Clustering of spermatozoa examined through flow cytometry provides more information than the conventional assessment: a resilience to osmotic stress example

Abstract

Context Conventional sperm quality tests may not be sufficient to predict the fertilising ability of a given ejaculate; thus, rapid, reliable and sensitive tests are necessary to measure sperm function. Aims This study sought to address whether a cluster analysis approach based on flow cytometry variables could provide more information about sperm function. Methods Spermatozoa were exposed to either isotonic (300 mOsm/kg) or hypotonic (180 mOsm/kg) media for 5 and 20 min, and were then stained with SYBR14 and propidium iodide (PI). Based on flow cytometry dot plots, spermatozoa were classified as either viable (SYBR14+/PI−) or with different degrees of plasma membrane alteration (SYBR14+/PI+ and SYBR14−/PI+). Moreover, individual values of electronic volume (EV), side scattering (SS), green (FL1) and red (FL3) fluorescence were recorded and used to classify sperm cells through cluster analysis. Two strategies of this approach were run. The first one was based on EV and the FL3/FL1 quotient, and the second was based on EV, SS and the FL3/FL1 quotient. Key results The two strategies led to the identification of more than three sperm populations. In the first strategy, EV did not differ between membrane-intact and membrane-damaged sperm, but it was significantly (P < 0.01) higher in spermatozoa losing membrane integrity. In the second strategy, three out of five subpopulations (SP2, SP3 and SP4) showed some degree of alteration in their plasma membrane with significant (P < 0.01) differences in EV. In both cluster analyses, SP5 (intact-membrane spermatozoa) presented the lowest EV. Besides, SP3 and SP4 (Strategy 1) and SP5 (Strategy 2) were found to be significantly (P < 0.05) correlated with sperm functional competence. Conclusions Cluster analysis based on flow cytometry variables provides more information about sperm function than conventional assessment does. Implications Combining flow cytometry with cluster analysis is a more robust approach for sperm evaluation.