error of the generated data signal.

[0137] In certain embodiments, the computer program has instructions for continuously calculating and updating the mean square error of the baseline noise signal over a sampling window.For example, the duration of the sampling window can be 1 μs or more, for example 10 μs or more, for example 25 μs or more, for example 50 μs or more, for example 100 μs or more, for example 500 μs or more, for example 1 ms or more, for example 10 ms or more, for example 25 ms or more, for example 50 ms or more, for example 100 ms or more, for example 500 ms or more, for example 1 second or more, for example 5 seconds or more, for example 10 seconds or more, for example 25 seconds or more, for example 50 seconds or more, for example 100 seconds or more, including over a sampling window duration of 500 seconds or more. In these embodiments, the computer program may have instructions for calculating the mean squared error over all or a portion of the sampling window duration, such as for example 5% or more, for example 10% or more, for example 15% or more, for example 25% or more, for example 50% or more, for example 75% or more, for example 90% or more, for example 95% or more, for example 97% or more, and for example 99% or more of the sampling window duration. In certain embodiments, the computer program has instructions for continuously calculating the mean squared error over the entire sampling window duration (100%).

[0138] In some embodiments, the computer program has instructions for measuring the baseline noise of each photodetector as the square of the difference between the current sample value and the calculated baseline. In some cases, the computer program has instructions for sampling the baseline noise every 2^baseline sample interval clocks over 2^baseline window size clocks. The computer program may have instructions for dividing the sum of the baseline noise samples by the number of accumulated noise samples to obtain a mean-squared baseline noise measurement. In certain embodiments, an approximate average of this value is used for each sample, and for example, for each sample, the sum of squared noise is continuously updated according to embodiments of the present disclosure as follows: (current sum) - (current mean) + (new baseline noise squared sample). In certain embodiments, the computer program has instructions for periodically updating the baseline sampling over the course of data acquisition, for example once every 1 μs or more, for example once every 10 μs or more, for example once every 25 μs or more, for example once every 50 μs or more, such as once every 100 μs or more, for example once every 500 μs or more, for example once every 1 ms or more, for example once every 10 ms or more, for example once every 25 ms or more, for example once every 50 ms or more, for example once every 100 ms or more, for example once every 500 ms or more, for example once every 1 second or more, for example once every 5 seconds or more, for example once every 10 seconds or more, for example once every 25 seconds or more, for example once every 50 seconds or more, for example once every 100 seconds or more, including where the computer program has instructions for updating the baseline sampling once every 500 seconds or more.

[0139] In certain embodiments, the computer program has instructions stored thereon that, when executed by the processor, cause the processor to update the baseline noise of each photodetector at a predetermined time period before light is detected from a particle in the sample. For example, in some cases, the computer program has instructions for updating the baseline noise of each photodetector immediately before generating a data signal from light detected from a particle in the sample. In other cases, the computer program has instructions for updating the baseline noise of each photodetector from 0.0001 μs to 500 μs, for example, 0.0005 μs to 450 μs, for example, 0.001 μs to 400 μs, for example, 0.005 μs to 350 μs, for example, 0.01 μs to 300 μs, for example, 0.05 μs to 250 μs, for example, 0.1 μs to 200 μs, for example, 0.5 μs to 150 μs, before generating a data signal from light detected from particles in the sample, and also includes instructions for updating the baseline noise of each photodetector from 1 μs to 100 μs before light is detected from particles in the sample.

[0140] In certain embodiments, the computer program has instructions for adjusting the bandwidth of the calculated baseline noise for each photodetector. In some cases, the computer program has instructions for adjusting the bandwidth by increasing the calculated baseline noise bandwidth by, for example, 5% or more, such as 10% or more, such as 15% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, including increasing the calculated baseline noise bandwidth by 99% or more. For example, the computer program may have instructions to increase the bandwidth of the calculated baseline noise by 0.0001 μs or more, such as 0.0005 μs or more, for example 0.001 μs or more, such as 0.005 μs or more, for example 0.01 μs or more, such as 0.05 μs or more, for example 0.1 μs or more, such as 0.5 μs or more, for example 1 μs or more, such as 2 μs or more, for example 3 μs or more, such as 4 μs or more, for example 5 μs or more, such as 10 μs or more, for example 25 μs or more, such as 50 μs or more, including increasing the bandwidth of the calculated baseline noise by 100 μs or more. In other cases, the computer program may have instructions to adjust the bandwidth by decreasing the bandwidth of the calculated baseline noise by, for example, 5% or more, such as 10% or more, for example 15% or more, such as 25% or more, for example 50% or more, such as 75% or more, for example 90% or more, including decreasing the bandwidth of the calculated baseline noise by 99% or more. For example, the computer program may have instructions to reduce the bandwidth of the calculated baseline noise by 0.0001 μs or more, such as 0.0005 μs or more, for example 0.001 μs or more, such as 0.005 μs or more, for example 0.01 μs or more, such as 0.05 μs or more, for example 0.1 μs or more, such as 0.5 μs or more, for example 1 μs or more, such as 2 μs or more, for example 3 μs or more, such as 4 μs or more, for example 5 μs or more, such as 10 μs or more, for example 25 μs or more, such as 50 μs or more, including reducing the bandwidth of the calculated baseline noise by 100 μs or more.In certain embodiments, the computer program has instructions for matching the bandwidth of the calculated baseline noise to the bandwidth of the data signal generated from the particles in the sample. For example, the computer program may have instructions for adjusting the bandwidth of the calculated baseline noise to be 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more of the bandwidth of the data signal generated from the particles in the sample, including cases where the bandwidth of the calculated baseline noise is adjusted to be 99.9% or more of the bandwidth of the data signal generated from the particles in the sample. In certain embodiments, the computer program has instructions for matching the bandwidth of the calculated baseline noise to the bandwidth of the data signal generated from the particles in the sample (100%).

[0141] In certain embodiments, the computer program has instructions for spectral decomposition of light detected by multiple photodetectors (e.g., weighted using the calculated baseline noise of each photodetector), as described, for example, in International Patent Application No. PCT / US2019 / 068395, filed December 23, 2019, U.S. Provisional Patent Application No. 62 / 971,840, filed February 7, 2020, and U.S. Provisional Patent Application No. 63 / 010,890, filed April 16, 2020, the disclosures of which are incorporated herein by reference in their entireties. For example, the computer program may have instructions for spectrally decomposing light detected by the multiple photodetectors by solving a spectral separation matrix using one or more of: 1) a weighted least squares algorithm; 2) a Sherman-Morrison iterative inverse updater; 3) an LU matrix decomposition, such that a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix; 4) a modified Cholesky decomposition; 5) a weighted least squares algorithm calculation via QR decomposition; and 6) a singular value decomposition calculation.

[0142] In an embodiment, the system comprises an input module, a processing module, and an output module. The subject systems may comprise both hardware and software components, and the hardware components may take the form of one or more platforms, e.g., servers, such that the functional elements, i.e., those elements of the system that perform specific tasks of the system (such as managing the input and output of information, processing information, etc.), may be performed by running software applications on and across one or more computer platforms represented by the system.

[0143] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that can access memory in which instructions for executing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are available or become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor's coordination and execution of functions of various computer programs, which may be written in a variety of programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as are well known in the art. The operating system typically works in conjunction with the processor to coordinate and execute functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that allow a user to manually align the light source with the flow stream based on the first and second light signals, hi some embodiments, the processor comprises analog electronics that provide feedback control, e.g., negative feedback control.

[0144] The system memory can be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-and-write compact disk, a flash memory device, or other memory storage device. The memory storage device can be any of a variety of known or future devices, including a compact disk drive, tape drive, removable hard disk drive, or diskette drive. Such types of memory storage devices typically read from and write to a program storage medium (not shown), such as a compact disk, magnetic tape, removable hard disk, or magnetic diskette, respectively. Any of these program storage media, or others now in use or that may be developed in the future, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also referred to as computer control logic, are typically stored in the system memory and / or program storage devices used in conjunction with the memory storage devices.

[0145] In some embodiments, a computer program product is described that includes a computer-usable medium having stored thereon control logic (a computer software program including program code). The control logic, when executed by a processor of a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementation of hardware state machines to perform the functions described herein will be apparent to one skilled in the art.

[0146] The memory may be any suitable device from which the processor can store and retrieve data, for example, a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor that is suitably programmed from a computer-readable medium carrying the necessary program code. The programming may be provided to the processor remotely via a communications channel, or may be pre-stored in a computer program product, such as a memory or some other portable or fixed computer-readable storage medium, using any of these devices associated with the memory. For example, a magnetic or optical disk may carry the programming and can be read by a disk writing / reading device. The system of the present invention also includes programming, e.g., algorithms used in implementing the above-described methods, in the form of a computer program product. The programming of the present invention may be recorded on a computer-readable medium, e.g., any medium that can be read and accessed directly by a computer. Such media include, but are not limited to, magnetic storage media such as magnetic disks, hard disk storage media, and magnetic tape, optical storage media such as CD-ROMs, electronic storage media such as RAM and ROM, portable flash drives, and hybrids of these categories, such as magnetic / optical storage media.

[0147] The processor may also have access to a communication channel for communicating with a user at a remote location, where remote means that the user is not in direct contact with the system but relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a mobile phone (i.e., a smartphone).

[0148] In some embodiments, a system according to the present disclosure may be configured to include a communications interface. In some embodiments, the communications interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communications interface may be configured for wired or wireless communications, including, but not limited to, radio frequency (RF) communications (e.g., radio frequency identification (RFID), Zigbee communications protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communications protocol, and cellular communications such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0149] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port, to enable data communications between the subject system and other external devices, such as computer terminals (e.g., in a doctor's office or hospital environment) configured for similar complementary data communications.

[0150] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol that allows the subject system to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that a user may use together.

[0151] In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing the Internet Protocol (IP) via a cellular network, short message service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0152] In one embodiment, the subject system is configured to wirelessly communicate with a server device via a communications interface using common standards such as, for example, the 802.11 or Bluetooth® RF protocols, or the IrDA infrared protocol. The server device may be another portable device such as a smartphone, personal digital assistant (PDA), or notebook computer, or a larger device such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touch screen.

[0153] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate the subject system, e.g., data stored in the optional data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0154] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. When a display device provides visual information, this information may typically be logically and / or physically organized as an array of pixels. A graphical user interface (GUI) controller provides a graphical input / output interface between the system and the user and may include any of a variety of known or future software programs for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be achieved in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing modules to a remote user, for example, via the Internet, telephone, or satellite network, according to known techniques. Presentation of data by the output manager may be implemented according to various known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails, or other files, or data in other formats. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system may be any type of known or future-developed computer platform, but they will typically be of the class of computers commonly referred to as servers. However, they may also be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cable or other communication system, including wireless systems, whether networked or not. They may be co-located, or they may be physically separated. Various operating systems may be employed on any computer platform, possibly depending on the type and / or manufacturer of the computer platform selected.Suitable operating systems include Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, and others.

[0155] FIG. 6 illustrates the general architecture of an exemplary computing device 600 according to certain embodiments. The general architecture of computing device 600 illustrated in FIG. 6 includes an arrangement of computer hardware and software components. Computing device 600 may include more (or fewer) elements than those illustrated in FIG. 6 . However, not all of these typically conventional elements need be shown to provide an enabling disclosure. As illustrated, computing device 600 includes a processing unit 610, a network interface 620, a computer-readable medium drive 630, an input / output device interface 640, a display 650, and input devices 660, all of which may communicate with each other via a communications bus. Network interface 620 may provide connectivity to one or more networks or computing systems. Processing unit 610 may receive information and instructions from other computing systems or services via a network. Processing unit 610 may also communicate with memory 670 and may further provide output information to optional display 650 via input / output device interface 640. The input / output device interface 640 may also accept input from optional input devices 660, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0156] Memory 670 may include computer program instructions (grouped in some embodiments as modules or components) that processing unit 610 executes to implement one or more embodiments. Memory 670 generally includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. Memory 670 may store an operating system 672 that provides computer program instructions used by processing unit 610 in the general management and operation of computing device 600. Memory 670 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0157] Non-transitory computer-readable storage medium for measuring baseline noise of a photodetector in an optical detection system Aspects of the present disclosure further comprise a non-transitory computer-readable storage medium having instructions for implementing the subject methods. The computer-readable storage medium may be employed by one or more computers for fully or partially automating a system for implementing the methods described herein. In certain embodiments, instructions according to the methods described herein may be coded on a computer-readable medium in the form of "programming," and the term "computer-readable medium" as used herein refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include magnetic disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state disks, and network-attached storage (NAS), regardless of whether such devices are internal or external to the computer. A file containing information may be "stored" on a computer-readable medium, where "storing" means recording information so that the computer can access and retrieve it at a later date. The computer-implemented methods described herein may be implemented using programming that may be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Java (Sun Microsystems, Inc., Santa Clara, CA), Visual Basic (Microsoft Corp., Redmond, WA), and C++ (AT&T Corp., Bedminster, NJ), among many others.

[0158] In some embodiments, a computer-readable storage medium of interest comprises a computer program stored thereon, the computer program having instructions, when loaded into a computer, comprising an algorithm for calculating a running mean squared error of a data signal generated from light detected from illuminated particles of a sample in a flow stream. In some embodiments, the non-transitory computer-readable storage medium has an algorithm for calculating a running mean squared error of a generated data signal by measuring the squared difference between the generated data signal and a calculated baseline data signal. In certain embodiments, the non-transitory computer-readable storage medium has an algorithm for measuring the squared difference between a plurality of generated data signals and a calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals, and an algorithm for summing the baseline noise signals over the sampling period and dividing the summed baseline noise signal by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is between 0.001 μs and 100 μs in duration. In other cases, the predetermined sampling period is between 1 μs and 10 μs in duration.

[0159] In some embodiments, the non-transitory computer-readable storage medium has an algorithm for calculating the moving mean squared error of the generated data signal at predetermined time intervals. In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating the moving mean squared error of the generated data signal at a frequency of between once every millisecond and once every 1000 milliseconds. For example, the non-transitory computer-readable storage medium may include an algorithm for calculating the moving mean squared error of the generated data signal at a frequency of once every 1 ms or more, e.g., once every 5 ms or more, e.g., once every 10 ms or more, e.g., once every 25 ms or more, e.g., once every 50 ms or more, e.g., once every 100 ms or more, and once every 500 ms or more. In other embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating the moving mean squared error of the generated data signal at a frequency of between once every second and once every 60 seconds. In yet other embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a moving mean squared error of the generated data signal at a frequency between once every minute and once every 60 minutes. In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for continuously calculating a moving mean squared error of the generated data signal.

[0160] In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for detecting light from free fluorophores in the sample with a photodetector, an algorithm for generating a data signal from the detected light, and an algorithm for calculating a running mean square error of the data signal generated from the light emitted from the free fluorophores in the sample. In some cases, the non-transitory computer-readable storage medium has an algorithm for spectrally resolving the light from each type of fluorophore in the sample. In certain cases, the non-transitory computer-readable storage medium has an algorithm for resolving the light from each type of fluorophore by calculating a spectral separation matrix for the fluorescence spectrum of each type of fluorophore in the sample. In certain cases, the non-transitory computer-readable storage medium has an algorithm for calculating the spectral separation matrix by using a weighted least squares algorithm. In some embodiments, the non-transitory computer-readable storage medium has an algorithm for weighting the data signal generated from the light from free fluorophores in the sample based on the determined baseline noise of the photodetector.

[0161] The non-transitory computer-readable storage medium may be employed in one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module comprises a processor having access to a memory on which instructions for executing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are available or become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor's coordination and execution of functions of various computer programs, which may be written in a variety of programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as are known in the art. The operating system typically cooperates with the processor to coordinate and execute functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

[0162] Integrated Circuit Devices Aspects of the present disclosure also include an integrated circuit device programmed to calculate a running mean square error of a data signal generated from light detected from illuminated particles of a sample in a flow stream. In some embodiments, the integrated circuit device of interest comprises a field programmable gate array (FPGA). In other embodiments, the integrated circuit device comprises an application specific integrated circuit (ASIC). In yet other embodiments, the integrated circuit device comprises a complex programmable logic device (CPLD).

[0163] In some embodiments, the integrated circuit is programmed to calculate a moving average squared error of the generated data signal by measuring the squared difference between the generated data signal and a calculated baseline data signal. In particular embodiments, the integrated circuit is programmed to measure the squared difference between multiple generated data signals and the calculated baseline data signal over a predetermined sampling period to generate multiple baseline noise signals, sum the baseline noise signals over the sampling period, and divide the summed baseline noise signal by the number of baseline noise signals generated over the predetermined sampling period. In some cases, the predetermined sampling period is between 0.001 μs and 100 μs in duration. In other cases, the predetermined sampling period is between 1 μs and 10 μs in duration.

[0164] In some embodiments, an integrated circuit of the present disclosure is programmed to calculate the moving mean squared error of the generated data signal at predetermined time intervals. In some cases, the integrated circuit is programmed to calculate the moving mean squared error of the generated data signal at a frequency of between once every millisecond and once every 1000 milliseconds. For example, the integrated circuit may be programmed to calculate the moving mean squared error of the generated data signal once every 1 ms or more, e.g., once every 5 ms or more, e.g., once every 10 ms or more, e.g., once every 25 ms or more, e.g., once every 50 ms or more, e.g., once every 100 ms or more, and once every 500 ms or more. In other embodiments, the integrated circuit is programmed to calculate the moving mean squared error of the generated data signal at a frequency of between once every second and once every 60 seconds. In yet other embodiments, the integrated circuit is programmed to calculate the moving mean squared error of the generated data signal at a frequency of between once every minute and once every 60 minutes. In certain embodiments, the integrated circuit is programmed to continuously calculate the moving mean squared error of the generated data signal.

[0165] In certain embodiments, the integrated circuit is programmed to detect light from free fluorophores in the sample with a photodetector, generate data signals from the detected light, and calculate a running mean square error of the data signals generated from the light emitted from the free fluorophores in the sample. In some cases, the integrated circuit is programmed to spectrally resolve the light from each type of fluorophore in the sample. In certain cases, the integrated circuit is programmed to resolve the light from each type of fluorophore by calculating a spectral separation matrix for the fluorescence spectrum of each type of fluorophore in the sample. In certain cases, the integrated circuit is programmed to calculate the spectral separation matrix using a weighted least squares algorithm. In some embodiments, the integrated circuit is programmed to weight the data signals generated from the light from free fluorophores in the sample based on the determined baseline noise of the photodetector.

[0166] kit Aspects of the present disclosure further include kits, which include one or more of the components of the optical detection systems described herein. In some embodiments, the kits include an optical detector and programming for the subject systems, such as in the form of a computer-readable medium (e.g., a flash drive, USB storage, a compact disc, a DVD, a Blu-ray disc, etc.) or instructions for downloading the programming from an Internet web protocol or cloud server. In some embodiments, the kits include a trigger signal generator, such as a function generator or a function generator integrated circuit. The kits may also include optical conditioning components, such as lenses, mirrors, filters, optical fibers, wavelength separators, pinholes, slits, collimation protocols, and combinations thereof.

[0167] The kit may further comprise instructions for practicing the subject method. These instructions may be present in the subject kit in various forms, one or more of which may be present in the kit. One form in which these instructions may be present is as information printed on a suitable medium or substrate, such as a sheet or sheets of paper with the information printed thereon, kit packaging, package insert, etc. Another form in which these instructions may be present is as a computer-readable medium, such as a diskette, compact disc (CD), portable flash drive, etc., on which the information is recorded. Another form in which these instructions may be present is a website address that can be used via the Internet to access the information at the destination site.

[0168] utility The subject methods, systems, and computer systems find use in a variety of applications where it is desirable to calibrate or optimize photodetectors, such as in particle analyzers. The subject methods and systems also find use in photodetectors used to analyze and sort particle components in samples in fluid media, such as biological samples. The present disclosure also finds use in flow cytometry, where it is desirable to provide flow cytometers with improved cell sorting accuracy, enhanced particle collection, reduced energy consumption, improved particle charging efficiency, more accurate particle charging, and improved particle deflection during cell sorting. In embodiments, the present disclosure reduces the need for user input or manual adjustments during sample analysis by a flow cytometer. In certain embodiments, the subject methods and systems provide fully automated protocols, thereby requiring little, if any, human input to the flow cytometer during use.

[0169] Notwithstanding the scope of the appended claims, the disclosure set forth herein is also defined by the following appendix. 1. A method for determining the baseline noise of a photodetector in a particle analyzer, comprising: irradiating a sample containing particles in a flow stream; detecting light from the illuminated flow stream with a photodetector; generating a data signal from the detected light; calculating the moving mean square error of the generated data signal to determine the baseline noise of the photodetector; A method comprising: 2. The method of claim 1, comprising detecting light from particle-free components of the illuminated flow stream. 3. The method of claim 2, comprising detecting light emitted from the flow stream between the particles. 4. The method of any one of claims 1 to 3, wherein calculating a moving mean squared error of the generated data signal comprises measuring a squared difference between the generated data signal and a calculated baseline data signal. 5. Calculating the moving mean squared error of the generated data signal is measuring the squared difference between a plurality of generated data signals and a calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals; summing a baseline noise signal over a sampling period; Dividing the summed baseline noise signals by the number of baseline noise signals generated over a given sampling period; 5. The method of claim 4, comprising:

[0170] 6. The method of claim 5, wherein the predetermined sampling period has a duration of 0.001 μs to 100 μs. 7. The method of claim 5, wherein the predetermined sampling period has a duration of 1 μs to 10 μs. 8. The method of any one of claims 1 to 7, comprising calculating a moving mean squared error of the generated data signal at a predetermined time interval. 9. The method of claim 8, wherein the moving mean squared error of the generated data signal is calculated at a frequency between once every millisecond and once every 1000 milliseconds. 10. The method of claim 8, wherein the moving mean squared error of the generated data signal is calculated at a frequency between once every second and once every 60 seconds.

[0171] 11. The method of claim 8, wherein the moving mean squared error of the generated data signal is calculated at a frequency between once every minute and once every 60 minutes. 12. The method of any one of claims 1 to 7, comprising continuously calculating a moving mean squared error of the generated data signal. 13. The method of any one of claims 1 to 12, wherein the sample comprises multiple fluorophores with overlapping fluorescence spectra. 14. The method of claim 13, wherein the particles of the sample are functionally associated with a fluorophore. 15. The method of claim 13 or 14, wherein the flow stream comprises one or more free fluorophores that are not functionally associated with particles of the sample.

[0172] 16. Detecting light from one or more free fluorophores in the sample with a photodetector; generating a data signal from the detected light; calculating a running mean square error of a data signal generated from light emitted from one or more free fluorophores in the sample; 16. The method of claim 15, comprising: 17. The method of any one of appendices 13-16, further comprising spectrally resolving light from each fluorophore in the sample by calculating a spectral unmixing matrix for the fluorescence spectrum of each fluorophore in the sample. 18. The method of claim 17, wherein the spectral separation matrix is ​​calculated using a weighted least squares algorithm. 19. The method of claim 18, wherein the data signal generated from the photodetector is weighted based on the determined baseline noise of the photodetector. 20. The method of any one of claims 1 to 19, wherein the moving mean square error of the generated data signal is calculated on an integrated circuit. 21. The method of claim 20, wherein the integrated circuit is a field programmable gate array.

[0173] 22. A light source configured to illuminate a particle-containing sample in a flow stream; a light detection system having a light detector for detecting light from the illuminated flow stream; a processor having a memory operatively coupled thereto; It is equipped with The memory has instructions stored therein that, when executed by the processor, cause the processor to: generating a data signal from the detected light; calculating a moving mean square error of the generated data signal to determine the baseline noise of the photodetector; system. 23. The system of claim 22, wherein the memory has instructions stored thereon that, when executed by the processor, cause the processor to calculate a moving average squared error of the generated data signal by measuring the squared difference between the generated data signal and a calculated baseline data signal. 24. The memory has instructions stored therein that, when executed by the processor, cause the processor to: measuring the squared difference between the plurality of generated data signals and the calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals; summing the baseline noise signal over the sampling period; Dividing the summed baseline noise signals by the number of baseline noise signals generated over a given sampling period; 24. The system of claim 23. 25. The system of claim 23, wherein the predetermined sampling period has a duration of 0.001 μs to 100 μs. 26. The system of claim 25, wherein the predetermined sampling period has a duration of 1 μs to 10 μs.

[0174] 27. The system of any one of appendices 22 to 26, wherein the memory has instructions stored therein that, when executed by the processor, cause the processor to calculate a moving mean squared error of the generated data signal at predetermined time intervals. 28. The system of claim 27, wherein the memory has instructions stored thereon that, when executed by the processor, cause the processor to calculate a moving mean squared error of the generated data signal at a frequency between once every millisecond and once every 1000 milliseconds. 29. The system of claim 27, wherein the memory has instructions stored thereon that, when executed by the processor, cause the processor to calculate a moving mean squared error of the generated data signal at a frequency between once per second and once every 60 seconds. 30. The system of claim 27, wherein the memory has instructions stored thereon that, when executed by the processor, cause the processor to calculate a moving mean squared error of the generated data signal at a frequency between once every minute and once every 60 minutes. 31. The system of any one of appendices 22-30, wherein the memory has instructions stored therein that, when executed by the processor, cause the processor to continuously calculate a moving mean squared error of the generated data signal.

[0175] 32. The system of any one of claims 22 to 31, wherein the sample comprises multiple fluorophores with overlapping fluorescence spectra. 33. The system of claim 32, wherein the particles of the sample are functionally associated with a fluorophore. 34. The system of claim 32 or 33, wherein the flow stream comprises one or more free fluorophores that are not functionally associated with particles of the sample. 35. The memory has instructions stored therein that, when executed by the processor, cause the processor to: detecting light from one or more free fluorophores in the sample with a photodetector; generating a data signal from the detected light; calculating a running mean square error of data signals generated from light emitted from one or more free fluorophores in the sample; 35. The system of claim 34. 36. A system described in any one of appendices 32 to 35, wherein the memory has instructions stored therein that, when executed by the processor, cause the processor to spectrally resolve light from each fluorophore in the sample by calculating a spectral separation matrix for the fluorescence spectrum of each fluorophore in the sample.

[0176] 37. The system of claim 36, wherein the memory has instructions stored thereon that, when executed by the processor, cause the processor to calculate the spectral separation matrix using a weighted least squares algorithm. 38. The system of claim 37, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to weight the data signal generated from the photodetector based on the determined baseline noise of the photodetector. 39. The system of any one of appendices 22 to 38, wherein the moving mean squared error of the generated data signal is calculated on an integrated circuit. 40. The system of claim 39, wherein the integrated circuit is a field programmable gate array. 41. The system of any one of claims 22 to 40, wherein the light source comprises a laser.

[0177] 42. The system of claim 41, wherein the light source comprises multiple lasers. 43. The system of any one of claims 22 to 42, wherein the photodetector comprises a photodiode. 44. The system of any one of claims 22 to 43, wherein the photodetector comprises a photomultiplier tube. 45. The system of any one of claims 22 to 44, wherein the system is a particle analyzer. 46. ​​The system of claim 45, wherein the particle analyzer is part of a flow cytometer.

[0178] 47. An integrated circuit programmed to determine the baseline noise of a photodetector in an optical detection system of a particle analyzer, comprising: An integrated circuit programmed to calculate a running mean square error of a data signal generated from light detected from illuminated particles of a sample in the flow stream. 48. The integrated circuit of claim 47, programmed to calculate a moving average squared error of the generated data signal by measuring the squared difference between the generated data signal and a calculated baseline data signal. 49. Measuring the squared difference between the plurality of generated data signals and the calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals; summing the baseline noise signal over the sampling period, Divide the summed baseline noise signals by the number of baseline noise signals generated over a given sampling period 49. The integrated circuit of claim 48 programmed to 50. The integrated circuit of claim 49, wherein the predetermined sampling period has a duration between 0.001 μs and 100 μs. 51. The integrated circuit of claim 50, wherein the predetermined sampling period has a duration of 1 μs to 10 μs.

[0179] 52. The integrated circuit of any one of appendices 47 to 51, programmed to calculate a moving mean squared error of a data signal generated at a predetermined time interval. 53. The integrated circuit of claim 52 programmed to calculate a moving mean squared error of a data signal generated at a frequency between once every 1 millisecond and once every 1000 milliseconds. 54. The integrated circuit of claim 52 programmed to calculate a moving mean squared error of a data signal generated at a frequency between once every 1 second and once every 60 seconds. 55. The integrated circuit of claim 52 programmed to calculate a moving mean squared error of a data signal generated at a frequency between once every 1 minute and once every 60 minutes. 56. The integrated circuit of any one of appendices 49 to 55, programmed to continuously calculate a moving mean squared error of the generated data signal.

[0180] 57. The integrated circuit of any one of notes 49 to 56, wherein the sample comprises multiple fluorophores with overlapping fluorescence spectra. 58. The integrated circuit of claim 57, wherein the particles of the sample are functionally associated with a fluorophore. 59. The integrated circuit of claim 57 or 58, wherein the flow stream comprises one or more free fluorophores that are not functionally associated with particles of the sample. 60. Detecting light from one or more free fluorophores in the sample with a photodetector; generating a data signal from the detected light; Calculate the running mean squared error of the data signal generated from light emitted from one or more free fluorophores in the sample 60. The integrated circuit of claim 59, programmed to: 61. An integrated circuit according to any one of appendices 57 to 60, programmed to spectrally resolve light from each fluorophore in a sample by calculating a spectral separation matrix for the fluorescence spectrum of each fluorophore in the sample.

[0181] 62. The integrated circuit of claim 61, programmed to calculate a spectral separation matrix using a weighted least squares algorithm. 63. The integrated circuit of claim 62, programmed to weight the generated data signal from the photodetector based on the determined baseline noise of the photodetector. 64. The integrated circuit according to any one of appendices 47 to 63, wherein the integrated circuit is a field programmable gate array (FPGA). 65. The integrated circuit of any one of appendices 47 to 63, wherein the integrated circuit is an application specific integrated circuit (ASIC). 66. The integrated circuit of any one of appendices 47 to 63, wherein the integrated circuit is a complex programmable logic device (CPLD).

[0182] 67. A non-transitory computer-readable storage medium having stored thereon instructions for determining baseline noise of a photodetector in an optical detection system of a particle analyzer, comprising: A non-transitory computer readable storage medium having instructions thereon an algorithm for calculating a running mean square error of a data signal generated from light detected from illuminated particles of a sample in a flow stream. 68. The non-transitory computer-readable storage medium of claim 67 having an algorithm for calculating a moving mean squared error of a generated data signal by measuring the squared difference between the generated data signal and a calculated baseline data signal. 69. An algorithm for measuring the squared difference between a plurality of generated data signals and a calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals; an algorithm for summing the baseline noise signal over a sampling period; an algorithm for dividing the summed baseline noise signal by the number of baseline noise signals generated over a given sampling period; 69. The non-transitory computer-readable storage medium of claim 68, having: 70. The non-transitory computer-readable storage medium of Clause 69, wherein the predetermined sampling period has a duration between 0.001 μs and 100 μs. 71. The non-transitory computer-readable storage medium of Clause 70, wherein the predetermined sampling period has a duration of 1 μs to 10 μs.

[0183] 72. The non-transitory computer-readable storage medium of any one of Clauses 67-71, having an algorithm for calculating a moving mean squared error of a data signal generated at a predetermined time interval. 73. The non-transitory computer-readable storage medium of claim 72 having an algorithm for calculating a moving mean squared error of the generated data signal at a frequency between once every millisecond and once every 1000 milliseconds. 74. The non-transitory computer-readable storage medium of claim 72 having an algorithm for calculating a moving mean squared error of the generated data signal at a frequency between once every second and once every 60 seconds. 75. The non-transitory computer-readable storage medium of claim 72 having an algorithm for calculating a moving mean squared error of the generated data signal at a frequency between once every minute and once every 60 minutes. 76. The non-transitory computer-readable storage medium of any one of Clauses 69-75, having an algorithm for continuously calculating a moving mean squared error of a generated data signal.

[0184] 77. The non-transitory computer-readable storage medium of any one of claims 69 to 76, wherein the sample comprises multiple fluorophores having overlapping fluorescence spectra. 78. The non-transitory computer-readable storage medium of claim 77, wherein particles of the sample are functionally associated with a fluorophore. 79. The non-transitory computer-readable storage medium of any one of appendix 77 or 78, wherein the flow stream comprises one or more free fluorophores that are not functionally associated with particles of the sample. 80. An algorithm for detecting light from one or more free fluorophores in a sample with a light detector; an algorithm for generating a data signal from the detected light; an algorithm for calculating the running mean square error of a data signal generated from light emitted from one or more free fluorophores in a sample; 80. The non-transitory computer-readable storage medium of claim 79, having: 81. A non-transitory computer-readable storage medium according to any one of claims 77 to 80, having an algorithm for spectrally resolving light from each fluorophore in a sample by calculating a spectral separation matrix for the fluorescence spectrum of each fluorophore in the sample.

[0185] 82. The non-transitory computer-readable storage medium of claim 81 having an algorithm for calculating a spectral separation matrix using a weighted least squares algorithm. 83. The non-transitory computer-readable storage medium of claim 82 having an algorithm for weighting data signals generated from the photodetector based on the determined baseline noise of the photodetector.

[0186] Although the foregoing invention has been described in some detail by way of illustration and example for clarity of understanding, it will be readily apparent to those skilled in the art in light of the teachings of this invention that certain changes and modifications can be made thereto without departing from the spirit or scope of the appended claims.

[0187] Thus, the foregoing merely illustrates the principles of the present invention. It will be understood that those skilled in the art will be able to devise various configurations, not explicitly described or shown herein, which embody the principles of the present invention and are within its spirit and scope. Furthermore, all examples and conditional language set forth herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts the inventors have contributed to furthering the art, and should not be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein describing principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Furthermore, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended as a donation to the public, whether or not such disclosure is expressly recited in the claims.

[0188] Accordingly, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims. The claims expressly define that 35 U.S.C. §112(f) or 35 U.S.C. §112(6) apply to a claim limitation only if the precise phrase "means for" or the precise phrase "step for" appears in the claim at the beginning of such limitation; if such precise phrases are not used in a claim limitation, 35 U.S.C. §112(f) or 35 U.S.C. §112(6) does not apply. CROSS-REFERENCE TO RELATED APPLICATIONS

[0189] Pursuant to 35 U.S.C. § 119(e), this application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 081,660, filed September 22, 2020, the disclosure of which is incorporated herein by reference in its entirety.

Claims

1. 1. A method for determining baseline noise of a photodetector in a particle analyzer, comprising: irradiating a sample containing particles in a flow stream; detecting light from the particle-free components of the illuminated flowstream with a photodetector; generating a data signal from the detected light; calculating a moving mean square error of the generated data signal to determine a baseline noise of the photodetector; A method comprising:

2. The method of claim 1 , further comprising detecting light emitted from the flow stream between the particles.

3. 3. The method of claim 1, wherein calculating the moving mean squared error of the generated data signal comprises measuring a squared difference between the generated data signal and a calculated baseline data signal.

4. Calculating a moving mean squared error of the generated data signal comprises: measuring the squared difference between a plurality of generated data signals and a calculated baseline data signal over a predetermined sampling period to generate a plurality of baseline noise signals; summing the baseline noise signal over the predetermined sampling period; Dividing the summed baseline noise signals by the number of baseline noise signals generated over the predetermined sampling period; 4. The method of claim 3, comprising:

5. 5. The method of claim 4, wherein the predetermined sampling period has a duration of 0.001 μs to 100 μs, preferably 1 μs to 10 μs.

6. 6. A method according to any one of claims 1 to 5, comprising calculating a moving mean squared error of the generated data signal at predetermined time intervals of between once every millisecond and once every 1000 milliseconds, preferably between once every second and once every 60 seconds, more preferably between once every minute and once every 60 minutes.

7. A method according to any one of claims 1 to 6, comprising continuously calculating a moving mean squared error of the generated data signal.

8. The method of any one of claims 1 to 7, wherein the sample comprises a plurality of fluorophores with overlapping fluorescence spectra.

9. The method of any one of claims 1 to 8, wherein the moving mean square error of the generated data signal is calculated on an integrated circuit.

10. 10. The method of claim 9, wherein the integrated circuit is a field programmable gate array.

11. a light source configured to illuminate a sample containing particles within the flow stream; a light detection system having a light detector for detecting light from particle-free components of the illuminated flowstream; a processor having a memory operatively coupled thereto; It is equipped with The memory has instructions that, when executed by the processor, cause the processor to: generating a data signal from the detected light; calculating a moving mean square error of the generated data signal to determine a baseline noise of the photodetector; system.

12. The system of claim 11 , wherein the system is a particle analyzer.

13. The system of claim 12 , wherein the particle analyzer is part of a flow cytometer.

14. 1. An integrated circuit programmed to determine baseline noise of a photodetector in an optical detection system of a particle analyzer, comprising: Irradiating a particle-containing sample in a flow stream; detecting light from the particle-free components of the illuminated flow stream with a photodetector; generating a data signal from the detected light; an integrated circuit programmed to calculate a running mean square error of data signals generated from light detected from illuminated particles of said sample in the flow stream;

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