Characterization and optimization of flow cytometry voltages

Abstract

Methods and systems for characterization and optimization of flow cytometry voltages are described herein. According to one aspect of the present disclosure, a method can include applying a plurality of voltages to a detector of a flow cytometry system, the detector optionally comprising a photomultiplier tube (PMT); with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; measuring a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages; identifying, as an operating voltage setting, a voltage setting where the rCV is essentially asymptotic; and setting the applied voltage of the detector to the identified operating voltage setting.

Description

CHARACTERIZATION AND OPTIMIZATION OF FLOWCYTOMETRY VOLTAGESTECHNICAL FIELD

[0001] The present invention relates generally to optimizing voltages for flow cytometer detectors.BACKGROUND

[0002] Flow cytometry systems can include one or more detectors or sensors for receiving emissions, converting those emissions into electrical signals, and transmitting those electrical signals downstream for further processing. The detectors can include a selected voltage, which can affect the sensitivity and quality of the resulting electrical signals. For example, a photomultiplier tube (PMT) can include a tunable high voltage setting that applies a voltage through the PMT, which may facilitate the acceleration of electrons resulting from the received emissions.

[0003] Prior to interrogating a sample via a flow cytometry system, the voltage of a detector can be adjusted or optimized for collecting converting the emissions into electrical signals. For example, adjusting the voltage typically occurs via voltration, where a user manually adjusts the voltage of a detector, collects emissions from a test sample, and monitors the generated electrical signals. If the electrical signals are satisfactory, the user may select the most recent test voltage for use in interrogating the sample. However, manually monitoring the output for respective test voltages may not necessarily result in the optimal selected voltage. For example, the user may be selecting a voltage based on an outputted staining index (SI) for a respective test voltage, but the selected voltage may also limit the overall population range of the electrical signals. Further, manual voltration for a detector can be time-intensive, with multiple rounds of manual testing taking several minutes. As each flow cytometry system can include multiple detectors requiring voltage selection, manual voltration can take an unnecessarily large amount of time.

[0004] Accordingly, there is a need in the art for efficient and accurate voltage selection techniques for flow cytometry systems.SUMMARY

[0005] Methods and systems for characterization and optimization of flow cytometry voltages are described herein. According to the present disclosure, a flow cytometry system can perform a method that characterizes flow cytometry detector response and determine the optimal or minimum required voltages for use in flow cytometry experiments. The method can include performing an automated voltage walk, or voltration, and measuring the MFI, rCV and rSD of a moderately dim particle that can be excited across the instrument excitation range and that can emit across the detector wavelengths in the flow cytometry system. The method can also include determining a voltage in each detector using particles that can serve to: 1) maximize the separation of a negative and positively stained population; and 2) minimize noise added to the system by increasing voltages, thereby also decreasing the effective dynamic range of the detector response. This can occur by implementing a moderately dim particle having a fluorescence signature resolvable from the system noise, applying a method to measure the electronic noise of the system, and identifying a voltage where this dim particle exhibits one of the following properties: 1) the rSD of the dim bead is 3 times the rSD of the measured electronic noise; 2) the rCV of the dim bead reaches an asymptote where the rCV is no longer decreasing as function of PMT voltage; and 3) implementing an additional stained particle where the separation index or staining index (SI) can be calculated to determine an optimal voltage where the SI reaches a local maximum. The methods described herein can thus provide for a more accurate, and efficient, optimal voltage for a flow cytometry system.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] For the purpose of illustrating the invention, there is shown in the drawings a form that is presently preferred; it being understood, however, that this invention is not limited to the precise arrangements and instrumentalities shown.

[0007] FIG. 1 depicts a flow cytometry system according to the present disclosure.

[0008] FIG. 2 left panel provides a detector voltage vs. light intensity graph for fluorescein (BL1-A), and the right panel shows staining index (SI) data for BL1-A according to the present disclosure.

[0009] FIG. 3 provides a SI data graph according to the present disclosure.

[0010] FIG. 4 provides a robust coefficient of variance (rCV) data graph according to the present disclosure.

[0011] FIGS. 5-9 provide different data for characterization and optimization of flow cytometry voltages according to the present disclosure.

[0012] FIG. 10 depicts a process flow for characterization and optimization of flow cytometry voltages according to the present disclosure.

[0013] FIG. 11 depicts a controller according to the present disclosure.

[0014] FIGS. 12A-12B show an example of spatial transcriptomics.

[0015] FIG. 13 shows a block diagram of an example flow system.

[0016] FIG. 14 depicts a comparison of planar and line-driven capillary focusing.

[0017] FIGS. 15A and 15B depicts side and axial views of a line-driven acoustic focusing apparatus.

[0018] FIG. 16 depicts acoustically focused particles flowing across laminar flow lines in a line-driven acoustic focusing apparatus.

[0019] FIGS. 17A-17C depict exemplary acoustic focusing apparatuses.DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0020] The present disclosure may be understood more readily by reference to the following detailed description taken in connection with the accompanying figures and examples, which form a part of this disclosure. It is to be understood that this invention is not limited to the specific devices, methods, applications, conditions or parameters described and / or shown herein, and that the terminology used herein is for the purpose of describing particular embodiments by way of example only and is not intended to be limiting of the claimed invention. Also, as used in the specification including the appended claims, the singular forms “a,” “an,” and “the” include the plural, and reference to a particular numerical value includes at least that particular value, unless the context clearly dictates otherwise. The term “plurality”, as used herein, means more than one. When a range of values is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another embodiment. All ranges are inclusive and combinable, and it should be understood that steps can be performed in any order. Any documents cited herein are incorporated by reference in their entireties for any and all purposes.

[0021] It is to be appreciated that certain features of the invention which are, for clarity, described herein in the context of separate embodiments, can also be provided in combination in a single embodiment. Conversely, various features of the invention that are, for brevity,described in the context of a single embodiment, can also be provided separately or in any subcombination. Further, reference to values stated in ranges include each and every value within that range. In addition, the term “comprising” should be understood as having its standard, open-ended meaning, but also as encompassing “consisting” as well. For example, a device that comprises Part A and Part B can include parts in addition to Part A and Part B, but can also be formed only from Part A and Part B.

[0022] “Fluorophore” as used herein is a molecule that emits detectable electro-magnetic radiation upon excitation with electro-magnetic radiation at one or more wavelengths. A large variety of fluorophores are known in the art and are developed by chemists for use as detectable molecular labels and can be conjugated to affinity molecules described herein. The term “fluorophore” and “fluorescent dye” may be used interchangeably herein.

[0023] Fluorescence is commonly used to capture images of tissue stained with fluorophores (e.g., staining targets, dyes). Each fluorophore has a unique spectral emission profile spread over a plurality of fluorescence channels (e.g., wavelengths of light). For example, FIGS. 12A-12B show an example of spatial transcriptomics. In particular, FIG. 12A shows the spectral emission profiles of 9 fluorophores illustrating the spectral overlap of different dyes (DAPI, eFluor 506, Alexa Fluor 488, Alexa Fluor 514, Alexa Fluor 555, eFluor 615, Alexa Fluor 647, Alexa Fluor 700, Alexa Fluor 750). As shown in FIG. 1A, the spectral profile of DAPI ranges from approximately 375 nm to approximately 650 nm. For example, DAPI has a peak intensity at approximately 450 nm. Each fluorophore has a unique spectral range and peak intensity. However, when capturing particular fluorescence channels (for example, 500 nm), the spectral emission profile of multiple fluorophores may overlap.Accordingly, a spectral image acquisition operation may be performed to acquire both mixed and unmixed images of the sample. FIG. 12B shows example spectrally mixed (left) and unmixed (right) composite images of human tonsil tissue sample stained with 9 fluorophores. While the mixed image includes all fluorescent channels, the unmixed image includes only the desired fluorescent channels.

[0024] Examples of fluorophores include fluorescein or its derivatives, such as fluorescein- 5 -isothiocyanate (FITC), 5-(and 6)-carboxyfluorescein, 5- or 6-earboxyfluorescein, 6- (fluorescein)-5-(and 6)-carboxamido hexanoic acid, fluorescein isothiocyanate, rhodamine or its derivatives such as tetramethylrhodamine and tetramethylrhodamine-5-(and-6)- isothiocyanate (TRITC). Other example fluorophores that could be conjugated to affinity molecules include, but are not limited to: coumarin dyes such as (diethyl-amino)coumarin or 7-amino-4-methylcoumarin-3-acetic acid, succinimidyl ester (AMCA); sulforhodamine 101sulfonyl chloride (TexasRed™ or TexasRed™ sulfonyl chloride; 5-(and-6)- carboxyrhodamine 101, succinimidyl ester, also known as 5-(and-6)-carboxy-X-rhodamine, succinimidyl ester (CXR); lissamine or lissamine derivatives such as lissamine rhodamine B sulfonyl Chloride (LisR); 5-(and-6)-carboxyfluorescein, succinimidyl ester (CFI); fluorescein-5-isothiocyanate (FITC); 7-diethylaminocoumarin-3-carboxylic acid, succinimidyl ester (DECCA); 5-(and-6)-carboxytetramethylrhodamine, succinimidyl ester (CTMR); 7-hydroxycoumarin-3-carboxylic acid, succinimidyl ester (HCCA); 6-fluorescein- 5-(and-6)-carboxamidolhexanoic acid (FCHA); A-(4,4-difluoro-5,7-dimethyl-4-bora-3a,4a- diaza-3-indacenepropionic acid, succinimidyl ester; also known as 5,7-dimethylBODIPY™ propionic acid, succinimidyl ester (DMBP); “activated fluorescein derivative” (FAP), available from Molecular Probes, Inc.; eosin-5 -isothiocyanate (EITC); erythrosin-5- Isothiocyanate (ErlTC); and Cascade™ Blue acetylazide (CBAA) (the (9-acetylazide derivative of 1 -hydroxy-3, 6, 8-pyrenetrisulfonic acid). Yet other potential fluorophores useful herein include, but are not limited to, fluorescent proteins such as green fluorescent protein (GFP) and its analogs or derivatives, fluorescent amino acids such as tyrosine and tryptophan and their analogs, fluorescent nucleosides, and other fluorescent molecules such as Cy2, Cy3, Cy 3.5, Cy5, Cy5.5, Cy 7, IR dyes, Dyomics dyes, phycoerythrins, Oregon green 488, pacific blue, rhodamine green, and Alexa dyes. Yet other examples of fluorescent labels which may be used herein include conjugates of phycoerythrin, inorganic fluorescent labels such as particles based on semiconductor material like coated CdSe nanocrystallites. Several the fluorophores above, as well as others, are available commercially from companies such as Molecular Probes, Inc. (Eugene, Oregon.), Pierce Chemical Co. (Rockford, Illinois.), and Sigma-Aldrich Co. (St. Louis, Missouri.).

[0025] FIG. 1 depicts a flow cytometry system 100 according to the present disclosure. A sample can be sent through a nozzle 105 under pressurized conditions and can create a stream 110 at the output of the nozzle 105. The stream 110 breaks off into a series of droplets 115. Laser 120 can irradiate samples (e.g., cells) within the sample at interrogation site 115. Depending upon the type of sample that is irradiated by the laser 120, the sample can generate an optical response. The optical response can be forward scattered light or forward fluorescent emissions 130. Alternatively, or in addition, the response of the sample to the irradiation by laser 120 can be side-scattered light, or side fluorescent emissions. These responses are transmitted through spectral separation optics 135 (e.g., optical filters, dispersion components, and the like) to detector 140.

[0026] The detector 140 can generate output signals that have a magnitude that is indicative of the intensity of the light collected from the cell, at the specific frequencies of the spectral separation optics 135. In some cases, the detector 140 can be one or more photomultiplier tubes (PMTs). In some cases, the controller 145 can generate an event data signal based on the output signals received from the detector 140. For example, the event data signal can include a time stamp to identify the data with a particular cell. In some cases, the event data signal can include sorting information. For example, the controller 145 can implement sorting logic to perform sort decisions that are based upon the biological response of the particular types of cells that are being sorted. Statistical calculations of the likelihood of an event belonging to a certain population can be used for making the sort decision. The event data signal can be used to control deflector plates 150 to charge a droplet prior to breaking off from the stream 110 (e.g., for sorting purposes). In some cases, the output signals from the detector 140 can include a photocount value.

[0027] The disclosure provided herein is not limited to the particular flow cytometry system 100 depicted in FIG. 1 and is used for illustrative purposes. For example, the methods described herein can be implemented by a flow cytometry system having various lasers of different wavelengths, various spectral separation optics for filtering or directing emissions, various deflector plates or mechanisms for sorting samples (if any), and the like.

[0028] As another example of a system capable of performing the methods described herein. FIG. 13 shows a block diagram of an example flow system 1300. The flow system 1300 includes a controller component 1302, a light source 1304 (e.g., a laser light source), and fluorescence detectors 1306. The light source may include, for example, one or more light emitting diodes (LEDs), one or more laser diodes, one or more photomultiplier tubes (PMTs), a single-photon avalanche diode (SPAD) array, dichroic filters, emission filters, and combinations thereof. When the flow system 1300 initiates an analytical interrogation of the sample, the light source 1304 projects light, such as laser light, onto the sample. While not illustrated, the flow system 1300 may also include one or more light manipulating devices (for example, one or more filters, reflectors, lenses, or a combination thereof) to direct light from the light source 1304 onto a sample. Light contacting the sample may cause fluorophores within the sample to fluoresce. The fluorescent light from the fluorophores is captured by the fluorescence detectors 1306 as a response to the interrogation of the sample.

[0029] In some flow cytometry systems, a sample fluid is focused to a comparatively small core diameter of around 10-50 pm by flowing a sheath fluid around the sample fluid at a very high volumetric rate (about 100-1000 times the volumetric rate of the sample fluid). Theparticles in the sample fluid flow at very fast linear velocities (on the order of meters per second) and as a result spend only a very short time passing through an interrogation point (often only 1-10 [is ).

[0030] In other systems, acoustic radiation pressure can be used instead of (or with) sheath fluid so as to concentrate particles within a flow, which flow can be within a capillary or other conduit. Such a system can include (1) a capillary including a sample channel; (2) at least one vibration producing transducer coupled to the capillary, the at least one vibration producing transducer being configured to produce an acoustic signal inducing acoustic radiation pressure within the sample channel to acoustically concentrate particles flowing within a fluid sample stream in the sample channel. Concentrated particles can then be analyzed, for example by lasers or other illumination. Example systems and methods are described in United States patent application nos. 11 / 784,936, 11 / 784,928, 12 / 239,453, 12 / 283,491, 12 / 209,084, 12 / 955,282, 14 / 129,777, 14 / 607,677, and 15 / 941,791, all of which foregoing applications are incorporated herein by reference in their entireties.

[0031] FIG. 14 illustrates a comparison of planar and line-driven capillary focusing. In planar focusing 1403 / 1405, the particles 1402, which may include one or more rare event particles, may be focused as a two-dimensional sheet and have varying velocities (see different arrows) along the flow direction. In line-driven capillary focusing 1407 / 1409, the particles 1402 may be focused to the center axis of the capillary and have a common velocity along the flow. The capillary may have a round, oblate, or elliptical cross-section, for example. Alternatively, the particles 1402 may also be focused to another axis within the capillary, or along the walls of the capillary.

[0032] FIGS. 15A and 15B illustrate side and axial views of a line-driven acoustic focusing apparatus. The particles 1503, which may include one or more rare event particles, may be acoustically focused using a transducer 1505 to a pressure minimum in the center 1509 of a tube 1501, which may be cylindrical, for example. The particles 1503 may form a single line trajectory 1511, which may allow uniform residence time for particles with similar size and acoustic contrast, which may in turn allow high-throughput serial analysis of particles without compromising sensitivity and resolution.

[0033] FIG. 16 illustrates acoustically focused particles flowing across laminar flow lines in a line-driven acoustic focusing apparatus. The particles 1605, which may include one or more rare event particles, may be acoustically focused using transducer 303 from sample stream 1609 to the center 1607 of a flowing fluid / wash stream 1615 in a line-driven capillary1601. The particles 1605 may move across laminar flow lines and may then move as a single file line and be analyzed at analysis point 1611.

[0034] FIGS. 17A-17C illustrate acoustic focusing apparatuses. FIG. 17A shows a flow cytometry system 1700 a in which a sample 1715 a including particles 1712, which may include one or more rare event particles, and a wash buffer 1713 a are introduced in a capillary 1703. A line drive 1701 (e.g., a PZT drive, or other means capable of producing an acoustic standing wave) introduces an acoustic standing wave (not shown) at a user-defined mode (e.g., a dipole mode). As a result, the sample 1715 a and wash buffer 1713 a may be acoustically reoriented (as 1715 b and 1713 b) and the particles 712 may be acoustically focused (as 717) based upon their acoustic contrast. An illumination source 1709 (e.g., a laser or a group of lasers, or any suitable illumination source, such as a light emitting diode) illuminates the particles 1717 at an interrogation point 1716. The illumination source may be a violet laser (e.g., a 405 nm laser), a blue laser (e.g., a 488 nm laser), a red laser (e.g., a 640 nm laser), or a combination thereof. An optical signal 1719 from the interrogated sample may be detected by a detector or array of detectors 1705 (e.g., a PMT array, a photo-multiplier tube, avalanche photodiodes (APDs), a multi-pixel APD device, silicon PMTs, etc.).

[0035] FIG. 17B shows a flow cytometry system 1700 b where the clean stream 1713 a may be flowed independently through the optics cell. The particles 1702, which may include one or more rare event particles, may be acoustically focused to flow as line 1717, the sample buffer 1715 b may be discarded to waste 1721, and line 1717 may transit to a second acoustic wave inducing means 1714.

[0036] FIG. 17C shows a flow cytometry system 1700 c where the sample 1715 a is injected slightly to one side of the center and flows next to capillary wall 1703 while buffer 1713 a flows against the opposite wall. The transducer 1701 may acoustically reorient sample 1715 a (as 1715 b), may acoustically focus particles 1702, which may include one or more rare event particles, (as 1714 / 1717), and may acoustically reorient buffer 1713 a.

[0037] Computer-implemented voltration methods are described herein. The voltration methods can be performed in part by the detectors of a sample interrogation system, such as the detectors 140 of the system 100 of FIG. 1 or detectors 1306 of FIG. 13. In some cases, a controller of a sample interrogation system can implement the computer-implemented voltration methods described herein. For example, the controller 145 of FIG. 1 or controller component 1302 of FIG. 13 can execute computer-executable instructions to perform the described voltration methods. In some cases, the computer-executable instructions can be stored locally (e.g., via memory of the sample interrogation system 100 or flow system 1300).In some cases, the computer-executable instructions can be external to the corresponding sample interrogation system, such as stored in memory external to the sample interrogation system, or accessible through a network (e.g., a cloud network).

[0038] The sample interrogation system 100 can perform a voltration method according to the computer-executable instructions. The sample interrogation system 100 can input or provide a set of voltages to one or more detectors of the system 100. The sample interrogation system 100 can measure characteristics of a first sample as a given voltage is applied to a detector(s) 140. For example, the sample characteristic system 100 can apply a light to the first sample, and the detector(s) 140 can measure emissions emitted from the first sample and received by the detector(s) 140. The measurements for the first sample can be stored and analyzed by the system 100. The system can repeat this process for a plurality of voltages applied to the detector(s) 140 (e.g., every 10, 20, or 25 mV, between 200 mV and 800 mV, etc.), and can collect and store these measurements across a voltage range.

[0039] The system 100 can analyze the measurements and can determine various characteristics for the first sample across the range of voltages applied to the detector(s) 140. For example, the system 100 can determine a signal intensity, a robust coefficient of variance (rCV), a robust standard deviation (rSD), a mean fluorescence intensity (MFI), a combination thereof, or the like.

[0040] In some cases, the system can perform this process for a plurality of samples. For example, the system 100 can perform the process for a second sample. The sample interrogation system 100 can input or provide a set of voltages to one or more detectors of the system 100. The sample interrogation system 100 can measure characteristics of the second sample as a given voltage is applied to a detector(s) 140. For example, the sample characteristic system 100 can apply a light to the second sample, and the detector(s) 140 can measure emissions emitted from the second sample and received by the detector(s) 140. The measurements for the second sample can be stored and analyzed by the system 100. The system can repeat this process for a plurality of voltages applied to the detector(s) 140 and can collect and store these measurements across a voltage range. The system 100 can analyze the measurements and can determine various characteristics for the second sample across the range of voltages applied to the detector(s) 140. For example, the system 100 can determine a signal intensity, rCV, rSD, MFI, a combination thereof, or the like, across the applied voltage range of the detector(s).

[0041] In some cases, the system 100 can perform the process described above for a third sample. The sample interrogation system 100 can input or provide a set of voltages to one ormore detectors of the system 100. The sample interrogation system 100 can measure characteristics of the third sample as a given voltage is applied to a detector(s) 140. For example, the sample characteristic system 100 can apply a light to the third sample, and the detector(s) 140 can measure emissions emitted from the third sample and received by the detector(s) 140. The measurements for the third sample can be stored and analyzed by the system 100. The system can repeat this process for a plurality of voltages applied to the detector(s) 140 and can collect and store these measurements across a voltage range. The system 100 can analyze the measurements and can determine various characteristics for the third sample across the range of voltages applied to the detector(s) 140. For example, the system 100 can determine a signal intensity, rCV, rSD, MFI, a combination thereof, or the like, across the applied voltage range of the detector(s).

[0042] In some cases, the system 100 can perform the process for a fourth sample. The sample interrogation system 100 can input or provide a set of voltages to one or more detectors of the system 100. The sample interrogation system 100 can measure characteristics of the fourth sample as a given voltage is applied to a detector(s) 140. For example, the sample characteristic system 100 can apply a light to the fourth sample, and the detector(s) 140 can measure emissions emitted from the fourth sample and received by the detector(s) 140. The measurements for the fourth sample can be stored and analyzed by the system 100. The system can repeat this process for a plurality of voltages applied to the detector(s) 140 and can collect and store these measurements across a voltage range. The system 100 can analyze the measurements and can determine various characteristics for the fourth sample across the range of voltages applied to the detector(s) 140. For example, the system 100 can determine a signal intensity, rCV, rSD, MFI, a combination thereof, or the like, across the applied voltage range of the detector(s).

[0043] The different samples incorporated into the voltration process can each include different characteristics compared to one another. For example, the first sample can be a blank bead. The blank bead can be a bead that approximates instrument noise of the system 100. The blank bead can include physical properties that the system 100 can detect, but where the blank bead also does not emit a fluorescent signal, or emits a negligent amount of a fluorescent signal.

[0044] In some cases, the second sample can be a dim bead. The dim bead can include a limited, comparatively low amount of fluorescence, such that the dim bead exhibits low intensity characteristics. For example, a dim bead can exhibit one or more characteristics -such as fluorescence intensity, for example - that are within 3 SD of the system noise (e.g., as detected via the blank bead).

[0045] In some cases, the third sample can be an intermediary, or mid, bead. The mid bead can include a moderate fluorescence intensity, such as one that is between 3 and 5 SD of the system noise.

[0046] In some cases, the fourth sample can be a bright bead. The bright bead can include a high fluorescence intensity, for example, above 5 SD from the system noise. The beads can be, for example, Attune™ Performance Tracking beads of Thermo Fisher Scientific. The beads can include intensity levels ranging from 1-4, where 1 denotes a blank bead, 2 denotes a dim bead, 3 denotes a medium bead, and 4 denotes a bright bead. The beads can include a specified diameter. For example, the blank bead can have a nominal diameter of 2.4 pm, and the dim, medium, and bright beads can have a nominal diameter of 3.2 pm. The beads can be stained with one or more fluorophore types, which can be configured to be excitable by a corresponding system’s (e.g., system 100) light sources (e.g., lasers), and emit fluorescence signals at designated levels (e.g., based on the respective staining).

[0047] The system 100 can analyze the measurements of the one or more samples received by the detector(s). The analysis can include one or more sub-processes. The one or more subprocesses can each determine an optimal voltage for the detector(s). For example, one subprocess can include determining a staining index (SI) across the applied voltages, and determining a peak (SI) value across the applied voltages. In some cases, stained and unstained cells can be used in lieu of beads, where the stained and unstained cells act as the positive and negative population, respectively. In some cases, a stained bead (e.g., a bright bead) and a blank bead can be used for the positive and negative population, respectively. Determining the SI value can include the determining a MFI value for a positive bead population (MFI_positive), a MFI value for the negative bead population (MFI_negative), and the rSD of the negative bead population (rSD_negative) for a given applied voltage. An example equation for the SI value of a given applied voltage can be: SI = (MFI_positive - MFI_negative) / (2 * rS_neg). FIG. 2, left panel shows a voltage vs. intensity chart for a sample according to the present disclosure. The sample data shown is for sample including fluorescein (BL1-A). The right panel of FIG. 2 shows a SI graph based on the data shown in the left panel for fluorescein (BL1-A), according to the present disclosure. The SI graph shows different characteristics of the measured bead. For example, the SI graph shows data points for the SI values 205, data points for a voltration index (VI) 210, and data points for an alternative SI (alt SI) 215. In some cases, the voltration index can be calculated from theequation (Alt SI) / (Sqrt (Voltage)). In some cases, the alt SI can be calculated from the equation (Median Positive) / (SD Negative). In some cases, the system can rely on either the alt SI or VI values in lieu of the SI values in determining an optimal voltage. The system can determine a maximum, peak, or inflection point within the respective data points, and determine the voltage corresponding to this maximum, peak, or inflection of the data points. This determined voltage can be the optimal voltage for the detector(s). For example, in cases where the system relies on the SI values 205, the optimal voltage can be 510 mV in FIG. 2. In cases where the system relies on the VI values 210, the optimal voltage can be 400 mV in FIG. 2. In cases where the system relies on the alt SI values 215, the optimal voltage can be 510mV in FIG. 2.

[0048] Another sub-process can include determining an asymptote of rCV values for a sample. For example, FIG. 4 shows a graph of CV(%) vs. applied voltage to a detector(s). In some cases, the rCV values can be based emissions from a bead, such as a dim bead. The data points 405 can indicate where an asymptote exists along the applied voltages. For example, the asymptote can occur where the rCV is no longer decreasing as a function of the applied detector voltage. In the example of FIG. 4, the asymptote can occur at the inflection point 410. The system can further the voltage corresponding to the asymptote, which can be determined to be the optimal voltage for the detector(s). In the example of FIG. 4, the asymptote can be located at 300 mV.

[0049] Another sub-process can include determining a rSD threshold for a sample. For example, the system can determine a point where the rSD for a sample meets a predefined rSD threshold. In some cases, the sample can be a bead, such as a dim bead. In some cases, the predefined threshold can be 3 times the background of the noise of the system. The system can also determine an applied voltage corresponding to the threshold. The determined voltage can be the optimal voltage for the detector(s). In some cases, the voltage of the detector can be increased until saturation is reached. The selected voltage can be the highest voltage value where the rSD meets the predefined threshold (e.g., rSD equals 3).

[0050] In some cases, the one or more of the sub-processes can be incorporated into determining an optimal value. For example, in some cases, a combination of the SI subprocess, the rCV sub-process, and the rSD sub-process described above can be used to determine an optimal voltage. For example,, an average optimal voltage value can be determined between the combination of sub-processes. In some cases, the combination of sub-processes can be weighted and compared to one another. For example, the determined optimal voltage from one-sub process can be weighted greater than the determined optimalvoltage from a second sub-process. FIGS. 5-11 show graphs of a combination of data corresponding to the processes described above. Based on the combination of data, which may correspond to multiple subprocesses described above, the system can determine an optimal voltage for a detector(s).

[0051] The system may apply the determined optimal voltage to the detector(s). For example, the system may apply the determined optimal voltage to the detector(s) used for the sub-processes describe above. Thus, the processes described above may be performed for each detector used for measuring characteristics of a sample.

[0052] FIG. 10 shows a process according to the present disclosure. The process can be performed by a system, such as a sample interrogation system 100 as described with reference to FIG. 1. At Step 1005, measurements of sample can be taken. The sample can be a bead, such as a blank bead, a dim bead, an intermediate bead, or a bright bead. The system can apply a voltage to a detector for collecting measurements of the sample. A light source of the system can direct light to the sample, and the detector of the system can measure characterizations of the sample caused by the sample receiving the light. Step 1005 can include a repeat of the measurements taken, albeit across a multitude of applied voltages. For example, the system can direct light to a sample, and can collect measurements from the sample via the detector, for a plurality of applied voltages to the detector. Further, in some cases, the measuring can occur for one or more samples, which may each receive and interact with light for a plurality of applied voltages to the detector.

[0053] At Step 1010, the system can determine an optimal voltage for the detector. The optimal voltage can be based on the analysis of the collected measurements for the one or more samples. The optimal voltage can be determined based on one or more analysis subprocesses described above. For example, the optimal voltage can be determined according to a SI sub-process, a rCV sub-process, a rSD sub-process, or a combination thereof. At Step 1015, the system can apply the determined optima voltage to the detector.

[0054] FIG. 11 depicts a controller 1100 according to the present disclosure. The controller 1100 can be an example of controller 145 discussed with reference to FIG. 1.

[0055] The controller 1100 can be a computing device such as a microcontroller, general purpose computer (e.g., a personal computer or PC), workstation, mainframe computer system, and so forth. The controller 1100 can include a processor device (e.g., a central processing unit or “CPU”) 1102, a memory device 1104, a storage device 1106, a user interface 1108, a system bus 1110, and a communication interface 1112.

[0056] The processor 1102 can be any type of processing device for carrying out instructions, processing data, and so forth.

[0057] The memory device 1104 can be any type of memory device including any one or more of random access memory (“RAM”), read-only memory (“ROM”), Flash memory, Electrically Erasable Programmable Read Only Memory (“EEPROM”), and so forth.

[0058] The storage device 1106 can be any data storage device for reading / writing from / to any removable and / or integrated optical, magnetic, and / or optical-magneto storage medium, and the like, such as a hard disk, a compact disc-read-only memory “CD-ROM”, CD- ReWritable CDRW,” Digital Versatile Disc-ROM “DVD-ROM”, DVD-RW, and so forth. The storage device 1106 can also include a controller / interface for connecting to the system bus 810. Thus, the memory device 1104 and the storage device 1106 are suitable for storing data as well as instructions for programmed processes for execution on the processor 1 102.

[0059] The user interface 1108 can include a touch screen, control panel, keyboard, keypad, display or any other type of interface, which can be connected to the system bus 1110 through a corresponding input / output device interface / adapter.

[0060] The communication interface 1112 can be adapted and configured to communicate with any type of external device, or with other components sampler interrogation system. For example, arrowed lines, such as those illustrated in FIG. 1, can illustrate electronic communication between the controller - for example, controller 145 of FIG. 1 - and another component of the sample interrogation system, for example detector 140. The communication interface 1112 can further be adapted and configured to communicate with any system or network, such as one or more computing devices on a local area network (“LAN”), wide area network (“WAN”), the Internet, and so forth. The communication interface 1112 can be connected directly to the system bus 1110 or can be connected through a suitable interface.

[0061] The controller 1100 can, thus, provide for executing processes, by itself and / or in cooperation with one or more additional devices, that can include algorithms for controlling components of the sample interrogation system in accordance with the present disclosure.The controller 1100 can be programmed or instructed to perform these processes according to any communication protocol and / or programming language on any platform. Thus, the processes can be embodied in data as well as instructions stored in the memory device 1104 and / or storage device 1106, or received at the user interface 1108 and / or communication interface 1112 for execution on the processor 1102.

Claims

CLAIMS1. A method, comprising: applying a plurality of voltages to a detector of a flow cytometry system; with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; measuring a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages; identifying, as an operating voltage setting, a voltage setting where the rCV is essentially asymptotic; and setting an applied voltage of the detector to the identified operating voltage setting.

2. The method of claim 1 , wherein the at least one standard comprises a second standard, and wherein the method further comprises: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

3. The method of claim 1, wherein the bead comprises a dim bead.

4. The method of claim 1 , further comprising: identifying, as an operating voltage setting, a robust standard deviation (rSD) value for intensity levels of the collected emissions that is approximately 3 times a rSD value for measured background noise, wherein the identifying is further based on the rSD value.

5. The method of claim 4, further comprising: measuring a background noise of the flow cytometry system.

6. The method of claim 1 , wherein the detector comprises a photomultiplier tube (PMT).

7. A method, comprising: applying a plurality of voltages to a detector of a flow cytometry system; with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead;measuring a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages; identifying, as an operating voltage setting, a robust standard deviation (rSD) value for intensity levels of the collected emissions that is approximately 3 times a rSD value for measured background noise; and setting an applied voltage of the detector to the identified operating voltage setting.

8. The method of claim 7, further comprising: determining a voltage value where the rCV is essentially asymptotic, wherein the identifying the operating voltage setting is further based on the voltage value.

9. The method of claim 7, wherein the at least one standard comprises a second standard, and wherein the method further comprises: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

10. The method of claim 7, wherein the bead comprises a dim bead.

11. The method of claim 7, wherein the detector comprises a photomultiplier tube (PMT).

12. A method, comprising: applying a plurality of voltages to a detector of a flow cytometry system; with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; identifying, as an operating voltage setting, a voltage setting where (i) a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages is essentially asymptotic, (ii) a robust standard deviation (rSD) value for intensity levels of the collected emissions is approximately 3 times a rSD value for measured background noise, or (iii) both (i) and (ii); and setting an applied voltage of the detector to the identified operating voltage setting.

13. The method of claim 12, wherein the at least one standard comprises a second standard, and wherein the method further comprises: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

14. The method of claim 12, wherein the bead comprises a dim bead.

15. The method of claim 12, wherein the detector comprises a photomultiplier tube (PMT).

16. An apparatus, comprising: one or more processors; a memory; and a set of computer-executable instructions that, when executed by the one or more processors; cause: applying a plurality of voltages to a detector of a flow cytometry system; with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; measuring a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages; identifying, as an operating voltage setting, a voltage setting where the rCV is essentially asymptotic; and setting an applied voltage of the detector to the identified operating voltage setting.

17. The apparatus of claim 16, wherein the at least one standard comprises a second standard, and wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

18. The apparatus of claim 16, wherein the bead comprises a dim bead.

19. The apparatus of claim 16, wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: identifying, as an operating voltage setting, a robust standard deviation (rSD) value for intensity levels of the collected emissions that is approximately 3 times a rSD value for measured background noise, wherein the identifying is further based on the rSD value.

20. The apparatus of claim 19, wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: measuring a background noise of the flow cytometry system.

21. The apparatus of claim 16, wherein the detector comprises a photomultiplier tube (PMT).

22. An apparatus, comprising: one or more processors; a memory; and a set of computer-executable instructions that, when executed by the one or more processors; cause: applying a plurality of voltages to a detector of a flow cytometry system; with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; measuring a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages; identifying, as an operating voltage setting, a robust standard deviation (rSD) value for intensity levels of the collected emissions that is approximately 3 times a rSD value for measured background noise; and setting an applied voltage of the detector to the identified operating voltage setting.

23. The apparatus of claim 22, wherein the at least one standard comprises a second standard, and wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: determining a voltage value where the rCV is essentially asymptotic, wherein the identifying the operating voltage setting is further based on the voltage value.

24. The apparatus of claim 22, wherein the at least one standard comprises a second standard, and wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

25. The apparatus of claim 22, wherein the bead comprises a dim bead.

26. The apparatus of claim 16, wherein the detector comprises a photomultiplier tube (PMT).

27. An apparatus, comprising: one or more processors; a memory; and a set of computer-executable instructions that, when executed by the one or more processors; cause: applying a plurality of voltages to a detector of a flow cytometry system; with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; identifying, as an operating voltage setting, a voltage setting where (i) a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages is essentially asymptotic, (ii) a robust standard deviation (rSD) value for intensity levels of the collected emissions is approximately 3 times a rSD value for measured background noise, or (iii) both (i) and (ii); and setting an applied voltage of the detector to the identified operating voltage setting.

28. The apparatus of claim 27, wherein the at least one standard comprises a second standard, and wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

29. The apparatus of claim 27, wherein the bead comprises a dim bead.

30. The apparatus of claim 27, wherein the detector comprises a photomultiplier tube (PMT).

31. A non-transitory computer readable medium comprising a set of computerexecutable instructions that, when executed by one or more processors, cause: applying a plurality of voltages to a detector of a flow cytometry system; with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; measuring a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages; identifying, as an operating voltage setting, a voltage setting where the rCV is essentially asymptotic; and setting an applied voltage of the detector to the identified operating voltage setting.

32. The non-transitory computer readable medium of claim 31, wherein the at least one standard comprises a second standard, and wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

33. The non-transitory computer readable medium of claim 31, wherein the bead comprises a dim bead.

34. The non-transitory computer readable medium of claim 31, wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: identifying, as an operating voltage setting, a robust standard deviation (rSD) value for intensity levels of the collected emissions that is approximately 3 times a rSD value for measured background noise, wherein the identifying is further based on the rSD value.

35. The non-transitory computer readable medium of claim 34, wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: measuring a background noise of the flow cytometry system.

36. The non-transitory computer readable medium of claim 31, wherein the detector comprises a photomultiplier tube (PMT).

37. A non-transitory computer readable medium comprising a set of computerexecutable instructions that, when executed by one or more processors, cause: applying a plurality of voltages to a detector of a flow cytometry system, the detector optionally comprising a photomultiplier tube (PMT); with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; measuring a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages; identifying, as an operating voltage setting, a robust standard deviation (rSD) value for intensity levels of the collected emissions that is approximately 3 times a rSD value for measured background noise; and setting an applied voltage of the detector to the identified operating voltage setting.

38. The non-transitory computer readable medium of claim 37, wherein the at least one standard comprises a second standard, and wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: determining a voltage value where the rCV is essentially asymptotic, wherein the identifying the operating voltage setting is further based on the voltage value.

39. The non-transitory computer readable medium of claim 37, wherein the at least one standard comprises a second standard, and wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

40. The non-transitory computer readable medium of claim 37, wherein the bead comprises a dim bead.

41. A non-transitory computer readable medium comprising a set of computerexecutable instructions that, when executed by one or more processors, cause: applying a plurality of voltages to a detector of a flow cytometry system; with the detector, collecting emissions of at least one standard caused by exciting the at least one standard, the at least one standard optionally comprising a bead; identifying, as an operating voltage setting, a voltage setting where (i) a robust coefficient of variance (rCV) for intensity levels of the collected emissions across the plurality of applied voltages is essentially asymptotic, (ii) a robust standard deviation (rSD) value for intensity levels of the collected emissions is approximately 3 times a rSD value for measured background noise, or (iii) both (i) and (ii); and setting an applied voltage of the detector to the identified operating voltage setting.

42. The non-transitory computer readable medium of claim 41, wherein the at least one standard comprises a second standard, and wherein the set of computer-executable instructions, when executed by the one or more processors, further cause: determining a maximum separation index or staining index (SI) value for the emissions collected for the second standard, wherein the identifying is further based on the maximum SI value.

43. The non-transitory computer readable medium of claim 41, wherein the bead comprises a dim bead.

44. The non-transitory computer readable medium of claim 41 , wherein the detector comprises a photomultiplier tube (PMT).

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