WO2012061578A2 - Sperm motility analyzer and related methods - Google Patents

Abstract

Method and systems 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.

Description

SPERM MOTILITY ANALYZER AND RELATED METHODS

STATEMENT OF GOVERNMENT SUPPORT

[0001] This application was made with Government Support under grant numbers

U01 HD060481, ROl HD065024 and cooperative agreement U54 HD035041 as part of the Specialized Cooperative Centers Program in Reproduction and Infertility Research from the Eunice Kennedy Shriver National Institute of Child Health and Human Development and grant numbers IIS 0448392 and 0812464 from the National Science Foundation. The Government has certain rights to this invention.

RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application Serial No.

61/409,688, filed November 3, 2010, the disclosure of which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

[0003] The present invention relates to sperm motility analysis, and more particularly, to Computer Assisted Sperm Analysis (CASA).

BACKGROUND

[0004] 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.

[0005] Conventional methods of analyzing sperm motion include flagellar waveform analysis and videomicrography, both of which may require a high level of expertise and specialized analysis equipment. Computer Assisted Sperm Analysis (CASA) has been used to analyze large populations of sperm for total motility and other characteristics. CASA provides objective and quantitative measurements of sperm motility, including percentage of motile sperm, velocities and other kinematic parameters.

SUMMARY OF EMBODIMENTS OF THE INVENTION

[0006] 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

subcategories of vigorous motility, and slow and weak motility may be considered subcategories of non-vigourous motility In some embodiments, 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). In some embodiments, the

[0007] In some embodiments, classifying the movement parameters of individual ones of the plurality of sperm into one of at least four classifications comprises a first formula as follows: Eqi = C VAP + C_v2VSL + C_vJVCL + C ALH + C_vjBCF + kj, wherein Eq, is a value that is greater than or less than zero, C_vj.s are weighting coefficients, /cy 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, and if the value of Eqi is greater than zero, then the sperm motility is classified as vigorous, and if the value of Eqi is less than zero, then the sperm motility if classified as non- vigorous.

[0008] In some embodiments, classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises applying a second formula to individual sperm classified as vigorous as follows: Eq₂ = C/,i VAP + C/,₂ VSL + C/,5VCL + C/^ALH + C/,jBCF + /Q, wherein Eq₂ is a value that is greater than or less than zero, C_/,_/^ are weighting coefficients, /Q 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, and if the value of Eq₂ is greater than zero, then the sperm motility is classified as hyperactivated, and if the value of Eq₂ is less than zero, then the sperm motility if classified as non-hyperactivated.

[0009] In some embodiments, classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises applying a third formula to individual sperm classified as non-hyperactivated as follows: Eq₃ = C,_p/VAP + C^VSL + CjpsVCL + C^ALH + C^BCF + k , wherein Eq₃ is a value that is greater than or less than zero,

are weighting coefficients, ¾ 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, and if the value of Eq₃ is greater than zero, then the sperm motility is classified as having intermediate motility, and if the value of Eq₃ is less than zero, then the sperm motility if classified as having progressive motility.

[0010] In some embodiments, classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises applying a fourth formula to individual sperm classified as non-vigorous as follows: Eq₄ = ^._/VAP + C_s„,₂VSL + C^VCL + C ^ALH + C^BCF + k_4> wherein Eq₄ is a value that is greater than or less than zero, C_m>i-5 are weighting coefficients, ¾ 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, and if the value of Eq₄ is greater than zero, then the sperm motility is classified as having slow motility, and if the value of Eq₃ is less than zero, then the sperm motility if classified as having weak motility,

[0011] In some embodiments, classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises a regression analysis.

[0012] In some embodiments, 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

classifications and correlating a visual classification with the one or more movement parameters of a plurality of sperm.

[0013] In some embodiments, 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.

[0014] In some embodiments, 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.

[0015] In some embodiments, a system for classifying sperm motility includes a

Computer Assisted Sperm Analyzer (CASA) configured to detect one or more movement parameters of a plurality of sperm in a sample. 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.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain principles of the invention.

[0017] Figure 1 is a flowchart illustrating embodiments according to some embodiments of the present invention.

[0018] Figure 2 is a block diagram of operations, systems and methods according to some embodiments of the present invention.

[0019] Figures 3A-3H are images of sperm movement in a CASA environment according to some embodiments of the present invention.

[0020] Figure 4A is a three dimensional graph of the movement parameters for a sperm sample according to some embodiments of the present invention.

[0021] 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.

[0022] Figures 5A-5B are images of sperm incubated for 2 hours in HTF medium with bicarbonate (Figure 5A) and without bicarbonate (Figure 5B).

[0023] 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.

[0024] Figures 6A-6C and Figure 7 are bar graphs illustrating classification results according to some embodiments of the present invention,

[0025] 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.

[0026] 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

(▼) mice compared in Figures 6A-6C. Data are shown as mean values ± SEM. Differences between percent motility at corresponding time points were analyzed using two-tailed unpaired t-test for Figures 9 A and 9B. In Figure 9C, one-way ANOVA followed by Dunnett's posttest was used to determine significance relative to the outbred CD1 strain. * P < 0.05; ** < 0.01

[0027] Figures 10A-10B are bar graphs illustrating the profiles of sperm analyzed in

80 μηι vs. 100 μηι CASA chambers. Sperm from CD1 mice (n = 6) were incubated for 0 min (Figure 10A) or 90 min (Figure 10B) in HTF complete medium. At each time point, aliquots of sperm were placed in either 80 μιη (black bars) or 100 μπι (open bars) chambers and motility was assessed by CASA. Motility profiles were generated using the multiclass Support Vector Machines (SVM) model. Bars represent mean percentages ± SEM of motile tracks. No significant differences were detected between the two chambers with the two- tailed unpaired t-test.

[0028] Figure 1 1 is a bar graph illustrating a comparison of visual and multiclass

SVM assessments of hyperactivation levels in inbred strains (n = 4 mice/strain). The percentage of the motile population exhibiting hyperactivated motility after 90 min in HTF complete medium was determined visually (black bars) and with the multiclass SVM model (open bars). No significant differences were detected between the two methods with the two- tailed unpaired t-test.

[0029] Figures 12A-12B are graphs of motility comparing compounds in series A and

B, which were tested at a concentration of 50 μΜ to determine their effects on the motility of mouse sperm during in vitro capacitation in HTF medium.

[0030] 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.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

[0031] The present invention now will be described hereinafter with reference to the accompanying drawings and examples, in which embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0032] Like numbers refer to like elements throughout. In the figures, the thickness of certain lines, layers, components, elements or features may be exaggerated for clarity.

[0033] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. As used herein, phrases such as "between X and Y" and "between about X and Y" should be interpreted to include X and Y. As used herein, phrases such as "between about X and Y" mean "between about X and about Y." As used herein, phrases such as "from about X to Y" mean "from about X to about Y."

[0034] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Well-known functions or constructions may not be described in detail for brevity and/or clarity.

[0035] It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. Thus, a "first" element discussed below could also be termed a "second" element without departing from the teachings of the present invention. The sequence of operations (or steps) is not limited to the order presented in the claims or figures unless specifically indicated otherwise.

[0036] The present invention is described below with reference to block diagrams and/or flowchart illustrations of methods, apparatus (systems) and/or computer program products according to embodiments of the invention, It is understood that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program

instructions. 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.

[0037] 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.

[0038] 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.

[0039] Accordingly, 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.

[0040] 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).

[0041] Subjects to be evaluated with the methods, devices and systems of the present invention include both human subjects and animal subjects. In particular, 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. [0042] As illustrated in Figure 1 , 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). 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). Exemplary images of sperm tracks generated with a CASA device before and after incubation are illustrated in Figures 3A-3B, respectively. The movement parameters of individual ones of the plurality of sperm are classified into one of at least four classifications (Block 20). For example, 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. , vigorous movement with many seemingly random variations without a well-defined progressive path and turns of the sperm head of greater than 90 degrees)), Figure 3G (slow motility (i. e. , relatively slow movement that may progress generally along a path with net forward movement)), and Figure 3H (weak motility (i. e. , less active motility than slow non- vigorous motility, with little net forward movement )).

[0043] 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. As illustrated in Figure 2, 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. 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.

[0044] In particular, 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. In some embodiments of the present invention, the memory 136 can be a content addressable memory (CAM).

[0045] As further illustrated in Figure 2, 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. As will be appreciated by those of skill in the art, 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 Linux™. 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. Finally, 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.

[0046] 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,

[0047] While the present invention is illustrated with reference to the sperm analyzer module 120, the sperm motility data 124, and CASA parameter data 126 in Figure 1 , as will be appreciated by those of skill in the art, other configurations fall within the scope of the present invention. For example, rather than being an application program 154, these circuits and modules can also be incorporated into the operating system 152 or other such logical division of the data processing system. Furthermore, while the sperm analyzer module 120 in Figure 2 is illustrated in a single data processing system, as will be appreciated by those of skill in the art, such functionality can be distributed across one or more data processing systems. Thus, 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. For example, although 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. In some embodiments, the operating system 152, programs 154 and data 156 may be provided as an integrated part of the sample analyzer 125.

[0048] In some embodiments, 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:

1. Average path velocity (VAP) is a smoothing of the path of the center of brightness of the spermatozoon, which reduces the effect of the lateral head displacement.

2. Straight-line velocity (VSL) is the distance between the first and last tracked point of the spermatozoon trajectory divided by the time elapsed. 3. Curvilinear velocity (VCL) is the sum of the distances between each center of brightness, during each frame, divided by the time elapsed.

4. Amplitude of lateral head displacement (ALH) is the maximum value of the distance of any point on the track from the corresponding average path, multiplied by two.

5. Beat cross frequency (BCF) is the frequency with which the cell track crosses the cell path in either direction.

[0049] In some embodiments, 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. In all of the equations herein, it should be understood that the weighting coefficients, constants, and equation values may be either positive or negative. Moreover, the values of VAP, VSP, VCL, ALH and BCF are as follows: VAP is the average path velocity (μητ/sec), VSL is the straight-line velocity (μιη/sec), VCL is the curvilinear velocity (μηι/sec), ALH is the amplitude of lateral head displacement (μη ) and BCF is the beat cross frequency (Hz).

[0050] The probabilistic binary classification set of equations may be configured as illustrated in Figure 4B. As shown 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 = Cv_/VAP + C_v2VSL + C_v5VCL + C_WALH + C_v5BCF + k_h

where 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 and 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.

[0051] 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:

Eq₂ = C/,_/VAP + C_/;2VSL + C_WVCL + C_WALH + C_h5BCF + k₂,

where Eq₂ is a value that is greater than or less than zero, CMS are weighting coefficients, k₂ 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 Eq₂ is greater than zero, then the sperm motility is classified as hyperactivated. If the value of Eq₂ is less than zero, then the sperm motility if classified as non-hyperactivated.

[0052] 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:

Eq₃ = C _/VAP + C_ip2VSL + C_ip3VC + C^ALH + C_{; 5}BCF + k₃,

where Eq₃ is a value that is greater than or less than zero, C_ipi.s are weighting coefficients, /¾ 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 Eq₃ is greater than zero, then the sperm motility is classified as having intermediate motility. If the value of Eq₃ is less than zero, then the sperm motility if classified as having progressive motility.

[0053] 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:

Eq₄ = C^_/VAP + C_sw2YSL + C_iw5VCL + C_i ALH + C_NV5BCF + k₄,

where Eq₄ 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 Eq₄ is greater than zero, then the sperm motility is classified as having slow motility. If the value of Eq₃ is less than zero, then the sperm motility if classified as having weak motility.

[0054] The design of the multiclass SVM model to quickly distinguish and quantitatively identify at least four patterns of motility (and in some embodiments, five patterns of motility) along with its applicability to sperm from multiple mouse strains, suggests that this model will be useful for more detailed analyses of sperm motility under a variety of experimental conditions. Inclusion of the slow category in our model revealed that sperm from C57BL/6J mice are substantially less vigorous at initial time points than sperm from two other inbred strains (129S l/SvImJ and PWK/PhJ). This difference is interesting since both C57BL/6J and 129S l/SvImJ strains are used extensively in the production of knockout and transgenic mouse lines. In addition, all three of these inbred strains are founder strains of the Collaborative Cross, a project that is producing a large number of recombinant inbred lines with extensive genetic diversity. Given the phenotypic variation observed in these inbred lines, it may be of interest to complete a similar analysis of sperm motility with fully inbred Collaborative Cross lines. The multiclass SVM model may contribute to more quantitative analyses of genetic factors that influence the complex traits of sperm motility and male fertility.

[0055] Gene targeting strategies have produced more than forty mutant mouse models with defects in sperm motility. Matzuk MM, Lamb DJ. The biology of infertility: research advances and clinical challenges. Nat Med 2008; 14: 1 197-1213. These include a broad range of structural, metabolic and/or signaling defects that reduce the proportion of motile sperm and/or impair required aspects of sperm movement such as forward progression or hyperactivation. The inclusion of multiple categories in the SVM multiclass model may facilitate rapid assessment of motility deficits, as demonstrated for GAPDHS-null sperm. Although further validation may be useful for sperm with severe structural defects and/or unusual motility patterns, 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).

[0056] Toxicological studies are being conducted to investigate the effects of various compounds on reproductive function in mice. In cases where male reproductive toxicity is observed, use of the multiclass SVM model may improve discrimination of effects on physiologically relevant patterns of motility that are not easily discerned by comparison of individual CASA parameters. Similarly, the model may serve as an important screening tool for evaluating compounds as potential and/or known contraceptives that impair sperm motility, particularly when mechanisms that regulate hyperactivation are targeted.

[0057] Motility classification by the multiclass SVM model may be rapid,

reproducible and quantitative. 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. In some embodiments, 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.

[0058] Accordingly, a multiclass SVM model for classifying sperm motility is provided that 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. For example, 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.

[0059] For example, an agent, such as a potential and/or known toxin or contraceptive drug, may be evaluated as follows. 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. In some embodiments, 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. As another example, 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. Thus, 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).

[0060] Embodiments according to the present invention will now be described with respect to the following non-limiting examples.

[0061] Example 1

[0062] Support Vector Machines (SVM) 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.,

"Classification of Mouse Sperm Motility Patterns using an Automated Multiclass Support Vector Machines Model, Biology of Reproduction, 84, 1207-1215 (201 1).

[0063] Materials and Methods

[0064] 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).

[0065] 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 KH₂P0₄, 0.2 mM MgS0₄'7 H₂0, 2 mM CaCl₂, 25 mM NaHC0₃, 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. For all media, the osmolality was adjusted to -315 mOsm/kg with 5M NaCl using a Model 3300 micro-osmometer (Advanced Instruments, Norwood, MA).

[0066] Animals and Sperm Collection

[0067] Adult CD1 male mice were purchased from Charles River Laboratories

(Raleigh, NC) and allowed to acclimatize before use. C57BL/6J, 129Sl/SvImJ, and

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.

[0068] Sperm were collected from the cauda epididymides of sexually mature (>8 weeks) mice. Each cauda was carefully trimmed to remove adipose and other tissue, rinsed in PBS (140 mM NaCl, 3mM KC1, 4 mM NaH₂P0₄-7H₂0, 1.4 mM KH₂P0₄, pH 7.4), and placed in 1 ml HTF media lacking both bicarbonate and energy substrates. Four to six cuts were made in each cauda using iris scissors, and sperm were released into the media by incubation for 10 min at 37° C under 5% C0₂ and air. After the incubation, the tissue was removed and the suspension was mixed gently by swirling. This suspension was then diluted 1 : 20 to 1 :60 in HTF complete medium to a concentration of -2-4 x 10⁵ sperm/ml, equivalent to 50-120 sperm per microscope field for CAS A when using the following equipment and settings, For the analysis of motility under non-capacitating conditions, sperm were diluted at the same ratio into bicarbonate-free HTF. [0069] Analysis of Sperm Motility

[0070] After dilution in HTF complete medium or bicarbonate-free HTF, sperm were incubated for 2 h at 37° C under 5% C0₂ 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. The default Mouse 2 analysis settings provided by Hamilton Thorne were used, except that 90 frames were recorded and slow cells were counted as motile. These settings include: 60 frames per second, 90 frames acquired, minimum contrast = 30, minimum size = 4 pixels, default cell size = 13 pixels, default cell intensity = 75, cells progressive if VAP >50 μιτι/sec and STR > 50%, slow cells counted as motile, low VAP cut off = 10 μιη/sec , low VSL cutoff = 0 μηι/sec , minimum intensity gate = 0.10, maximum intensity gate = 1.52, minimum size gate = 0.13 pixels, maximum size gate = 2.43 pixels, minimum elongation gate = 5 pixels, and maximum elongation gate = 100 pixels.

[0071] Sperm suspensions were gently mixed before measuring motility. For each motility measurement, a 25 μΐ aliquot of sperm suspension was loaded by capillary action using a large bore pipet tip into one chamber of a pre-warmed Leja slide (100 μπι-deep, Leja, The Netherlands). To minimize drift in the media, excess liquid was removed from the outside of the slide by blotting with a laboratory tissue as recommended by the manufacturer, and loading was examined to ensure the absence of air pockets in the chamber. At least 10 fields were recorded for each sample analyzed, covering the entire viewable area of the chamber without overlapping successive fields. Tracks and kinematic parameters were recorded for individual sperm. 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.

[0072] SVM Model Training

[0073] A training set was created from sperm analyzed after 90 min of incubation in

HTF complete medium at 37° in an atmosphere of 5% C0₂ 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. As illustrated in Figure 4B, classified tracks were labeled as "vigorous" (progressive, intermediate, hyperactivated) or "non-vigorous" (slow and weakly motile). The function then generated an equation that best separates the two groups of data in

multidimensional space, This process was repeated within the vigorous and non-vigorous groups to generate four equations used to classify motility patterns as shown in Figure 4B

[0074] Statistical Analysis

[0075] Statistical analyses were performed using GraphPad Prism 5 (GraphPad

Software, La Jolla, CA), All data are shown as mean ± SEM. Statistical significance was determined using either two-tailed unpaired t-tests or by one-way ANOVA after arcsine transformation of percentages. Differences were considered significant if P < 0.05. The percent agreement of the model was evaluated by calculating Cohen's Kappa Coefficient. Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 1960; 20:37-46.

[0076] Results

[0077] Characterization of Sperm Motility Patterns

[0078] Current CASA instruments capture multiple images and generate tracks for each sperm by marking the position of the head in successive frames as shown in Figures 3 A- 3B. Visual examination of tracks recorded immediately after isolation of sperm from the cauda epididymis (Figure 3B) and after in vitro capacitation for 90 min (Figure 3B) indicates that motility changes during this interval from predominantly progressive to more varied profiles that are less linear. The first step in developing a quantitative model that

distinguishes these different types of motility was to generate a training set of sperm tracks for analysis. CASA tracks were collected of sperm incubated for 90 minutes in HTF complete medium and assessed both sperm motion and track pattern with the playback function in the software. Tracks of sperm from 12 CD1 mice were classified as progressive, intermediate, hyperactivated, slow, or weakly motile. Figures 3A-33C-3G illustrates examples of all motility patterns, and Table 1 provides the mean values of all kinematic parameters associated with each group.

Table 1

Table 1. CASA parameter means for groups identified in the multiclass training set.*

Group VAP VSL VCL ALH BCF

Progressive 146.9 ±31 .5 1 19.5 ±29.5 279.6 ±59.1 16.7 ±4.5 25.4 ± 5.2

Intermediate 183.3 ±31.1 140.4 ±28.5 406.3 ±61 .5 23.7 ±4.1 21 .1 ± 4.1

Hyperactivated 171.1 ±37.9 73.3 ±36.1 373.6 ±78.4 22.7 ±5.4 29.1 ±1 1.8

Slow 85.2 ±19.0 40.2 ±20.5 175.6 ±32.4 12.3 ±4.3 33.8 ±10.3

Weakly Motile 56.2 ±16.5 13.6 ± 6.8 127.2 ±36.4 9.9 ±4.0 45.3 ±12.6

*Values are means ±standard deviation. 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.

[0079] Sperm with vigorous motility (progressive, intermediate or hyperactivated) in the training set had mean VCL values >279 μηι/sec, while the non-vigorous groups (slow and weakly motile) had mean VCL values <176 μιη/sec . Additional examples of each motility pattern are shown at higher magnification in Figures 8A-8E. Tracks classified as progressive were typically very straight with little deviation of the head from the average path and angles between consecutive points of less than 90 degrees along the majority of the track (track "a" in Figures 3A and 3B and Figure 8A), Intermediate sperm tracks showed more vigorous motion, with larger deviations from the net direction of movement and angles of

approximately 90 degrees along most of the track length (track "b" in Figure 3B and Figure 8B). Sperm were classified as hyperactivated if they displayed highly vigorous motility accompanied by turns of greater than 90 degrees between consecutive points along the majority of the track. This category includes both the classic star-spin pattern of motility (track "c" in Figure 3B and Figure 8C) and sperm tracks that exhibit large deviations from the average path but maintain a more defined direction of movement (track "d" in Figure 3B and Figure 8C). Slow sperm tracks covered much less distance than progressive sperm and generally did not show a high level of displacement of the head from the path of movement (track "e" in Figure 3B and Figure 8D). 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). To maintain very strict criteria for defining the five motility patterns, 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.

[0080] Development of the Multiclass SVM Model

[0081] 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 (μιη/sec), VSL (μιη/sec), VCL (μηι/sec), ALH (μιη) 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. After generating the model using tracks from 90 min time points, 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.

[0082] 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) clustered separately from progressive sperm tracks (red data points). Intermediate tracks (green data points) clustered between these groups, indicating that this motility pattern may constitute a phase that occurs between progressive and hyperactivated states. Tracks classified as weakly motile (cyan data points) generally had the lowest velocities and were grouped below the slow tracks (black points).

[0083] The clustering of sperm tracks based on their visual classification as shown in

Figure 4A suggested that sperm motility patterns could be mathematically separated into groups using support vector machines. A set of equations were developed that

mathematically define the boundaries which distinguish these clusters as shown in Table 2. Each equation includes all independent CASA parameters, and the number multiplied by each parameter reflects its relative importance in that equation. In 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,

hyperactivated) and non-vigorous (slow and weakly motile). 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.

Table 2

TABLE 2. Multiclass Support Vector Machine (SVM) equations.

SV 1 (0.0424 x VAP) + (0.0315 x VSL) + (0.0133 x VCL) - (0.0202 x ALH) - (0.0090 x BCF) - 8 .8690 SVM2 (0.017 x VAP) - (0.0987 x VSL) + (0.0266 x VCL) + (0.0526 x ALH) + (0.0252 x BCF) - 3.8861 SVM3 (0.0216 x VAP) - (0.0708 x VSL) + (0.0330 x VCL) + (0.0337 x ALH) - (0.0235 x BCF) -6.3523 SV 4 (0.0393 x VAP) + (0.1004x VSL) - (0.0094 x VCL) + (0.0053 x ALH) - (0.0296 x BCF) - 4.2373

VAP = average path velocity in μΓη/sec, VSL = straight line velocity in μιτι/sec, VCL = curvilinear velocity in μηη/sec, ALH = amplitude of lateral head displacement in μηη, BCF = beat cross frequency in Hz. [0084] Within the vigorous group, two additional SVM equations were developed,

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.

[0085] 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). 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.

[0086] To simplify the classification process, 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.

[0087] Accuracy of Track Identification with the Multiclass SVM Model

[0088] To assess the ability of the multiclass SVM model to accurately classify sperm motility tracks, the motility patterns of 1,068 sperm tracks from four additional CD 1 mice after incubation for 90 min in HTF complete medium were visually classified. It was determined how many of these tracks were correctly assessed by the motility model, and the percent agreement was calculated as shown in Table 3. All tracks were included in these assessments. In agreement with the visual classification, it was noted that tracks of sperm with hairpin bends at the annulus were identified almost exclusively as slow or progressive. Tracks from agglutinated sperm were typically excluded from the analysis because of size exclusions in the software or short tracks with less than 45 points. Tracks derived from sperm collisions and errors in tracking were also greatly reduced by requiring a minimum of 45 points. Agreement in all groups, except intermediate, exceeded 83% and the overall agreement was 88.2%. Just over half of the visually-identified intermediate tracks in this set (52.1%) agreed with the model classification, while the model classified the remaining tracks in this group as progressive or hyperactivated in approximately equal proportions. Cohen's Kappa, a measurement of interrater reliability (Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 1960; 20:37-46), was also used to assess the overall level of agreement between visual and multiclass SVM model classifications. This index indicates that the strength of agreement between these

classifications is very good, with a Kappa coefficient of 0.848, which is within the 95% confidence interval.

Table 3

TABLE 3. Agreement between visual and model-assigned tracks.

Track group agree/total % agreement

Progressive 197/231 85.3

Intermediate 38/73 52.1

Hyperactivated 249/265 94.0

Slow 229/248 92.3

Weakly Motile 64/83 77.1

Total 777/900 86.3

[0089] Motility Profiles of Sperm Incubated in Capacitating and Non- capacitating Medium

[0090] Bicarbonate is required for sperm capacitation as well as the acquisition of hyperactivated motility. To assess the ability of the model to distinguish motility pattern distributions of capacitated and non-capacitated sperm, 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. Among vigorously motile tracks, both intermediate and hyperactivated tracks increased by 60 min and reached maximum values by 90 min (box, Figure 5A), In this experiment, progressive tracks declined to 22.2%o of all motile tracks by 90 min, while intermediate tracks increased to 9% and hyperactivated tracks increased to 18.9%. The proportion of sperm with non-vigorous motility (slow or weakly motile) also increased during in vitro capacitation, reaching 49.9% by 90 min. Further increases in non-vigorous motility were frequently observed by the 2 h time point. We confirmed that the model is effective when sperm are analyzed in chambers with depths of either 100 μιτι (Leja) or 80 μηι (2X-cel slides, Hamilton Thome) (Figures 10A-10B).

[0091] Sperm incubated in medium that does not stimulate capacitation (Figure 5B) showed a significantly different motility distribution than sperm incubated for the same period in HTF complete medium (Figure 5A). When bicarbonate was omitted from the medium, the mean proportion of progressive sperm tracks remained above 50% over the entire time course, in agreement with visual observations. The proportion of non-vigorous tracks increased throughout the incubation, although the percentage of weakly motile sperm remained lower than observed in HTF complete medium. As expected, intermediate and hyperactivated tracks were rarely observed throughout the incubation, reaching mean values of 0.4% and 1.2%, respectively, by 90 min (red box, Fig. 3B).

[0092] Analysis of Sperm with Motility Defects

[0093] To determine how the multiclass SVM model classifies sperm with severe defects in motility, we assessed the motility distribution of mice lacking the sperm-specific glycolytic enzyme glyceraldehyde 3 -phosphate dehydrogenase (GAPDHS) (Figure 7). These males are infertile and produce sperm that are motile (Figure 9B), but exhibit little forward progression. The model classified 99% of GAPDHS-null sperm as weakly motile or slow (Figure 7, open bars). Sperm from wild-type mice with the same genetic background (mixed 129S6/SvEvTac and C57BL/6NCii) displayed predominantly progressive motility (Figure 7, black bars). Approximately 17% of wild-type sperm were classified as slow immediately after isolation, perhaps reflecting contributions of the C57BL/6 genetic background.

[0094] Motility Profiles of Sperm from Inbred Mouse Strains

[0095] The multiclass SVM model using sperm tracks from CD1 outbred mice was developed. To determine its suitability for assessing sperm motility in other mouse strains, the model was applied to sperm from C57BL/6J (BL6), 129S l/SvImJ (129) and PWK/PhJ (PWK) inbred mouse strains over a 90 min incubation in HTF complete medium (assayed in parallel, n = 4 mice per strain). Motility profiles were generated for each strain and compared to CD1 profiles (Figure 6A). Sperm from two inbred strains (129 and PWK) exhibited motility profiles that were comparable to CD1 sperm immediately after isolation, except that a higher percentage of 129 sperm were classified as slow (0 min). At this initial time point, BL6 sperm tracks contained significantly fewer progressive tracks (mean = 23%, P < 0.001) and significantly more slow tracks (mean = 66.4%, P <0.001) compared to CD1 sperm (79.2% progressive, 4.2% slow). This proportion of slow tracks persisted in BL6 sperm throughout the assay.

[0096] Throughout the time course, sperm from the three inbred strains showed the expected decrease in the percentage of progressive motility and concomitant increase in other motility classes (Figure 6A). After 90 min of incubation, the mean percentage of slow tracks was significantly higher for both BL6 (59.5%) and 129 (60.6%) sperm than for CD1 (40.9%) sperm. At this time point, the mean percentages of hyperactivated sperm in both BL6 (16%) and 129 (15%) mice were significantly lower than the levels observed in CD1 mice (32%), while PWK hyperactivation was comparable to CD1. To determine whether reduced hyperactivation was due to the inability of the model to identify hyperactivated sperm in BL6 and 129 mice, the percentage of hyperactivated sperm for each strain was visually assessed. There were no statistically significant differences between hyperactivation levels determined visually or by using the multiclass SVM model (Figure 1 1). BL6 sperm did display more asymmetric tracks than 129 sperm at 90 min, but many of these tracks had low velocities and were not classified as hyperactivated by the model or visual assessment.

[0097] The multiclass SVM model classifies all motile sperm in each population.

When the number of sperm analyzed or the percent motility varies substantially between animals, additional considerations may be needed for a more complete assessment of motility. Fewer sperm were typically recovered from these inbred strains (mean = 5.7 - 16.7 x 10⁶) than from CD1 mice (mean = 31 x 10⁶), but analyzed >150 tracks/mouse at each time point to provide robust assessments of sperm motility in each strain. The percentage of motile sperm at later time points was significantly lower in PWK mice (mean = 26.5% at 90 minutes) compared to the other strains (37% - 50%, Figure 9C). Therefore, we also calculated the percentage of hyperactivated sperm as a function of the total number of sperm analyzed by CASA at the 90 min time point (Figure 6B). When immotile sperm are included in the calculation, the percentage of hyperactivated sperm in PWK mice falls to levels that are comparable to those observed for the other two inbred strains.

[0098] Analysis

[0099] During capacitation 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.

[00100] Immediately after isolation from the cauda epididymis, 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 μιτι/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. In contrast, 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 classifies sperm as progressive only if they have motility that is both linear and vigorous, while sperm that have linear tracks with substantially lower velocities (mean VAP = 85.2 ± 19.0 in our training set) are classified as slow. Inclusion of the non-vigorous classifications in this model provides better discrimination of sperm velocities and clearly shows that the motility of many sperm becomes less vigorous as capacitation proceeds, with >40% of the sperm classified as slow or weakly motile by 60 min (Figures 5A-5D).

[00101] The multiclass SVM model also identifies intermediate and hyperactivated tracks, the vigorous patterns of sperm motility that develop during capacitation. In the training set 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. At initial time points, hyperactivation is essentially absent by visual inspection of sperm tracks (Figures 3A- 3G) and multiclass analysis reflects this observation (Figures 5A-5D). Consequently, 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. As expected, 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.

[00102] The percentage of intermediate tracks increases to -9% of the motile population over the same time course (Figures 5A-5D). Analyses of multidimensional scatter plots show that the intermediate tracks cluster between the progressive and hyperactivated groups (Figures 4A-4B), suggesting that the intermediate category may represent sperm shifting from progressive to hyperactivated motility. Similar transitional patterns of sperm motility have been described in other species. The loss of both intermediate and

hyperactivated tracks when bicarbonate is omitted from the medium (Figures 5A-5D) provides support for considering intermediate sperm as a subset of the population developing asymmetric flagellar beats, and highlights the ability of the multiclass SVM model to accurately identify physiological alterations in motility that occur during capacitation.

[00103] Example 2

[00104] 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.

[00105] For example, Glyceraldehyde 3 -phosphate dehydrogenase, spermatogenic (GAPDHS) is expressed only during the post-meiotic period of spermatogenesis and is the sole GAPDH isozyme in mammalian sperm. Gene targeting studies in mice demonstrate that GAPDHS and other sperm-specific isozymes in the glycolytic pathway are essential for sperm motility and male fertility. These studies demonstrate the importance of glycolysis for sperm energy production. They also confirm that inhibition of GAPDHS should not impair testicular function, since sperm production appears normal in mice lacking this enzyme. Earlier studies identified competitive substrate inhibitors of GAPDHS that suppress sperm glycolysis and motility at concentrations that do not inhibit GAPDH, the isozyme present in all somatic tissues. Homology modeling efforts also identified structural features near the active site in GAPDHS that are distinct from GAPDH. Therefore, 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.

[00106] Ninety thousand compounds were screened, and two series of compounds that inhibit recombinant human GAPDHS with IC50 values below 10 μΜ were identified. Initial assays indicate that multiple compounds in each series inhibit sperm motility. The motility of mouse sperm at 30 min intervals was assessed using the classification systems described herein to identify a percentage of sperm in the control and test samples that are progressive, intermediate, hyperactivated, slow and weakly motile. Control sperm incubated for 90 min in HTF medium displayed -60% vigorous motility, including progressive, intermediate and hyperactivated patterns. In contrast, the addition of GAPDHS inhibitors reduced the percentage of motile sperm and/or eliminated vigorous motility, including hyperactivation. Both medicinal chemistry efforts and additional assays of sperm function are underway to further develop these compounds for the selective inhibition of GAPDHS to achieve post- testicular, non-hormonal contraception as indicated in Table 4. TABLE 4

[00107] Compounds in series A and B were tested at a concentration of 50 μΜ to determine their effects on the motility of mouse sperm during in vitro capacitation in HTF medium. Motility was assessed by computer-assisted sperm analysis (CAS A). Alterations in motility profiles were observed with all 9 inhibitors. Three compounds in series A (Al, A2, A3) and one compound in series B (B4) reduced motility below 10% during the incubation period as shown in Figure 12A-12B.

[00108] As illustrated in Figures 13A-13H, the concentration-dependent effects on mouse sperm motility and viability were assayed during in vitro capacitation in HTF medium in three independent experiments. Effects of A2 and B4 on motility are shown in Figures 13A-13H. As illustrated, both compounds reduced the percentage of motile sperm at 20 μΜ, with more pronounced and earlier inhibition at higher concentrations. The analysis of motility profiles demonstrated that both compounds reduced vigorous motility and inhibited capacitation-dependent hyperactivation. Adverse effects on plasma membrane permeability were not observed at these concentrations.

[00109] 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.

[00110] Any agent of interest can be screened according to embodiments of the present invention. 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. In particular embodiments, the agent is an antisense nucleic acid, an siRNA, shRNA, miRNA or a ribozyme that inhibits production of a target polypeptide.

[00111] Small organic compounds (e.g. , "non-oligomers") 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,

[00112] Oligomers include oligopeptides, oligonucleotides, oligosaccharides, polylipids, polyesters, polyamides, polyurethanes, polyureas, polyefhers, and poly

(phosphorus derivatives), e.g. phosphates, phosphonates, phosphoramides, phosphonamides, phosphites, phosphinamides, etc., poly (sulfur derivatives) e.g., sulfones, sulfonates, sulfites, sulfonamides, sulfenamides, etc. , where for the phosphorous and sulfur derivatives the indicated heteroatom for the most part will be bonded to C,H,N,0 or S, and combinations thereof. Such oligomers may be obtained from combinatorial libraries in accordance with known techniques.

[00113] Further, 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. 5,817,751), a polypeptide library, a cDNA library, a library of antisense nucleic acids, siRNA, shRNA, miRNA and the like, or an arrayed collection of agents such as polypeptide and nucleic acid arrays. [00114] The foregoing is illustrative of the present invention and is not to be construed as limiting thereof. Although a few exemplary embodiments of this invention have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope of this invention as defined in the claims. Therefore, it is to be understood that the foregoing is illustrative of the present invention and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The invention is defined by the following claims, with equivalents of the claims to be included therein.

Claims

Attorney Docket No. 5470.574WO THAT WHICH IS CLAIMED IS:

1. A method of classifying sperm motility, the method comprising:

detecting one or more movement parameters of a plurality of sperm in a sample using a computer-assisted sperm analysis device; and

classifying the movement parameters of individual ones of the plurality of sperm into one of at least four classifications, the at least four classifications selected from

hyperactivated motility, intermediate motility, progressive motility, slow motility, and weak motility.

2. The method of Claim 1 , wherein the movement parameters comprise average path velocity (VAP), straight-line velocity (VSL), curvilinear velocity (VCL), amplitude of lateral head displacement (ALH) and beat cross frequency (BCF).

3. The method of Claim 2, wherein classifying the movement parameters of individual ones of the plurality of sperm into one of at least four classifications comprises a first formula as follows:

Eqi = Cv_/VAP + C_v2VSL + C_viVCL + C ALH + C_vJBCF + kj,

wherein Eqi is a value that is greater than or less than zero, C_vis 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,

wherein if the value of Eqi is greater than zero, then the sperm motility is classified as vigorous, and if the value of Eqi is less than zero, then the sperm motility if classified as non- vigorous.

4. The method of Claim 3, wherein classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises applying a second formula to individual sperm classified as vigorous as follows:

Eq₂ = C_/,₇VAP + C/,₂VSL + C_/;3VCL + C_WALH + C_/) BCF + k₂, wherein Eq₂ is a value that is greater than or less than zero, CMS are weighting coefficients, /¾ 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,

wherein if the value of Eq₂ is greater than zero, then the sperm motility is classified as hyperactivated, and if the value of Eq₂ is less than zero, then the sperm motility if classified as non-hyperactivated.

5. The method of Claim 4, wherein classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises applying a third formula to individual sperm classified as non-hyperactivated as follows:

Eq₃ = C_LPLVAP + C^VSL + C^VCL + Q*ALH + C^BCF + k₃,

wherein Eq₃ is a value that is greater than or less than zero, C_ipis are weighting coefficients, /¾ 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,

wherein if the value of Eq₃ is greater than zero, then the sperm motility is classified as having intermediate motility, and if the value of Eq₃ is less than zero, then the sperm motility if classified as having progressive motility.

6, The method of Claim 5, wherein classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises applying a fourth formula to individual sperm classified as non-vigorous as follows:

Eq₄ = Q. VAP + C_w2VSL + C_MV3VCL + C^ALH + C._s,„jBCF + k₄,

wherein Eq4 is a value that is greater than or less than zero, C_sw;s 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,

wherein if the value of Eq₄ is greater than zero, then the sperm motility is classified as having slow motility, and if the value of Eq₃ is less than zero, then the sperm motility if classified as having weak motility.

7. The method of any of Claims 1-6, wherein classifying the movement parameters of individual ones of the plurality of sperm into one of the at least four

classifications comprises a regression analysis.

8. The method of any of Claims 1-7, wherein 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.

9. The method of Claim 8, wherein the empirically-based model of actual clinical experience comprises visually classifying individual ones of the plurality of sperm into one of the at least four classifications and correlating a visual classification with the one or more movement parameters of a plurality of sperm.

10. The method of any of Claims 1 -9, further comprising:

dividing the sample into a control sample and at least one test sample;

adding an agent to the test sample;

determining a change in a percentage of sperm in each of the at least four

classifications for the test sample and the control sample.

1 1. The method of Claim 10, wherein the agent is a potential and/or known contraceptive, toxin and/or fertility therapeutic agent.

12. The method of any of Claims 1-9, further comprising:

determining a percentage of the plurality of sperm in each of the at least four classifications for a plurality of samples; and

comparing the percentage of the plurality of sperm in each of the at least four classifications.

13. A system for classifying sperm motility, the system comprising:

a Computer Assisted Sperm Analyzer (CASA) configured to detect one or more movement parameters of a plurality of sperm in a sample; a motility classification module configured to classify the movement parameters of individual ones of the plurality of sperm into one of at least four classifications, the at least four classifications comprising hyperactivated motility, intermediate motility, progressive motility, motility, and weak motility.

14. The system of Claim 14, further wherein the motility classification module comprises support vector machines.

15. The system of Claim 14, wherein the movement parameters comprise average path velocity (VAP), straight-line velocity (VSL), curvilinear velocity (VCL), amplitude of lateral head displacement (ALH) and/or beat cross frequency (BCF).

16. The system of Claim 15, wherein the motility classification module is further configured to classify the movement parameters of individual ones of the plurality of sperm into one of at least four classifications by a first formula as follows:

Eq, = C_v/VAP + C VSL + C_v5VCL + C ALH + C_v5BCF + k

wherein Eqi is a value that is greater than or less than zero, C_vy.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 and BCF is the beat cross frequency,

wherein if the value of Eqi is greater than zero, then the sperm motility is classified as vigorous, and if the value of Eqi is less than zero, then the sperm motility if classified as non- vigorous,

17. The system of Claim 16, wherein the motility classification module is further configured to classify the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications by applying a second formula to individual sperm classified as vigorous as follows:

Eq₂ = C/,_/VAP + C/,₂VSL + C_/,₅VCL + C_WALH + C_WBCF + k₂,

wherein Eq₂ is a value that is greater than or less than zero, Chis are weighting coefficients, 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, wherein if the value of Eq₂ is greater than zero, then the sperm motility is classified as hyperactivated, and if the value of Eq₂ is less than zero, then the sperm motility if classified as non-hyperactivated.

18. The system of Claim 17, wherein the motility classification module is further configured to classify the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications by applying a third formula to individual sperm classified as non-hyperactivated as follows:

Eq₃ = C^VAP + C_ip2VSL + C_ip3VC + C_ip4ALH + C_ip5BC¥ + /¾,

wherein Eq₃ is a value that is greater than or less than zero,

are weighting coefficients, /¾ 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,

wherein if the value of Eq₃ is greater than zero, then the sperm motility is classified as having intermediate motility, and if the value of Eq₃ is less than zero, then the sperm motility if classified as having progressive motility.

19. The system of Claim 18, wherein the motility classification module is further configured to classify the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications by applying a fourth formula to individual sperm classified as non-vigorous as follows:

Eq₄ = C^/VAP + C^VSL + C^VCL + C_sw4ALH + C_S ,₅BCF + k₄,

wherein Eq₄ is a value that is greater than or less than zero, C_swi-5 are weighting coefficients, k₄ 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,

wherein if the value of Eq₄ is greater than zero, then the sperm motility is classified as having slow motility, and if the value of Eq₃ is less than zero, then the sperm motility if classified as having weak motility,

20. The system of any of Claims 13-19, wherein the motility classification module is further configured to classify the movement parameters of individual ones of the plurality of sperm into one of the at least four classifications comprises a regression analysis.

21. The system of any of Claims 13-20, wherein the motility classification module is further configured to classify 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.

22. The system of Claim 21, wherein the empirically-based model of actual clinical experience comprises visually classifying individual ones of the plurality of sperm into one of the at least four classifications and correlating a visual classification with the one or more movement parameters of a plurality of sperm.

23. The system of any of Claims 13-22, wherein the motility classification module is configured to compare the motility of a control sample and a test sample, wherein an agent is added to the test sample, and the motility classification module is configured to determine a change in a percentage of sperm in each of the at least four classifications for the test sample and the control sample.

24. The system of Claim 23, wherein the agent is a potential and/or known contraceptive, toxin and/or fertility therapeutic agent.

25. The system of any of Claims 13-21, wherein the motility classification module is further configured to determine a percentage of the plurality of sperm in each of the at least four classifications for a plurality of samples and compare the percentage of the plurality of sperm in each of the at least four classifications.

26. A method of identifying an agent that modulates sperm motility in a sperm ample, the method comprising:

dividing the sample into a control sample and at least one test sample;

adding the agent to the test sample;

detecting one or more movement parameters of a plurality of sperm in the control sample and the test sample using a computer-assisted sperm analysis device; and

classifying the movement parameters of individual ones of the plurality of sperm of the control sample and the test sample into one of at least four classifications, the at least four classifications being selected from hyperactivated motility, intermediate motility, progressive motility, slow motility, and weak motility;

determining a modulation in a percentage of sperm in each of the at least four classifications for the test sample and the control sample.