Additional value of computer assisted semen analysis (CASA) compared to conventional motility assessments in pig artificial insemination

Artificial intelligence and porcine breeding

Livestock management is evolving into a new era, characterized by the analysis of vast quantities of data (Big Data) collected from both traditional breeding methods and new technologies such as sensors, automated monitoring system, and advanced analytics. Artificial intelligence (A-In), which refers to the capability of machines to mimic human intelligence, including subfields like machine learning and deep learning, is playing a pivotal role in this transformation. A wide array of A-In techniques, successfully employed in various industrial and scientific contexts, are now being integrated into mainstream livestock management practices. In the case of swine breeding, while traditional methods have yielded considerable success, the increasing amount of information requires the adoption of new technologies such as A-In to drive productivity, enhance animal welfare, and reduce environmental impact. Current findings suggest that these techniques have the potential to match or exceed the performance of traditional methods, often being more scalable in terms of efficiency and sustainability within the breeding industry. This review provides insights into the application of A-In in porcine breeding, from the perspectives of both sows (including welfare and reproductive management) and boars (including semen quality and health), and explores new approaches which are already being applied in other species.

Influence of the coincubation of post-thawed bull semen with elevated β-hydroxybutyrate concentrations on sperm characteristics

In this study, the relationships between post-thaw bull sperm characteristics and hyperketonemic conditions after coincubation with cow plasma or media were determined to investigate if such a condition could affect bull sperm characteristics. Two experiments were conducted. In experiment 1, blood samples were collected from 31 cows to prepare plasma. Cows were independently categorized into two groups according to plasma β-hydroxybutyrate (BHB) concentrations (above or below 1.2 mM). Thawed bull semen was diluted and incubated with diluted plasma; motility parameters were evaluated using Computer Assisted Semen Analysis (CASA). In experiment 2, a pooled sample of thawed semen was diluted and divided into three aliquots: without BHB (control) and treated with either 1.2 mM (1.2) or 3 mM (3) BHB. In addition to motility, flow cytometric analyses were carried out. In experiment 1, the overall motility decreased significantly in plasma containing high (≥1.2 mM) BHB compared to plasma containing low (<1.2 mM) BHB. In experiment 2, the overall motility tended to be lower in BHB (3 mM)-supplemented samples. The supplementation of 3 mM BHB increased the proportion of live superoxide-positive sperm and sperm with high mitochondrial potential, while the DNA fragmentation index decreased.

Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Artificial insemination in the swine industry, as in other species, demands adequate semen handling and accurate evaluation for the preparation of seminal doses. Sperm concentration and motility estimates are part of the semen evaluation process and are considered important for maximizing the yield of doses for insemination. In this study, methods were examined for their accuracy in the estimation of boar sperm concentration and motility. Assessments of sperm concentration were carried out using iSperm^®, ISAS^® v1, Open CASA v2, and the Accuread^® photometer. Analyses of sperm motility were performed with iSperm^®, ISAS^® v1, and Open CASA v2 systems. In this study, boar semen samples were collected from 10 healthy males from two genetic lines. There were no relevant differences between sire lines when sperm concentration was assessed. A Bayesian analysis was applied to the four methods used to assess sperm concentration to examine whether there are relevant differences between them. Results suggested differences in the four methods, with a probability of relevance (PR) of 0.86-1.00. The iSperm^® method revealed higher concentration values within the highest posterior density region at 95% confidence interval (HPD_95%) = 167.0, 224.2 M/mL, whereas Open CASA v2 showed the lowest values, with HPD_95% = 99.3, 155.9 M/mL. The iSperm^® demonstrated higher reliability in measuring sperm concentration compared to other methods or devices within the given range of confidence. ANOVAs revealed relevant differences in the three methods of motility estimation. Overall, differences in boar sperm concentration and motility estimates were found using various methods, but further studies are needed for better characterization of these differences.

Animal board invited review: An update on the methods for semen quality evaluation in swine - from farm to the lab

Pig breeding is mainly conducted through artificial insemination with liquid-stored semen. It is, therefore, crucial to ensure that sperm quality is over the standard thresholds, as reduced sperm motility, morphology or plasma membrane integrity are associated with reduced farrowing rates and litter sizes. This work aims to summarise the methods utilised in farms and research laboratories to evaluate sperm quality in pigs. The conventional spermiogram consists in the assessment of sperm concentration, motility and morphology, which are the most estimated variables in farms. Yet, while the determination of these sperm parameters is enough for farms to prepare seminal doses, other tests, usually carried out in specialised laboratories, may be required when boar studs exhibit a decreased reproductive performance. These methods include the evaluation of functional sperm parameters, such as plasma membrane integrity and fluidity, intracellular levels of calcium and reactive oxygen species, mitochondrial activity, and acrosome integrity, using fluorescent probes and flow cytometry. Furthermore, sperm chromatin condensation and DNA integrity, despite not being routinely assessed, may also help determine the causes of reduced fertilising capacity. Sperm DNA integrity can be evaluated through direct (Comet, transferase deoxynucleotide nick end labelling (TUNEL) and its in situ nick variant) or indirect tests (Sperm Chromatin Structure Assay, Sperm Chromatin Dispersion Test), whereas chromatin condensation can be determined with Chromomycin A3. Considering the high degree of chromatin packaging in pig sperm, which only have protamine 1, growing evidence suggests that complete decondensation of that chromatin is needed before DNA fragmentation through TUNEL or Comet can be examined.

Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled Dataset

Infertility has recently emerged as a severe medical problem. The essential elements in male infertility are sperm morphology, sperm motility, and sperm density. In order to analyze sperm motility, density, and morphology, laboratory experts do a semen analysis. However, it is simple to err when using a subjective interpretation based on laboratory observation. In this work, a computer-aided sperm count estimation approach is suggested to lessen the impact of experts in semen analysis. Object detection techniques concentrating on sperm motility estimate the number of active sperm in the semen. This study provides an overview of other techniques that we can compare. The Visem dataset from the Association for Computing Machinery was used to test the proposed strategy. We created a labelled dataset to prove that our network can detect sperms in images. The best not-super tuned result is mAP 72.15.

Cryopreservation of jaguar (Panthera onca) sperm cells using different cryoprotectants and different thawing temperatures

The cryopreservation of jaguar semen must be improved to produce high-quality biobanking doses. Until now, the rare studies of semen freezing in the species have only evaluated glycerol, always with a significant reduction in sperm quality in thawed semen. The purpose of this study was to assess the efficacy of three cryoprotectants, dimethylsulfoxide (DMSO), glycerol (GLY), and methanol (MET), in the cryopreservation of jaguar semen in an LDL-based extender, as well as the effect of thawing temperature on dosage quality. Five mature males with a history of reproduction were used. On the males, an infrared thermal image (IRT) was captured, the spicules and testes were analyzed, and the CASA system was used to evaluate the quality of fresh and thawed sperm. The superficial IRT was 4.6 ± 1.2 °C cooler than the anal sphincter, and the semen measured between 27.3 and 28.7 °C shortly after exiting the urethra. The total motility of fresh sperm was 55.3 ± 22.6%, and progressive motility was 36.3 ± 18%. The total motility of thawed sperm was 5.28 ± 2.51%, 4.49 ± %2.49, and 0.51 ± 0.62% for DMSO, GLY, and MET, respectively. DMSO and GLY performed better than MET, and there was no difference in thawing temperature (37°C 30 s vs. 50°C 12 s). All animals exhibit a considerable level of morphological changes in sperm. Low amounts of total and progressive motility were found in the thawed sperm. Males with a high level of sperm morphological changes were found to be fertile, but the lone male with normospermia was infertile. Thus, we contest the applicability of the commonly used morphological classification for bovines to felid species.

Motility Assessment of Ram Spermatozoa

For successful fertilisation to occur, spermatozoa need to successfully migrate through the female reproductive tract and penetrate the oocyte. Predictably, poor sperm motility has been associated with low rates of fertilisation in many mammalian species, including the ram. As such, motility is one of the most important parameters used for in vitro evaluation of ram sperm quality and function. This review aims to outline the mechanical and energetic processes which underpin sperm motility, describe changes in motility which occur as a result of differences in sperm structure and the surrounding microenvironment, and assess the effectiveness of the various methods used to assess sperm motility in rams. Methods of subjective motility estimation are convenient, inexpensive methods widely used in the livestock industries, however, the subjective nature of these methods can make them unreliable. Computer-assisted sperm analysis (CASA) technology accurately and objectively measures sperm motility via two-dimensional tracing of sperm head motion, making it a popular method for sperm quality assurance in domesticated animal production laboratories. Newly developed methods of motility assessment including flagellar tracing, three-dimensional sperm tracing, in vivo motility assessment, and molecular assays which quantify motility-associated biomarkers, enable analysis of a new range of sperm motion parameters with the potential to reveal new mechanistic insights and improve ram semen assessment. Experimental application of these technologies is required to fully understand their potential to improve semen quality assessment and prediction of reproductive success in ovine artificial breeding programs.

A Sperm Quality Detection System Based on Microfluidic Chip and Micro-Imaging System

Sperm quality assessment is the main method to predict the reproductive ability of livestock. The detection of sperm quality of livestock is of great significance to the application of artificial insemination and in vitro fertilization. In order to comprehensively evaluate sperm quality and improve the real-time and portability of sperm quality detection, a portable microscopic imaging system based on microfluidic chip is developed in this paper. The system can realize the comprehensive evaluation of sperm quality by detecting sperm vitality and survival rate. On the hardware side, a microfluidic chip is designed, which can automatically mix samples. A set of optical system with a magnification of 400 times was developed for microscopic observation of sperm. In the aspect of software, aiming at the comprehensive evaluation of sperm quality based on OpenCV, a set of algorithms for identifying sperm motility and survival rate is proposed. The accuracy of the system in detecting sperm survival rate is 94.0%, and the error rate is 0.6%. The evaluation results of sperm motility are consistent with those of computer-aided sperm analysis (CASA). The system's identification time is 9 s. Therefore, the system is absolutely suitable for sperm quality detection.

Mass Sperm Motility Is Correlated to Sperm Motility as Measured by Computer-Aided Sperm Analysis (CASA) Technology in Farmed Ostriches

Semen analyses have gained momentum in various livestock industries. However, in farmed ostriches, semen analysis is still in its experimental stage, and males are not screened for sperm quality before breeding. This study investigated the correlations between computer-aided sperm analysis (CASA) technology and the traditional, yet affordable, mass sperm motility score. Semen was collected from nine South African Black ostrich males (mean age ± SD: 5.25 ± 1.21 years), using the dummy female method for 5 consecutive days monthly, for 8 months. Mass sperm motility scores were recorded on a scale of 1−5 (1: little to no sperm movement; 5: rapid sperm movement). The CASA traits recorded were: total motility (MOT), progressive motility (PMOT), curve−linear velocity (VCL), straight-line velocity (VSL), average path velocity (VAP), amplitude of lateral head displacement (ALH), linearity (LIN), straightness (STR), wobble (WOB), and beat-cross frequency (BCF). The results revealed positive correlations between mass sperm motility and PMOT, MOT, VCL, and VAP ranging from 0.34 to 0.59 (p < 0.0001). In contrast, negative correlations were recorded between mass sperm motility and LIN, STR, and BCF, with correlations ranging from −0.20 to −0.39 (p < 0.0001). VSL, ALH, and WOB were not correlated to mass sperm motility (p > 0.05). Ostrich farmers may thus be able to evaluate sperm motility reliably and potentially select breeding males by using the affordable mass sperm motility scoring method. Determining the correlation between these methods and fertility after artificial insemination or natural mating is however needed.

The effect of adjusting settings within a Computer-Assisted Sperm Analysis (CASA) system on bovine sperm motility and morphology results

Semen motility is the most widely recognized semen quality parameter used by Artificial Insemination (AI) centers. With the increasing worldwide export of semen between AI centers there is an increasing need for standardized motility assessment methods. Computer-Assisted Sperm Analysis (CASA) technology is thought to provide an objective motility evaluation; however, results can still vary between laboratories. The aim of present study was to verify the impact of different setting values of the CASA IVOS II on motility, concentration, and morphology of bovine semen samples frozen in an extender with or without egg yolk and then decide on optimal settings for a further validation step across AI centers. Semen straws from 30 different bulls were analyzed using IVOS II with twelve modified settings. No significant changes were observed in semen concentration, percentage of motile sperm or kinetic results for either extender type. However, increasing settings for both STR and VAP progressive (%) from Low, Medium, and High cut-off values significantly (p<0.05) reduced the percentage of detected progressive spermatozoa, in egg yolk extender from 49.5±15.2, 37.2±11.9 to 11.9±5.3%, and in clear extender from 51.9±9.1, 35.8±7.3 to 10.0±2.4%, respectively. In clear extender only, the modification of droplet proximal head length significantly affected the detection of normal sperm percentages (88.0± 4.7 to 95.0±0.6 and 96.0±0.6%) and of the percentage of detected proximal droplets (12.2±4.7, 2.5±2.7 to 0.6±0.2%) for Low, Medium and High values respectively (p<0.05). The identification of sensitivity within the CASA system to changes in set parameters then led to the determination of an optimal IVOS II setting. The existing variability among centers for these phenotypes was reduced when the standardized settings were applied across different CASA units. The results clearly show the importance of applied settings for the final CASA results and emphasize the need for standardized settings to obtain comparable data.

Systematic Analysis of Breed, Methodological, and Geographical Impact on Equine Sperm Progressive Motility

Simple Summary

Semen quality is an important indicator of reproductive health and fertility. With adverse temporal trends in human semen quality over the past 50 years paralleled in male animals, there is increasing concern for the causes and implications of perturbed male reproductive health. The evaluation of equine progressive motility (PM), a parameter closely associated with fertility, provides information on the fertilising capacity of equine ejaculate and current reproductive health of the equine stallion. Using systematic analysis, recent trends in equine PM were determined from 696 estimates from 280 individual studies. Temporal trends indicate equine PM has not significantly changed between the years 1990 and 2018. Significant breed, methodological, and geographical variations observed in equine PM may considerably influence actual and reported stallion fertility potential. Information on stallion PM meaningfully contributes to the wider literature on semen quality and provides avenues for future stallion fertility research. This systematic analysis presents the wider challenges associated with semen quality assessment, particularly within the equine sector, and provides recommendations to promote consistency across industry and research.

Abstract

Over the past five decades, there has been increasing evidence to indicate global declines in human semen quality. Parallel adverse trends measured in male animals indicate a potential environmental aetiology. This study evaluated the progressive motility (PM) of stallion ejaculate through a systematic review and meta-analysis. A total of 696 estimates of equine PM from 280 studies, which collected semen samples between the years 1990 and 2018, were collated for meta-analysis. The method of motility analysis, breed, season of collection, and geographical location were extracted. Simple linear regression determined temporal trends in stallion PM. Studies using microscopy estimated PM to be significantly greater compared to computer-automated methods (p ≤ 0.001). For Arabian breeds, PM was consistently higher than other breeds. Over time, there was a significant decline in PM for studies from Europe (n = 267) but a significant increase for studies from North America (n = 259). Temporal trends indicate the fertilising capacity of equine ejaculate has remained consistently high in the last three decades. That being so, variations observed suggest methodological, geographical, and individual stallion differences may significantly influence actual and reported stallion fertility potential.

Total Aseptization of Boar Semen, to Increase the Biosecurity of Reproduction in Swine

The aim of the study was to establish the complete microbiological profile of boar semen (Sus scrofa domesticus) and to choose the most effective antiseptic measures in order to control and optimize AI reproduction in pig farms. One hundred and one semen samples were collected and analyzed from several pig farms. The microbiological profile of ejaculates was determined by evaluating the degree of contamination of fresh semen and after dilution with specific extenders. The bacterial and fungal load of fresh boar semen recorded an average value of 82.41/0.149 × 10³ CFU/mL, while after diluting the ejaculates the contamination value was 0.354/0.140 × 10³ CFU/mL. Twenty-four germs (15 bacterial and 9 fungal species) were isolated, the most common being Candida parapsilosis/sake (92%) and Escherichia coli (81.2%). Modification of the sperm collection protocol (HPBC) reduced contamination in raw sperm by 49.85% in bacteria (significant (p < 0.00001) and by 9.67% in fungi (non-significant (p < 0.111491). The load in bacteria and filamentous fungi can be controllable, but not in levuras fungi. Some fluconazole-added extenders (12.5 mg%), ensure fungal aseptization, and even an increase in sperm progressivity (8.39%) for at least a 12 h shelf life after dilution. Validation of sperm aseptization was done by maintaining sow fecundity unchanged after AI (insignificant p > 0.05).

Computer-assisted sperm analysis (CASA) in veterinary science: A review

Computer-assisted sperm analysis (CASA) allows an assessment of sperm motion and morphology more accurately and objectively than by subjective evaluation. Although, CASA instruments have improved significantly during last 40 years especially in terms of software, image capture and computer settings, little has changed regarding processes for analyzing sperm motion attributes. The main problem is related to validation, consistency and optimization of equipment and procedures. Differences among CASA systems denote problems of objective analysis of results between different semen processing units. If validated, CASA systems can provide a great tool to objectively compare sperm motility and morphology. Sperm motility is one of the indicators most evaluated before and after cryopreservation vis-à-vis quality and fertilizing ability. Researchers have determined a possible relationship of CASA outputs with bull fertility in vivo; however, a clear association has not yet been confirmed. Most CASA measures depend upon concentration, sample volume, type of extender, duration of analysis and thawing temperature. For each attribute, CASA software should provide outputs based on a range rather than means or medians for transformed data. The current review describes development, validation requirements, limitations and future expansions associated with CASA technology.

DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos

BACKGROUND AND OBJECTIVE

Object detection is a primary research interest in computer vision. Sperm-cell detection in a densely populated bull semen microscopic observation video presents challenges that are more difficult than those presented by other general object-detection cases. These challenges include partial occlusion, vast number of objects in a single video frame, tiny size of the object, artifacts, low contrast, low video resolution, and blurry objects because of the rapid movement of the sperm cells. This study proposes a deep neural network architecture, called DeepSperm, that solves the aforementioned problems and is more accurate and faster than state-of-the-art architectures.

METHODS

In the proposed architecture, we use only one detection layer, which is specific for small object detection. For handling overfitting and increasing accuracy, we set a higher input network resolution, use a dropout layer, and perform data augmentation on saturation and exposure. Several hyper-parameters are tuned to achieve better performance. Mean average precision (mAP), confusion matrix, precision, recall, and F1-score are used to measure accuracy. Frame per second (fps) is used to measure speed. We compare our proposed method with you only look once (YOLO) v3 and YOLOv4.

RESULTS

In our experiment, we achieve 94.11 mAP on the test dataset, F1-score of 0.93, and a processing speed of 51.9 fps. In comparison with YOLOv4, our proposed method is 2.18 x faster on testing, and 2.9 x faster on training with a small dataset, while achieving comparative detection accuracy. The weights file size was also reduced significantly, with one-twentieth that of YOLOv4. Moreover, it requires a 1.07 x less graphical processing unit (GPU) memory than YOLOv4.

CONCLUSIONS

This study proposes DeepSperm, which is a simple, effective, and efficient deep neural network architecture with its hyper-parameters and configuration to detect bull sperm cells robustly in real time. In our experiments, we surpass the state-of-the-art in terms of accuracy, speed, and resource needs.

Effect of Extender, Storage Time and Temperature on Kinetic Parameters (CASA) on Bull Semen Samples

Simple Summary

The bull accounts for a great part of the economic value of beef herds managed with natural mating, with the Bull Breeding Soundness Evaluation (BBSE) performed in the field becoming essential. Part of the BBSE is the semen-quality evaluation (classical and kinetic parameters) that can be performed in situ (by trained practitioners) or at laboratories, with semen being short-term stored and shipped. The extender used as well as the storage temperature and duration may affect its quality. Thus, our aim was to explore the best conditions to preserve CASA kinetic parameters, to be reliably assessed afterwards. CASA parameters were preserved for up to 4–6 h post-ejaculation, except for AndroMed^® in most of the parameters. Motility decreased from 4–6 h up to 24 h, with the best values obtained with BIOXcell^® at 5 °C. The rest of kinetic parameters worsened when analyses were performed at 24 h. Therefore, we suggest evaluating seminal quality as soon as possible. However, using BIOXcell^®, either at 5 °C or room temperature, and INRA96^® at room temperature, sperm motility can be reliably evaluated for up to 6 h. These results help to fix adequate protocols for the short-term storage and shipment of bovine semen collected under field conditions.

Abstract

CASA kinetic parameters are often evaluated in a diagnostic centre. How storage conditions affect ejaculates up to evaluation is unclear. We assessed, in 25 commercial bulls electroejaculated in the field, the impact of time until evaluation (0–2 h, 4–6 h, and 24 h post-ejaculation), holding temperature (5 °C vs. room temperature), and extender (AndroMed^®, BIOXcell^® or INRA96^®) on CASA kinetic parameters. Total and progressive motility, VCL, VAP, VCL, ALH, BCF, STR, LIN, and WOB were assessed. CASA kinetic parameters were preserved for up to 4–6 h post-ejaculation, except for AndroMed^®. Regardless of extender or temperature, motility decreased from 4–6 h up to 24 h, with the best values obtained with BIOXcell^® at 5 °C. Our results suggest that BIOXcell^® can preserve sperm motility for up to 6 h, either at 5 °C or room temperature, and also INRA96^® at room temperature, with motility assessments and the percentage of the most rapid sperms being the lowest with INRA96^® at 5 °C. The kinetic parameters decreased when analyses were performed at 24 h. Therefore, we suggest evaluating seminal quality as soon as possible, before 6 h after collection. These results help to fix adequate protocols for the short-term storage and shipment of bovine semen collected under field conditions.

Relationship between Fertility Traits and Kinematics in Clusters of Boar Ejaculates

Simple Summary

Swine reproduction efficiency is determined by the fertility potential of the sow and sperm quality. The objective of this study is to compare boar sperm motility and kinematic features to evaluate their relationships with reproductive success after artificial insemination (AI). In this study, the movement patterns of boar ejaculates were analyzed by a computer-assisted semen analysis (CASA)-Mot system, and the kinematic values of ejaculate clusters were assessed. The semen of the Pietrain boars showed more linear trajectory of the spermatozoa, while curvilinear velocity and oscillatory movement characterized the semen of the Duroc × Pietrain boars. The offspring of sows inseminated with Pietrain boars showed significantly lower number of stillbirths. In addition, ejaculate grouping into clusters did not have a predictive capacity on litter size variables. Nevertheless, the kinematic variables of the ejaculate may have a predictive, albeit reduced, capacity regarding litter size variables. The results of this study therefore open up possibilities for future assessments of fertility.

Abstract

The aim was to determine the relationship between kinematic parameters of boar spermatozoa and fertility rates of sow, as well as to assess the effect of sperm clusters on the fertility capacity of the ejaculate. Semen samples were collected from 11 sexually mature boars. Samples were analyzed by an ISAS^®v1 CASA-Mot system for eight kinematic parameters. Ejaculate clusters were characterized using multivariate procedures, such as principal factors (PFs) analysis and clustering methods (the k-means model). Four different ejaculate clusters were identified from two kinematic PFs which involved linear trajectory and velocity. There were differences (p < 0.05) between the sperm kinematic variables by sire line. There was no statistical difference (p > 0.05) between dam lines and ejaculate clusters in fertility variables. The discriminant ability of the different kinematics of sperm variables to predict litter size fertility was analyzed using receiver operating characteristics (ROC) curve analysis. Curvilinear velocity (VCL), average path velocity (VAP), amplitude of lateral head displacement (ALH), and beat-cross frequency (BCF) showed significant, albeit limited, predictive capacity for litter size fertility variables (range: 0.55–0.58 area under curve, AUC). The kinematic analysis of the ejaculates in clusters did not have a predictive capacity for litter size variables.

Semen evaluation: methodological advancements in sperm quality-specific fertility assessment - A review

Assessment of male fertility is based on the evaluation of sperm. Semen evaluation measures various sperm quality parameters as fertility indicators. However, semen evaluation has limitations, and it requires the advancement and application of strict quality control methods to interpret the results. This article reviews the recent advances in evaluating various sperm-specific quality characteristics and methodologies, with the help of different assays to assess sperm-fertility status. Sperm evaluation methods that include conventional microscopic methods, computer-assisted sperm analyzers (CASA), and flow cytometric analysis, provide precise information related to sperm morphology and function. Moreover, profiling fertility-related biomarkers in sperm or seminal plasma can be helpful in predicting fertility. Identification of different sperm proteins and diagnosis of DNA damage has positively contributed to the existing pool of knowledge about sperm physiology and molecular anomalies associated with different infertility issues in males. Advances in methods and sperm-specific evaluation has subsequently resulted in a better understanding of sperm biology that has improved the diagnosis and clinical management of male factor infertility. Accurate sperm evaluation is of paramount importance in the application of artificial insemination and assisted reproductive technology. However, no single test can precisely determine fertility; the selection of an appropriate test or a set of tests and parameters is required to accurately determine the fertility of specific animal species. Therefore, a need to further calibrate the CASA and advance the gene expression tests is recommended for faster and field-level applications.

Rapid Multi-Hybridisation FISH Screening for Balanced Porcine Reciprocal Translocations Suggests a Much Higher Abnormality Rate Than Previously Appreciated

With demand rising, pigs are the world’s leading source of meat protein; however significant economic loss and environmental damage can be incurred if boars used for artificial insemination (AI) are hypoprolific (sub-fertile). Growing evidence suggests that semen analysis is an unreliable tool for diagnosing hypoprolificacy, with litter size and farrowing rate being more applicable. Once such data are available, however, any affected boar will have been in service for some time, with significant financial and environmental losses incurred. Reciprocal translocations (RTs) are the leading cause of porcine hypoprolificacy, reportedly present in 0.47% of AI boars. Traditional standard karyotyping, however, relies on animal specific expertise and does not detect more subtle (cryptic) translocations. Previously, we reported development of a multiple hybridisation fluorescence in situ hybridisation (FISH) strategy; here, we report on its use in 1641 AI boars. A total of 15 different RTs were identified in 69 boars, with four further animals XX/XY chimeric. Therefore, 4.5% had a chromosome abnormality (4.2% with an RT), a 0.88% incidence. Revisiting cases with both karyotype and FISH information, we reanalysed captured images, asking whether the translocation was detectable by karyotyping alone. The results suggest that chromosome translocations in boars may be significantly under-reported, thereby highlighting the need for pre-emptive screening by this method before a boar enters a breeding programme.

Optimization of CASA-Mot Analysis of Donkey Sperm: Optimum Frame Rate and Values of Kinematic Variables for Different Counting Chamber and Fields

Simple Summary

A reliable sperm motility exam is important for semen analysis and breeding soundness examination. Different parameters can affect the Computer Assisted Sperm Analysis (CASA) motility results. Today, new high-resolution cameras and different chambers are introduced to CASA systems, and protocol optimization is required to render the estimation results for donkey sperm. The objective of this study is the optimization of the conditions used for donkey semen motility analysis with CASA-Mot by defining the optimum frame rate for different chamber types. Additionally, to study the effect of different chamber types, chamber field and sperm dilution on the sperm kinematic parameters with higher frame rates are examined.

Abstract

In order to optimize the donkey sperm motility analysis by the CASA (Computer Assisted Sperm Analysis)-Mot system, twelve ejaculates were collected from six jackasses. Capillary loaded chamber (CLC), ISAS^®D4C depths 10 and 20 µm, ISAS^®D4C Leja 20 and drop displacement chamber (DDC), Spermtrack^® (Spk) depths 10 and 20 µm were used. Sperm kinematic variables were evaluated using each chamber and a high-resolution camera capable of capturing a maximum of 500 frames/second (fps). The optimum frame rate (OFR) (defined according to curvilinear velocity—VCL) was dependent on chamber type. The highest OFR obtained was 278.46 fps by Spk20. Values for VCL, straight-line velocity (VSL), straightness (STR), amplitude of lateral head displacement (ALH) and beat cross frequency (BCF) were high in DDC and 10 µm depth. In both DDC 10 and 20 µm, the sperm velocities (VCL, VSL, VAP) and ALH values decreased significantly from the centre to the edges, while Wobble and BCF increased. No defined behavior was observed along the CLC. However, all the kinematic variables had a higher value in a highly concentrated sample, in both chamber types. In conclusion, analyzing a minimum of nine fields at 250 fps from the centre to the edges in Spk10 chamber using a dilution of 30 × 10⁶ sperm/mL offers the best choice for donkey computerised sperm motility analysis.

Machine learning to further improve the decision which boar ejaculates to process into artificial insemination doses

Current artificial insemination (AI) laboratory practices assess semen quality of each boar ejaculate to decide which ones to process into AI doses. This decision is aided with two, world-wide used, motility parameters that come available through computer assisted semen analysis (CASA). This decision process, however, still results in AI doses with variable and sometimes suboptimal fertility outcomes (e.g., small litter size). The hypothesis was that the decision which ejaculates to process into AI doses can be improved by adding more data from CASA systems, and data from other sources, in combination with a data-driven model. Available data consisted of ejaculates that passed the initial decision, and thus, were processed into AI doses and used to inseminate sows. Data were divided into a training set (6793 records) and a validation set (1191 records) for model development, and an independent test set (1434 records) for performance assessment. Gradient Boosting Machine (GBM) models were developed to predict four fertility phenotypes of interest (gestation length, total number born, number born alive, and number of stillborn piglets). Each fertility phenotype was considered as a numeric and as a binary outcome parameter, totaling to eight different fertility phenotypes. Data used to further improve the decision process originated from four sources: 1) CASA information, 2) boar ejaculate information, 3) breeding value estimations, and 4) weather information. These data were used to create seven prediction sets, where each new set added parameters to the ones included in the previous set. The GBM models predicted fertility phenotypes with low correlations (for numeric phenotypes) and area under the curve values (for binary phenotypes) on the test data. Hence, results demonstrated that a combination of more data and GBM did not enable further improvement of the AI dose quality checks, resulting in the rejection of our hypothesis. However, our study revealed parameters affecting boar ejaculate fertility which were not used in today's decision process. These parameters (listed in the top 10 in at least four GBM models) included one parameter associated with boar ejaculate information, two with breeding value estimations, five with CASA information, and one with weather information. These parameters, therefore, should be further investigated for their potential value when assessing the quality of boar ejaculates in daily routine AI doses processing.

Proteomic identification of boar seminal plasma proteins related to sperm resistance to cooling at 17 °C

The modern pig industry relies on extensive use of artificial insemination with cooled semen. It is important that semen doses maintain their quality during processing, transport and storage before insemination to guarantee maximum fertility rates. However, ejaculates may respond differently to liquid preservation at 17 °C, despite the optimal quality assessed before cooling. Thus, the aim of this study was to identify differences in seminal plasma proteome of ejaculates with a higher or lower seminal resistance to storage at 17 °C. A total of 148 ejaculates from 65 sexually mature healthy boars were classified as: High Resistance to cooling (HR, total motility > 60% at 144h) and Low resistance to cooling (LR, total motility <60 at 72h). To identify differentially expressed seminal plasma proteins between HR and LR ejaculates, ten ejaculates of each group were analyzed by 2D SDS-PAGE and ESI-Q-TOF mass spectrometry. The proteins associated with HR ejaculates were cathepsin B (spot 2803 and 6601, p < 0.01); spermadhesin PSP-I (spots 3101 and 3103, p < 0.05); epididymal secretory protein E1 precursor (spot 2101, p < 0.05) and IgGFc binding protein (spot 1603, p < 0.01). The protein associated with LR group was the Major seminal plasma PSPI (spot 9103, p < 0.01). To our knowledge, this is the first report of the association of boar seminal plasma proteins to semen resistance to cold storage at 17 °C. These results suggest the use of these proteins as biomarkers for semen resistance to preservation at 17 °C.

Combined effects of type and depth of counting chamber, and rate of image frame capture, on bull sperm motility and kinematics

Semen quality assessment requires accurate, reliable and objective methods for examination of sperm variables including sperm motility. For preparation of semen samples for artificial insemination, as a genetic resource, samples that are used for insemination need to have the capacity to result in a highly acceptable fertility rate. Several methods have been developed for evaluation of bull sperm in laboratory conditions and for preparation of doses for artificial insemination. Computer-assisted semen analyses can provide objective information on various sperm variables. Nevertheless, this equipment requires fine-calibrations considering differences among species, breeds and conditions for sample evaluation and data analyses. In the present study, there was examination of the interaction between factors such as image frame rate and type and depth of counting chamber in which sperm were evaluated, together with differences between bulls of four breeds. The use of the Spermtrack® reusable 10 μm-depth chamber provided more reliable results than results obtained using disposable chambers (10 and 20 μm depth). A capture rate of at least 90 fps is required for assessment of sperm motility percentage, whereas a rate of 250 fps is needed for obtaining consistent kinematic data. Differences among breeds in the present study indicate conditions for sperm analyses should include specific equipment calibrations for each breed. These results contribute to development of more precise conditions for assessments of bull sperm quality taking into account breed differences and the requirement each breed has for the adequate evaluation and preparation of samples for artificial insemination.

Implications of boar sperm kinematics and rheotaxis for fertility after preservation

Artificial insemination (AI) is the single most important assisted reproductive technique devised to facilitate the genetic improvement of livestock. In the swine industry, it has broadly replaced natural service over the last number of decades which has been made possible by the high pregnancy rates and litter sizes obtainable with semen extended, up to, and sometimes beyond 5 d. Central to achieving good reproductive performance is the ability of boar studs to monitor semen quality, the basis of which has long been the assessment of sperm motility by subjective and, more recently, by more objective computerised systems. In this review, the literature on the relationship between sperm motility and kinematic parameters and field fertility is summarised. We discuss how this relationship is dependent on factors such as the viscosity of the media and the use of standard operating procedures. Emerging evidence is discussed regarding the importance of sperm rheotaxis and thigmotaxis as long-distance sperm guidance mechanisms, which enable motile functional spermatozoa to avoid the backflow of fluid, mucus and semen from the sow's uterus in the hours post AI, facilitating the establishment of sperm reservoirs in the oviducts. The literature on the use of microfluidics in studying sperm rheotaxis in vitro is also summarised, and we discuss how these systems, when combined with techniques such as lensless microscopy, have the potential to offer more physiological assessments of the swimming patterns of boar spermatozoa. Finally, possible future avenues of further investigation are proposed.

Effect of dietary grape marc on fresh and refrigerated boar semen

In several studies there has been evaluation of the dietary addition of antioxidants to improve the quality of fresh and stored semen in domestic animals. Grape marc (GM), as the residue of vinification, contains large amounts of polyphenols with antioxidant, anti-inflammatory, antimicrobial, and antiaging effects. In this study, two regimens of dietary GM supplementation (2% and 4%) were tested regarding effects on the characteristics and lipid peroxidation of fresh and stored semen. The dietary supplementation of GM improved sperm characteristics in fresh semen, especially at 4% of GM. There were greater values for all kinetic variables and membrane integrity, and lesser values for sperm abnormalities and lipid peroxidation with 2% and 4% GM supplementation. Lipid peroxidation of the pellet was less with both 2% and 4% GM supplementation. During sperm storage, the dietary supplementation of GM improved the quality of sperm, with greater values for kinetic variables and membrane integrity at day 15 of storage. In the present study, supplementation of GM in boar diets improved fresh semen characteristics and reduced the lipid peroxidation of ejaculated spermatozoa, possibly due to the effect of polyphenols present in the GM. Consequently, this likely resulted in improved sperm quality during storage.

Evaluation of a portable device for assessment of motility in stallion semen

In horse breeding, quality assessment of semen before insemination is often requested. Non‐laboratory‐based techniques for objective analysis of sperm motility are thus of interest. The aim of this study was evaluating a portable device for semen analysis (Ongo sperm test) and its comparison with computer‐assisted semen analysis (CASA). Semen was collected from 10 stallions, diluted to 100, 50 and 25 × 10⁶ sperm/ml and analysed for total (TM) and progressive motility (PM). The final sperm concentration influenced total motility analysed by Ongo (p < 0.05) which was higher at 100 × 10⁶ sperm/ml when compared to 25 × 10⁶ sperm/ml (p < 0.05) but not when compared to 50 × 10⁶ sperm/ml (n.s.). Sperm concentration did not influence total motility when assessed by SpermVision (n.s.). Agreement between methods was evaluated by correlation analysis and Bland–Altman plot. Intra‐assay variation of Ongo was 5.2% ± 3.0 for TM and 6.9% ± 3.4 for PM. Correlation between Ongo and CASA was r = 0.79, 0.88 and 0.83 for 100, 50 and 25 × 10⁶ sperm/ml for TM, and r = 0.87, 0.89 and 0.87 for PM, respectively (all p < 0.001). At the 100 and 25 mio/ml dilutions, the difference between the two systems deviated significantly from 0, while no such bias existed at the 50 mio/ml dilution (TM Ongo 85.0%, CASA 82.3%; PM Ongo 64.1%, CASA 66.1%). The 95% confidence interval was 19.9%, 18.9% and 19.2% ± mean for TM and 20.7%, 17.4% and 20.3% ± mean for 100, 50 and 25 × 10⁶ sperm/ml, respectively. In conclusion, Ongo sperm test sperm motility data were strongly correlated with data obtained by CASA. In addition, at a concentration of 50 × 10⁶ sperm/ml values measured with both systems were close to identical. At this concentration, which is recommended in equine AI, Ongo and CASA can be used interchangeably.

Combined effect of type and capture area of counting chamber and diluent on Holstein bull sperm kinematics

The evaluation of sperm motion is crucial for processing of seminal doses for artificial insemination. Here, the combined effect of the type and capture area of three counting chambers, together with the type of diluent employed, on sperm motility was analysed. Ejaculates from thirteen Holstein bulls were used for sperm kinematic analysis with the ISAS^® v1 CASA-Mot system, using two capillary-loaded counting chambers (Leja^® and Cell-Vu^® ) and one drop displacement chamber (Makler^® ). Nine fixed positions were analysed per chamber type, considering central and lateral and three longitudinal fields. Independent of the diluent used, differences were found between the three chambers. Independent of the extender, no differences in x-axis were observed with Cell-Vu^® , while using Leja^® , some parameters showed lower values in the centre than in lateral areas. In both counting chambers, the lowest values were observed in the distal area. Results obtained with the two diluents were highly different with a very low correlation between them. In conclusion, the capture area inside the chambers leads to significant changes in sperm kinematic parameters and different dilution media introduce considerable differences in the motility patterns. It is necessary to optimise sampling methods and specific set-ups to be used with CASA-Mot technology.

Objective evaluation of ram and buck sperm motility by using a novel sperm tracker software

This work offers researchers the first version of an open-source sperm tracker software (Sperm Motility Tracker, V1.0) containing a novel suit of algorithms to analyze sperm motility using ram and buck sperm as models. The computer-assisted semen analysis is used in several publications with increasing trend worldwide in the last years, showing the importance of objective methodologies to evaluate semen quality. However, commercial systems are costly and versatility is constrained. In the proposed method, segmentation is applied and the tracking stage is performed by using individual Kalman filters and a simplified occlusion handling method. The tracking performance in terms of precision (number of true tracks), the percentage of fragmented paths and percentage of correctly detected particles were manually validated by three experts and compared with the performance of a commercial motility analyzer (Microptic's SCA). The precision obtained with our sperm motility tracker was higher than the one obtained with a commercial software at the current acquisition frame rate of 25 fps (P < 0.0001), concomitantly with a similar percentage of fragmentized tracks (P = 0.0709) at sperm concentrations ranging 25-37 × 10⁶ cells/mL. Moreover, our tracker was able to detect trajectories that were unseen by SCA. Kinetic values obtained by using both methods were contrasted. The higher values found were explained based on the better performance of our sperm tracker to report speed parameters for very fast motile sperm. To standardize results, acquisition conditions are suggested. This open-source sperm tracker software has a good plasticity allowing researchers to upgrade according requirements and to apply the tool for sperm from a variety of species.

Genetic parameters for semen quality and quantity traits in five pig lines

We aimed to estimate genetic parameters for semen quality and quantity traits as well as for within-boar variation of these traits to evaluate their inclusion in breeding goals. Genetic parameters were estimated within line using a multiple-trait (4 × 4) repeatability animal model fitted for 5 pig lines, considering 4 semen traits: sperm motility (MOT), sperm progressive motility (PROMOT), log-transformed number of sperm cells per ejaculate (lnN), and total morphological abnormalities (ABN). The within-boar variation of these traits was analyzed based on a multiple-trait (2 × 2) approach for SD and average (AVG) and a single-trait analysis for CV. The average heritabilities across the 5 lines estimated by multiple-trait analysis were 0.18 ± 0.07 (MOT), 0.22 ± 0.08 (PROMOT), 0.16 ± 0.04 (lnN), and 0.20 ± 0.04 (ABN). The average genetic correlations were favorable between MOT and PROMOT (0.86 ± 0.10), between MOT and ABN (-0.66 ± 0.25), and between PROMOT and ABN (-0.65 ± 0.25). As determined by within-boar variation analysis, AVG exhibited the greatest heritabilities followed by SD and CV, respectively, for the traits MOT and ABN. For PROMOT, average SD heritability was lower than CV heritability, whereas for lnN, they were the same. The average genetic correlations between AVG and SD were favorable for MOT (-0.60 ± 0.13), PROMOT (-0.79 ± 0.14), and ABN (0.78 ± 0.17). The moderate heritabilities indicate the possibility of effective selection of boars based on semen traits. Average and SD are proposed as appropriate traits for selection regarding uniformity.

Effect of chamber characteristics, loading and analysis time on motility and kinetic variables analysed with the CASA-mot system in goat sperm

Several factors unrelated to the semen samples could be influencing in the sperm motility analysis. The aim of the present research was to study the effect of four chambers with different characteristics, namely; slide-coverslip, Spermtrack, ISAS D4C10, and ISAS D4C20 on the sperm motility. The filling procedure (drop or capillarity) and analysis time (0, 120 and 240s), depth of chamber (10 or 20μm) and field on motility variables were analysed by use of the CASA-mot system in goat sperm. Use of the drop-filling chambers resulted in greater values than capillarity-filling chambers for all sperm motility and kinetic variables, except for LIN (64.5% compared with 56.3% of motility for drop- and capillarity-filling chambers respectively, P<0.05). There were no significant differences in total sperm motility between different chamber depths, however, use of the 20μm-chambers resulted in greater sperm progressive motility rate, VSL and LIN, and less VCL and VAP than chambers with a lesser depth. There was less sperm motility and lesser values for kinetic variables as time that elapsed increased between sample loading and sperm evaluation. For sperm motility, use of droplet-loaded chambers resulted in similar values of MOT in all microscopic fields, but sperm motility assessed in capillarity-loaded chambers was less in the central fields than in the outermost microscopic fields. For goats, it is recommended that sperm motility be analysed using the CASA-mot system with a drop-loaded chamber within 2min after filling the chamber.

Efficient Boar Semen Production and Genetic Contribution: The Impact of Low-Dose Artificial Insemination on Fertility

Diluting semen from high fertile breeding boars, and by that inseminating many sows, is the core business for artificial insemination (AI) companies worldwide. Knowledge about fertility results is the reason by which an AI company can lower the concentration of a dose. Efficient use of AI boars with high genetic merit by decreasing the number of sperm cells per insemination dose is important to maximize dissemination of the genetic progress made in the breeding nucleus. However, a potential decrease in fertility performance in the field should be weighed against the added value of improved genetics and, in general, is not tolerated in commercial production. This overview provides some important aspects that influence the impact of low-dose AI on fertility: (i) the importance of monitoring field fertility, (ii) the need for accurate and precise semen assessment, (iii) the parameters that are taken into account, (iv) the application of information from genetic and genomic selection and (v) the optimization when using different AI techniques. Efficient semen production, processing and insemination in combination with increasing use of genetic and genomic applications result in maximum impact of genetic trend.

Influence of chamber type integrated with computer-assisted semen analysis (CASA) system on the results of boar semen evaluation

The objective of the study was to evaluate the effect of different types of chambers used in computer-assisted semen analysis (CASA) on boar sperm concentration and motility parameters. CASA measurements were performed on 45 ejaculates by comparing three commonly used chambers: Leja chamber (LJ), Makler chamber (MK) and microscopic slide-coverslip (SL). Concentration results obtained with CASA were verified by manual counting on a Bürker hemocytometer (BH). No significant differences were found between the concentrations determined with BH vs. LJ and SL, whereas higher (p<0.01) values of this parameter were obtained with MK. Compared to MK and SL, significantly higher values were recorded in LJ for velocity (VCL and VAP) as well as amplitude of the lateral head displacement (ALH) and beat cross frequency (BCF), which was associated with significantly higher percentages of motile, progressively motile and rapidly progressive motile spermatozoa. Higher values for the linearity (LIN) and straightness (STR) of sperm movement were obtained for the analysis performed in MK and SL. In both these chambers, the results of all the linearity and kinetic parameters of sperm were similar (p>0.05). The results obtained show that CASA assessment of boar semen should account for the effect of counting chamber on the results of sperm motility and concentration, which confirms the need for further study on standardizing the automatic analysis of boar semen.

Sperm quality and fertility of boar seminal doses after 2 days of storage: does the type of extender really matter?

The present approach was designed to evaluate the extender effects on sperm quality and fertility of short-term refrigerated seminal doses from Landrace boars lodged in husbandry-controlled conditions. For this purpose, we analyzed the sperm quality of seminal doses diluted in short-term (Beltsville Thawing Solution) and extra-long-term (Duragen) extenders from Days 0 to 2 of storage at 17 °C during an 8-month period. Pregnancy rates and litter size were evaluated from double inseminations within an interval of 12 hours (36 and 48 hours of refrigeration) of multiparous females using seminal doses diluted in each extender type. Sperm quality was assessed from the analyses of sperm motility and kinetics, sperm viability, expressed as plasma and acrosome membrane integrity, membrane lipid disorder, intracellular calcium levels, and acrosin activity. Results indicated significant differences between the extenders in the sperm quality of seminal doses. Therefore, the seminal doses diluted in Duragen had higher percentages of progressive motile spermatozoa and membrane-intact spermatozoa than those diluted in Beltsville Thawing Solution throughout all the experimental months. Nevertheless, despite these differences in preserving the sperm quality, pregnancy rates (>90%) and litter sizes (>10 piglets born per litter) were similar between the extenders. Our results had great relevance from a practical point of view because they reported lack of an extender effect on the reproductive performance of seminal doses during short-tem storage.

A genome-wide association study reveals a novel candidate gene for sperm motility in pigs

Sperm motility is one of the most widely used parameters in order to evaluate boar semen quality. However, this trait can only be measured after puberty. Thus, the use of genomic information appears as an appealing alternative to evaluate and improve selection for boar fertility traits earlier in life. With this study we aimed to identify SNPs with significant association with sperm motility in two different commercial pig populations and to identify possible candidate genes within the identified QTL regions. We performed a single-SNP genome-wide association study using genotyped animals from a Landrace-based (L1) and a Large White-based (L2) pig populations. For L1, a total of 602 animals genotyped for 42,551 SNPs were used in the association analysis. For L2, a total of 525 animals genotyped for 40,890 SNPs were available. After the association analysis, a false discovery rate q-value ≤0.05 was used as the threshold for significant association. No SNPs were significantly associated with sperm motility in L1, while six SNPs on Sus scrofa chromosome 1 (position 117.26-119.56Mb) were significant in L2. The mitochondrial methionyl-tRNA formyltransferase (MTFMT) gene, which affects translation efficiency of proteins in sperm cells, was identified as a putative candidate gene. The significant markers identified in this study may be useful to enhance the genetic improvement of sperm motility by selection of boars at an earlier age under a marker assisted selection strategy.

Semen effects on insemination outcomes in sows

Sows (n=1205) were artificially inseminated with semen from single sires (n=166). Semen was previously analysed for sperm concentration, motility, velocity, morphology (using DIC microscopy) and membrane integrity, sperm clump score, temperature on arrival and pH. Percent normal sperm influenced both numbers of pigs born alive (P<0.01) and litter size (P<0.05) which, in turn, was also influenced by abnormal sperm head morphology (P<0.05) and retained distal cytoplasmic droplets (P<0.01). Percent stillbirths were influenced by sperm flagellar beat cross frequency (P<0.05) and semen arrival temperature (P<0.05).

Computer-assisted sperm analysis (CASA): capabilities and potential developments

Computer-assisted sperm analysis (CASA) systems have evolved over approximately 40 years, through advances in devices to capture the image from a microscope, huge increases in computational power concurrent with amazing reduction in size of computers, new computer languages, and updated/expanded software algorithms. Remarkably, basic concepts for identifying sperm and their motion patterns are little changed. Older and slower systems remain in use. Most major spermatology laboratories and semen processing facilities have a CASA system, but the extent of reliance thereon ranges widely. This review describes capabilities and limitations of present CASA technology used with boar, bull, and stallion sperm, followed by possible future developments. Each marketed system is different. Modern CASA systems can automatically view multiple fields in a shallow specimen chamber to capture strobe-like images of 500 to >2000 sperm, at 50 or 60 frames per second, in clear or complex extenders, and in <2 minutes, store information for ≥ 30 frames and provide summary data for each spermatozoon and the population. A few systems evaluate sperm morphology concurrent with motion. CASA cannot accurately predict 'fertility' that will be obtained with a semen sample or subject. However, when carefully validated, current CASA systems provide information important for quality assurance of semen planned for marketing, and for the understanding of the diversity of sperm responses to changes in the microenvironment in research. The four take-home messages from this review are: (1) animal species, extender or medium, specimen chamber, intensity of illumination, imaging hardware and software, instrument settings, technician, etc., all affect accuracy and precision of output values; (2) semen production facilities probably do not need a substantially different CASA system whereas biology laboratories would benefit from systems capable of imaging and tracking sperm in deep chambers for a flexible period of time; (3) software should enable grouping of individual sperm based on one or more attributes so outputs reflect subpopulations or clusters of similar sperm with unique properties; means or medians for the total population are insufficient; and (4) a field-use, portable CASA system for measuring one motion and two or three morphology attributes of individual sperm is needed for field theriogenologists or andrologists working with human sperm outside urban centers; appropriate hardware to capture images and process data apparently are available.

Multivariate analyses for determining the association of field porcine fertility with sperm motion traits analysed by computer-assisted semen analysis and with sperm morphology

This study investigates the association of semen traits with boar fertility. The fertility outcome (farrowing rate - FR and total piglets born - TB) of 14 boars was obtained from a field trial conducted during 10 week of breeding period on a commercial farm using multiparous sows (n = 948) through single-sire mating with 2 × 10(9) motile sperm cells per artificial insemination (AI) dose. Sperm motion parameters, evaluated with computer-assisted semen analysis system in raw and stored semen at 17°C for 240 h, in addition to morphological sperm defects, measured on the collection day, were included in the analysis to determine which semen traits were important to discriminate the fertility potential of ejaculates from these boars. The data underwent multivariate cluster, canonical and discriminant analyses. Four clusters of boars were formed based on fertility outcome. One boar, with the lowest FR and TB values (89.7% and 11.98), and two boars, with the highest FR and TB values (97.8% and 14.16), were placed in different clusters. The other boars were separated in two distinct clusters (four and seven boars), including boars with intermediate TB (12.64 and 13.22) but divergent values for FR (95.9% vs 91.8%). Semen traits with higher discriminatory power included total motility, progressive motility, amplitude of lateral head displacement and cytoplasmatic droplets. Through multivariate discriminant analysis, more than 80% of the 140 ejaculates were correctly classified into their own group, showing that this analysis may be an efficient statistical tool to improve the discrimination of potential fertility of boars. Nevertheless, the validation of the relationship between fertility and semen traits using this statistical approach needs to be performed on a larger number of farms and with a greater number of boars.

Semen quality assessments and their significance in reproductive technology

Semen quality assessment methods are very important in predicting the fertilizing ability of persevered spermatozoa and to improve animal reproductive technology. This review discusses some of the current laboratory methods used for semen quality assessments, with references to their relevance in the evaluation of male fertility and semen preservation technologies. Semen quality assessment methods include sperm motility evaluations, analyzed with the computer-assisted semen analysis (CASA) system, and plasma membrane integrity evaluations using fluorescent stains, such as Hoechst 33258 (H33258), SYBR-14, propidium iodide (PI), ethidium homodimer (EthD) and 6-carboxyfluorescein diacetate (CFDA), and biochemical tests, such as the measurement of malondialdehyde (MDA) level. This review addresses the significance of specific fluorochromes and ATP measurements for the evaluation of the sperm mitochondrial status. Laboratory methods used for the evaluation of chromatin status, DNA integrity, and apoptotic changes in spermatozoa have been discussed. Special emphasis has been focused on the application of proteomic techniques, such as two-dimensional (2-D) gel electrophoresis and liquid chromatography mass spectrometry (LC-MS/MS), for the identification of the properties and functions of seminal plasma proteins in order to define their role in the fertilization-related processes.

Characteristics of high-quality Asian elephant (Elephas maximus) ejaculates and in vitro sperm quality after prolonged chilled storage and directional freezing

The in vitro quality of spermatozoa from one elephant (Elephas maximus) was examined after chilled storage and directional freezing (DF). High-quality, non-contaminated ejaculates (77.6 ± 6.0% progressive motility, 3.9 ± 1.5 µg creatinine mL–1 raw semen, 2.7 ± 0.6% detached heads) were cryopreserved after 0 (0hStor), 12 (12hStor) and 24 h (24hStor) of chilled storage. At 0 h and 6 h post-thawing, total motility, plasma membrane integrity, acrosome integrity, mitochondrial activity and normal morphology were similar (P > 0.05) across treatments. In contrast, progressive motility, rapid velocity and several kinematic parameters were lower (P < 0.05) for 24Stor compared with 0hStor at 0 h post-thaw. By 6 h post-thaw, amplitude of lateral head displacement and velocity parameters (average pathway, straight-line and curvilinear velocity) were lower (P < 0.05) for 24hStor compared with 0hStor and 12hStor. DNA integrity was high and remained unchanged (P > 0.05) across all groups and processing stages (1.6 ± 0.6% of cells contained fragmented DNA). Results indicate that DF after up to 12 h of chilled storage results in a post-thaw sperm population of acceptable quality for artificial insemination. These findings have implications for the cryopreservation of sex-sorted spermatozoa, which typically undergo more than 12 h of chilled storage prior to sorting and preservation.