Multidimensional data analysis algorithms facilitate and speed the analysis, and provide improved visualization of flow cytometry data in spermatology

Modern flow cytometers enable the simultaneous analysis of an increasing number of parameters or dimensions, offering significant advantages for andrological assessments. However, relying on conventional procedures for data processing increases the complexity of the analysis and the time required to complete it. We propose a simplified and efficient protocol that leverages commercial software and dimensional reduction algorithms for multiparametric flow cytometry data analysis, to improve data visualization and speed up the analysis. Ejaculates from six stallions were split into two media, a modified Tyrode's containing 67 mM glucose and 10 mM pyruvate or a commercial media containing 67 mM glucose but not pyruvate and stored at room temperature for 48 h. A five-color panel was designed to assess key sperm parameters, including viability, membrane permeability, mitochondrial membrane potential, mitochondrial mass, and apoptotic changes. Data were uploaded to FlowJo 10.10 for Mac, where automatic compensation and conventional 2D plot analyses were performed on the compensated files. In a second analysis, individual files were concatenated (merged) in FlowJo, generating new.fcs files, which were subsequently downsampled from 50,000 to 3000 events (spermatozoa) per file. The downsampled, concatenated files were exported and analyzed in Cytobank (https://premium.cytobank.org). Preprocessing included Arcsinh transformation for improved population visualization, automated compensation, and the exclusion of non-sperm debris, clumps, and doublets. Initial 2D plots were generated for each condition, providing a baseline comparison. Dimensionality reduction and clustering analyses were then performed on the same merged files. Our results showed that the proportions of sperm subpopulations identified by advanced analytical approaches, such as downsampling, dimensional reduction, and clustering, were consistent with those obtained via traditional 2D dot-plot analysis. This study demonstrates the feasibility and accuracy of advanced methodologies for flow cytometric analysis of spermatozoa. These approaches enhance processing speed, improve data visualization, and offer deeper insights into the dynamics of sperm subpopulations under varying metabolic conditions.