Studying microglia in the central nervous system (CNS) is crucial in our understanding of the etiology and progression of neuroinflammatory diseases (Cătălin et al., 2013; Cojocaru et al., 2021). While direct electrophysiological and morphological studies of microglia are difficult, due to how fast microglia are reacting to their environment (Cătălin et al., 2017; Boboc et al., 2023), the link between microglial function and morphology allows researchers to accurately predict microglial involvement in such diseases (Hermann and Gunzer, 2020; Popova et al., 2021; Cornell et al., 2022). However, microglia exhibit a remarkable capacity for morphological plasticity, continuously surveilling their microenvironment through dynamic extension and retraction of cellular processes. The continuous morphological changes serve as a fundamental mechanism underlying their diverse functions, including synaptic pruning (Harry, 2013; Kettenmann et al., 2013; Arcuri et al., 2017; Weinhard et al., 2018), immune surveillance (Davalos et al., 2005; Nimmerjahn et al., 2005) and modulation of neuroinflammatory responses (Surugiu et al., 2019; Anton et al., 2021; Li et al., 2021). Different microglia phenotypes have been associated with both aging and disease, highlighting the importance of studying morphological changes as potential biomarkers for disease progression. Advancements in imaging techniques, such as laser scanning microscopy (LSM) have enabled detailed visualization and analysis of microglia morphology in vitro (Mitran et al., 2018; Stopper et al., 2018) and in vivo (Davalos et al., 2005; Nimmerjahn et al., 2005; Cătălin et al., 2013). These technological innovations offer unprecedented opportunities to unravel the intricate dynamics of microglial morphology in health and disease. Using such technologies, scientists have shown that microglia can change their morphology up to 8 h after death (Nimmerjahn et al., 2005; Block et al., 2007; Hanisch and Kettenmann, 2007; Ransohoff and Perry, 2009; Dibaj et al., 2010), and that as little as 5 min of global ischemia can be enough for microglia morphology change (Cătălin et al., 2017). Innovation in scientific research, particularly in the domains of imaging and microscopy, has historically been a catalyst for transformative discoveries. Central to the progress of microscopy is the quest for enhanced resolution, a critical parameter defining the performance of imaging systems. While traditional fluorescence microscopy has long been constrained by diffraction-limited resolution, recent strides have led to the emergence of super-resolution techniques capable of achieving resolutions at the nanoscale (Hell, 2007; Montgomery et al., 2013; Montgomery et al., 2016). However, all these advances pose a significant limitation: the high costs associated with acquiring them. While the total cost of confocal LSM (cLSM) has decreased, the level of the investment can still be restrictive for low-income countries. The introduction of optical sectioning microscopy using structured illumination (OSM) has emerged as a transformative imaging technique capable of providing high-resolution, three-dimensional visualization of biological specimens. While the base of OSM is widefield microscopy, by placing a grid between the sample and the detector to generate a pattern of intensity differences, the method can filter out-of-focus and by repetitive grid movements a true optical section is calculated (Neil et al., 1997). Although a direct comparison between the two methods has been made (Weigel et al., 2009), in the present study we aim to investigate if OSM can be used to investigate microglia morphology, and try to establish, using the difference in acquisition time and work force costs, a limit in which it does and does not become economically viable to acquire such devices compared to a cLSM system.

Because light microscopy is considered a fundamental tool in most areas of life science research, its use in any research laboratory seems like a given. From the use of glass lenses to the optical systems of today’s microscopes, researchers can choose from a great variety of techniques including: confocal microscopy (Minsky, 1988), STED microscopy (Hell and Wichmann, 1994; Okada and Nakagawa, 2015), Adaptive Optics (AO) z-STED Microscopy (Barbotin et al., 2019), Spinning Disc Confocal Microscopy (SPDM) (Oreopoulos et al., 2014), Airyscan Microscopy (Wu and Hammer, 2021) or OSM (ApoTome, Zeiss) (Ryan et al., 2017; Tröger et al., 2020). As with most technologies, the initial cost is high, but as production and competition begins, the overall market drives the price down. However, due to economic disparities around the world (Holstein et al., 2009; Peterson, 2017; Lee, 2023) the affordability of a product varies (Lipsey and Swedenborg, 2007). In contrast, access to information is getting easier. For example, a group of researchers in any country connected to the Internet can access the latest results in any known field in seconds. Thus, while curiosity and knowledge may be present in low-income labs, lacking infrastructure may prevent testing different theories. The introduction of new methods that use simple ideas can lead to surprising results, from an increase in diagnosis of certain diseases (Bhamla et al., 2017) to every day house comforts (Graettinger et al., 2005). While there are major differences between state-of-the-art techniques and cheaper alternatives, in certain situations and for certain questions it is just not economically viable (and necessary) to opt for the top-of-the-line solution.

Here, we could show that advances in OSM research make this technique a suitable, albeit time-consuming, alternative for cLSM. While the image quality of the two microscopic techniques is influenced by several factors, including the type of sample, the expertise of the team, and the requirement for identifying cellular or subcellular structures, by utilizing the same tissue sample and the same experienced team proficient in analyzing morphological parameters of microglia, we have mitigated some of those variables. Thus, the major differing factor lies in the technical disparities between the two microscopes. Microglia were chosen both because our laboratory has already gained experience with microglia morphology (Cătălin et al., 2017) and because of the high morphological variability of microglia (Godeanu et al., 2023). We also chose to investigate the microglia from the somatosensitive cortex of 3-week-old mice, as they were shown to have a complex branching pattern, compared to microglia obtained from aged animals (Godeanu et al., 2023; Pinosanu et al., 2023), hoping that differences between the two techniques would become apparent using this complex cell.

To date, resolution comparisons between the two microscopy techniques have primarily been performed within customized setups (Jonkman and Brown, 2015). Due to our experience in brain imaging and glial cells (Cătălin et al., 2017), we chose to investigate the somatosensory cortical microglial cells for their morphological appearance as dynamic and highly ramified cells under physiological conditions (Davalos et al., 2005; Nimmerjahn et al., 2005; Garaschuk and Verkhratsky, 2019; Lier et al., 2021). The microglial processes in our study were traced and labeled starting with primary branches (the first order branches that arise from the soma) and terminal branches (that are the most distant from the cell body located ramifications). No differences were seen in the analyzed morphological parameters between our two groups, showing that OSM can be a viable alternative to cLSM in certain morphological studies.

cLSM was the only technique that allowed for the rejection of out-of-focus light (Weigel et al., 2009). The development of OSM (which does not require an excitation source or pixel-by-pixel scanning of the object) has demonstrated the ability to achieve a resolution that, according to previous studies, can potentially surpass that of cLSM (Gustafsson, 2000) and has proven its usefulness with remarkable diagnostic results (Das et al., 2012; Heintzmann and Huser, 2017). As such, choosing the best method, between the two, in order to answer a biological question boils down to potential artifacts that accompany OSM. Although the quality of images generated by both methods can be affected by photobleaching or vibration-induced artifacts, OSM may provide better images from very weak photo-samples due to detection device (CCD camera instead of photomultiplier) but is additionally affected by artifacts induced by light scattering in thicker samples (≥30 μm) (Weigel et al., 2009). As a result, image quality degrades with increasing tissue depth because the grid pattern is projected onto the focal plane, which is contaminated by scattered light (Weigel et al., 2009). In the present study we used image stacks of approximately 15 μm acquired from a 35 μm slice. Therefore, a possible reason for the comparable results between the two techniques may be that the depth of our acquisition is insufficient for such artifacts to manifest, or at least to significantly affect the overall results. Moreover, other factors were shown to impact the image quality of OSM. For example, noise can be quantified as information, and the final image can thus become corrupted. This was shown to be the case for image noise picked up by OSM, especially starting with depths more than z = 27 μm, due to increasingly low contrast (Weigel et al., 2009). This low contrast, resulting from the increased noise, leads to a fragmented view of thin structures, which can cause a false reduction in branch numbers, especially in a manual analysis approach.

We found smaller average area-based parameters obtained by OSM compared to cLSM. With microglia being highly dynamic and their surveilled area always shifting due to microglia constantly emitting and retracting their processes with the highest-order extensions being the most mobile (Cătălin et al., 2017), one might argue that the difference between the two methods can be a consequence of the measured populations. However, the present reported mean surveilled area and mean occupied area obtained with the cLSM were comparable to our previous reports (p = 0.1314, respectively p = 0.3469 Supplementary Figure 1). As discussed, OSM imaging scattering is greater as the depth of the tissue is increasing. The bright fluorescence emitted by the soma or first-order branches can produce so much noise, that it can be counted as information, resulting in a larger false value when evaluating the exact edge of the surveillance area, for example. With a difference of approximately 10% between the average surveillance area determined by OSM and cLSM, for the resolution of the analyzed pictures, the difference in choosing the edge is around 2 μm (Supplementary Figure 2). Furthermore, it is possible that we have incorporated a z averaging effect on our investigations. As mentioned in the image acquisition and analysis’s part z-stacks were used for analyses and as proven structured illumination methods can generate enhanced optical sectioning compared to confocal methods (Wilson, 2011). These differences in optical narrowing of sectioning intensity can be as much as 20–25%, which may be one reason for a reduced fluorescence signal. In addition, structured illumination methods (such as Apotome) are not suitable methods for the study of living moving specimens due to the technical limitations of having to acquire three images at three different positions of the Ronchi rule for each z, which can increase scattering. Furthermore, the difference in resolution between the two methods generated by different objectives and specific machine bound parameters can also add to the observed changes.

While both techniques have advantages and disadvantages that the investigator needs to be aware of, another important aspect that should be taken into consideration is the financial one. With the average initial considerable investment difference between an OSM and a cLSM system (approximately 100,000 USD), planning the questions that need answering, seems to be just one aspect in deciding between the two methods. Although today’s society is evolving exponentially in terms of discoveries made, research and scientific results are still dominated by the resources a particular laboratory has at its disposal (Ioannidis and Garber, 2011), major improvements can be made “on a budget” (Sendi et al., 2004). Beyond the initial acquisition cost, the ongoing financial burden of routine maintenance, servicing, and repairs of advanced instruments poses a substantial challenge. This is particularly pronounced in less developed countries, where the availability of technical expertise and replacement parts may be limited, leading to increased costs. In addition, funding and grants are highly dependent on other factors such as country’s economic status and although the differences between developed and developing countries have narrowed in the 21st century, they remain or are unevenly distributed, including in the field of research (Paprotny, 2021). For example, a study that analyzed the biomedical publications from several developed and emerging countries (1994–2013), showed that even tough countries like Malaysia are catching up with some developed countries in terms of the number of publications, there are still large gaps. Moreover, the percentage of gross domestic product (GDP) spent on research is lower, especially when comparing the United States (2.7% of their GDP in 2011) to Malaysia (1.06%) or Qatar (0.33%) (Tang et al., 2016). Hence, based on our findings, allocating funding smartly (considering outcome and labor cost), particularly in the context of developing countries, may be more beneficial than investing it in state-of-the-art fast microscopy systems. Thus 100,000 USD could cover the salary of a researcher for 6.11 ± 2.93 years in the case of countries with an HDI = (7–9) and for 7.06 ± 3.13 years for HDI < 7 (Figure 4) (Table 2).

Since there are no significant differences in the results provided by the morphological analysis done for this study, with area-based parameters being the exception, we can presume that for this scope and other basic histological evaluation, OSM systems like the ApoTome could be an appropriate choice given a reduced initial budget and a lower labor cost. For projects that require fast moving in vivo samples, more detailed analyses or focus on a volumetric image or an increased resolution one should consider cLSM systems as the first choice.