Seminal fluid and sperm diluent affect sperm metabolism in an insect: Evidence from NAD(P)H and flavin adenine dinucleotide autofluorescence lifetime imaging

Sperm metabolism is fundamental to sperm motility and male fertility. Its measurement is still in its infancy, and recommendations do not exist as to whether or how to standardize laboratory procedures. Here, using the sperm of an insect, the common bedbug, Cimex lectularius, we demonstrate that standardization of sperm metabolism is required with respect to the artificial sperm storage medium and a natural medium, the seminal fluid. We used fluorescence lifetime imaging microscopy (FLIM) in combination with time‐correlated single‐photon counting (TCSPC) to quantify sperm metabolism based on the fluorescent properties of autofluorescent coenzymes, NAD(P)H and flavin adenine dinucleotide. Autofluorescence lifetimes (decay times) differ for the free and protein‐bound state of the co‐enzymes, and their relative contributions to the lifetime signal serve to characterize the metabolic state of cells. We found that artificial storage medium and seminal fluid separately, and additively, affected sperm metabolism. In a medium containing sugars and amino acids (Grace's Insect medium), sperm showed increased glycolysis compared with a commonly used storage medium, phosphate‐buffered saline (PBS). Adding seminal fluid to the sperm additionally increased oxidative phosphorylation, likely reflecting increased energy production of sperm during activation. Our study provides a protocol to measure sperm metabolism independently from motility, stresses that protocol standardizations for sperm measurements should be implemented and, for the first time, demonstrates that seminal fluid alters sperm metabolism. Equivalent protocol standardizations should be imposed on metabolic investigations of human sperm samples.


| INTRODUCTION
Sperm metabolism is central to male reproduction. For species with motile sperm, sperm metabolism will fuel motility to achieve fertilization. As any other eukaryotic cells, sperm cells can produce ATP by two pathways, glycolysis and oxidative phosphorylation. However, to what extent both pathways are used switches occur between them is not known for most species nor are details of internal and external metabolic substrates in sperm. For example, even in the well-studied model organism Drosophila, aspects of the sperm metabolism have only been revealed in the last few years, except for two early contributions (Geer, Kelley, Pohlman, & Yemm, 1975;Osanai & Chen, 1993). Recent contributions show that sperm cells employ surprisingly strong glycolysis in both the male and the female storage organ (Wetzker & Reinhardt, 2019) but also employ oxidative phosphorylation (Turnell & Reinhardt, 2020;Wetzker & Reinhardt, 2019).
Sperm metabolism is not currently included in parameters of standard clinical semen testing (World Health Organization, 2010). However, such inclusion seems desirable, because motility alone is not a sufficient readout for fertilization ability. For example, sperm that is not motile does not necessarily lack metabolism and can still be used for in-vitro fertilization. In addition, most currently used clinical sperm parameters have low, or no, predictive power for fertility or the ability of the partner to conceive (Ferlin, 2012;Glazener, Ford, & Hull, 2000).
Attempting to standardize the measurement of sperm metabolism, we start with two factors that appear most fundamental to sperm metabolism: the experimental sperm diluent used (henceforth, medium) and seminal fluid. The sperm diluent varies widely between studies and may explain differences between publications even in sperm viability (Eckel et al., 2017). The effect of seminal fluid on sperm function has been reviewed earlier (Davis, 1965;Mann & Lutwak-Mann, 1981;Poiani, 2006) and received particular attention because of its potent antioxidant effect (Aitken, Jones, & Robertson, 2012;Davis, 1965;Wathes, Abayasekara, & Aitken, 2007).
Few methods are currently available to study sperm metabolism.
They include nuclear magnetic resonance -spectroscopy (reviewed by Kamp, Büsselmann, & Lauterwein, 1996), biochemical measurements of oxygen consumption and acidification rate (Magdanz, Boryshpolets, Ridzewski, Eckel, & Reinhardt, 2019;Paynter et al., 2017), and metabolic flux analysis using radiolabeled substrates (Weiner, Crosier, & Keefer, 2019). Ribou and Reinhardt (2012) and Reinhardt and Ribou (2013) introduced a method based on the fluorescence decay of a probe sensitive to oxygen radicals. These authors revealed that sperm metabolic rate and oxygen radicals production decreased rapidly as soon as sperm entered the female sperm storage organ (Reinhardt & Ribou, 2013;Ribou & Reinhardt, 2012). In another insect species, the common bedbug Cimex lectularius, oxygen radicals produced by sperm also decreased in females but increased after sperm had resided several weeks in the female sperm store (Reinhardt & Ribou, 2013). Simultaneously with the increase of oxygen radicals, fertility declined (Reinhardt & Ribou, 2013).
Fluorescence lifetime imaging microscopy (FLIM) makes use of the autofluorescent properties of the cellular coenzyme nicotinamide adenine dinucleotide in its reduced form (NADH) and of flavin adenine dinucleotide (FAD) in its oxidized form to characterize cellular metabolism. NADH/FAD FLIM is commonly employed to study cancer cells Wallrabe et al., 2018) and stem cell differentiation (Meleshina et al., 2016;Meleshina et al., 2017;Stringari et al., 2011). Metabolic FLIM has recently been employed as a label-free technique to study sperm metabolism (Reinhardt, Breunig, Uchugonova, & König, 2015;Ribou & Reinhardt, 2012;Wetzker & Reinhardt, 2019). Based on timecorrelated single-photon counting (TCSPC; Becker, 2012), FLIM quantifies the duration of excited states of fluorophores, here, NAD(P)H and FAD. NAD(P)H subsumes NADH and its phosphorylated form NADPH due to their highly similar fluorescence behavior (Huang, Heikal, & Webb, 2002). In FLIM, fluorophores are excited by a pulsed laser, and the arrival time of emitted photons that reaches the detector is measured relative to the corresponding laser pulse at high temporal sensitivity. The photon arrival times add up to fluorescence decay curves for each pixel of the image. They allow for the statistical calculation of the fluorescence lifetime if sufficient photons are detected. The technique can disentangle decay curves of several components, such as several molecules or different structural forms of one molecule, as well as the relative contribution of each component.
In case of NAD(P)H and FAD, these are free and protein-bound states of both coenzymes. For NAD(P)H, the short and long lifetimes, τ 1 and τ 2 , represent the decay of free and protein-bound molecules, respectively. Their relative abundances are termed a 1 and a 2 (Becker, 2012;Lakowicz, Szmacinski, Nowaczyk, & Johnson, 1992;Leben, Köhler, Radbruch, Hauser, & Niesner, 2019;Sharick et al., 2018). FAD lifetimes are the shorter τ 1 for the protein-bound state and the longer τ 2 for the free state of the coenzyme (Becker, 2012), again with relative abundances of the two.
The lifetime patterns of both markers, particularly the abundance of both lifetime states, serve as a metabolic fingerprint of cells and tissues. While a higher free-to-bound ratio of NAD(P)H is indicative of a more glycolytic state, a lower free-to-bound ratio is a hallmark of more oxidative states (Evers et al., 2018;Stringari et al., 2011;Wallrabe et al., 2018). For NAD(P)H, the relative contributions to the intensity peak of free and bound NAD(P)H lifetimes characterize the relative rate between glycolysis and oxidative phosphorylation (Stringari, Nourse, Flanagan, & Gratton, 2012). This procedure is, for example, used to identify cancer metabolism by the Warburg effect (Warburg, 1956) compared with normal tissue (Skala, Riching, Bird, et al., 2007;Wallrabe et al., 2018). The relative contribution of free and bound NAD(P)H also served to quantify stem cell differentiation (Meleshina et al., 2016;Quinn et al., 2013;Stringari et al., 2011;. For example, a 1 can differ 5-10% points between normal and pre-grade cancer cells (Skala, Riching, Bird, et al., 2007; or between stem cells and differentiated cells (Meleshina et al., 2016;Meleshina et al., 2017), and as much as 20% points between different tissues in Drosophila (Wetzker & Reinhardt, 2019). The method detects FAD autofluorescence lifetime changes in a similar way (Becker, Bergmann, Suarez Ibarrola, Müller, & Braun, 2019;Islam, Honma, Nakabayashi, Kinjo, & Ohta, 2013;Wallrabe et al., 2018). Both NAD(P)H and FAD can be recorded from the same sample by the use of excitation light of different wavelengths and separate emission filter sets. The pattern of FAD variation provides important additional information on the metabolic state of cells. For example, NAD(P)H a 1 did not differ between sperm extracted from Drosophila melanogaster males compared with sperm extracted from females; however, the respective FAD a 1 values differed by 25% points indicative of differences of FAD-related biochemistry (Wetzker & Reinhardt, 2019). The so-called FLIM redox ratio (FLIRR), a lifetime-based redox ratio independent of fluorescence intensities, implements lifetime parameters of both NAD(P)H and FAD (Wallrabe et al., 2018). For further information about metabolic measurements via FLIM, see Blacker and Duchen (2016); Kolenc and Quinn (2019);and Schaefer, Kalinina, Rueck, von Arnim, and von Einem (2019).
Here, we use FLIM and exemplify in an urban pest insect, the common bedbug C. lectularius (Doggett, Miller, & Lee, 2018;Reinhardt & Siva-Jothy, 2007), how the sperm energy metabolism varies with the medium used and in the presence or absence of seminal fluid.
This species was chosen, because it is one of the few for which previous data exist on FLIM-based sperm metabolism, and where sperm metabolism was linked to fertility (Reinhardt et al., 2015;Reinhardt & Ribou, 2013). Bedbugs produce sperm in sufficiently large numbers (Kaldun & Otti, 2016;Reinhardt, Naylor, & Siva-Jothy, 2011) to satisfy the split-sample design that is necessary to examine seminal fluid effects in individual males. A final advantage is that in this species, sperm is stored in separate anatomical compartments from seminal fluid (Davis, 1965) and so does (a) not activate sperm metabolism before the investigation and (b) the manufactured seminal fluid is stored "ready-to-go" without extraction from the accessory glands, and in containers that are large enough for easy fluid extraction and precisely controllable addition to sperm. Our data show that the artificial medium and the natural seminal fluid separately and additively affect sperm metabolism.

| Experimental animals
Male bedbugs were taken from a large stock colony (>1,000 individuals) that has been maintained as a standard culture for several years in the authors' laboratory at the TU Dresden (Germany). Bedbugs were housed at 70% r.H. as described earlier (Reinhardt, Naylor, & Siva-Jothy, 2003) but at slightly lower temperatures, between 21 and 23 C. The population used, named F4, originates from a field collection in London (England) in 2006 and has been extensively used in previous experiments (Bellinvia, Johnston, Reinhardt, & Otti, 2020;Bellinvia, Spachtholz, Borgwardt, Schauer, & Otti, 2020;Otti, Deines, Hammerschmidt, & Reinhardt, 2017;Otti, McTighe, & Reinhardt, 2013;Reinhardt et al., 2011;Reinhardt, Naylor, & Siva-Jothy, 2009a, 2009b including for a study investigating the protein composition of the seminal fluid (Reinhardt, Wong, & Georgiou, 2009). The function of seminal fluid in C. lectularius was described by Davis, 1965. Throughout this study, males were sexually isolated for several weeks before use. During isolation, males were fed twice to stimulate sperm production (Kaldun & Otti, 2016). Feedings were separated by 1 week. Measurements were started 10 days after the last feeding.
The bedbug male has an asymmetric copulatory organ (facing left) but testes, adjacent sperm storage organs, the seminal vesicles (SVs), and seminal fluid containers are paired, symmetrical structures ( Figure 1). The SVs are large enough to split them in half to examine sperm in the presence and absence of seminal fluid.
Similar to many other insects, bedbug sperm is very long (800 μm; Cragg, 1920) and has a filamentous structure that prevents an easy distinction between head and tail. Moreover, insect sperm has no midpiece but two mitochondrial derivatives (the nebenkerns) that are wound around the tail. For an overview of insect sperm morphology and motility, see Werner and Simmons (2008).

| Experimental design
We used a paired, full-factorial design whereby sperm was kept in either of two buffers (see Sample preparation) and, for the same male, measured with and without seminal fluid (SF), i.e., SF + and SF 0 , respectively. The split-sample nature of the paired SF + -SF 0 design was achieved by adding SF to one-half of the sperm container but not the other half. This design represents a paired analysis of variance (ANOVA) design of sperm metabolism in response to medium and SF. Because the current study is a technical baseline study, we also F I G U R E 1 Schematic representation of the anatomy of the reproductive system of the male bedbug, Cimex lectularius, in dorsal view examined the variation of sperm metabolism between the two paired SVs. We accounted for the fact that both SVs (first or second dissected) stem from the same male (see Section 2.7).

| Sample preparation
Dissection and sample preparations were performed in the respective medium of the allocated treatment, being either Grace's Insect medium (GM; Sigma-Aldrich, Pr.-Nr. G8142) or phosphate-buffered saline (PBS; Chemsolute Pr.-Nr. 8461). Both the two-paired SVs and the attached SF reservoirs ( Figure 1) were dissected and transferred as a whole into a drop of medium onto a microscope slide. The SV that was picked first for the analysis was labeled SV1, and the second SV2. While SV1 was processed, SV2 remained intact in its assigned medium. SV1 and SV2 were random with respect to left or right. We took the first (SV1) measurements $10 min after dissection, and the second (SV2) about 15-20 min thereafter.
The SV was cut in half, and each half was placed in a separate drop of 5 μl medium. Sperm was squeezed out from one SV half into the drop of medium. That bedbug sperm can survive in similar volumes, without evaporation, and can stay even motile for 24 hr was shown by Rao & Davis (1969).
In SF + samples, the seminal fluid container was added to the sperm, ruptured and thereby SF released onto the sperm, immediately before the analysis. Both, SF 0 and SF + samples were then covered with a coverslip (18 Â 18 mm) and analyzed using FLIM. The order of SF + or SF 0 treatment was random within any SV. SV2 was analyzed in the same way as SV1. Dissection was successful in 14 males, 5 GM, and 9 PBS, but not all four paired measurements were obtained for all males (Table S1).

| NAD(P)H and FAD autofluorescence
We measured several metabolic parameters. The mean lifetime (τ m ), a commonly used parameter, is defined as: where τ 1 is the lifetime of free NAD(P)H or bound FAD and a 1 the relative contribution of τ 1 to τ m , and τ 2 is the lifetime of bound NAD(P)H or free FAD with a 2 marking the relative contribution of τ 2 to τ m . The FLIRR (Wallrabe et al., 2018) defined as: 2.5 | Time-correlated single-photon countfluorescence lifetime imaging microscopy The measurements were executed using a multiphoton, pulsed titanium: sapphire femtosecond laser (Chameleon Ultra II, Coherent, Santa Clara, CA) and the microscopic setup described earlier (Wetzker & Reinhardt, 2019). In brief, the average laser power on the sample was around 12 mW. The microscope setup consisted of a

| FLIM data extraction and analysis
Autofluorescence lifetimes were calculated from the fluorescence decay curves using the software SCPImage 8.0 (Becker & Hickl GmbH). The calculation of the lifetimes requires a correction of the temporal convolution of the decay signal generated by the measuring system. This correction is achieved by incorporating an instrument response function (IRF) generated using a urea crystal. A scatter of 0 and a fixed shift was set for each image. The offset was not fixed.
The weighted least square methods implemented in SCPImage 8.0 was used to fit the decay data. Increased photon numbers to calculate more reliable lifetime decays were achieved by pixel binning of two (i.e., 25 pixels per image) for NAD(P)H, and a binning of four (81 pixels) for FAD images. Lifetimes were calculated using a bi-exponential decay; that is, the fluorescence decay of each component was assumed to arise from two fluorophores (i.e., free and bound NAD(P) H, or FAD). This has the consequence that in our case, a 2 , the relative contribution of the long lifetime simply was a 2 = 100% À a 1 and so is images were sorted into a multichannel stack with the slides representing the individual samples and the channels representing the lifetime parameters (τ m , τ 1 , τ 2 , and a 1 ). The background was excluded using a threshold of 110 photons for NAD(P)H photon intensity images, thereby also excluding pixels with insufficient photon counts.
Free active bedbug sperm aggregates (Ruknudin & Veera Raghavan, 1988), and the relatively high threshold was set to predominantly capture such regions of dense sperm. Sperm aggregations are the natural situation and would also retain sufficient sperm density over the time of the measurements, thus reducing background effects at low sperm density. All pixels in an image that passed the background threshold were defined as a region of interest (ROI). We used the threshold to generate a binary mask that was then used to create a selection of the pixels. This selection was stored for every slice of the stack as ROIs and used to extract the lifetime data. To have comparable data, the ROIs generated from NADH were also used for FAD. For ROI handling, the ROI Manager (Ferreira & Rasband, 2010 in FIJI was used. 2.8 | The effect of a possible time delay on the measurement of the sperm metabolism

| Statistical analysis
During the analysis (see Section 3), the factor "SV" sometimes remained in the minimal model (e.g., for NAD τ 1 and NAD a 1 ), suggesting that first and second SV could differ in sperm metabolism.
Because a functional asymmetry for a paired, symmetric organ is relatively unlikely, we assumed the differences may have been related to the fact that SV2, although intact had stayed in the medium for longer before measurement than SV1. However, the time passed between measuring SV1 and SV2 was not correlated with the metabolic difference between SV1 and SV2 (Table S4), suggesting the time effect is small or masked by some other factor.
Future sperm metabolism protocols are unlikely to measure both SV but only one. We mimicked this situation by rerunning all statistical analyses using data from only the first SV (then excluding SV as a factor). We compared the two results throughout Section 3 but we note the latter approach has less statistical power, because the sample size is halved.
3 | RESULTS  (Table S2 and Figure 3d) and was significantly affected by the medium (Table 1 and   Figure 3b) and was significantly affected by medium (Table 1). For SF 0 sperm, τ 1 was higher in SV1 than in SV2 (695 vs. 675 ps) in PBS, but not in GM (645 vs. 644 ps). SF in the sample lead to a slight decrease in τ 1 in PBS (SV1: 687 ps; SV2: 660 ps) but a slight increase in GM (SV1: 652 ps; SV2: 689 ps) ( Table S2). The minimal model retained most terms from the full model (Table 1), and this included the two-way interaction of Medium:SV and Medium:SF. This is suggesting that a complex mix of effects governs τ 1 . None of these terms except medium were significant.
Using SV1 data only confirmed medium as a significant effect on τ 1 and rejected more complex interactions, such as medium Â SF (Table 2). τ 2 ranged from 2,519.7 to 2,861.5 ps (Table S2). Medium treatments themselves contributed relatively little to a shift of τ 2 (Figure 3c)-no significant influences on τ 2 were detected ( Table 1). The minimal model included medium, SF, and medium Â SF (Table 1 and Figure 3c). For SV2, SF + samples differed substantially between GM and PBS ( Figure 3c). Using only SV1 data showed no significant influence on τ 2 (Table 2).

| Mean lifetime of NAD(P) (τ m )
For SF 0 sperm, τ m was about 10% lower in GM (mean 1,169.5 ps) than in PBS (mean 1,298.4 ps). τ m increased for SF + samples in both media ( Figure 3a). The minimal model (Table 1) retained medium and SF as explanatory variables (SF significant). Using only SV1 data retained the same terms with SF being close to significance and medium remaining highly significant (Table 2).

| FAD
All lifetime values of FAD showed large variation, which was exaggerated by medium (Figure 4 and Table S3). For example, τ m of FAD in T A B L E 1 Summary of the statistical analyses using a generalized linear modeling approach    GM varied between 1,044.1 and 1,379.7 ps but in PBS between 888.8 and 2,121.5 ps (Table S3).

| Proportion of protein-bound FAD (a 1 )
FAD a 1 varied from 37.5 to 76.5% (Table S3), significantly explained by medium, and medium Â SF interaction (Table 1). SF itself was not significant but stayed in the minimal model (Table 1). a 1 was lower when SF was added to sperm in GM for both SV1 and SV2 ( Figure 4d), but for sperm in PBS, a 1 was higher for SV1 when SF was added to sperm and was only slightly higher in SV2 (Figure 4d). Using SV1 data confirmed medium and SF to explain variation in a 1 (not significant) and their interaction (Table 2).

| Lifetime of free FAD (τ 2 )
τ 2 ranged between 2,489.1 and 4,192.9 ps (Table S3), significantly affected by medium (Table 1). τ 2 showed 16% lower values in GM than in PBS (Table S3 and Figure 4c). Rerunning the model with SV1 data only confirmed that only medium remained in the model (Table 2).

| Lifetime of protein-bound FAD (τ 1 )
FAD τ 1 ranged from 304.7 to 1,368.8 ps (Table S3). It was affected only by medium (Table 1) and confirmed using only SV1 data (Table 2). Similar to τ 2 , τ 1 also showed lower values in GM than in PBS (Table S3 and Figure 4b). Here, the values in GM were $4% lower than in PBS.

| Mean lifetime of FAD (τ m )
τ m varying between 888.7 and 2,121.5 ps was affected by medium, SF, and the interaction of medium and SF (Figure 4a and Table 1), confirmed for the case that only SV1 data were used (Table 2).

| FLIRR
Model reduction procedures suggested that FLIRR was significantly explained only by medium (Table 1 and Figure 5). Rerunning the model with SV1, the terms medium, SF, and medium Â SF are being significant (Table 2). We provide this analysis mainly for comparative purposes to other articles. It should be interpreted with caution because FLIRR is a ratio, its statistical treatment violating basic principles, because a ratio assumes a linear relationship between the proportions of the bound fractions of NAD(P)H and FAD.

| Photon intensity
The photon intensity of NAD(P)H was between 201 and 2,765 photons (Table S2 and Figure 3e) and for FAD between 16 and 483 (Table S3 and Figure 4e). In the minimal model for NAD(P)H, the interaction SF:SV and the three-way interaction were removed from the model. Of the remaining terms, only the interaction of Medium:SV was significant (p = .011, see Table 1). For FAD, all single terms and the two-way interaction of Medium:SF remained in the minimal model (Table 1). None of the remaining terms were significant. We used Spearman's rank correlation test to see if photon intensity correlated with estimates of lifetime values. NAD(P)H τ 2 correlated positively with photon intensity (rho = .322, p = .020), FAD τ 1 (rho = À.347, p = .013,), and a 1 (rho = À.609, p = 3.4E-6) negatively with the intensity (Table S5). These correlations could either mean that estimates are low when photon counts are low, that high metabolism increased photon counts, or that more photons are emitted by more dense sperm aggregates. When we ranked sperm density in the microscopy images from low (one) to high density (five) by a person blind to treatment and not involved in study, we found that NAD(P)H intensity was higher at higher sperm density (rho = .299, p = .031), suggesting a normal, biological effect. For FAD intensity where photon counts were low in a few cases, but here sperm density and photon intensity were not correlated (rho = .192, p = .178), suggesting FAD parameters were not biased by low photon counts.

| DISCUSSION
We present a detailed FLIM protocol to examine sperm metabolism, using an insect as an example. We quantified two of the seemingly most important sources of variation, a methodological one, the sperm diluent used (GM or PBS), and a biological one, the presence and absence of seminal fluid. We found that the diluent affected all parameters examined, and SF most of them (Table 1), suggesting that procedures to measure sperm metabolism need to be highly standard-

| Photon counts and the quality of metabolic estimates
The photon count per pixel in a sample determines the quality of parameter estimates-low photon counts can distort the lifetime estimates. NAD(P)H photon counts increase as glycolysis increases (NADH production) and/or as oxidative phosphorylation decreases (NADH consumption; Evers et al., 2018). We found that NAD(P)H photon intensity was significantly explained by the interaction of Medium and SV but that none of the lifetime values of NAD(P)H were significantly explained by this interaction. We conclude that photon intensity did not distort our NAD(P)H lifetime estimates. Also, τ 2 correlated positively with NAD(P)H photon counts. Although τ 2 was not affected by our treatments, it is possible that long lifetimes may require more photons for more precise lifetime calculation.
FAD photon count was not significantly influenced by treatment. impact on τ 1 , as FAD in aqueous solution comprises a multiexponential decay with a fast and a longer lifetime (Islam et al., 2013;Islam, Susdorf, Penzkofer, & Hegemann, 2003), or both.

| The effect of seminal fluid on sperm metabolism
SF affects sperm function in several ways. For example, SF can either incapacitate or support rival sperm (Holman, 2009;Holman & Snook, 2008), an effect that seems to depend, in part, on the relatedness of rivals (Den Boer, Baer, & Boomsma, 2010). In C. lectularius, SF activates sperm travel through the female (Davis, 1965) but not in the related Cimex hemipterus (Ruknudin & Veera Raghavan, 1988). However, in the latter species, SF contained substrates that extended sperm motility in vitro (Ruknudin & Veera Raghavan, 1988). In C. lectularius, we found that SF increased τ 1 and lowered a 1 of NAD(P) H in PBS (Figure 2b,d), indicating that SF increased sperm oxidative phosphorylation, and provided substances that were not already contained in the medium. The stimulation of oxidative phosphorylation by SF likely explains the lower FAD a 1 values observed for GM ( Figure 3d). With increased FADH consumption during oxidative phosphorylation, free FAD increases and, thus, reduces the relative amount of bound FAD (a 1 ). Bedbug SF contains proteins, sugars, and amino acids (Rao, 1974;Reinhardt, Wong, & Georgiou, 2009), as does hemolymph (Rao, 1974) through which the sperm travel. None of these components are found in PBS but GM contains sugars and amino acids, including L-alanine, for which in vitro evidence suggests it might be metabolized by bedbug sperm (Rao, 1974). However, it seems that amino acid catabolism may not be responsible for the increased oxidative phosphorylation in GM, because in GM, sperm was overall more glycolytic. Sperm may favor sugars over amino acids, or simply metabolize whatever is more abundant, which in GM would be sugar (e.g., Sucrose 26.68 g/L) not amino acids (0.05-0.7 g/L). In addition, the amount of sugar in SF in relation to that in GM seems negligible but future experiments will have to isolate the effects of individual components of GM. NAD(P)H τ 2 was nearly significantly explained by the interaction of Medium:SF. τ 2 is affected by various enzymes involved in both glycolysis and oxidative phosphorylation which together produce a wide range of lifetimes (Leben et al., 2019;Sharick et al., 2018

| The effect of time and medium
Bedbug sperm motility depends on oxygen (Rao & Davis, 1969;Ruknudin & Veera Raghavan, 1988) if oxidative phosphorylation fuels motility but glycolysis is possible in oxygen-poor environments. The relatively higher glycolytic state of bedbug sperm in GM than PBS suggests that GM fosters glycolysis and could support motility for a prolonged time. Similarly, in honeybees, where the female sperm storage organ is low in oxygen, sperm switch toward oxygen-independent glycolysis (Paynter et al., 2017). In our study species, the biology is even more complex, because both oxygen and sperm metabolism are likely to differ when sperm leave the female copulatory organ compared with when they travel through the female hemolymph or when entering the ovaries. It needs further investigation if the change from oxygen-dependent to oxygen-independent is a switch, or whether simply the oxygen-dependent pathway is turned off.
FAD τ 1 increases with oxidative phosphorylation (Wallrabe et al., 2018) and so would also lead to a decrease in FAD a 1 . This is what we found for sperm in GM (Figure 3d). The GM-FAD data for the sperm that were dissected later (SV2) also suggest some oxidative phosphorylation. However, for NAD(P)H, the SF-related increase in oxidative phosphorylation was less pronounced between SV1 and SV2 ( Figure 3d). One possible explanation may be that in the 20-25 min between dissection and measurement, sperm in SV2 was shielded from substrates and oxygen and, therefore, may have metabolized substrates within the vesicle. As sperm metabolism is likely to favor sugars over amino acids, a slightly higher glycolytic state may be expected. Especially, if sperm have no other substrates available to feed the oxidative phosphorylation. Possibly, the SV effect we observed simultaneously produced a higher glycolytic rate in GM and a higher oxidative phosphorylation in PBS.

| Methodological results
The literature on metabolic FLIM often uses τ m as an easy lifetime readout. For NAD(P)H, τ m was almost identical to the inverse of a 1 (Figure 3a), suggesting that NAD(P)H τ m differences are mainly caused by an increase in a 2 or a decrease in a 1 . a 1 is the common indicator for higher glycolytic or oxidative phosphorylation rate (Stringari, Nourse, et al., 2012). Thus, while a 1 strongly predicts τ m , the reverse is not true. In other words, τ m alone is not a suitable indicator of NAD(P)H.
We recommend that researchers report τ m measurements in concert with at least those of a 1 . We further recommend that researcher does not set τ 1 and τ 2 at fixed values, because τ m is determined by the product of a 1 and τ 1 , and fixing τ 1 will not allow one to accommodate τ m changes caused by additive effects of a 1 and τ 1 . That such concerns are not merely a theoretical issue, which is shown by our SF effects on NAD(P)H where a 1 and τ 1 were both non-significant but τ m was significant. Had we fixed τ 1 we might not have detected the effect of SF on τ m . Such fixing also has an impact when using the lifetime of free NAD(P)H to examine changes in the physical and chemical environment of the cell (Ogikubo et al., 2011;Scott, Spencer, Leonard, & Weber, 1970). For example, viscosity varies between mitochondria and the cytoplasm (Evers et al., 2018). τ 1 is often measured as a mixture of cytoplasmatic and mitochondrial τ 1 and can, therefore, change based on either the amount of free NAD(P)H in the cytoplasm and/or the mitochondria. During glycolysis, more free NAD(P)H is produced in the cytoplasm, shifting the overall sample mean toward smaller τ 1 .

| FLIM as a suitable tool to examine sperm quality
The relatively poor suitability of sperm motility as an indicator of fertility in humans may make sperm metabolism an additional indicator of fertility. We suggest our protocol is a promising basis from which to develop the measurement of sperm metabolism in other species, including humans. Our data indicate that GM produced minimal variation between the samples compared to the variation of the FAD lifetime values in PBS. Possibly, the low variation arose, because the excess of substrates in GM pushed sperm to optimal metabolism, whereas in PBS sperm show different states of starvation. Therefore, we suggest using GM or other glycolysis-supporting media for sample storage but an energetically more demanding medium if sperm function is to be elucidated ("stress test"). An important next step will be to examine the actual predictive power of sperm metabolism for fertility. Our method is non-destructive; should FLIM be applied in assisted reproduction techniques, the fertilization ability of sperm that have undergone FLIM needs to be established, and currently, it seems that glycolysis-supporting media provide the best starting point.
However, we suggest that our method may be applicable to livestock, for example, to monitor the quality of cryo-preserved sperm after thawing and before insemination as well as to optimize sperm storage media.
In our study, we focused on free sperm but it is important to note that FLIM allows the measurement of sperm metabolism in intact organs (Wetzker & Reinhardt, 2019). An ambitious, though probably distant, vision is to use FLIM to monitor sperm metabolic health in vivo in humans. If current FLIM approaches using simple skin contact to detect skin cancer (König, 2020)

| CONCLUSION
We confirmed that FLIM is a powerful tool to measure sperm metabolism. We also showed for free sperm, that is, for sperm in contact to oxygen, that medium and seminal fluid impact sperm metabolism in a way that easily mirrors differences that are, otherwise, seen between normal and cancer cells, or between stem cells and differentiated cells. Furthermore, we were able to show, using FLIM, that sperm metabolism is plastic and responds to the environment. This also implicates that this method could be used as a measurement of plasticity of sperm when, for example, sperm of different species would be compared. Also, the activation effect of SF appeared to be independent from the two media we used and pushed the metabolism toward a higher rate of oxidative phosphorylation, although this does not need to be the case for all media. Generally, our results call for highly standardized media and experimental conditions when analyzing sperm metabolism and other sperm functions.

ACKNOWLEDGMENTS
Thanks to Biz Turnell for help with statistics and Yvette von Bredow for help with Figure 1. The study was funded by the DFG to OO (OT 521/4-1) and KR (KR 1666/4-1), as well as by GACR to OB (18-08468J) and TB (18-08468J).