Customized sample handling in biological analysis

Abstract

The processing of a sample may be dynamically adjusted. This may be done via a method which comprises obtaining a first set of measurements from a first portion of a patient sample. Such a method may also include deriving blood cell data for at least one population of cells in the patient sample based on the first set of measurements. Additionally, such a method may include determining one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample. Using those one or more sample optimization parameters determined for the patient sample, the method may then include processing a second portion of the patient sample. In such a case, processing the second portion of the patient sample may comprise obtaining a second set of measurements from the second portion of the patient sample. In such a method, at least one of obtaining the first set of measurements and obtaining the second set of measurements comprises imaging.

Description

[0001] CUSTOMIZED SAMPLE HANDLING IN BIOLOGICAL ANALYSIS

[0002] PRIORITY

[0003] This claims the benefit of U.S. provisional patent application 63 / 615,854, entitled “Customized Sample Preparation,” filed December 29, 2023, the disclosure of which is hereby incorporated by reference in its entirety.

[0004] BACKGROUND

[0005] Blood cell analysis is one of the most commonly performed medical tests for providing an overview of a patient's health status. A sample (e.g., a blood sample) can be drawn from a patient's body and stored in a test tube containing an anticoagulant to prevent clotting. The sample can then be processed to determine information such as the number or percent of cells of various different categories that it includes. This information, in turn, can be applied in treatment and / or diagnosis of a patient.

[0006] While traditional cell processing technology can be effective, it does have drawbacks. Lor example, preparing a sample for analysis can involve the addition of lysing and / or staining agents, but the amount of such agents for the particles of interest to be fully stained or for particles which are not of interest to be fully lysed may depend on information about the composition of the sample which may not be known until after the sample has been analyzed. Similarly, the composition of the sample may impact the measurement of the sample. Lor example, the time necessary to collect measurements, the way a sample is presented for measurement (e.g., thickness of a sample stream in an imaging system), etc. In some cases, this can result in issues such as overuse of reagents, unnecessarily lengthy measurement periods, or failure to capture sufficient data about a sample. Accordingly, there is a need for improvements in cell analysis techniques allowing for samples to be handled in a manner which is customized for their particular requirements. SUMMARY

[0007] Described herein are devices, systems and methods which can be used for sample handling and / or optimization, such as through customization of the sample’s preparation and / or analysis.

[0008] An illustrative implementation of the technology described herein relates to a method for dynamic sample optimization. Such a method may comprise obtaining a first set of measurements from a first portion of a patient sample. Such a method may also include deriving blood cell data for at least one population of cells in the patient sample based on the first set of measurements. Additionally, such a method may include determining one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample. Using those one or more sample optimization parameters determined for the patient sample, the method may then include processing a second portion of the patient sample. In such a case, processing the second portion of the patient sample may comprise obtaining a second set of measurements from the second portion of the patient sample. In such a method, at least one of obtaining the first set of measurements and obtaining the second set of measurements comprises imaging. Corresponding systems and computer readable media may also be implemented based on this disclosure.

[0009] While multiple examples arc described herein, still other examples of the described subject matter will become apparent to those skilled in the art from the following detailed description and drawings, which show and describe illustrative examples of disclosed subject matter. As will be realized, the disclosed subject matter is capable of modifications in various aspects, all without departing from the spirit and scope of the described subject matter. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.

[0010] BRIEF DESCRIPTION OF THE DRAWINGS

[0011] 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:

[0012] FIG. 1 is a schematic illustration, partly in section and not to scale, showing operational aspects of an exemplary flowcell, autofocus system and high optical resolution imaging device for sample image analysis using digital image processing.

[0013] FIG. 2 schematically depicts a schematic representation of a cellular analysis system.

[0014] FIG. 3 illustrates a transducer module.

[0015] FIG. 4 illustrates a system block diagram illustrating aspects of a cellular analysis system.

[0016] FIG. 5 depicts a method for dynamic reagent adjustment.

[0017] FIG. 6 illustrates a method which may be used in processing an aliquot of a sample.

[0018] FIG. 7 illustrates an architecture for a machine learning model which can be used in some embodiments in classifying images.

[0019] FIG. 8 illustrates a portion of the architecture of FIG. 7.

[0020] FIG. 9 depicts an illustrative flow cytometer that may be utilized in measuring cell characteristics using fluorescence.

[0021] FIG. 10 illustrates a high level method for sample optimization.

[0022] FIG. 1 1 illustrates acts which may be performed in determining one or more sample optimization parameter(s).

[0023] FIG. 12A-12B illustrate acts which may be performed in processing an aliquot of a sample with one or more modified sample optimization parameters. FIG. 13 illustrates a configuration which may he used to control relative pressure of sample and alignment fluid flow streams.

[0024] 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.

[0025] DETAILED DESCRIPTION

[0026] The present disclosure relates to apparatus, systems, compositions, and methods for sample optimization. In this disclosure, sample optimization should be understood as referring to all activities that take place to change something with respect to a sample based on observed system behavior. One embodiment may include an automated particle imaging system which comprises an analyzer which may be, for example, a visual analyzer. In some embodiments, the visual analyzer may further comprise a processor to facilitate automated analysis of the images.

[0027] According to some aspects of this disclosure, a system comprising a visual analyzer may be provided for obtaining images of a sample comprising particles suspended in a liquid. Such a system 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 are also contemplated.

[0028] The customized handling of blood cells in a blood sample is an exemplary application for which the subject matter is particularly well suited, though other types of body fluid samples may be used. For example, aspects of the disclosed technology may be used in analysis of a non-blood body fluid sample comprising blood cells (e.g., white blood cells and / or red blood cells), such as scrum, bone marrow, lavage fluid, effusions, exudates, cerebrospinal fluid, pleural fluid, peritoneal fluid, urine, and amniotic fluid. It is also possible that the sample can be a solid tissue sample, e.g., a biopsy sample that has been treated to produce a cell suspension. The sample may also be a suspension obtained from treating a fecal sample. A sample may also be a laboratory or production line sample comprising particles, such as a cell culture sample. The term sample may be used to refer to a sample obtained from a patient or laboratory or any fraction, portion or aliquot thereof. The sample can be diluted, divided into portions, or stained in some processes. An aliquot refers to a portion of a patient sample, and can be subdivided into additional aliquots, portions or subsamples. The term aliquot can be used interchangeably with portion as they both refer to a partial amount of the patient sample.

[0029] In some aspects, samples are presented, imaged and analyzed in an automated manner. In the case of blood samples, the sample may be substantially diluted with a suitable diluent or saline solution, which reduces the extent to which the view of some cells might be hidden by other cells in an undiluted or less-diluted sample. The cells can be treated with agents that enhance the contrast of some cell aspects, for example using permeabilizing agents to render cell membranes permeable, and histological stains to adhere in and to reveal features, such as granules and the nucleus. In some cases, it may be desirable to stain an aliquot of the sample for counting and characterizing particles which include reticulocytes, nucleated red blood cells, and platelets, and for white blood cell differential, characterization and analysis. In other cases, samples containing red blood cells may be diluted before introduction to the flow cell and / or imaging in the flow cell or otherwise.

[0030] The particulars of sample preparation apparatus and methods for sample dilution, permeabilizing and histological staining, generally may be accomplished using precision pumps and valves operated by one or more programmable controllers. Examples can be found in patents such as U.S. Pat. No. 7,319,907. Likewise, techniques for distinguishing among certain cell categories and / or subcategories by their attributes such as relative size and color can be found in U.S. Pat. No. 5,436,978 in connection with white blood cells. The disclosures of these patents arc hereby incorporated by reference in their entirety.

[0031] I. Imaging Systems

[0032] 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 (PIO AL), such as a clear glycerol solution having a viscosity that is greater than the viscosity of the sample fluid.

[0033] 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 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. 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 PIO AL envelope as the PIO AL 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.

[0034] 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. 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 U.S. provisional patent application 63 / 291,044 titled “Autofocusing Through Multi-Layer Processing,” filed on December 17, 2021 , 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. II. Non-Imaging Systems

[0035] In addition to the imaging based systems and modules described herein, in some cases the disclosed technology may be implemented in connection with other systems. These may include, for example, impedance systems, fluorescence systems, light scatter systems, VCS systems (integration of volume, conductivity, and scatter together), spectrophotometry systems, or any other suitable systems as would be apparent to one skilled in the art in view of the teachings herein. Such systems may be re I erred to as alternative systems or “non-imaging”, as those systems may not capture high quality images of microscopic particles. Some nonimaging systems may include systems that utilize a different imaging analysis process (e.g., different than the flow imaging described herein) to obtain data, etc. Non-imaging systems may collect sample fluid information including identical, similar, and / or different parameters compared to the information obtained by imaging systems described above. These nonimaging systems may be helpful in order to obtain certain particle information that may be difficult and / or time consuming to derive from images. For example, the imaging system may not be able to assess volumetric data related to cells, and thus a non-imaging system may be included with the imaging system in order to establish this volumetric data. Additional examples of how multiple types of systems may be integrated are provided in U.S. Patent 9,702,806, titled “Hematology Systems and Methods” and filed March 18, 2014, U.S. Patent 10,429,292, titled “Dynamic Range Extension Systems and Methods for Particle Analysis in Blood Samples” and filed March 17, 2014, and U.S. Patent 9,429,524, titled “Systems and Methods for Imaging Fluid Samples” and filed October 17, 2014, the disclosures of each of which are hereby incorporated by reference in their entirety.

[0036] A. Impedance Systems

[0037] Referring now to FIG. 2, a schematic representation of a cellular analysis system 200 is shown. In some embodiments, and as shown, system 200 may include a preparation system 210, a transducer module 220, and an analysis system 230. While the system 200 is described herein at a very high level, with reference to the three core system blocks (e.g., 210, 220, and 230), the skilled artisan would readily understand that system 200 includes many other system components such as central control proccssor(s), display systcm(s), fluidic systcm(s), temperature control system(s), user-safety control system(s), and the like. In operation, a fluid sample (e.g., a whole blood sample (WBS)) 240 can be presented to the system 200 for analysis. In some instances, the sample 240 is aspirated into system 200. Exemplary aspiration techniques are known to the skilled artisan. After aspiration, the sample 240 can be delivered to a preparation system 210. Preparation system 210 receives the sample 240 and can perform operations involved with preparing the sample 240 for further measurement and analysis. For example, preparation system 210 may separate the sample 240 into predefined aliquots for presentation to transducer module 220. Preparation system 210 may also include mixing chambers so that appropriate reagents may be added to the aliquots. For example, where an aliquot is to be tested for differentiation of white blood cell subset populations, a lysing reagent (e.g., ERYTHROEYSE, a red blood cell lysing buffer made available by Bio-Rad Laboratories, Inc.) may be added to the aliquot to break up and remove the Red Blood Cells (RBCs). Preparation system 210 may also include temperature control components (not shown) to control the temperature of the reagents and / or mixing chambers. Appropriate temperature controls can improve the consistency of the operations of preparation system 210. As discussed elsewhere herein, sample data such as light scatter data, light absorption data, and / or current data can be obtained (e.g., using a transducer) and processed or used to determine various blood cell status indications of an individual patient.

[0038] In some instances, predefined aliquots can be Iran si erred from preparation system 210 to transducer module 220. As described in further detail below, transducer module 220 may be able to perform direct current (DC) impedance, radiofrequency (RF) conductivity, light transmission, and / or light scatter measurements of cells from the sample 240 passing individually therethrough. Measured DC impedance, RF conductivity, and light propagation (e.g., light transmission, light scatter) parameters can be provided or transmitted to analysis system 230 for data processing. In some instances, analysis system 230 may include computer processing features and / or one or more modules or components which can evaluate the measured parameters, identify and enumerate the blood cellular constituents, and correlate a subset of data characterizing elements of the sample 240 with a White Blood Cell Count (WBC) status of the individual for providing in a report 250. Excess biological sample from transducer module 220 can be directed to an external (or alternatively internal) waste system 260.

[0039] In one embodiment transducer module 220 comprises an impedance detector which utilizes impedance, also known as the Coulter principle, to count individual cells as they pass through an aperture (correlating a displacement, and corresponding electrical response to cell size / volume). In one embodiment, the impedance detector is configured to measure one or more of red blood cells, white blood cells, and platelets. In one embodiment, the impedance detector is configured to measure red blood cells and platelets (e.g., configuring a threshold to only count cells in the range of a blood cell and platelet), mean corpuscular volume (average volume of red blood cells), and mean platelet volume (average volume of platelets).

[0040] In the context of FIG. 3, which illustrates a transducer module (and references an impedance portion of a transducer module), there are electrodes 334, 336 for performing DC impedance measurements of cells passing through an interrogation zone (e.g., two tanks separated by an aperture which cells pass through). Signals from electrodes 334, 336 are transmitted to an analysis system 304 to process the data and establish a cell count and other numeric cell parameters (e.g., volumetric data). This data is then output to report 306. Any remaining fluid is discharged to waste 308.

[0041] In one example, the use of solely an impedance detector may have particular utility for red blood cells and platelets, or also counting white blood cells where discrimination between the various types of white blood cells is not needed. This is since it may be difficult to distinguish between various types of white blood cells (e.g., at least neutrophils, lymphocytes, monocytes, eosinophils, basophils) solely through an impedance measurement which would count the white blood cell and assess its size, but would need additional analysis to differentiate the type of white blood cell. By way of example, the impedance detector can be used on one or more of: red blood cell count, platelet count, mean corpuscular volume, mean platelet volume, and / or white blood cell count.

[0042] B. Conductivity Systems

[0043] FIG. 3 illustrates in more detail a potential implementation of a transducer module which integrates impedance (DC) and conductivity measurement. In some embodiments, and as shown, system 300 may include a sample source 302 from which fluid can be transferred to transducer module 310. The transducer module 310 may have a flow cell 330, which may include an electrode assembly having first and second electrodes 334, 336 for performing DC impedance and RF conductivity measurements of cells passing through a cell interrogation zone. Signals from electrodes 334, 336 can be transmitted to analysis system 304. The electrode assembly can analyze volume and conductivity characteristics of the cells using low- frequency current and high-frequency current, respectively. For example, low-frequency DC impedance measurements can be used to analyze the volume of each individual cell passing through the cell interrogation zone. Relatedly, high-frequency RF current measurements can be used to determine the conductivity of cells passing through the cell interrogation zone. Because cell walls act as conductors to high frequency current, the high frequency current can be used to detect differences in the insulating properties of the cell components, as the current passes through the cell walls and through each cell interior. High frequency current can be used to characterize nuclear and granular constituents and the chemical composition of the cell interior.

[0044] Wires or other transmission or connectivity mechanisms can transmit signals from the electrode assembly (e.g., electrodes 334, 336) to analysis system 304 for processing. For example, measured DC impedance or RF conductivity parameters can be provided or transmitted to analysis system 304 for data processing. In some instances, analysis system 304 may include computer processing features and / or one or more modules or components which can evaluate the measured parameters, identify and enumerate biological sample constituents, and correlate a subset of data characterizing elements of the biological sample with a status of the individual. As shown here, cellular analysis system 300 may generate or output a report 306 containing the predicted status and / or a prescribed treatment regimen for the individual. In some instances, excess biological sample from transducer module 310 can be directed to an external (or alternatively internal) waste system 308. In some instances, a cellular analysis system 300 may include one or more features of a transducer module or blood analysis instrument such as those described in previously incorporated U.S. Pat. Nos. 5,125,737; 6,228,652; 8,094,299; and 8,189,187.

[0045] In some embodiments, a conductivity system can be standalone (e.g., would not include an impedance detector), or could be paired with an impedance detector as described in the context of FIG. 3 to provide additional particle information.

[0046] C. Light Scatter Systems

[0047] Turning now to FIG. 4, as illustrated in that figure, a cellular analysis system may include a transducer module 2910 having a light or irradiation source such as a laser 2912 emitting a beam 2914. The laser 2912 can be, for example, a 635 nm, 5 mW, solid-state laser. In some instances, system 2900 may include a focus-alignment system 2920 that adjusts beam 2914 such that a resulting beam 2922 is focused and positioned at a cell interrogation zone 2932 of a flowcell 2930. In some instances, flowcell 2930 receives a sample aliquot from a preparation system 2902. Note, as described below, the light scatter detection system may be combined with DC (impedance) and RF (conductivity) measurement systems, though it should be understood that a light scatter system may alternatively be implemented as a standalone module without impedance or conductivity measurement functionality.

[0048] In some instances, the aliquot generally flows through the cell interrogation zone 2932 such that its constituents pass through the cell interrogation zone 2932 one at a time. In some cases, a system 2900 may include a cell interrogation zone or other feature of a transducer module or blood analysis instrument such as those described in U.S. Pat. Nos. 5,125,737; 6,228,652; 7,390,662; 8,094,299; and 8,189,187, the contents of each of which are incorporated herein by reference in their entirety. For example, a cell interrogation zone 2932 may be defined by a square transverse cross-section measuring approximately 50x50 microns, and having a length (measured in the direction of flow) of approximately 65 microns. Flow cell 2930 may include an electrode assembly having first and second electrodes 2934, 2936 for performing DC impedance and RF conductivity measurements of the cells passing through cell interrogation zone 2932. Signals from electrodes 2934, 2936 can be transmitted to analysis system 2904 for further processing as described previously in the context of FIGS. 2 and 3.

[0049] Incoming beam 2922 travels along beam axis AX and irradiates the cells passing through cell interrogation zone 2932, resulting in light propagation within an angular range a (e.g. scatter, transmission) emanating from the zone 2932. Exemplary systems are equipped with sensor assemblies that can detect light within three, four, five, or more angular ranges within the angular range a, including light associated with an extinction or axial light loss measure as described elsewhere herein. As shown here, light propagation 2940 can be detected by a light detection assembly 2950, optionally having a light scatter detector unit 2950A and a light scatter and transmission detector unit 2950B. In some instances, light scatter detector unit 2950A includes a photoactive region or sensor zone for detecting and measuring upper median angle light scatter (UMALS), for example light that is scattered or otherwise propagated at angles relative to a light beam axis within a range from about 20 to about 42 degrees. In some instances, UMALS corresponds to light propagated within an angular range from between about 20 to about 43 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. Light scatter detector unit 2950A may also include a photoactive region or sensor zone for detecting and measuring lower median angle light scatter (LMALS), for example light that is scattered or otherwise propagated at angles relative to a light beam axis within a range from about 10 to about 20 degrees. In some instances, LMALS corresponds to light propagated within an angular range from between about 9 to about 19 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. A combination of UMALS and LMALS is defined as median angle light scatter (MALS), which is light scatter or propagation at angles between about 9 degrees and about 43 degrees relative to the incoming beam axis which irradiates cells flowing through the interrogation zone.

[0050] As shown in FIG. 4, the light scatter detector unit 2950A may include an opening 2951 that allows low angle light scatter or propagation 2940 to pass beyond light scatter detector unit 2950A and thereby reach and be detected by light scatter and transmission detector unit 2950B. According to some embodiments, light scatter and transmission detector unit 2950B may include a photoactive region or sensor zone for detecting and measuring lower angle light scatter (LALS), for example light that is scattered or propagated at angles relative to an irradiating light beam axis of about 5.1 degrees. In some instances, LALS corresponds to light propagated at an angle of less than about 9 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of less than about 10 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 1.9 degrees+0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 3.0 degrees+0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 3.7 degrees±0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 5.1 degrees±0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 7.0 degrees±0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone.

[0051] According to some embodiments, light scatter and transmission detector unit 2950B may include a photoactive region or sensor zone for detecting and measuring light transmitted axially through the cells, or propagated from the irradiated cells, at an angle of 0 degrees relative to the incoming light beam axis. In some cases, the photoactive region or sensor zone may detect and measure light propagated axially from cells at angles of less than about 1 degree relative to the incoming light beam axis. In some cases, the photoactive region or sensor zone may detect and measure light propagated axially from cells at angles of less than about 0.5 degrees relative to the incoming light beam axis less. Such axially transmitted or propagated light measurements correspond to axial light loss (ALL or AL2). As noted in previously incorporated U.S. Pat. No. 7,390,662, when light interacts with a particle, some of the incident light changes direction through the scattering process (i.e. light scatter) and pail of the light is absorbed by the particles. Both of these processes remove energy from the incident beam. When viewed along the incident axis of the beam, the light loss can be referred to as forward extinction or axial light loss. Additional aspects of axial light loss measurement techniques are described in U.S. Pat. No. 7,390,662 at column 5, line 58 to column 6, line 4.

[0052] As such, the cellular analysis system 2900 provides means for obtaining light propagation measurements, including light scatter and / or light transmission, for light emanating from the irradiated cells of the biological sample at any of a variety of angles or within any of a variety of angular ranges, including ALL and multiple distinct light scatter or propagation angles. For example, light detection assembly 2950, including appropriate circuitry and / or processing units, provides a means for detecting and measuring UMALS, LMALS, LALS, MALS, and ALL.

[0053] D. Fluorescence Systems

[0054] FIG. 9 depicts an illustrative flow cytometer 2000 that may be utilized in measuring cell characteristics using fluorescence. In some instances, prior to this fluorescence measurement, cells from a hematological sample may be treated with a hemolytic agent to lyse erythrocytes, thereby leaving behind white blood cells in the sample fluid. Further, the remaining white blood cells may then be stained with a fluorescent dye which can make a difference in the fluorescence intensity. Sample fluid containing stained cells may then be introduced into flow cytometer 2000 to measure scattered light and fluorescence of the respective cells when the cells arc irradiated with a laser.

[0055] Flow cytometer 2000 includes a light source 2021 (e.g. a red semiconductor laser), configured to emit a beam of light (e.g. a laser beam with a wavelength of 633nm) into an orifice part of a sheath flow cell 2023 via a collimating lens 2022. Simultaneously, particles from the sample fluid (e.g., cells - such as blood cells or body fluid cells) individually pass through nozzle 2020 into the orifice pail of sheath flow cell 2023. Therefore, the particles are directed into the sheath fluid and configured to pass through an emitted beam of light from light source 2021 within sheath flow cell 2023. The light source 2021 irradiates an orifice part of a flow cell into which the prepared measuring sample has been introduced, with light which can excite a dye used in treatment of a sample, and is selected depending on a fluorescent dye which stains a particle (e.g., blood cell or body fluid cell) in a sample. Therefore, depending on a kind of a fluorescent dye used, in addition to the semiconductor laser, for example, an red argon laser, a He— Ne laser, and a blue semiconductor laser may be used.

[0056] Forward scattered light radiated from the particle is introduced into a forward scattered light detector 2026 (e.g., a photodiode) via a condensing lens 2024 and a pinhole plate 2025. Additionally, side scattered light radiated from the particle is introduced into a side scattered light detector 2029 (e.g., photomultiplier tube) via a condensing lens 2027 and a dichroic mirror 2028. Side fluorescent light radiated from the particle is also introduced into a side fluorescent light detector 2031 (e.g., photomultiplier tube) via condensing lens 2027, a dichroic mirror 2028, a filter 2028’ and a pinhole plate 2030. A forward scattered light signal outputted from the forward scattered light detector 2026, a side scattered light signal outputted from the side scattered light detector 2029, and a side fluorescent signal outputted from the side fluorescent light detector 2031 are amplified with amplifiers 2032, 2033, 2034, respectively, and are inputted into the control part 2006. Control part 2006 analyses these signals, and calculates received signal intensities. Control part 2006, or any other suitable components of a fluorescent system, may utilize these scattered light intensities in order to calculate and display suitable measured parameters, as would be apparent to one skilled in the ail in view of the teachings herein. Further information on fluorescence systems which may be applied to cell analysis in some embodiments is provided in U.S. patents 7,625,730 and 7,892,841, the disclosures of each of which are hereby incorporated by reference in their entirety.

[0057] Note, the fluorescence systems are sometimes referred to as an optical system in the art, as they leverage laser excitation and the use of mirrors in a non-imaging arrangement, thus the fluorescence systems can also be referred to as an optical system.

[0058] Some fluorescence technologies may also leverage imaging as part of an analytical process (e.g., fluorescence in situ hybridization, aka FISH). A fluorescence imaging module (e.g., FISH) may be used as part of an additional module used to assess biological samples (e.g., blood cells) as a different module from the flow imaging modules described earlier. In this context, the use of fluorescence can apply to imaging or non-imaging systems or modules, as appropriate. For instance, a multi-module analysis system can include a flow imaging module (e.g., FIG. 1) and a fluorescent imaging module - as separate imaging modules. Alternatively, a multi-module analysis system can include a flow imaging module (e.g., FIG. 1) and a separate fluorescence module which may comprise a fluorescent imaging component. Alternatively, a multi-module analysis system can include an imaging module (e.g., flow imaging of FIG. 1 or FISH), and at least one separate module that does not utilize imaging (e.g., impedance, spectrophotometry, fluorescence cytometry, light scatter, or conductivity).

[0059] III. Sample Optimization Process

[0060] Systems such as shown and discussed in the context of sections I and II, above may be used in capturing data that can be applied in sample optimization. High level methods for sample optimization are described below in the context of FIGS. 5-6 and 10-12.

[0061] A. Obtaining Measurements

[0062] As shown in FIGS. 5 and 10, sample optimization may begin with obtaining 501 measurements. These measurements may be measurements from a first portion of a patient sample, such as measurements of one or more cells in a first aliquot of a patient sample. This may be done, for example, by flowing the first aliquot of the blood sample through a nonimaging system (e.g., an impedance system, a conductivity system, a light scatter system, and / or a fluorescence system) and using it to obtain the appropriate measurements (e.g., obtaining 502 impedance measurements of cells, obtaining conductivity measurements of cells, obtaining light scatter measurements of cells, obtaining fluorescence measurements of cells, etc.).

[0063] Alternatively, in some embodiments obtaining 501 the measurements may be performed by capturing 503 images of one or more cells in the first aliquot of the patient sample. This may be done by simply flowing the first aliquot of the sample through a flow cell and capturing the images of the one or more cells as they passed through the flow cell’s viewing area, though it is possible that other acts may also be performed in connection with this. For example, in some cases, one or more preparation steps may be performed on the first aliquot prior to capturing 503 the images, such as adding a diluent to reduce the risk that there would be overlapping cells in the captured images. Additionally, in cases where one or more images of the first aliquot are captured 503, those images may be captured using the same components as could later (see discussion of FIG. 6, infra) be used in capturing images of the second aliquot, or may be captured using different components (e.g., if a method such as shown in FIG. 5 was performed using an analyzer which included two imaging systems of the type shown in FIG. 1). Other variations are also possible, and will be immediately apparent to those of skill in the art in light of this disclosure. Accordingly, the above examples of how measurements may be obtained 501 in various embodiments of the disclosed technology should be understood as being illustrative only, and should not be treated as limiting.

[0064] B. Deriving Blood Cell Data

[0065] Once the measurements had been obtained, those measurements may be used as a basis for deriving 502 blood cell data, such as by deriving 504 data indicative of red blood cells and / or white blood cells. As with the act of obtaining 501 measurements, this blood cell data derivation 504 may be performed in a variety of manners. For instance, in a case where obtaining 501 measurements was performed using an impedance based system, the impedance measurements may be used to determine the sizes of the cells in a first aliquot, and those sizes can be used to classify the measured cells as either being white blood cells or red blood cells (e.g., cells with a volume of less than a threshold value, such as 175 fL can be classified as red blood cells, though other volume thresholds, such as 90 fL, 100 fL or 150 fL may alternatively be used). The classifications may then be used to generate counts of white and red cells (e.g., by multiplying the number of red / white blood cells in the first aliquot by a scaling factor which would compensate for any size differences which may exist between the first and second aliquots), which, as described below, can form a basis for determining 1003 sample optimization parameters (e.g., determining 508 amounts of staining and / or lysing agent to use when preparing 601 the second aliquot). However, derivation 504 of blood cell data is not limited to cases where measurements are obtained 501 using an impedance system. For instance, in a cases where the measurements comprise images from one or more cells in the patient sample, blood cell data may be derived 504 from those images using various types of image processing, an example of which is described below.

[0066] In a case where one or more cells in the first aliquot are measured via capturing 503 images, deriving 504 blood cell data may include defining 505 a set of masks for each image of a blood cell from the one or more blood cells. This may include defining a dark mask (e.g., a mask made up of the pixels with average red, green and blue values when the image is encoded in RGB color space) and a center mask (e.g., a mask made up of pixels within a 20 pixel radius of the center of a cell image). The set of masks may then be used to extract 506 a set of features for the cell depicted in the image corresponding to the masks. These features may include features such as pixel count (i.e., the number of pixels in the mask for which the pixel count is derived), channel ratios (e.g., a ratio of the average green channel values of pixels in a mask to the average red channel values of pixels in the mask), etc. These features may then be used to classify 507 the cell depicted in the image to which the features relate as either a red blood cell or a white blood cell. This may be done, for instance, using decision rules such as those set forth below in table 1 , where dark_G_mean_over_R_mean is the ratio of average green channel values to average red channel values in the dark mask, ccntcr_G_mcan_ovcr_R_mcan is the ratio of average green channel values to average red channel values in the center mask, and dark_pixel_count is the number of pixels in the dark mask. Table 1

[0067] Alternative approaches to classifying 507 imaged cells are also possible. For example, in some cases the number of pixels in a dark mask of an image depicting a particle may be treated as representing the particle’s cross sectional area. That cross sectional area may then be used to derive a volume measure (e.g., using the equation volume = 4 / 3'7f1 / 2'A3 / 2, in which A is the particle’s cross sectional area) which, in turn, can be used to classify 507 the cell in a manner similar to that described above in the context of deriving 504 blood cell data from impedance measurements. These classifications can then for a basis for determining counts for red and / or white blood cells which, as described below, can form a basis for determining 508 amounts of staining and / or lysing agent to use when preparing 601 the second aliquot. C. Determining Sample Optimization Parameters As noted above, once blood cell data had been derived 504, that data, such as red and / or white blood cell counts, can be used to determine 1003 one or more sample optimization parameters. These sample optimization parameters may be used for sample preparation optimization, for sample analysis optimization, or for both. As shown in FIG. 11, determining 1003 these types of sample optimization parameters may include determining 1101 a compensation ratio (e.g., a ratio between expected and derived cell data) and using it to modify 1102 a parameter (e.g., an amount of stain or lyse, discussed below in illustrating sample preparation optimization). Similarly, determining 1003 sample optimization parameters may include comparing 1103 derived blood cell data to one or more ranges (e.g., percentile ranges) and then modifying 1104 a parameter based on that comparison (e.g., if a determined amount is below a threshold amount at the low end of a predefined range, then a sample optimization parameter may be modified 1104 based on that comparison). Concrete illustrations of how these types of optimization may be determined 1003 are provided below.

[0068] 1. Sample Preparation Optimization

[0069] One example of determining 1003 sample preparation optimization parameters is determining 508 an amount of stain and / or lyse for preparing a second aliquot. To illustrate what this may entail, consider the example above of deriving counts of the numbers of white blood cells and red blood cells which would be expected to be in the second aliquot. In such a case, to facilitate determining 508 amounts of stain and / or lyse to use in sample preparation, an analyzer used in performing the method of FIG. 5 may be configured with data indicating optimal amounts of one or more staining or lysing agents given predefined white and red blood cell counts, respectively. With this information, ratios can be determined 509 between the red and / or white blood cell counts derived 504 previously and the predefined counts for the red and white blood cells. Then, to determine 508 the amount of stain to use in preparation of the second aliquot, the ratio of derived and predefined white blood cell counts can be multiplied 510 by the corresponding predefined amount of stain. Similarly, to determine 508 the amount of lyse to use in preparation of the second aliquot, the ratio of derived and predefined red blood cell counts can be multiplied 510 by the corresponding predefined amount of lyse. To put it another way, in some implementations, staining and / or lysing amounts to use in preparation of a second aliquot may be determined 508 using equations such as equations 1 and 2, below, in which WBC_x is the white blood cell count derived for the second aliquot, RBC_x is the red blood cell count derived for the second aliquot, WBC_0 is the predefined white blood cell count corresponding to an optimal stain amount with which the analyzer had been configured, RBC_O is the predefined red blood cell count corresponding to an optimal lyse amount with which the analyzer had been configured, p_stain is the optimal stain amount corresponding to WBC_0, and p_lyse is the optimal lyse amount corresponding to RBC_O.

[0070] Staining amount = (WBC_x / WBC_0) * p_stain

[0071] Equation 1

[0072] Lysing Amount = (RBC_x / RBC_O) * p_lyse

[0073] Equation 2

[0074] Other approaches to determining 508 staining and / or lysing amounts are also possible. For instance, and to continue the example of making the determination 508 based on red and / or white blood cell counts, in some cases, rather than utilizing ratios, the red and / or white blood cell counts may be classified 511 into blood cell count classes (e.g., high classes representing counts greater than the 50thpercentile for their particular blood cell type, and low classes representing counts less than or equal to the 50thpercentile for their particular blood cell type). The lysing and / or staining amounts could then be defined 512 as being equal to predefined amounts of staining and / or lysing reagent (as applicable) identified in data stored by the analyzer used to perform the method of FIG. 5 as corresponding to count classes for the second aliquot. Some embodiments may also feature combined approaches. For instance, in some embodiments lysing amounts may be determined using ratios while staining amounts may be determined by count classes, or vice versa. Similarly, some embodiments may be configured to determine a lysing or staining amount using both ratios and count classes. For example, it is possible that an embodiment may have classes for white blood cell counts which only cover white blood cell counts falling into the 20th-40thpercentile (a low count class) or the 60th-80dlpercentiles (a high count class). In this type of embodiment, if a white blood cell count derived for the second aliquot didn’t fall into either the low or high count class, then the amount of stain to use in preparing the second aliquot may be determined using equations 3-5, below, in which equation 3 is applicable when the derived white blood cell count is below the minimum value for the low count class, equation 4 is applicable when the derived white blood cell count falls between the low and high count classes, equation 5 is applicable when the derived white blood cell count is above the maximum for the high count class, WBC_low_min is the minimum value in the low count class, low_stain is the amount of staining agent corresponding to the low count class, WBC_low_max is the maximum value in the low count class, WBC_high_min is the minimum value in the high count class, high_stain is the amount of staining agent corresponding to the high count class, and WBC_high_max is the maximum value in the high count class.

[0075] Staining amount = (WBC_x / WBC_low_min) * low_stain

[0076] Equation 3

[0077] Staining WBC x * low stain + high stain amount (WBC_low_niax + WBC_high_niin) / 2 2

[0078] Equation 4

[0079] Staining amount = (WBC_x I WBC_high_max) * high_stain

[0080] Equation 5

[0081] To further illustrate the types of variations in how the determination 508 of staining and lysing amounts may be implemented, consider the relationship between that determination 508 and the subsequent preparation 601 of the second aliquot. In some cases, staining and lysing reagents may be separate compounds that may be added to the second aliquot in different amounts. However, in other cases stain and lysing reagents may be in one composition containing both a stain and a lyse together - where the composition includes saponin, a plurality of stains (e.g., combinations of new methylene blue, crystal violet, and basic fuchsin), and glutaraldehyde (additional information on stain and lyse compositions can be found in U.S. patent 9,279,750 and U.S. published patent application 2021 / 0108994, the disclosures of each of which are incorporated herein by reference in their entirety). In such a case, where the second aliquot may be prepared with a single staining + lysing reagent, there may not be separate staining and lysing amounts, but instead a single staining + lysing amount (e.g., the maximum of staining and lysing amounts determined using approaches as set forth above) may be determined 508.

[0082] Other variations are also possible, and will be immediately apparent to those of skill in the art in light of this disclosure. For example, while the above disclosure described various examples of how staining and / or lysing amounts may be determined, it is also possible that other types of preparation parameters, such as amount of diluent to add, may be optimized as well. For example, just as the amount of stain and / or lyse to add may be modified based on differences between derived and expected amount data, other preparation parameters may be similarly optimized. For example, an amount of diluent added to a second aliquot of a patient sample may be increased or decreased based on a compensation ratio (e.g. ratio of actual cell count to predefined cell count, such as WBC_x / WBC_0 or RBC_x I RBC_O from equations 1 and 2, above) and / or based on the relationship between actual amount data and one or more predefined ranges (e.g., in direct proportion to the observed cell ratio, or involving dilution ranges where a range of observed particles compensation ratios is associated with a particular dilution value). The general considerations would involve a relatively large amount of cells being observed (e.g., a large number of red or white blood cells or platelets) then resulting in more dilution (e.g., to reduce overlapping of cells), whereas a relatively low amount of cells being observed (e.g., a low number of red or white blood cells or platelets) then resulting in less dilution (e.g., to minimize large gaps between observed cells). In various examples there is a preset dilution amount which can be adjusted up or down depending on observation from a first aliquot for a second aliquot (e.g., based on a compensation ratio), or the dilution amount itself is completely customized for the second aliquot and configured based on the observed cell count in the first aliquot (e.g., the second aliquot dilution is completely customized depending on the first aliquot dilution observations). Accordingly, the examples of how lysing and / or staining amounts or diluent amounts may be determined should be understood as being illustrative only, and should not be treated as implying limitations on the type of sample preparation parameters optimization which may he protected hy this document or any related document.

[0083] 2. Sample Analysis Optimization

[0084] Just as determining 1003 one or more sample optimization parameters may include determining 1004 one or more sample preparation optimization parameters, it may also (or alternatively) include determining 1005 one or more sample analysis optimization parameters in some cases. For example, in some cases, the time over which measurements of cells in patient sample are collected may be modified based on differences between derived and expected amount data for a patient sample. For instance, in the event that a cell concentration for a patient sample is below a threshold amount, the time for collecting measurements from a second aliquot of that sample can be extended to ensure that sufficient data is collected for subsequent analysis despite the lower concentration value. Similarly, if the cell concentration is above a predefined expected amount, the measurement time for a second aliquot of the patient sample may be decreased, thereby potentially increasing the throughput of the analyzer used in processing the sample.

[0085] Other sample analysis optimization parameters may also be determined in some cases. To illustrate, consider FIG. 13, which illustrates an exemplary system which may be used in conveying a sample to (and through) a flowcell in a system which allows modification of the sample analysis optimization parameter of sample stream thickness. In the system of FIG. 13, a pressurized reservoir of sheath fluid 1301 is connected to first and second flow paths 1302 1303 via a T valve 1304. Along the first flow path 1302, the sheath fluid could be conveyed to a sample valve 1305, which would be connected to a sample source 1306 that would use an external pressure source (not shown) to drive a portion of the sample (e.g., the second aliquot) into a passageway 1307 downstream of the sample valve 1305. Once in the passageway 1307, the sample would be driven by sheath fluid from the first flow path 1302 through a cannula 1308 of the flowcell. Meanwhile, sheath fluid from the second flow path 1303 would enter the flow cell through a second channel 1309, and would envelop the sample stream after it exited the cannula 1308. The combined stream (i.e., the sample stream enveloped by the sheath fluid stream from the second flow path) would then proceed through the flowcell so that pictures of the cells it conveyed could be captured by an imaging device 1310 before the stream was discharged to waste 1311. In such a case, the thickness of the sample stream that flows through the flowcell may be determined by the relative pressure of the sample stream and the alignment fluid which surrounds it (i.e., the higher the pressure of the sample stream relative to the pressure of the alignment fluid, the thicker the sample stream would be as it flowed through the flowcell) and this relative pressure may, in turn, be controlled by first and second restrictors 1312 1313 on the first and second flow paths 1302 1303. This pressure control can allow the thickness of the sample stream to be adjusted based on the derived blood cell data, such as by increasing the sample stream thickness in response to deriving relatively low amount data, or decreasing the thickness in response to deriving relatively high amount data. Alternatively, the system can utilize individualized pumps connected to the flow restrictors or replace the flow restrictors with pumps in order to help adjust sample stream and sheath stream velocity, flow rates, and / or thickness. Accordingly, the previous example of determining a measurement duration as a sample analysis optimization parameter should be understood as being illustrative only, and should not be treated as limiting on the scope of protection provided by this document or by any related document.

[0086] D. Processing a Second Portion

[0087] Once one or more sample optimization parameter(s) had been determined 1003, the parameter(s) may be used in the processing 1006 of a second portion of the patient sample (e.g., obtaining one or more measurements of cells in a second aliquot). As shown in FIGS. 6 and 12A-12B, this may include preparing 601 the second portion of the patient sample (e.g., a second aliquot), for example, by mixing 1201 it with an amount of stain and / or mixing 1202 it with an amount of lyse determined based on amount data derived as described previously. Preparing 601 the second portion may also include mixing 1203 it with an amount of diluent which may also have been determined based on amount data. The second portion may then be analyzed 1204. This may include establishing 602 a flow of alignment fluid from an alignment fluid reservoir to a flow cell, and creating 603 a sample stream comprising the second aliquot and a sheath of alignment fluid such as by injecting 1205 the second aliquot into the flow of alignment fluid at a relative pressure determined based on the amount data as described above in the context of FIG. 13. Measurements of the second portion may then be obtained 1206 over a duration determined based on the amount data (e.g., by capturing 604 a plurality of images as a sample stream flows through the flow cell), and additional amount data could be derived from those measurements (e.g., a white blood cell count could be determined 605 from captured images).

[0088] IV. Variations and additional embodiments

[0089] While the above disclosure described several additional embodiments including variations in how the disclosed technology may be implemented, it should be understood that these were not intended to be exhaustive, and that other embodiments and variations may also be practiced based on this disclosure. For example, in some cases, amount data may be determined 1105 for various subpopulations of cells from a first portion (e.g., particular types of white blood cells, such as granulocytes and lymphocytes), and this subpopulation data may be used to modify 1106 a sample optimization parameter. For instance, in a case where measurements were taken of the first aliquot using default sample optimization parameters (e.g., default amounts of stain and / or lyse, default imaging time, etc.) and those measurements indicated that a particular cell subpopulation was low, one or more of the sample optimization parameters may be modified to reduce the risk that insufficient data regarding that subpopulation would be gathered from a second aliquot (e.g., imaging time for the second aliquot may be increased based on a compensation ratio for the subpopulation, or based on a comparison of the amount data for that subpopulation with a predefined range for that subpopulation).

[0090] Variations are also possible in terms of how implementation approaches may be combined with each other. For example, in some cases, a system implemented based on this disclosure may be configured to combine different modifications to minimize how much any particular parameter was changed. For instance, in some cases, the duration over which the second set of measurements are obtained 1206 and the relative pressure at which the sample stream is injected 1205 may be varied together in order to achieve a total imaged sample volume which would be expected to feature a number of cells needed for subsequent analysis given the type of test which was being performed on the sample. As another illustration of a combined approach, in some cases a system implemented based on this disclosure may initially seek to account for derived blood cell data using one approach (e.g., changing the relative pressure at which a sample stream is injected 1205), but may have limits beyond which that approach would not be effective (e.g., the thickness of the sample stream couldn’t be increased to such an extent that it would not be surrounded by at least a minimum amount of sheath fluid), and the system may be configured to use a different approach (e.g., increasing the duration over which the second set of measurements are obtained 1206) to account for any residual discrepancy from the amount data once those limits were reached

[0091] Variations are possible in how optimizations are applied to portions of a sample which are processed using the disclosed technology. For example, a first aliquot may be processed by one channel (e.g., an impedance channel) to determine amount data for one population of cells (e.g., red blood cells) and the second aliquot may be processed by another channel (e.g., an imaging channel) to provide measurements of another population of cells (e.g., white blood cells and / or platelets), or the first and second aliquots may both be processed using a single channel (e.g., both the first and second aliquots may be processed using an imaging channel). Similarly, the first and second aliquots may both be processed in the context of performing a single test on a sample (e.g., taking a complete blood count, which may include taking a red blood cell count with a first aliquot and determining counts of platelets and white blood cells using the second aliquot), but it is also possible that the second aliquot may be process in performing a reflex test on the sample (e.g., based on a result of a test performed with the first aliquot). It is also possible that a second portion of a sample which is processed using one or more sample optimization parameters may be pail of the same aliquot whose measurements are used to derive amount data. Other variations are also possible, and could be implemented without undue experimentation by those of skill in the art in light of the material disclosed herein.

[0092] Additional embodiments or variations can utilize an aliquot where a portion of the aliquot is measured which then assesses optimization for the rest of the aliquot. For instance, while an aliquot or portion is being stained and / or lysed, the system can image part of the aliquot to determine if the staining and / or lysing is sufficient and then either continue with a default staining and / or lysing regimen if sufficient, or adjust the staining and / or lysing reagents if insufficient. Similarly, this process or subtyping or sub-analyzing a portion of the aliquot can be used for alternative optimization processes involving sample dilution, or sample stream thickness adjustments.

[0093] As another example of a type of variation which may be present in some implementations, while it is possible that sample optimization parameters (e.g., staining and / or lysing amounts) may be determined based on cell counts derived from measurements of a first aliquot, other amount data for various populations of cells (e.g., red and / or white blood cells), such as total volume and / or surface area may also (or alternatively) be derived and used in determining sample optimization parameters. Similarly, while some embodiments may use dark and / or center masks and cell sizes in classifying imaged cells, it is also possible that imaged cells may be classified using other types of masks and other features of imaged cells. For instance, image information like that set forth in table 2 may be determined to generate masks like those described in table 3, while features like those set forth in table 4 may ultimately be used in classifying cells.

[0094]

[0095] Table 2

[0096] Table 3

[0097] Table 4

[0098] Further information on masks, features, and their use in cell identification is provided in international patent application PCT / US23 / 85714, titled “Population Based Cell Classification” and filed on December 22, 2023, the disclosure of which is hereby incorporated by reference in its entirety.

[0099] Other masks, features and techniques for determining and applying them are also possible, and so the additional examples provided above in the context of tables 2-4 should not be treated as limiting. Indeed, it should be understood that the approach of defining 505 masks and extracting 506 features to classify cells is itself only illustrative, and that other approaches are also possible. For example, in some cases, rather than classifying cells as red blood cells and white blood cells, cell classification 507 may be performed with classes of “red blood cell,” “white blood cell” (and / or other types of identifiable blood cells, like platelets) and “other,” with the “other” class used for particles which could not be classified (or classified with sufficient confidence). As another example, it is possible that cells may be classified in manners which do not rely on masking or feature extraction, such as by providing images captured of the first aliquot as input to a machine learning model which was trained to provide classifications for the cells captured in those images. An example of an architecture which could be used for such a machine learning model is illustrated and described below in the context of FIGS. 7 and 8.

[0100] Turning now to FIG. 7, that figure illustrates an architecture for a machine learning model which can be used in some embodiments in classifying images. In the architecture of FIG. 7, an input image 701 would be analyzed in a series of stages 702a-702n, each of which may be referred to as a “layer,” and which is illustrated in more detail in FIG. 8. As shown in FIG. 8, an input 801 (which, in the initial layer 802a of FIG. 8 would be the input image 701, and otherwise would be the output of the preceding layer) is provided to a layer 802 where it would be processed to generate one or more transformed images 803a-803n. This processing may include convolving the input 801 with a set of filters 804a-804n, each of which would identify a type of feature from the underlying image that would then be captured in that filter’s corresponding transformed image. For instance, as a simple example, convolving an image with the filter shown in table 5 could generate a transformed image capturing the edges from the input image 801.

[0101] [ -1 -1 -1 ]

[0102] [ -1 8 -1 ]

[0103] [ -1 -1 -1 ]

[0104] Table 5

[0105] As shown in FIG. 8, in addition to generating transformed images 803a-803n a layer may also generate a pooled image 8O5a-8O5n for each of the transformed images 8O3a-8O3n. This may be done, for example, by organizing the appropriate transformed image into a set of regions, and then replacing the values in that region with a single value, such as the maximum value for the region or the average of the values for the region. The result would be a pooled image whose resolution would be reduced relative to its corresponding transformed image based on the size of the regions it was split into (e.g., if the transformed image had NxN dimensions, and it was split into 2x2 regions, then the pooled image would have size (N / 2)x(N / 2)). These pooled images 8O5a-8O5n could then be combined into a single output image 806, in which each of the pooled images 8O5a-8O5n is treated as a separate channel in the output image 806. This output image 806 can then be provided as input to the next layer as shown in FIG. 7.

[0106] Returning to the discussion of FIG. 7, after a final output image 703 has been created through the various stages 702a-702n of processing, the final output image 703 could be provided as input to a neural network 704. This may be done, for example, by providing the value of each channel of each pixel in the output image 703 to an input node of a densely connected single layer network. The output of the neural network 704 could then be treated a classification of the original input image 701. For example, in the case such as shown in FIG. 7, where a neural network 704 has multiple output nodes each of those output nodes may be treated as corresponding to a cell classification (e.g., one output node corresponding to white blood cells, one node corresponding to red blood cells, and one node corresponding to other), and the corresponding classification for the output node with the highest value could be treated as the classification for the cell depicted in the input image that resulted in that value being reached.

[0107] Machine learning models such as illustrated in FIGS. 7 and 8 can be trained to classify cells using blood cell images having known classes to minimize cross entropy loss among the output nodes of the neural network 704. Such blood cell images can be acquired through human annotation of images produced during normal operation of an analyzer (e.g., a human inspecting images and then labeling them with cell classes). Once the training data was available, the training may include splitting the classified images up multiple subsets, or folds, and then training and evaluating the model multiple times, with a different fold of training images being held back as a validation set each time (i.e., K-fold cross validation). In this way, performance metrics from each training instance can be averaged to verify the model’s generalization performance and, assuming the performance is acceptable, a final trained version of the model (e.g., whichever trained model had the best individual performance) can be used to make inferences (i.e., classify cell images) in production. Accordingly, the above descriptions of how imaged cells could potentially be classified based on defining masks and extracting features should be understood as being illustrative only, and should not be treated as limiting.

[0108] Variations are also possible in terms of the physical components which may be utilized in applying the teachings of this disclosure. For example, while the configuration of components illustrated in FIG. 13 may be used to allow for changes in sample stream thickness, other configurations are also possible and may be used when implementing the disclosed technology. These could include, for example, pumping sheath fluid from an unpressurized reservoir, or having different pressurized reservoirs of sheath fluid feeding first and second flow paths, rather than having a single pressurized sheath fluid reservoir as shown in FIG. 13. Other variations (e.g., increasing numbers of valves, adding pressure sensors along flow paths, increasing numbers of restrictors, removing restrictor and replacing them with pumps that control the pressure on the various flow paths, including both restrictors and pumps on the individual flow paths, adding additional pumps to the flowpath connected to the restrictors, etc.) are also possible, and can be seen in, for example, U.S. published patent application 2024 / 0342715, filed on June 21, 2024 for a “Biological Sample Driving System and Method,” which is hereby incorporated by reference in its entirety. As another example, in some cases, actions such as obtaining measurements 501, deriving 504 blood cell data and determining 508 staining and / or lysing amounts may all be performed under the control of a single computer located locally to components such as cameras and flow cells used to process a patient sample. However, it is also possible that one or more of these activities (or portions of these activities) may be performed externally, such as by being sent to a cloud based processing system over a wide area network, so that processing intensive operations (e.g., application of machine learning models) could be performed more efficiently using dedicated hardware before their results are returned, for example, to be utilized in the processing of a second aliquot as depicted in FIG. 6. Accordingly, the examples and illustrations of physical components and configurations set forth in this document should be understood as being illustrative only, and should not be treated as limiting on the scope of protection provided by this document or any related document.

[0109] V. Examples

[0110] To further illustrate potential implementations and embodiments of the disclosed technology, the following sets of exemplary implementations which could be practiced based on this disclosure are set forth below.

[0111] A. Example set 1

[0112] Example 1

[0113] A computer-implemented method of dynamic reagent adjustment comprising: obtaining a set of measurements of one or more cells in a first aliquot of a patient sample; deriving data indicative of at least one of red blood cells and white blood cells based on the set of measurements; and determining, based on the data indicative of at least one of red blood cells and white blood cells, at least one of a staining amount and a lysing amount to use in preparation of a second aliquot of the patient sample.

[0114] Example 2

[0115] The computer-implemented method of example 1, wherein the method comprises: preparing the second aliquot using the staining amount and the lysing amount; establishing a flow of alignment fluid from an alignment fluid reservoir into a flow cell; creating a sample stream comprising the second aliquot and a sheath of alignment fluid surrounding the second aliquot based on injecting the second aliquot from a channel into the flow of alignment fluid; using a camera focused on a viewing area of the flow cell to capture a plurality of images as the sample stream is flowing through the viewing area of the flow cell; and determining a white blood cell count for the patient sample based on the plurality of images.

[0116] Example 3 The computer-implemented method of example 2, wherein obtaining the set of measurements is performed using a non-imaging system.

[0117] Example 4

[0118] The computer-implemented method of example 3, wherein obtaining the set of measurements comprises obtaining impedance measurements.

[0119] Example 5

[0120] The computer-implemented method of example 2, wherein obtaining the set of measurements comprises capturing images of the one or more cells in the first aliquot of the patient sample.

[0121] Example 6

[0122] The computer-implemented method of example 5, wherein deriving data indicative of at least one of red blood cells and white blood cells comprises, for each image of a cell from the one or more cells: defining a set of masks for that image; extracting a set of features for that image using the set of masks; classifying the cell in that image as a red blood cell or a white blood cell based on the set of features.

[0123] Example 7

[0124] The computer-implemented method of any of examples 2-6, wherein preparing the second aliquot of the patient sample is performed using a combined staining and lysing reagent.

[0125] Example 8

[0126] The computer-implemented method of any of examples 1-7, wherein the data indicative of at least one of red blood cells and white blood cells comprises at least one of white blood cell count, white blood cell volume, white blood cell surface area, red blood cell count, red blood cell volume, and red blood cell surface area.

[0127] Example 9 The computer implemented method of any of examples 1-8, wherein: deriving data indicative of at least one of red blood cells and white blood cells comprises deriving at least one of a white blood cell count and a red blood cell count; and determining at least one of the staining amount and the lysing amount to use in preparation of the second aliquot of the patient sample comprises at least one of: determining the staining amount based on determining a staining ratio from the derived white blood cell count and a predefined white blood cell count, and multiplying the staining ratio by a predefined staining amount corresponding to the predefined white blood cell count; and determining the lysing amount based on determining a lysing ratio from the derived red blood cell count and a predefined red blood cell count, and multiplying the lysing ratio by a predefined lysing amount corresponding to the predefined red blood cell count.

[0128] Example 10

[0129] The computer implemented method of any of examples 1-9, wherein: deriving data indicative of at least one of red blood cells and white blood cells comprises deriving at least one of a white blood cell count and a red blood cell count; and determining at least one of the staining amount and the lysing amount to use in preparation of the second aliquot of the patient sample comprises at least one of: determining the staining amount based on classifying the derived white blood cell count into one of a plurality of white blood cell count classes, and defining the staining amount based on the white blood cell count class for the derived white blood cell count; and determining the lysing amount based on classifying the derived red blood cell count into one of a plurality of red blood cell count classes, and defining the lysing amount based on the red blood cell count class for the derived red blood cell count.

[0130] Example 11

[0131] A system comprising: one or more processors; one or more non-transitory computer readable mediums storing instructions to perform a method comprising: obtaining a set of measurements of one or more cells in a first aliquot of a patient sample; deriving data indicative of at least one of red blood cells and white blood cells based on the set of measurements; and determining, based on the data indicative of at least one of red blood cells and white blood cells, at least one of a staining amount and a lysing amount to use in preparation of a second aliquot of the patient sample.

[0132] Example 12

[0133] The system of example 11, wherein the method comprises: preparing the second aliquot using the staining amount and the lysing amount; establishing a flow of alignment fluid from an alignment fluid reservoir into a flow cell; creating a sample stream comprising the second aliquot and a sheath of alignment fluid surrounding the second aliquot based on injecting the second aliquot from a channel into the flow of alignment fluid; using a camera focused on a viewing area of the flow cell to capture a plurality of images as the sample stream is flowing through the viewing area of the flow cell; and determining a white blood cell count for the patient sample based on the plurality of images.

[0134] Example 13

[0135] The system of example 12, wherein obtaining the set of measurements is performed using a non-imaging system.

[0136] Example 14

[0137] The system of example 13, wherein obtaining the set of measurements comprises obtaining impedance measurements.

[0138] Example 15

[0139] The system of example 12, wherein obtaining the set of measurements comprises capturing images of the one or more cells in the first aliquot of the patient sample.

[0140] Example 16 The system of example 15, wherein deriving data indicative of at least one of red blood cells and white blood cells comprises, for each image of a cell from the one or more cells: defining a set of masks for that image; extracting a set of features for that image using the set of masks; classifying the cell in that image as a red blood cell or a white blood cell based on the set of features.

[0141] Example 17

[0142] The system of any of examples 12-16, wherein preparing the second aliquot of the patient sample is performed using a combined staining and lysing reagent.

[0143] Example 18

[0144] The system of any of examples 11-17, wherein the data indicative of at least one of red blood cells and white blood cells comprises at least one of white blood cell count, white blood cell volume, white blood cell surface area, red blood cell count, red blood cell volume, and red blood cell surface area.

[0145] Example 19

[0146] The system of any of examples 11-18, wherein: deriving data indicative of at least one of red blood cells and white blood cells comprises deriving at least one of a white blood cell count and a red blood cell count; and determining at least one of the staining amount and the lysing amount to use in preparation of the second aliquot of the patient sample comprises at least one of: determining the staining amount based on determining a staining ratio from the derived white blood cell count and a predefined white blood cell count, and multiplying the staining ratio by a predefined staining amount corresponding to the predefined white blood cell count; and determining the lysing amount based on determining a lysing ratio from the derived red blood cell count and a predefined red blood cell count, and multiplying the lysing ratio by a predefined lysing amount corresponding to the predefined red blood cell count.

[0147] Example 20 The system of any of examples 11 -19, wherein: deriving data indicative of at least one of red blood cells and white blood cells comprises deriving at least one of a white blood cell count and a red blood cell count; and determining at least one of the staining amount and the lysing amount to use in preparation of the second aliquot of the patient sample comprises at least one of: determining the staining amount based on classifying the derived white blood cell count into one of a plurality of white blood cell count classes, and defining the staining amount based on the white blood cell count class for the derived white blood cell count; and determining the lysing amount based on classifying the derived red blood cell count into one of a plurality of red blood cell count classes, and defining the lysing amount based on the red blood cell count class for the derived red blood cell count.

[0148] B. Example set 2

[0149] Example 1

[0150] A computer-implemented method for sample optimization in biological analysis comprising: obtaining a first set of measurements from a first portion of a patient sample; deriving blood cell data for at least one population of cells in the patient sample based on the first set of measurements; determining one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample; and processing a second portion of the patient sample utilizing at least one of the one or more sample optimization parameters determined for the patient sample, wherein processing the second portion of the patient sample comprises obtaining a second set of measurements from the second portion of the patient sample; wherein at least one of obtaining the first set of measurements and obtaining the second set of measurements comprises imaging.

[0151] Example 2

[0152] The computer-implemented method of example 1, wherein obtaining the second set of measurements comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into a flow cell; creating a sample stream comprising the second portion and a sheath of alignment fluid surrounding the second portion based on injecting the second portion from a channel into the flow of alignment fluid; and using a camera focused on a viewing area of the flow cell to capture a plurality of images as the sample stream is flowing through the viewing area of the flow cell.

[0153] Example 3

[0154] The computer-implemented method of example 2, wherein obtaining the first set of measurements is performed using a non-imaging system.

[0155] Example 4

[0156] The computer- implemented method of example 3, wherein obtaining the first set of measurements comprises obtaining impedance measurements.

[0157] Example 5

[0158] The computer-implemented method of example 2, wherein obtaining the first set of measurements comprises capturing images of the one or more cells in the first portion of the patient sample.

[0159] Example 6

[0160] The computer-implemented method of example 5, wherein deriving the blood cell data for the at least one population of cells in the patent sample comprises, for each image of a cell from one or more cells: defining a set of masks for that image; extracting a set of features for that image using the set of masks; classifying the cell in that image based on the set of features.

[0161] Example 7

[0162] The computer implemented method of any of examples 2-6, wherein: the one or more sample optimization parameters determined for the patient sample comprise a sample stream thickness; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises injecting the second portion of the patient sample at a pressure having a value relative to a pressure of the alignment fluid which is based on the derived blood cell data.

[0163] Example 8

[0164] The computer implemented method of any of examples 1-7, wherein: the one or more sample optimization parameters determined for the patient sample comprise a sample concentration; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of diluent which is based on the derived blood cell data.

[0165] Example 9

[0166] The computer implemented method of any of examples 1-8, wherein: the one or more sample optimization parameters determined for the patient sample comprise measurement time; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises obtaining the second set of measurements over a duration which is based on the derived blood cell data..

[0167] Example 10

[0168] The computer implemented method of any of examples 1-9, wherein: the one or more sample optimization parameters determined for the patient sample comprise stain amount; and the one or more sample optimization parameters determined for the patient sample comprise stain amount; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of staining reagent which is based on the derived blood cell data.

[0169] Example 11 The computer implemented method of any of examples 1-10, wherein: the one or more sample optimization parameters determined for the patient sample comprise lyse amount; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of lysing reagent which is based on the derived blood cell data.

[0170] Example 12

[0171] The computer implemented method of any of examples 10-11, wherein preparing the second portion of the patient sample is performed using a combined staining and lysing reagent.

[0172] Example 13

[0173] The computer implemented method of any of examples 1-12, wherein determining the one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: performing a comparison of the blood cell data for the at least one population of cells to a range; and modifying the at least one of the one or more sample optimization parameters based on the comparison indicating that the blood cell data for the at least one population of cells is outside of the range..

[0174] Example 14

[0175] The computer implemented method of any of examples 1-13, wherein determining the one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: determining a compensation ratio based on: the blood cell data for the at least one population of cells in the patient sample; and a predefined amount for the at least one population of cells in the patient sample; and modifying the at least one of the one or more sample optimization parameters based on the compensation ratio.

[0176] Example 15 The computer implemented method of any of examples 1- 14, wherein determining the one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: determining amounts for a plurality of subpopulations in the patient sample; and modifying at least one of the one or more sample optimization parameters based on at least one amount for a subpopulation from the plurality of subpopulations.

[0177] Example 16

[0178] The computer implemented method of any of examples 1-15, wherein the blood cell data for the at least one population of cells comprises at least one of: white blood cell count, white blood cell volume, white blood cell surface area, red blood cell count, red blood cell volume, red blood cell surface area, and platelet count.

[0179] Example 17

[0180] The computer implemented method of any of examples 1-16, wherein processing the second portion of the patient sample comprises performing a reflex text on the patient sample.

[0181] Example 18

[0182] The computer implemented method of any of examples 1-17, wherein: the blood cell data for at least one population of cells in the patient sample comprises blood cell data for red blood cells in the patient sample; and the second set of measurements is a set of measurements of one or more non-red blood cell cells in the second portion of the patient sample.

[0183] Example 19

[0184] A biological analysis system, comprising: one or more processors; and a non-transitory computer readable medium having stored thereon instructions operable to, when executed by the one or more processors, perform the method of any of examples 1-18.

[0185] Example 20 A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of any one of the methods of examples 1 to 18.

[0186] Example 21

[0187] A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of any one of the methods of examples 1 to 18.

[0188] Example 22

[0189] A system comprising: one or more processors; one or more non-transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising: obtaining a first set of measurements from a first portion of a patient sample; deriving blood cell data for at least one population of cells in the patient sample based on the first set of measurements; determining one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample; and processing a second portion of the patient sample utilizing at least one of the one or more sample optimization parameters determined for the patient sample, wherein processing the second portion of the patient sample comprises obtaining a second set of measurements from the second portion of the patient sample; wherein at least one of obtaining the first set of measurements and obtaining the second set of measurements comprises imaging..

[0190] Example 23

[0191] The system of example 22, wherein obtaining the second set of measurements comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into a flow cell; creating a sample stream comprising the second portion and a sheath of alignment fluid surrounding the second portion based on injecting the second portion from a channel into the flow of alignment fluid; and using a camera focused on a viewing area of the flow cell to capture a plurality of images as the sample stream is flowing through the viewing area of the flow cell.

[0192] Example 24

[0193] The system of example 23, wherein obtaining the first set of measurements is performed using a non-imaging system.

[0194] Example 25

[0195] The system of example 24, wherein obtaining the first set of measurements comprises obtaining impedance measurements.

[0196] Example 26

[0197] The system of example 23, wherein obtaining the first set of measurements comprises capturing images of the one or more cells in the first portion of the patient sample.

[0198] Example 27

[0199] The system of example 26, wherein deriving the blood cell data for the at least one population of cells in the patent sample comprises, for each image of a cell from one or more cells: defining a set of masks for that image; extracting a set of features for that image using the set of masks; classifying the cell in that image based on the set of features.

[0200] Example 28

[0201] The system of any of examples 23-27, wherein: the one or more sample optimization parameters determined for the patient sample comprise a sample stream thickness; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises injecting the second portion of the patient sample at a pressure having a value relative to a pressure of the alignment fluid which is based on the derived blood cell data. Example 29

[0202] The system of any of examples 22-28, wherein: the one or more sample optimization parameters determined for the patient sample comprise a sample concentration; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of diluent which is based on the derived blood cell data.

[0203] Example 30

[0204] The system of any of examples 22-29, wherein: the one or more sample optimization parameters determined for the patient sample comprise measurement time; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises obtaining the second set of measurements over a duration which is based on the derived blood cell data.

[0205] Example 31

[0206] The system of any of examples 22-30, wherein: the one or more sample optimization parameters determined for the patient sample comprise stain amount; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of staining reagent which is based on the derived blood cell data.

[0207] Example 32

[0208] The system of any of examples 22-31, wherein: the one or more sample optimization parameters determined for the patient sample comprise lyse amount; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of lysing reagent which is based on the derived blood cell data. Example 33

[0209] The system of any of examples 31-32, wherein preparing the second portion of the patient sample is performed using a combined staining and lysing reagent.

[0210] Example 34

[0211] The system of any of examples 22-33, wherein determining the one or more sample optimization parameters for the patient sample based on the amount data for the at least one population of cells in the patient sample comprises: performing a comparison of the amount data for the at least one population of cells to a range; and modifying at least one of the one or more sample optimization parameters based on the comparison indicating that the amount data for the at least one population of cells is outside of the range.

[0212] Example 35

[0213] The system of any of examples 22-34, wherein determining the one or more sample optimization parameters for the patient sample based on the amount data for the at least one population of cells in the patient sample comprises: determining a compensation ratio based on: the amount data for the at least one population of cells in the patient sample; and a predefined amount for the at least one population of cells in the patient sample; and modifying at least one of the one or more sample optimization parameters based on the compensation ratio.

[0214] Example 36

[0215] The system of any of examples 22-35, wherein determining the one or more sample optimization parameters for the patient sample based on the amount data for the at least one population of cells in the patient sample comprises: determining amounts for a plurality of subpopulations for at least one population of cells in the patient sample; and modifying at least one of the one or more sample optimization parameters based on at least one amount for a subpopulation from the plurality of subpopulations. Example 37

[0216] The system of any of examples 22-36, wherein the amount data for the at least one population of cells comprises at least one of: white blood cell count, white blood cell volume, white blood cell surface area, red blood cell count, red blood cell volume, red blood cell surface area, and platelet count.

[0217] Example 38

[0218] The system of any of examples 22-37, wherein processing the second portion of the patient sample comprises performing a reflex text on the patient sample.

[0219] Example 39

[0220] The system of any of examples 22-38, wherein: the amount data for at least one population of cells in the patient sample comprises amount data for red blood cells in the patient sample; and the second set of measurements is a set of measurements of one or more non-red blood cell cells in the second portion of the patient sample.

[0221] Example 40

[0222] A method of computer implemented biological analysis comprising performing the set of acts the instructions stored on the non-transitory computer readable medium of the system of any of examples 22-39 are to perform when executed.

[0223] Example 41

[0224] A non-transitory computer readable medium storage medium comprising instructions to perform the set of acts which the instructions stored on the non-transitory computer readable medium of the system of any of examples 22-39 are to perform when executed.

[0225] Example 42 A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the set of acts which the instructions stored on the non-transitory computer readable medium of the system of any of examples 22-39 are to perform when executed.

[0226] VI. Interpretation

[0227] 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.

[0228] It should be understood that a statement that “one or more” or “at least one” of a type of item have a characteristic indicates that the items in the indicated group collectively have the characteristic. To indicate that each item in a group has a characteristic, the phrase “each of’ will be used with the group identifier (e.g., “one or more” or “at least one”).

[0229] It should be understood that, in the claims, “set” should be understood as referring to one or more thing of similar nature, design or function.

[0230] It should be understood that any of the examples described herein may include various other features in addition to or in lieu of those described above. By way of example only, any of the examples described herein may also include one or more of the various features disclosed in any of the various references that are incorporated by reference herein.

[0231] It should be understood that any one or more of the teachings, expressions, embodiments, examples, etc. described herein may be combined with any one or more of the other teachings, expressions, embodiments, examples, etc. that are described herein. The above-described teachings, expressions, embodiments, examples, etc. should therefore not be viewed in isolation relative to each other. Various suitable ways in which the teachings herein may be combined will be readily apparent to those of ordinary skill in the art in view of the teachings herein. Such modifications and variations arc intended to be included within the scope of the claims.

[0232] It should be appreciated that any patent, publication, or other disclosure material, in whole or in part, that is said to be incorporated by reference herein is incorporated herein only to the extent that the incorporated material does not conflict with existing definitions, statements, or other disclosure material set forth in this disclosure. As such, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions, statements, or other disclosure material set forth herein will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.

[0233] Having shown and described various versions of the present invention, further adaptations of the methods and systems described herein may be accomplished by appropriate modifications by one of ordinary skill in the art without departing from the scope of the present invention. Several of such potential modifications have been mentioned, and others will be apparent to those skilled in the art. For instance, the examples, versions, geometries, materials, dimensions, ratios, steps, and the like discussed above are illustrative and are not required. Accordingly, the scope of the present invention should be considered in terms of the following claims and is understood not to be limited to the details of structure and operation shown and described in the specification and drawings.

Claims

CLAIMS1. A computer- implemented method for sample optimization in biological analysis comprising: obtaining a first set of measurements from a first portion of a patient sample; deriving blood cell data for at least one population of cells in the patient sample based on the first set of measurements; determining one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample; and processing a second portion of the patient sample utilizing at least one of the one or more sample optimization parameters determined for the patient sample, wherein processing the second portion of the patient sample comprises obtaining a second set of measurements from the second portion of the patient sample; wherein at least one of obtaining the first set of measurements and obtaining the second set of measurements comprises imaging.

2. The computer- implemented method of claim 1, wherein obtaining the second set of measurements comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into a flow cell; creating a sample stream comprising the second portion and a sheath of alignment fluid surrounding the second portion based on injecting the second portion from a channel into the flow of alignment fluid; and using a camera focused on a viewing area of the flow cell to capture a plurality of images as the sample stream is flowing through the viewing area of the flow cell.

3. The computer-implemented method of claim 2, wherein obtaining the first set of measurements is performed using a non-imaging system.

4. The computer-implemented method of claim 3, wherein obtaining the first set of measurements comprises obtaining impedance measurements.

5. The computer-implemented method of claim 2, wherein obtaining the first set of measurements comprises capturing images of the one or more cells in the first portion of the patient sample.

6. The computer-implemented method of claim 5, wherein deriving the blood cell data for the at least one population of cells in the patent sample comprises, for each image of a cell from one or more cells: defining a set of masks for that image; extracting a set of features for that image using the set of masks; classifying the cell in that image based on the set of features.

7. The computer implemented method of any of claims 2-6, wherein: the one or more sample optimization parameters determined for the patient sample comprise a sample stream thickness; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises injecting the second portion of the patient sample at a pressure having a value relative to a pressure of the alignment fluid which is based on the derived blood cell data.

8. The computer implemented method of any of claims 1-7, wherein: the one or more sample optimization parameters determined for the patient sample comprise a sample concentration; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of diluent which is based on the derived blood cell data.

9. The computer implemented method of any of claims 1 -8, wherein: the one or more sample optimization parameters determined for the patient sample comprise measurement time; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises obtaining the second set of measurements over a duration which is based on the derived blood cell data.

10. The computer implemented method of any of claims 1-9, wherein: the one or more sample optimization parameters determined for the patient sample comprise stain amount; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of staining reagent which is based on the derived blood cell data.

11. The computer implemented method of any of claims 1-10, wherein: the one or more sample optimization parameters determined for the patient sample comprise lyse amount; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of lysing reagent which is based on the derived blood cell data.

12. The computer implemented method of any of claims 10-11, wherein preparing the second portion of the patient sample is performed using a combined staining and lysing reagent.

13. The computer implemented method of any of claims 1-12, wherein determining the one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: performing a comparison of the blood cell data for the at least one population of cells to a range; andmodifying the at least one of the one or more sample optimization parameters based on the comparison indicating that the blood cell data for the at least one population of cells is outside of the range.

14. The computer implemented method of any of claims 1-13, wherein determining the one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: determining a compensation ratio based on: the blood cell data for the at least one population of cells in the patient sample; and a predefined amount for the at least one population of cells in the patient sample; and modifying the at least one of the one or more sample optimization parameters based on the compensation ratio.

15. The computer implemented method of any of claims 1-14, wherein determining the one or more sample handling optimization for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: determining amounts for a plurality of subpopulations in the patient sample; and modifying at least one of the one or more sample optimization parameters based on at least one amount for a subpopulation from the plurality of subpopulations.

16. The computer implemented method of any of claims 1-15, wherein the blood cell data for the at least one population of cells comprises at least one of: white blood cell count, white blood cell volume, white blood cell surface area, red blood cell count, red blood cell volume, red blood cell surface area, and platelet count.

17. The computer implemented method of any of claims 1-16, wherein processing the second portion of the patient sample comprises performing a reflex text on the patient sample.

18. The computer implemented method of any of claims 1-17, wherein: the blood cell data for the at least one population of cells in the patient sample comprises blood cell data for red blood cells in the patient sample; and the second set of measurements is a set of measurements of one or more non-red blood cell cells in the second portion of the patient sample.

19. A biological analysis system, comprising: one or more processors; and a non-transitory computer readable medium having stored thereon instructions operable to, when executed by the one or more processors, perform the method of any preceding claim.

20. A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of any one of the methods of claims 1 to 18.

21. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of any one of the methods of claims 1 to 18.

22. A biological analysis system for sample optimization comprising: one or more processors; one or more non-transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising: obtaining a first set of measurements from a first portion of a patient sample; deriving blood cell data for at least one population of cells in the patient sample based on the first set of measurements; determining one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample; andprocessing a second portion of the patient sample utilizing at least one of the one or more sample optimization parameters determined for the patient sample, wherein processing the second portion of the patient sample comprises obtaining a second set of measurements from the second portion of the patient sample; wherein at least one of obtaining the first set of measurements and obtaining the second set of measurements comprises imaging.

23. The system of claim 22, wherein obtaining the second set of measurements comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into a flow cell; creating a sample stream comprising the second portion and a sheath of alignment fluid surrounding the second portion based on injecting the second portion from a channel into the flow of alignment fluid; and using a camera focused on a viewing area of the flow cell to capture a plurality of images as the sample stream is flowing through the viewing area of the flow cell.

24. The system of claim 23, wherein obtaining the first set of measurements is performed using a non-imaging system.

25. The system of claim 24, wherein obtaining the first set of measurements comprises obtaining impedance measurements.

26. The system of claim 23, wherein obtaining the first set of measurements comprises capturing images of the one or more cells in the first portion of the patient sample.

27. The system of claim 26, wherein deriving the blood cell data for the at least one population of cells in the patent sample comprises, for each image of a cell from one or more cells: defining a set of masks for that image; extracting a set of features for that image using the set of masks;classifying the cell in that image based on the set of features.

28. The system of any of claims 23-27, wherein: the one or more sample optimization parameters determined for the patient sample comprise a sample stream thickness; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises injecting the second portion of the patient sample at a pressure having a value relative to a pressure of the alignment fluid which is based on the derived blood cell data.

29. The system of any of claims 22-28, wherein: the one or more sample optimization parameters determined for the patient sample comprise a sample concentration; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of diluent which is based on the derived blood cell data.

30. The system of any of claims 22-29, wherein: the one or more sample optimization parameters determined for the patient sample comprise measurement time; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises obtaining the second set of measurements over a duration which is based on the derived blood cell data.

31. The system of any of claims 22-30, wherein: the one or more sample optimization parameters determined for the patient sample comprise stain amount; andprocessing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of staining reagent which is based on the derived blood cell data.

32. The system of any of claims 22-31, wherein: the one or more sample optimization parameters determined for the patient sample comprise lyse amount; and processing the second portion of the patient sample using the one or more sample optimization parameters determined for the patient sample comprises preparing the second portion by mixing it with an amount of lysing reagent which is based on the derived blood cell data.

33. The system of any of claims 31-32, wherein preparing the second portion of the patient sample is performed using a combined staining and lysing reagent.

34. The system of any of claims 22-33, wherein determining the one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: performing a comparison of the blood cell data for the at least one population of cells to a range; and modifying at least one of the one or more sample optimization parameters based on the comparison indicating that the blood cell data for the at least one population of cells is outside of the range.

35. The system of any of claims 22-34, wherein determining the one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: determining a compensation ratio based on: the blood cell data for the at least one population of cells in the patient sample; and a predefined amount for the at least one population of cells in the patient sample;and modifying at least one of the one or more sample optimization parameters based on the compensation ratio.

36. The system of any of claims 22-35, wherein determining the one or more sample optimization parameters for the patient sample based on the blood cell data for the at least one population of cells in the patient sample comprises: determining amounts for a plurality of subpopulations for at least one population of cells in the patient sample; and modifying at least one of the one or more sample optimization parameters based on at least one amount for a subpopulation from the plurality of subpopulations.

37. The system of any of claims 22-36, wherein the blood cell data for the at least one population of cells comprises at least one of: white blood cell count, white blood cell volume, white blood cell surface area, red blood cell count, red blood cell volume, red blood cell surface area, and platelet count.

38. The system of any of claims 22-37, wherein processing the second portion of the patient sample comprises performing a reflex text on the patient sample.

39. The system of any of claims 22-38, wherein: the blood cell data for at least one population of cells in the patient sample comprises blood cell data for red blood cells in the patient sample; and the second set of measurements is a set of measurements of one or more non-red blood cell cells in the second portion of the patient sample.

40. A method of computer implemented biological analysis comprising performing the set of acts the instructions stored on the non-transitory computer readable medium of the system of any of claims 22-39 are to perform when executed.

41. A non-transitory computer readable medium storage medium comprising instructions to perform the set of acts which the instructions stored on the non-transitory computer readable medium of the system of any of claims 22-39 are to perform when executed.

42. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the set of acts which the instructions stored on the non-transitory computer readable medium of the system of any of claims 22-39 are to perform when executed.

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