A Modular Platform for Automated Organoid Culture and Longitudinal Imaging

A Modular Platform for Automated Organoid Culture and Longitudinal Imaging | 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 A Modular Platform for Automated Organoid Culture and Longitudinal Imaging Sebastian Torres-Montoya, Sebastian Hernandez, Spencer T Seiler, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7313439/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Organoids, 3D tissue cultures that mimic real organs, offer valuable models for research. Traditional culture methods rely on manual feeding and orbital shakers, making them labor-intensive and inconsistent. Microfluidic systems have shown their potential to improve reproducibility by controlling media exchange and culture conditions, yet most still require standard incubators, which limit continuous monitoring due to space and humidity constraints. To address this, we developed a modular platform that integrates automated feeding, real-time imaging, and environmental control, eliminating the need for a conventional incubator. A key feature is a vertically oriented PDMS/glass chip that supports precise media delivery and monitoring while preserving incubation conditions, making it ideal for morphological studies. We demonstrated the platform’s ability to maintain metabolic stability and media distribution over time using cerebral organoids. This platform improves organoid research by combining microfluidics, automation, and imaging, enhancing disease modeling, drug testing, and regenerative medicine applications. Biological sciences/Biological techniques Biological sciences/Biotechnology Physical sciences/Engineering Health sciences/Health care Health sciences/Medical research Neural Development Brain Organoid Microfluidics Stem Cells Automation Cell culture Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Advances in stem cell technology have enabled the development of organoids, 3D in vitro tissue cultures that replicate key aspects of biological organs. Various tissues, including brain 1 , gut 2 , liver 3 , kidney 4 , and retina 5 , have been generated using this approach. However, standard culture methods, such as manual media feeding, are labor-intensive and prone to variability 6 . Microfluidic technologies have emerged as a solution, enhancing reproducibility 7 , 8 , 9 through consistent culture conditions 6 , 10 , 11 , 12 , 13 , and optimized media exchange 14 , 15 , 16 , 17 , 18 , 19 , 20 . Despite these advancements, maintaining the necessary environmental conditions while tracking tissue development remains challenging 17 , 18 , 19 , 20 , as most microfluidic systems are designed for standard incubators 6 , which are poorly suited for continuous monitoring due to space limitations and the incompatibility of electrical components with the warm, humid environment in cell culture incubators 21 . Integrating autonomous feeding with real-time imaging and multimodal monitoring in out-of-incubator systems may provide valuable insights into tissue development 6 , cell migration 22 , 23 , proliferation 24 , and differentiation 25 . At the same time, an instrumented platform with tracking capabilities could significantly improve biological systems monitoring 26 , 27 , disease modeling 28 , and pharmacological research 13 . Previous studies have explored integrating media supply distribution and tracking capabilities for organoid cultures 28 , 29 . However, most existing solutions fail to fully address the combination of automated feeding, tracking capabilities, and environmental incubation requirements 24 , 30 . Current approaches can be categorized into three main groups: (1) feeding systems that rely on standard incubators 6 , 7 , 10 , 11 , 13 , 31 , which restrict continuous imaging given the high humidity conditions; (2) systems that operate outside incubators but lack continuous media delivery and the ability to switch between different media types 12 , 22 , 24 ; and (3) tank reactors that not require standard incubators, but can not be adjusted to conventional imaging techniques 8 , 9 , 32 . Key integration challenges include, but are not limited to, handling microfluidic chips within incubators without disturbing cultures 14 , 33 , ensuring proper media distribution for 3D cell survival, and maintaining adequate gas exchange to prevent necrosis in organoid cores 10 . Addressing these limitations is crucial for longitudinal cell studies 24 , 33 , and tracking the organoid growth process 34 . Here, we developed a modular platform designed to overcome limitations in longitudinal tracking. The platform integrates three key modules: (1) an automated feeding module, (2) a live imaging module, and (3) an environment feedback control module, which support organoid viability and enable real-time monitoring. A key feature of this system is its ability to regulate fluid flow within the culture media, maintaining stable pH and temperature levels while capturing time-lapse images and minimizing contamination risks during handling. To enhance 3D tissue tracking, a vertically oriented PDMS/glass chip was incorporated as a culture plate. This chip is easy to fabricate and replicate, playing a fundamental role in real-time monitoring of fluid flow distribution while maintaining incubation conditions, making it particularly suitable for morphological studies 16 , 35 , 36 . By integrating microfluidics, automated feeding technologies, real-time imaging, and controlled tissue culture conditions, we demonstrated the longitudinal tracking capabilities of the platform by using cerebral organoids, providing valuable insights into their viability, morphology, metabolic homeostasis, incubation stability, and media distribution. Results Organoid culture platform The platform was designed modularly to enable experimental flexibility, integrating off-the-shelf 3D-printed parts mounted on an anodized aluminum optical breadboard. The modular framework comprises three subsystems: Environmental Feedback Control (blue), Automated Media Feeding (red), Live Imaging Acquisition (green). Some support accessories were used to ensure the standard functionality (Fig. 1 a); however, these accessories are not considered as a subsystem as they lack logic control. The setup also allows for microscopic observation and analysis of samples while maintaining stable temperature and pH levels. The allocation of each component is illustrated in Fig. 1 b, and Fig. 1 c. The platform physical configuration (Fig. 1 d-e) supports the semi-continuous circulation of culture media, ensuring uniform distribution, efficient nutrient exchange, impurity removal, and sterility maintenance. A Raspberry Pi screen (Fig. 1 b(11)) provides a user-friendly interface for real-time visualization and system control. Figure 1 outlines the system architecture, and detailed component lists are provided in Supplementary Tables 1 and 2. The platform was functionally tested over 120 hours in preparation for tissue culture experiments. The platform demonstrated robust imaging capabilities (Fig. 1 f). To test the control capabilities of the environmental control, we ran several test bench tests 37°C and a 7.5 pH value (Fig. 1 g-h). The complete set of data is available in the Supplemental Material section. The environmental control module comprises two feedback loop controllers: one for pH regulation (Fig. 1 a(2)), which injects carbon dioxide (Fig. 1 a(1)) into the media (Fig. 1 a(3)), and another for temperature control (Fig. 1 a(7)), The feeding module includes a programmable pump (Fig. 1 a(5)), which connects the media reservoir (Fig. 1 a(3)) to the PDMS/glass chip (Fig. 1 a(6)) through a recirculation mechanism, preceded by a polyvinylidene fluoride (PVDF) filtration system (Fig. 1 a(4)) to ensure media cleanliness and quality. The imaging module, positioned near the culture chamber, is equipped with a fluorescence-capable camera (Fig. 1 a(8)), enabling high-resolution visualization. An additional temperature sensor (Fig. 1 a(9)) was added to verify the measures. This setup allows for real-time monitoring of experimental conditions within the specially designed PDMS/glass chips, providing valuable insights into organoid development. All modules were designed to function in an integrated manner, ensuring the operation and enhanced experimental control. To accommodate biological samples, we designed and fabricated an optically transparent modular PDMS/glass microfluidic chip that can be easily tailored for specific experimental conditions and is particularly useful for tracking media flow effects. The chip was constructed by bonding a molded and cured silicone well, formed using a 3D-printed mold, to a transparent glass microscope slide (Fig. 2 a-j), and can be adjusted for multiwell applications (Fig. 2 k-l) to allow multi-organoid experiments, see (Supplementary Fig. 2 shows the configuration of the serial and parallel wells used here). For a detailed description of the fabrication process, see the “Organoid culture platform” subsection in the methods. The well is accessible via 2 mm square inlet and outlet channels at the edges of the chip surface, positioned 5 mm above the bottom to prevent sample loss through the outflow channel. A PDMS interface securely seals the Tygon tubing, preventing leaks within the platform. The setup is open to the air but can be sealed with a cap that holds the temperature sensor (Fig. 1 c(13)). The opening is sufficiently large to facilitate organoid insertion using a standard micropipette while also allowing the removal of air bubbles and promoting free gas exchange. Unlike traditional culture methods, the chip is positioned vertically to enable tracking of media flow as it approaches the tissue (Fig. 2 m-n) and can be adapted for longitudinal monitoring of media feeding (Fig. 2 m-n). The entire fabrication process was completed in less than 16 hours. This chip was designed as a customizable alternative to conventional cell culture plates; the proposed chip costs USD 5.31 (see Supplementary Table 1) and methods. At the core of the automated feeding module is a programmable servo pump capable of delivering culture media with 5 nL resolution and adjustable stroke durations ranging from 1 second to 5 hours, directly into the PDMS/glass chip. To better replicate the physiological circulation of bodily fluids 37 , we implemented a media recirculation system that enables semi-continuous feeding with a higher exchange rate compared to conventional tissue culture methods 28 , 29 . The real-time image module integrates a Dino-Lite (AM4115T-GRFBY) microscope and the PDMS/glass chip, enabling video capture and imaging at up to 60 frames per second. This setup supports sample identification, accurate data acquisition, and analysis, and is capable of detecting mCherry (ex:580nm, em:610-720nm ), and GFP(ex: 465nm, em:505-540nm) fluorescence 38 . The system features a ~ 20 mm field of view and can be integrated with alternative camera systems. The environmental feedback control module integrates a pH stabilizer, which regulates CO₂ injection, and a heat controller for temperature regulation. It operates across a temperature range of 20°C to 100°C and a pH range of 0.1 to 14. For validation, the system was set to maintain 37°C (Fig. 1 h) and a pH of 7.6 (Fig. 1 i). The platform maintained stability within ± 0.6°C and ± 0.3 pH units, demonstrating reliable environmental control. The experimental conditions were set to dispense 53 µL and aspirate 50 µL of media every 60 seconds to compensate for media losses due to liquid evaporation from the heat source. The platform maintained a temperature of 37°C and a pH of 7.6. Additionally, the platform was programmed to capture images every 24 hours; a detailed description can be found in the methods section. This module enhances the precision and reproducibility of cell culture experiments, supporting detailed and consistent data acquisition. Viability of cerebral organoids Tracking capabilities were tested using mouse cerebral organoids (Fig. 3 a). Three batches (B) of ten organoids were divided into two groups: five were cultured in a standard incubator (control) and five in the platform, for each chip configuration (Fig. 3 b). In both setups, wells were pre-filled with culture media to ensure consistent initial conditions. The cerebral organoid differentiation protocol used in this study has been extensively characterized by our group through immunohistochemistry and single-cell RNA sequencing across multiple time points, as detailed in Hernandez et al 39 . These analyses revealed the presence of all major neural cell types, including neural progenitor cells (NPCs), intermediate progenitors, excitatory and inhibitory neurons, and astrocytes, confirming the robustness and cellular diversity of the organoids used in this study. After six days, the cell survival rate was assessed using the CytoPainter Cell Plasma Membrane Staining Kit (ab219941), a standard dye for labeling live cells 40 , 41 , 42 , 43 . Tissue cytoarchitecture was evaluated by immunohistochemistry of Map2 for neuronal cell bodies and dendrites and nuclear counterstaining with DAPI. To control for CytoPainter autofluorescence, cortical organoids from the same batch were fixed in 4% paraformaldehyde before staining, serving as negative controls. Organoids cultured on the platform maintained comparable viability (orange fluorescence) to those in a standard incubator after six days, no effect of the chip to chip design variation was observed in the survival rate (Fig. 3 c). Additionally, platform-cultured organoids (Fig. 3 d) exhibited cytoarchitecture consistent with incubator controls (Fig. 3 a), supporting the platform’s suitability for longitudinal tracking experiments. Cortical organoids are susceptible to metabolic stress, which can impair neuronal specification 44 . To further validate the organoids viability, a comprehensive metabolic analysis was performed using the Vi-CELL MetaFLEX™ bioanalyte analyzer (Fig. 4 a). Media aliquots were collected from three independent batches of organoids cultured on the chip, and levels of pH, glucose, sodium, calcium, chloride, and potassium were measured (Fig. 4 ; Supplementary Tables 3 and 4). For each experiment, samples were taken at days 3 and 6 of culture, representing mid- and end-points of the experiment. These values were compared to matched organoids cultured in a standard tissue culture incubator with media exchanged every other day. Fresh media was also analyzed as a control (Fig. 4 a; Supplementary Table 3). Across all conditions, pH levels remained consistent between platform- and incubator-grown organoids (Fig. 4 b; Table 4; p > 0.05). Similarly, concentrations of glucose and all measured ions showed no significant differences between the two culture methods (Fig. 4 c-f; p > 0.05), confirming metabolic stability of organoids maintained on the platform. Metabolic analysis was conducted on three representative single-well batches of chip-cultured organoids. The imaging module was used to evaluate the integrity and growth of cerebral organoids over six days (Fig. 5 ; Supplementary Tables 5–10). Time-lapse images were acquired, and a custom algorithm was applied to detect organoid perimeters and calculate projected areas (Fig. 5 a-c). Organoids exhibited consistent growth throughout the experiment (Fig. 5 d-i). Unlike organoids grown using traditional methods—which often require embedding in the base of tissue culture plates for horizontal tracking—organoids cultured on the platform maintained a uniform and consistent perimeter. This suggests that the platform may better preserve organoid structure compared to previous approaches 6 , 25 , To quantify growth dynamics, organoid measurements were normalized 45 , revealing a comparable increase in growth rate between days 3 and 6 in cell culture (day 15–20) for the six batches (Fig. 5 j-k). Normalization values are provided in Supplementary Table 11. Calculation details are described in the Methods section. B1 organoids had the slowest growth rates (average ~ 0.019 mm²/day), B2 showed the highest growth rate overall, particularly PG0 at ~ 0.124 mm²/day showing a merging tendency between the organoids, and B3 showed intermediate and more consistent growth (~ 0.06–0.08 mm²/day), neither B1, B2, nor B4 showed sample fusion. Multiwell batches showed a similar trend to batch 1 in the single well in the growth diameter, while the percentage of growth of MW1 is similar to batches 1 and 3, MW2 behaves similarly to batch 2. A noticeable advantage of the multiwell approach is the cleaner visualization of the sample and the space provided for the organoids, which helps avoid fusion between samples, which is a common issue in traditional culture techniques 46 , 47 . The Kruskal-Wallis test found significant differences in growth rate across batches, confirming the observed results in the imaging analysis (see supplementary Table 12 for p-value). Longitudinal tracking of media circulation To demonstrate the platform’s tracking capabilities, a media circulation profile was generated by monitoring the real-time absorption of a green BODIPY dye to assess organoid permeability. A pulsatile flow, controlled by the feeding module, was used to inject the dye. The dye was diluted in ACSF to final concentrations ranging from 0 to 10 mM, as shown in Supplementary Fig. 3. Intensity calibration for the highest concentrations is provided in Supplementary Tables 13 and 14. A Computational Fluid Dynamics (CFD) simulation was performed under conditions adapted from previous work 6 with parameters listed in Supplementary Table 15 to validate the experimental findings. Fluorescence intensity was recorded over time (Fig. 6 a) in three regions of interest (ROIs) within the organoid (Fig. 6 b), using an illumination power of 0.52 W/mm² (Fig. 6 c). The first 300 seconds of the absorption profile are shown in Fig. 6 d. The corresponding CFD simulation (Fig. 6 f) revealed similar trends, with dye concentration increasing in a pulsatile manner, consistent with the media flow profile generated by the feeding module. The agreement between experimental and computational data highlights the platform’s capability to continuously monitor dynamic media transport using fluorescence, which is difficult or infeasible with conventional cell culture systems. The experiment was completed without interruptions, supporting the reliability and consistency of both the hardware and computational models. Discussion Advancements in digital manufacturing have significantly simplified the development of customized hardware for biotechnology applications 15 , 25 , 48 , 49 . Imaging biological samples within conventional tissue culture incubators remains challenging due to limited space and environmental conditions that can compromise electronic components 21 . The presented platform addresses these limitations by integrating an automated feeding system with a vertical imaging module, allowing in-incubator-compatible live imaging without bulky or sensitive external equipment. This design provides a self-contained alternative for organoid culture, incorporating control and sensing elements that enhance experimental sustainability. Moreover, the platform enables real-time monitoring of media flow and component absorption at the organoid surface 14 , 31 , 50 , a capability not achievable with standard systems that rely on orbital shakers and non-physiological culture conditions 51 , 52 , 53 , 54 . The modular architecture allows for a wide range of experimental configurations. For instance, the feeding module can simulate dynamic media circulation patterns, supporting morphogen gradients and spatial patterning studies in multiregional organoids 30 , 36 , 50 , 51 , 55 , 56 , and drug screening applications 57 . The environmental control module expands the platform’s applicability to a broader range of biological models, including those that require dynamic or non-standard culturing conditions such as variable temperature 58 , gas composition, or pH, which conventional incubators are not designed to support. It also helps mitigate anatomical constraints that affect pH regulation and media acidification in large 3D organoids 59 , where rapid nutrient exchange is essential. Previous microengineered culture systems have demonstrated diverse strategies to improve organoid viability, maturation, and accessibility. Low-cost 3D-printed bioreactors integrate imaging and drug delivery for long-term monitoring 12 , while millifluidic devices enhance oxygen and nutrient supply to reduce necrosis 10 , 32 . Automated microfluidic platforms enable combinatorial drug screening 11 , and stirred-tank reactors improve reproducibility through real-time sensing 8 . More specialized systems include ex utero embryogenesis platforms for mouse development 9 , modular perfusion devices that limit glycolytic stress 6 , gradient generators that mimic morphogen signaling 51 , and compact CO₂ mini-incubators that enable stable long-term imaging 21 . Integrated brain organoid platforms further combine automated culture with electrophysiological recording to link structure and function 20 . The vertical chip and imaging system enables precise tracking of compound diffusion and cellular responses within fluidic environments, opening new opportunities for real-time drug delivery studies and dynamic tissue patterning 30 , 36 , 55 . These capabilities support applications in modeling complex biological processes, such as morphogenic cell fate induction 16 , 50 , 51 , 56 , 60 , 61 , 62 . The dye absorption experiment further demonstrated the platform’s ability to synchronize physical flow profiles with computational predictions. The close agreement between experimental and simulated dye diffusion highlights the system’s utility not only for organoid culture but also for validating fluid dynamics models under biologically relevant conditions. This integration of experimental and computational tools is particularly valuable for designing media delivery protocols, assessing tissue permeability, and modeling dynamic signaling events that are difficult to capture using conventional static culture systems. The inclusion of multiple independent batches provides biological replication and enables comparison across device architectures. The unequal batch sizes in growth measurements are a consequence of imaging limitations, as shown in Figs. 5 f and 5 g, given the overlapping of the organoids in the well, which we acknowledge as a technical constraint of the current platform rather than a biological failure of the system. Future iterations of the device will incorporate optimized well spacing and optical access to enable consistent imaging of all organoids per batch, thereby improving statistical robustness. This study presents a prototype platform, comprising three integrated subsystems, which were characterized to assess the stability of temperature, pH, and organoid growth. For each technical assessment, standard deviations were computed, ensuring that variability within device operation was quantified and reported transparently. In addition, while six experimental batches were conducted, imaging constraints reduced the number of analyzable organoids per batch. For instance, batch 1 yielded five complete datasets, while batches 2–6 yielded between two and five usable organoids each, depending on image quality and overlap. To enable rapid validation, we used mouse forebrain organoids, which mature much faster than human ones. This choice allowed shorter culture periods while providing an efficient proof of concept for testing platform viability, stability, and growth dynamics without the variability associated with long-term organoid cultures. The results clearly demonstrate the platform's feasibility and potential for automated organoid culture and monitoring. Repeated sterilization of PDMS-glass devices presents a potential limitation for long-term applications. PDMS can absorb water and chemicals during autoclaving or repeated chemical sterilization, leading to swelling, microcracking, or delamination from the glass substrate over time 63 , 64 , 65 . High-temperature exposure can also alter its mechanical stability. In this study, we observed evaporation-related issues in the chip that resulted from an adjusted filling rate of 3 µL, calculated based on the osmolality to compensate for this loss 66 , 67 . Methods Organoid culture platform The prototype platform shown here was designed and fabricated to sustain and track 3D tissue cultures (Fig. 1 ), throughout this study. This prototype contained three integrated subsystems (temperature, pH, and culture monitoring). The components include a servo-driven fluidic pump module, a temperature control system with an aluminum heating pad, an environmental chamber sensor module, a pH gas controller module with a 3D-printed gas diffuser valve, and a microscope with light source generation capabilities. A fluorescence chamber was integrated into this modular platform, consisting of a microscope device, a PDMS chip, and a microscope glass slide. The chip was supplied with fluids through fluidic lines, and an LED ring (NeoPixel, Adafruit) was used for illumination. Figure 1 b presents a schematic representation of the modular platform, illustrating the key components and their interactions within the system. A custom relay board was integrated into the platform, connecting the Arduino to the solenoid valve. This board regulated the current in the solenoid valve and allowed the Arduino to close down the valve during overheating. Given the excess electric current, it is essential to prevent overinjection of CO2 into the culture media, which could acidify the microenvironment where the organoids are submerged. The setup was mounted on a stainless steel, height-adjustable base, with customized 3D-printed parts made of PLA and holder components printed with commercial resin (Formlabs model V2, Formlabs, Massachusetts). Additionally, the platform could be remotely controlled using open-source software packages such as TeamViewer® and RealVNC®. The PDMS/glass microfluidic chip comprises one microscope slide and 6 grams of silicone. The fabrication process involves several sequential steps. Figure 2 illustrates the creation of a solid mold fabricated using Model V2 resin (Form 3, Formlabs) on an SLA printer. The mold was thoroughly cleaned in isopropanol (IPA) for 20 minutes in an ultrasonic bath to remove unwanted materials. Nitrogen gas was then used to dry the molds, followed by a 40-minute UV light exposure (405nm) at 60ºC to ensure complete curing. After cleaning, a PDMS (Sylgard 184, Dow Corning) and curing agent mixture was prepared in a 10:1 weight ratio of PDMS to curing agent. The mixture was degassed for two hours in a vacuum chamber, then cured in an oven at 60ºC for six hours to facilitate physical cross-linking. A 5-minute surface activation step was carried out to bond the polymer and glass surfaces using O 2 plasma reactive ion etching (RIE) on a 25mm × 75mm microscope glass slide (Fisherbrand™ Superfrost™, Fisher) at 50W power, 125sccm of oxygen, and a chamber pressure of 0.254mTorr. The PDMS and glass were then carefully aligned to form a polymer/glass bond. The assembled bio-chamber was left to settle in a vacuum oven for six hours at 60ºC, with 10 grams of pressure applied to enhance adhesion between the PDMS and glass interface. The components of the chip are listed as non-reactive to cells by their respective manufacturing companies. All the PDMS/Glass chip versions were fabricated using the same protocol, with modifications made only to the design. The multiwell v2 chip features microscope-grade flexible slides between the steps to separate the wells, as shown in detail in Supplementary Fig. 2. The fabrication cost for one single PDMS/glass chip was determined by adding the commercial value of one microscope slide at $ 0.64 USD, 6 grams of silicone at $ 1.84 USD, and 0.019 L of Formlabs model V2 resin at $ 2.83 USD; the total cost of the material for the chip fabrication was $ 5.31 USD. The price per hour of the equipment used to print, cure, and bond the PMDS to the glass was not factored into this calculation. The quantity price of the component was obtained from a cost estimation relation between the total price of the commercial presentation of the material and the amount used for the device fabrication. The attachment of the PDMS to the glass using the procedure described above achieved a high success rate (approximately 80%). Failures were primarily due to small imperfections in the uniformity of the PDMS surface that contacts the glass. After fabrication, the glass–PDMS chips were tested for leakage using a fluorescent medium that was pumped through the system. Most of the chips that passed the leakage test (approximately 80%) were reused multiple times without any subsequent failures. The data presented in this paper were collected using five chips: three single-well chips, one serial multi-well chip, and one parallel multi-well chip (see Supplemental Material, Fig. 2 ). The device-to-device variation is primarily determined by the precision of the 3D printer used to fabricate the PDMS mold. Here we used a Formlabs Form 3 + Bio stereolithography (SLA) printer, which achieves a maximum XY resolution of 25 µm and a layer thickness of 25 µm, with dimensional accuracy of approximately ± 100 µm across the build volume. Therefore, considering that the principal dimension of the well is 3 mm, the device-to-device variation is several orders of magnitude smaller than the well size. The culture media, essential for maintaining cell viability and growth, were semi-continuously circulated using a servo pump (Cavro Centris, Tecan) and Tygon tubing, sealed with couplings to prevent leaks in the platform. A control script, MainPumpProgramExe.py , was implemented to program the experiment's desired speed, period, and volume. This module has a volume capacity of 500 mL and is designed to facilitate media recirculation. For the platform's functional test, 50 mL, 4.2 times the volume used in standard culture protocols was utilized. A sterilization protocol, detailed in the "Platform Sterilization" section, was employed to ensure sterility, utilizing a 0.22µm hydrophilic PVDF filter. To achieve specific absorption profiles, a velocity of 1.27 × 10⁻⁴ m/s was used, with inlet and outlet volumes of 53 µL and 50 µL, respectively, to compensate for media losses by evaporation. This calibration was performed using the supplementary values in Supplementary Table 3. The adjustment was based on the osmolality calculation for the salts present in the culture media. An increase in media osmolality has been reported as an indicator of culture medium evaporation 47 , 66 . The osmolality value was calculated for both fresh unused and the last day experiment media, using the following formula, $$\:C(mol/L)=\frac{Osmolality(Osm/kg)}{i}\cdot\:\frac{1}{\rho\:(kg/L)}$$ C is the molar concentration, i is the dissociation factor (van’t Hoff factor), and \(\:\rho\:\) is the fluid density. The dissociations used were K = 1, Na = 1, and Ca = 2, for potassium, sodium, and calcium, respectively. Then the volume loss was calculated using the following equation, $$\:Vfinal=Vinitial\cdot\:\frac{OsmolalityFreshMedia}{OsmolalityDay6}$$ Where Vfinal is the final media volume, Vinitial is the initial media volume, and OsmolalityFreshMedia and OsmolalityDay6 are the osmolality values of the initial and final recorded days, respectively. The fraction lost was calculated, $$\:Fraction\:Lost=1-\frac{OsmolalityFreshMedia}{OsmolalityDay6}$$ For the potassium (K) salt, the osmolality was 3.87 mOsm/kg and 5.57 mOsm/kg for the fresh and last day media, respectively. Its osmolality ratio was 0.69, and the fraction lost was 0.30 for the complete experiment. The fraction lost for each of the experimental days was calculated by dividing the total experiment fraction lost by 6, then a 0.05 ratio per day was used to estimate the loss per day. With an initial volume of 50 µL, the loss volume was calculated to be 50 µL × 0.05, resulting in a loss of 2.50 µL per day and a final volume of 47.45 µL. The values were calculated for sodium (Na) at 0.86 µL and calcium (Ca) at 0.39 µL. Given these calculations, we adjust the flow rate to inject three µL more to compensate for evaporation losses. The imaging acquisition process was conducted using the Dino-Lite Edge AM4115T-GRFBY, a digital microscope with a resolution of 1.3 MP and a rapid capture rate of 30 frames per second (FPS). This microscope was equipped with two sets of LEDs for fluorescent imaging of objects. The first set consisted of 4 Blue LEDs with an excitation wavelength of 480 nm, while the second set included 4 Yellow LEDs with an excitation wavelength of 575 nm. The LED sets were switchable through the accompanying software, allowing for flexibility in the imaging process. The emission wavelengths were 510 nm and 610 nm, respectively. The Dino-Lite microscope provided magnification capabilities of up to 220x, enabling detailed examination of the specimens. Videocapture 2.0 (DinoEdge, Dinolite), a freeware version of the software provided by the same company, was employed to record the acquired videos. This software facilitated efficient video recording and data management during imaging experiments. The incubation environment was carefully controlled using a combination of instrumented devices. To maintain physiological conditions, the media was conditioned with bone-dry carbon dioxide (Airgas) at a concentration of 100%, resulting in an average carbon dioxide level of approximately 5% within the incubator. A pH stabilizer module was positioned in the media reservoir, using an Atlas Scientific mini-lab probe (Atlas Scientific, New York) with a resolution of ± 0.001 and an accuracy of ± 0.002. pH values were measured every minute, and aliquots were taken on specific days for analysis using the Vi-CELL MetaFLEX (see supplementary table 3) to validate the measurements. Depending on the pH value, a pneumatic-electric solenoid valve (Tailonz) was controlled by the ArduinoMegapHSolenoid.ino script using an Arduino Mega 2560 control board (clock speed of 16 MHz) to maintain the value of the media between 7.2 and 7.6. The solenoid valve's inlet port was connected to a bone-dry CO₂ high-pressure steel cylinder (Airgas, California) via a plastic hose, and the outlet port was connected to a lab-grade hose leading to a diffuser submerged in the media, forming a closed-loop circuit (Fig. 1 a). The pH level was continuously monitored every minute over six days, with the system programmed to prevent the pH from exceeding the 7.6 threshold (Fig. 1 b). To ensure temperature stability, an NTC10K temperature sensor with an accuracy of ± 5%, a W1209 PID controller with an accuracy of ± 0.1°C, and an aluminum PCT heater (220°C at 5- 28W) were positioned at the back of the chip (Fig. 1 b(10)). The PID controller was set to maintain 37°C and was placed inside the PDMS/glass chip containing the culture media. A second SCD-40 sensor, with an accuracy of ± 0.8°C, was added near the PCT heater to verify the environmental temperature, with data acquisition managed using the CO2TempHumidityRecording.py script. All relevant files are provided in the data availability section. A dual-channel thermometer (Leaton) was also placed next to the heater sensor for further temperature validation. Temperature and pH records are shown in Fig. 1 g and Fig. 1 h, respectively. Experimental design A test bench without organoids was performed three times on the platform device to test variability. Then, the organoids were cultured across a total of seven independent batches: one batch with five control organoids culture in shaker conditions inside the incubator, four batches of five organoids each in the single-well PDMS/glass chip format, one batch of five organoids in the multiwell PDMS/glass chip arrangement (v1), and one batch of five organoids in the multiwell arrangement (v2). For each batch, all organoids were stained with a live-cell dye to assess viability (biological replicates = individual organoids; technical replicate = one staining/measurement per organoid). Media samples from three independent single-well batches were analyzed using the Vi-CELL MetaFLEX™ analyzer to generate metabolic panels. Organoid growth was assessed by diameter measurements obtained via brightfield microscopy. Due to the optical constraints of the device geometry, not all organoids could be imaged with sufficient resolution, resulting in uneven sample sizes for growth analysis (four organoids in batch 1, two in batch 2, three in batch 3, five in batch 4, two in multiwell v1, and five in multiwell v2). Viability analysis The organoids in the chip were fed following the protocol described in the Organoid Culture Platform section. At the same time, the controls received 2 mL of media every other day, adhering to standard lab protocols 46 , 39 . The experiment started on differentiation 14, a stage when neurons had already formed 68 , 69 , and continued until day (D) 20. The experimental conditions were set to dispense 53 µL and aspirate 50 µL of media every 60 seconds to compensate for media losses due to liquid evaporation from the heat source. The platform maintained a temperature of 37°C and a pH of 7.6. Additionally, the platform was programmed to capture images every 24 hours. This conditions were the same for the three types of PDMS/Glass chips used in the experiment. The CytoPainter Cell Plasma Membrane Staining Kit with Orange Fluorescence (Abcam # ab219941), employing an orange cell membrane probe (Ex/Em = 540/590 nm), was used in this experiment. This probe allows for the uniform staining of cell membranes across various mammalian cell types 42 , 43 , 70 . Complementary evidence of the staining can be found in supplementary Fig. 4, Fig. 5 , and Fig. 6 , showing the multiwell approaches results. The Orange Dye was reconstituted by adding 100 µL of dimethyl sulfoxide (DMSO) to the vial, preparing a 500X orange dye stock solution. The working solution was obtained by diluting 20 µL of the dye solution into 10 mL of assay buffer. Subsequently, 100 µL of the solution was added to the well and incubated with cells for 20 minutes at 37ºC with 5% CO2, protected from light. Following incubation, the cortical organoids underwent three washes with 1x phosphate-buffered saline (PBS) (Thermo Fisher Scientific # 10-010-023) and were fixed with a 4% paraformaldehyde solution for 40 minutes. The organoids were washed three times with 1x PBS and then left in 30% sucrose (Millipore Sigma # S8501) diluted in 1x PBS overnight. The organoids were then embedded in a solution containing equal parts Tissue-Tek O.C.T. Compound (Sakura # 4583) and 30% sucrose solution. The samples were directly sectioned to 12 µm using a cryostat (Leica Biosystems # CM3050) onto glass slides. After two 5-minute washes in 1X PBS and one wash in deionized water (Chem world # CW-DW-2G). The sections were then stored at 4°C and were observed using an EVOS-FL Auto-2 (Thermo Fisher Scientific) fluorescence microscope equipped with a TRITC filter set (Ex/Em = 540/590 nm). Organoids were collected and fixed in 4% paraformaldehyde (PFA) (Thermo Fisher Scientific # 28908) and cryopreserved in 30% sucrose solution. They were then embedded in a solution containing equal parts of Tissue-Tek O.C.T. Compound (Sakura # 4583) and 30% sucrose solution. They were then directly sectioned to 12 µm using a cryostat (Leica Biosystems # CM3050) onto glass slides. After two washes of five minutes in 1X PBS, the sections and one wash in deionized water (Chem world # CW-DW-2G), sections were incubated in blocking solution, 5% v/v donkey serum (Millipore Sigma # D9663), and 0.1% Triton X-100 (Millipore Sigma # X100) for 1 hour. The sections were then incubated in primary antibodies overnight at 4°C. They were then washed thrice for 10 minutes in PBS and incubated in secondary antibodies for 90 minutes at room temperature. They were then washed thrice for 10 minutes in PBS and covered with Fluoromount-G Mounting Medium (Thermo Fisher Scientific # 00-4958-02). The primary antibodies used were mouse and rabbit anti-Map2 (Proteintech # 17490-1-AP, 1:2,000). The secondary antibodies were of the Alexa series (Thermo Fisher Scientific), used at a concentration of 1:750. The nuclear counterstain was 300 nM DAPI (4’,6-Diamidino-2-Phenylindole, Dihydrochloride) (Thermo Fisher # D1306). Imaging was done using an inverted confocal microscope (Zeiss 880) and Zen Blue software (Zeiss). Images were processed using Zen Black (Zeiss) and ImageJ software (NIH). Metabolic measurements were conducted using the Vi-CELL MetaFLEX™ analyte analyzer (Fig. 4 a). The media were sampled in aliquots from 3 batches of organoids grown in the platform, and the pH and metabolites, including glucose, sodium, calcium, chloride, and potassium, were measured. Two different time points were used for each batch: three and six days. The values were compared to organoids grown in a tissue culture incubator and under the standard culturing protocol (media exchange every other day). As a control, fresh media were sampled. Kruskal-Wallis(Kw) test, a statistical test used to compare two or more groups for a continuous or discrete variable 71 , was employed to track the significant differences between groups. A total of n = 70 records were measured in the MetaFLEX, dividing the data into seven variables with ten replicas for each condition, such as fresh media, incubator, and platform incubation. K(w) It is a non-parametric test, which assumes no particular distribution of the data and is analogous to the one-way analysis of variance (ANOVA) at a 95% confidence level. This test was run in RStudio. Version 1.3.1093 retrieved the data from supplementary table 3. The imaging acquisition process was conducted using Videocapture 2.0 (DinoEdge, Dinolite), a freeware version of the software provided by the same company, which was employed to record the acquired videos. Three batches of organoids were recorded over six days and measured with the perimeter and area tool provided by the software. To observe the growth trend, we calculate the quantitative values by subtracting and dividing each organoid cross section by the average of the batch cross section, with the following formula; $$\:Normalized\:Value\left(t\right)=\left(CS\right(t)-CS(1)/CS\:Average(1\left)\right)$$ , CS(t) is the cross-sectional measure of each organoid at a specific time, and CS(1) is the cross-sectional measure at the first day of the experiment. Supplementary Figs. 7, 8, 9, 10, 11, and 12 provide complementary evidence of organoid monitoring. Supplementary tables 5, 6, 7, 8, 9, and 10 show the organoid measures in the single-well and multiwell experiments. The calculated growth rates (slopes) for each organoid, along with the results of the Kruskal-Wallis(Kw) test comparing growth rates across batches. Longitudinal tracking of media circulation The green BODIPY dye was imaged using the AM4115T-GRFBY Dinolite, which provides camera capture and light source generation capabilities. The light intensity was measured in lux (LX) using a LX1332B light meter corresponding to the blue square. The microscope could switch between six intensity levels, with the two highest levels selected for constructing the calibration curves. ImageJ measured the intensity levels from the microscope camera, corresponding to the black square. LX was converted to lumens and power intensity (W/mm²). The calibration curve was generated using ten concentration points and replicated thrice. Figure 6 b shows the calibration curve for the 0.52 W/mm² value, corresponding to the second highest intensity, and Fig. 6 c shows the calibration curve for the highest intensity of 0.79 W/mm². Other intensity points were not considered due to their lack of emission. The computational fluid dynamics (CFD) analysis helped validate the design and assess its performance by simulating the fluid flow under various conditions 52 , 53 , 54 , 72 , 73 , 74 , 75 , 76 , 77 . These conditions were simulated using COMSOL® Multiphysics 5.5 (Stockholm, Sweden). The experimental setup involved a linear speed 1.2710 − 3 ms − 1 and a 70 µL media injection. The well size is characterized by a depth of 5 mm and a diameter of 5.60 mm. The liquid phase was modeled using water properties, while air properties were employed to represent the gas phase. The properties of water under incubator conditions were considered, with a density of 997kgm − 3 and a viscosity of 6.92010 − 3 kgm − 1 s − 1 . The temporal evolution of the concentration field is considered, and the concentration source term is specified at the inlet of the geometry, serving as a boundary condition. This study focuses on a single species utilized for live cell membrane staining, specifically CellTracker™ Green BODIPY™ Dye (Thermo Fisher Scientific # C2102). The concentration value chosen for this physical process is 10molm − 3 , while the diffusion coefficient between the media and the organoid surface is set to 1x10 − 10 m 2 s −1 . The reference temperature for the platform is maintained at 37°C, and the governing properties associated with the fluid flow are determined from the Navier-Stokes set of boundary conditions 77 , 78 . The fluid dynamics conditions for the homogeneous domains were derived from previous experiments conducted in shaker domains, as referenced. The level set method was implemented, as referenced, to capture the phase boundary of the free surface flow interface accurately. Using the level set function, this approach facilitated the control of integration between the two domains at the boundary. The value of the level set function ranged from 0 to 1, depending on the fluid phase, and a value of 0.5 was selected for this investigation to achieve an equal distribution at the surface. This specific choice was based on considerations regarding surface tension and computational efficiency presented in previous literature. The solution domain, encompassing both phases, was enclosed by a solid wall, with only the top surface exposed to incubator conditions. A non-slip condition was imposed on the solid wall, while a slip condition was applied to the open surface. For this simulation, a total of 519,830 tetrahedral elements were generated. Additionally, a phantom spherical geometry was approximated based on images of the organoid batch taken on day 13, with a diameter of 1.8 mm. The atmospheric conditions were set to a pressure of 1 atm, a temperature of 37°C, and a gas composition of 5% carbon dioxide, 17% oxygen, and 78% nitrogen. Lastly, a mesh independence study was conducted to ensure consistent element sizing and to provide accurate numerical analysis. The results of the mesh convergence study are shown in supplementary Fig. 13. The test was performed up to 1.939.940, and the graph shows that the simulation result stabilizes beyond ~ 400,000 elements, confirming mesh independence of the numerical solution. These results validate the suitability of the mesh resolution used in the main simulations. The resulting velocity fields, expressed in SI units, were visualized using stream arrow lines combined with a middle plane slide 77 , 78 . Embryonic stem cell line maintenance All experiments used the ES-E14TG2a mouse embryonic stem cell (ESC) line (ATCC # CRL-1821), derived from a 129/Ola male mouse strain. Before experimentation, the ESCs were subjected to mycoplasma testing. ESCs were cultured on plates coated with Recombinant Protein Vitronectin (Thermo Fisher Scientific # A14700). The maintenance medium for mESCs consisted of Glasgow Minimum Essential Medium (Thermo Fisher Scientific # 11710035), supplemented with Embryonic Stem Cell-Qualified Fetal Bovine Serum (Thermo Fisher Scientific # 10439001), 0.1 mM MEM Non-Essential Amino Acids (Thermo Fisher Scientific # 11140050), 1 mM Sodium Pyruvate (Millipore Sigma # S8636), 2 mM Glutamax supplement (Thermo Fisher Scientific # 35050061), 0.1 mM 2-Mercaptoethanol (Millipore Sigma # M3148), and 0.05 mgmL − 1 Primocin (Invitrogen # ant-pm-05). The mESC maintenance media were supplemented with 1,000 units mL − 1 of Recombinant Mouse Leukemia Inhibitory Factor (Millipore Sigma # ESG1107). The culture medium was changed daily to maintain optimal conditions for ESC growth. ReLeSR passaging reagent (Stem Cell Technologies # 05872) was used according to the manufacturer’s instructions for cell dissociation and passaging. For cell preservation, ESCs were frozen using mFreSR cryopreservation medium (Stem Cell Technologies # 05855) following the manufacturer’s guidelines. Generation of organoids Mouse embryonic stem cells (mESCs) were dissociated into single cells using TrypLE Express (Thermo Fisher #12604021) for 5 minutes at 37°C, following previous protocols 62 , 63 , 32 . Cells were then re-aggregated in Lipidure-coated 96-well V-bottom plates at a density of 3,000 cells per well in 150 µL of mESC maintenance medium, supplemented with 10 µM ROCK inhibitor Y-27632 (Tocris #1254) and 1,000 U/mL recombinant mouse LIF (Millipore Sigma #ESG1107). After 24 hours, the medium was replaced with dorsal forebrain patterning medium consisting of DMEM/F12 with GlutaMAX (Thermo Fisher #10565018), 10% KnockOut Serum Replacement (Thermo Fisher #10828028), 0.1 mM MEM Non-Essential Amino Acids (Thermo Fisher #11140050), 1 mM sodium pyruvate (Sigma #S8636), 1X N-2 supplement (Thermo Fisher #17502048), 2X B-27 minus Vitamin A (Thermo Fisher #12587010), 0.1 mM 2-mercaptoethanol (Sigma #M3148), and 0.05 mg/mL Primocin (InvivoGen #ant-pm-05). To promote dorsal forebrain identity, this medium was further supplemented with 10 µM Y-27632, 5 µM WNT inhibitor XAV939 (StemCell Technologies #100–1052), and 5 µM TGF-β inhibitor SB431542 (Tocris #1614). Media was changed daily, with N-2 and B-27 supplements added after filtration to retain hydrophobic components. Platform sterilization The PDMS/Glass chip, temperature feedback control sensor, thermocouple, media hoses, and programmable pump underwent thorough cleaning using a total of 45mL high-level disinfectant solution (CIDEX® OPA, ASP), which was recirculated within the system, pump, and hoses at a rate of 100µL per 20 seconds, using a PVDF 0.22µM filter to clear the impurities for 20 minutes. Following the disinfection step, the components were rinsed meticulously with ultra-pure distilled water (Invitrogen™ Ultra-Pure™, ThermoFisher) for 20 minutes, under the same conditions used with the CIDEX®, to remove any residual disinfectant. As a final cleaning step, we pump air through the system to dry out any liquid residual for 4 hours using the same rates of the CIDEX® and the purified water. Moreover, the chamber, chip holder, environmental sensor, power cable, and microscope were wiped down using hydrogen peroxide (Oxivir TB, Diversey) for 1 minute each, ensuring a comprehensive decontamination process. These stringent sterilization measures were implemented to maintain a sterile experimental environment and minimize the potential for contamination during the study. Data availability All custom scripts, pH calibration, feeding rates, temperature records, 3D-printed files, microscope images, and CFD videos are available at [ https://github.com/sebtomon89/braingeneersdifussionproject ]. Additional modified scripts can be accessed upon request. All other relevant data are available from the corresponding author upon request. Code availability Details of publicly available software used in the study are given in the “Data availability” section. Apart from this, no unique custom code or mathematical algorithms were central to reaching the conclusions of this work. Declarations Author Contributions Statement S.T.-M. and S.T.S. worked on hardware, software, and platform assembly. S.T.-M., S.H., H.E.S and S.V-C. worked in cell culture and cell staining. S.T.-M., S.H., H.E.S., and G.K. performed biological experiments. S.T.-M., M.A.M.-R., and M.T. conceived the experiments. D.H., M.A.M.-R., and M.T. supervised the team and secured funding. S.T.-M., H.E.S., M.A.M.-R., S.T.S., and M.T. wrote the manuscript with contributions from all authors. List of software used in this work This work was produced using, COMSOL® Multiphysics 5.5, URL: https://www.comsol.com/. Autodesk Fusion 360, URL:https://www.autodesk.com/products/fusion-360/overview. Matlab R2022a, URL:https://www.mathworks.com/products/new_products/release2022a.html. R Studio Version 1.3.1093. URL: https://www.r-project.org/, PyCharm 2020.3, URL: https://www.jetbrains.com/pycharm/. Adobe Illustrator 29.3.1, URL: https://www.adobe.com/products/illustrator.html. BioRender, URL: https://www.biorender.com/. Draw.io, URL: https://www.drawio.com/. DinoCapture 2.0, URL: https://www.dinolite.us/features/dinocapture/?srsltid=AfmBOoqyQw4rmUoVHNKJLBDRRkWOa8mKBfaHKbG4zwAO00TnrFHzIJyQ. ImageJ 1.54g, URL: https://imagej.net/ij/. Microsoft Excel 363, URL: https://www.microsoft.com/en-us/microsoft-365/excel. Grammarly, URL: https://app.grammarly.com/. Funding This work was supported by the Schmidt Futures Grant SF857 (D.H., and M.T.); the National Human Genome Research Institute Grant 1RM1HG011543 (D.H. and M.T.); National Science Foundation Grants 2134955 (to D.H. and M.T.), 2034037 (to M.T.), and 2515389 (to D.H., M.A.M.-R. and M.T.); the National Institute of Mental Health Grant 1U24MH132628 and U24NS146314 (to D.H. and M.A.M.-R.); the California Institute for Regenerative Medicine DISC4-16285 (to M.A.M.-R. and M.T.), and DISC4-16337 (to M.A.M.-R).; by the University of California Office of the President M25PR9045 (to M.A.M.-R. and M.T.); and H.E.S. is a National Science Foundation Graduate Student Research Fellowship grantee. S.H. received support from the UC Doctoral Diversity Initiative (DDI-UCSC-IBSC). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the National Science Foundation, the University of California, CIRM or any other agency of the State of California. Author Contribution S.T.-M. and S.T.S. worked on hardware, software, and platform assembly. S.T.-M., S.H., H.E.S and S.V-C. worked in cell culture and cell staining. S.T.-M., S.H., H.E.S., and G.K. performed biological experiments. S.T.-M., M.A.M.-R., and M.T. conceived the experiments. D.H., M.A.M.-R., and M.T. supervised the team and secured funding. S.T.-M., H.E.S., M.A.M.-R., S.T.S., and M.T. wrote the manuscript with contributions from all authors. Acknowledgement We thank the UCSC Life Sciences Microscopy Center, RRID: SCR_021135, for providing the confocal microscope to acquire the images. Some illustrations were generated using Biorender. We gratefully acknowledge contributions from Dr. Sofie R. Salama and Dr. Kateryna Voitiuk for the feedback on the preparation of this manuscript. During the preparation of this work, the authors used ChatGPT and Grammarly to improve clarity and sentence structure. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Data Availability All custom scripts, pH calibration, feeding rates, temperature records, 3D-printed files, microscope images, and CFD videos are available at [ [https://github.com/sebtomon89/braingeneersdifussionproject](https:/github.com/sebtomon89/braingeneersdifussionproject) ]. Additional modified scripts can be accessed upon request. All other relevant data are available from the corresponding author upon request. References Sasai, Y. Next-Generation Regenerative Medicine: Organogenesis from Stem Cells in 3D Culture. Cell. Stem Cell. 12 , 520–530 (2013). Sasai, Y., Eiraku, M. & Suga, H. In vitro organogenesis in three dimensions: self-organising stem cells. Development 139 , 4111–4121 (2012). Mun, S. J. et al. Generation of expandable human pluripotent stem cell-derived hepatocyte-like liver organoids. J. Hepatol. 71 , 970–985 (2019). Takasato, M., Er, P. X., Chiu, H. S. & Little, M. H. Generation of kidney organoids from human pluripotent stem cells. Nat. Protoc. 11 , 1681–1692 (2016). Völkner, M. et al. Retinal Organoids from Pluripotent Stem Cells Efficiently Recapitulate Retinogenesis. Stem Cell. Rep. 6 , 525–538 (2016). Seiler, S. T. et al. Modular automated microfluidic cell culture platform reduces glycolytic stress in cerebral cortex organoids. Sci. Rep. 12 , 20173 (2022). Cho, A. N. et al. Microfluidic device with brain extracellular matrix promotes structural and functional maturation of human brain organoids. Nat. Commun. 12 , 4730 (2021). Schwedhelm, I. et al. Automated real-time monitoring of human pluripotent stem cell aggregation in stirred tank reactors. Sci. Rep. 9 , 12297 (2019). Aguilera-Castrejon, A. & Hanna, J. H. Ex Utero Culture of Mouse Embryos from Pregastrulation to Advanced Organogenesis. JoVE 63160 10.3791/63160 (2021). Berger, E. et al. Millifluidic culture improves human midbrain organoid vitality and differentiation. Lab. Chip . 18 , 3172–3183 (2018). Schuster, B. et al. Automated microfluidic platform for dynamic and combinatorial drug screening of tumor organoids. Nat. Commun. 11 , 5271 (2020). Khan, I. et al. A low-cost 3D printed microfluidic bioreactor and imaging chamber for live-organoid imaging. Biomicrofluidics 15 , 024105 (2021). Maisonneuve, B. G. C. et al. Deposition chamber technology as building blocks for a standardized brain-on-chip framework. Microsyst. Nanoeng . 8 , 86 (2022). Lee, H. N. et al. Effect of biochemical and biomechanical factors on vascularization of kidney organoid-on-a-chip. Nano Convergence . 8 , 35 (2021). Castiglione, H. et al. Human Brain Organoids-on-Chip: Advances, Challenges, and Perspectives for Preclinical Applications. Pharmaceutics 14 , 2301 (2022). Sanchis-Calleja, F. et al. Decoding morphogen patterning of human neural organoids with a multiplexed single-cell transcriptomic screen. bioRxiv 2024–02 (2024). 10.1016/j.jmbbm.2021.105024 Boussaad, I. et al. OPEN Integrated, automated maintenance, expansion. Scientific Reports . Understrup, K. G. & Programming and Control of Flow-based Microfluidic biochips. Maurer, B. et al. Inkube: An all-in-one solution for neuron culturing, electrophysiology, and fluidic exchange. 12.06.627248 Preprint at (2024). https://doi.org/10.1101/2024.12.06.627248 (2024). Voitiuk, K. et al. A feedback-driven IoT microfluidic, electrophysiology, and imaging platform for brain organoid studies. 03.15.585237 Preprint at (2024). https://doi.org/10.1101/2024.03.15.585237 (2024). Talebipour, A., Saviz, M., Vafaiee, M. & Faraji-Dana, R. Facilitating long-term cell examinations and time-lapse recordings in cell biology research with CO2 mini-incubators. Sci. Rep. 14 , 3418 (2024). Wiggins, L. et al. The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition. Nat. Commun. 14 , 1854 (2023). Svensson, C., Medyukhina, A., Belyaev, I., Al-Zaben, N. & Figge, M. T. Untangling cell tracks: Quantifying cell migration by time lapse image data analysis. Cytometry Pt A . 93 , 357–370 (2018). Hashimoto, H. et al. Time-lapse imaging of cell cycle dynamics during development in living cardiomyocyte. J. Mol. Cell. Cardiol. 72 , 241–249 (2014). Ly, V. T. et al. Picroscope: low-cost system for simultaneous longitudinal biological imaging. Commun. Biol. 4 , 1261 (2021). Paulsen, B. et al. Autism genes converge on asynchronous development of shared neuron classes. Nature 602 , 268–273 (2022). Mostajo-Radji, M. A., Schmitz, M. T., Montoya, S. T. & Pollen, A. A. Reverse engineering human brain evolution using organoid models. Brain Res. 1729 , 146582 (2020). Xinaris, C., Brizi, V. & Remuzzi, G. Organoid Models and Applications in Biomedical Research. Nephron 130 , 191–199 (2015). Rajan, D. K. et al. Monitoring pH, temperature and humidity in long-term stem cell culture in CO 2 incubator. in. IEEE International Symposium on Medical Measurements and Applications (MeMeA) 470–474 (IEEE, 2017). (2017). 10.1109/MeMeA.2017.7985922 Smith, R. L., Demers, C. J. & Collins, S. D. Microfluidic device for the combinatorial application and maintenance of dynamically imposed diffusional gradients. Microfluid Nanofluid . 9 , 613–622 (2010). Homan, K. A. et al. Flow-enhanced vascularization and maturation of kidney organoids in vitro. Nat. Methods . 16 , 255–262 (2019). Rosen, Y. et al. Incubator-Free Organoid Culture in a Sealed Recirculatory System. Brunner, M., Doppler, P., Klein, T., Herwig, C. & Fricke, J. Elevated pCO. affects lactate metabolic shift CHO cell. Cult. processes Eng. Life Sci. 18 , 204–214 (2018). Miura, Y. & Pașca, S. P. Mapping human brain organoids on a spatial atlas. Cell. Stem Cell. 28 , 983–984 (2021). Chu, J. & Anderson, S. A. Development of Cortical Interneurons. Neuropsychopharmacol 40 , 16–23 (2015). Pavon, N. et al. Patterning Ganglionic Eminences in Developing Human Brain Organoids Using Morphogen Gradient Inducing Device . http://biorxiv.org/lookup/doi/ (2023). 10.1101/2023.06.20.545705 doi:10.1101/2023.06.20.545705. Kazim, S. F., Enam, S. A. & Shamim, M. S. Possible detrimental effects of neurosurgical irrigation fluids on neural tissue: an evidence based analysis of various irrigants used in contemporary neurosurgical practice. Int. J. Surg. 8 , 586–590 (2010). AF4115T-GRFBY —. Dino-Lite Digital Microscope | Americas. https://www.dinolite.us/products/usb-microscopes/af4115t-grfby/ Hernandez, S. et al. Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly. 05.01.651773 Preprint at (2025). https://doi.org/10.1101/2025.05.01.651773 (2025). Lei, Y., Shkolnikov, V. & Xin, D. 3D Biological Cell Reconstruction with Multi-View Geometry. in IEEE 17th International Symposium on Biomedical Imaging (ISBI) 495–498 (IEEE, 2020). 495–498 (IEEE, 2020). (2020). 10.1109/ISBI45749.2020.9098564 Tiburcius, S. et al. Egg-yolk core–shell mesoporous silica nanoparticles for high doxorubicin loading and delivery to prostate cancer cells. Nanoscale 14 , 6830–6845 (2022). Sher, J., Miller, C. & Sharma, D. Effect of Bisphosphonates on the Osteogenic Activity of Osteoprogenitor Cells Cultured on Titanium Surfaces. Int. J. Oral Maxillofac. Implants . 35 , 939–947 (2020). Munro, T., Miller, C. M., Antunes, E. & Sharma, D. Interactions of Osteoprogenitor Cells with a Novel Zirconia Implant Surface. JFB 11 , 50 (2020). Bhaduri, A. et al. Cell stress in cortical organoids impairs molecular subtype specification. Nature 578 , 142–148 (2020). Mazziotta, M. & Pareto, A. Normalization methods for spatio-temporal analysis of environmental performance: Revisiting the Min–Max method. Environmetrics 33 , e2730 (2022). Xiang, Y. et al. Generation and Fusion of Human Cortical and Medial Ganglionic Eminence Brain Organoids. Curr. Protoc. Stem Cell. Biol. 47 , e61 (2018). Giandomenico, S. L., Sutcliffe, M. & Lancaster, M. A. Generation and long-term culture of advanced cerebral organoids for studying later stages of neural development. Nat. Protoc. 16 , 579–602 (2021). Sohn, L. L. et al. How Can Microfluidic and Microfabrication Approaches Make Experiments More Physiologically Relevant? Cell. Syst. 11 , 209–211 (2020). Amirifar, L. et al. Brain-on-a-chip: Recent advances in design and techniques for microfluidic models of the brain in health and disease. Biomaterials 285 , 121531 (2022). Rifes, P. et al. Modeling neural tube development by differentiation of human embryonic stem cells in a microfluidic WNT gradient. Nat. Biotechnol. 38 , 1265–1273 (2020). Suong, D. N. A. et al. Induction of inverted morphology in brain organoids by vertical-mixing bioreactors. Commun. Biol. 4 , 1213 (2021). Salek, M. M., Sattari, P. & Martinuzzi, R. J. Analysis of Fluid Flow and Wall Shear Stress Patterns Inside Partially Filled Agitated Culture Well Plates. Ann. Biomed. Eng. 40 , 707–728 (2012). Goto-Silva, L. et al. Computational fluid dynamic analysis of physical forces playing a role in brain organoid cultures in two different multiplex platforms. BMC Dev. Biol. 19 , 3 (2019). Murasiewicz, H. et al. Engineering considerations on the use of liquid/liquid two-phase systems as a cell culture platform: Engineering considerations on the use of liquid/liquid two-phase systems. J. Chem. Technol. Biotechnol. 92 , 1690–1698 (2017). Demers, C. J. et al. Development-on-chip: in vitro neural tube patterning with a microfluidic device. Development 143 , 1884–1892 (2016). Amin, N. D. et al. Generating Human Neural Diversity with a Multiplexed Morphogen Screen in Organoids . http://biorxiv.org/lookup/doi/10.1101/ (2023). 2023.05.31.541819 doi:10.1101/2023.05.31.541819. Wolak, D. J. & Thorne, R. G. Diffusion of macromolecules in the brain: implications for drug delivery. Mol. Pharm. 10 , 1492–1504 (2013). Puschhof, J. et al. Derivation of snake venom gland organoids for in vitro venom production. Nat. Protoc. 16 , 1494–1510 (2021). Liu, Y. et al. Intracellular pH dynamics regulates intestinal stem cell lineage specification. Nat. Commun. 14 , 3745 (2023). Borello, U. & Pierani, A. Patterning the cerebral cortex: traveling with morphogens. Curr. Opin. Genet. Dev. 20 , 408–415 (2010). Kutys, M. L. et al. Uncovering mutation-specific morphogenic phenotypes and paracrine-mediated vessel dysfunction in a biomimetic vascularized mammary duct platform. Nat. Commun. 11 , 3377 (2020). Xue, X. et al. A Patterned Human Neural Tube Model Using Microfluidic Gradients. Nature https://doi.org/10.1038/s41586-024-07204-7 (2024). Rodríguez, C. F. et al. Breaking the clean room barrier: exploring low-cost alternatives for microfluidic devices. Front. Bioeng. Biotechnol. 11 , 1176557 (2023). Effects of Sterilization Methods on the Integrity and Functionality of Covalent Mucin Coatings on Medical Devices. - Rickert – 2022 - Advanced Materials Interfaces - Wiley Online Library. https://advanced.onlinelibrary.wiley.com/doi/full/ 10.1002/admi.202101716 Less is More. Oligomer extraction and hydrothermal annealing increase PDMS bonding forces for new microfluidics assembly and for biological studies | bioRxiv. https://www.biorxiv.org/content/10.1101/150953v1.full Heo, Y. S. et al. Characterization and Resolution of Evaporation-Mediated Osmolality Shifts that Constrain Microfluidic Cell Culture in Poly(dimethylsiloxane) Devices. Anal. Chem. 79 , 1126–1134 (2007). Chuah, Y. J. et al. Simple surface engineering of polydimethylsiloxane with polydopamine for stabilized mesenchymal stem cell adhesion and multipotency. Sci. Rep. 5 , 18162 (2015). Park, Y. et al. Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters. Cell. Rep. Methods . 4 , 100686 (2024). Kadoshima, T. et al. Self-organization of axial polarity, inside-out layer pattern, and species-specific progenitor dynamics in human ES cell–derived neocortex. Proceedings of the National Academy of Sciences 110, 20284–20289 (2013). Rosch, J. C., Neal, E. H., Balikov, D. A., Rahim, M. & Lippmann, E. S. CRISPR-Mediated Isogenic Cell-SELEX Approach for Generating Highly Specific Aptamers Against Native Membrane Proteins. Cel Mol. Bioeng. 13 , 559–574 (2020). Theory of Nonparametric Tests . (Springer Berlin Heidelberg, New York, NY, (2018). McMurtrey, R. J. Analytic Models of Oxygen and Nutrient Diffusion, Metabolism Dynamics, and Architecture Optimization in Three-Dimensional Tissue Constructs with Applications and Insights in Cerebral Organoids. Tissue Eng. Part. C: Methods . 22 , 221–249 (2016). Patrachari, A. R., Podichetty, J. T. & Madihally, S. V. Application of computational fluid dynamics in tissue engineering. J. Biosci. Bioeng. 114 , 123–132 (2012). Ramanujan, S. et al. Diffusion and Convection in Collagen Gels: Implications for Transport in the Tumor Interstitium. Biophys. J. 83 , 1650–1660 (2002). Shin, W. et al. Spatiotemporal Gradient and Instability of Wnt Induce Heterogeneous Growth and Differentiation of Human Intestinal Organoids. iScience 23, 101372 (2020). Saglam-Metiner, P. et al. Spatio-temporal dynamics enhance cellular diversity, neuronal function and further maturation of human cerebral organoids. Commun. Biol. 6 , 173 (2023). Lomax, H., Pulliam, T. H. & Zingg D. W. Fundamentals of Computational Fluid Dynamics. Computational Fluid Dynamics for Engineers . (Cambridge University Press, Cambridge; New York, (2012). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialV2.pdf SupplementaryMaterialV3.pdf Cite Share Download PDF Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Submission checks completed at journal 10 Oct, 2025 First submitted to journal 09 Oct, 2025 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-7313439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":540065934,"identity":"76cf409b-0d5d-4df4-862c-e4d095c505a5","order_by":0,"name":"Sebastian Torres-Montoya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYPACGzl+KIuxgaBiNjCZZizZQKKWw4kbDhCrRX5+87HPFRXMjJtvJD/+8IPBRhamFycwOMaWPPPMGTZmsxtpZpI9QBcS1sLGY8zY2MbDZnYjh40Z2YW4HdYG0vJPgsd4Rg7zZwaG/4S1MBwDaWkwkDCQyGGQZmA4QFiLwbG0ZMaGYwkGEmeeAf1ikGw8k6DDmg8fZmyo+V/f3w4KsQo72T6CDkOzlDTlo2AUjIJRMApwAAD4cD6MrzsLXwAAAABJRU5ErkJggg==","orcid":"","institution":"University of California Santacruz","correspondingAuthor":true,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Torres-Montoya","suffix":""},{"id":540065935,"identity":"f4d76e36-bc63-44d0-8959-79cac0e4184f","order_by":1,"name":"Sebastian Hernandez","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Hernandez","suffix":""},{"id":540065939,"identity":"b0e1277c-98e9-44f0-ad72-b7bfceec6a82","order_by":2,"name":"Spencer T Seiler","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"Spencer","middleName":"T","lastName":"Seiler","suffix":""},{"id":540065941,"identity":"77a009e8-d36a-4a10-b224-47b0853ab8b6","order_by":3,"name":"Hunter E. Schweiger","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"Hunter","middleName":"E.","lastName":"Schweiger","suffix":""},{"id":540065942,"identity":"ec4c196e-9276-46fc-8035-37588ad19e8b","order_by":4,"name":"Samira Vera-Choqqueccota","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"Samira","middleName":"","lastName":"Vera-Choqqueccota","suffix":""},{"id":540065947,"identity":"c1f20369-755e-409d-a584-a3c1483678c8","order_by":5,"name":"Gregory Kaurala","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Kaurala","suffix":""},{"id":540065951,"identity":"d7089899-4bd5-42c7-ae3b-a796780c7b04","order_by":6,"name":"Tal Sharf","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"Tal","middleName":"","lastName":"Sharf","suffix":""},{"id":540065953,"identity":"ee7fea9d-0d0c-4178-8cf5-56066b28d1bf","order_by":7,"name":"David Haussler","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Haussler","suffix":""},{"id":540065954,"identity":"5cfc644c-39db-4d28-b81c-bcfd413c701e","order_by":8,"name":"Mohammed A. Mostajo-Radji","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"A.","lastName":"Mostajo-Radji","suffix":""},{"id":540065955,"identity":"dede6b75-2ffe-4706-bdd8-03e1df1437b1","order_by":9,"name":"Mircea Teodorescu","email":"","orcid":"","institution":"University of California Santacruz","correspondingAuthor":false,"prefix":"","firstName":"Mircea","middleName":"","lastName":"Teodorescu","suffix":""}],"badges":[],"createdAt":"2025-08-07 00:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7313439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7313439/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-40231-0","type":"published","date":"2026-02-18T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95527889,"identity":"b321b31f-5392-4b52-85c5-9134a2b3e76f","added_by":"auto","created_at":"2025-11-10 10:15:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10977660,"visible":true,"origin":"","legend":"","description":"","filename":"AModularPlatformforAutomatedOrganoidCultureandLongitudinalTrackingCleanVersion.docx","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/3b5ec3cb75e8f1687c0ebe7b.docx"},{"id":95528125,"identity":"0090fb21-d71a-40aa-ac5a-1d34a8fc6560","added_by":"auto","created_at":"2025-11-10 10:15:38","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11153,"visible":true,"origin":"","legend":"","description":"","filename":"6d702ad35886490a99b4f09d98df040d.json","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/9a60d54a25cd668babca2df5.json"},{"id":95528867,"identity":"4947a5c6-5275-4096-951e-7af0e6249c7e","added_by":"auto","created_at":"2025-11-10 10:16:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17851415,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/b6dd40d0791acd93801f4306.pdf"},{"id":95499342,"identity":"447e70e7-e7a4-4c33-99a1-54e4da5ff291","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25579810,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialV3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/29b6c1085a376f2c0ac40447.pdf"},{"id":95528024,"identity":"be22289c-f5f0-46c0-8f29-cab1ecbbdf7b","added_by":"auto","created_at":"2025-11-10 10:15:25","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178881,"visible":true,"origin":"","legend":"","description":"","filename":"6d702ad35886490a99b4f09d98df040d1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/235bfdc970f430ac95a857e8.xml"},{"id":95528857,"identity":"866e02ba-428e-4608-b034-5b8c9a975399","added_by":"auto","created_at":"2025-11-10 10:16:32","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2455572,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/d18ee9bbf9986d40ebf9d37d.png"},{"id":95499325,"identity":"9372e533-7747-4342-be00-c205cecf4e97","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1285886,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/b2dc9afc62f39dade1039ea2.png"},{"id":95499335,"identity":"6d2fd96c-6769-471a-b692-8e535bcfcd2d","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2817371,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/d9f96278d4011751eda1c80b.png"},{"id":95528046,"identity":"df7b28b4-a969-4cff-9949-bdc3485f4a13","added_by":"auto","created_at":"2025-11-10 10:15:27","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":229335,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/f057d176e1ab54b585273c19.png"},{"id":95499332,"identity":"eb58013b-a16e-484f-9315-5d270691b034","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2914415,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/4a9c43a6ce76bec71252093a.png"},{"id":95499329,"identity":"2cd895e0-398a-4cab-9e95-6a4937b732d6","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":771354,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/c77a08f22ee6b5895d2eecec.png"},{"id":95499336,"identity":"07caf96d-640b-4d7a-b7b1-098cc77edf56","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":313005,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/d75ac6a103040629e0058f95.png"},{"id":95499339,"identity":"cdc2b74d-3cde-44f5-afa3-c52a9cfd67a8","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156110,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/dd0719826af3b9908322578b.png"},{"id":95499333,"identity":"508fb9b5-19d4-4ee2-97ee-b13d33670410","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":289675,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/7fa5778c66816111d2e0c20a.png"},{"id":95528468,"identity":"4b68961b-04e5-4e1e-afda-a35bdcbaa54c","added_by":"auto","created_at":"2025-11-10 10:16:10","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45897,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/81bd0ca7a2fac336ab8463f2.png"},{"id":95528381,"identity":"6a41dcd9-fa77-4f8c-9279-4af120c75959","added_by":"auto","created_at":"2025-11-10 10:16:00","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":356346,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/839359f8a9d42451a6792a79.png"},{"id":95499337,"identity":"167de796-44cf-4ffb-82e6-4d58ca24767e","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98016,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/b80184dcab75ae77f76fb1a8.png"},{"id":95499338,"identity":"460c881c-8d86-4432-9469-0093b04c54f7","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179164,"visible":true,"origin":"","legend":"","description":"","filename":"6d702ad35886490a99b4f09d98df040d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/fceed2a102e502969ecd7a62.xml"},{"id":95529044,"identity":"011e01a8-7e72-418f-8819-6003f3f68d46","added_by":"auto","created_at":"2025-11-10 10:16:43","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195856,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/e081d6cbbdc87e97b92a6bd2.html"},{"id":95528644,"identity":"f80a747c-03be-40db-8487-1368181c4147","added_by":"auto","created_at":"2025-11-10 10:16:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1508322,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the modular platform for automated organoid culture and longitudinal imaging. a) Block diagram showing the main modules: environmental control (2, 7), automated feeding (5), and live imaging (8). b) CAD model of the whole system: heater controller (7), biochamber (6, highlighted in red and detailed in panel c), portable microscope (8), environmental sensor control module (10), LED visualization station (11), media reservoir with pH probe (3), pH controller (2), programmable servo pump (5), and power supply (12). c) exploded view of the biochamber: heater pad (14), media temperature sensor (13), PDMS/glass chip. Figure 2 shows the fabrication, and Figure 5 shows the chip during testing (15), and an external temperature sensor for verification (9). (d,e) Photographs of the physical setup corresponding to panels b and c. f) Magnification of the PDMS/glass chip showing an example of an object (4 mm diameter nut) at the bottom of the well. Supplemental Figure 1 shows a USAF 1951 resolution test target. (g,h) Recorded temperature and pH values during the first 120 hours of continuous operation. This test bench evaluation was performed without organoids. Figure 1a was produced using Draw.io from Google and Adobe Illustrator. Figures 1b and 1c were made using Autodesk Fusion 360 and Adobe Illustrator. Figures 1d and 1e were created using Adobe Illustrator in images taken by the authors. Figure 1f was obtained from the microscope software DinoCapture 2.0 and Adobe Illustrator. Figures 1g and 1h were produced using the Python package matplotlib in PyCharm and final edits in Adobe Illustrator.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/c67a66925a0f2ebef8e5df69.png"},{"id":95499319,"identity":"12bb47ad-b223-449a-8a30-db9e6521bc4a","added_by":"auto","created_at":"2025-11-10 05:15:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":915077,"visible":true,"origin":"","legend":"\u003cp\u003ePDMS/glass chip fabrication process. a) Chip design and b) 3D printing of the mold. c) Casting the elastic PDMS, followed by d) curing. e) Degassing the mold in a vacuum chamber, f) cleaning surfaces, and g) activating both PDMS and glass slide using oxygen plasma etching to bond the layers. Heat is applied to strengthen the bond, resulting in h) the final chip. i) Final assembled chip and cross-sectional view of the layered structure. j) PDMS/glass chip shown in Figure 1c15. The chip can be adapted for multiwell applications where experiments can be performed in serial or in parallel (Supplementary Fig. 2), k) Version 1 of the Multiwell approach shows a serial configuration and l) shows a parallel one. m) CFD simulation during aspiration and n) during filling actions. Figures 1a to 1i were produced using Biorender and Adobe Illustrator. Figure 1j was produced using Adobe Illustrator. Figures 1k and 1l were produced in COMSOL® Multiphysics 5.5 and Adobe Illustrator.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/bf274746bdd3bb683100a141.png"},{"id":95499316,"identity":"e7652906-046e-4118-9aa2-11b269d04d80","added_by":"auto","created_at":"2025-11-10 05:15:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1289678,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemistry and viability assessment of cortical organoids cultured on the platform versus standard conditions. (a) Representative immunostaining of cortical organoids before the experiment, showing the presence of the neuronal marker Map2. (b) Experimental setup: Cortical organoids were divided into two groups - cultured either in a standard incubator (control) or on the proposed platform, across three different types of chips, single well, multiwell v1 (serial wells) and multiwell v2 (parallel wells). The geometry of these chips is described in Supplementary Figure 2 (c) Day 20: CytoPainter dye was used to assess cell viability in both conditions. “Standard culture” and “Standard culture negative control\" are grown on an orbital shaker. All of the organoids were fixed in paraformaldehyde before staining. The geometry of the serial Multi-well configuration (v1) and the parallel one (v2) are described in Supplemental material Figure 2. (d) Day 20 immunostaining confirmed the presence of Map2-positive neurons in cortical organoids cultured on the platform for 6 days. Figure 3b was produced using Biorender and Adobe Illustrator.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/b3c642eaa310bb86d4c4db4e.png"},{"id":95528198,"identity":"cd344231-da1e-481e-8f30-ebbbff9f4ed6","added_by":"auto","created_at":"2025-11-10 10:15:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":378243,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic Analysis. a) Experimental design: Media aliquots were taken from both the PDMS/glass chip (life support platform) and a standard tissue culture plate incubator, then analyzed using the Vi-CELL MetaFLEX™ bioanalyte analyzer. b-g) Metabolite concentrations measured include: b) pH, c) glucose, d) sodium, e) calcium, f) chloride, and g) potassium. Colors represent different conditions: Blue = fresh media; Yellow = cortical organoids cultured in the platform for 3 days; Green = cortical organoids cultured in the incubator for 6 days; Orange = cortical organoids cultured in the platform for 6 days. Observations were independently replicated in three different organoid groups (B1, B2, and B3), and statistical comparisons are presented in Supplementary Tables 3 and 4. No statistically significant differences were found in pH or metabolite concentrations between the platform and incubator conditions (all p \u0026gt; 0.05). Specifically, pH ( p = 0.1874), glucose (p = 0.4012), potassium (p = 0.0623), sodium (p = 0.2073), calcium (p = 0.1630), and chloride (p = 0.1781) levels were maintained across all tested conditions, confirming metabolic stability within the platform. Error bars represent the 95% confidence interval. Figure 4a was produced using Biorender and Adobe Illustrator; the image of the Metaflex equipment was obtained by a photograph carried out by one of the authors, and edited in Adobe Illustrator to enhance the image.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/895ed6abdbb13b0738a2ff48.png"},{"id":95499321,"identity":"677c07d0-c429-4f1e-a060-6c6c62e1f146","added_by":"auto","created_at":"2025-11-10 05:15:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1558857,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated cross-sectional area of cortical organoids cultured from day 15 to day 20: a) PDMS/glass single well chip, b) multiwell v1 (serial wells), and c) multiwell v2 (parallel wells) where the organoids were placed, the red rectangle indicates. The geometry of v1 and v2 chips is described in Supplementary Figure 2 d) Batch 1 (n=4 visible organoids), e) Batch 2 (n=2 visible organoids), f) Batch 3 (n=3 visible organoids) location, the white arrow indicates an organoid (PG3) that could not be analyzed due the overlap with a concurrent organoids, and g) Batch 4 (n=3 visible organoids), the white arrows indicates a couple of organoids (PG0 and PG4) that could not be analysed due the overlap with other organoids. h) Multiwell v1 shows the serial multi-well configuration. Only one well was immaged (The blue square indicates the area marked in Figure 5.b) batch (n=2), i) Multiwell v2 shows the parallel multi-well configuration (The magenta square indicates the area marked in Figure 5.c) Batch (n=5). The geometric layout of the serial and parallel well designs are shown in Supplementary Figure. 2. j) Normalized growth trajectories, scaled to day 15. k) Growth curves display increasing area over time for each organoid. A kruskal-wallis test revealed a statistically significant difference in growth rates between batches (p \u0026lt; 0.05). Multi-well and single well batches showed a similar trend, the overall trends suggest reproducible organoid expansion and platform compatibility with sustained culture across different conditions. Complete growth data are reported in Supplementary Tables 5-10.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/ec2f76f74fa84c0cb05d3017.png"},{"id":95499324,"identity":"eb6dff44-786b-48cb-b5bd-21a226ba0de8","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1050703,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental and computational analysis of dye absorption in cerebral organoids. (a) Schematic of the experimental setup for real-time monitoring of dye intensity: three regions of interest (ROIs) were defined within the organoid. (b) Chip design representation. (c) Experimental conditions used a light intensity of 0.52 W/mm². (d) Measured dye concentration profile over the first 300 seconds. (e) Dye distribution during the experiment over the first 300 seconds. (f) Computational Fluid Dynamics (CFD) simulation showing velocity field and BODIPY dye distribution during the experiment over the first 300 seconds, corresponding to the experiment in (e). Figure 6a was produced using Biorender, DinoCapture 2.0, and Adobe Illustrator. Figure 6b was produced using Autodesk Fusion 360 and Adobe Illustrator.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/102d7bc834fe6fe02109c68f.png"},{"id":103251990,"identity":"a0c57f5e-c4ea-40a1-8c68-284b6ea7046f","added_by":"auto","created_at":"2026-02-23 16:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7667655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/caccaf98-204e-4562-a2df-7ecf48759183.pdf"},{"id":95528715,"identity":"9d40a519-6f9b-4f16-9b44-1c24e23dbc65","added_by":"auto","created_at":"2025-11-10 10:16:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17851415,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/e4234afc1c17651f13abbb7e.pdf"},{"id":95499343,"identity":"7364791f-5fdf-42f0-a08d-0fee4ef67b65","added_by":"auto","created_at":"2025-11-10 05:15:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25579810,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialV3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313439/v1/8916c5f3a62e7ab343e0f593.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Modular Platform for Automated Organoid Culture and Longitudinal Imaging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdvances in stem cell technology have enabled the development of organoids, 3D \u003cem\u003ein vitro\u003c/em\u003e tissue cultures that replicate key aspects of biological organs. Various tissues, including brain\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, gut\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, liver\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, kidney\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, and retina\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, have been generated using this approach. However, standard culture methods, such as manual media feeding, are labor-intensive and prone to variability\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Microfluidic technologies have emerged as a solution, enhancing reproducibility\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e through consistent culture conditions\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and optimized media exchange\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Despite these advancements, maintaining the necessary environmental conditions while tracking tissue development remains challenging\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, as most microfluidic systems are designed for standard incubators\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, which are poorly suited for continuous monitoring due to space limitations and the incompatibility of electrical components with the warm, humid environment in cell culture incubators\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Integrating autonomous feeding with real-time imaging and multimodal monitoring in out-of-incubator systems may provide valuable insights into tissue development\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, cell migration\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, proliferation\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and differentiation\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. At the same time, an instrumented platform with tracking capabilities could significantly improve biological systems monitoring\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, disease modeling\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and pharmacological research\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePrevious studies have explored integrating media supply distribution and tracking capabilities for organoid cultures\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, most existing solutions fail to fully address the combination of automated feeding, tracking capabilities, and environmental incubation requirements\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Current approaches can be categorized into three main groups: (1) feeding systems that rely on standard incubators\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, which restrict continuous imaging given the high humidity conditions; (2) systems that operate outside incubators but lack continuous media delivery and the ability to switch between different media types\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e; and (3) tank reactors that not require standard incubators, but can not be adjusted to conventional imaging techniques\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Key integration challenges include, but are not limited to, handling microfluidic chips within incubators without disturbing cultures\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, ensuring proper media distribution for 3D cell survival, and maintaining adequate gas exchange to prevent necrosis in organoid cores\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Addressing these limitations is crucial for longitudinal cell studies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and tracking the organoid growth process\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHere, we developed a modular platform designed to overcome limitations in longitudinal tracking. The platform integrates three key modules: (1) an automated feeding module, (2) a live imaging module, and (3) an environment feedback control module, which support organoid viability and enable real-time monitoring. A key feature of this system is its ability to regulate fluid flow within the culture media, maintaining stable pH and temperature levels while capturing time-lapse images and minimizing contamination risks during handling. To enhance 3D tissue tracking, a vertically oriented PDMS/glass chip was incorporated as a culture plate. This chip is easy to fabricate and replicate, playing a fundamental role in real-time monitoring of fluid flow distribution while maintaining incubation conditions, making it particularly suitable for morphological studies\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. By integrating microfluidics, automated feeding technologies, real-time imaging, and controlled tissue culture conditions, we demonstrated the longitudinal tracking capabilities of the platform by using cerebral organoids, providing valuable insights into their viability, morphology, metabolic homeostasis, incubation stability, and media distribution.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eOrganoid culture platform\u003c/h2\u003e\u003cp\u003eThe platform was designed modularly to enable experimental flexibility, integrating off-the-shelf 3D-printed parts mounted on an anodized aluminum optical breadboard. The modular framework comprises three subsystems: Environmental Feedback Control (blue), Automated Media Feeding (red), Live Imaging Acquisition (green). Some support accessories were used to ensure the standard functionality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea); however, these accessories are not considered as a subsystem as they lack logic control. The setup also allows for microscopic observation and analysis of samples while maintaining stable temperature and pH levels. The allocation of each component is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec. The platform physical configuration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed-e) supports the semi-continuous circulation of culture media, ensuring uniform distribution, efficient nutrient exchange, impurity removal, and sterility maintenance. A Raspberry Pi screen (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb(11)) provides a user-friendly interface for real-time visualization and system control. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the system architecture, and detailed component lists are provided in Supplementary Tables\u0026nbsp;1 and 2. The platform was functionally tested over 120 hours in preparation for tissue culture experiments. The platform demonstrated robust imaging capabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). To test the control capabilities of the environmental control, we ran several test bench tests 37\u0026deg;C and a 7.5 pH value (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg-h). The complete set of data is available in the Supplemental Material section.\u003c/p\u003e\u003cp\u003eThe environmental control module comprises two feedback loop controllers: one for pH regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(2)), which injects carbon dioxide (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(1)) into the media (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(3)), and another for temperature control (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(7)), The feeding module includes a programmable pump (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(5)), which connects the media reservoir (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(3)) to the PDMS/glass chip (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(6)) through a recirculation mechanism, preceded by a polyvinylidene fluoride (PVDF) filtration system (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(4)) to ensure media cleanliness and quality. The imaging module, positioned near the culture chamber, is equipped with a fluorescence-capable camera (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(8)), enabling high-resolution visualization. An additional temperature sensor (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea(9)) was added to verify the measures. This setup allows for real-time monitoring of experimental conditions within the specially designed PDMS/glass chips, providing valuable insights into organoid development. All modules were designed to function in an integrated manner, ensuring the operation and enhanced experimental control.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo accommodate biological samples, we designed and fabricated an optically transparent modular PDMS/glass microfluidic chip that can be easily tailored for specific experimental conditions and is particularly useful for tracking media flow effects. The chip was constructed by bonding a molded and cured silicone well, formed using a 3D-printed mold, to a transparent glass microscope slide (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-j), and can be adjusted for multiwell applications (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ek-l) to allow multi-organoid experiments, see (Supplementary Fig.\u0026nbsp;2 shows the configuration of the serial and parallel wells used here). For a detailed description of the fabrication process, see the \u0026ldquo;Organoid culture platform\u0026rdquo; subsection in the methods. The well is accessible via 2 mm square inlet and outlet channels at the edges of the chip surface, positioned 5 mm above the bottom to prevent sample loss through the outflow channel. A PDMS interface securely seals the Tygon tubing, preventing leaks within the platform. The setup is open to the air but can be sealed with a cap that holds the temperature sensor (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec(13)). The opening is sufficiently large to facilitate organoid insertion using a standard micropipette while also allowing the removal of air bubbles and promoting free gas exchange. Unlike traditional culture methods, the chip is positioned vertically to enable tracking of media flow as it approaches the tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003em-n) and can be adapted for longitudinal monitoring of media feeding (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003em-n). The entire fabrication process was completed in less than 16 hours. This chip was designed as a customizable alternative to conventional cell culture plates; the proposed chip costs USD 5.31 (see Supplementary Table\u0026nbsp;1) and methods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the core of the automated feeding module is a programmable servo pump capable of delivering culture media with 5 nL resolution and adjustable stroke durations ranging from 1 second to 5 hours, directly into the PDMS/glass chip. To better replicate the physiological circulation of bodily fluids\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, we implemented a media recirculation system that enables semi-continuous feeding with a higher exchange rate compared to conventional tissue culture methods\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe real-time image module integrates a Dino-Lite (AM4115T-GRFBY) microscope and the PDMS/glass chip, enabling video capture and imaging at up to 60 frames per second. This setup supports sample identification, accurate data acquisition, and analysis, and is capable of detecting mCherry (ex:580nm, em:610-720nm ), and GFP(ex: 465nm, em:505-540nm) fluorescence\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The system features a\u0026thinsp;~\u0026thinsp;20 mm field of view and can be integrated with alternative camera systems.\u003c/p\u003e\u003cp\u003eThe environmental feedback control module integrates a pH stabilizer, which regulates CO₂ injection, and a heat controller for temperature regulation. It operates across a temperature range of 20\u0026deg;C to 100\u0026deg;C and a pH range of 0.1 to 14. For validation, the system was set to maintain 37\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh) and a pH of 7.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei). The platform maintained stability within \u0026plusmn;\u0026thinsp;0.6\u0026deg;C and \u0026plusmn;\u0026thinsp;0.3 pH units, demonstrating reliable environmental control. The experimental conditions were set to dispense 53 \u0026micro;L and aspirate 50 \u0026micro;L of media every 60 seconds to compensate for media losses due to liquid evaporation from the heat source. The platform maintained a temperature of 37\u0026deg;C and a pH of 7.6. Additionally, the platform was programmed to capture images every 24 hours; a detailed description can be found in the methods section. This module enhances the precision and reproducibility of cell culture experiments, supporting detailed and consistent data acquisition.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eViability of cerebral organoids\u003c/h3\u003e\n\u003cp\u003eTracking capabilities were tested using mouse cerebral organoids (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Three batches (B) of ten organoids were divided into two groups: five were cultured in a standard incubator (control) and five in the platform, for each chip configuration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In both setups, wells were pre-filled with culture media to ensure consistent initial conditions. The cerebral organoid differentiation protocol used in this study has been extensively characterized by our group through immunohistochemistry and single-cell RNA sequencing across multiple time points, as detailed in Hernandez et al\u003csup\u003e39\u003c/sup\u003e. These analyses revealed the presence of all major neural cell types, including neural progenitor cells (NPCs), intermediate progenitors, excitatory and inhibitory neurons, and astrocytes, confirming the robustness and cellular diversity of the organoids used in this study.\u003c/p\u003e\u003cp\u003eAfter six days, the cell survival rate was assessed using the CytoPainter Cell Plasma Membrane Staining Kit (ab219941), a standard dye for labeling live cells\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Tissue cytoarchitecture was evaluated by immunohistochemistry of Map2 for neuronal cell bodies and dendrites and nuclear counterstaining with DAPI. To control for CytoPainter autofluorescence, cortical organoids from the same batch were fixed in 4% paraformaldehyde before staining, serving as negative controls. Organoids cultured on the platform maintained comparable viability (orange fluorescence) to those in a standard incubator after six days, no effect of the chip to chip design variation was observed in the survival rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Additionally, platform-cultured organoids (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) exhibited cytoarchitecture consistent with incubator controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), supporting the platform\u0026rsquo;s suitability for longitudinal tracking experiments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCortical organoids are susceptible to metabolic stress, which can impair neuronal specification\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. To further validate the organoids viability, a comprehensive metabolic analysis was performed using the Vi-CELL MetaFLEX\u0026trade; bioanalyte analyzer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Media aliquots were collected from three independent batches of organoids cultured on the chip, and levels of pH, glucose, sodium, calcium, chloride, and potassium were measured (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplementary Tables\u0026nbsp;3 and 4). For each experiment, samples were taken at days 3 and 6 of culture, representing mid- and end-points of the experiment. These values were compared to matched organoids cultured in a standard tissue culture incubator with media exchanged every other day. Fresh media was also analyzed as a control (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea; Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eAcross all conditions, pH levels remained consistent between platform- and incubator-grown organoids (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; Table\u0026nbsp;4; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, concentrations of glucose and all measured ions showed no significant differences between the two culture methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec-f; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), confirming metabolic stability of organoids maintained on the platform. Metabolic analysis was conducted on three representative single-well batches of chip-cultured organoids.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe imaging module was used to evaluate the integrity and growth of cerebral organoids over six days (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Supplementary Tables\u0026nbsp;5\u0026ndash;10). Time-lapse images were acquired, and a custom algorithm was applied to detect organoid perimeters and calculate projected areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). Organoids exhibited consistent growth throughout the experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed-i). Unlike organoids grown using traditional methods\u0026mdash;which often require embedding in the base of tissue culture plates for horizontal tracking\u0026mdash;organoids cultured on the platform maintained a uniform and consistent perimeter. This suggests that the platform may better preserve organoid structure compared to previous approaches\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, To quantify growth dynamics, organoid measurements were normalized\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, revealing a comparable increase in growth rate between days 3 and 6 in cell culture (day 15\u0026ndash;20) for the six batches (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ej-k). Normalization values are provided in Supplementary Table\u0026nbsp;11. Calculation details are described in the Methods section. B1 organoids had the slowest growth rates (average\u0026thinsp;~\u0026thinsp;0.019 mm\u0026sup2;/day), B2 showed the highest growth rate overall, particularly PG0 at ~\u0026thinsp;0.124 mm\u0026sup2;/day showing a merging tendency between the organoids, and B3 showed intermediate and more consistent growth (~\u0026thinsp;0.06\u0026ndash;0.08 mm\u0026sup2;/day), neither B1, B2, nor B4 showed sample fusion. Multiwell batches showed a similar trend to batch 1 in the single well in the growth diameter, while the percentage of growth of MW1 is similar to batches 1 and 3, MW2 behaves similarly to batch 2. A noticeable advantage of the multiwell approach is the cleaner visualization of the sample and the space provided for the organoids, which helps avoid fusion between samples, which is a common issue in traditional culture techniques\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The Kruskal-Wallis test found significant differences in growth rate across batches, confirming the observed results in the imaging analysis (see supplementary Table\u0026nbsp;12 for p-value).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eLongitudinal tracking of media circulation\u003c/h3\u003e\n\u003cp\u003eTo demonstrate the platform\u0026rsquo;s tracking capabilities, a media circulation profile was generated by monitoring the real-time absorption of a green BODIPY dye to assess organoid permeability. A pulsatile flow, controlled by the feeding module, was used to inject the dye. The dye was diluted in ACSF to final concentrations ranging from 0 to 10 mM, as shown in Supplementary Fig.\u0026nbsp;3. Intensity calibration for the highest concentrations is provided in Supplementary Tables\u0026nbsp;13 and 14. A Computational Fluid Dynamics (CFD) simulation was performed under conditions adapted from previous work\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e with parameters listed in Supplementary Table\u0026nbsp;15 to validate the experimental findings.\u003c/p\u003e\u003cp\u003eFluorescence intensity was recorded over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) in three regions of interest (ROIs) within the organoid (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), using an illumination power of 0.52 W/mm\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). The first 300 seconds of the absorption profile are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed. The corresponding CFD simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef) revealed similar trends, with dye concentration increasing in a pulsatile manner, consistent with the media flow profile generated by the feeding module. The agreement between experimental and computational data highlights the platform\u0026rsquo;s capability to continuously monitor dynamic media transport using fluorescence, which is difficult or infeasible with conventional cell culture systems. The experiment was completed without interruptions, supporting the reliability and consistency of both the hardware and computational models.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAdvancements in digital manufacturing have significantly simplified the development of customized hardware for biotechnology applications\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Imaging biological samples within conventional tissue culture incubators remains challenging due to limited space and environmental conditions that can compromise electronic components\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The presented platform addresses these limitations by integrating an automated feeding system with a vertical imaging module, allowing in-incubator-compatible live imaging without bulky or sensitive external equipment. This design provides a self-contained alternative for organoid culture, incorporating control and sensing elements that enhance experimental sustainability.\u003c/p\u003e\u003cp\u003eMoreover, the platform enables real-time monitoring of media flow and component absorption at the organoid surface\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, a capability not achievable with standard systems that rely on orbital shakers and non-physiological culture conditions\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. The modular architecture allows for a wide range of experimental configurations. For instance, the feeding module can simulate dynamic media circulation patterns, supporting morphogen gradients and spatial patterning studies in multiregional organoids\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, and drug screening applications\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The environmental control module expands the platform\u0026rsquo;s applicability to a broader range of biological models, including those that require dynamic or non-standard culturing conditions such as variable temperature\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, gas composition, or pH, which conventional incubators are not designed to support. It also helps mitigate anatomical constraints that affect pH regulation and media acidification in large 3D organoids\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, where rapid nutrient exchange is essential.\u003c/p\u003e\u003cp\u003ePrevious microengineered culture systems have demonstrated diverse strategies to improve organoid viability, maturation, and accessibility. Low-cost 3D-printed bioreactors integrate imaging and drug delivery for long-term monitoring\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, while millifluidic devices enhance oxygen and nutrient supply to reduce necrosis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Automated microfluidic platforms enable combinatorial drug screening\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and stirred-tank reactors improve reproducibility through real-time sensing\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. More specialized systems include ex utero embryogenesis platforms for mouse development\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, modular perfusion devices that limit glycolytic stress\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, gradient generators that mimic morphogen signaling\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and compact CO₂ mini-incubators that enable stable long-term imaging\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Integrated brain organoid platforms further combine automated culture with electrophysiological recording to link structure and function\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe vertical chip and imaging system enables precise tracking of compound diffusion and cellular responses within fluidic environments, opening new opportunities for real-time drug delivery studies and dynamic tissue patterning\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. These capabilities support applications in modeling complex biological processes, such as morphogenic cell fate induction\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The dye absorption experiment further demonstrated the platform\u0026rsquo;s ability to synchronize physical flow profiles with computational predictions. The close agreement between experimental and simulated dye diffusion highlights the system\u0026rsquo;s utility not only for organoid culture but also for validating fluid dynamics models under biologically relevant conditions. This integration of experimental and computational tools is particularly valuable for designing media delivery protocols, assessing tissue permeability, and modeling dynamic signaling events that are difficult to capture using conventional static culture systems.\u003c/p\u003e\u003cp\u003eThe inclusion of multiple independent batches provides biological replication and enables comparison across device architectures. The unequal batch sizes in growth measurements are a consequence of imaging limitations, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg, given the overlapping of the organoids in the well, which we acknowledge as a technical constraint of the current platform rather than a biological failure of the system. Future iterations of the device will incorporate optimized well spacing and optical access to enable consistent imaging of all organoids per batch, thereby improving statistical robustness.\u003c/p\u003e\u003cp\u003eThis study presents a prototype platform, comprising three integrated subsystems, which were characterized to assess the stability of temperature, pH, and organoid growth. For each technical assessment, standard deviations were computed, ensuring that variability within device operation was quantified and reported transparently. In addition, while six experimental batches were conducted, imaging constraints reduced the number of analyzable organoids per batch. For instance, batch 1 yielded five complete datasets, while batches 2\u0026ndash;6 yielded between two and five usable organoids each, depending on image quality and overlap.\u003c/p\u003e\u003cp\u003eTo enable rapid validation, we used mouse forebrain organoids, which mature much faster than human ones. This choice allowed shorter culture periods while providing an efficient proof of concept for testing platform viability, stability, and growth dynamics without the variability associated with long-term organoid cultures. The results clearly demonstrate the platform's feasibility and potential for automated organoid culture and monitoring. Repeated sterilization of PDMS-glass devices presents a potential limitation for long-term applications. PDMS can absorb water and chemicals during autoclaving or repeated chemical sterilization, leading to swelling, microcracking, or delamination from the glass substrate over time\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. High-temperature exposure can also alter its mechanical stability. In this study, we observed evaporation-related issues in the chip that resulted from an adjusted filling rate of 3 \u0026micro;L, calculated based on the osmolality to compensate for this loss\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eOrganoid culture platform\u003c/h2\u003e\u003cp\u003eThe prototype platform shown here was designed and fabricated to sustain and track 3D tissue cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), throughout this study. This prototype contained three integrated subsystems (temperature, pH, and culture monitoring). The components include a servo-driven fluidic pump module, a temperature control system with an aluminum heating pad, an environmental chamber sensor module, a pH gas controller module with a 3D-printed gas diffuser valve, and a microscope with light source generation capabilities. A fluorescence chamber was integrated into this modular platform, consisting of a microscope device, a PDMS chip, and a microscope glass slide. The chip was supplied with fluids through fluidic lines, and an LED ring (NeoPixel, Adafruit) was used for illumination. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb presents a schematic representation of the modular platform, illustrating the key components and their interactions within the system.\u003c/p\u003e\u003cp\u003eA custom relay board was integrated into the platform, connecting the Arduino to the solenoid valve. This board regulated the current in the solenoid valve and allowed the Arduino to close down the valve during overheating. Given the excess electric current, it is essential to prevent overinjection of CO2 into the culture media, which could acidify the microenvironment where the organoids are submerged. The setup was mounted on a stainless steel, height-adjustable base, with customized 3D-printed parts made of PLA and holder components printed with commercial resin (Formlabs model V2, Formlabs, Massachusetts). Additionally, the platform could be remotely controlled using open-source software packages such as TeamViewer\u0026reg; and RealVNC\u0026reg;.\u003c/p\u003e\u003cp\u003eThe PDMS/glass microfluidic chip comprises one microscope slide and 6 grams of silicone. The fabrication process involves several sequential steps. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the creation of a solid mold fabricated using Model V2 resin (Form 3, Formlabs) on an SLA printer. The mold was thoroughly cleaned in isopropanol (IPA) for 20 minutes in an ultrasonic bath to remove unwanted materials. Nitrogen gas was then used to dry the molds, followed by a 40-minute UV light exposure (405nm) at 60\u0026ordm;C to ensure complete curing.\u003c/p\u003e\u003cp\u003eAfter cleaning, a PDMS (Sylgard 184, Dow Corning) and curing agent mixture was prepared in a 10:1 weight ratio of PDMS to curing agent. The mixture was degassed for two hours in a vacuum chamber, then cured in an oven at 60\u0026ordm;C for six hours to facilitate physical cross-linking. A 5-minute surface activation step was carried out to bond the polymer and glass surfaces using O\u003csub\u003e2\u003c/sub\u003e plasma reactive ion etching (RIE) on a 25mm \u0026times; 75mm microscope glass slide (Fisherbrand\u0026trade; Superfrost\u0026trade;, Fisher) at 50W power, 125sccm of oxygen, and a chamber pressure of 0.254mTorr. The PDMS and glass were then carefully aligned to form a polymer/glass bond. The assembled bio-chamber was left to settle in a vacuum oven for six hours at 60\u0026ordm;C, with 10 grams of pressure applied to enhance adhesion between the PDMS and glass interface. The components of the chip are listed as non-reactive to cells by their respective manufacturing companies. All the PDMS/Glass chip versions were fabricated using the same protocol, with modifications made only to the design. The multiwell v2 chip features microscope-grade flexible slides between the steps to separate the wells, as shown in detail in Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eThe fabrication cost for one single PDMS/glass chip was determined by adding the commercial value of one microscope slide at \u003cspan\u003e$\u003c/span\u003e0.64 USD, 6 grams of silicone at \u003cspan\u003e$\u003c/span\u003e1.84 USD, and 0.019 L of Formlabs model V2 resin at \u003cspan\u003e$\u003c/span\u003e2.83 USD; the total cost of the material for the chip fabrication was \u003cspan\u003e$\u003c/span\u003e5.31 USD. The price per hour of the equipment used to print, cure, and bond the PMDS to the glass was not factored into this calculation. The quantity price of the component was obtained from a cost estimation relation between the total price of the commercial presentation of the material and the amount used for the device fabrication.\u003c/p\u003e\u003cp\u003eThe attachment of the PDMS to the glass using the procedure described above achieved a high success rate (approximately 80%). Failures were primarily due to small imperfections in the uniformity of the PDMS surface that contacts the glass. After fabrication, the glass\u0026ndash;PDMS chips were tested for leakage using a fluorescent medium that was pumped through the system. Most of the chips that passed the leakage test (approximately 80%) were reused multiple times without any subsequent failures. The data presented in this paper were collected using five chips: three single-well chips, one serial multi-well chip, and one parallel multi-well chip (see Supplemental Material, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The device-to-device variation is primarily determined by the precision of the 3D printer used to fabricate the PDMS mold. Here we used a Formlabs Form 3\u0026thinsp;+\u0026thinsp;Bio stereolithography (SLA) printer, which achieves a maximum XY resolution of 25 \u0026micro;m and a layer thickness of 25 \u0026micro;m, with dimensional accuracy of approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;100 \u0026micro;m across the build volume. Therefore, considering that the principal dimension of the well is 3 mm, the device-to-device variation is several orders of magnitude smaller than the well size.\u003c/p\u003e\u003cp\u003eThe culture media, essential for maintaining cell viability and growth, were semi-continuously circulated using a servo pump (Cavro Centris, Tecan) and Tygon tubing, sealed with couplings to prevent leaks in the platform. A control script, \u003cem\u003eMainPumpProgramExe.py\u003c/em\u003e, was implemented to program the experiment's desired speed, period, and volume. This module has a volume capacity of 500 mL and is designed to facilitate media recirculation. For the platform's functional test, 50 mL, 4.2 times the volume used in standard culture protocols was utilized. A sterilization protocol, detailed in the \"Platform Sterilization\" section, was employed to ensure sterility, utilizing a 0.22\u0026micro;m hydrophilic PVDF filter. To achieve specific absorption profiles, a velocity of 1.27 \u0026times; 10⁻⁴ m/s was used, with inlet and outlet volumes of 53 \u0026micro;L and 50 \u0026micro;L, respectively, to compensate for media losses by evaporation. This calibration was performed using the supplementary values in Supplementary Table\u0026nbsp;3. The adjustment was based on the osmolality calculation for the salts present in the culture media. An increase in media osmolality has been reported as an indicator of culture medium evaporation\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. The osmolality value was calculated for both fresh unused and the last day experiment media, using the following formula,\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:C(mol/L)=\\frac{Osmolality(Osm/kg)}{i}\\cdot\\:\\frac{1}{\\rho\\:(kg/L)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e is the molar concentration, \u003cem\u003ei\u003c/em\u003e is the dissociation factor (van\u0026rsquo;t Hoff factor), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e is the fluid density. The dissociations used were K\u0026thinsp;=\u0026thinsp;1, Na\u0026thinsp;=\u0026thinsp;1, and Ca\u0026thinsp;=\u0026thinsp;2, for potassium, sodium, and calcium, respectively. Then the volume loss was calculated using the following equation,\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Vfinal=Vinitial\\cdot\\:\\frac{OsmolalityFreshMedia}{OsmolalityDay6}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eVfinal\u003c/em\u003e is the final media volume, \u003cem\u003eVinitial\u003c/em\u003e is the initial media volume, and OsmolalityFreshMedia and OsmolalityDay6 are the osmolality values of the initial and final recorded days, respectively. The fraction lost was calculated,\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Fraction\\:Lost=1-\\frac{OsmolalityFreshMedia}{OsmolalityDay6}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor the potassium (K) salt, the osmolality was 3.87 mOsm/kg and 5.57 mOsm/kg for the fresh and last day media, respectively. Its osmolality ratio was 0.69, and the fraction lost was 0.30 for the complete experiment. The fraction lost for each of the experimental days was calculated by dividing the total experiment fraction lost by 6, then a 0.05 ratio per day was used to estimate the loss per day. With an initial volume of 50 \u0026micro;L, the loss volume was calculated to be 50 \u0026micro;L \u0026times; 0.05, resulting in a loss of 2.50 \u0026micro;L per day and a final volume of 47.45 \u0026micro;L. The values were calculated for sodium (Na) at 0.86 \u0026micro;L and calcium (Ca) at 0.39 \u0026micro;L. Given these calculations, we adjust the flow rate to inject three \u0026micro;L more to compensate for evaporation losses.\u003c/p\u003e\u003cp\u003eThe imaging acquisition process was conducted using the Dino-Lite Edge AM4115T-GRFBY, a digital microscope with a resolution of 1.3 MP and a rapid capture rate of 30 frames per second (FPS). This microscope was equipped with two sets of LEDs for fluorescent imaging of objects. The first set consisted of 4 Blue LEDs with an excitation wavelength of 480 nm, while the second set included 4 Yellow LEDs with an excitation wavelength of 575 nm. The LED sets were switchable through the accompanying software, allowing for flexibility in the imaging process. The emission wavelengths were 510 nm and 610 nm, respectively. The Dino-Lite microscope provided magnification capabilities of up to 220x, enabling detailed examination of the specimens. Videocapture 2.0 (DinoEdge, Dinolite), a freeware version of the software provided by the same company, was employed to record the acquired videos. This software facilitated efficient video recording and data management during imaging experiments.\u003c/p\u003e\u003cp\u003eThe incubation environment was carefully controlled using a combination of instrumented devices. To maintain physiological conditions, the media was conditioned with bone-dry carbon dioxide (Airgas) at a concentration of 100%, resulting in an average carbon dioxide level of approximately 5% within the incubator. A pH stabilizer module was positioned in the media reservoir, using an Atlas Scientific mini-lab probe (Atlas Scientific, New York) with a resolution of \u0026plusmn;\u0026thinsp;0.001 and an accuracy of \u0026plusmn;\u0026thinsp;0.002. pH values were measured every minute, and aliquots were taken on specific days for analysis using the Vi-CELL MetaFLEX (see supplementary table 3) to validate the measurements. Depending on the pH value, a pneumatic-electric solenoid valve (Tailonz) was controlled by the \u003cem\u003eArduinoMegapHSolenoid.ino\u003c/em\u003e script using an Arduino Mega 2560 control board (clock speed of 16 MHz) to maintain the value of the media between 7.2 and 7.6. The solenoid valve's inlet port was connected to a bone-dry CO₂ high-pressure steel cylinder (Airgas, California) via a plastic hose, and the outlet port was connected to a lab-grade hose leading to a diffuser submerged in the media, forming a closed-loop circuit (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The pH level was continuously monitored every minute over six days, with the system programmed to prevent the pH from exceeding the 7.6 threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eTo ensure temperature stability, an NTC10K temperature sensor with an accuracy of \u0026plusmn;\u0026thinsp;5%, a W1209 PID controller with an accuracy of \u0026plusmn;\u0026thinsp;0.1\u0026deg;C, and an aluminum PCT heater (220\u0026deg;C at 5- 28W) were positioned at the back of the chip (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb(10)). The PID controller was set to maintain 37\u0026deg;C and was placed inside the PDMS/glass chip containing the culture media. A second SCD-40 sensor, with an accuracy of \u0026plusmn;\u0026thinsp;0.8\u0026deg;C, was added near the PCT heater to verify the environmental temperature, with data acquisition managed using the \u003cem\u003eCO2TempHumidityRecording.py\u003c/em\u003e script. All relevant files are provided in the data availability section. A dual-channel thermometer (Leaton) was also placed next to the heater sensor for further temperature validation. Temperature and pH records are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh, respectively.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eA test bench without organoids was performed three times on the platform device to test variability. Then, the organoids were cultured across a total of seven independent batches: one batch with five control organoids culture in shaker conditions inside the incubator, four batches of five organoids each in the single-well PDMS/glass chip format, one batch of five organoids in the multiwell PDMS/glass chip arrangement (v1), and one batch of five organoids in the multiwell arrangement (v2). For each batch, all organoids were stained with a live-cell dye to assess viability (biological replicates\u0026thinsp;=\u0026thinsp;individual organoids; technical replicate\u0026thinsp;=\u0026thinsp;one staining/measurement per organoid). Media samples from three independent single-well batches were analyzed using the Vi-CELL MetaFLEX\u0026trade; analyzer to generate metabolic panels. Organoid growth was assessed by diameter measurements obtained via brightfield microscopy. Due to the optical constraints of the device geometry, not all organoids could be imaged with sufficient resolution, resulting in uneven sample sizes for growth analysis (four organoids in batch 1, two in batch 2, three in batch 3, five in batch 4, two in multiwell v1, and five in multiwell v2).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eViability analysis\u003c/h2\u003e\u003cp\u003eThe organoids in the chip were fed following the protocol described in the Organoid Culture Platform section. At the same time, the controls received 2 mL of media every other day, adhering to standard lab protocols\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The experiment started on differentiation 14, a stage when neurons had already formed\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, and continued until day (D) 20. The experimental conditions were set to dispense 53 \u0026micro;L and aspirate 50 \u0026micro;L of media every 60 seconds to compensate for media losses due to liquid evaporation from the heat source. The platform maintained a temperature of 37\u0026deg;C and a pH of 7.6. Additionally, the platform was programmed to capture images every 24 hours. This conditions were the same for the three types of PDMS/Glass chips used in the experiment.\u003c/p\u003e\u003cp\u003eThe CytoPainter Cell Plasma Membrane Staining Kit with Orange Fluorescence (Abcam # ab219941), employing an orange cell membrane probe (Ex/Em\u0026thinsp;=\u0026thinsp;540/590 nm), was used in this experiment. This probe allows for the uniform staining of cell membranes across various mammalian cell types\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Complementary evidence of the staining can be found in supplementary Fig.\u0026nbsp;4, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, showing the multiwell approaches results. The Orange Dye was reconstituted by adding 100 \u0026micro;L of dimethyl sulfoxide (DMSO) to the vial, preparing a 500X orange dye stock solution. The working solution was obtained by diluting 20 \u0026micro;L of the dye solution into 10 mL of assay buffer. Subsequently, 100 \u0026micro;L of the solution was added to the well and incubated with cells for 20 minutes at 37\u0026ordm;C with 5% CO2, protected from light.\u003c/p\u003e\u003cp\u003eFollowing incubation, the cortical organoids underwent three washes with 1x phosphate-buffered saline (PBS) (Thermo Fisher Scientific # 10-010-023) and were fixed with a 4% paraformaldehyde solution for 40 minutes. The organoids were washed three times with 1x PBS and then left in 30% sucrose (Millipore Sigma # S8501) diluted in 1x PBS overnight. The organoids were then embedded in a solution containing equal parts Tissue-Tek O.C.T. Compound (Sakura # 4583) and 30% sucrose solution. The samples were directly sectioned to 12 \u0026micro;m using a cryostat (Leica Biosystems # CM3050) onto glass slides. After two 5-minute washes in 1X PBS and one wash in deionized water (Chem world # CW-DW-2G). The sections were then stored at 4\u0026deg;C and were observed using an EVOS-FL Auto-2 (Thermo Fisher Scientific) fluorescence microscope equipped with a TRITC filter set (Ex/Em\u0026thinsp;=\u0026thinsp;540/590 nm).\u003c/p\u003e\u003cp\u003eOrganoids were collected and fixed in 4% paraformaldehyde (PFA) (Thermo Fisher Scientific # 28908) and cryopreserved in 30% sucrose solution. They were then embedded in a solution containing equal parts of Tissue-Tek O.C.T. Compound (Sakura # 4583) and 30% sucrose solution. They were then directly sectioned to 12 \u0026micro;m using a cryostat (Leica Biosystems # CM3050) onto glass slides. After two washes of five minutes in 1X PBS, the sections and one wash in deionized water (Chem world # CW-DW-2G), sections were incubated in blocking solution, 5% v/v donkey serum (Millipore Sigma # D9663), and 0.1% Triton X-100 (Millipore Sigma # X100) for 1 hour. The sections were then incubated in primary antibodies overnight at 4\u0026deg;C. They were then washed thrice for 10 minutes in PBS and incubated in secondary antibodies for 90 minutes at room temperature. They were then washed thrice for 10 minutes in PBS and covered with Fluoromount-G Mounting Medium (Thermo Fisher Scientific # 00-4958-02).\u003c/p\u003e\u003cp\u003eThe primary antibodies used were mouse and rabbit anti-Map2 (Proteintech # 17490-1-AP, 1:2,000). The secondary antibodies were of the Alexa series (Thermo Fisher Scientific), used at a concentration of 1:750. The nuclear counterstain was 300 nM DAPI (4\u0026rsquo;,6-Diamidino-2-Phenylindole, Dihydrochloride) (Thermo Fisher # D1306). Imaging was done using an inverted confocal microscope (Zeiss 880) and Zen Blue software (Zeiss). Images were processed using Zen Black (Zeiss) and ImageJ software (NIH).\u003c/p\u003e\u003cp\u003eMetabolic measurements were conducted using the Vi-CELL MetaFLEX\u0026trade; analyte analyzer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The media were sampled in aliquots from 3 batches of organoids grown in the platform, and the pH and metabolites, including glucose, sodium, calcium, chloride, and potassium, were measured. Two different time points were used for each batch: three and six days. The values were compared to organoids grown in a tissue culture incubator and under the standard culturing protocol (media exchange every other day). As a control, fresh media were sampled.\u003c/p\u003e\u003cp\u003eKruskal-Wallis(Kw) test, a statistical test used to compare two or more groups for a continuous or discrete variable\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, was employed to track the significant differences between groups. A total of n\u0026thinsp;=\u0026thinsp;70 records were measured in the MetaFLEX, dividing the data into seven variables with ten replicas for each condition, such as fresh media, incubator, and platform incubation. K(w) It is a non-parametric test, which assumes no particular distribution of the data and is analogous to the one-way analysis of variance (ANOVA) at a 95% confidence level. This test was run in RStudio. Version 1.3.1093 retrieved the data from supplementary table 3.\u003c/p\u003e\u003cp\u003eThe imaging acquisition process was conducted using Videocapture 2.0 (DinoEdge, Dinolite), a freeware version of the software provided by the same company, which was employed to record the acquired videos. Three batches of organoids were recorded over six days and measured with the perimeter and area tool provided by the software. To observe the growth trend, we calculate the quantitative values by subtracting and dividing each organoid cross section by the average of the batch cross section, with the following formula;\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:Normalized\\:Value\\left(t\\right)=\\left(CS\\right(t)-CS(1)/CS\\:Average(1\\left)\\right)$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e\u003cp\u003eCS(t) is the cross-sectional measure of each organoid at a specific time, and CS(1) is the cross-sectional measure at the first day of the experiment. Supplementary Figs.\u0026nbsp;7, 8, 9, 10, 11, and 12 provide complementary evidence of organoid monitoring. Supplementary tables 5, 6, 7, 8, 9, and 10 show the organoid measures in the single-well and multiwell experiments. The calculated growth rates (slopes) for each organoid, along with the results of the Kruskal-Wallis(Kw) test comparing growth rates across batches.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLongitudinal tracking of media circulation\u003c/h2\u003e\u003cp\u003eThe green BODIPY dye was imaged using the AM4115T-GRFBY Dinolite, which provides camera capture and light source generation capabilities. The light intensity was measured in lux (LX) using a LX1332B light meter corresponding to the blue square. The microscope could switch between six intensity levels, with the two highest levels selected for constructing the calibration curves. ImageJ measured the intensity levels from the microscope camera, corresponding to the black square. LX was converted to lumens and power intensity (W/mm\u0026sup2;). The calibration curve was generated using ten concentration points and replicated thrice. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb shows the calibration curve for the 0.52 W/mm\u0026sup2; value, corresponding to the second highest intensity, and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec shows the calibration curve for the highest intensity of 0.79 W/mm\u0026sup2;. Other intensity points were not considered due to their lack of emission.\u003c/p\u003e\u003cp\u003eThe computational fluid dynamics (CFD) analysis helped validate the design and assess its performance by simulating the fluid flow under various conditions\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. These conditions were simulated using COMSOL\u0026reg; Multiphysics 5.5 (Stockholm, Sweden). The experimental setup involved a linear speed 1.2710\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003ems\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a 70 \u0026micro;L media injection. The well size is characterized by a depth of 5 mm and a diameter of 5.60 mm. The liquid phase was modeled using water properties, while air properties were employed to represent the gas phase. The properties of water under incubator conditions were considered, with a density of 997kgm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and a viscosity of 6.92010\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003ekgm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The temporal evolution of the concentration field is considered, and the concentration source term is specified at the inlet of the geometry, serving as a boundary condition. This study focuses on a single species utilized for live cell membrane staining, specifically CellTracker\u0026trade; Green BODIPY\u0026trade; Dye (Thermo Fisher Scientific # C2102). The concentration value chosen for this physical process is 10molm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, while the diffusion coefficient between the media and the organoid surface is set to 1x10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003em\u003csup\u003e2\u003c/sup\u003es\u003csup\u003e\u0026minus;1\u003c/sup\u003e. The reference temperature for the platform is maintained at 37\u0026deg;C, and the governing properties associated with the fluid flow are determined from the Navier-Stokes set of boundary conditions\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. The fluid dynamics conditions for the homogeneous domains were derived from previous experiments conducted in shaker domains, as referenced. The level set method was implemented, as referenced, to capture the phase boundary of the free surface flow interface accurately. Using the level set function, this approach facilitated the control of integration between the two domains at the boundary. The value of the level set function ranged from 0 to 1, depending on the fluid phase, and a value of 0.5 was selected for this investigation to achieve an equal distribution at the surface. This specific choice was based on considerations regarding surface tension and computational efficiency presented in previous literature. The solution domain, encompassing both phases, was enclosed by a solid wall, with only the top surface exposed to incubator conditions. A non-slip condition was imposed on the solid wall, while a slip condition was applied to the open surface. For this simulation, a total of 519,830 tetrahedral elements were generated.\u003c/p\u003e\u003cp\u003eAdditionally, a phantom spherical geometry was approximated based on images of the organoid batch taken on day 13, with a diameter of 1.8 mm. The atmospheric conditions were set to a pressure of 1 atm, a temperature of 37\u0026deg;C, and a gas composition of 5% carbon dioxide, 17% oxygen, and 78% nitrogen. Lastly, a mesh independence study was conducted to ensure consistent element sizing and to provide accurate numerical analysis. The results of the mesh convergence study are shown in supplementary Fig.\u0026nbsp;13. The test was performed up to 1.939.940, and the graph shows that the simulation result stabilizes beyond ~\u0026thinsp;400,000 elements, confirming mesh independence of the numerical solution. These results validate the suitability of the mesh resolution used in the main simulations. The resulting velocity fields, expressed in SI units, were visualized using stream arrow lines combined with a middle plane slide\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEmbryonic stem cell line maintenance\u003c/h2\u003e\u003cp\u003eAll experiments used the ES-E14TG2a mouse embryonic stem cell (ESC) line (ATCC # CRL-1821), derived from a 129/Ola male mouse strain. Before experimentation, the ESCs were subjected to mycoplasma testing. ESCs were cultured on plates coated with Recombinant Protein Vitronectin (Thermo Fisher Scientific # A14700). The maintenance medium for mESCs consisted of Glasgow Minimum Essential Medium (Thermo Fisher Scientific # 11710035), supplemented with Embryonic Stem Cell-Qualified Fetal Bovine Serum (Thermo Fisher Scientific # 10439001), 0.1 mM MEM Non-Essential Amino Acids (Thermo Fisher Scientific # 11140050), 1 mM Sodium Pyruvate (Millipore Sigma # S8636), 2 mM Glutamax supplement (Thermo Fisher Scientific # 35050061), 0.1 mM 2-Mercaptoethanol (Millipore Sigma # M3148), and 0.05 mgmL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Primocin (Invitrogen # ant-pm-05). The mESC maintenance media were supplemented with 1,000 units mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of Recombinant Mouse Leukemia Inhibitory Factor (Millipore Sigma # ESG1107). The culture medium was changed daily to maintain optimal conditions for ESC growth. ReLeSR passaging reagent (Stem Cell Technologies # 05872) was used according to the manufacturer\u0026rsquo;s instructions for cell dissociation and passaging. For cell preservation, ESCs were frozen using mFreSR cryopreservation medium (Stem Cell Technologies # 05855) following the manufacturer\u0026rsquo;s guidelines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eGeneration of organoids\u003c/h2\u003e\u003cp\u003eMouse embryonic stem cells (mESCs) were dissociated into single cells using TrypLE Express (Thermo Fisher #12604021) for 5 minutes at 37\u0026deg;C, following previous protocols\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Cells were then re-aggregated in Lipidure-coated 96-well V-bottom plates at a density of 3,000 cells per well in 150 \u0026micro;L of mESC maintenance medium, supplemented with 10 \u0026micro;M ROCK inhibitor Y-27632 (Tocris #1254) and 1,000 U/mL recombinant mouse LIF (Millipore Sigma #ESG1107). After 24 hours, the medium was replaced with dorsal forebrain patterning medium consisting of DMEM/F12 with GlutaMAX (Thermo Fisher #10565018), 10% KnockOut Serum Replacement (Thermo Fisher #10828028), 0.1 mM MEM Non-Essential Amino Acids (Thermo Fisher #11140050), 1 mM sodium pyruvate (Sigma #S8636), 1X N-2 supplement (Thermo Fisher #17502048), 2X B-27 minus Vitamin A (Thermo Fisher #12587010), 0.1 mM 2-mercaptoethanol (Sigma #M3148), and 0.05 mg/mL Primocin (InvivoGen #ant-pm-05). To promote dorsal forebrain identity, this medium was further supplemented with 10 \u0026micro;M Y-27632, 5 \u0026micro;M WNT inhibitor XAV939 (StemCell Technologies #100\u0026ndash;1052), and 5 \u0026micro;M TGF-β inhibitor SB431542 (Tocris #1614). Media was changed daily, with N-2 and B-27 supplements added after filtration to retain hydrophobic components.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePlatform sterilization\u003c/h2\u003e\u003cp\u003eThe PDMS/Glass chip, temperature feedback control sensor, thermocouple, media hoses, and programmable pump underwent thorough cleaning using a total of 45mL high-level disinfectant solution (CIDEX\u0026reg; OPA, ASP), which was recirculated within the system, pump, and hoses at a rate of 100\u0026micro;L per 20 seconds, using a PVDF 0.22\u0026micro;M filter to clear the impurities for 20 minutes.\u003c/p\u003e\u003cp\u003eFollowing the disinfection step, the components were rinsed meticulously with ultra-pure distilled water (Invitrogen\u0026trade; Ultra-Pure\u0026trade;, ThermoFisher) for 20 minutes, under the same conditions used with the CIDEX\u0026reg;, to remove any residual disinfectant. As a final cleaning step, we pump air through the system to dry out any liquid residual for 4 hours using the same rates of the CIDEX\u0026reg; and the purified water. Moreover, the chamber, chip holder, environmental sensor, power cable, and microscope were wiped down using hydrogen peroxide (Oxivir TB, Diversey) for 1 minute each, ensuring a comprehensive decontamination process. These stringent sterilization measures were implemented to maintain a sterile experimental environment and minimize the potential for contamination during the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eAll custom scripts, pH calibration, feeding rates, temperature records, 3D-printed files, microscope images, and CFD videos are available at [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sebtomon89/braingeneersdifussionproject\u003c/span\u003e\u003cspan address=\"https://github.com/sebtomon89/braingeneersdifussionproject\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e].\u003c/span\u003e Additional modified scripts can be accessed upon request. All other relevant data are available from the corresponding author upon request.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCode availability\u003c/h2\u003e\u003cp\u003eDetails of publicly available software used in the study are given in the \u0026ldquo;Data availability\u0026rdquo; section. Apart from this, no unique custom code or mathematical algorithms were central to reaching the conclusions of this work.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e\n\u003cp\u003eS.T.-M. and S.T.S. worked on hardware, software, and platform assembly. S.T.-M., S.H., H.E.S and S.V-C. worked in cell culture and cell staining. S.T.-M., S.H., H.E.S., and G.K. performed biological experiments. S.T.-M., M.A.M.-R., and M.T. conceived the experiments. D.H., M.A.M.-R., and M.T. supervised the team and secured funding. S.T.-M., H.E.S., M.A.M.-R., S.T.S., and M.T. wrote the manuscript with contributions from all authors.\u003c/p\u003e\n\u003ch1\u003e\u003cstrong\u003eList of software used in this work\u0026nbsp;\u003c/strong\u003e\u003c/h1\u003e\n\u003cp\u003eThis work was produced using,\u0026nbsp;\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eCOMSOL\u0026reg; Multiphysics 5.5, URL: https://www.comsol.com/.\u003c/li\u003e\n \u003cli\u003eAutodesk Fusion 360, URL:https://www.autodesk.com/products/fusion-360/overview.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMatlab R2022a, URL:https://www.mathworks.com/products/new_products/release2022a.html.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eR Studio Version 1.3.1093. URL: \u0026nbsp;https://www.r-project.org/,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePyCharm 2020.3, URL: https://www.jetbrains.com/pycharm/.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAdobe Illustrator 29.3.1, URL: https://www.adobe.com/products/illustrator.html.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBioRender, URL: https://www.biorender.com/.\u003c/li\u003e\n \u003cli\u003eDraw.io, URL: https://www.drawio.com/.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDinoCapture 2.0, URL: https://www.dinolite.us/features/dinocapture/?srsltid=AfmBOoqyQw4rmUoVHNKJLBDRRkWOa8mKBfaHKbG4zwAO00TnrFHzIJyQ.\u003c/li\u003e\n \u003cli\u003eImageJ 1.54g, URL: https://imagej.net/ij/.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMicrosoft Excel 363, URL: https://www.microsoft.com/en-us/microsoft-365/excel.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGrammarly, URL: https://app.grammarly.com/.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Schmidt Futures Grant SF857 (D.H., and M.T.); the National Human Genome Research Institute Grant 1RM1HG011543 (D.H. and M.T.); National Science Foundation Grants 2134955 (to D.H. and M.T.), 2034037 (to M.T.), and 2515389 (to D.H., M.A.M.-R. and M.T.); the National Institute of Mental Health Grant 1U24MH132628 and U24NS146314 (to D.H. and M.A.M.-R.); the California Institute for Regenerative Medicine DISC4-16285 (to M.A.M.-R. and M.T.), and DISC4-16337 (to M.A.M.-R).; by the University of California Office of the President M25PR9045 (to M.A.M.-R. and M.T.); and H.E.S. is a National Science Foundation Graduate Student Research Fellowship grantee. S.H. received support from the UC Doctoral Diversity Initiative (DDI-UCSC-IBSC). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the National Science Foundation, the University of California, CIRM or any other agency of the State of California.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.T.-M. and S.T.S. worked on hardware, software, and platform assembly. S.T.-M., S.H., H.E.S and S.V-C. worked in cell culture and cell staining. S.T.-M., S.H., H.E.S., and G.K. performed biological experiments. S.T.-M., M.A.M.-R., and M.T. conceived the experiments. D.H., M.A.M.-R., and M.T. supervised the team and secured funding. S.T.-M., H.E.S., M.A.M.-R., S.T.S., and M.T. wrote the manuscript with contributions from all authors.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank the UCSC Life Sciences Microscopy Center, RRID: SCR_021135, for providing the confocal microscope to acquire the images. Some illustrations were generated using Biorender. We gratefully acknowledge contributions from Dr. Sofie R. Salama and Dr. Kateryna Voitiuk for the feedback on the preparation of this manuscript. During the preparation of this work, the authors used ChatGPT and Grammarly to improve clarity and sentence structure. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll custom scripts, pH calibration, feeding rates, temperature records, 3D-printed files, microscope images, and CFD videos are available at [ [https://github.com/sebtomon89/braingeneersdifussionproject](https:/github.com/sebtomon89/braingeneersdifussionproject) ]. Additional modified scripts can be accessed upon request. All other relevant data are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSasai, Y. Next-Generation Regenerative Medicine: Organogenesis from Stem Cells in 3D Culture. \u003cem\u003eCell. Stem Cell.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 520\u0026ndash;530 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSasai, Y., Eiraku, M. \u0026amp; Suga, H. In vitro organogenesis in three dimensions: self-organising stem cells. \u003cem\u003eDevelopment\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e, 4111\u0026ndash;4121 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMun, S. J. et al. Generation of expandable human pluripotent stem cell-derived hepatocyte-like liver organoids. \u003cem\u003eJ. Hepatol.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 970\u0026ndash;985 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakasato, M., Er, P. X., Chiu, H. S. \u0026amp; Little, M. H. Generation of kidney organoids from human pluripotent stem cells. \u003cem\u003eNat. Protoc.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1681\u0026ndash;1692 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eV\u0026ouml;lkner, M. et al. Retinal Organoids from Pluripotent Stem Cells Efficiently Recapitulate Retinogenesis. \u003cem\u003eStem Cell. Rep.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 525\u0026ndash;538 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeiler, S. T. et al. Modular automated microfluidic cell culture platform reduces glycolytic stress in cerebral cortex organoids. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 20173 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho, A. N. et al. Microfluidic device with brain extracellular matrix promotes structural and functional maturation of human brain organoids. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 4730 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwedhelm, I. et al. Automated real-time monitoring of human pluripotent stem cell aggregation in stirred tank reactors. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 12297 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAguilera-Castrejon, A. \u0026amp; Hanna, J. H. Ex Utero Culture of Mouse Embryos from Pregastrulation to Advanced Organogenesis. \u003cem\u003eJoVE\u003c/em\u003e \u003cb\u003e63160\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3791/63160\u003c/span\u003e\u003cspan address=\"10.3791/63160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerger, E. et al. Millifluidic culture improves human midbrain organoid vitality and differentiation. \u003cem\u003eLab. Chip\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 3172\u0026ndash;3183 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchuster, B. et al. Automated microfluidic platform for dynamic and combinatorial drug screening of tumor organoids. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 5271 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan, I. et al. A low-cost 3D printed microfluidic bioreactor and imaging chamber for live-organoid imaging. \u003cem\u003eBiomicrofluidics\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 024105 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaisonneuve, B. G. C. et al. Deposition chamber technology as building blocks for a standardized brain-on-chip framework. \u003cem\u003eMicrosyst. Nanoeng\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 86 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee, H. N. et al. Effect of biochemical and biomechanical factors on vascularization of kidney organoid-on-a-chip. \u003cem\u003eNano Convergence\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 35 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastiglione, H. et al. Human Brain Organoids-on-Chip: Advances, Challenges, and Perspectives for Preclinical Applications. \u003cem\u003ePharmaceutics\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 2301 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanchis-Calleja, F. et al. Decoding morphogen patterning of human neural organoids with a multiplexed single-cell transcriptomic screen. \u003cem\u003ebioRxiv\u003c/em\u003e 2024\u0026ndash;02 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmbbm.2021.105024\u003c/span\u003e\u003cspan address=\"10.1016/j.jmbbm.2021.105024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoussaad, I. et al. OPEN Integrated, automated maintenance, expansion. \u003cem\u003eScientific Reports\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnderstrup, K. G. \u0026amp; Programming and Control of Flow-based Microfluidic biochips.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaurer, B. et al. Inkube: An all-in-one solution for neuron culturing, electrophysiology, and fluidic exchange. 12.06.627248 Preprint at (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2024.12.06.627248\u003c/span\u003e\u003cspan address=\"10.1101/2024.12.06.627248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVoitiuk, K. et al. A feedback-driven IoT microfluidic, electrophysiology, and imaging platform for brain organoid studies. 03.15.585237 Preprint at (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2024.03.15.585237\u003c/span\u003e\u003cspan address=\"10.1101/2024.03.15.585237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalebipour, A., Saviz, M., Vafaiee, M. \u0026amp; Faraji-Dana, R. Facilitating long-term cell examinations and time-lapse recordings in cell biology research with CO2 mini-incubators. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 3418 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWiggins, L. et al. The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1854 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSvensson, C., Medyukhina, A., Belyaev, I., Al-Zaben, N. \u0026amp; Figge, M. T. Untangling cell tracks: Quantifying cell migration by time lapse image data analysis. \u003cem\u003eCytometry Pt A\u003c/em\u003e. \u003cb\u003e93\u003c/b\u003e, 357\u0026ndash;370 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHashimoto, H. et al. Time-lapse imaging of cell cycle dynamics during development in living cardiomyocyte. \u003cem\u003eJ. Mol. Cell. Cardiol.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 241\u0026ndash;249 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLy, V. T. et al. Picroscope: low-cost system for simultaneous longitudinal biological imaging. \u003cem\u003eCommun. Biol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 1261 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaulsen, B. et al. Autism genes converge on asynchronous development of shared neuron classes. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e602\u003c/b\u003e, 268\u0026ndash;273 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMostajo-Radji, M. A., Schmitz, M. T., Montoya, S. T. \u0026amp; Pollen, A. A. Reverse engineering human brain evolution using organoid models. \u003cem\u003eBrain Res.\u003c/em\u003e \u003cb\u003e1729\u003c/b\u003e, 146582 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXinaris, C., Brizi, V. \u0026amp; Remuzzi, G. Organoid Models and Applications in Biomedical Research. \u003cem\u003eNephron\u003c/em\u003e \u003cb\u003e130\u003c/b\u003e, 191\u0026ndash;199 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRajan, D. K. et al. Monitoring pH, temperature and humidity in long-term stem cell culture in CO 2 incubator. in. \u003cem\u003eIEEE International Symposium on Medical Measurements and Applications (MeMeA)\u003c/em\u003e 470\u0026ndash;474 (IEEE, 2017). (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/MeMeA.2017.7985922\u003c/span\u003e\u003cspan address=\"10.1109/MeMeA.2017.7985922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith, R. L., Demers, C. J. \u0026amp; Collins, S. D. Microfluidic device for the combinatorial application and maintenance of dynamically imposed diffusional gradients. \u003cem\u003eMicrofluid Nanofluid\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 613\u0026ndash;622 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoman, K. A. et al. Flow-enhanced vascularization and maturation of kidney organoids in vitro. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 255\u0026ndash;262 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosen, Y. et al. Incubator-Free Organoid Culture in a Sealed Recirculatory System.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrunner, M., Doppler, P., Klein, T., Herwig, C. \u0026amp; Fricke, J. Elevated pCO. \u003cem\u003eaffects lactate metabolic shift CHO cell. Cult. processes Eng. Life Sci.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 204\u0026ndash;214 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiura, Y. \u0026amp; Pașca, S. P. Mapping human brain organoids on a spatial atlas. \u003cem\u003eCell. Stem Cell.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 983\u0026ndash;984 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChu, J. \u0026amp; Anderson, S. A. Development of Cortical Interneurons. \u003cem\u003eNeuropsychopharmacol\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 16\u0026ndash;23 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePavon, N. \u003cem\u003eet al. Patterning Ganglionic Eminences in Developing Human Brain Organoids Using Morphogen Gradient Inducing Device\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biorxiv.org/lookup/doi/\u003c/span\u003e\u003cspan address=\"http://biorxiv.org/lookup/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2023.06.20.545705\u003c/span\u003e\u003cspan address=\"10.1101/2023.06.20.545705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e doi:10.1101/2023.06.20.545705.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKazim, S. F., Enam, S. A. \u0026amp; Shamim, M. S. Possible detrimental effects of neurosurgical irrigation fluids on neural tissue: an evidence based analysis of various irrigants used in contemporary neurosurgical practice. \u003cem\u003eInt. J. Surg.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 586\u0026ndash;590 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAF4115T-GRFBY \u0026mdash;. Dino-Lite Digital Microscope | Americas. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dinolite.us/products/usb-microscopes/af4115t-grfby/\u003c/span\u003e\u003cspan address=\"https://www.dinolite.us/products/usb-microscopes/af4115t-grfby/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHernandez, S. et al. Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly. 05.01.651773 Preprint at (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2025.05.01.651773\u003c/span\u003e\u003cspan address=\"10.1101/2025.05.01.651773\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLei, Y., Shkolnikov, V. \u0026amp; Xin, D. 3D Biological Cell Reconstruction with Multi-View Geometry. in \u003cem\u003eIEEE 17th International Symposium on Biomedical Imaging (ISBI)\u003c/em\u003e 495\u0026ndash;498 (IEEE, 2020). 495\u0026ndash;498 (IEEE, 2020). (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ISBI45749.2020.9098564\u003c/span\u003e\u003cspan address=\"10.1109/ISBI45749.2020.9098564\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTiburcius, S. et al. Egg-yolk core\u0026ndash;shell mesoporous silica nanoparticles for high doxorubicin loading and delivery to prostate cancer cells. \u003cem\u003eNanoscale\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 6830\u0026ndash;6845 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSher, J., Miller, C. \u0026amp; Sharma, D. Effect of Bisphosphonates on the Osteogenic Activity of Osteoprogenitor Cells Cultured on Titanium Surfaces. \u003cem\u003eInt. J. Oral Maxillofac. Implants\u003c/em\u003e. \u003cb\u003e35\u003c/b\u003e, 939\u0026ndash;947 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMunro, T., Miller, C. M., Antunes, E. \u0026amp; Sharma, D. Interactions of Osteoprogenitor Cells with a Novel Zirconia Implant Surface. \u003cb\u003eJFB 11\u003c/b\u003e, 50 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhaduri, A. et al. Cell stress in cortical organoids impairs molecular subtype specification. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e578\u003c/b\u003e, 142\u0026ndash;148 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMazziotta, M. \u0026amp; Pareto, A. Normalization methods for spatio-temporal analysis of environmental performance: Revisiting the Min\u0026ndash;Max method. \u003cem\u003eEnvironmetrics\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, e2730 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiang, Y. et al. Generation and Fusion of Human Cortical and Medial Ganglionic Eminence Brain Organoids. \u003cem\u003eCurr. Protoc. Stem Cell. Biol.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, e61 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiandomenico, S. L., Sutcliffe, M. \u0026amp; Lancaster, M. A. Generation and long-term culture of advanced cerebral organoids for studying later stages of neural development. \u003cem\u003eNat. Protoc.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 579\u0026ndash;602 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSohn, L. L. et al. How Can Microfluidic and Microfabrication Approaches Make Experiments More Physiologically Relevant? \u003cem\u003eCell. Syst.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 209\u0026ndash;211 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmirifar, L. et al. Brain-on-a-chip: Recent advances in design and techniques for microfluidic models of the brain in health and disease. \u003cem\u003eBiomaterials\u003c/em\u003e \u003cb\u003e285\u003c/b\u003e, 121531 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRifes, P. et al. Modeling neural tube development by differentiation of human embryonic stem cells in a microfluidic WNT gradient. \u003cem\u003eNat. Biotechnol.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 1265\u0026ndash;1273 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuong, D. N. A. et al. Induction of inverted morphology in brain organoids by vertical-mixing bioreactors. \u003cem\u003eCommun. Biol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 1213 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalek, M. M., Sattari, P. \u0026amp; Martinuzzi, R. J. Analysis of Fluid Flow and Wall Shear Stress Patterns Inside Partially Filled Agitated Culture Well Plates. \u003cem\u003eAnn. Biomed. Eng.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 707\u0026ndash;728 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoto-Silva, L. et al. Computational fluid dynamic analysis of physical forces playing a role in brain organoid cultures in two different multiplex platforms. \u003cem\u003eBMC Dev. Biol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 3 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurasiewicz, H. et al. Engineering considerations on the use of liquid/liquid two-phase systems as a cell culture platform: Engineering considerations on the use of liquid/liquid two-phase systems. \u003cem\u003eJ. Chem. Technol. Biotechnol.\u003c/em\u003e \u003cb\u003e92\u003c/b\u003e, 1690\u0026ndash;1698 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemers, C. J. et al. Development-on-chip: \u003cem\u003ein vitro\u003c/em\u003e neural tube patterning with a microfluidic device. \u003cem\u003eDevelopment\u003c/em\u003e \u003cb\u003e143\u003c/b\u003e, 1884\u0026ndash;1892 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmin, N. D. \u003cem\u003eet al. Generating Human Neural Diversity with a Multiplexed Morphogen Screen in Organoids\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biorxiv.org/lookup/doi/10.1101/\u003c/span\u003e\u003cspan address=\"http://biorxiv.org/lookup/doi/10.1101/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023). \u003cdiv class=\"ExternalRefDOI\"\u003e2023.05.31.541819\u003c/div\u003e doi:10.1101/2023.05.31.541819.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWolak, D. J. \u0026amp; Thorne, R. G. Diffusion of macromolecules in the brain: implications for drug delivery. \u003cem\u003eMol. Pharm.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1492\u0026ndash;1504 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuschhof, J. et al. Derivation of snake venom gland organoids for in vitro venom production. \u003cem\u003eNat. Protoc.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1494\u0026ndash;1510 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, Y. et al. Intracellular pH dynamics regulates intestinal stem cell lineage specification. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 3745 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorello, U. \u0026amp; Pierani, A. Patterning the cerebral cortex: traveling with morphogens. \u003cem\u003eCurr. Opin. Genet. Dev.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 408\u0026ndash;415 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKutys, M. L. et al. Uncovering mutation-specific morphogenic phenotypes and paracrine-mediated vessel dysfunction in a biomimetic vascularized mammary duct platform. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 3377 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue, X. et al. A Patterned Human Neural Tube Model Using Microfluidic Gradients. \u003cem\u003eNature\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-024-07204-7\u003c/span\u003e\u003cspan address=\"10.1038/s41586-024-07204-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez, C. F. et al. Breaking the clean room barrier: exploring low-cost alternatives for microfluidic devices. \u003cem\u003eFront. Bioeng. Biotechnol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1176557 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEffects of Sterilization Methods on the Integrity and Functionality of Covalent Mucin Coatings on Medical Devices. - Rickert \u0026ndash;\u0026thinsp;2022 - Advanced Materials Interfaces - Wiley Online Library. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://advanced.onlinelibrary.wiley.com/doi/full/\u003c/span\u003e\u003cspan address=\"https://advanced.onlinelibrary.wiley.com/doi/full/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/admi.202101716\u003c/span\u003e\u003cspan address=\"10.1002/admi.202101716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLess is More. Oligomer extraction and hydrothermal annealing increase PDMS bonding forces for new microfluidics assembly and for biological studies | bioRxiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biorxiv.org/content/10.1101/150953v1.full\u003c/span\u003e\u003cspan address=\"https://www.biorxiv.org/content/10.1101/150953v1.full\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeo, Y. S. et al. Characterization and Resolution of Evaporation-Mediated Osmolality Shifts that Constrain Microfluidic Cell Culture in Poly(dimethylsiloxane) Devices. \u003cem\u003eAnal. Chem.\u003c/em\u003e \u003cb\u003e79\u003c/b\u003e, 1126\u0026ndash;1134 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChuah, Y. J. et al. Simple surface engineering of polydimethylsiloxane with polydopamine for stabilized mesenchymal stem cell adhesion and multipotency. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 18162 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark, Y. et al. Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters. \u003cem\u003eCell. Rep. Methods\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, 100686 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKadoshima, T. et al. Self-organization of axial polarity, inside-out layer pattern, and species-specific progenitor dynamics in human ES cell\u0026ndash;derived neocortex. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 110, 20284\u0026ndash;20289 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosch, J. C., Neal, E. H., Balikov, D. A., Rahim, M. \u0026amp; Lippmann, E. S. CRISPR-Mediated Isogenic Cell-SELEX Approach for Generating Highly Specific Aptamers Against Native Membrane Proteins. \u003cem\u003eCel Mol. Bioeng.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 559\u0026ndash;574 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003eTheory of Nonparametric Tests\u003c/em\u003e. (Springer Berlin Heidelberg, New York, NY, (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcMurtrey, R. J. Analytic Models of Oxygen and Nutrient Diffusion, Metabolism Dynamics, and Architecture Optimization in Three-Dimensional Tissue Constructs with Applications and Insights in Cerebral Organoids. \u003cem\u003eTissue Eng. Part. C: Methods\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e, 221\u0026ndash;249 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatrachari, A. R., Podichetty, J. T. \u0026amp; Madihally, S. V. Application of computational fluid dynamics in tissue engineering. \u003cem\u003eJ. Biosci. Bioeng.\u003c/em\u003e \u003cb\u003e114\u003c/b\u003e, 123\u0026ndash;132 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamanujan, S. et al. Diffusion and Convection in Collagen Gels: Implications for Transport in the Tumor Interstitium. \u003cem\u003eBiophys. J.\u003c/em\u003e \u003cb\u003e83\u003c/b\u003e, 1650\u0026ndash;1660 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShin, W. et al. Spatiotemporal Gradient and Instability of Wnt Induce Heterogeneous Growth and Differentiation of Human Intestinal Organoids. \u003cem\u003eiScience\u003c/em\u003e 23, 101372 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaglam-Metiner, P. et al. Spatio-temporal dynamics enhance cellular diversity, neuronal function and further maturation of human cerebral organoids. \u003cem\u003eCommun. Biol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 173 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLomax, H., Pulliam, T. H. \u0026amp; Zingg D. W. Fundamentals of Computational Fluid Dynamics.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003eComputational Fluid Dynamics for Engineers\u003c/em\u003e. (Cambridge University Press, Cambridge; New York, (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neural Development, Brain Organoid, Microfluidics, Stem Cells, Automation, Cell culture","lastPublishedDoi":"10.21203/rs.3.rs-7313439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7313439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOrganoids, 3D tissue cultures that mimic real organs, offer valuable models for research. Traditional culture methods rely on manual feeding and orbital shakers, making them labor-intensive and inconsistent. Microfluidic systems have shown their potential to improve reproducibility by controlling media exchange and culture conditions, yet most still require standard incubators, which limit continuous monitoring due to space and humidity constraints. To address this, we developed a modular platform that integrates automated feeding, real-time imaging, and environmental control, eliminating the need for a conventional incubator. A key feature is a vertically oriented PDMS/glass chip that supports precise media delivery and monitoring while preserving incubation conditions, making it ideal for morphological studies. We demonstrated the platform\u0026rsquo;s ability to maintain metabolic stability and media distribution over time using cerebral organoids. This platform improves organoid research by combining microfluidics, automation, and imaging, enhancing disease modeling, drug testing, and regenerative medicine applications.\u003c/p\u003e","manuscriptTitle":"A Modular Platform for Automated Organoid Culture and Longitudinal Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:15:00","doi":"10.21203/rs.3.rs-7313439/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T10:53:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T23:00:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T14:53:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T03:10:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193785879137318686071042341551528104769","date":"2025-11-04T23:18:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91652811873273625814029563575465675763","date":"2025-11-03T06:41:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239726913937463956131354878280704078827","date":"2025-11-03T02:53:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T12:16:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-11T01:28:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-09T09:45:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b32abc7b-10bb-4079-b6d6-afcc9a88ea22","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57447718,"name":"Biological sciences/Biological techniques"},{"id":57447719,"name":"Biological sciences/Biotechnology"},{"id":57447720,"name":"Physical sciences/Engineering"},{"id":57447721,"name":"Health sciences/Health care"},{"id":57447722,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-02-23T16:08:22+00:00","versionOfRecord":{"articleIdentity":"rs-7313439","link":"https://doi.org/10.1038/s41598-026-40231-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-18 15:58:24","publishedOnDateReadable":"February 18th, 2026"},"versionCreatedAt":"2025-11-10 05:15:00","video":"","vorDoi":"10.1038/s41598-026-40231-0","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40231-0","workflowStages":[]},"version":"v1","identity":"rs-7313439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7313439","identity":"rs-7313439","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}