| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
From the * Section of Animal Reproduction and
Obstetrics, Department of Herd Health and Medicine, Faculty of Veterinary
Medicine, University of Extremadura, Cáceres, Spain; the
Division of Comparative Reproduction,
Obstetrics and Udder Health, Department of Clinical Sciences and the
Department of Anatomy and Physiology, Faculty
of Veterinary Medicine and Animal Sciences, Swedish University of Agricultural
Sciences, Uppsala, Sweden.
| Correspondence to: Dr Fernando J. Peña, Section of Animal Reproduction and Obstetrics, Department of Herd Health and Medicine, Faculty of Veterinary Medicine, University of Extremadura, Avd de la Universidad s/n, 10071 Cáceres, Spain (e-mail: fjuanpvega{at}unex.es). |
| Received for publication February 9, 2005; accepted for publication May 24, 2005. |
 |
Abstract |
|---|
 Top Abstract Materials and MethodsResultsDiscussionReferences |
|---|
Key words: ASMA, sperm subpopulations, cryopreservation, cluster analysis
The coexistence of different sperm subpopulations within the mammalian ejaculate is nowadays widely accepted by the scientific community. The origin of these subpopulations is not clear yet, but it has been hypothesized that they correspond to differences in the assembly of individual spermatozoa during spermatogenesis, as well as to their differential maturational status and age when leaving the cauda epididymides at ejaculation (Abaigar et al, 1999). Characteristics of these subpopulations have been studied by means of flow cytometry and computer-assisted sperm analysis (CASA) and analyzed using multivariate approaches to identify sperm subpopulations in mammals (Abaigar et al, 2001; Martínez-Pastor et al, 2005). Although sperm morphology can be considered a good indicator of semen quality in bull sires (Phillips et al, 2004) and it is recommended as part of the spermiogram for domestic animals (Rodríguez-Martínez, 2003), the investigation of morphometric sperm subpopulations in boar semen has received little attention, there being only 2 studies dealing with this issue (Hirai et al, 2001; Thurston et al, 2001), while no data exist regarding sperm morphometric subpopulations in other species.
We have previously demonstrated that sperm quality after cryopreservation of boar semen differs depending on the fraction of the seminal plasma the boar spermatozoa were fortuitously contained in (Peña et al, 2003a,b, 2004). Thus, spermatozoa present in the first 10 mL of the sperm-rich fraction (Portion I) could withstand handling procedures (extension, handling, and freezing-thawing) better than those contained in the latter part of a fractionated ejaculate (second portion of the sperm-rich fraction and the rest of the bulk ejaculate). The reasons for these differences, although not yet disclosed in detail, may be related to differences in electrolyte composition or protein components (Zhu et al, 2000). However other factors may be related to these different abilities, as subtle morphometric differences in sperm head morphology have been related to differences in sperm quality among ejaculates. Most of the studies on sperm subpopulations have been performed using specific software such as the PATN software (Abaigar et al, 1999; Thurston et al, 1999), although a few have used more popular statistical packages (Martínez-Pastor et al, 2004; Quintero-Moreno et al, 2005) such as the SAS software. The aims of the present study were to 1) develop a simple multistep procedure to identify sperm subpopulations within the boar ejaculate based on data gathered with assisted morphology sperm analysis (ASMA), using a commercially available statistical package (SPSS), 2) test the hypothesis that subtle morphometric differences exist among spermatozoa located in different ejaculate portions, and 3) test the hypothesis that the existence of these morphometric sperm subpopulations may at least in part explain differences in the ability of different ejaculates and/or portions within the ejaculate to sustain cryopreservation procedures.
|
Materials and Methods |
|---|
|
TopAbstractMaterials and MethodsResultsDiscussionReferences |
|---|
Sperm Staining for Morphometric Analysis
Sperm samples were adjusted in phosphate-buffered saline (PBS) to 100
x 106 cells per mL. Thereafter, 10 µL of the sperm
suspension was placed on the edge of a slide and extended. Preparations were
allowed to dry and were fixed and stained for 10 minutes in an eosin solution
(Panreac, Barcelona, Spain) and 10 minutes in a methylene blue solution
(Panreac). The excess of stain was removed by washing, and the slide was
allowed to dry before being permanently mounted with Eukitt (Panreac).
Computerized Morphometric Analysis
The prepared slides were examined using a Nikon Labophot microscope
equipped with a 100x bright field objective lens and a 3.3x
photo-ocular lens. The video signal was acquired by a Sony CCD AVC-D7CE video
camera (Sony Corporation, Tokyo, Japan) interfaced with a Sperm-Class
Analyser® (SCA) version 99 CASMA system (Microptic S.L., Barcelona,
Spain). The array size of the video grabber was 512 x 512 x 8
bits, providing digitized images of 262 144 pixels and 256 gray levels.
Resolution of images was 0.083 µm per pixel in the horizontal and vertical
axes.
At least 200 spermatozoa per sample were captured in 2 slides per
ejaculate. Sperm cells were displayed on the monitor at equivalent brightness,
and all the cells that did not present any overlap with debris or other cells
were considered for analysis. From each sample sperm heads were captured and
analyzed using the SCA program as previously described
(Buendía et al, 2002). After treatment of the images, some of the sperm images had to be discarded
because of defective binarization, as observed by false correspondence between
the original image and its mask. Each sperm head was measured for 7 primary
parameters (head area [A, µM2], head perimeter [P, µM], head
length [L, µM], head width [W, µM], midpiece width [w, µM], mid-piece
area [a, µM2], distance [d, µM] between the major axes of the
head and midpiece) and 4 derived parameters of head shape (FUN1 [L/W], FUN2
[4
A/P2], FUN3 [(L-W)/(L+W)], FUN 4 [LW/4A]).
Semen Freezing Protocol
After 60 minutes of holding time at room temperature (20°C to
22°C), the semen was extended (1+1) with Beltsville Thawing Solution (BTS)
(206 mM glucose, 20.4 mM Na3 citrate, 14.9 mM NaHCO3,
3.4 mM Na2-EDTA, 10 mM KCl, penicillin G Na 0.6 g/L,
Dihydrostreptomycin 1.0 g/L). The extended semen was allowed to stand in a
cooling centrifuge (Centra MP4R, IEC, Needham Heights, Mass) set at 15°C
for 3 hours, after which it was centrifuged at 800 x g for 10
minutes. The supernatant was discarded and the volume (graduated vial) and
sperm concentration (Bürker chamber) were measured. The sperm pellet was
re-extended with a second extender (Ext II 80 mL [80% v/v 310 mM]
ß-lactose + 20 mL egg yolk) at a ratio of 1 part of semen to 1 part of
extender. After thorough mixing, the semen was further cooled to 5°C for 2
hours in the centrifuge. At this temperature, the semen was slowly mixed with
a third extender, consisting of 89.5 mL Ext II, 9 mL glycerol, and 1.5 mL of
Equex STM (Nova Chemicals Sales Inc, Scituate, Mass), at a ratio of 2 parts of
semen to 1 part of extender, giving a final glycerol concentration of 3%. The
final sperm concentration was 1 x 109 spermatozoa, checked in
a Bürker Chamber. The work at 15°C and 5°C was done in a cooled
cabinet (IMV, L'Aigle France) where semen was loaded in 0.5 mL straws (IMV).
After sealing, the straws were transferred to the chamber of a programmable
freezer (Mini Digitcool 1400, IMV) and frozen horizontally in racks. The
cooling rate was as follows: -3°C/min from 5°C to -5°C;
thereafter, -40°C/min from -5°C to -140°C. The frozen straws then
were plunged into liquid nitrogen (LN2, -196°C). After 4 weeks
of storage, samples were removed from the LN2 and thawed in a water
bath at 50°C for 12 seconds.
Motility Analyses
Semen samples were diluted at 20°C to 22°C, 1:20 in an extender
consisting of 95 mL BTS and 5 mL lactose-egg yolk solution to prevent the
spermatozoa from sticking to the glassware during motility analysis. The
extended semen, containing approximately 50 x 106/mL, was
held in an incubator at 38°C for 30 minutes, and then 5 µL of the
sample was placed into a 10 µm deep Makler counting chamber (Sefi Medical
Instruments, Haifa, Israel) for motility analysis using a CASA system
(Strömberg-Mika-CMA, Windows version 1.1, MTM Medical Technologies,
Montreux, Switzerland). The setting parameters for the SMCMA program were 32
frames in which spermatozoa had to be present in at least 16 to be counted,
and time resolution, 20 ms (50Hz). Spermatozoa with an average path velocity
(VAP) less than 10 µm/s were considered immotile, and spermatozoa with a
VAP greater than 25 µm/s were considered motile. A minimum of 8
predetermined fields all around the central reticulum of the chamber were
evaluated, counting a minimum of 200 spermatozoa in duplicates. Spermatozoa
deviating less than 10% from a straight line were designated linear motile
spermatozoa, and those having a circular motion of radius less than 25 µm
were classified as circularly motile. The analysis yielded the following
motility parameters: motile (% of motile spermatozoa), linearly motile (% of
spermatozoa moving linearly), circle (% of spermatozoa with circular
motility), VSL (straight linear velocity, µm/s), VAP (average path
velocity, µm/s), VCL (curvilinear velocity, µm/s) and ALH (lateral head
displacement, µm).
Annexin-V/PI Flow Cytometry Analysis
Staining for annexin-V/PI was performed using the annexin-V(A) conjugated
with fluorescein isothiocyanate (FITC)-apoptosis detection kit II (Pharmingen,
San Diego, Calif) and Propidium Iodide (PI, Molecular Probes, Eindhoven, The
Netherlands) as previously described for boar semen
(Peña et al, 2003a).
The samples were analyzed by a triple-laser LSR cytometer (Becton Dickinson
Immunochemistry Systems, San José, Calif) equipped with standard
optics. An Ar-ion laser (INNOVA 90, Coherent, Santa Clara, Calif) tuned at 488
nm and running at 200 mW was used as light source. From each cell, forward
light scatter (FSC), orthogonal light scatter (SSC), A-FITC fluorescence
(FL1), and PI fluorescence (FL3) were evaluated using Cellquest version 3.3
(Becton Dickinson) software. A gate was applied in the FSC/SSC dot-plot to
restrict the analysis to spermatozoa. For the gated cells, the percentages of
annexin-V-positive (A+), PI-positive (PI+), and double-positive cells were
evaluated, based on quadrants determined from single-stained and unstained
control samples. Cells in the lower left quadrant were not fluorescent
(A-/PI-) and were recorded as live cells, (eg, without membrane dysfunction).
Apoptotic but viable spermatozoa (A+/PI-) were labeled with annexin-V but not
with PI and fell in the lower right quadrant. Early necrotic spermatozoa
(A+/PI+) that bound both annexin-V and PI (upper right quadrant) are assumed
to maintain some degree of membrane integrity although having damaged
permeable membranes, and thus still bind Annexin-V. Late necrotic spermatozoa
(A-/PI+), however, were labeled by PI but not annexin-V (upper left quadrant).
It is assumed that these latter spermatozoa have completely lost sperm
membrane integrity and are thus unable to bind annexin-V
(Peña et al,
2003b).
Statistical Analysis
The main objective of the analysis was to identify sperm sub-populations
using the morphometric data obtained from each boar and ejaculate portion by
means of clustering procedures
(Martínez-Pastor et al,
2005). The first step was to perform a principal components
analysis of the morphometric data. The purpose of the first step was to derive
a small number of linear combinations (principal components) that retained as
much of the information in the original variables as possible. This allowed us
to summarize many variables in few jointly uncorrelated principal components.
A good result was considered when few principal components accounting for a
high proportion of the total variance were obtained. The second step was to
perform a nonhierarchical analysis using the k-means model that uses euclidean
distances to calculate the center of the clusters. We used the selected
principal components as variables. The third step was to perform a step-wise
discriminant analysis of the clusters obtained. This kind of analysis is often
used to reduce the number of clusters and to help in the interpretation of the
data obtained in the k-means procedure
(Hair et al, 1998). To study
the distributions of observations (individual spermatozoa) within portions and
within subpopulations, a 
2 test was used.
Data regarding postthaw sperm quality (CASA and annexin-V assay) were first tested using a Kolgomorov-Smirnov test to determine the normality of data distribution. In view of the Gaussian distribution of the data gathered, an analysis of variance was used to determine the motility and membrane integrity values in each boar and ejaculate portion considered. Linear regression analyses were used to investigate relationships between morphometric parameters in fresh semen and sperm quality measurements postthaw. The level of significance was set to P < .05. All analyses were performed using the SPSS ver 11.0 for Windows software (SPSS Inc Chicago, Ill).
|
Results |
|---|
|
TopAbstractMaterials and MethodsResultsDiscussionReferences |
|---|
|
|
Sperm Subpopulations Within the 2 Portions of the Boar Ejaculate
Within each subpopulation (cluster) the percentage of spermatozoa
represented by portion I or II was more or less the same, namely 50% ±
5%, except for cluster 15 where it was 44% in portion I versus 56% in portion
II. Additionally, within portion I, the representation of the 4 subpopulations
varied from 13% to 33%; in portion II, from 13% to 36%. Clusters 10, 11, and
especially cluster 15 were relatively more present in portion II
(Table 4).
|
Sperm Subpopulations Within Each Portion of Individual Boars
Except in boar 1352, where the proportion of the sperm subpopulations did
not differ within each portion of the boar ejaculate, there were significant
differences within both boar and ejaculate portion (Figures
1 and
2).
|
|
|
Boars and ejaculate portions 1352-II, 407-I, and 1044-II showed the best membrane integrity postthaw (P < .05). The best motility was observed in 684-I, 1277-II, and 684-II. In respect to sperm velocities, VCL was better in 684-I, 684-II, and 407-II, while VSL and VAP were better in 1277-II, 684-II, and 407-II.
Relationship Between Sperm Morphometry and Sperm Quality Postthaw (Table 5)— Linear regression analysis revealed significant relations among different parameters of sperm quality postthaw and sperm head morphometry in fresh samples. The percentage of intact sperm membranes postthaw (A-/PI-) was explained by 2 models. The first one included the sperm head shape factor FUN2 (R2 = 0.257, adjusted R2 = 0.257, P < .01). The second model included 2 sperm head factors, FUN2 and FUN4, (R2 = 0.367 adjusted R2 = 0.312 P < .01). This model nominally explains 36.7% of the variation. In relation to sperm kinematics postthaw, midpiece width was a predictor of motility (R2 = 0.06, adjusted R2 = 0.047, P < .05). Two models explained the percentage of linear mo-tile sperm postthaw; the first included midpiece width (R2 = 0.131, adjusted R2 = 0.119, P < .01), and the second one included midpiece width and the distance between the major axes of the head and midpiece (R2 = 0.210, adjusted R2 = 0.190, P < .01). The VAP model contained the terms distance between the major axes of the head and midpiece, FUN4, midpiece width, head area, mid-piece area, FUN3, FUN 2, FUN1, head width, head length, and head perimeter (R2 = 0.273, adjusted R2 = 0.155, P < .05). VCL was explained by a model including the terms distance between the major axes of the head and midpiece, FUN4, midpiece width, midpiece area, FUN3, FUN2, FUN1, and sperm head width, length, and perimeter (R2 = 0.275, adjusted R2 = 0.157, P < .05). Finally, VSL was explained by a model containing the terms distance between the major axes of the head and midpiece, FUN4, midpiece width, head area, midpiece area, FUN3, FUN2, FUN1, head width, length, and perimeter (R2 = 0.194, adjusted R2 = 0.064, P < .05).
|
|
Discussion |
|---|
|
TopAbstractMaterials and MethodsResultsDiscussionReferences |
|---|
The statistical procedure hereby used was simple and appeared useful to detect sperm subpopulations. In our study we have used the SPSS software and a different approach from that used in studies on sperm subpopulations derived from motility data (Abaigar et al, 1999; Quintero-Moreno et al, 1999, 2002; Martínez-Pastor et al, 2005); these authors used the PATN or SAS software. Like Martínez-Pastor et al (2005), we included in our approach a PCA as a first step to reduce the number of variables to few informative ones. This first step also facilitated the further management of the data. As a second step, we performed a k-means cluster procedure, a kind of clustering indicated when there is a large set of data, as was the case in our study (5780 individual spermatozoa). This procedure has the relative disadvantage that the operator must set the number of clusters a priori. Such cluster numbers were determined in a series of preliminary tests, until the optimal number of clusters was found. On the other hand, a major advantage of the k-means cluster procedure is the easy detection of outliers. As a third step, to further reduce the number of clusters and to help in the interpretation of data, we performed a discriminant analysis, using the approach described by Davis et al (1995) to study sperm kinematics in human semen. These authors carried out a multistep iterative procedure combining the k-means model with multivariate discriminative analysis.
In our study we used as a first step a PCA to reduce the number and select the type of variables to be included in the analysis. This first step facilitated the management of the data; in fact in a number of studies on sperm sub-populations based on sperm kinematics, one of the major critical points was the selection of variables to enter in the analysis. The first objective of our study was to use a simple method using SPSS software to identify sperm morphometric subpopulations. The statistical tool is not new, but its use for this type of data is new. Since the number of variables obtained from the ASMA analysis was high, the inclusion of a PCA as a first step was considered as essential to simplify the statistical procedure.
The origin of these subpopulations is not clear. Genetically derived variation on sperm morphology has been demonstrated as the base for phenotypic differences observed between spermatozoa of different strains of mice (Beatty, 1972). Studies in animal species other than pigs seem to indicate that it is plausible that variation in sperm morphology arises during spermatogenesis, when genotypic effects influence sperm structure. Sperm morphology phenotype appears to be controlled by genes transcribed in the pre-meiotic phase of development (Burgoyne, 1975). Inbreeding coefficients have been related to poor ejaculate quality, further demonstrating the genetic control of sperm morphology (Roldan et al, 1998). This fact, together with the easy identification (albeit few boars were used) of differences on sperm ejaculates between boars and ejaculate portions within the population of normal spermatozoa, points out the possibility of identifying those boars—or sperm portions—more suitable for biotechnological procedures such as sperm cryopreservation or sorting. However, the results may not necessarily apply to other boars, as 5 boars from 1 breed were investigated. The fact that the results were significant although only 5 boars were included can be misleading because the unit of measurement was not the boar.
Relationship Between Sperm Subpopulations and Sperm Quality Postthaw
Both portions of the ejaculate varied in the percentage of spermatozoa
within each subpopulation except in boar 1352, where no significant
differences were observed in the size of the sperm subpopulations. It is
noteworthy that these 2 sperm subpopulations showed a different ability to
sustain freezing-thawing procedures (Peña et al,
2003a,b,
2004). Cryopreservation
implies many insults to the spermatozoa
(Mazur, 1984). Perhaps the 2
main ones are the osmotic stress and the formation/reshaping of intracellular
ice during freezing and again during thawing. It is noteworthy that when
comparisons are made among species for their ability to sustain cold shock,
clear sperm differences are evident
(Watson and Plummer, 1985);
the spermatozoa of those species less sensitive to cold shock are smaller and
more rounded in shape. Obviously, many other factors are involved in
cryoresistance, but we hypothesized that sperm shape influences sperm area,
thus causing differences in heat exchange as well as in movements of water and
ions. It is, therefore, plausible to think that spermatozoa may vary in their
physical properties depending on their shape. Although many other factors can
also be related, the importance of shape factors is that these are probably
inherited traits (Thurston et al,
2001), which points to the possibility of identifying boars with
"good" or "bad" sperm freezability through the
morphometric study of the ejaculates. Considering conventional
cryopreservation of boar semen, while 50% of the original spermatozoa remain
motile postthaw, not more than 2.5% of the motile sperm remain fully competent
for fertilization (Holt et al,
2005). Therefore, approaches such as selection of "good
freezers" (either as individual boars or as well-defined ejaculate
portions) could have a tremendous impact on the success of cryopreservation.
In fact, in our study, regression analysis models were able to predict up to
the 36% of the variance in the percentage of sperm membranes postthaw, and
other models predicted sperm velocities and motility after freezing-thawing
procedures.
We have found in some ejaculate portions a sperm motility rate substantially higher than membrane integrity. This is an unusual finding that, however, can be explained by the technique used to assess sperm membrane integrity. The use of the A/PI assay allows a better discrimination of sperm membranes than a classical combination of probes such as SYBR-14/PI (Peña et al, 2003b). In fact, the subpopulation of live sperm as assessed using SYBR-14/PI is an heterogeneous population in which can be found spermatozoa with intact membranes and spermatozoa showing translocation of the phospholipid phosphatidylserine (PS) from the inner to the outer leaflet of the sperm membrane. This is an early change in the process of cryodamage, and sperm motility is still not seriously compromised at this stage, so a proportion of spermatozoa classified as damaged using the A/PI assay may still remain motile.
In conclusion, we have developed a simple statistical procedure to identify sperm morphometric subpopulations within the boar ejaculate. The ASMA protocol used was able to detect subtle morphometric differences within different portions of the boar ejaculate. Such a combination of multivariate analysis with ASMA analysis could be considered as a powerful tool to improve the spermiogram of stud boars.
|
Footnotes |
|---|
|
References |
|---|
|
TopAbstractMaterials and MethodsResultsDiscussionReferences |
|---|
Beatty RA. The genetics of size and shape of spermatozoa organelles. In: Beatty RA, Gluecksson-Waelsch S, eds. The Genetics of Spermatozoa. Copenhagen, Denmark: Bogtrykkerit Forum; 1972 : 97-115.
Buendía P, Soler C, Paolicchi F, Gago G, Urquieta B, Pérez Sánchez F, Bustos Obregón E. Morphometric characterization and classification of alpaca sperm heads using the sperm class analyzer® system. Theriogenology. 2002; 57: 1207 -1218.[CrossRef][Medline]
Burgoyne PS. Sperm phenotype and its relationship to somatic and germ line genotype: a study using mouse aggregation chimeras. Dev Biol. 1975;44: 63 -76.[CrossRef][Medline]
Darin-Bennet A, White IG. Influence of the cholesterol content of mammalian spermatozoa on susceptibility to cold shock. Cryobiology. 1977; 14: 466 -470.[CrossRef][Medline]
Davis RO, Drobnis EZ, Overstreet JW. Application of multivariate cluster, discriminate function and stepwise regression analysis to variable selection and predictive modeling of sperm survival. Fertil Steril. 1995;63: 1051 -1057.[Medline]
ESHRE Andrology Special Interest Group. Guidelines on the
application of CASA technology in the analysis of spermatozoa. Hum
Reprod. 1998;13: 142
-145.
Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate Data Analysis. 5th ed. London, NJ: Prentice Hall Int; 1998 .
Hirai M, Boersma A, Hoeflich A, Wolf E, Föll J, Aumüller R, Braum J. Objectively measured sperm motility and sperm head morphometry in boars (Sus scrofa): relation to fertility and seminal plasma growth factors. J Androl. 2001;22: 104 -110.[Abstract]
Holt WV, Medrano A, Thurston L, Watson PF. The significance of cooling rates and animal variability for boar sperm cryopreservation: insights from the cryomicroscope. Theriogenology. 2005; 63: 370 -382.[Medline]
Martínez-Pastor F, García-Macias V, Alvarez M,
Herráez P, Anel L, de Paz P. Sperm subpopulations in Iberian red deer
epididymal sperm and their changes through the cryopreservation process.
Biol Reprod. 2005; 72: 316
-327.
Mazur P. Freezing of living cells: mechanisms and implications. Am J Physiol. 1984; 16: C125 -C142.
Ostermeier GCH, Sargeant GA, Yandell BS, Evenson DP, Parrish JJ. Relationship of bull fertility to sperm nuclear shape. J Androl. 2001;22: 595 -603.[Abstract]
Peña FJ, Johannisson A, Wallgren M, Rodríguez-Martínez H. Antioxidant supplementation in vitro improves boar sperm motility and mitochondrial membrane potential after cryopreservation of different fractions of the ejaculate. Anim Reprod Sci. 2003a;78: 85 -98.[CrossRef][Medline]
Peña FJ, Johannisson A, Wallgren M, Rodríguez-Martínez H. Assessment of fresh and frozen-thawed boar semen using an annexin-v assay: a new method to evaluate sperm membrane integrity. Theriogenology. 2003b; 60: 677 -689.[CrossRef][Medline]
Peña FJ, Johannisson A, Wallgren M, Rodríguez-Martínez H. Antioxidant supplementation of boar spermatozoa from different fractions of the ejaculate improves cryopreservation: changes in sperm membrane lipid architecture. Zygote. 2004;12: 117 -124.[CrossRef][Medline]
Phillips NJ, Mcgowan MR, Johnston SD, Mayer DG. Relationship between thirty post-thaw spermatozoal characteristics and the field fertility of 11 high use Australian dairy AI sires. Anim Reprod Sci. 2004;81: 47 -61.[Medline]
Quintero-Moreno A, Miró J, Rigau T, Rodríguez-Gil JE. Identification of sperm subpopulations with specific motility characteristics in stallion ejaculates. Theriogenology. 2003; 59: 1973 -1990.[CrossRef][Medline]
Quintero-Moreno A, Rigau T, Rodríguez-Gil JE. Regression analyses and motile sperm subpopulations structure study as improving tools in boar semen quality analysis. Theriogenology. 2004; 61: 673 -690.[CrossRef][Medline]
Rodríguez-Martínez H. Laboratory semen assessment and prediction of fertility: still utopia? Reprod Dom Anim. 2003;38: 312 -318.
Roldan ERS, Cassinello J, Abaigar T, Gomendio M. Inbreeding, fluctuating asymmetry and ejaculate quality in an endangered ungulate. Proc R Soc Lond B. 1998; 265: 243 -248.[Medline]
Thurston LM, Siggins K, Mileham AJ, Watson PF, Holt W.
Identification of amplified restriction fragment length polymorphism markers
linked to genes controlling boar sperm viability following cryopreservation.
Biol Reprod. 2002; 66: 545
-554.
Thurston LM, Watson PF, Mileham AJ, Holt WV. Morphological sperm subpopulations defined by fourier shape descriptors in fresh ejaculates correlate with variation in boar semen quality following cryopreservation. J Androl. 2001;22: 382 -394.[Abstract]
Watson PF, Plummer JM. The responses of boar sperm membranes to cold shock and cooling. In: Proceedings of The First International Conference on Deep Freezing of Boar Semen. Uppsala, Sweden: The Swedish University of Agricultural Sciences; 1985: 113 -127.
Zhu J, Xu X, Cosgrove JR, Foxcroft GR. Effects of semen plasma from different fractions of individual ejaculates on IVF in pigs. Theriogenology. 2000; 54: 1443 -1452.[CrossRef][Medline]
This article has been cited by other articles:
|
|
 |
|
  M. C. Esteso, A. J. Soler, M. R. Fernandez-Santos, A. A. Quintero-Moreno, and J. J. Garde Functional Significance of the Sperm Head Morphometric Size and Shape for Determining Freezability in Iberian Red Deer (Cervus elaphus hispanicus) Epididymal Sperm Samples J Androl, September 1, 2006; 27(5): 662 - 670. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
I. N. Martinez, J. M. Moran, and F. J. Pena Two-Step Cluster Procedure After Principal Component Analysis Identifies Sperm Subpopulations in Canine Ejaculates and Its Relation to Cryoresistance J Androl, July 1, 2006; 27(4): 596 - 603. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
