Patient and control sample based analyzer evaluation

Abstract

A blood analysis instrument may be assessed using a combination of images of control particles of a control samples and stained blood particles of a blood sample (e.g., a patient sample). This may be done using a method which comprises receiving images of control particles of a control sample and assessing a first performance characteristic of the blood analysis instrument based on the images of control particles. Such a method may also comprise receiving images of stained blood particles of a blood sample and assessing a second performance characteristic of the blood analysis instrument based on the images of the stained blood particles.

Description

PATIENT AND CONTROL SAMPLE BASED ANALYZER EVALUATIONCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This is related to, and claims the benefit of, provisional patent application 63 / 651,600, filed in the United States Patent Office on May 24, 2024 for “Patient and Control Sample Based Analyzer Evaluation,” which application is hereby incorporated by reference in its entirety.BACKGROUND

[0002] Blood cell analysis is a commonly performed medical test for providing an overview of a patient's health status. A blood sample can be drawn from a patient's body and stored in a test tube containing an anticoagulant to prevent clotting. A whole blood sample normally comprises three major classes of blood cells including red blood cells (erythrocytes), white blood cells (leukocytes) and platelets (thrombocytes). Each class can be further divided into subclasses of members. For example, five major types or subclasses of white blood cells (WBCs) have different shapes and functions. White blood cells may include neutrophils, lymphocytes, monocytes, eosinophils, and basophils. There are also subclasses of the red blood cell types. The appearances of particles in a sample may differ according to pathological conditions, cell maturity and other causes. Red blood cell subclasses may include reticulocytes and nucleated red blood cells.

[0003] To evaluate and document whether an analyzer is able to effectively perform its tasks, such as, but not limited to, analysis of blood samples, it may be provided with a control sample having known characteristics, and the results of analysis by the analyzer compared with what would be expected based on the control samples’ known characteristic(s). Currently, the state of the art uses material that is the same as or similar to blood as a control, for example, using human red blood cells as a control for the detection of red blood cells. However, such approaches have many problems. For example, blood-based control samples are expensive, and difficult to shipand store, requiring refrigeration to safely transport. Further, blood-based products have a limited shelf-life even when refrigerated, which can alter their characteristics and add both cost and complexity.

[0004] Accordingly, there is a need for improved technology for evaluating whether an analyzer is able to effectively perform its tasks.BRIEF SUMMARY

[0005] The present disclosure relates to systems and methods for evaluating effectiveness of an analyzer. For example, in some aspects the disclosed technology may be used to implement a method for assessing a blood analysis instrument. As set forth herein, such a method may include receiving images of control particles of a control sample and assessing a first performance characteristic of the blood analysis instrument based on the images of control particles. A method implemented based on this disclosure may also include receiving images of stained blood particles of a blood sample and assessing a second performance characteristic of the blood analysis instrument based on the images of the stained blood particles.

[0006] The disclosed technology may also be implemented in other manners, such as in the form of systems or computer readable media programmed to performed methods such as described above. Accordingly, the above description of a method which may be implemented based on this disclosure should be understood as being illustrative only, and should not be treated as limiting.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] While the specification concludes with claims which particularly point out and distinctly claim the invention, it is believed the present invention will be better understood from the following description of certain examples taken in conjunction with the accompanying drawings, in which like reference numerals identify the same elements and in which:

[0008] FIG. 1 is a schematic illustration, partly in section and not to scale, showing operational aspects of an exemplary flowcell which may be used in an analyzer configured to capture and analyze images.

[0009] FIG. 2A illustrates a perspective view of an exemplary preparation module for mixing a staining and / or lysing agent with a sample to form a sample mixture, and for incubating the sample mixture, showing the lamination of ferromagnetic sheets to a housing of the preparation module.

[0010] FIG. 2B illustrates a perspective view of the preparation module of FIG. 2A, showing the wrapping of the ferromagnetic sheets to the housing with adhesive tape.

[0011] FIG. 2C illustrates a perspective view of the preparation module of FIG. 2A, showing the winding of a heating coil of the preparation module about the housing.

[0012] FIG. 3 illustrates a method which may be performed to assess a blood analysis instrument using both a control sample and a blood sample (e.g., a patient sample).

[0013] FIG. 4 illustrates a process which may be performed to account for an assessment of a performance characteristic using a blood sample.

[0014] FIG. 5 illustrates acts which may be performed in assessing an analyzer’s staining function.

[0015] FIG. 6 illustrates acts which may be performed in assessing an analyzer’s staining function using absorbance statistics.

[0016] FIG. 7 illustrates steps which may be performed to determine if a particular imaged cell was under-stained.

[0017] FIG. 8 illustrates a potential distribution of cells from a stained sample.

[0018] FIG. 9 illustrates a potential method of using an analyzer’s staining function in analyzing a sample.

[0019] FIG. 10 illustrates a method which may be used to determine optimized channels for subsequent staining assessment.

[0020] The drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be carried out in a variety of other ways, including those not necessarily depicted in the drawings. The accompanying drawings incorporated in and forming a part of the specification illustrate several aspects of the present invention, and together with the description serve to explain the principles of the invention; it being understood, however, that this invention is not limited to the precise arrangements shown.DETAILED DESCRIPTION

[0021] The present disclosure relates to evaluating the effectiveness of analyzers. In some aspects, the analyzers may be visual analyzers comprising processors to facilitate automated conversion and / or analysis of images. Such analyzers may be useful, for example, in characterizing particles in biological fluids, such as detecting and quantifying erythrocytes, reticulocytes, nucleated red blood cells, platelets, and white blood cells, including white blood cell differential counting, categorization and subcategorization and analysis. Other similar uses such as characterizing blood cells from other fluids (serum, bone marrow, lavage fluid, effusions, exudates, cerebrospinal fluid, pleural fluid, peritoneal fluid, and amniotic fluid) are also contemplated.

[0022] Turning now to the drawings, FIG. 1 schematically shows an exemplary flow cell 22 for conveying a sample fluid through a viewing zone 23 of a high optical resolution imaging device 24 in a configuration for imaging microscopic particles in a sample flow stream 32 using digital image processing. Flow cell 22 is coupled to a source 25 of sample fluid which may have been subjected to processing, such as contact with a particle contrast agent composition and heating. Flow cell 22 is also coupled to one or more sources 27 of a particle and / or intracellular organelle alignment liquid (PIOAL), such as a clear glycerol solution having a viscosity that is greater than the viscosity of the sample fluid, an example ofwhich is disclosed in U.S. Pat. Nos. 9,316,635 and 10,451,612, the disclosures of which are hereby incorporated by reference in their entirety.

[0023] The sample fluid is injected through a flattened opening at a distal end 28 of a sample feed tube 29, and into the interior of the flow cell 22 at a point where the PIOAL flow has been substantially established resulting in a stable and symmetric laminar flow of the PIOAL around / surrounding (e.g., circumferentially in a circular cross-sectional arrangement, or surrounding a plurality of sides of in a non-circular (e.g., rectangular) cross-sectional arrangement) the ribbon-shaped sample stream. The sample and PIOAL streams may be supplied by precision metering pumps that move the PIOAL with the injected sample fluid along a flowpath that narrows substantially. The PIOAL envelopes and compresses the sample fluid in the zone 21 where the flowpath narrows. Hence, the decrease in flowpath thickness at zone 21 can contribute to a geometric focusing of the sample stream 32. The sample fluid ribbon 32 is enveloped and carried along with the PIOAL downstream of the narrowing zone 21, passing in front of, or otherwise through the viewing zone 23 of, the high optical resolution imaging device 24 where images are collected, for example, using a charge coupled device (CCD) 48. In this way, flow imaging is performed where images from the flowing sample stream and the cellular material contained therein are collected. Processor 18 can receive, as input, pixel data from CCD 48. The sample fluid ribbon flows together with the PIOAL to a discharge 33.

[0024] As shown here, the narrowing zone 21 can have a proximal flowpath portion 21a having a proximal thickness PT and a distal flowpath portion 21b having a distal thickness DT, such that distal thickness DT is less than proximal thickness PT. The sample fluid can therefore be injected through the distal end 28 of sample tube 29 at a location that is distal to the proximal portion 21a and proximal to the distal portion 21b. Hence, the sample fluid can enter the PIOAL envelope as the PIOAL stream is compressed by the zone 21. wherein the sample fluid injection tube has a distal exit port through which sample fluid is injected into flowing sheath fluid, the distal exit port bounded by the decrease in flowpath size of the flow cell.

[0025] The digital high optical resolution imaging device 24 with objective lens 46 is directed along an optical axis that intersects the ribbon-shaped sample stream 32. The relative distance between the objective 46 and the flow cell 33 is variable by operation of a motor drive 54, for resolving and collecting a focused digitized image on a photosensor array. In one example, objective 46 can be thought of as a microscope or a part of a microscope, in that it magnifies an imaging area. In various examples, such relative distance is achieved whereby the motor drive 54 can move the objective relative to the fixed flow cell, the flow cell relative to the fixed objective, or the camera relative to at least one of the fixed objective or fixed flowcell. Additional information regarding the construction and operation of an exemplary flow cell such as shown in FIG. 1 is provided in U.S. Patent 9,322,752, entitled “Flow cell Systems and Methods for Particle Analysis in Blood Samples,” filed on March 17, 2014, the disclosure of which is hereby incorporated by reference in its entirety. Descriptions of approaches which may be used for focusing in an imaging system such as shown in FIG. 1 are provided in Published App. No. 2024 / 0357232 titled “Focus Quality Determination through Multi-Layer Processing,” filed on June 11, 2024, U.S. Patent 9,857,361 titled “Flowcell, Sheath fluid, and Autofocus Systems and Methods for Particle Analysis in Urine Samples”, filed on March 17, 2014, U.S. Patent 10,705,008 titled “Autofocus Systems and Methods for Particle Analysis in Blood Samples”, filed on March 17, 2014, U.S. Patent 10,705,011, titled “Dynamic Focus System and Methods”, filed October 5, 2017, and international application W02023 / 150064 titled “Measure Image Quality of Blood Cell Images”, filed January 27, 2023, the disclosures of each of which are hereby incorporated by reference in their entirety. Descriptions of approaches which may be used for focusing in an imaging system such as shown in FIG. 1 are provided in Published App. No. 2024 / 0357232 titled “Focus Quality Determination through Multi-Layer Processing,” filed on June 11, 2024, U.S. Patent 9,857,361 titled “Flowcell, Sheath fluid, and Autofocus Systems and Methods for Particle Analysis in Urine Samples”, filed on March 17, 2014, U.S. Patent 10,705,008 titled “Autofocus Systems and Methods for Particle Analysis in Blood Samples”, filed on March 17, 2014, U.S. Patent 10,705,011, titled “Dynamic Focus System and Methods”, filed October 5, 2017, and international applicationWO2023 / 150064 titled “Measure Image Quality of Blood Cell Images”, fded January 27, 2023, the disclosures of each of which are hereby incorporated by reference in their entirety.

[0026] In addition to a flowcell such as that shown in FIG. 1, an analyzer which may be used for blood cell analysis may also include additional components, such as a staining and / or lysing component which can be used to prepare a sample for analysis. An example of such a component is provided in FIGS. 2A-2C, which depict a preparation module 400 which may be used to both mix a sample with a staining agent and / or a lysing agent (e.g., a combined staining and lysing agent) and to incubate the sample mixture via heating prior to the cells withing the sample mixture being imaged by a camera such as described in the context of FIG. 1. For example, preparation module 400 may be incorporated in place of the source 25 shown in FIG. 1 or between the source 25 and the sample feed tube 29 shown in FIG. 1, to facilitate mixing of the sample with the staining and / or lysing agent and incubation of the sample mixture prior to capturing of images of the sample by the high optical resolution imaging device 24. The staining and / or lysing agent may include any suitable composition. For example, it may be composed in accordance with any one or more teachings of U.S. Pat. No. 9,279,750, entitled “Method and Composition for Staining and Sample Processing,” issued on March 8, 2016, the disclosure of which is hereby incorporated by reference in its entirety; and / or U.S. Pat. No. 9,322,753, entitled “Method and Composition for Staining and Processing a Urine Sample,” issued on April 26, 2016, the disclosure of which is hereby incorporated by reference in its entirety; and / or US Pub. No. 2021 / 0108994, entitled “Method and Composition for Staining and Sample Processing,” published on April. 15, 2021, the disclosure of which is hereby incorporated by reference in its entirety.

[0027] In the example of FIGS. 2A-2C, the preparation module 400 includes a housing 410, a pair of ferromagnetic sheets 412, and a heater in the form of a heating coil 414 (FIG. 6C). Heating coil 414 can comprise a resistive coil, or alternatively an inductive coil. As best shown in FIG. 6A, the housing 410 includes a plurality of (e.g., four) sidewalls 420 which collectively define an interior chamber 422 (also referred to as a sample reservoir) for receiving the agent and the sample, mixing the agent and the sample to form a sample mixture, and incubating the samplemixture. The housing 410 also includes a top wall 424 and a port 426 extending through the top wall 424 to the interior chamber 422. The port 426 may permit a stain / lyse dispenser (not shown) to deliver the agent to the interior chamber 422, and / or may permit a sample dispenser (not shown) to deliver the sample to the interior chamber 422 so as to be added to the agent.

[0028] The housing 410 may comprise a metallic material having relatively high thermal conductivity, such as aluminum, in order to promote uniform heating of the housing 410 and likewise uniform heating of the contents of the interior chamber 422. In the example of FIGS. 2A-2C, the sidewalls 420 of the housing 410 are laminated with respective ferromagnetic sheets 412 to improve the efficiency of the heating (e.g., resistive heating, or alternatively inductive heating) performed by preparation module 400 (e.g., due to the relatively low ferromagnetic properties of aluminum). More particularly, each ferromagnetic sheet 412 is secured to the outer surfaces of a corresponding pair of sidewalls 420. It will be appreciated that any suitable number of ferromagnetic sheets 412 may be used to laminate the sidewalls 420. In the example of FIGS. 2A-2C a thermally conductive compound 430 is deposited on the outer surfaces of the sidewalls 420 for adhering the ferromagnetic sheets 412 to the sidewalls 420. As best shown in FIG. 2B, an adhesive tape 432 is tightly wrapped about the ferromagnetic sheets 412 to securely engage the inner surfaces of the ferromagnetic sheets 412 with the outer surfaces of the sidewalls 420.

[0029] As best shown in FIG. 6C, the heating coil 414 includes a wire 440 wound about the sidewalls 420 of the housing 410 (and about ferromagnetic sheets 412). The wire 440 may comprise a metallic material having relatively high electrical conductivity, such as copper. The wire 440 may have any suitable cross-sectional area and / or thickness, and may be wound to define any suitable number of turns for the heating coil 414. The heating coil 414 may functions as an inductor or induction coil, and is operatively coupled to a power unit 450, which may be configured to drive the heating coil 414 to a frequency at which the heating coil 414 behaves as a resonant circuit that under excitation produces an alternating current thereby producing an alternating magnetic field at or near the heating coil 414. This field may generate an electromagnetic field (EMF) on the outer surfaces of the sidewalls 420, which may in turncause an alternating current. This current, in conjunction with the resistivity of the housing 410, may yield power dissipation and heat up the outer surfaces of the sidewalls 420. Such heat may be transferred to the contents of the chamber 422, such as the agent and / or the sample. It will be appreciated that such induction heating may be performed using relatively low input power, and / or may achieve homogeneous heating of the contents of the chamber 422 and thereby improve staining and / or lysing performance. In this regard, exciting the circuit at the resonant frequency may deliver maximum power, and exciting the circuit at an increasing frequency may effectively adjust the power delivery. Alternatively, in some cases a resistive heater / resi stance heating coil may be used for the heater coil 414.

[0030] In some cases, a preparation module 400 such as shown in FIGS. 2A-2C may include a temperature sensor such as a thermistor (not shown) which is configured to continuously sense the temperature of the contents of the chamber 422. The temperature sensor may be configured to send feedback signals indicative of the sensed temperatures to a controller (not shown) which may in turn be configured to send control signals to the power unit 450 for selectively driving the heating coil 414. In this manner, the controller may cease heating of the contents of the chamber 422 upon reaching a threshold temperature. In one example, the controller utilizes heating control algorithms and the feedback signals are incorporated into elements of the algorithms or computer-driven instructions provided to the power unit 450 and / or heating coil 414 to optimally regulate temperature. In some embodiments, the controller may be configured to send control signals to a maintenance heater (not shown) for maintaining the contents of the chamber 422 at the threshold temperature.

[0031] In some analyzers which may be assessed using the disclosed technology, there may be a plurality of preparation modules, each utilizing the structure of FIGS. 2A-2C (i.e., a plurality of structural elements 400). In this way, a plurality of samples can be stained, incubated, or otherwise prepared at a similar time. In some cases, each staining module may have its own unique heating element. Similarly, in some case, a preparation module may have a plurality of chambers 422, each capable of receiving a sample, and a common heating structure connected to the entire module (e.g., a single housing 410 with a plurality of chambers 422 anda common heating coil 414 surrounding housing 410). Other configurations for preparing a sample for analysis, such as those described in U.S. patent application 18 / 224,947, filed on July 21, 2023 for Biological Sample Staining Module and Biological Analysis Systems And Methods, the disclosure of which is incorporated by reference in its entirely, may also be used either in addition to, or in combination with, the preparation module of FIGS. 2A-2C. Accordingly, the above discussion of components which may be used for staining and / or lysing a sample should be understood as being illustrative only, and should not be treated as limiting.

[0032] In operation, an analyzer such as an analyzer incorporating a flowcell based imaging system as illustrated in FIG. 1 and the preparation module of FIGS. 2A-2C may analyze images of particles (e.g., cells) in a sample. Control particles may be used to ensure the analyzer is functioning correctly, such that the analyzer is capable of effectively and correctly counting or otherwise determining analyzed particles (e.g., blood cells). Additionally, using the technology disclosed herein, patient samples may also be used to evaluate the effectiveness of an analyzer’s operations, with some characteristics of the analyzer’s performance being assessed using control particles, and other aspects being assessed using patient samples. A method which may be performed to provide this type of dual control sample and patient sample assessment, and thereby avoid or mitigate some of the drawbacks of existing analyzer assessment technology, is discussed below in the context of FIGS. 3 and 4.

[0033] Turning first to FIG. 3, that figures shows a method which may be performed to assess a blood analysis instrument using both a control sample and a blood sample (e.g., a patient sample). As shown in that figure, such a method may include receiving 301 a set of control particle images. This may be done by passing a control sample through an imaging region of a flowcell and taking images of the control particles in that sample with an image capture device (e.g., the high optical resolution imaging device 24 of FIG. 1). This type of imaging may take place during a dedicated control assessment process, such as at the start of the day before the instrument is used on patient samples so that issues may be identified and addressed before patient samples are processed.

[0034] In various cases, the control particles whose images may be received 301 may include biological material (e.g., animal-derived or human-derived blood cells) and may contain a known number of blood components, such as white blood cells, red blood cells, and platelets, similar to those found in patient samples. In some examples, the biological control material requires refrigeration to maintain cell integrity for certain analytical control processes (e.g., control processes that require size such as volumetric information, or morphological related information). In some examples, at least a portion of the biological control material does not require refrigeration because the control procedure relates to a parameter (e g., count) where in-depth data such as size, volume, or other morphological related information is not needed. Other examples may involve stabilization of the biological control such that refrigeration is not required (e.g., the biological controls can be kept at room temperature).

[0035] Stabilization can involve techniques to keep morphological features of control particles / cells intact for an extended period of time. For example, stabilization of control particles at room temperature may be maintained for days, for at least a week or for longer than a week, or for as long as a month, or for a month or longer. Such techniques would utilize one or more stabilization agents during the preparation process to help stabilize the cells. In some examples, stabilization involves utilizing a stabilization agent which comprises nourishing media during the control cell preparation process to help stabilize the cells by expanding the timeframe that degradation of the cells occurs, or other techniques known to one skilled in the art. By example, suitable stabilizing agents can be an aqueous solution including a cell nutrient (e g., lactose or AMP) and contain one or more of the following: a cell nutrient (e.g., lactose, AMP), a fungicide (e.g., methyl paraben, ethyl paraben, propyl paraben), an antimicrobial (e.g., kanamycin sulfate, neomycin sulfate, sodium penicillin, gentamicin sulfate), a surfactant (e.g., Pluronic F60, Pluronic 25R8, Pluronic F127, Kolliphor P188), a plasma protein (e.g., albumin, lipoproteins, globulins, fibrinogens and mixtures thereof), a buffer (e.g., citrate, phosphate), a metal chelating agent (e.g., citrate, EDTA), an agent to maintain tonicity (potassium chloride, sodium phosphate monobasic, and combinations thereof.

[0036] In some examples, stabilization utilizes a fixation process and a fixative agent, wherein cells are exposed to a fixative agent (e.g., an aldehyde, such as glutaraldehyde, formaldehyde, paraformaldehyde, and combinations thereof) which chemically cross-links the cell membrane to stabilize it. Additional information on stabilization and stabilization agents can be found in, for example: US7393688, US4213876, US4299726, US6569682, US5320964 and US4358394, the contents of which are hereby incorporated by reference in their entirety.

[0037] It is also possible that the control particles may comprise synthetic control particles formulated to function with imaging technology (e.g., the system of FIG. 1). One advantage of a synthetic control is an extended shelf life compared to blood-based controls derived from human or animal blood (e.g., if the blood-based controls do not utilize stabilizing processes such as described above), which need extensive quality handling procedures and may have a limited use timeframe before degrading. The synthetic control particles can utilize characteristics suited for imaging in order to provide an image-based representation of the target particle (e.g., various colors, shapes, surface characteristics such as projections, dimples, or combinations thereof, different sizes, surface detection patterns, internal structures, surface functionalization). In some aspects, the synthetic control particles utilize one or more uniquely identifiable parameters which may not directly visually correlate with their target particle (e.g., a synthetic control neutrophil may not actually visually represent a neutrophil), but are sufficiently demarcated from other control particle types such that a control process can properly identify a control particle type, and use this information to ensure that an analyzer is functioning correctly.

[0038] The control particles may also comprise both blood-based control particles composed of biological material (e.g., animal-derived or human-derived blood cells), and synthetic control particles composed of synthetic material (e.g., polymers), or a mixture of biological material and synthetic material. In one example, all the control particles in a control sample may utilize animal blood cells which are altered or engineered to provide imaging characteristics (e.g., size, color, and / or shape) meant to reflect the intended particle they are meant to represent. Such alterations or engineering can include utilizing various processes to shrink, expand, re-shape or otherwise alter a physical / imaging characteristic of the cell to reflect the intended particle they are meant to represent. In one example, all the control particles in a control sample may be synthetic control particles and may utilize synthetic material (e.g., polymers, beads, metals, alloys, hydrogels, combinations thereof) engineered to provide imaging characteristics (e g., size, color, shape and / or surface characteristics) meant to reflect the intended particle they are meant to represent. For instance, the synthetic material can be engineered to have a size similar to that of the represented particle, or be engineered to have an inner colored structure similar to that of a nucleus (e.g., for nucleated controls, such as white blood cell controls and nucleated-red blood cells). In one example, some synthetic control particles such as red blood cell synthetic control particles, provide imaging characteristics to reflect the intended particle they are meant to represent (e.g., sized or shaped similar to a red blood cell). By way of example, some blood cell types (e.g., white blood cells) contain a nucleus as described above and herein, so a synthetic control particle may utilize an internal structure to simulate a nucleus - for instance, a synthetic material can utilize an internal structure that will resemble a nucleus when analyzed by the analyzer.

[0039] Where they are present, the synthetic control particles may comprise any suitable material. Non-limiting materials that may be used to form the synthetic control particles include, but are not limited to, cellulose, silica (silicon dioxide), polymethyl methacrylate) (PMMA) / hydrogel coated materials, melamine (melamine formaldehyde resin), cross-linked agarose, polyvinylacetate (PVA), polystyrene, metals, hydrogels, and combinations thereof. In one aspect, the synthetic control particle may comprise a transparent, or semi-transparent material. In this aspect, detection of the synthetic control particle may be based, in whole or in part, via detection of the light refracting properties of the synthetic control particle. In a further aspect, the synthetic control particle may comprise a color, the synthetic control particle color being used as a detectible label.

[0040] A synthetic control particle may be a synthetic bead, for example, a polystyrene microsphere.The synthetic control particle may be provided in a control particle composition, the control particle composition comprising synthetic beads of various sizes and colors. For example,beads of different colors may be used to represent the particles to be detected in different ways. For example, smaller, red synthetic beads may represent red blood cells, larger red beads may represent eosinophils, smaller, white beads may represent platelets, medium white beads may represent lymph, and larger, blue beads may represent basophils. In this aspect, the size and color of the synthetic bead may be used as characteristics to represent the whole blood cell. In one aspect, WBC imaging may use different types of beads / shapes (e.g., five part diff + nRBC + Retie).

[0041] A synthetic control particle may be a size that is from about 1 pm to about 25 pm in diameter, or from about 3 pm to about 20 pm in diameter, or from about 5 pm to about 15 pm in diameter, or from about 10 pm to about 13 pm in diameter. In certain aspects, the synthetic control sample may comprise one or more synthetic control particles of different sizes. For example, the synthetic control sample may comprise synthetic control particles that have a size that is comparable to that of a white blood cell, in addition to control particles that have a size that is comparable to that of a red blood cell (e.g., smaller than a white blood cell control). Likewise, the synthetic control sample may comprise synthetic control particles of a size that does not correspond in size to any expected blood sample components.

[0042] A synthetic control particle may comprise a surface having a detection pattern thereon. In this aspect, the detection pattern may be detected by the instrument (e.g., by recognizing the detection pattern via imaging) and allow for detection or quantification of the synthetic control particle. In one aspect, the detection pattern may provide surface characteristics that affect light scatter. In this aspect, the light scatter caused by the synthetic control pattern may be detected and may further provide information pertaining the sample being measured.

[0043] In further aspects, a synthetic control particle may comprise a surface functionalization. For example, the synthetic control particle surface may comprise a functionalized particle or DNA molecule. Exemplary groups that may be used to functionalize the synthetic control particle include, but are not limited to, mercapto groups, hydroxyl groups, carboxyl groups, disulfide groups, polyvinylalcohol groups, amine groups (primary and secondary ammonium),maleimido groups, tertiary ammonium groups, quaternary ammonium groups, epoxy groups, carboxylsulfonate groups, and octadecyl (Cl 8) groups.

[0044] The synthetic control samples may be provided in a carrier at a concentration of particles much lower than that of a blood-based control, avoiding the need for further dilution. In one aspect, the synthetic control particles may further be provided in a solution having a concentration such that the control particle composition does not require further dilution for use in the analyzer. For example, for a “red blood cell” bead synthetic control (having a size of about five microns) may be provided at a concentration of 0.2 x 106 / uL, and a “platelet” bead synthetic control (having a size of about 3 microns) may be provided at a concentration of 10 x 103 / UL. HGB surrogates may comprise a red dye or a red-color compound that binds imidazole, e.g, the red color may be used to test absorbance to be similar to RBCs. “White blood cell” synthetic control beads (greater than or equal to about 5 microns) may be provided at a concentration of about 7 x 103 / uL with an appropriate ratio, for example, %NE = 60%, %LY = 30%, %MO = 6%, %EO = 3%, and %BA = 1%. NRBC beads (at a size of less than or equal to 5 micron) for a positive level may be provided at a concentration of about 1 x 103 / uL; “reties” beads (about 5 microns in size) may be provided at a concentration of 3 x 103 / uL. In this aspect, the synthetic control may provide enhanced performance, if the dilution ratio for RBC / PLT bath is altered during control runs. For example, if the current dilution ratio is 1 :250 (blood:diluent), the ideal dilution ratio may be about 1 : 10, or about 1 : 12, or about 1 : 15 (control: diluent) for control runs. This would result in “normal” RBC and PLT recoveries: [RBC] = 4 x 106 / UL and [PLT] = 200 x 103 / uL. Acronyms: WBC: White blood cells; %NE: Percentage of neutrophils; %LY: Percentage of lymphocytes; %MO: Percentage of monocytes; %EO: Percentage of eosinophils; %BA: Percentage of basophils; %IG: Percentage of immature granulocytes; NRBC / W: Nucleated red blood cells per 100 white blood cells; RETC: Reticulocyte count; RBC: Red blood cells; MCV: Mean corpuscular volume; PLT: Platelets; MPV: Mean platelet volume; HGB: Hemoglobin.

[0045] In some aspects, the control particles may be included in a single control sample (e.g., a single tube containing each of the types of control particles), such that the control sample contains aplurality of types of control particles. In other aspects, the control particles may be provided as at least two control samples, such as a blood-based control sample and a synthetic control sample as described herein, or control particles may be provided as separate samples based on their cell type (e.g., a first sample for control red blood cells, and a second sample for control white blood cells). The control particles may be provided in a carrier fluid, for example, a carrier fluid that is isotonic to and / or having the same osmolarity, and / or the same pH as that of the biological sample. For example, a control particle may be provided in a carrier fluid that is isotonic to that of the blood sample.

[0046] After the images of the control particles have been received 301, they can be used to assess 302 a first performance characteristic of the blood analysis instrument. This performance characteristic may be quantitative features, such as a counting accuracy. For example, to establish a control particle cell count, a cell may be compared with a threshold based on known characteristics of the control. To illustrate, in certain aspects, a measurement of a quantitative feature may employ a synthetic control to evaluate performance characteristics such as the ability to determine complete blood count (CBC) parameters (from an impedance based analytical portion of a blood analysis machine): Red blood cell count (RBC), mean corpuscular volume (MCV), Platelet count (PLT), mean platelet volume (MPV), hemoglobin (HGB) ; and white blood cell related parameters from an imaging based analytical portion of a blood analysis machine: White blood cell count (WBC), Neutrophil percentage (%NE), lymphocyte percentage (%LY), monocyte percentage (%MO), eosinophil percentage (%EO), basophil percentage (%BA), immature granulocyte percentage (%IG), Nucleated red blood cells per 100 white blood cells (NRBC / W), and reticulocyte count (RETC). The quantitative features may also be measurements which are demarcated in terms of multiple levels, for example, the concentration of synthetic control particles may be provided at a “low”, “normal”, or “high” concentration, to represent concentrations that may be found in a blood sample. In one example, images for a control particle are used in an imaging analyzer, but where an analyzer comprises multiple submodules (e.g., an imaging module and an impedance module), the control particle analysis can be configured to each such that the control particles are imagedfor the imaging module and a similar or different control particle can be analyzed (e.g., via impedance analysis instead of imaging) in the non-imaging (e.g., impedance) module. Additional information on use of multiple channels or multiple portions (e.g., imaging and impedance together) is disclosed in PCT publication No. WO / 2024 / 123780 entitled “Hematology Flow System” and filed December 5, 2023, which is hereby incorporated by reference in its entirety.

[0047] In addition to receiving 301 and assessing 302 a first performance characteristic using images of control particles, the method of FIG. 3 also includes receiving 303 images of stained blood particles of a blood sample (e.g., a patient sample). This may be done, for example, using a portion of a patient sample during a dedicated assessment run before any tests are performed. However, the images may also (or alternatively) be received 303 during normal operation of the blood analysis instrument (e.g., in performing tests specified in orders for patient samples). In cases where images are received 303 during normal operation of the blood analysis instrument, each time a sample is processed, images for that sample may be received, though it is also possible that images may only be received 303 for the purpose of assessing the instrument for a subset of samples (e.g., every fifth sample), even though the images may be captured for all samples in the course of performing ordered tests.

[0048] After the images of the stained blood cells had been received 303, those images may be used to assess 304 a second performance characteristic of the blood analysis instrument. This second performance characteristic may be, for example, a qualitative characteristic such as effectiveness of the instrument’s lysing and / or staining functionality, and it may be assessed using an artificial intelligence model (e.g., a model comprising one or more convolution layers connecting to a dense network with one or more output nodes corresponding to potential assessment results, such as pass / fail, levels on a 1-5 scale, or a number between 0 and 1) trained to take images of stained blood particles as input and provide the assessment result as an output. Other approaches to assessment are also possible. For example, in some cases the received 303 images may be analyzed to generate masks for isolating cells or various portions of the cells (e.g., the nucleus) so that characteristics of the cells and / or portions of the cells could be usedfor assigning the imaged cells to clusters corresponding to different populations (e.g., a neutrophil cluster, a basophil cluster, etc.). The actual characteristics of these clusters could then be compared with the characteristics the clusters would be expected to have under ideal instrument operation to assess 304 the second characteristic. For instance, if the clusters had lower blueness values than expected, then this could be treated as indicating that the instrument’s staining module was not operating as expected. Other approaches to image analysis based assessment of an instrument’s performance characteristics, such as described in international patent application PCT / US23 / 85714 for “Population Based Cell Classification,” the disclosure of which is hereby incorporated by reference in its entirety, are also possible and may be used in some implementations. Accordingly, the above descriptions of how an assessment 304 of a second performance characteristic may be performed should be understood as illustrative only, and should not be treated as limiting.

[0049] Turning next to FIG. 4, that figure illustrates a process which may be performed to account for the assessment 304 of the second performance characteristic after it had been made. As shown in that figure, if the assessment indicated that the second performance characteristic was acceptable (e.g., if the assessment provided a pass on a pass / fail evaluation, or if a numeric evaluation from the assessment was above an acceptability threshold), the blood sample may be used to determine 401 a blood cell analysis result. For example, if the blood sample was a patient sample, then the test(s) set forth in an associated order (e.g., a complete blood count) could be performed on the sample and the results of those tests would be the blood cell analysis result. Alternatively, if the assessment indicated that the second performance characteristic was not acceptable, then the sample may be re-prepared (e.g., another aliquot could be taken of the sample and that aliquot could be stained and lysed in preparation for imaging) for further imaging. This re-preparation may be done simply by performing the preparation in the same manner as when preparing to capture the images which had previously been received 303. However, it is possible that one or more parameters for the preparation could be adjusted to account for the assessment 304. For example, in a case where the assessment indicated insufficient staining, an increased stain amount, or an increased incubation time could be usedwhen re-preparing 402 the sample. It is also possible that other types of steps may be taken to account for an assessment 304 based on images of stained blood particles. For instance, in some cases (e.g., if there was not sufficient volume to re-prepare and re-image a sample), a blood cell analysis result may be determined 401 based on the originally received 303 images, but that result may be flagged to indicate that further review may be necessary. Other types of responses are also possible, and could be implemented without undue experimentation based on this disclosure. Accordingly, the exemplary responses described above in the context of FIG. 4 should be understood as being illustrative only, and should not be treated as limiting.

[0050] While FIGS. 3 and 4 and the associated discussion indicated how control samples and blood samples may be combined to evaluate performance characteristics of an analyzer, it should be understood that, in some cases, additional evaluation steps may also be performed. For example, in some cases calibration particles may be used to calibrate the analyzer to ensure it is outputting correct information (e.g., by setting a particular parameter, such as output of numeric channels or electronics configurations). In this way a calibration particle can be used to adjust a parameter of the analyzer to ensure it is correctly analyzing presented specimens, while control particles and particles from patient samples may be used to ensure the analyzer is working correctly. By way of the example, if a control sample is run and the analyzer does not read the control particles correctly (e.g., does not read a correct number or range), the data is flagged and an operator can input a calibration particle to adjust the functioning of the machine, then reintroduce a control particle sample to see if the analyzer is now reading the particles correctly. Accordingly, the above discussion of using images of control particles and stained blood particles to assess various performance characteristics of an analyzer should be treated as illustrative only, and not limiting.

[0051] Other approaches to assessing an analyzer’s functionality are also possible. For example, as shown in FIG. 5, in some cases assessing 304 the staining function of a biological analyzer may include determining a plurality of absorbance values by, for each of the stained particle images, calculating 501 an incident light intensity based on light intensities of a plurality of non-particle pixels to remove luminance non-uniformity across images, and then using thatincident light intensity to determine a set of absorbance values for a cell depicted in that image. For example, in the case of a patch image depicting an individual cell, calculating 501 the incident light intensity for that image may be done by calculating the mean or median intensities of pixels on the border of the image. This incident light intensity can then be used to determine the absorbance of each pixel in the image using a calculation such as equation 1, below.Ap= logEquation 1In that equation Apis the absorbance at pixel p, I0pis the intensity of incident light at pixel p (i.e., the value calculated 501 in the preceding step), and Ipis the value of transmitted light at pixel p.

[0052] With the absorbance values determined 502, those values may be used to assess 503 the staining functionality of the analyzer. As shown in FIG. 6, this may be done by generating 601 one or more absorbance statistics, and evaluating 602 the staining function of the analyzer using those statistics (e.g., comparing one of the statistics with a threshold which had been previously established as distinguishing analyzers whose staining function was working properly from analyzers which were under-staining samples). Examples of statistics which may be generated 601 for this purpose include those described below in table 1.Table 1

[0053] Other statistics which may be generated 601 in some cases could include color channel specific statistics. For example, in some cases statistics such as ABceii red (representing the total absorbance of a cell in the red color channel) and / or ABN2nucieus_green (representing the area normalized value of the total absorbance in the green color channel of pixels depicting the cell nucleus) may be calculated. It is also possible that, in addition to cell level statistics such as those from table 1, there may also be sample level statistics generated 601 in some cases. For instance, in some cases, the total, mean, median, 90thpercentile, standard deviation, variance or other population statistics may be calculated for one or more of the cell level statistics (e.g.,a MeanABcvtopiasm statistic may be calculated, representing the average value of total absorbance of cytoplasm pixels across the cell population). Such population statistics may be for an entire cell population (e.g., all white blood cells), or may be for specific subpopulations of cells (e.g., there may be population statistics calculated specifically for white blood cell subtypes such as lymphocytes, monocytes, neutrophils, eosinophils and basophils).

[0054] Other types of statistics may also be generated in some cases. To illustrate, consider FIG. 7, which illustrates steps which may be performed to determine if a particular imaged cell was under-stained - information which could be used to generate 601 the population statistic of a percentage of cells which are under-stained. In the method of FIG. 7, initially an absorbance value for a cell’s nucleus would be determined 701 (e g., a value for ABnucieus which may be normalized or limited to a particular color channel would be determined). This value could then be compared 702 to a threshold to determine if the cell was or was not under-stained. When performing a method such as shown in FIG. 7, a variety of approaches may be used to determine the threshold which would be compared 702 with the absorbance for the cell’s nucleus. For example, in some cases, a predetermined threshold (e.g., a threshold of 0.94 for ABN2nucieus red) defined using observed values for well-stained versus under-stained cells may be used. Alternatively, in some cases a threshold may be defined based on a distribution of absorbance values for a sample. For instance, in a case where values of ABnucieus red are distributed as shown in FIG. 8, it can be seen that there is a horizontal gap along Y = 0.8. in such a case, that horizontal gap (i.e., a value of 0.8 for ABnucieus red) can be used as a threshold to separate under-stained from well-stained cells in the sample under analysis.

[0055] It should be understood that, while statistics such as the cell and population level statistics described above may be used to assess an analyzer’s staining function, such statistics may also be used for other purposes. An example of how statistics such as described may be used for purposes in addition to assessing an analyzer’s staining functionality is shown in FIG. 9. In that figure, in addition to determining 501 absorbance values, there would be a calculation 901 of the amount of stain available per cell (SAPC) (e.g., by dividing the volume of stain used in the staining process, by the number of cells in the sample). The ratio of this value to variousabsorbance statistics can then be used as a measure for how hard or easy it is to stain imaged cells, which, in turn, may be used to analyze 902 the sample, such as by classifying cells and / or diagnosing various disease states which may impact staining.

[0056] Another example of a potential use of absorbance statistics is shown in FIG. 10, which depicts a method which may be used to determine optimized channels for subsequent staining assessment (or disease diagnosis, or cell identification, or other application of staining information). As shown in FIG. 10, optimized channel optimization may begin with obtaining images of well-stained and under-stained cells 1001 1002. This may be done, for example, by using a human reviewer to examiner and classify images of cells as well-stained or understained, intentionally under-staining cells through reducing the amount of reagent used in the staining process, etc. Once the image have been obtained 1001 1002, they may be used to, for each absorbance statistic generated by the analyzer (e.g., ABNlceii, ABN 1 cytoplasm, etc.) calculate 1003 1004 two sets of values, a first set of values using the well-stained images and a second set of values using the under-stained images. These sets of values may each include one value for each channel of the pixels in the relevant images. For example, in an analyzer which captures images having values in red, green and blue color channels (e.g., RGB images) and which would calculate the cell level statistic ABNlnucleus, the first set of values may be ABN 1 nucleus red, ABN 1 nucleus green, and ABNlnucleus blue calculated based on the well-stained images, while the second set of values may be ABN1 nucleus red, ABN1 nucleus green, and ABNlnucleus blue calculated based on the under-stained images. Those values may then be used to determine 1005 the coefficients which would maximize the difference between the well- stained and under-stained images for the relevant statistics in a weighted average of the channel values, which coefficients could potentially be combined across image sets (e.g., by averaging) to determine 1005 a set of optimized coefficients.

[0057] Subsequently, once the optimized coefficients had been determined 1005, they could be used to calculate absorbance statistics that may be better able to evaluate and / or apply an analyzer’s staining function. For instance, there may be a statistic such as ABN 1 nucleus optimized which could be Calculated as ABN 1 nucleus red * &1 optimized + ABNlnucleus green * ^ optimized + ABNlnucleus blue *as optimized and which may be able to make finer grained distinctions than either the ABNlnudeus statistic or any of the channel specific versions of that statistic.

[0058] As a further illustration of potential implementations and applications of the disclosed technology, the following examples are provided of non-exhaustive ways in which the teachings herein may be combined or applied. It should be understood that the following examples are not intended to restrict the coverage of any claims that may be presented at any time in this application or in subsequent filings of this application. No disclaimer is intended. The following examples are being provided for nothing more than merely illustrative purposes. It is contemplated that the various teachings herein may be arranged and applied in numerous other ways. It is also contemplated that some variations may omit certain features referred to in the below examples. Therefore, none of the aspects or features referred to below should be deemed critical unless otherwise explicitly indicated as such at a later date by the inventors or by a successor in interest to the inventors. If any claims are presented in this application or in subsequent filings related to this application that include additional features beyond those referred to below, those additional features shall not be presumed to have been added for any reason relating to patentability.

[0059] Example 1

[0060] A method of assessing a blood analysis instrument, comprising: receiving images of control particles of a control sample; assessing a first performance characteristic of the blood analysis instrument based on the images of control particles; receiving images of stained blood particles of a blood sample; and assessing a second performance characteristic of the blood analysis instrument based on the images of stained blood particles.

[0061] Example 2

[0062] The method of example 1, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a staining performance.

[0063] Example 3

[0064] The method of any of examples 1-2, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a lysing performance.

[0065] Example 4

[0066] The method of any of examples 1-3, wherein assessing the first performance characteristic comprises establishing a control particle cell count and comparing the control particle cell count with a threshold.

[0067] Example 5

[0068] The method of any of examples 1-4, wherein assessing the second performance characteristic comprises determining a staining performance from analyzing the images of stained blood particles.

[0069] Example 6

[0070] The method of example 5, wherein determining the staining performance comprises: determining a plurality of absorbance values by, for each of the images of stained blood particles from the stained sample: calculating an inciden t light intensity value for that image based on light-intensities of a plurality of non-particle pixels; and determining a set of absorbance values for a cell depicted in that image using the incident light intensity value for that image; and determining the staining performance using the plurality of absorbance values.

[0071] Example 7

[0072] The method of any of examples 6, wherein determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; determining an individual cellthreshold based on a distribution of absorbance values in the images of stained blood particles; and generating an under-stained percentage based on comparing absorbance values for the images of stained blood particles with the individual cell threshold.

[0073] Example 8

[0074] The method of any of examples 5-7, wherein: each of the images of stained blood particles from the blood sample comprises values in each of a plurality of channels; and determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; and for each of the one or more absorbance statistics, generating that absorbance statistic comprises calculating an optimized sum, wherein the optimized sum is a sum of, for each of the plurality of channels, a value for that absorbance statistic in that channel, multiplied by a corresponding coefficient from a plurality of optimized coefficients.

[0075] Example 9

[0076] The method of any of example 8, wherein: the blood sample is a patient sample; and the method comprises determining a blood cell analysis result for the patient sample based on the images of stained blood particles.

[0077] Example 10

[0078] The method of example 9, wherein the method comprises: calculating an amount of stain available in the blood sample; and using ratios of absorbance values for stained blood particles to stain available per particle in the blood sample to analyze the sample by: classifying one or more cells in the blood sample; and / or identifying a disease in the blood sample.

[0079] Example 11

[0080] The method of any of examples 1-10, wherein receiving images of control particles of the control sample comprises passing the control sample through an imaging region of a flowcell and taking images of the control particles with an image capture device, and the receiving images of stained blood particles of the blood sample comprises passing the blood sample through an imaging region of the flowcell and taking images of the stained blood particles with the image capture device.

[0081] Example 12

[0082] The method of any of examples 1-11, wherein the control particles of the control sample are passed through a flowcell.

[0083] Example 13

[0084] The method of any of examples 1-12, wherein the stained blood particles of the blood sample are passed through a flowcell.

[0085] Example 14

[0086] A system comprising one or more processors; and a non-transitory computer readable medium having stored thereon instructions for performing the method of any of examples 1-13 when executed using the one or more processors.

[0087] Example 15

[0088] A non-transitory computer readable medium having stored thereon instructions for performing the method of any of examples 1-13.

[0089] Example 16

[0090] A system for assessing a blood analysis instrument, the system comprising a processor programmed with instructions stored on a non-transitory computer readable medium toperform a set of acts comprising: receiving images of control particles of a control sample; assessing a first performance characteristic of the blood analysis instrument based on the images of control particles; receiving images of stained blood particles of a blood sample; and assessing a second performance characteristic of the blood analysis instrument based on the images of stained blood particles.

[0091] Example 17

[0092] The system of example 16, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a staining performance.

[0093] Example 18

[0094] The system of any of examples 16-17, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a lysing performance.

[0095] Example 19

[0096] The system of any of examples 16-18, wherein assessing the first performance characteristic comprises establishing a control particle cell count and comparing the control particle cell count with a threshold.

[0097] Example 20

[0098] The system of any of examples 16-19, wherein assessing the second performance characteristic comprises determining a staining performance from analyzing the images of stained blood particles.

[0099] Example 21

[0100] The system of example 20, wherein determining the staining performance comprises: determining a plurality of absorbance values by, for each of the images of stained blood particles from the stained sample: calculating an incident light intensity value for that image based on light-intensities of a plurality of non-particle pixels; and determining a set of absorbance values for a cell depicted in that image using the incident light intensity value for that image; and determining the staining performance using the plurality of absorbance values.

[0101] Example 22

[0102] The system of any of examples 20-21, wherein determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; determining an individual cell threshold based on a distribution of absorbance values in the images of stained blood particles; and generating an under-stained percentage based on comparing absorbance values for the images of stained blood particles with the individual cell threshold.

[0103] Example 23

[0104] The system of any of examples 20-22, wherein: each of the images of stained blood particles from the blood sample comprises values in each of a plurality of channels; and determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; and for each of the one or more absorbance statistics, generating that absorbance statistic comprises calculating an optimized sum, wherein the optimized sum is a sum of, for each of the plurality of channels, a value for that absorbance statistic in that channel, multiplied by a corresponding coefficient from a plurality of optimized coefficients.

[0105] Example 24

[0106] The system of any of examples 20-23, wherein the processor is programmed to determine a blood cell analysis result for the blood sample from analyzing the images of the stained blood particles.

[0107] Example 25

[0108] The system of example 24, wherein the set of acts comprises: calculating an amount of stain available in the blood sample; and using ratios of absorbance values for stained blood particles to stain available per particle in the blood sample to analyze the sample by: classifying one or more cells in the blood sample; and / or identifying a disease in the blood sample.

[0109] Example 26

[0110] The system of any of examples 16-25, wherein: the system comprises the blood analysis instrument; and the processor is programmed to control the blood analysis instrument to: pass the control sample through an imaging region of a flowcell and take images of the control particles with an image capture device; and pass the blood sample through the imaging region of the flowcell and take images of the stained blood particles with the image capture device.

[0111] Example 27

[0112] The system of any of examples 16-26, wherein the processor is programmed to control the blood analysis instrument to pass the control particles of the control sample through a flowcell.

[0113] Example 28

[0114] The system of any of examples 16-27, wherein the processor is programmed to control the blood analysis instrument to pass the stained blood particles of the blood sample through a flowcell.

[0115] Example 29

[0116] A method comprising performing the set of acts the instructions on the non-transitory computer readable medium of the system of any of examples 16-28 are to perform when executed.

[0117] Example 30

[0118] A non-transitory computer readable medium having stored thereon instructions for performing the set of acts the instructions stored on the non-transitory computer readable medium of the system of any of examples 16-28 are to perform when executed.

[0119] Example 31

[0120] An analyzer comprising: a flowcell; a camera; one or more processors; a non-transitory computer readable medium having stored thereon instructions for performing a set of acts comprising: receiving images of control particles of a control sample; assessing a first performance characteristic of the blood analysis instrument based on the images of control particles; receiving images of stained blood particles of a blood sample; and assessing a second performance characteristic of the blood analysis instrument based on the images of stained blood particles.

[0121] Example 32

[0122] The analyzer of example 31, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a staining performance.

[0123] Example 33

[0124] The analyzer of any of examples 31-32, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a lysing performance.

[0125] Example 34

[0126] The analyzer of any of examples 31-33, wherein assessing the first performance characteristic comprises establishing a control particle cell count and comparing the control particle cell count with a threshold.

[0127] Example 35

[0128] The analyzer of any of examples 31-34, wherein assessing the second performance characteristic comprises determining a staining performance from analyzing the images of stained blood particles.

[0129] Example 36

[0130] The analyzer of example 35, wherein determining the staining performance comprises: determining a plurality of absorbance values by, for each of the images of stained blood articles from the stained sample: calculating an incident light intensity value for that image based on light-intensities of a plurality of non-particle pixels; and determining a set of absorbance values for a cell depicted in that image using the incident light intensity value for that image; and determining the staining performance using the plurality of absorbance values.

[0131] Example 37

[0132] The analyzer of any of examples 35-36, wherein determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; determining an individual cell threshold based on a distribution of absorbance values in the images of stained blood particles; and generating an under-stained percentage based on comparing absorbance values for the images of stained blood particles with the individual cell threshold.

[0133] Example 38

[0134] The analyzer of any of examples 35-37, wherein: each of the images of stained blood particles from the blood sample comprises values in each of a plurality of channels; and determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; and for each of the one or more absorbance statistics, generating that absorbance statistic comprises calculating an optimized sum, wherein the optimized sum is a sum of, for each of the plurality of channels, a value for that absorbance statistic in that channel, multiplied by a corresponding coefficient from a plurality of optimized coefficients.

[0135] Example 39

[0136] The analyzer of any of examples 35-38, wherein the processor is programmed to determine a blood cell analysis result for the blood sample from analyzing the images of the stained blood particles.

[0137] Example 40

[0138] The analyzer of example 39, wherein the set of acts comprises: calculating an amount of stain available in the blood sample; and using ratios of absorbance values for stained blood particles to stain available per particle in the blood sample to analyze the sample by: classifying one or more cells in the blood sample; and / or identifying a disease in the blood sample.

[0139] Example 41

[0140] The analyzer of any of examples 31-40, wherein receiving images of control particles of the control sample comprises passing the control sample through an imaging region of the flowcell and taking images of the control particles with the camera, and the receiving images of stained blood particles of the blood sample comprises passing the blood sample through the imaging region of the flowcell and taking images of the stained blood particles with the camera.

[0141] Example 42

[0142] The analyzer of any of examples 31-41, wherein the set of acts comprises passing the control sample through the flowcell.[00143J Example 43

[0144] The analyzer of any of examples 31-42, wherein the set of acts comprises passing the stained blood particles of the blood sample through a flowcell.

[0145] Example 44

[0146] A method comprising performing the set of acts the instructions on the non-transitory computer readable medium of the analyzer of any of examples 31-43 are to perform when executed.

[0147] Example 45

[0148] A non-transitory computer readable medium having stored thereon instructions for performing the set of acts the instructions stored on the non-transitory computer readable medium of the analyzer of any of examples 31-43 are to perform when executed.

[0149] All patents, patent publications, patent applications, journal articles, books, technical references, and the like discussed in the instant disclosure are incorporated herein by reference in their entirety for all purposes.

[0150] Different arrangements of the components depicted in the drawings or described above, as well as components and steps not shown or described are possible. Similarly, some features and sub-combinations are useful and may be employed without reference to other features and sub-combinations. Embodiments of the invention have been described for illustrative and not restrictive purposes, and alternative embodiments will become apparent to readers of this patent. In certain cases, method steps or operations may be performed or executed in differing order, or operations may be added, deleted or modified. It can be appreciated that, in certain aspects of the invention, a single component may be replaced by multiple components, andmultiple components may be replaced by a single component, to provide an element or structure or to perform a given function or functions. Except where such substitution would not be operative to practice certain embodiments of the invention, such substitution is considered within the scope of the invention. Accordingly, the claims should not be treated as limited to the examples, drawings, embodiments and illustrations provided above, but instead should be understood as having the scope provided when their terms are given their broadest reasonable interpretation as provided by a general -purpose dictionary, except that when a term or phrase is indicated as having a particular meaning under the heading Explicit Definitions, it should be understood as having that meaning when used in the claims.

[0151] Explicit Definitions

[0152] It should be understood that, in the above examples and the claims, a statement that something is “based on” something else should be understood to mean that it is determined at least in part by the thing that it is indicated as being based on. To indicate that something must be completely determined based on something else, it is described as being “based EXCLUSIVELY on” whatever it must be completely determined by.

[0153] It should be understood that, in the above examples and claims, the term “set” should be understood as one or more things which are grouped together.

Claims

CLAIMSWhat is claimed is:

1. A method of assessing a blood analysis instrument, comprising: receiving images of control particles of a control sample; assessing a first performance characteristic of the blood analysis instrument based on the images of control particles; receiving images of stained blood particles of a blood sample; and assessing a second performance characteristic of the blood analysis instrument based on the images of stained blood particles.

2. The method of claim 1, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a staining performance.

3. The method of any of claims 1-2, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a lysing performance.

4. The method of any of claims 1-3, wherein assessing the first performance characteristic comprises establishing a control particle cell count and comparing the control particle cell count with a threshold.

5. The method of any of claims 1-4, wherein assessing the second performance characteristic comprises determining a staining performance from analyzing the images of stained blood particles.

6. The method of claim 5, wherein determining the staining performance comprises: determining a plurality of absorbance values by, for each of the images of stained bloodparticles from the stained sample: calculating an incident light intensity value for that image based on light-intensities of a plurality of non-particle pixels; and determining a set of absorbance values for a cell depicted in that image using the incident light intensity value for that image; and determining the staining performance using the plurality of absorbance values.

7. The method of any of claims 6, wherein determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; determining an individual cell threshold based on a distribution of absorbance values in the images of stained blood particles; and generating an under-stained percentage based on comparing absorbance values for the images of stained blood particles with the individual cell threshold.

8. The method of any of claims 5-7, wherein: each of the images of stained blood particles from the blood sample comprises values in each of a plurality of channels; and determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; and for each of the one or more absorbance statistics, generating that absorbance statistic comprises calculating an optimized sum, wherein the optimized sum is a sum of, for each of the plurality of channels, a value for that absorbance statistic in that channel, multiplied by a corresponding coefficient from a plurality of optimized coefficients.

9. The method of any of claims 8, wherein: the blood sample is a patient sample; andthe method comprises determining a blood cell analysis result for the patient sample based on the images of stained blood particles.

10. The method of claim 9, wherein the method comprises: calculating an amount of stain available in the blood sample; and using ratios of absorbance values for stained blood particles to stain available per particle in the blood sample to analyze the sample by: classifying one or more cells in the blood sample; and / or identifying a disease in the blood sample.

11. The method of any of claims 1-10, wherein receiving images of control particles of the control sample comprises passing the control sample through an imaging region of a flowcell and taking images of the control particles with an image capture device, and the receiving images of stained blood particles of the blood sample comprises passing the blood sample through an imaging region of the flowcell and taking images of the stained blood particles with the image capture device.

12. The method of any of claims 1-11, wherein the control particles of the control sample are passed through a flowcell.

13. The method of any of claims 1-12, wherein the stained blood particles of the blood sample are passed through a flowcell.

14. A system comprising: one or more processors; and a non-transitory computer readable medium having stored thereon instructions for performing the method of any of claims 1-13 when executed using the one or more processors.

15. A non-transitory computer readable medium having stored thereon instructions for performing the method of any of claim 1-13.

16. A system for assessing a blood analysis instrument, the system comprising a processor programmed with instructions stored on a non-transitory computer readable medium to perform a set of acts comprising: receiving images of control particles of a control sample; assessing a first performance characteristic of the blood analysis instrument based on the images of control particles; receiving images of stained blood particles of a blood sample; and assessing a second performance characteristic of the blood analysis instrument based on the images of stained blood particles.

17. The system of claim 16, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a staining performance.

18. The system of any of claims 16-17, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a lysing performance.

19. The system of any of claims 16-18, wherein assessing the first performance characteristic comprises establishing a control particle cell count and comparing the control particle cell count with a threshold.

20. The system of any of claims 16-19, wherein assessing the second performance characteristic comprises determining a staining performance from analyzing the images of stained blood particles.21 . The system of claim 20, wherein determining the staining performance comprises: determining a plurality of absorbance values by, for each of the images of stained blood particles from the stained sample: calculating an incident light intensity value for that image based on light-intensities of a plurality of non-particle pixels; and determining a set of absorbance values for a cell depicted in that image using the incident light intensity value for that image; and determining the staining performance using the plurality of absorbance values.

22. The system of any of claims 20-21, wherein determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; determining an individual cell threshold based on a distribution of absorbance values in the images of stained blood particles; and generating an under-stained percentage based on comparing absorbance values for the images of stained blood particles with the individual cell threshold.

23. The system of any of claims 20-22, wherein: each of the images of stained blood particles from the blood sample comprises values in each of a plurality of channels; and determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; and for each of the one or more absorbance statistics, generating that absorbance statistic comprises calculating an optimized sum, wherein the optimized sum is a sum of, for each of the plurality of channels, a value for that absorbance statistic in that channel, multiplied by a corresponding coefficient from a plurality of optimized coefficients.

24. The system of any of claims 20-23, wherein the processor is programmed to determine a blood cell analysis result for the blood sample from analyzing the images of the stained blood particles.

25. The system of claim 24, wherein the set of acts comprises: calculating an amount of stain available in the blood sample; and using ratios of absorbance values for stained blood particles to stain available per particle in the blood sample to analyze the sample by: classifying one or more cells in the blood sample; and / or identifying a disease in the blood sample.

26. The system of any of claims 16-25, wherein: the system comprises the blood analysis instrument; and the processor is programmed to control the blood analysis instrument to: pass the control sample through an imaging region of a flowcell and take images of the control particles with an image capture device; and pass the blood sample through the imaging region of the flowcell and take images of the stained blood particles with the image capture device.

27. The system of any of claims 16-26, wherein the processor is programmed to control the blood analysis instrument to pass the control particles of the control sample through a flowcell.

28. The system of any of claims 16-27, wherein the processor is programmed to control the blood analysis instrument to pass the stained blood particles of the blood sample through a flowcell.

29. A method comprising performing the set of acts the instructions on the non-transitory computer readable medium of the system of any of claims 16-28 are to perform when executed.

30. A non-transitory computer readable medium having stored thereon instructions for performing the set of acts the instructions stored on the non-transitory computer readable medium of the system of any of claims 16-28 are to perform when executed.

31. An analyzer comprising: a flowcell; a camera; one or more processors; a non-transitory computer readable medium having stored thereon instructions for performing a set of acts comprising: receiving images of control particles of a control sample; assessing a first performance characteristic of the blood analysis instrument based on the images of control particles; receiving images of stained blood particles of a blood sample; and assessing a second performance characteristic of the blood analysis instrument based on the images of stained blood particles.

32. The analyzer of claim 31, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a staining performance.

33. The analyzer of any of claims 31-32, wherein assessing the first performance characteristic comprises a counting accuracy and assessing the second performance characteristic comprises a lysing performance.

34. The analyzer of any of claims 31-33, wherein assessing the first performance characteristic comprises establishing a control particle cell count and comparing the control particle cell count with a threshold.

35. The analyzer of any of claims 31-34, wherein assessing the second performance characteristic comprises determining a staining performance from analyzing the images of stained blood particles.

36. The analyzer of claim 35, wherein determining the staining performance comprises: determining a plurality of absorbance values by, for each of the images of stained blood particles from the stained sample: calculating an incident light intensity value for that image based on light-intensities of a plurality of non-particle pixels; and determining a set of absorbance values for a cell depicted in that image using the incident light intensity value for that image; and determining the staining performance using the plurality of absorbance values.

37. The analyzer of any of claims 35-36, wherein determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one or more absorbance values for the images of stained blood particles; determining an individual cell threshold based on a distribution of absorbance values in the images of stained blood particles; and generating an under-stained percentage based on comparing absorbance values for the images of stained blood particles with the individual cell threshold.

38. The analyzer of any of claims 35-37, wherein: each of the images of stained blood particles from the blood sample comprises values in each of a plurality of channels; and determining the staining performance comprises: generating one or more absorbance statistics for the blood sample based on one ormore absorbance values for the images of stained blood particles; and for each of the one or more absorbance statistics, generating that absorbance statistic comprises calculating an optimized sum, wherein the optimized sum is a sum of, for each of the plurality of channels, a value for that absorbance statistic in that channel, multiplied by a corresponding coefficient from a plurality of optimized coefficients.

39. The analyzer of any of claims 35-38, wherein the processor is programmed to determine a blood cell analysis result for the blood sample from analyzing the images of the stained blood particles.

40. The analyzer of claim 39, wherein the set of acts comprises: calculating an amount of stain available in the blood sample; and using ratios of absorbance values for stained blood particles to stain available per particle in the blood sample to analyze the sample by: classifying one or more cells in the blood sample; and / or identifying a disease in the blood sample.

41. The analyzer of any of claims 31-40, wherein receiving images of control particles of the control sample comprises passing the control sample through an imaging region of the flowcell and taking images of the control particles with the camera, and the receiving images of stained blood particles of the blood sample comprises passing the blood sample through the imaging region of the flowcell and taking images of the stained blood particles with the camera.

42. The analyzer of any of claims 31-41, wherein the set of acts comprises passing the control sample through the flowcell.

43. The analyzer of any of claims 31-42, wherein the set of acts comprises passing the stained blood particles of the blood sample through a flowcell.

44. A method comprising performing the set of acts the instructions on the non-transitory computer readable medium of the analyzer of any of claims 31-43 are to perform when executed.

45. A non-transitory computer readable medium having stored thereon instructions for performing the set of acts the instructions stored on the non-transitory computer readable medium of the analyzer of any of claims 31-43 are to perform when executed.

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