From 3D Printer to Microscope: A Customizable Platform for Fluorescence Microscopy

From 3D Printer to Microscope: A Customizable Platform for Fluorescence Microscopy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From 3D Printer to Microscope: A Customizable Platform for Fluorescence Microscopy Jovan Badzoka, Javier Ureña, Benjamin Göllner, Andreas Leismüller, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6548991/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fluorescence microscopy is a powerful method for visualizing biological structures with high specificity, yet commercial systems remain expensive, complex, and limited by proprietary hardware and software. To address these barriers, we present a low-cost, open-source fluorescence microscope built primarily from 3D-printed components, off-the-shelf optical elements, and accessible electronics. The system is based on a modified Rook CoreXY 3D printer, which provides precise motorized sample positioning, and a custom-designed fluorescence detection unit comprising optical filters, an achromatic lens, and a Raspberry Pi Camera v2. Controlled by a flexible Python script, the platform enables automated image acquisition, programmable scanning, and user-defined workflows not possible with conventional systems. Imaging tests using fluorescein diacetate-stained Bacillus subtilis confirmed single-cell resolution and strong fluorescence contrast under suitable conditions. Field of view and pixel resolution were quantified, and mechanical stability was demonstrated through 100-cycle positioning tests with submicron average drift. This work establishes a robust and extensible framework for fluorescence imaging that bridges the gap between educational DIY tools and functional laboratory instrumentation, offering a highly accessible alternative for researchers, educators, and innovators working outside of traditional infrastructure. Biological sciences/Microbiology Physical sciences/Optics and photonics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Fluorescence microscopy is an essential tool for visualizing biological structures, chemical interactions, and microscale systems with high specificity and sensitivity[ 1 – 4 ]. However, commercial fluorescence microscopes are often expensive, complex, and reliant on proprietary components and software, limiting their accessibility to institutions with significant resources.[ 5 – 9 ] In recent years, the growing availability of affordable microcontrollers, 3D printing, and open-source hardware has enabled the development of do-it-yourself (DIY) fluorescence microscopes—offering functional and flexible alternatives for researchers, educators, and hobbyists.[ 10 – 14 ] In this work, we present the development and validation of a low-cost, customizable fluorescence microscope built using primarily 3D-printed components, open-source electronics, and off-the-shelf optical elements. At its core, the system is based on a modified Rook CoreXY 3D printer, which provides high-precision motorized sample movement. A custom-designed fluorescence detection unit, including filters, an achromatic lens, and a Raspberry Pi Camera v2, enables the selective imaging of fluorescence signals. One of the defining features of this system is its programmability and adaptability. The microscope is controlled by a custom Python script that manages image acquisition, motion control, and optional features such as automatic plane correction and large-area scanning. Unlike commercial systems, which are typically locked behind proprietary software, this open-source setup allows users to implement custom imaging routines tailored to their specific experimental needs—including advanced workflows that would not be possible on conventional systems. The system was evaluated by imaging Bacillus subtilis bacteria stained with fluorescein diacetate, a viability indicator that fluoresces upon enzymatic hydrolysis in living cells. Although no formal intensity calibration was performed, the system successfully captured high-contrast fluorescence images and demonstrated single-cell resolution under suitable sample conditions. This paper details the mechanical, optical, and software architecture of the microscope, discusses the imaging results, and outlines areas for future development. The system is intended as a robust, extensible, and accessible platform for both educational and research-focused fluorescence microscopy, bridging the gap between low-cost DIY projects and functional laboratory instrumentation. 2. Results 2.1. Fluorescence Imaging Performance The fluorescence microscope successfully detected and imaged Bacillus subtilis stained with fluorescein diacetate (FDA), confirming the system’s ability to capture biologically relevant fluorescence signals. The imaging performance was strongly dependent on the distribution of bacteria on the sample surface. In cases where cells were densely agglomerated, the resulting fluorescence intensity was sufficiently high to overexpose the Raspberry Pi Camera v2 sensor, saturating parts of the image and masking structural details. However, when bacteria were evenly distributed, the system produced well-resolved, high-contrast images, clearly revealing individual cells with distinguishable fluorescence signals. This confirmed the system’s single-cell resolution capability under suitable conditions. The acquired images were consistent with the expected green fluorescence emission from FDA and demonstrated a strong signal-to-background ratio in non-overexposed regions. Although no formal intensity calibration was performed, the raw images reflected reliable contrast and reproducibility under identical acquisition settings. Proper optical alignment is essential for microscopy. In earlier versions of the fluorescence detection unit, bacterial structures were visible—albeit distorted—when using low-magnification objectives, but not detectable at higher magnifications. This issue was traced to a misalignment along the Z-axis between the achromatic lens and the Raspberry Pi camera sensor. In the latest version, this misalignment was corrected, as demonstrated by the system's ability to produce clear images across different objective magnifications. Figure 1 and Fig. 2 show images captured of the same sample spot with 10× and 40× magnification objectives, respectively, revealing a high bacterial density due to uneven sample distribution. At higher magnification, slight image distortion is visible near the edges of the field of view, implying that optical alignment could be further improved 2.2. Optical Performance and Mechanical Stability To enable quantitative interpretation of spatial measurements, the pixel size and field of view (FOV) were estimated using a caliper gauge with a known marking width. While the marking itself was non-fluorescent, the metallic surface of the gauge produced sufficient background reflectivity to allow visualization under high exposure and sensitivity settings. The physical width of the selected marking was measured using a calibrated commercial microscope and found to be 123 µm on average. In the fluorescence microscope image, the same marking spanned 464 pixels, resulting in an estimated pixel size of approximately 0.265 µm. Given the Raspberry Pi Camera v2’s resolution of 3280 × 2464 pixels, the resulting FOV was calculated to be approximately 869 × 653 µm. The mechanical stability of the system was assessed by repeatedly imaging a single-cell fluorescent bacterium to evaluate positional accuracy over multiple movement cycles. The microscope was programmed to move 10 mm in both X and Y directions, return to the original imaging location, and capture an image. This sequence was repeated 100 times, and the resulting image stack was analyzed using a custom Python script to calculate positional deviation in both axes. Results of the alignment accuracy are displayed in Fig. 3 and Fig. 4 , showing both the relative deviation between sequential images and the absolute deviation from the initial position. The relative deviation confirmed that the positional drift remained within ± 2.5 µm across all cycles. The absolute deviation analysis revealed a slight cumulative shift in the Y-direction, while X-positioning remained within ± 3 µm. The Y-axis drift was calculated to average approximately 0.08 µm per movement cycle, which is likely due to minor tension inconsistencies in the timing belts. However, this gradual drift is considered negligible in the context of the system’s intended applications, particularly considering the DIY nature of the setup. 2.3 Key findings and achievements The microscope successfully captured fluorescence images of Bacillus subtilis stained with fluorescein diacetate, demonstrating clear fluorescence contrast and single-cell resolution. Overexposure occurred in regions with high bacterial agglomeration, while even distribution produced high-quality, well-resolved images. The field of view was determined to be approximately 869 × 653 µm, with a pixel size of ~ 0.265 µm. Mechanical stability tests showed that the system maintained positional accuracy within ± 2.5 µm, sufficient for repeated imaging and automated scanning tasks. A slight Y-direction drift (~ 0.08 µm per cycle) was observed across 100 repetitions, attributed to timing belt tension, but deemed negligible for most applications. The system demonstrated consistent optical performance, with effective separation of excitation and emission signals and stable focusing across extended operation. 3. Discussion The development of a modular, low-cost fluorescence microscope demonstrates that reliable single-cell imaging can be achieved using open-source hardware, 3D-printed components, and accessible optical elements. The results confirm that the system is capable of detecting biologically relevant fluorescence signals and maintaining the mechanical precision necessary for applications such as time-lapse imaging, automated scanning, and quantitative fluorescence analysis at the microscale. Compared to commercial fluorescence microscopes, the presented DIY system offers a compelling balance of functionality and affordability. The programmable Python-based control enables users to create customized imaging protocols that go beyond the fixed workflows of proprietary systems. This flexibility is particularly valuable in research environments requiring adaptable imaging strategies, such as tile-based acquisition, multi-point scanning, or automated exposure adjustment. The optical configuration proved sufficient for visualizing FDA-stained Bacillus subtilis, providing strong signal-to-background contrast and enabling single-cell resolution under favorable conditions. However, the limited dynamic range of the Raspberry Pi Camera v2 became apparent in overexposed regions, highlighting a key limitation in using consumer-grade imaging sensors for fluorescence microscopy. In addition to upgrading to a more sensitive camera, HDR imaging could be implemented by capturing and combining multiple exposures, enhancing the visualization of both weak and strong fluorescence signals within a single frame. This is particularly beneficial for samples with uneven signal distribution—and readily achievable within the open-source framework of the system, as all imaging parameters can be freely modified via Python. Mechanical stability tests confirmed that the system reliably returned to predefined positions with subcellular precision. While a slight cumulative drift in the Y-direction was observed, it remained minimal over 100 cycles and was likely caused by minor mechanical inconsistencies, such as belt tension. For the intended applications—particularly in research and teaching environments—this level of deviation is well within acceptable limits. Additionally, the determined pixel size of ~ 0.265 µm and field of view of ~ 869 × 653 µm provide a quantitative framework for spatial interpretation of image data, enabling measurements of particle size, displacement, and fluorescence intensity profiles in future use cases. Overall, the system performs robustly in its current configuration and offers a solid foundation for further development. Future improvements could include motorized focusing, multi-channel fluorescence capabilities, and quantitative calibration using reference fluorophores. These additions would broaden the system’s applicability and bring it even closer to professional-grade performance while preserving its open-source and low-cost nature. 4. Materials and Methods 4.1. Design and 3D-Printed Components The mechanical foundation of the microscope is based on the Rook MK1 3D printer, a CoreXY motion system that offers a theoretical positioning accuracy of 12.5 µm per microstep, for the built configuration. The theoretical positioning accuracy can be calculated from the number of steps the stepper motor needs for one complete revolution (360°/1.8°) and the pulley circumference (20 teeth x 2 mm/tooth). $$\:Linear\:Movement\:per\:Full\:Step=\frac{20x2\:mm}{\frac{360^\circ\:}{1.8^\circ\:}}=\frac{40\:mm}{200}=0.2\:mm$$ To achieve finer resolution, stepper motors have the option to microstep by dividing each full step by the number of microsteps chosen. For this assembly the microstepping option 1/16 was chosen, resulting in an accuracy of 12.5 µm: $$\:Linear\:Movement\:per\:Microstep=\frac{0.2}{16}=12.5\:\mu\:m$$ The original design, developed by rolohaun 3D/Rook, is made available under a Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license [ https://github.com/rolohaun/Rook ; https://www.rolohaun3d.ca/3d-printers ]. The corresponding CAD files were imported into Fusion360, where targeted modifications were made to adapt the system for fluorescence microscopy. These included mechanical adjustments to improve motion reliability, secure optical mounting, and integration with the fluorescence detection unit. The fluorescence detection unit itself was custom-designed from scratch using Fusion360 and was not based on existing open-source models. Its design was inspired by concepts from the OpenFlexure project, particularly regarding compact optical alignment, but was significantly modified to suit the unique spatial and functional requirements of this system[ 13 , 15 – 18 ]. To improve mechanical rigidity and alignment precision, the unit’s housing was reinforced with additional mounting points and structural support, ensuring that the optical elements remain fixed during extended imaging sequences. All custom components for the fluorescence detection unit were fabricated using black PLA filament, chosen for its low reflectivity to minimize stray light and internal reflections. The modular nature of the mechanical design allows for replacement or modification of individual parts, including optical holders, camera mounts, and filter slots. 4.2. Optical Setup The optical configuration of the fluorescence detection unit follows a standard epifluorescence layout, optimized for selective excitation and detection of fluorescence signals.[ 19 – 22 ] The light path consists of an excitation source, a shortpass excitation filter, a dichroic mirror, an emission (longpass) filter, and an achromatic focusing lens, which together ensure efficient separation of excitation and emission wavelengths while preserving image quality. The system employs a high power LED (XPEBBL-L1-0000-00301-SB01, Mouser Electronics) as the excitation light source. The LED beam first passes through a 475 nm shortpass filter (12.5 mm diameter, OD 4.0; Edmund Optics) to remove any out-of-band spectral components. The filtered light is then reflected by a 495 nm dichroic mirror (12.5 × 17.6 mm; Edmund Optics) positioned at a 45° angle, directing the excitation light toward the sample. Fluorescence emitted from the sample at longer wavelengths passes back through the dichroic mirror and then through a 500 nm longpass emission filter (12.5 mm, OD 4.0, High Performance Longpass Filter). This configuration ensures that only fluorescence emission above 500 nm reaches the detector while blocking residual excitation light. An achromatic doublet lens (Thorlabs AC127-050-A, f = 50 mm, Ø1/2", ARC 400–700 nm) is positioned after the microscope objective to focus the fluorescence signal onto the Raspberry Pi Camera v2 sensor. This design allows objectives to be easily swapped for different magnifications by simply screwing them onto the holder. The modular design of the optical path allows for straightforward replacement or reconfiguration of optical components, enabling adaptation of the system to other fluorophores or imaging requirements. A cross-sectional schematic of the assembled detection unit is shown in Fig. 7 . 4.3. Software and Image Acquisition Image acquisition and motion control are fully managed by a custom Python script running on a Raspberry Pi, which serves as the central control unit for the microscope. The software coordinates communication between the Raspberry Pi Camera v2, responsible for capturing fluorescence images, and the Bigtreetech SKR Mini E3 v3.0 motion controller, which interprets standard GCode instructions to drive the CoreXY motion system. This script-based control architecture allows for a high degree of flexibility and customization, enabling users to develop and implement custom acquisition routines beyond the capabilities of commercial microscope software. The system supports real-time motorized positioning, programmable scanning of defined sample regions, and integration of experimental logic, such as exposure adjustments or timed captures. This makes the microscope suitable for diverse applications, including multi-region scanning, time-lapse imaging, and automated fluorescence mapping. Images are acquired at the native resolution of 3280 × 2464 pixels provided by the Pi Camera v2. Exposure time is set manually, depending on the fluorescence intensity of the sample, while all other image settings remain at default. Importantly, no post-processing, image enhancement, or color correction is applied, ensuring that the fluorescence signal captured in each image represents the raw optical data as seen by the sensor. The open-source nature of the system allows users to freely modify the acquisition script to suit their needs, from automating full-slide scans to performing selective imaging based on image content. This software-level adaptability is one of the system’s core strengths, offering a degree of programmability and integration not typically available in commercial fluorescence microscopes. 4.4. Sample Preparation and Fluorescence Imaging To evaluate the imaging capabilities of the system, Bacillus subtilis was selected, due to the easy handling and good fluorophore uptake, as a model organism and stained using fluorescein diacetate (FDA), a non-fluorescent viability marker that becomes fluorescent upon enzymatic hydrolysis within living cells.[ 23 – 25 ] This transformation produces green fluorescence, allowing for the selective visualization of metabolically active bacteria. A stock solution of FDA was prepared by dissolving 5 mg of fluorescein diacetate in acetone. From this, a working solution was obtained by diluting 40 µL of the stock in 10 mL of DPBS buffer. 100 µL of the working solution was then added to a liquid culture of Bacillus subtilis and incubated to allow for intracellular uptake and enzymatic conversion of the dye.[ 25 – 28 ] After staining, 5 µL of the bacterial suspension was transferred to a clean glass microscope slide and allowed to air-dry. This ensured that the bacteria adhered to the surface, reducing movement during imaging and enabling clear fluorescence signal detection. This staining method provided a straightforward and reliable means of validating the microscope’s ability to detect biologically relevant fluorescence signals at the single-cell level, while also offering sufficient signal intensity to test the sensor’s dynamic range and focus consistency. 4.5. Field of View Determination and Mechanical Stability To determine the field of view (FOV) and evaluate the system’s spatial resolution, the markings on a caliper gauge were used, whose width was determined precisely with conventional microscopy. While the markings on the caliper themselves did not emit fluorescence, the metallic surface reflected ambient light, making the scale visible under extreme imaging conditions. By maximizing the camera exposure time and sensitivity, a contrast was created between the reflective metal background and the non-reflective black marking, enabling visualization of the scale’s edge. For this determination a microscope objective with 10x magnification has been used. The width of the marking was measured using a calibrated commercial microscope. This known distance was then used as a reference to calculate the FOV, based on the number of corresponding pixels detected by the Raspberry Pi Camera v2. Additionally, the mechanical stability of the system was evaluated through repeatability tests. To assess the precision and repeatability in positioning, a fluorescent sample of Bacillus subtilis was prepared as described previously. The sample, where a single cell was visible, has been used as a reference target. The system was programmed to capture and image, move 10 mm in both X and Y directions, return to the original reference location and restart the procedure. This was repeated for 100 times. The resulting image stack was analyzed to detect positional drift, using pixel-wise comparison of the fluorescent signal location across frames. 4.6. Ethics declaration No ethical approval was required for this study as the bacterial strain used is non-pathogenic and not subject to biosafety restrictions Declarations Data Availability All designed, modified, and 3D-printed models are available in both .STL and .CAD formats in the supplementary information. The supplementary materials also include Python scripts, Marlin configuration files, and a detailed bill of materials provided as an Excel file. Files and specific programming options related to the motion platform (Rook 3D Printer) are accessible online at https://github.com/rolohaun/Rook and https://www.rolohaun3d.ca/3d-printers. Author contributions Jovan Badzoka: Experimental, Concept and Writing – original Draft; Christian W. Huck : Supervision, Christoph Kappacher : Optical Design Concept; Jakob Lauß : Review & Editing; Javier Ureña: Programming; Benjamin Göllner: Acquisition contributions; Andreas Leismüller: Acquisition contributions Competing interests The author(s) declare no competing interests. References Jaafar, I. H. et al. Improving fluorescence imaging of biological cells on biomedical polymers. Acta biomaterialia 7, 1588–1598 (2011). Hickey, S. M. et al. Fluorescence Microscopy-An Outline of Hardware, Biological Handling, and Fluorophore Considerations. 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An improved method to determine cell viability by simultaneous staining with fluorescein diacetate-propidium iodide. The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society 33, 77–79 (1985). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6548991","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":458898949,"identity":"775b9eff-0a2c-4140-8192-602c387d06d2","order_by":0,"name":"Jovan Badzoka","email":"","orcid":"","institution":"Leopold-Franzens-University Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Jovan","middleName":"","lastName":"Badzoka","suffix":""},{"id":458898950,"identity":"b5197a25-cd01-4870-84f7-77f914188517","order_by":1,"name":"Javier Ureña","email":"","orcid":"","institution":"Leopold-Franzens-University Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Ureña","suffix":""},{"id":458898951,"identity":"1a638b10-8800-413d-9da3-1451f63156bd","order_by":2,"name":"Benjamin Göllner","email":"","orcid":"","institution":"hollu Systemhygiene GmbH","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Göllner","suffix":""},{"id":458898952,"identity":"5464f325-066f-4a28-bdbd-511a590313aa","order_by":3,"name":"Andreas Leismüller","email":"","orcid":"","institution":"hollu Systemhygiene GmbH","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Leismüller","suffix":""},{"id":458898953,"identity":"e387408a-5745-45bc-a6f5-c67153d6451c","order_by":4,"name":"Christoph Kappacher","email":"","orcid":"","institution":"Leopold-Franzens-University Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Kappacher","suffix":""},{"id":458898954,"identity":"97275309-667c-4613-a356-42e8c4e25af0","order_by":5,"name":"Jakob Lauß","email":"","orcid":"","institution":"Leopold-Franzens-University Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Jakob","middleName":"","lastName":"Lauß","suffix":""},{"id":458898955,"identity":"34ff20a1-2d69-4542-9bd0-5d22b666bb9a","order_by":6,"name":"Christian W. 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2","display":"","copyAsset":false,"role":"figure","size":613433,"visible":true,"origin":"","legend":"\u003cp\u003eFluorescence image of Bacillus subtilis stained with fluorescein diacetate (FDA), captured at a high-density region using a 10X magnification objective.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6548991/v1/7d445848759dd0382dee52ee.png"},{"id":83334730,"identity":"4056dd99-e8e1-4adf-a9c3-e2a6d20a7899","added_by":"auto","created_at":"2025-05-23 08:53:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1784098,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of the Field-of-View by comparing the width of the same marker on a caliper by a commercial microscope (b) and the do-it-yourself fluorescence microscope (a)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6548991/v1/ccf472ff42e1ff9ad4ca9579.png"},{"id":83335434,"identity":"03be2123-68c2-4c29-a3ff-e609dac09a7f","added_by":"auto","created_at":"2025-05-23 09:01:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154707,"visible":true,"origin":"","legend":"\u003cp\u003eRelative Accuracy in X and Y Direction\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6548991/v1/2e86ac0b8a49cb802a18da6e.png"},{"id":83334727,"identity":"5dcdd916-ba2d-419c-a28d-f3957871bb58","added_by":"auto","created_at":"2025-05-23 08:53:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72796,"visible":true,"origin":"","legend":"\u003cp\u003eAbsolute Accuracy in X and Y Direction\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6548991/v1/44f47759539b698ddcc61355.png"},{"id":83334184,"identity":"da5fa336-0be2-4535-b538-d5a2da477039","added_by":"auto","created_at":"2025-05-23 08:45:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":352355,"visible":true,"origin":"","legend":"\u003cp\u003eFinal 3D Model of the designed fluorescence unit in combination with the Rook 3D Printer and the Raspberry Pi holder\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6548991/v1/379c3a819cc868eca9a9555d.png"},{"id":83334190,"identity":"b942a007-3eb2-45f5-90a1-666a787836f2","added_by":"auto","created_at":"2025-05-23 08:45:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":189747,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic section analysis of the fluorescence detection unit by Fusion 360 with 3D printed parts in gray\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6548991/v1/366c458b7e030f6be3b849c4.png"},{"id":84947777,"identity":"c7a69ac1-bb46-46e4-a447-e572423c5ea0","added_by":"auto","created_at":"2025-06-19 06:39:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4082310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6548991/v1/eff33ff0-bfed-4450-a977-d802053b9f65.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From 3D Printer to Microscope: A Customizable Platform for Fluorescence Microscopy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFluorescence microscopy is an essential tool for visualizing biological structures, chemical interactions, and microscale systems with high specificity and sensitivity[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, commercial fluorescence microscopes are often expensive, complex, and reliant on proprietary components and software, limiting their accessibility to institutions with significant resources.[\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] In recent years, the growing availability of affordable microcontrollers, 3D printing, and open-source hardware has enabled the development of do-it-yourself (DIY) fluorescence microscopes\u0026mdash;offering functional and flexible alternatives for researchers, educators, and hobbyists.[\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn this work, we present the development and validation of a low-cost, customizable fluorescence microscope built using primarily 3D-printed components, open-source electronics, and off-the-shelf optical elements. At its core, the system is based on a modified Rook CoreXY 3D printer, which provides high-precision motorized sample movement. A custom-designed fluorescence detection unit, including filters, an achromatic lens, and a Raspberry Pi Camera v2, enables the selective imaging of fluorescence signals.\u003c/p\u003e \u003cp\u003eOne of the defining features of this system is its programmability and adaptability. The microscope is controlled by a custom Python script that manages image acquisition, motion control, and optional features such as automatic plane correction and large-area scanning. Unlike commercial systems, which are typically locked behind proprietary software, this open-source setup allows users to implement custom imaging routines tailored to their specific experimental needs\u0026mdash;including advanced workflows that would not be possible on conventional systems.\u003c/p\u003e \u003cp\u003eThe system was evaluated by imaging Bacillus subtilis bacteria stained with fluorescein diacetate, a viability indicator that fluoresces upon enzymatic hydrolysis in living cells. Although no formal intensity calibration was performed, the system successfully captured high-contrast fluorescence images and demonstrated single-cell resolution under suitable sample conditions.\u003c/p\u003e \u003cp\u003eThis paper details the mechanical, optical, and software architecture of the microscope, discusses the imaging results, and outlines areas for future development. The system is intended as a robust, extensible, and accessible platform for both educational and research-focused fluorescence microscopy, bridging the gap between low-cost DIY projects and functional laboratory instrumentation.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Fluorescence Imaging Performance\u003c/h2\u003e \u003cp\u003eThe fluorescence microscope successfully detected and imaged Bacillus subtilis stained with fluorescein diacetate (FDA), confirming the system\u0026rsquo;s ability to capture biologically relevant fluorescence signals.\u003c/p\u003e \u003cp\u003eThe imaging performance was strongly dependent on the distribution of bacteria on the sample surface. In cases where cells were densely agglomerated, the resulting fluorescence intensity was sufficiently high to overexpose the Raspberry Pi Camera v2 sensor, saturating parts of the image and masking structural details. However, when bacteria were evenly distributed, the system produced well-resolved, high-contrast images, clearly revealing individual cells with distinguishable fluorescence signals. This confirmed the system\u0026rsquo;s single-cell resolution capability under suitable conditions.\u003c/p\u003e \u003cp\u003eThe acquired images were consistent with the expected green fluorescence emission from FDA and demonstrated a strong signal-to-background ratio in non-overexposed regions. Although no formal intensity calibration was performed, the raw images reflected reliable contrast and reproducibility under identical acquisition settings.\u003c/p\u003e \u003cp\u003eProper optical alignment is essential for microscopy. In earlier versions of the fluorescence detection unit, bacterial structures were visible\u0026mdash;albeit distorted\u0026mdash;when using low-magnification objectives, but not detectable at higher magnifications. This issue was traced to a misalignment along the Z-axis between the achromatic lens and the Raspberry Pi camera sensor. In the latest version, this misalignment was corrected, as demonstrated by the system's ability to produce clear images across different objective magnifications. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show images captured of the same sample spot with 10\u0026times; and 40\u0026times; magnification objectives, respectively, revealing a high bacterial density due to uneven sample distribution. At higher magnification, slight image distortion is visible near the edges of the field of view, implying that optical alignment could be further improved\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Optical Performance and Mechanical Stability\u003c/h2\u003e \u003cp\u003eTo enable quantitative interpretation of spatial measurements, the pixel size and field of view (FOV) were estimated using a caliper gauge with a known marking width. While the marking itself was non-fluorescent, the metallic surface of the gauge produced sufficient background reflectivity to allow visualization under high exposure and sensitivity settings. The physical width of the selected marking was measured using a calibrated commercial microscope and found to be 123 \u0026micro;m on average. In the fluorescence microscope image, the same marking spanned 464 pixels, resulting in an estimated pixel size of approximately 0.265 \u0026micro;m. Given the Raspberry Pi Camera v2\u0026rsquo;s resolution of 3280 \u0026times; 2464 pixels, the resulting FOV was calculated to be approximately 869 \u0026times; 653 \u0026micro;m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mechanical stability of the system was assessed by repeatedly imaging a single-cell fluorescent bacterium to evaluate positional accuracy over multiple movement cycles. The microscope was programmed to move 10 mm in both X and Y directions, return to the original imaging location, and capture an image. This sequence was repeated 100 times, and the resulting image stack was analyzed using a custom Python script to calculate positional deviation in both axes.\u003c/p\u003e \u003cp\u003eResults of the alignment accuracy are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, showing both the relative deviation between sequential images and the absolute deviation from the initial position. The relative deviation confirmed that the positional drift remained within \u0026plusmn;\u0026thinsp;2.5 \u0026micro;m across all cycles. The absolute deviation analysis revealed a slight cumulative shift in the Y-direction, while X-positioning remained within \u0026plusmn;\u0026thinsp;3 \u0026micro;m. The Y-axis drift was calculated to average approximately 0.08 \u0026micro;m per movement cycle, which is likely due to minor tension inconsistencies in the timing belts. However, this gradual drift is considered negligible in the context of the system\u0026rsquo;s intended applications, particularly considering the DIY nature of the setup.\u003c/p\u003e\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Key findings and achievements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe microscope successfully captured fluorescence images of Bacillus subtilis stained with fluorescein diacetate, demonstrating clear fluorescence contrast and single-cell resolution.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eOverexposure occurred in regions with high bacterial agglomeration, while even distribution produced high-quality, well-resolved images.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe field of view was determined to be approximately 869 \u0026times; 653 \u0026micro;m, with a pixel size of ~\u0026thinsp;0.265 \u0026micro;m.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMechanical stability tests showed that the system maintained positional accuracy within \u0026plusmn;\u0026thinsp;2.5 \u0026micro;m, sufficient for repeated imaging and automated scanning tasks.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eA slight Y-direction drift (~\u0026thinsp;0.08 \u0026micro;m per cycle) was observed across 100 repetitions, attributed to timing belt tension, but deemed negligible for most applications.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe system demonstrated consistent optical performance, with effective separation of excitation and emission signals and stable focusing across extended operation.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe development of a modular, low-cost fluorescence microscope demonstrates that reliable single-cell imaging can be achieved using open-source hardware, 3D-printed components, and accessible optical elements. The results confirm that the system is capable of detecting biologically relevant fluorescence signals and maintaining the mechanical precision necessary for applications such as time-lapse imaging, automated scanning, and quantitative fluorescence analysis at the microscale.\u003c/p\u003e \u003cp\u003eCompared to commercial fluorescence microscopes, the presented DIY system offers a compelling balance of functionality and affordability. The programmable Python-based control enables users to create customized imaging protocols that go beyond the fixed workflows of proprietary systems. This flexibility is particularly valuable in research environments requiring adaptable imaging strategies, such as tile-based acquisition, multi-point scanning, or automated exposure adjustment.\u003c/p\u003e \u003cp\u003eThe optical configuration proved sufficient for visualizing FDA-stained Bacillus subtilis, providing strong signal-to-background contrast and enabling single-cell resolution under favorable conditions. However, the limited dynamic range of the Raspberry Pi Camera v2 became apparent in overexposed regions, highlighting a key limitation in using consumer-grade imaging sensors for fluorescence microscopy. In addition to upgrading to a more sensitive camera, HDR imaging could be implemented by capturing and combining multiple exposures, enhancing the visualization of both weak and strong fluorescence signals within a single frame. This is particularly beneficial for samples with uneven signal distribution\u0026mdash;and readily achievable within the open-source framework of the system, as all imaging parameters can be freely modified via Python.\u003c/p\u003e \u003cp\u003eMechanical stability tests confirmed that the system reliably returned to predefined positions with subcellular precision. While a slight cumulative drift in the Y-direction was observed, it remained minimal over 100 cycles and was likely caused by minor mechanical inconsistencies, such as belt tension. For the intended applications\u0026mdash;particularly in research and teaching environments\u0026mdash;this level of deviation is well within acceptable limits.\u003c/p\u003e \u003cp\u003eAdditionally, the determined pixel size of ~\u0026thinsp;0.265 \u0026micro;m and field of view of ~\u0026thinsp;869 \u0026times; 653 \u0026micro;m provide a quantitative framework for spatial interpretation of image data, enabling measurements of particle size, displacement, and fluorescence intensity profiles in future use cases.\u003c/p\u003e \u003cp\u003eOverall, the system performs robustly in its current configuration and offers a solid foundation for further development. Future improvements could include motorized focusing, multi-channel fluorescence capabilities, and quantitative calibration using reference fluorophores. These additions would broaden the system\u0026rsquo;s applicability and bring it even closer to professional-grade performance while preserving its open-source and low-cost nature.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Design and 3D-Printed Components\u003c/h2\u003e \u003cp\u003eThe mechanical foundation of the microscope is based on the Rook MK1 3D printer, a CoreXY motion system that offers a theoretical positioning accuracy of 12.5 \u0026micro;m per microstep, for the built configuration.\u003c/p\u003e \u003cp\u003eThe theoretical positioning accuracy can be calculated from the number of steps the stepper motor needs for one complete revolution (360\u0026deg;/1.8\u0026deg;) and the pulley circumference (20 teeth x 2 mm/tooth).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Linear\\:Movement\\:per\\:Full\\:Step=\\frac{20x2\\:mm}{\\frac{360^\\circ\\:}{1.8^\\circ\\:}}=\\frac{40\\:mm}{200}=0.2\\:mm$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo achieve finer resolution, stepper motors have the option to microstep by dividing each full step by the number of microsteps chosen. For this assembly the microstepping option 1/16 was chosen, resulting in an accuracy of 12.5 \u0026micro;m:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Linear\\:Movement\\:per\\:Microstep=\\frac{0.2}{16}=12.5\\:\\mu\\:m$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe original design, developed by rolohaun 3D/Rook, is made available under a Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rolohaun/Rook\u003c/span\u003e\u003cspan address=\"https://github.com/rolohaun/Rook\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rolohaun3d.ca/3d-printers\u003c/span\u003e\u003cspan address=\"https://www.rolohaun3d.ca/3d-printers\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. The corresponding CAD files were imported into Fusion360, where targeted modifications were made to adapt the system for fluorescence microscopy. These included mechanical adjustments to improve motion reliability, secure optical mounting, and integration with the fluorescence detection unit.\u003c/p\u003e \u003cp\u003eThe fluorescence detection unit itself was custom-designed from scratch using Fusion360 and was not based on existing open-source models. Its design was inspired by concepts from the OpenFlexure project, particularly regarding compact optical alignment, but was significantly modified to suit the unique spatial and functional requirements of this system[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To improve mechanical rigidity and alignment precision, the unit\u0026rsquo;s housing was reinforced with additional mounting points and structural support, ensuring that the optical elements remain fixed during extended imaging sequences.\u003c/p\u003e \u003cp\u003eAll custom components for the fluorescence detection unit were fabricated using black PLA filament, chosen for its low reflectivity to minimize stray light and internal reflections. The modular nature of the mechanical design allows for replacement or modification of individual parts, including optical holders, camera mounts, and filter slots.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Optical Setup\u003c/h2\u003e \u003cp\u003eThe optical configuration of the fluorescence detection unit follows a standard epifluorescence layout, optimized for selective excitation and detection of fluorescence signals.[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] The light path consists of an excitation source, a shortpass excitation filter, a dichroic mirror, an emission (longpass) filter, and an achromatic focusing lens, which together ensure efficient separation of excitation and emission wavelengths while preserving image quality.\u003c/p\u003e \u003cp\u003eThe system employs a high power LED (XPEBBL-L1-0000-00301-SB01, Mouser Electronics) as the excitation light source. The LED beam first passes through a 475 nm shortpass filter (12.5 mm diameter, OD 4.0; Edmund Optics) to remove any out-of-band spectral components. The filtered light is then reflected by a 495 nm dichroic mirror (12.5 \u0026times; 17.6 mm; Edmund Optics) positioned at a 45\u0026deg; angle, directing the excitation light toward the sample.\u003c/p\u003e \u003cp\u003eFluorescence emitted from the sample at longer wavelengths passes back through the dichroic mirror and then through a 500 nm longpass emission filter (12.5 mm, OD 4.0, High Performance Longpass Filter). This configuration ensures that only fluorescence emission above 500 nm reaches the detector while blocking residual excitation light.\u003c/p\u003e \u003cp\u003eAn achromatic doublet lens (Thorlabs AC127-050-A, f\u0026thinsp;=\u0026thinsp;50 mm, \u0026Oslash;1/2\", ARC 400\u0026ndash;700 nm) is positioned after the microscope objective to focus the fluorescence signal onto the Raspberry Pi Camera v2 sensor. This design allows objectives to be easily swapped for different magnifications by simply screwing them onto the holder.\u003c/p\u003e \u003cp\u003eThe modular design of the optical path allows for straightforward replacement or reconfiguration of optical components, enabling adaptation of the system to other fluorophores or imaging requirements. A cross-sectional schematic of the assembled detection unit is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Software and Image Acquisition\u003c/h2\u003e \u003cp\u003eImage acquisition and motion control are fully managed by a custom Python script running on a Raspberry Pi, which serves as the central control unit for the microscope. The software coordinates communication between the Raspberry Pi Camera v2, responsible for capturing fluorescence images, and the Bigtreetech SKR Mini E3 v3.0 motion controller, which interprets standard GCode instructions to drive the CoreXY motion system.\u003c/p\u003e \u003cp\u003eThis script-based control architecture allows for a high degree of flexibility and customization, enabling users to develop and implement custom acquisition routines beyond the capabilities of commercial microscope software. The system supports real-time motorized positioning, programmable scanning of defined sample regions, and integration of experimental logic, such as exposure adjustments or timed captures. This makes the microscope suitable for diverse applications, including multi-region scanning, time-lapse imaging, and automated fluorescence mapping.\u003c/p\u003e \u003cp\u003eImages are acquired at the native resolution of 3280 \u0026times; 2464 pixels provided by the Pi Camera v2. Exposure time is set manually, depending on the fluorescence intensity of the sample, while all other image settings remain at default. Importantly, no post-processing, image enhancement, or color correction is applied, ensuring that the fluorescence signal captured in each image represents the raw optical data as seen by the sensor.\u003c/p\u003e \u003cp\u003eThe open-source nature of the system allows users to freely modify the acquisition script to suit their needs, from automating full-slide scans to performing selective imaging based on image content. This software-level adaptability is one of the system\u0026rsquo;s core strengths, offering a degree of programmability and integration not typically available in commercial fluorescence microscopes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Sample Preparation and Fluorescence Imaging\u003c/h2\u003e \u003cp\u003eTo evaluate the imaging capabilities of the system, Bacillus subtilis was selected, due to the easy handling and good fluorophore uptake, as a model organism and stained using fluorescein diacetate (FDA), a non-fluorescent viability marker that becomes fluorescent upon enzymatic hydrolysis within living cells.[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] This transformation produces green fluorescence, allowing for the selective visualization of metabolically active bacteria.\u003c/p\u003e \u003cp\u003eA stock solution of FDA was prepared by dissolving 5 mg of fluorescein diacetate in acetone. From this, a working solution was obtained by diluting 40 \u0026micro;L of the stock in 10 mL of DPBS buffer. 100 \u0026micro;L of the working solution was then added to a liquid culture of Bacillus subtilis and incubated to allow for intracellular uptake and enzymatic conversion of the dye.[\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAfter staining, 5 \u0026micro;L of the bacterial suspension was transferred to a clean glass microscope slide and allowed to air-dry. This ensured that the bacteria adhered to the surface, reducing movement during imaging and enabling clear fluorescence signal detection.\u003c/p\u003e \u003cp\u003eThis staining method provided a straightforward and reliable means of validating the microscope\u0026rsquo;s ability to detect biologically relevant fluorescence signals at the single-cell level, while also offering sufficient signal intensity to test the sensor\u0026rsquo;s dynamic range and focus consistency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Field of View Determination and Mechanical Stability\u003c/h2\u003e \u003cp\u003eTo determine the field of view (FOV) and evaluate the system\u0026rsquo;s spatial resolution, the markings on a caliper gauge were used, whose width was determined precisely with conventional microscopy. While the markings on the caliper themselves did not emit fluorescence, the metallic surface reflected ambient light, making the scale visible under extreme imaging conditions. By maximizing the camera exposure time and sensitivity, a contrast was created between the reflective metal background and the non-reflective black marking, enabling visualization of the scale\u0026rsquo;s edge. For this determination a microscope objective with 10x magnification has been used.\u003c/p\u003e \u003cp\u003eThe width of the marking was measured using a calibrated commercial microscope. This known distance was then used as a reference to calculate the FOV, based on the number of corresponding pixels detected by the Raspberry Pi Camera v2.\u003c/p\u003e \u003cp\u003eAdditionally, the mechanical stability of the system was evaluated through repeatability tests. To assess the precision and repeatability in positioning, a fluorescent sample of Bacillus subtilis was prepared as described previously. The sample, where a single cell was visible, has been used as a reference target. The system was programmed to capture and image, move 10 mm in both X and Y directions, return to the original reference location and restart the procedure. This was repeated for 100 times. The resulting image stack was analyzed to detect positional drift, using pixel-wise comparison of the fluorescent signal location across frames.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Ethics declaration\u003c/h2\u003e \u003cp\u003eNo ethical approval was required for this study as the bacterial strain used is non-pathogenic and not subject to biosafety restrictions\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All designed, modified, and 3D-printed models are available in both .STL and .CAD formats in the supplementary information. The supplementary materials also include Python scripts, Marlin configuration files, and a detailed bill of materials provided as an Excel file. Files and specific programming options related to the motion platform (Rook 3D Printer) are accessible online at https://github.com/rolohaun/Rook and https://www.rolohaun3d.ca/3d-printers.\u003c/p\u003e\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJovan Badzoka:\u003c/strong\u003e Experimental, Concept and Writing \u0026ndash; original Draft; \u003cstrong\u003eChristian W. Huck\u003c/strong\u003e: Supervision, \u003cstrong\u003eChristoph Kappacher\u003c/strong\u003e: Optical Design Concept; \u003cstrong\u003eJakob Lau\u0026szlig;\u003c/strong\u003e: Review \u0026amp; Editing; \u003cstrong\u003eJavier Ure\u0026ntilde;a:\u0026nbsp;\u003c/strong\u003eProgramming; \u003cstrong\u003eBenjamin G\u0026ouml;llner:\u003c/strong\u003e Acquisition contributions; \u003cstrong\u003eAndreas Leism\u0026uuml;ller:\u003c/strong\u003e Acquisition contributions\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJaafar, I. H.\u003cem\u003e et al. \u003c/em\u003eImproving fluorescence imaging of biological cells on biomedical polymers. \u003cem\u003eActa biomaterialia \u003c/em\u003e\u003cstrong\u003e7, \u003c/strong\u003e1588\u0026ndash;1598 (2011).\u003c/li\u003e\n\u003cli\u003eHickey, S. 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An improved method to determine cell viability by simultaneous staining with fluorescein diacetate-propidium iodide. \u003cem\u003eThe journal of histochemistry and cytochemistry : official journal of the Histochemistry Society \u003c/em\u003e\u003cstrong\u003e33, \u003c/strong\u003e77\u0026ndash;79 (1985).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6548991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6548991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFluorescence microscopy is a powerful method for visualizing biological structures with high specificity, yet commercial systems remain expensive, complex, and limited by proprietary hardware and software. To address these barriers, we present a low-cost, open-source fluorescence microscope built primarily from 3D-printed components, off-the-shelf optical elements, and accessible electronics. The system is based on a modified Rook CoreXY 3D printer, which provides precise motorized sample positioning, and a custom-designed fluorescence detection unit comprising optical filters, an achromatic lens, and a Raspberry Pi Camera v2. Controlled by a flexible Python script, the platform enables automated image acquisition, programmable scanning, and user-defined workflows not possible with conventional systems. Imaging tests using fluorescein diacetate-stained Bacillus subtilis confirmed single-cell resolution and strong fluorescence contrast under suitable conditions. Field of view and pixel resolution were quantified, and mechanical stability was demonstrated through 100-cycle positioning tests with submicron average drift. This work establishes a robust and extensible framework for fluorescence imaging that bridges the gap between educational DIY tools and functional laboratory instrumentation, offering a highly accessible alternative for researchers, educators, and innovators working outside of traditional infrastructure.\u003c/p\u003e","manuscriptTitle":"From 3D Printer to Microscope: A Customizable Platform for Fluorescence Microscopy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 08:45:19","doi":"10.21203/rs.3.rs-6548991/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cedbf361-ec23-44bf-8916-730686921a6c","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48750804,"name":"Biological sciences/Microbiology"},{"id":48750805,"name":"Physical sciences/Optics and photonics"}],"tags":[],"updatedAt":"2025-06-19T06:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-23 08:45:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6548991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6548991","identity":"rs-6548991","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}