WO2012061578A2 - Analyseur de la motilité des spermatozoïdes et procédés associés - Google Patents

Analyseur de la motilité des spermatozoïdes et procédés associés Download PDF

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WO2012061578A2
WO2012061578A2 PCT/US2011/059112 US2011059112W WO2012061578A2 WO 2012061578 A2 WO2012061578 A2 WO 2012061578A2 US 2011059112 W US2011059112 W US 2011059112W WO 2012061578 A2 WO2012061578 A2 WO 2012061578A2
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sperm
motility
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WO2012061578A3 (fr
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Summer Goodson
Zhaojun Zhang
James Tsuruta
Wei Wang
Deborah O'brien
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The University Of North Carolina At Chapel Hill
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5091—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing the pathological state of an organism

Definitions

  • the present invention relates to sperm motility analysis, and more particularly, to Computer Assisted Sperm Analysis (CASA).
  • CASA Computer Assisted Sperm Analysis
  • Sperm motility and the capacity to undergo hyperactivation in the female reproductive tract is required for fertilization in mammals.
  • Sperm motility analysis may be useful in understanding genetic, iological, biomolecular, and pharmaceutical effects on sperm function.
  • CASA Computer Assisted Sperm Analysis
  • Methods of classifying sperm motility include detecting one or more movement parameters of a plurality of sperm in a sample using a computer-assisted sperm analysis device.
  • the movement parameters of individual ones of the plurality of sperm are classified into one of at least four classifications.
  • the at least four classifications are selected from the group consisting of hyperactivated, intermediate, progressive, slow and weakly motile. Hyperactivated, intermediate and progressive motility may be considered
  • the movement parameters used to classify motility include average path velocity (VAP), straight-line velocity (VSL), curvilinear velocity (VCL), amplitude of lateral head displacement (ALH) and/or beat cross frequency (BCF).
  • VAP average path velocity
  • VSL straight-line velocity
  • VCL curvilinear velocity
  • AH amplitude of lateral head displacement
  • BCF beat cross frequency
  • classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises a regression analysis.
  • classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises determining a weighting coefficient based on an empirically-based model of actual clinical experience.
  • the empirically-based model of actual clinical experience may include visually classifying individual ones of the plurality of sperm into one of the at least four
  • a sample is divided into a control sample and a test sample, an agent is added to the test sample, and a change in a percentage of sperm in each of the at least four classifications for the test sample and the control sample is determined.
  • the agent may be alterations in the constituents of the culture medium (metabolic substrates, proteins, ions, etc.), inhibitors that block specific metabolic processes or other cellular processes such as ion transport or protein phosphorylation, potential and/or known contraceptive or fertility therapeutic agents.
  • a percentage of the plurality of sperm in each of the at least four classifications for a plurality of samples is determined, and a percentage of the plurality of sperm in each of the at least four classifications is compared.
  • a system for classifying sperm motility includes a
  • a motility classification module is configured to classify the movement parameters of each of the plurality of sperm into one of at least four classifications, the at least four classifications comprising hyperactivated motility, intermediate, progressive, slow, and weakly motile.
  • Figure 1 is a flowchart illustrating embodiments according to some embodiments of the present invention.
  • Figure 2 is a block diagram of operations, systems and methods according to some embodiments of the present invention.
  • Figures 3A-3H are images of sperm movement in a CASA environment according to some embodiments of the present invention.
  • Figure 4A is a three dimensional graph of the movement parameters for a sperm sample according to some embodiments of the present invention.
  • Figure 4B is a decision tree using the equations of Table 2 to classify sperm motility based on detected parameters according to some embodiments of the present invention.
  • Figures 5A-5B are images of sperm incubated for 2 hours in HTF medium with bicarbonate (Figure 5A) and without bicarbonate ( Figure 5B).
  • Figures 5C-5D are bar graphs of motility profiles of sperm incubated for 90 minutes in HTF medium with and without bicarbonate as shown in Figures 5A-5B.
  • Figures 6A-6C and Figure 7 are bar graphs illustrating classification results according to some embodiments of the present invention.
  • Figures 8A-8E are digital images of examples of sperm motility patterns identified during in vitro capacitation. Representative CASA tracks of sperm identified as progressive ( Figure 8A), intermediate ( Figure 8B), hyperactivated (Figure 8C), slow (Figure 8D), and weakly motile (Figure 8E) after 90 min incubation in HTF complete medium. VAP, VCL, and VSL values for each track are shown. Track images are magnified to better illustrate the patterns of movement, but are not to the same scale relative to each other.
  • Figures 9A-9C are graphs of the percent motility of sperm populations. The percentage of motile sperm was assessed by CASA as follows: Figure 9A are samples used in Figures 5C-5D incubated under capacitating (#) or non-capacitating conditions ( ⁇ ).
  • Figure 9B are samples from WT or Gapd s 'A mice compared in Figure 7.
  • Figure 9C are samples from C57BL/6J (BL6, ⁇ ), 129Sl/SvlmJ (129, ⁇ ), PWK/PhJ (PW , A), and CD1
  • Figures 10A-10B are bar graphs illustrating the profiles of sperm analyzed in
  • Figure 1 1 is a bar graph illustrating a comparison of visual and multiclass
  • Figures 12A-12B are graphs of motility comparing compounds in series A and
  • Figures 13A-13H are graphs comparing the motility of sperm incubated with different concentrations of compounds A2 ( Figures 13A-13D) and B4 ( Figures 13E-13H).
  • the stacked bar graphs in Figures 13B-13D and 13F-13H show that both compounds reduced vigorous motility and inhibited capacitation-dependent hyperactivation.
  • phrases such as “between X and Y” and “between about X and Y” should be interpreted to include X and Y.
  • phrases such as “between about X and Y” mean “between about X and about Y.”
  • phrases such as “from about X to Y” mean “from about X to about Y.”
  • These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, and/or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.
  • These computer program instructions may also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function/act specified in the block diagrams and/or flowchart block or blocks.
  • the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer- implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.
  • the present invention may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). Furthermore,
  • embodiments of the present invention may take the form of a computer program product on a computer-usable or computer-readable non-transient storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system.
  • the computer-usable or computer-readable medium may be, for example but not limited to, an electronic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer- readable medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM).
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CD-ROM portable compact disc read-only memory
  • Subjects to be evaluated with the methods, devices and systems of the present invention include both human subjects and animal subjects.
  • embodiments of the present invention may be used with sperm samples from human and/or animal subjects, including but not limited mammalian subjects such as rodents (rats, mice), pigs, goats, sheep, cows, horses, cats, dogs, non-human primates (monkeys, chimps, gorillas, baboons), etc. for evaluation, medical research or veterinary purposes.
  • methods of classifying sperm motility include detecting movement parameters of a plurality of sperm in a sample using a Computer Assisted Sperm Analysis (CAS A) device (Block 10).
  • CAS A Computer Assisted Sperm Analysis
  • the movement parameters may include average path velocity (VAP, the velocity over the average direction of movement), straight- line velocity (VSL, the velocity calculated using only the starting point and end point of the sperm trajectory), curvilinear velocity (VCL, the velocity determined as a function of the total distance traveled by the sperm over the measured time), amplitude of lateral head displacement (ALH, the deviation of the sperm head from the average path of movement) and beat cross frequency (BCF, the number of times the sperm head crosses the path of movement).
  • VAP average path velocity
  • VSL straight- line velocity
  • VCL curvilinear velocity
  • AH amplitude of lateral head displacement
  • BCF beat cross frequency
  • the classifications may include progressive, intermediate, hyperactivated, slow, and/or weak motility.
  • Exemplary images of sperm motility are shown in Figure 3C (progressive motility (i. e. , movement generally progressing along path in a direction with turns of the head of less than 90 degrees)), Figure 3D (intermediate motility (/. e. , movement that is similar to progressive vigorous motility, but has a larger variance from the path and turns of the sperm head of approximately 90 degrees, such as an oscillating movement)), Figure 3E-3F (hyperactivated motility (i. e.
  • Figure 2 illustrates an exemplary data processing system that may be included in devices operating in accordance with some embodiments of the present invention, e.g. , to carry out the operations illustrated in Figure 1.
  • a data processing system 1 16 which can be used to carry out or direct operations includes a processor 100, a memory 136 and input/output circuits 146.
  • the data processing system can be incorporated in a portable communication device and/or other components of a network, such as a server.
  • the processor 100 communicates with the memory 136 via an address/data bus 148 and communicates with the input/output circuits 146 via an address/data bus 149.
  • the input/output circuits 146 can be used to transfer information between the memory (memory and/or storage media) 136 and another component, such as a sample analyzer 125 (e.g. , a CASA analyzer) for analyzing a sample.
  • a sample analyzer 125 e.g. , a CASA analyzer
  • These components can be conventional components such as those used in many conventional data processing systems, which can be configured to operate as described herein.
  • the processor 100 can be a commercially available or custom microprocessor, microcontroller, digital signal processor or the like.
  • the memory 136 can include any memory devices and/or storage media containing the software and data used to implement the functionality circuits or modules used in accordance with embodiments of the present invention.
  • the memory 136 can include, but is not limited to, the following types of devices: cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, DRAM and magnetic disk.
  • the memory 136 can be a content addressable memory (CAM).
  • the memory (and/or storage media) 136 can include several categories of software and data used in the data processing system: an operating system 152; application programs 154; input/output device circuits 146; and data 156.
  • the operating system 152 can be any operating system suitable for use with a data processing system, such as IBM®, OS/2®, AIX® or zOS® operating systems or Microsoft® Windows® operating systems Unix or LinuxTM.
  • the input/output device circuits 146 typically include software routines accessed through the operating system 152 by the application program 154 to communicate with various devices.
  • the application programs 154 are illustrative of the programs that implement the various features of the circuits and modules according to some embodiments of the present invention.
  • the data 156 represents the static and dynamic data used by the application programs 154, the operating system 152 the input/output device circuits 146 and other software programs that can reside in the memory 136.
  • the data processing system 1 16 can include several modules, including a sperm analyzer module 120 and the like.
  • the modules can be configured as a single module or additional modules otherwise configured to implement the operations described herein for analyzing the motility profile of a sample.
  • the data 156 can include sperm motility data 124 and/or CASA parameter data 126, for example, that can be used by the sperm analyzer module 120 to detect and/or analyze a biological sample such as a sperm sample and/or to control the sample analyzer 125,
  • the present invention should not be construed as limited to the configurations illustrated in Figure 2, but can be provided by other arrangements and/or divisions of functions between data processing systems.
  • Figure 2 is illustrated as having various circuits and modules, one or more of these circuits or modules can be combined, or separated further, without departing from the scope of the present invention.
  • the operating system 152, programs 154 and data 156 may be provided as an integrated part of the sample analyzer 125.
  • a training data set may be used to classify sperm motility based on one or more movement parameters identified in a CASA environment.
  • the movement parameters include average path velocity (VAP), straight-line velocity (VSL), curvilinear velocity (VCL), amplitude of lateral head displacement (ALH) and beat cross frequency (BCF) or other movement parameters including parameters known to those of skill in the art,
  • the movement parameters may be one or more of the standard parameters such as those provided by the Hamilton Tho ne Integrated Visual Optical System (IVOS) or CEROS motility analyzers and defined as follows:
  • VAP Average path velocity
  • VSL Straight-line velocity
  • VCL Curvilinear velocity
  • Amplitude of lateral head displacement is the maximum value of the distance of any point on the track from the corresponding average path, multiplied by two.
  • Beat cross frequency is the frequency with which the cell track crosses the cell path in either direction.
  • a probabilistic binary classification set of equations may be used to identify various sperm motility groups.
  • the motility of individual sperm may be classified visually, and a regression analysis and/or machine assisted learning may be used to identify classification rules based on the visual classification of the motility of individual sperm and the associated CASA movement parameters.
  • the weighting coefficients, constants, and equation values may be either positive or negative.
  • VAP is the average path velocity ( ⁇ /sec)
  • VSL is the straight-line velocity ( ⁇ /sec)
  • VCL is the curvilinear velocity ( ⁇ /sec)
  • ALH is the amplitude of lateral head displacement ( ⁇ )
  • BCF beat cross frequency (Hz).
  • the probabilistic binary classification set of equations may be configured as illustrated in Figure 4B.
  • a first equation 200 is used to classify the sperm motility of a sample in two groups: vigorous or non- vigorous.
  • the first equation 200 may be as follows:
  • Eqi is a value that is greater than or less than zero
  • C v/ .j are weighting coefficients
  • /c / is a constant
  • VAP is the average path velocity
  • VSL is the straight-line velocity
  • VCL is the curvilinear velocity
  • ALH is the amplitude of lateral head displacement
  • BCF is the beat cross frequency. If the value of Eqi is greater than zero, then the sperm motility is classified as vigorous. If the value of Eqi is less than zero, then the sperm motility if classified as non- vigorous.
  • a second and third probabilistic binary classification equation may be applied to the vigorous sperm to further classify the vigorous motility sperm into hyperactivated, intermediate or progressive sperm motility.
  • the second equation 202 may be used to classify the sperm into hyperactivated and non-hyperactivated motility sperm.
  • the second equation 202 may be applied to the individual sperm classified as vigorous as follows:
  • Eq 2 is a value that is greater than or less than zero
  • CMS are weighting coefficients
  • k 2 is a constant
  • VAP is the average path velocity
  • VSL is the straight-line velocity
  • VCL is the curvilinear velocity
  • ALH is the amplitude of lateral head displacement
  • BCF is the beat cross frequency. If the value of Eq 2 is greater than zero, then the sperm motility is classified as hyperactivated. If the value of Eq 2 is less than zero, then the sperm motility if classified as non-hyperactivated.
  • the third probabilistic binary classification equation 204 may be applied to the non-hyperactivated sperm to further classify the non-hyperactivated sperm as having either progressive or intermediate motility.
  • the third equation 204 may be as follows:
  • Eq 3 C / VAP + C ip2 VSL + C ip3 VC + C ⁇ ALH + C ; 5 BCF + k 3 ,
  • Eq 3 is a value that is greater than or less than zero
  • C ip i.s are weighting coefficients
  • /3 ⁇ 4 is a constant
  • VAP is the average path velocity
  • VSL is the straight-line velocity
  • VCL is the curvilinear velocity
  • ALH is the amplitude of lateral head displacement
  • BCF is the beat cross frequency.
  • a fourth probabilistic binary classification equation 206 may be applied to the non-vigorous sperm to further classify the non-vigorous sperm as either slow or weak.
  • the fourth equation 206 may be as follows:
  • Eq 4 C ⁇ / VAP + C sw2 YSL + C iw5 VCL + C i ALH + C NV5 BCF + k 4 ,
  • Eq 4 is a value that is greater than or less than zero, are weighting coefficients, k is a constant, VAP is the average path velocity, VSL is the straight-line velocity, VCL is the curvilinear velocity, ALH is the amplitude of lateral head displacement and BCF is the beat cross frequency. If the value of Eq 4 is greater than zero, then the sperm motility is classified as having slow motility. If the value of Eq 3 is less than zero, then the sperm motility if classified as having weak motility.
  • the multiclass SVM model may be useful for establishing a standardized assessment of sperm motility that automatically discriminates between motility patterns based on the mathematical relationship of CAS A parameters. Standardization may facilitate more meaningful comparisons among mutant mouse lines that may significantly enhance our understanding of the regulation of motility transitions required for fertilization.
  • Embodiments according to the present invention may be used to analyze sperm motility and may be useful to identify genes important to fertility in veterinary models (e.g. , for livestock breeding purposes, evaluation of endangered species, and the like).
  • Motility classification by the multiclass SVM model may be rapid
  • This model may provide an objective assessment of the entire motile population of sperm analyzed by CASA that is consistent between experiments, thereby facilitating standardization of motility pattern analyses.
  • Computer program code using the model described herein may be easy to use and may quickly calculates both the number and percentage of motile sperm in each of the categories. It may also generate a list showing the CASA parameters and multiclass SVM classification for each track.
  • Classification of sperm motility patterns using the multiclass SVM model shows good agreement with visual classifications by observers with extensive training (Table 3), reducing or eliminating the need for visual verification of results or complex training to achieve accurate, detailed analyses.
  • CASA and machine learning tools may be used and/or combined to identify physiologically relevant patterns of sperm movement, and the multiclass SVM model has the potential for being a valuable tool for assessing genetic, biomolecular, and pharmaceutical effects on sperm motility.
  • a multiclass SVM model for classifying sperm motility may be capable of identifying at least four, or more distinct motility groups in a mixed population of sperm quickly, efficiently, and with a high level of accuracy. Because this model may utilitze the relationship between CASA parameters for specific types of motility and not absolute values, it may more accurately reflect visual assessments of the percentages of sperm within each motility group. This model may be capable of accurately assessing sperm motility in mice with distinct genetic backgrounds as well as in sperm populations with defective motility with no need for modification of the model. Used in conjunction with CASA, this model may be used for research and development in the areas of reproduction and fertility.
  • embodiments of the invention may be used for investigating the efficacy of potential and/or known contraceptives or fertility drugs, as well as identifying compounds that may be reproductive toxins.
  • Embodiments of the present invention may be used to analyze human sperm in order to facilitate sperm analysis in a clinical setting, such as fertility treatment.
  • an agent such as a potential and/or known toxin or contraceptive drug
  • a sperm sample from a subject may be divided into a control and one or more test samples.
  • the agent may be added to the test sample, and the control and test samples may be evaluated as described herein. Differences in sperm motility may be observed in the control and test sample to observe the effects of the agent on sperm motility.
  • agents that modulate hyperactivation or motility may be identified in the test sample as compared to the control sample, for example, for further testing as potential and/or known contraceptive drugs or as potential and/or known toxins, which may reduce motility or hyperactivation.
  • a fertility drug may increase motility and/or the percentage of hyperactivated sperm.
  • Test and control samples may also be used to identify and evaluate various conditions for storing sperm.
  • embodiments according to the invention provide methods of identifying an agent or condition that modulates sperm motility, e.g. , as a candidate for a male contraceptive drug, spermicide, fertility agent (including male fertility agents), and/or toxins (e.g. , an environmental toxin that may reduce male fertility).
  • Support Vector Machines were used to develop a multiclass SVM model that classifies hyperactivated sperm, and four other distinct patterns of sperm motility in mice based on standard CASA parameters. SVM equations were incorporated into a software program for automated sperm motility analyses. See S.G. Goodson et al.,
  • All reagents were purchased from Sigma- Aldrich Co. (St. Louis, MO) except sodium chloride and glucose (Fisher Scientific), sodium pyruvate (Invitrogen, Carlsbad, CA), sodium bicarbonate (EM Science, Gibbstown, NJ), potassium chloride, magnesium sulfate heptrahydrate, and potassium phosphate (Mallinckrodt Chemical, Phillipsburg, NJ), and penicillin/streptomycin l OOx stock solution containing 10,000 U/ml penicillin G and 10 mg/ml streptomycin (Gemini Bioproducts, West Sacramento, CA).
  • HTF the medium used for all sperm motility assays, is based on the composition of human oviductal fluid and has been used extensively for both mouse and human in vitro fertilization (IVF).
  • HTF complete medium consists of 101.6 mM NaCl, 4.7 mM KC1, 0.37 mM KH 2 P0 4 , 0.2 mM MgS0 4 '7 H 2 0, 2 mM CaCl 2 , 25 mM NaHC0 3 , 2.78 mM glucose, 0.33 mM pyruvate, 21.4 mM lactate, 5mg/ml BSA , 100 U/ml penicillin G and 0.1 mg/ml streptomycin.
  • HTF medium without energy substrates did not include glucose, lactate, or pyruvate.
  • Bicarbonate-free HTF replaced 25 mM sodium bicarbonate with 21 mM HEPES.
  • the osmolality was adjusted to -315 mOsm/kg with 5M NaCl using a Model 3300 micro-osmometer (Advanced Instruments, Norwood, MA).
  • mice PWK PhJ male mice were kindly provided by Fernando Pardo-Manuel de Villena (University of North Carolina). Gapdhs '1' and wildtype mice were obtained from an established breeding colony. At least three mice of each strain or genotype were used for each experiment. All procedures involving mice were approved in advance by the Institutional Animal Care and Use Committee of the University of North Carolina at Chapel Hill.
  • sperm were incubated for 2 h at 37° C under 5% C0 2 in air, and motility was assessed at 30 min intervals. Initial time points were completed within two minutes of dilution into HTF. Quantitative parameters of sperm motility were recorded by CASA using the CEROS sperm analysis system (software version 12.3, Hamilton Thorne Biosciences, Beverly, MA).
  • the CEROS system includes an Olympus CX41 microscope equipped with a MiniTherm stage warmer and a Sony model XC-ST50 CCD camera. Sperm tracks (1.5 sec) were captured at 37°C with a 4x negative phase contrast objective and a frame acquisition rate of 60 Hz.
  • Tracks included in subsequent analyses were required to have a minimum of 45 points which represents half the number of total frames, as in previous studies using extended tracking intervals.
  • Individual database text (DBT) files with track details were generated for every sperm population analyzed at every time point, providing Field #, Track #, average path velocity (VAP), straight-line velocity (VSL), curvilinear velocity (VCL), amplitude of lateral head displacement (ALH), beat cross frequency (BCF), straightness (STR), and linearity (LIN) values for every track.
  • VAP average path velocity
  • VSL straight-line velocity
  • VCL curvilinear velocity
  • AH amplitude of lateral head displacement
  • BCF beat cross frequency
  • STR straightness
  • LIN linearity
  • a training set was created from sperm analyzed after 90 min of incubation in
  • HTF complete medium at 37° in an atmosphere of 5% C0 2 and air. This time point was selected because high levels of vigorous motility were maintained consistently at 90 min and five motility patterns (progressive, intermediate, hyperactivated, slow, and weakly motile) were well represented. Individual sperm tracks were assessed visually and assigned to one of these five motility patterns (see results for details of the criteria for each group). The kinematic parameters for these tracks were identified in the CASA-generated DBT files and copied into an Excel worksheet, along with their visual classification to create the training data set. All classified tracks and parameters were loaded into Matlab (software version 2009b, The Mathworks, Natick, MA). The "svmtrain" LibSVM function was used to generate the model.
  • CASA parameter means for groups identified in the multiclass training set.*
  • VAP average path velocity in pm/sec
  • VSL straight line velocity in pm/sec
  • VCL curvilinear velocity in pm/sec
  • ALH amplitude of lateral head displacement in pm
  • BCF beat cross frequency in Hz.
  • Tracks that were motile but were not vigorous and did not have significant forward motion were characterized as weakly motile (track "f ' in Figure 3B and Figure 8E).
  • tracks were excluded that were derived from sperm with abnormalities such as flagellar bending at the annulus or adherence to other sperm ( ⁇ 400 tracks), were the result of sperm collisions (-300 tracks), or could not be identified confidently (-400 tracks).
  • a total of 2,043 tracks were included in the final training set used to develop the multiclass model.
  • the Hamilton Thorne CASA systems generate data files that list parameter values for each sperm track analyzed in an experiment. Independent kinematic parameters were used to develop our multiclass SVM model, including VAP ( ⁇ /sec), VSL ( ⁇ /sec), VCL ( ⁇ /sec), ALH ( ⁇ ) and BCF (Hz). Since STR (VSL/VAP) and LIN (VSL/VCL) are ratios of other parameters, they were not used in building our prediction model. CASA parameters for visually classified tracks in our training set were grouped into separate Excel® files for each motility pattern.
  • This set of tracks from sperm incubated for 90 min under capacitating conditions included 539 progressive tracks, 236 intermediate tracks, 515 hyperactivated tracks, 556 slow tracks, and 197 weakly motile tracks.
  • progressive tracks recorded at time 0 were incorporated to determine their effect on the multiclass equations. Since these time 0 tracks did not significantly alter the multiclass SVM model equations (data not shown), they were not included in the final model.
  • Three-dimensional scatter plots of CASA parameters associated with the five motility groups revealed clustering of tracks according to their visual classification as shown in Figure 4A.
  • Hyperactivated sperm tracks blue data points
  • Intermediate tracks green data points
  • Tracks classified as weakly motile cyan data points
  • were grouped below the slow tracks black points.
  • Equation SVM1 For example, VAP is the most important determinant and BCF is the least important.
  • the decision tree shown in Figure 4B summarizes how these equations are sequentially applied to sort sperm tracks into the five motility groups. Equations available in LibSVM were used to divide the tracks into two principal groups: vigorous (progressive, intermediate,
  • the program generated a binary equation that separates these two groups in the training set (Table 2, SVM1). If CASA parameters from an unclassified track are applied to this equation and the result is greater than 0, the track is classified as vigorous. Otherwise, the track is classified as non-vigorous. After defining two groups with the initial equation, the process was repeated to further subdivide these populations into discrete motility groups.
  • VAP average path velocity in ⁇ /sec
  • VSL straight line velocity in ⁇ /sec
  • VCL curvilinear velocity in ⁇ /sec
  • ALH amplitude of lateral head displacement in ⁇
  • BCF beat cross frequency in Hz.
  • SVM2 and SVM3 (Table 2).
  • SVM2 classifies tracks as hyperactivated if the value of SVM2 is greater than 0, and removes them from further examination. Vigorous sperm tracks that have a SVM2 ⁇ 0 are further analyzed by SVM3. Tracks are classified as intermediate if their SVM3 >0, or progressive if SVM3 ⁇ 0.
  • Tracks with SVM1 values less than 0 are classified as non-vigorous. This non- vigorous group can be further classified as slow or weakly motile based on SVM4 (Table 2).
  • SVM4 The SVM4 equation classifies a sperm track as slow if its value is greater than 0, while SVM4 values less than 0 are categorized as weakly motile.
  • a batch file program was created that utilizes CASA-generated DBT files with all CASA parameters for each motile track.
  • This batch file applies the SVM equations to individual CASA tracks, generates a summary showing the number of sperm that were classified into each motility group, and calculates the percentage of tracks in each group as a function of the motile population.
  • This program also generates a detailed list showing each track analyzed, along with its CASA parameters and multiclass SVM classification.
  • Bicarbonate is required for sperm capacitation as well as the acquisition of hyperactivated motility.
  • motility profiles of sperm were generated from six CD1 mice incubated in HTF complete medium ⁇ 25 mM bicarbonate over a 2 hr time course as shown in Figure 5A-5D. While the percentage of motile sperm in both media remained above 50% throughout the time course, as shown in Figure 9 A, there were marked differences in the sperm motility profiles. In complete medium containing bicarbonate (Figure 5 A), the number of progressive tracks steadily decreased over time. This decrease in progressive motility was accompanied by increases in all other motility groups.
  • the multiclass SVM model classifies all motile sperm in each population.
  • sperm motility patterns shift from largely progressive tracks at early time points to more varied patterns of movement, including hyperactivation.
  • Prior CASA-based approaches for identifying sperm motility patterns in the mouse have focused predominantly on distinguishing progressive and hyperactivated sperm populations, although there is no consensus on the parameters that best define hyperactivation.
  • CASA parameters from 2,043 sperm tracks (1.5 sec, 90 frames) were used to develop an automated model that identifies and quantitates five distinct patterns of sperm movement in large populations of mouse sperm.
  • the model is built upon a series of SVM equations (Table 2) that take into account both the relationships between CASA parameters and the relative importance of each parameter in assigning tracks to specific motility groups.
  • This approach classifies all recorded tracks simultaneously, providing a more comprehensive analysis of the changes in motility that occur during capacitation compared to identifying only the percentage of hyperactivated sperm by visual assessment or the use of thresholds for selected CASA parameters.
  • the SVM model was developed with mouse sperm tracks captured at 60 Hz using a Hamilton Thorne CEROS instrument. Although CASA systems typically calculate similar kinematic parameters, further validation studies may be used to test the applicability of this model for other CASA platforms.
  • mouse sperm display vigorous motility with -80% of the motile population classified as progressive by the multiclass SVM model. The percentage of motile sperm is typically maintained during a 120 min in vitro capacitation period. In addition, the percentage of sperm displaying progressive motility does not change substantially during this interval when standard CASA cutoffs are used.
  • the Mouse 2 default settings recommended by Hamilton Thorne categorize sperm as progressive if VAP >50 ⁇ /sec and STR >50, a broad definition that includes virtually all linear tracks.
  • the Hamilton Thorne software also identifies sperm as rapid if VAP exceeds the progressive threshold of 50 ⁇ im/sec.
  • the progressive tracks in our training set were linear and had mean values for VAP of 146.9 ⁇ 31.5 ⁇ im/sec.
  • the multiclass SVM model also identifies intermediate and hyperactivated tracks, the vigorous patterns of sperm motility that develop during capacitation.
  • both intermediate and hyperactivated sperm had higher mean values for VCL and ALH than progressive sperm (Table 1), reflecting the increased vigor expected during hyperactivation.
  • Hyperactivated motility patterns including both star-spin tracks and tracks that show some directional movement, were classified with 94% accuracy.
  • hyperactivation is essentially absent by visual inspection of sperm tracks ( Figures 3A- 3G) and multiclass analysis reflects this observation ( Figures 5A-5D).
  • the multiclass SVM model reduces or eliminates the need for the subtraction of noise detected at time zero from the levels of hyperactivation detected at later time points.
  • this model detects an increase in the proportion of hyperactivated sperm over the course of a 2 h period of in vitro capacitation.
  • the percentage of hyperactivated sperm reaches ⁇ 15%-35% by 90 min, consistent with levels reported in mouse and other species using validated approaches.
  • a sperm sample from a subject may be divided into a control and a test sample, and an agent may be added to the test sample, and the control and test sample may be evaluated according to embodiments of the present invention. Differences in sperm motility may be observed in the control and test sample to observe the effects of the agent on sperm motility. In some embodiments, agents that reduce hyperactivation and/or motility may be identified, for example, for further testing as potential contraceptive drugs or as potential toxins.
  • Glyceraldehyde 3 -phosphate dehydrogenase, spermatogenic is expressed only during the post-meiotic period of spermatogenesis and is the sole GAPDH isozyme in mammalian sperm.
  • GAPDH GAPDH isozyme in mammalian sperm.
  • embodiments of the present invention may provide a high throughput screening approach with the goal of identifying potent and selective inhibitors of GAPDHS, e.g. , for contraceptive development.
  • Embodiments according to the present invention may be used to analyze sperm motility and may be useful to identify genes important to fertility in human and veterinary models (e.g. , for livestock breeding purposes, evaluation of endangered species, and the like), and/or to identify reproductive toxicology in environmental conditions or drugs or other ingested substances.
  • Suitable agents include small organic compounds (e.g. , non-oligomers), oligomers or combinations thereof, and inorganic molecules.
  • Suitable organic molecules can include but are not limited to polypeptides (including enzymes, antibodies and Fab' fragments), carbohydrates, lipids, coenzymes, and nucleic acid molecules (including DNA, R A and chimerics and analogs thereof) and nucleotides and nucleotide analogs.
  • the agent is an antisense nucleic acid, an siRNA, shRNA, miRNA or a ribozyme that inhibits production of a target polypeptide.
  • Small organic compounds include a wide variety of organic molecules, such as heterocyclics, aromatics, alicyclics, aliphatics and combinations thereof, comprising steroids, antibiotics, enzyme inhibitors, ligands, hormones, drugs, alkaloids, opioids, terpenes, porphyrins, toxins, catalysts, as well as combinations thereof,
  • Oligomers include oligopeptides, oligonucleotides, oligosaccharides, polylipids, polyesters, polyamides, polyurethanes, polyureas, polyefhers, and poly
  • oligomers may be obtained from combinatorial libraries in accordance with known techniques.
  • the methods of the invention can be practiced to screen a library of agents, e.g. , a combinatorial chemical compound library (e.g. , benzodiazepine libraries as described in U.S. Patent No. 5,288,514; phosphonate ester libraries as described in U.S. Patent No. 5,420,328, pyrrolidine libraries as described in U.S. Patent Nos. 5,525,735 and 5,525,734, and diketopiperazine and diketomorpholine libraries as described in U.S. Patent No.
  • a combinatorial chemical compound library e.g. , benzodiazepine libraries as described in U.S. Patent No. 5,288,514; phosphonate ester libraries as described in U.S. Patent No. 5,420,328, pyrrolidine libraries as described in U.S. Patent Nos. 5,525,735 and 5,525,734, and diketopiperazine and diketomorpholine libraries as described in U.S. Patent No.

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Abstract

L'invention concerne un procédé et des systèmes permettant de classer la motilité des spermatozoïdes, consistant à détecter un ou plusieurs paramètres de mouvement d'une pluralité de spermatozoïdes dans un échantillon à l'aide d'un dispositif d'analyse des spermatozoïdes assisté par ordinateur. Les paramètres de mouvement de certains de la pluralité de spermatozoïdes sont classés comme appartenant à l'une d'au moins quatre classes.
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WO2015153691A3 (fr) * 2014-04-03 2015-11-26 Drexel University Procédés mis en œuvre par ordinateur, supports lisibles par ordinateur, et systèmes permettant de suivre une pluralité de spermatozoïdes
JPWO2015137466A1 (ja) * 2014-03-12 2017-04-06 一般社団法人家畜改良事業団 精子の検査方法及び装置
CN106872630A (zh) * 2017-03-29 2017-06-20 山东大学 与重度少弱精子症相关的生物标志物的筛选与应用
FR3058223A1 (fr) * 2016-11-02 2018-05-04 Institut National De La Recherche Agronomique Procede de mesure objective de la motilite massale dans la semence
US10470798B1 (en) 2018-11-30 2019-11-12 Ohana Biosciences, Inc. Methods for promoting fertilization
WO2024037724A1 (fr) 2022-08-19 2024-02-22 Consejo Nacional De Investigaciones Científicas Y Técnicas (Conicet) Procédés pour améliorer la fonction du sperme et la capacité de fertilisation pour des techniques de reproduction assistée

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2015137466A1 (ja) * 2014-03-12 2017-04-06 一般社団法人家畜改良事業団 精子の検査方法及び装置
EP3118321A4 (fr) * 2014-03-12 2017-11-08 Livestock Improvement Association of Japan, Inc. Procédé et dispositif d'inspection de sperme
US10593036B2 (en) 2014-03-12 2020-03-17 Livestock Improvement Association Of Japan, Inc. Sperm inspection method and device
WO2015153691A3 (fr) * 2014-04-03 2015-11-26 Drexel University Procédés mis en œuvre par ordinateur, supports lisibles par ordinateur, et systèmes permettant de suivre une pluralité de spermatozoïdes
FR3058223A1 (fr) * 2016-11-02 2018-05-04 Institut National De La Recherche Agronomique Procede de mesure objective de la motilite massale dans la semence
CN106872630A (zh) * 2017-03-29 2017-06-20 山东大学 与重度少弱精子症相关的生物标志物的筛选与应用
US10470798B1 (en) 2018-11-30 2019-11-12 Ohana Biosciences, Inc. Methods for promoting fertilization
US10603075B1 (en) 2018-11-30 2020-03-31 Ohana Biosciences, Inc. Compositions and methods for enhancing sperm function
WO2024037724A1 (fr) 2022-08-19 2024-02-22 Consejo Nacional De Investigaciones Científicas Y Técnicas (Conicet) Procédés pour améliorer la fonction du sperme et la capacité de fertilisation pour des techniques de reproduction assistée

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