Fluorescence lifetime imaging microscopy as an instrument for human sperm assessment

Andrology laboratory techniques for micro-TESE/IVF/ICSI: a narrative review

ABSTRACT

Since the early days of assisted reproductive technology (ART), the importance of sperm processing, employed to separate the motile, morphologically normal sperm from the semen, has been shown to be beneficial. The aim of the semen processing technique has been to remove seminal plasma and facilitate capacitation. Additionally, the presence of leukocytes, bacteria, and dead spermatozoa has been shown to be detrimental as it may cause oxidative stress that has an adverse effect on oocyte fertilization and embryo development. Hence, removal of leukocytes, bacteria, and dead spermatozoa is an important step of sperm processing for assisted reproduction. Currently, several sperm processing techniques have been evolved and optimized in the field of assisted reproduction. The requirements for in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and testicular sperm extraction (TESE) are different than those of intrauterine insemination (IUI). The yield of as many motile, morphologically normal sperm as possible is a prerequisite for the success of IVF insemination procedure. In ICSI, where injection of a single spermatozoon into the oocyte is performed by the embryologist, sperm selection techniques play a crucial role in the ICSI procedure. Finally, sperm retrieval in TESE samples with very low number of sperm may be challenging and requires extra care during sample processing. Additionally, sperm cryopreservation is necessary in TESE cases in order to avoid multiple biopsies.

Label-free metabolic fingerprinting of motile mammalian spermatozoa with subcellular resolution

BACKGROUND

Sperm metabolic pathways that generate energy for motility are compartmentalized within the flagellum. Dysfunctions in metabolic compartments, namely mitochondrial respiration and glycolysis, can compromise motility and male fertility. Studying these compartments is thus required for fertility treatment. However, it is very challenging to capture images of metabolic compartments in motile spermatozoa because the fast beating of the flagellum introduces motion blur. Therefore, most approaches immobilize spermatozoa prior to imaging.

RESULTS

Our findings indicate that immobilizing sperm alters their metabolic profile, highlighting the necessity for measuring metabolism in spermatozoa during movement. We achieved this by encapsulating mouse epididymis in a hydrogel followed by two-photon fluorescence lifetime imaging microscopy for imaging motile sperm in situ. The autofluorescence of endogenous metabolites-FAD, NADH, and NADPH-enabled us to visualize sperm metabolic compartments without staining. We trained machine learning for automated image segmentation and generated metabolic fingerprints using object-based phasor analysis. We show that metabolic fingerprints of spermatozoa and the mitochondrial compartment (1) can distinguish individual males by genetic background, age, or fecundity status, (2) correlate with fertility, and (3) change with age likely due to increased oxidative metabolism.

CONCLUSIONS

Our approach eliminates the need for sperm immobilization and labeling and captures the native state of sperm metabolism. This technique could be adapted for metabolism-based sperm selection for assisted reproduction.

Bioenergetics of human spermatozoa in patients with testicular germ cell tumours

In patients with testicular germ cell tumours (TGCT), sperm cryopreservation prior to anti-cancer treatment represents the main fertility preservation approach. However, it is associated with a low sperm recovery rate after thawing. Since sperm is a high-energy demanding cell, which is supplied by glycolysis and oxidative phosphorylation (OXPHOS), mitochondrial dysfunctionality can directly result in sperm anomalies. In this study, we investigated the bioenergetic pattern of cryopreserved sperm of TGCT patients in comparison with normozoospermic samples using two state-of-the-art methods: the Extracellular Flux Analyzer (XF Analyzer) and two-photon fluorescence lifetime imaging microscopy (2P-FLIM), in order to assess the contributions of OXPHOS and glycolysis to energy provision. A novel protocol for the combined measurement of OXPHOS (oxygen consumption rate: OCR) and glycolysis (extracellular acidification rate: ECAR) using the XF Analyzer was developed together with a unique customized AI-based approach for semiautomated processing of 2P-FLIM images. Our study delivers optimized low-HEPES modified human tubal fluid media (mHTF) for sperm handling during pre-analytical and analytical phases, to maintain sperm physiological parameters and optimal OCR, equivalent to OXPHOS. The negative effect of cryopreservation was signified by the deterioration of both bioenergetic pathways represented by modified OCR and ECAR curves and the derived parameters. This was true for normozoospermic as well as samples from TGCT patients, which showed even stronger damage within the respiratory chain compared to the level of glycolytic activity impairment. The impact of cryopreservation and pathology are supported by 2P-FLIM analysis, showing a significant decrease in bound NADH in contrast to unbound NAD(P)H, which reflects decreased metabolic activity in samples from TGCT patients. Our study provides novel insights into the impact of TGCT on sperm bioenergetics and delivers a verified protocol to be used for the assessment of human sperm metabolic activity, which can be a valuable tool for further research and clinical andrology.

Applications of machine learning in time-domain fluorescence lifetime imaging: a review

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.