Cell Culture Monitoring Research Articles

Application of semi-quantitative loop-mediated isothermal amplification for gene expression study in Expi293 cells.

BACKGROUND: Mammalian cell cultures play a key role in the pharmaceutical industry, requiring constant monitoring of the cell conditions during fermentation. In addition to monitoring of the physical-chemical parameters, it is important to evaluate the internal state of cells, for which gene expression analysis is used. Currently, quantitative reverse transcription polymerase chain reaction (qPCR) is the dominant method. However, the loop-mediated isothermal amplification (LAMP) technique also attracts attention due to its high specificity, sensitivity and reaction rate. LAMP is becoming a promising tool for rapid analysis of gene expression, especially under the conditions of limited biological material or a large volume of samples. AIM: Development of a technique for quantifying the expression level of target genes in human cell culture Expi293. METHODS: For LAMP, a recombinant large fragment of Bacillus stearothermophilus DNA polymerase (Bst-pol) was obtained, purified, and optimal reaction conditions were determined. SYBR Green I and LUCS13 were used as intercalating dyes. The amplification parameters for different concentrations of the enzyme and the dye were analyzed. Standard SYBR Green I kits were used for qPCR. Both methods were compared when analyzing the expression of IGF 1, FGF2 and EIF3i genes in cell lines with an increased expression level of these genes. RESULTS: It has been shown that using LUCS13 dye provides the classic S-shape of the signal accumulation curve in LAMP, while using SYBR Green I dye causes artifacts. The optimal concentration of Bst-pol was 40 ng/µl. When comparing the two methods, it was found that LAMP has greater sensitivity, allows determining gene expression with an accuracy comparable to qPCR, demonstrating a shorter reaction time (up to 35 minutes). CONCLUSION: Although quantitative PCR remains the main method for assessing the level of gene expression, loop-mediated isothermal amplification offers a number of advantages that make it an attractive alternative for various biotechnological purposes. Due to its high speed, ease of execution and accessibility, as well as high sensitivity and specificity, LAMP is a valuable technique for rapid analysis of gene expression during cell culture monitoring.

Device for automated aseptic sampling: Automated sampling solution for future cell and gene manufacturing.

Cell sampling is a key step performed regularly throughout the cell manufacturing process to gather cell samples for cell growth, progress, and characteristics analysis. While the current method of sampling by pipetting in a biosafety cabinet is commonly used, it is labour-intensive and susceptible to contamination risks. We have developed Device for Automated Aseptic Sampling (DAAS), to enable automated, small volume (0.02-1.00mL) aseptic sampling with minimal dead volume primarily for cell and gene therapy manufacturing. The aim of DAAS is to enable an accurate and consistent sampling process, with minimal contamination risks and interruption to the cells in culture. DAAS can potentially interface with other automated solutions to enable automated and streamlined cell manufacturing workflow and reduce overall manufacturing costs. DAAS has been verified as an aseptic sampling solution via repeated microbial ingression tests. It has also been tested for achieving comparable cell density and viability compared to manual pipetting, with negligible cross-sample carryover when used to sample Jurkat cells of different cell concentrations. The application of using DAAS to sample cell periodically and monitor cell growth and viability continuously for prolonged cell culture was successfully demonstrated with Jurkat cell culture in a static culture flask and donor T cell culture in an automated bioreactor system over a culture duration of 10days in a Biosafety Level-2 laboratory. Overall, DAAS presents great potential as an automated and aseptic sampling solution, offering cell and gene therapy manufacturers easier and more frequent access to cell samples with minimal interruptions to the cell culture. This enables close monitoring of cell culture and a more automated, connected and cost-effect cell and gene therapy manufacturing process.

Improving Raman-Based Models for Real-Time Monitoring the CHO Cell Culture Process with Effective Variable Selection Strategies

Research has shown that Raman spectroscopy could be applied to monitor various components in mammalian cell culture in real time. In the process of application, it is necessary to ensure the performance of the Raman-based model. The variable selection strategy is an effective method that significantly influences the model performance and simplification. In this study, different variable selection strategies were evaluated, and the optimal variable selection strategy was determined for monitoring the CHO cell culture process. Firstly, a wide variety of spectral regions involving the Raman fingerprinting region and the C-H stretching region were investigated. Secondly, six different variable selection algorithms were meticulously assessed. Thirdly, the combination of different variable selection algorithms was used to improve model performance and simplify the model. Finally, the monitoring of cell culture processes was implemented. The findings underscored that commonly used spectral regions could improve the model performance but could not simplify the model well. Moving-window partial least square (MWPLS), genetic algorithm (GA), and random frog (RF) are more suitable for Raman modeling of the cell culture process, but they must be used after the spectral region selection. The combination of three variable selection algorithms (MWPLS-GA-RF) improved the model’s performance by 16–70% by selecting 30–60 variables, effectively simplifying the model. For glucose, lactate, viable cell density, and ammonium ion, real-time monitoring was performed well. This study will be helpful for researchers to select suitable variable selection strategies for building models for the real-time monitoring of cell culture.

Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment

Manufacturing regenerative medicine requires continuous monitoring of pluripotent cell culture and quality assessment while eliminating cell destruction and contaminants. In this study, we employed a novel method to monitor the pluripotency of stem cells through image analysis, avoiding the traditionally used invasive procedures. This approach employs machine learning algorithms to analyze stem cell images to predict the expression of pluripotency markers, such as OCT4 and NANOG, without physically interacting with or harming cells. We cultured induced pluripotent stem cells under various conditions to induce different pluripotent states and imaged the cells using bright-field microscopy. Pluripotency states of induced pluripotent stem cells were assessed using invasive methods, including qPCR, immunostaining, flow cytometry, and RNA sequencing. Unsupervised and semi-supervised learning models were applied to evaluate the results and accurately predict the pluripotency of the cells using only image analysis. Our approach directly links images to invasive assessment results, making the analysis of cell labeling and annotation of cells in images by experts dispensable. This core achievement not only contributes for safer and more reliable stem cell research but also opens new avenues for real-time monitoring and quality control in regenerative medicine manufacturing. Our research fills an important gap in the field by providing a viable, noninvasive alternative to traditional invasive methods for assessing pluripotency. This innovation is expected to make a significant contribution to improving regenerative medicine manufacturing because it will enable a more detailed and feasible understanding of cellular status during the manufacturing process.

Monitoring of three-dimensional live-cell cultures using a multimode, multiscale imaging system combining confocal fluorescence microscopy and optical coherence microscopy

Live-cell monitoring involves long-term observation and analysis of living cells in tissue cultures, which develop cells or tissues in an artificial environment and are essential for tissue engineering. Fluorescence microscopy (FM) is widely used as a monitoring tool owing to its powerful ability to visualize protein distribution within cells and tissues, thereby providing information on their functions in biological processes. To reduce the photobleaching effect, which is a major limiting factor in FM, FM is generally combined with other imaging modalities, such as phase contrast and differential interference contrast microscopy, which are nondestructive to fluorochromes, to selectively use fluorescence signals only in specific areas of interest within a sample. However, these methods are restricted to thin samples and cannot produce depth-resolved images. Here, we propose a method to determine three-dimensional (3D) positions in volumetric samples for application in high-resolution 3D fluorescence imaging using a multimode and multiscale imaging system that combines optical coherence microscopy (OCM) and line confocal FM (LC–FM). We also demonstrate the benefits of multimodal imaging in 3D cell culture monitoring. To evaluate the performance of the proposed system and method, we rapidly and accurately located the regions of interest in 3D tissue culture models, such as tumor spheroids and microvascular bed, and instantaneously acquired volumetric high-resolution fluorescence images. We also used an integrated system to monitor tumor spheroids mixed with extracellular matrix (ECM) over five days of the culture process, and the FM–OCM integrated technique successfully imaged the overall 3D structures, such as the distribution and boundaries of the spheroids and ECM as well as stained tumor cells. This complementary information is useful for understanding the relationship between cellular behaviors, such as the proliferation and migration of tumor cells, and microenvironments, such as the ECM.

A hierarchical porous structure integrated three-dimensional electrochemical biosensor for cell culture and real-time monitoring

Three-dimensional (3D) porous scaffolds are arousing tremendous interests in tissue engineering, in-vitro biological models and drug discovery for the potential on integration in-vivo 3D culture stimulation and real-time electrochemical sensing. However, the development on this field is still hindered by the lack of basic 3D conductive materials, unsatisfactory electrochemical performance and complicated construction strategy. Herein, we developed a hierarchical porous 3D electrochemical biosensor with facile preparation strategy for cell culture and real-time detection of cell-released molecules. In this biosensor, the micropores and nanopores were concurrently integrated within the Ni foam 3D porous scaffold, through which the fast mass transfer and the large specific surface area can be accomplished. As a proof of concept, the sensing materials, Pt nanoparticles were functionalized on the 3D porous scaffold. The fabricated biosensor presented a wide linear range from 200 nM to 20 μM with a high sensitivity of 423.1 nA/μM toward hydrogen peroxide. More importantly, HeLa cells cultured thereon demonstrated high viability and the cell-released H2O2 were also real-time monitored, illustrating its great application prospects on the in-vivo 3D modeling systems.

Development of generic metabolic Raman calibration models using solution titration in aqueous phase and data augmentation for in-line cell culture analysis.

This study presents a novel approach for developing generic metabolic Raman calibration models for in-line cell culture analysis using glucose and lactate stock solution titration in an aqueous phase and data augmentation techniques. First, a successful set-up of the titration method was achieved by adding glucose or lactate solution at several different constant rates into the aqueous phase of a bench-top bioreactor. Subsequently, the in-line glucose and lactate concentration were calculated and interpolated based on the rate of glucose and lactate addition, enabling data augmentation and enhancing the robustness of the metabolic calibration model. Nine different combinations of spectra pretreatment, wavenumber range selection, and number of latent variables were evaluated and optimized using aqueous titration data as training set and a historical cell culture data set as validation and prediction set. Finally, Raman spectroscopy data collected from 11 historical cell culture batches (spanning four culture modes and scales ranging from 3to 200 L) were utilized to predict the corresponding glucose and lactate values. The results demonstrated a high prediction accuracy, with an average root mean square errors of predictionof 0.65 g/L for glucose, and 0.48 g/L for lactate. This innovative method establishes a generic metabolic calibration model, and its applicability can be extended to other metabolites, reducing the cost of deploying real-time cell culture monitoring using Raman spectroscopy in bioprocesses.

Comprehensive cell culture monitoring: Leveraging in-line Raman spectroscopy for enhanced biopharmaceutical manufacturing insights

Many biopharmaceutical manufacturers and researchers seek a comprehensive understanding and real-time monitoring of cell growth and metabolism to enhance the cell culture process. However, the biological complexity of cells and the intricacies of cell culture process have impeded the development of analytical and monitoring techniques. This study aims to present a complete picture of cell culture processes using in-line Raman spectroscopy. Initially, we proposed an efficient method for discriminating and removing anomalous spectra. Subsequently, Raman-based models for 27 components including amino acid, organic acid, lipid, organic alkali, alkaloid, alcohol, saccharide, biomass, physicochemical parameter, ion, and protein titer were established. The Q2 obtained by these models was greater than 0.8, and the relative percent difference (RPD) from external validation was greater than 2.0 except for glucose. Additionally, the proposed control charts effectively monitored normal batches and abnormal conditions such as bacterial contamination and insufficient feeding. In cases of unexplained culture abnormalities, the proposed workflow offered explanations.

Influence of the internal structure of straight microchannels on inertial transport behavior of particles

The rapid advancement of Micro-Electro-Mechanical Systems (MEMS) technology has established microfluidics as a pivotal field. This technology marks the onset of a new era in various applications, including drug testing, cell culture, and disease monitoring, underscoring its extensive practicality and potential for future exploration. This research delves into the intricate behavior of particle inertial migration within microchannels, particularly focusing on the impact of different channel structures and Reynolds numbers (Re). Our studies reveal that particles in microchannels with one-sided sharp-cornered microstructures migrate towards the sharp corner at a relative position of 0.4 under low flow rates, and towards the straight wall side at a relative position of 0.8 under high flow rates. The migration pattern of equilibrium positions varies with different arrangements of sharp-corner structures, achieving stability at the channel's center only when the sharp corners are symmetrically arranged on both sides. Our investigation into the shape of microstructures indicates that sharp-cornered structures generate a more stable secondary flow compared to rectangular and semi-circular structures, preventing particle aggregation at the outlet. To address the challenges associated with handling variable cross-section geometries and solid-wall boundaries in dissipative particle dynamics methods effectively, we have developed a dissipative particle dynamics model specifically for analyzing such microchannels. Building upon our previous research, this model introduces a conservative force coefficient for particles within the microstructured region and an interaction zone that only involves repulsive forces, aligning well with experimental outcomes. Through the study of microstructures' geometric shapes, this paper offers guidance for designing microchannels for particle enrichment. Furthermore, the dissipative particle dynamics model established for the particle flow and solid structure interaction within microstructured channels provides insights into the mesoscale dynamics of liquid-solid two-phase flow and particle motion. In conclusion, this paper aims to enhance particle motion sample preparation techniques, thereby broadening the scope of microfluidic applications in the biomedical field.

Detecting abnormal cell behaviors from dry mass time series

The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96% in the automatic detection of anomalies. Additionally, anomaly detection was also associated with cell measurement errors inherent to the acquisition or analysis pipelines, leading to an improvement of the upstream methods for feature extraction. Our results pave the way to novel architectures for the continuous monitoring of cell cultures in applied research or bioproduction applications, and for the prediction of pathological cellular changes.

Long-term stable pH sensor array with synergistic bilayer structure for 2D real-time mapping in cell culture monitoring

Pursuing accurate, swift, and durable pH sensors is important across numerous fields, encompassing healthcare, environmental surveillance, and agriculture. In particular, the emphasis on real-time pH monitoring during cell cultivation has become increasingly pronounced in the current scientific environment—a crucial element being diligently researched to ensure optimal cell production. Both polyaniline (PANi) and iridium oxide (IrOx) show their worth in pH sensing, yet they come with challenges. Single-PANi-layered pH sensors often grapple with diminished sensitivity and lagging responses, while electrodeposited IrOx structures exhibit poor adhesion, leading to their separation from metallic substrates—a trait undesirable for a consistently stable, long-term pH sensor. This paper introduces a bi-layered PANi-IrOx pH sensor, strategically leveraging the advantages of both materials. The results presented here underscore the sensitivity enhancement of binary-phased framework, faster response time, and more robust structure than prior work. Through this synergistic strategy, we demonstrate the potential of integrating different phases to overcome the inherent constraints of individual materials, setting the stage for advanced pH-sensing solutions.

Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method.

Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

Robust platform for inline Raman monitoring and control of perfusion cell culture.

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process. However, implementation of Raman for perfusion cell culture has been very limited mainly due to challenges with high-cell density and long running times during perfusion which cause extremely high fluorescence interference to Raman spectra and consequently it is exceedingly difficult to develop robust chemometrics models. In this work, a platform based on Raman measurement of permeate has been proposed for effective analysis of perfusion process. It has been demonstrated that this platform can effectively circumvent the fluorescence interference issue while providing rich and timely information about perfusion dynamics to enable efficient process monitoring and robust bioreactor feed control. With the highly consistent spectral data from cell-free sample matrix, development of chemometrics models can be greatly facilitated. Based on this platform, Raman models have been developed for good measurement of several analytes including glucose, lactate, glutamine, glutamate, and permeate titer. Performance of Raman models developed this way has been systematically evaluated and the models have shown good robustness against changes in perfusion scale and variations in permeate flowrate; thus models developed from small lab scale can be directly transferred for implementation in much larger scale of perfusion. With demonstrated robustness, this platform provides a reliable approach for automated glucose feed control in perfusion bioreactors. Glucose model developed from small lab scale has been successfully implemented for automated continuous glucose feed control of perfusion cell culture at much larger scale.

Large-scale smart bioreactor with fully integrated wireless multivariate sensors and electronics for long-term in situ monitoring of stem cell culture.

Achieving large-scale, cost-effective, and reproducible manufacturing of stem cells with the existing devices is challenging. Traditional single-use cell-bag bioreactors, limited by their rigid and single-point sensors, struggle with accuracy and scalability for high-quality cell manufacturing. Here, we introduce a smart bioreactor system that enables multi-spatial sensing for real-time, wireless culture monitoring. This scalable system includes a low-profile, label-free thin-film sensor array and electronics integrated with a flexible cell bag, allowing for simultaneous assessment of culture properties such as pH, dissolved oxygen, glucose, and temperature, to receive real-time feedback for up to 30 days. The experimental results show the accurate monitoring of time-dynamic and spatial variations of stem cells and myoblast cells with adjustable carriers from a plastic dish to a 2-liter cell bag. These advances open up the broad applicability of the smart sensing system for large-scale, lower-cost, reproducible, and high-quality engineered cell manufacturing for broad clinical use.

Compact lens-free imager using a thin-film transistor for long-term quantitative monitoring of stem cell culture and cardiomyocyte production.

With advancements in human induced pluripotent stem cell (hiPSC) technology, there is an increasing demand for quality control techniques to manage the long-term process of target cell production effectively. While monitoring systems designed for use within incubators are promising for assessing culture quality, existing systems still face challenges in terms of compactness, throughput, and available metrics. To address these limitations, we have developed a compact and high-throughput lens-free imaging device named INSPCTOR. The device is as small as a standard culture plate, which allows for the installation of multiple units within an incubator. INSPCTOR utilises a large thin-film transistor image sensor, enabling simultaneous observation of six independent culture environments, each approximately 1 cm2. With this device, we successfully monitored the confluency of hiPSC cultures and identified the onset timing of epithelial-to-mesenchymal transition during mesodermal induction. Additionally, we quantified the beating frequency and conduction of hiPSC-derived cardiomyocytes by using high-speed imaging modes. This enabled us to identify the onset of spontaneous beating during differentiation and assess chronotropic responses in drug evaluations. Moreover, by tracking beating frequency over 10 days of cardiomyocyte maturation, we identified week-scale and daily-scale fluctuations, the latter of which correlated with cellular metabolic activity. The metrics derived from this device would enhance the reproducibility and quality of target cell production.

Electric Cell-Substrate Impedance Sensing as a Tool to Characterize Wound Healing Dynamics.

The electric cell-substrate impedance sensing (ECIS) is a well-established technique that allows for the real-time monitoring of cell cultures growing on gold-electrodes embedded in culture dishes. Its foundation lays on the insulating effect that cells present against the free-flow of electrons, as these passive electrical properties generate a characteristic complex impedance spectrum when a small-amplitude, non-invasive alternating current (AC) is provided through the electrodes, the living cells, and the culture media in the culture ware. In addition, it possesses the ability to create a wound that is highly confined to the electrode area by simply increasing the amplitude of the AC current in dependence of the pre-resistor strength for a defined pulse duration and at a specific frequency. Therefore, it represents a controlled and reproducible tool to carry out in vitro wound healing experiments. Accordingly, in this methods protocol, the use of the ECIS will be described in the context of the wound healing research: cardiac 3T3 fibroblasts will be wounded and their recovery dynamics analyzed based on the typical methodologies applied to the processing of ECIS data. In addition, cellular micromotions will be evaluated. Finally, fluorescence immunostaining of ECIS samples will be described in order to showcase the potential of the ECIS in combination with other well-established techniques to add further knowledge depth to the understanding of the complex wound healing dynamics.

Validation of the cell culture monitoring using a Raman spectroscopy calibration model developed with artificially mixed samples and investigation of model learning methods using initial batch data.

The development of calibration models using Raman spectra data has long been challenged owing to the substantial time and cost required for robust data acquisition. To reduce the number of experiments involving actual incubation, a calibration model development method was investigated by measuring artificially mixed samples. In this method, calibration datasets were prepared using spectra from artificially mixed samples with adjusted concentrations based on design of experiments. The precision of these calibration models was validated using the actual cell culture sample. The results showed that when the culture conditions were unchanged, the root mean square error of prediction (RMSEP) of glucose, lactate, and antibody concentrations was 0.34, 0.33, and 0.25g/L, respectively. Even when variables such as cell line or culture mediawere changed, the RMSEPs of glucose, lactate, and antibody concentrations remained within acceptable limits, demonstrating the robustness of the calibration models with artificially mixed samples. To further improve accuracy, a model training method for small datasets was also investigated. The spectral pretreatment conditions were optimized using error heat maps based on the first batch of each cell culture condition and applied these settings to the second and third batches. The RMSEPs improved for glucose, lactate, and antibody concentration, with values of 0.44, 0.19, and 0.18g/L under constant culture conditions, 0.37, 0.12, and 0.12g/L for different cell lines, and 0.26, 0.40, and 0.12g/L when the culture media was changed. These results indicated the efficacy of calibration modeling with artificially mixed samples for actual incubations under various conditions.

Advances in Sensor Developments for Cell Culture Monitoring (4/2023)

Advances in Sensor Developments for Cell Culture Monitoring (4/2023)

Automated cell counting for Trypan blue-stained cell cultures using machine learning.

Cell counting is a vital practice in the maintenance and manipulation of cell cultures. It is a crucial aspect of assessing cell viability and determining proliferation rates, which are integral to maintaining the health and functionality of a culture. Additionally, it is critical for establishing the time of infection in bioreactors and monitoring cell culture response to targeted infection over time. However, when cell counting is performed manually, the time involved can become substantial, particularly when multiple cultures need to be handled in parallel. Automated cell counters, which enable significant time reduction, are commercially available but remain relatively expensive. Here, we present a machine learning (ML) model based on YOLOv4 that is able to perform cell counts with a high accuracy (>95%) for Trypan blue-stained insect cells. Images of two distinctly different cell lines, Trichoplusia ni (High FiveTM; Hi5 cells) and Spodoptera frugiperda (Sf9), were used for training, validation, and testing of the model. The ML model yielded F1 scores of 0.97 and 0.96 for alive and dead cells, respectively, which represents a substantially improved performance over that of other cell counters. Furthermore, the ML model is versatile, as an F1 score of 0.96 was also obtained on images of Trypan blue-stained human embryonic kidney (HEK) cells that the model had not been trained on. Our implementation of the ML model comes with a straightforward user interface and can image in batches, which makes it highly suitable for the evaluation of multiple parallel cultures (e.g. in Design of Experiments). Overall, this approach for accurate classification of cells provides a fast, bias-free alternative to manual counting.

A Cell State Monitoring System with Integrated In Situ Imaging and pH Detection

Cell models are one of the most widely used basic models in biological research, and a variety of in vitro cell culture techniques and models have been developed recently to simulate the physiological microenvironment in vivo. However, regardless of the technique or model, cell culture is the most fundamental but crucial component. As a result, we have developed a cell culture monitoring system to assess the functional status of cells within a biochip. This article focuses on a mini-microscope made from a readily available camera for in situ continuous observation of cell growth within a biochip and a pH sensor based on optoelectronic sensing for measuring pH. With the aid of this monitoring system, scientists can keep an eye on cell growth in real time and learn how the pH of the culture medium affects it. This study offers a new approach for tracking cells on biochips and serves as a valuable resource for enhancing cell culture conditions.