Characterizing Metabolic Shifts in Septic Murine Kidney Tissue Using 2P-FLIM for Early Sepsis Detection

1. Introduction

In cases where an infection encounters a dysregulated host response, this can lead to the failure of one or more organs. This phenomenon is described as sepsis, a disease with a high mortality rate [1,2]. In 2017, there were 50 million cases of sepsis worldwide, resulting in 11 million deaths. This represents 20% of global deaths, emphasising the importance of studying this disease [3]. The World Health Organization has identified the prevention, diagnosis and clinical treatment of sepsis as a priority area in a resolution published in 2017 [4]. Biophotonic imaging using predominantly spectroscopic imaging approaches is a promising tool for the study of such diseases, with the potential to improve our understanding and facilitate the development of rapid and effective treatments [5,6]. Compared to other imaging modalities, FLIM provides spatially resolved insights into metabolic processes without the need for exogenous markers, which is crucial for understanding dynamic changes in sepsis pathology. In this context fluorescence imaging using one- or multi-photon excitation schemes is particularly noteworthy [7,8]. In addition to the fluorescence intensity, the fluorescence lifetime is especially susceptible to change in response to small changes in the chemical environment [9]. The advantages of two-photon excited fluorescence lifetime imaging ((2P) FLIM) over single-photon excited (1P) FLIM include the elimination of overlap between excitation and emission spectra and the temporal and spatial concentration of fluorescence in the focal plane [10]. Metabolic FLIM does not require the introduction of an exogenous fluorescent label because it is based on the fluorescence of two metabolic autofluorophores, nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD), in their protein-bound and free forms, which have different lifetimes [11,12]. This differentiation is essential, as it allows precise monitoring o metabolic shifts from oxidative phosphorylation to glycolysis, which are hallmark processes in sepsis induced tissue damage. In order to observe NADH and FAD independently, it is necessary to use appropriate excitation and detection wavelengths due to the spectral overlap in the excitation and emission spectra of NADH and FAD [13,14,15]. The ratio of the relative amplitudes of the bound fluorophores (fluorescence lifetime imaging redox ratio, FLIRR) gives insight into the metabolism of the tissue under investigation and summarizes the measurements at one location into a single value [16]. A reductive metabolic pathway via glycolysis results in a lower FLIRR than that observed for oxidative phosphorylation. This parameter has already been established for the detection of cancerous tissue and is promising for use in infectious disease diagnostics such as sepsis as well [17].

Because of the filter function of the kidney, sepsis often results in acute kidney injury (AKI) [18,19]. This is the rationale behind the selection of murine kidney thin sections as the specimen for this study. To gain insight into the diverse metabolic responses, healthy mice and mice with chronic and acute sepsis were studied using metabolic FLIM imaging.

2. Materials and Methods

2.1. Animal Handling and Thin Section Preparation

Polymicrobial sepsis was induced in mice by intraperitoneal injection of a slurry suspension of human feces (peritoneal contamination and infection (PCI) model [20]) and their behaviour in the following hours up to 14 days was examined and scored. The scoring sheet reflects body weight, general condition, activity and the reaction to external stimuli (see S1 in the Supplementary Material for details, adapted from [19]). The resulting scores are shown in Table 1. Healthy mice from the non-infected control group were scored every three days. The chronic and acute mice were sacrificed (perfusion following anesthetization with 3.5% isoflurane) 24 hours and 14 days after sepsis induction, respectively. All mice were housed under standardized conditions with controlled diet and environment to minimize external factors influencing metabolic responses.

The right kidney was extracted and shock frozen. Frozen kidneys were cut into 20 µm thin sections on a cryostat and thawed in nitrogen flow before refreezing and storing at -80 °C under nitrogen atmosphere. Prior to measurements, samples were thawed under nitrogen atmosphere and measured in room air. All the experiments were conducted in accordance with approved ethical guidelines (see the section on the Institutional Review Board Statement).

2.2. Measurement Setup

A DeltaEmerald S09670 (APE, Berlin, Germany) laser was employed with a tuneable pump beam that was set to 711, 798 or 876 nm and second laser at 1032.6 nm for overview images with temporal and spatial overlap of pulses.

Images were obtained using an inverse laser scanning microscope (Leica Microsystems, SP8 Falcon, Mannheim, Germany) equipped with a HC PL APO CS2 20x/0.75NA dry objective. The fluorescence signal was collected by a hybrid photon detector (R10467U-40, Hamamatsu, counting mode) in an adjustable wavelength range in the epi direction, with the excitation light separated from the signal by an MFP SP665 and MP1 SP680 filter. Furthermore, the FLIM signal of the detailed images was transmitted to a SPC-180NX S/N 3H0066 card (Becker & Hickl, Berlin, Germany).

To obtain overview images, rectangular multi-tile images with 10% overlap were acquired. Detailed images were obtained using 500 frame accumulations. The laser power was set to cause an initial photon count rate of 250 kcps for all measurements. Three positions each in the outer and inner cortex of the sample were measured, first with 876 nm excitation and 550-650 nm detection, and then with 711 nm excitation and 380-450 nm detection. A preliminary scan was performed with minimal laser power to identify potential measurement locations, followed by acquisition of images at these locations and an overview image. Two spectral scans were performed on a healthy sample at one position in the cortex, first with excitation at 876 nm, Δλ = 0.7 nm, 53 mW (250 kcps at 550-650 nm) and second with excitation at 711 nm, Δλ = 0.7 nm, 53 mW (200 kcps at 380-450 nm). The 10 nm wide detection window was shifted in 57 steps of 5 nm within a total detection window of 380-670 nm. For each step, 20 frames were accumulated and two of these stacks were measured in succession for each excitation wavelength.

For imaging details, see also Appendix A.

3. Results

In this study, the effect of sepsis on the metabolism of murine kidneys was studied at two different time points: 24 hours (acute, three mice: A1, A2, A3) and 14-days (chronic, three mice: C1, C2, C3) after sepsis induction. Three healthy mice (H1, H2, H3) were used as a control group. First, we present the changes in behavioural scores associated with sepsis. Secondly, we describe the different parts of murine kidney sections using a broad detection window in 2P-FLIM with a tile scanning overview. Based on these findings, positions for detailed FLIM images were chosen. We then performed a spectral FLIM scan to decide on suitable detection ranges for NADH and FAD. Finally, these ranges were used to obtain detailed FLIM images for thin sections of the nine mice at the chosen locations in the inner (i1, i2, i3) and outer renal cortex (o1, o2, o3).

3.1. Behavioural Scores

Table 1 shows the behaviour of nine mice, of which six were infected to induce sepsis. Acute sepsis displays an early infection state. These mice were sacrificed 24 hours after the infection. The scores at this time point vary greatly, one mouse shows no behavioural abnormalities (a score of 0, see Table 1) despite the infection. This difference in the biological immune answer can be attributed to biological variance. Chronic infection is the classification of mice sacrificed 14 days after the infection. In this time frame, in all animals an initial rise of the score followed by a decrease can be observed (see mice C1, C2, C3 in Table 1) indicating a recovery).

3.2. Mean Photon Arrival Time Overview Images

Mean photon arrival time overview images of murine kidney tissue sections (see Figure 1) provide an efficient and rapid overview of the respective sample, facilitating the localization of important structures while limiting the overall exposure and thus the risk of inducing photodamage. While there is observable fluorescence in the renal cortex (labelled “co” in Figure 1, sample H1), the renal medulla (labelled “me” in Figure 1, sample H1) exhibits minimal to no autofluorescence. It is thus possible to distinguish between these two main parts of the kidney and to localize the positions of detail measurements on the outer and inner border of the cortex, as illustrated by the labelled white squares in Figure 1. In comparison to human kidneys, the cortex is more pronounced in murine kidneys, occupying at least half of the section in these images [21,22].

These images demonstrate no notable distinction between the mean photon arrival times in healthy and chronic samples (see samples H1, H2, H3 (healthy) and C1, C2, C3 (chronic) in Figure 1). In the acute samples, the outer cortex exhibits a lower mean photon arrival time than the inner cortex (see samples A1, A2, A3 in Figure 1). This feature is exclusive in acute samples, hinting at a change in the metabolism specifically in the outer cortex at an early time point of the infection that is reverted later for chronic infections.

The fluorescence intensity in these overview FLIM images (see Figure 1) demonstrates that only in the cortex is there a sufficient number of photons for a two-component fit, which accounts for protein-bound and unbound NADH and FAD. The overall photon count is insufficient for a two-component fit in the entire overview image (especially the medulla, see for example area marked with “me” in sample H1 in Figure 1), due to the low frame accumulation of 12 frames. Three positions were selected in each outer and inner cortex for detailed FLIM images with 500 frame accumulations for each position. These are indicated by white squares in Figure 1 and will be presented in Section 3.4.

The fluorescence lifetimes of the blood vessels, which can be identified by their morphology, differ from those of the surrounding tissue (indicated by arrows in sample H3 in Figure 1). These structures exhibit high fluorescence lifetimes (see yellow arrow in sample H3 in Figure 1), which can be attributed to elastin and collagen [23,24]. In contrast, the outer part of the blood vessels exhibits very low mean photon arrival times (see purple arrow in sample H3 in Figure 1). This may be correlated with the degree of collagen cross-linking present within the tissue. The presence of cross-linking results in an elevated fluorescence lifetime [23]. While these features are prominent in the images (see arrows in Figure 1), their elevated collagen content exceeds the NADH/FAD model's applicability, necessitating the avoidance of such positions in detailed measurements.

Another notable feature of the tissue is the presence of regions exhibiting elevated mean photon arrival times, which are discernible in multiple thin sections of the kidney (see circles in Figure 1). These regions are identified as proximal tubules, which exhibit distinct functional and metabolic characteristics compared to their distal tubule counterparts. These differences are also reflected in their fluorescence properties [25]. Distal and proximal tubules represent the two main structures in the renal cortex and are therefore the only possible structures that these areas can be attributed to.

3.3. Spectral Scan

A spectral scan was performed on a healthy sample (mouse H2, parallel section to the one studied in other sections) to examine the fluorescence behaviour at two different excitation and several ranges of detection wavelengths. The decay curves of all pixels in the FLIM image were aggregated and fitted to equation 1. Using a measured instrument response function (IRF) convoluting the bi-exponential decay, the influence of the laser width, and other characteristics of the optical setup can be considered (see equation 1).

$$I=IRF\ast \left(a_1^{\ast }{e}^{-{\displaystyle \frac{t}{{\tau }_1}}}+a_2^{\ast }{e}^{-{\displaystyle \frac{t}{{\tau }_2}}}\right)$$

(1)

Instead of the absolute amplitudes $a_1^{\ast }$ and $a_2^{\ast }$, the relative amplitude $a_1={\displaystyle \frac{a_1^{\ast }}{a_1^{\ast }+a_2^{\ast }}}$ is used because it summarizes both amplitudes independent of the absolute fluorescence intensity. The resulting parameters of the two-component fit are presented alongside the normalized fluorescence intensity in Figure 2. This information was used to identify suitable detection windows for the distinct detection of NADH and FAD, which is critical due to their overlapping excitation and emission spectra [12].

Two-photon excitation of both NADH and FAD is achieved at a wavelength of 711 nm, resulting in the promotion of these molecules to an excited state [13,14]. It is therefore essential to select a suitable detection range to obtain lifetime information for NADH alone. The emission of NADH is blue shifted compared to FAD. Therefore, a lower wavelength window with constant lifetimes and amplitudes should be selected, considering the fluorescence intensity, in order to obtain a sufficiently large detection window for a reasonable measurement time. For both excitation wavelengths (876 nm and 711 nm), the recorded emission maximum of the sample was at approximately 530 nm (see Figure 2a). This corresponds to the emission maximum of FAD, indicating that the sample contains a greater amount of FAD than NADH. This is because FAD is excited by both excitation wavelengths whereas NADH is only excited by the lower wavelength of 711 nm. The difference between the two normalized spectra in the region below 500 nm is analogous to the NADH emission spectrum (see Figure 2a) [13]. To minimize the FAD signal in the NADH channel, a detection window of 380-450 nm was selected for NADH, as illustrated by the purple rectangle in Figure 2. The lifetimes ${\tau }_1$ and ${\tau }_2$, as well as the relative amplitude $a_1$, remain largely constant within this region (see Figure 2b,c,d). The observed variation can be attributed to the relatively low fluorescence intensity and the excitation of a different fluorophore that exhibits a maximum of fluorescence intensity around 625 nm as visible in the pink difference spectrum in Figure 2a. This fluorophore is most likely responsible for the difference in ${\tau }_2$ between the two excitation wavelengths and the rise in $a_1$ above 575 nm for an excitation wavelength of 711 nm (see Figure 2b and d). It does not interfere with the measurement of NADH because its emission spectrum does not overlap with the one of NADH.

For FAD, an excitation wavelength of 876 nm is already sufficient to separate the FAD signal from the NADH signal. A detection window of 550-650 nm is appropriate for achieving a reasonable photon count rate for the FLIM measurements. The differences in the lifetimes ${\tau }_1$ and ${\tau }_2$ and the relative amplitude $a_1$ between the two excitation wavelengths (see Figure 2) can be attributed to the excitation of another fluorophore that is excited using 711 nm. This is evident in the emission spectrum, which cannot be described with the two-component fit that was used.

Table 2 summarizes the chosen excitation and detection wavelengths for NADH and FAD, completed with approximate lifetimes and relative amplitudes which are despite some small deviations still in a similar wavelength range as values reported in the literature [26,27]. As fluorescence lifetime parameters are very prone to change with changes in the chemical environment, a change to these between different models – from isolated molecules to single cells to organs - is likely.

3.4. Detailed FLIM Images

Detailed images acquired subsequently for FAD and NADH were fit pixel-wise to equation 1 to obtain lifetimes and relative amplitudes for their protein-bound and unbound forms (see Section 3.2 for details, described there not for a pixel-wise fit but for a fit of the sum of the decay curves in all pixels). To obtain FLIRR images, the relative amplitudes of bound FAD and NADH were divided for each pixel according to equation 2.

$$FLIRR={\displaystyle \frac{a_{bound}\left(\mathrm{F}\mathrm{A}\mathrm{D}\right)}{a_{bound}\left(\mathrm{N}\mathrm{A}\mathrm{D}\mathrm{H}\right)}}={\displaystyle \frac{a_2\left(\mathrm{F}\mathrm{A}\mathrm{D}\right)}{a_1\left(\mathrm{N}\mathrm{A}\mathrm{D}\mathrm{H}\right)}}$$

(2)

Figure 3 provides an overview of the combined FLIRR and ratiometric fluorescence intensity maps, accompanied by the corresponding histograms. Figure 3a illustrates the distinction between convoluted proximal and distal tubules in the outer cortex, as identified by their FLIRR and ratiometric NADH/FAD intensity with proximal tubules having a higher FLIRR and being brighter due to their higher NADH content [28,29]. There are no discernible differences evident between healthy samples and those infected with chronic and acute sepsis. To facilitate comparison, histograms of FLIRR, lifetimes ${\tau }_1$, ${\tau }_2$ and the relative amplitude $a_1$ were generated (see Figure 3b and Figure S1ac in the Supplementary Materials). Of these values, FLIRR is the most promising and is therefore used for further analysis. The lifetimes are not expected to change significantly because the system of protein-bound and unbound NADH and FAD stays the same regardless of the disease state. Their ratio, reflected by the relative amplitudes, will change with a change in metabolism. The FLIRR value comprises the change of the protein-bound/unbound ratio for both NADH and FAD in one value, facilitating the comparison of different samples. Figure 3b shows that FLIRR changes for some mice, but no apparent trend for acute or chronic sepsis is visible.

As shown in Figure 3b, the histograms for healthy outer cortex images show considerable overlap. This is to be expected as the metabolic processes occurring in healthy mice are likely to be comparable. In contrast, the outer cortex images from infected mice show less overlap (see Figure 3b). Except for samples H3 and C3 in the inner cortex, the variation between the three images of a single sample is minimal compared to the variation between samples (see Figure 3b). This observation can be attributed to biological variation in the immune response of the mice. The observed metabolic switch in the outer cortex may reflect its higher vulnerability to early sepsis induced hypoxia, a hypothesis that aligns with the outer cortex’s proximity to systemic circulation.

The images of the inner cortex show a greater degree of variation (see Figure 3b), particularly in samples H3 and C3, which can be attributed to medullary entanglement in the cortex. This attribution is reinforced by the overview images in Figure 2 where the squares indicating the position of the detailed images show that, partly, low-fluorescence medulla tissue is included in the measured region. The use of low-resolution preview images with short acquisition times to minimize bleaching did not allow the identification of a suitable measurement position in the inner cortex for every position of a detailed image and therefore not all medullary traces could be excluded. Another reason for the variation for sample C3 in the histogram in Figure 3b are the bright squares of different mean photon arrival times in the overview images of the same sample (see Figure 1). Sample H3, while also showing more variance in the histogram (see Figure 3b), does not exhibit bright tiles in Figure 1 but bright tubular structures.

For a more detailed comparison of the histograms shown in Figure 3b, it is necessary to examine their key values. If the FLIRR values are normally distributed, a Gaussian fit can be performed. The histogram in Figure 4a shows that this assumption is incorrect. The reason for this discrepancy is that the images in question have two distinct regions, namely the proximal and distal tubules, as previously described and illustrated in Figure 3. A two-Gaussian fit should be able to accommodate this discrepancy and accurately represent the shape of the histogram, as shown in Figure 4b. A full description of the fitting process can be found in Appendix B. In the two regions of the proximal and distal tubules, the fitted lifetimes, amplitudes and the resulting FLIRRs are normally distributed.

The mean values of the Gaussian curves ${\mu }_1$ and ${\mu }_2$ can be used to facilitate a comparison of the FLIRR in the distal (${\mu }_1$) and proximal (${\mu }_2$) tubules between the different disease states. The scatter plots shown in Figure 4c and d illustrate these fitted values for FLIRR and ${\tau }_1$ in the distal tubules, and no obvious differences were observed between the different infection states. This is a positive result, as it shows that the protein-bound/free NADH/FAD model remains applicable and that the relative amplitudes $a_1$ and $a_2$ and therefore the FLIRRs can be compared. The variation in the lifetimes can be associated with the biological variation of these samples.

Leaving out the outlier samples previously described in this section (H3 and C3, see variation in the histograms in Figure 3b and areas in the overview images in Figure 1), all healthy and chronic samples show FLIRRs of about 0.45 in the distal tubules of the outer cortex and similar or slightly higher values in the distal tubules of the inner cortex (see Figure 4c). One image of the inner cortex of H2 shows a considerably higher FLIRR of about 0.5 (see Figure 4c), this can be related to the relatively low number of pixels that can be attributed to distal tubules compared to proximal tubules and therefore a relatively high influence of proximal tubules which show a higher FLIRR (see Figure 3a, H2-i3). The lower FLIRR of inner cortex images of sample H3 can be related to the bright tubular structures visible in the overview image in Figure 1. The metabolism in the distal tubules of the outer cortex in chronic and healthy samples can therefore be assumed to be oxidative phosphorylation, the standard metabolic pathway in healthy tissue [30]. In acute samples, the FLIRR of inner cortex distal tubules stays roughly the same, but a shift of outer cortex distal tubules FLIRR is visible for all three samples (see Figure 4c and Figure S2 in the Supplementary Material). This is consistent with the lower mean photon arrivals times of the outer cortex in Figure 1 and relates to a switch of the metabolism from oxidative phosphorylation to glycolysis [17]. A similar trend is visible for the proximal tubules, as shown in Figure S2 in the Supplementary Material. This is in accordance with the metabolic switch observed in tubular cells in the early phase of sepsis-induced AKI [31]. It is hypothesized that this shift serves to protect the tubules during this acute phase [32].

4. Discussion

The results presented in the preceding section show that in the acute stage, at 24 hours after sepsis induction, the metabolism is only changed in the outer cortex of the kidney (see Figure 4c). Even in mice showing almost no symptoms (see A2, Table 1) this change in metabolism occurs. The assignment of a specific region where the metabolism is affected was made possible by using FLIM as an imaging technique compared to common biochemical methods including blood analysis and the analysis of homogenised renal (cortical) tissue, where only cells of a broader region (kidney or renal cortex) can be distinguished by dissection of the tissue prior to homogenization [30,33].

One possible reason for a change of properties closer to the outer perimeter of the kidney, like the mean photon arrival times visible in Figure 1, is freezing. This can be ruled out because it would also have affected the structure of the tissue. Freezing artefacts like cracks and holes can be found throughout all samples or at least in several samples across different infection states. (see Figure 1 and detailed images in Figure 3a). While 2P-FLIM provides valuable spatial resolution, the reliance on specific autofluorophores like NADH and FAD limits its applicability to tissues with sufficient endogenous fluorescence. Future studies should explore its integration with complementary techniques for broader applicability.

Some overview images show bright squares or bright tubular structures (see Figure 1, samples C3 and H3). The bright squares visible in sample C3 in Figure 1 can be explained by the use of relatively high laser powers for the investigation of this sample. They were required to achieve the desired initial count rate of 250 kcps (see Table S3 in the Supplementary Material) but resulted in photodamage evident by these tiles leading to an elevated fluorescence intensity (see bright tiles in Figure 1) and changes in the lifetimes and relative amplitudes (see Figure S1 in the Supplementary Material). This results in a shift in FLIRR (see Figure 3b). The higher intensity shows that not only (partial) bleaching of the fluorophores occurred which would also lead to different lifetimes and amplitudes but to a reduced fluorescence intensity. A new fluorophore seems to be induced by the high laser power, resulting in a higher fluorescence intensity. The origin of the bright tubular structures in sample H3 (see Figure 1) is unclear, but they are most likely the reason for the exceptional behaviour of this sample in comparison to the other healthy samples measured with similar excitation laser powers (see FLIRR histograms in Figure 3b and excitation powers in Table S3 in the Supplementary Material). Excluding these two samples, the variance of the lifetimes in the distal and proximal tubules is low (see Figure 4d and Figure S2 in the Supplementary Material). Because of this, the influence of other fluorophores can be ruled out for these samples. The results demonstrate the presence of only NADH and FAD, which is in accordance with the implemented excitation and detection wavelengths as a result of the spectral scan (see Section 3.3 and Figure 2). The reversible metabolic changes observed in the outer cortex suggest that early interventions targeting glycolytic pathways could mitigate sepsis-induced kidney damage, offering new therapeutic avenues.

In cases of chronic kidney disease, the cells are not able to switch back to oxidative phosphorylation [34]. In the study presented here, chronic refers to the time observation point after induction of sepsis (14-days), regardless of the disease state/score values. These findings based on the FLIRR results visible in Figure 4c show that the switch in metabolism in the outer cortex from oxidative phosphorylation to glycolysis as a reaction to acute sepsis after induction with PCI was reversible. It can be assumed that the mice labelled chronic here are recovered because their FLIRRs are similar in the inner and outer cortex and close to those of healthy specimen. This fits to the lack of symptoms (see Table 1), however, a lack of symptoms alone cannot be used to assign the disease state, as the study of sample A2 showed. The lack of symptoms might imply that the variation of the fluorescence parameters in the outer cortex and therefore the change in metabolism is not induced by the infection but other influences on the standardized animals. However, the equal treatment of the mice following the infection implies that the observed differences in FLIRR and mean photon arrival times (see Figure 4c and Figure 1) are a result of the different disease states of the mice. It is also known that infections do not always result in immediate symptoms. Sepsis might be asymptomatic, making it even more important to understand the metabolic pathways to be able to initiate treatment of infected patients as soon as possible [35,36].

5. Conclusions

We have demonstrated that 2P-FLIM can effectively differentiate between oxidative phosphorylation and glycolysis in tubular kidney cells from healthy mice and those with acute sepsis. The average photon arrival time overview images revealed distinct differences in the outer and inner cortex of the acute sepsis group, which indicates metabolic changes. Interestingly, not all tubular cells are affected uniformly and at the same time. Initially, 24 hours after infection, cells near the outer edge of the kidney exhibit a metabolic shift toward glycolysis, as detected by FLIM and described by FLIRR. However, by 14 days post-infection, this shift reverses, and the metabolism returns to oxidative phosphorylation. This process is visible for the FLIRR of both distal and proximal tubules. The FLIRR histograms were fitted by a two Gaussian fit and the mean values of the two Gaussians represent the mean FLIRR in the proximal and distal tubules. Another approach to identify the FLIRR of the two different kinds of tubules would be to use masks to analyse the pixels in the two regions individually. The analysis of the histograms proved to be sufficient, but this more complex approach might be of use in the future. To our knowledge, we are the first to report using FLIM that during the early stages of sepsis, the metabolism of the outer renal cortex undergoes a reversible transition from oxidative phosphorylation to glycolysis, while the inner cortex remains unchanged. This research establishes a foundation for more detailed FLIM studies on the effects of sepsis and other infections on the metabolism across different regions of various organs. These findings not only enhance our understanding of sepsis pathology but also highlight the potential of FLIM as a diagnostic tool for early therapeutic interventions in septic patients.