Measurement Of Cell Concentration Research Articles

Assessing the suitability of cell counting methods during different stages of a cell processing workflow using an ISO 20391-2 guided study design and analysis.

Cell counting is a fundamental measurement for determining viable cell numbers in biomanufacturing processes. The properties of different cell types and the range of intended uses for cell counts within a biomanufacturing process can lead to challenges in identifying suitable counting methods for each potential application. This is further amplified by user subjectivity in identifying the cells of interest and further identifying viable cells. Replacement of traditionally used manual counting methods with automated systems has alleviated some of these issues. However, a single cell type can exhibit different physical properties at various stages of cell processing which is further compounded by process impurities such as cell debris or magnetic beads. These factors make it challenging to develop a robust cell counting method that offers a high level of confidence in the results. Several initiatives from standards development organizations have attempted to address this critical need for standardization in cell counting. This study utilizes flow-based and image-based methods for the quantitative measurement of cell concentration and viability in the absence of a reference material, based on the tools and guidance provided by the International of Standards (ISO) and the US National Institute of Standards and Technology (NIST). Primary cells were examined at different stages of cell processing in a cell therapy workflow. Results from this study define a systematic approach that enables the identification of counting methods and parameters that are best suited for specific cell types and workflows to ensure accuracy and consistency. Cell counting is a foundational method used extensively along various steps of cell and gene therapy. The standard used in this study may be applied to other cell and gene therapy processes to enable accurate measurement of parameters required to guide critical decisions throughout the development and production process. Using a framework that confirms the suitability of the cell counting method used can minimize variability in the process and final product.

Cytotoxic T Lymphocytes Control Growth of B16 Tumor Cells in Collagen-Fibrin Gels by Cytolytic and Non-Lytic Mechanisms.

Cytotoxic T lymphocytes (CTLs) are important in controlling some viral infections, and therapies involving the transfer of large numbers of cancer-specific CTLs have been successfully used to treat several types of cancers in humans. While the molecular mechanisms of how CTLs kill their targets are relatively well understood, we still lack a solid quantitative understanding of the kinetics and efficiency by which CTLs kill their targets in vivo. Collagen-fibrin-gel-based assays provide a tissue-like environment for the migration of CTLs, making them an attractive system to study T cell cytotoxicity in in vivo-like conditions. Budhu.et al. systematically varied the number of peptide (SIINFEKL)-pulsed B16 melanoma cells and SIINFEKL-specific CTLs (OT-1) and measured the remaining targets at different times after target and CTL co-inoculation into collagen-fibrin gels. The authors proposed that their data were consistent with a simple model in which tumors grow exponentially and are killed by CTLs at a per capita rate proportional to the CTL density in the gel. By fitting several alternative mathematical models to these data, we found that this simple "exponential-growth-mass-action-killing" model did not precisely describe the data. However, determining the best-fit model proved difficult because the best-performing model was dependent on the specific dataset chosen for the analysis. When considering all data that include biologically realistic CTL concentrations (E≤107cell/mL), the model in which tumors grow exponentially and CTLs suppress tumor's growth non-lytically and kill tumors according to the mass-action law (SiGMA model) fit the data with the best quality. A novel power analysis suggested that longer experiments (∼3-4 days) with four measurements of B16 tumor cell concentrations for a range of CTL concentrations would best allow discriminating between alternative models. Taken together, our results suggested that the interactions between tumors and CTLs in collagen-fibrin gels are more complex than a simple exponential-growth-mass-action killing model and provide support for the hypothesis that CTLs' impact on tumors may go beyond direct cytotoxicity.

Predictive Cell Culture Time Evolution Based on Electric Models.

Obtaining cell concentration measurements from a culture assay by using bioimpedance is a very useful method that can be used to translate impedances to cell concentration values. The purpose of this study was to find a method to obtain the cell concentration values of a given cell culture assay in real time by using an oscillator as the measurement circuit. From a basic cell-electrode model, enhanced models of a cell culture immersed in a saline solution (culture medium) were derived. These models were used as part of a fitting routine to estimate the cell concentration in a cell culture in real time by using the oscillation frequency and amplitude delivered by the measurement circuits proposed by previous authors. Using real experimental data (the frequency and amplitude of oscillations) that were obtained by connecting the cell culture to an oscillator as the load, the fitting routine was simulated, and real-time data of the cell concentration were obtained. These results were compared to concentration data that were obtained by using traditional optical methods for counting. In addition, the error that we obtained was divided and analyzed in two parts: the first part of the experiment (when the few cells were adapting to the culture medium) and the second part of the experiment (when the cells exponentially grew until they completely covered the well). Low error values were obtained during the growth phase of the cell culture (the relevant phase); therefore, the results obtained were considered promising and show that the fitting routine is valid and that the cell concentration can be measured in real time by using an oscillator.

A Rapid Assessment of Microalgal Concentration Using Turbidity Measurement for Shellfish Hatchery Seed Production

The goal of this study was to develop a rapid assessment of microalgal concentration using a turbidity meter to serve commercial shellfish hatcheries for feeding shellfish larvae and broodstock. We used four commonly used algal species, Tetraselmis suecica, Isochrysis galbana, Chaetoceros calcitrans, and Chaetoceros gracilis, generating serial dilutions of each and measuring cell concentrations and turbidity of each sample. We identified linear correlations between cell concentration and turbidity and established standard equations between cell concentration and turbidity for each species. We sampled and quantified algae from a shellfish hatchery using these equations and compared hemocytometer counts. No differences were found (P ≥ 0.174), indicating the accuracy of the equations. We expect that this method can provide a quick, accurate, easy method for shellfish hatcheries to quantify algal concentration and avoid either over- or underfeeding larvae and broodstock.

Lens Free Holographic Imaging for Urinary Tract Infection Screening.

The diagnosis of urinary tract infection (UTI) currently requires precise specimen collection, handling infectious human waste, controlled urine storage, and timely transportation to modern laboratory equipment for analysis. Here we investigate holographic lens free imaging (LFI) to show its promise for enabling automatic urine analysis at the patient bedside. We introduce an LFI system capable of resolving important urine clinical biomarkers such as red blood cells, white blood cells, crystals, and casts in 2 mm thick urine phantoms. This approach is sensitive to the particulate concentrations relevant for detecting several clinical urine abnormalities such as hematuria and pyuria, linearly correlating to ground truth hemacytometer measurements with R 2 = 0.9941 and R 2 = 0.9973, respectively. We show that LFI can estimate E. coli concentrations of 10 3 to 10 5 cells/mL by counting individual cells, and is sensitive to concentrations of 10 5 cells/mL to 10 8 cells/mL by analyzing hologram texture. Further, LFI measurements of blood cell concentrations are relatively insensitive to changes in bacteria concentrations of over seven orders of magnitude. Lastly, LFI reveals clear differences between UTI-positive and UTI-negative urine from human patients. LFI is sensitive to clinically-relevant concentrations of bacteria, blood cells, and other sediment in large urine volumes. Together, these results show promise for LFI as a tool for urine screening, potentially offering early, point-of-care detection of UTI and other pathological processes.

Au/Ni Coaxial Nanopillar Microarray Electrodes for Red Blood Cell Concentration Detection

In this study, an Au/Ni nanopillar microarray electrode for red blood cell (RBC) concentration detection was proposed. Electrodes were fabricated using anodic aluminum oxide (AAO) as the template, followed by photolithography, electroforming, and gold immersion to form an Au/Ni coaxial nanopillar array electrode. The diameter of the Au/Ni nanopillar microarray electrode was <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$50\,\mu \text{m}$ </tex-math></inline-formula> . The average size of the coaxial nanopillar was measured to be approximately 140 nm, indicating that the thickness of the gold shell was approximately 25 nm. The weight and atomic percentage ratio of Au/Ni were measured to be 84.94/19.06 and 55.87/44.13, respectively. The stochastic collision nanoelectrochemistry (SCNEC) of insulating particles, along with chronoamperometry, was implemented to measure cell concentration. The characteristics of the fabricated electrode were first verified by the cell concentration detection of KU812. The subsequent whole blood sample detection verified that the proposed sensing scheme has two linear detection ranges, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times 10^{\sf 7}$ </tex-math></inline-formula> –2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times \,\,10^{\sf 9}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 10^{\sf 9}$ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.88\times 10^{\sf 9}$ </tex-math></inline-formula> cells/mL, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula> of 0.999 and 0.999, respectively. The detection recovery rates of the two blood samples were measured to be 102.4% and 109.8%, respectively. The experimental results confirmed that the fabricated electrodes are feasible for detecting RBC concentrations in real blood samples.

Non-invasive real-time monitoring of cell concentration and viability using Doppler ultrasound.

Bioprocess optimization towards higher productivity and better quality control relies on real-time process monitoring tools to measure process and culture parameters. Cell concentration and viability are among the most important parameters to be monitored during bioreactor operations that are typically determined using optical methods on an extracted sample. In this paper, we have developed an online non-invasive sensor to measure cell concentration and viability based on Doppler ultrasound. An ultrasound transducer is mounted outside the bioreactor vessel and emits a high frequency tone burst (15 MHz) through the vessel wall. Acoustic backscatter from cells in the bioreactor depends on cell concentration and viability. The backscattered signal is collected through the same transducer and analyzed using multivariate data analysis (MVDA) to characterize and predict the cell culture properties. We have developed accurate MVDA models to predict the Chinese hamster ovary (CHO) cell concentration in a broad range from 0.1×106 cells/mL to 100×106 cells/mL, and cell viability from 3% to 99%. The non-invasive monitoring is ideal for single use bioreactor and the in-situ measurements removes the burden for offline sampling and dilution steps. This method can be similarly applied to other suspension cell culture modalities.

Absorption Spectra Description for T-Cell Concentrations Determination and Simultaneous Measurements of Species during Co-Cultures

Advanced Therapy Medicinal Products are promising drugs for patients in therapeutic impasses. Their complex fabrication process implies regular quality controls to monitor cell concentration. Among the different methods available, optical techniques offer several advantages. Our study aims to measure cell concentration in real time in a potential closed-loop environment using white light spectroscopy and to test the possibility of simultaneously measuring concentrations of several species. By analyzing the shapes of the absorption spectra, this system allowed the quantification of T-cells with an accuracy of about 3% during 30 h of cultivation monitoring and 26 h of doubling time, coherent with what is expected for normal cell culture. Moreover, our system permitted concentration measurements for two species in reconstructed co-cultures of T-cells and Candida albicans yeasts. This method can now be applied to any single or co-culture, it allows real-time monitoring, and can be easily integrated into a closed system.

Cell monitoring with optical coherence tomography

Background aimsWe evaluated a commercially available instrument, OCTiCell (chromologic.com/octicell), for monitoring cell growth in suspended agitated bioreactors based on optical coherence tomography. OCTiCell is an in-line, completely non-invasive instrument that can operate on any suspended-cell bioreactor with a window or transparent wall. In traditional optical coherence tomography, the imaging beam is rastered over the sample to form a three-dimensional image. OCTiCell, instead uses a fixed imaging beam and takes advantage of the motion of the media to move the cells across the interrogating optical beam. ResultsWe found strong correlations between the non-invasive, non-contact, reagent-free OCTiCell measurements of cell concentration and viability and those obtained from the automated cell counter, and the XTT viability assay, which is a colorimetric assay for quantifying metabolic activity. ConclusionsThis novel cell monitoring method can adapt to different bioreactor form factors and could reduce the labor cost and contamination risks associated with cell growth monitoring.

Muc5b plays a role in the development of inflammation and fibrosis in hypersensitivity pneumonitis induced by Saccharopolyspora rectivirgula.

Previously we have shown that a gain-of-function MUC5B promoter variant (rs35705950) is the strongest risk factor for the development of idiopathic pulmonary fibrosis. We have also found that Muc5b overexpression reduces mucociliary clearance in mice, potentially leading to recurrent injury to the bronchoalveolar epithelia. Hypersensitivity pneumonitis (HP) is induced by inhalation of numerous causative antigens that may be affected by mucociliary clearance. We conducted this study to determine the role of Muc5b in a mouse model of HP induced by Saccharopolyspora rectivirgula (SR) antigen. We used Muc5b-deficient and wild-type (WT) mice to determine whether Muc5b plays a role in inflammation and fibrosis at 3 and 6 wk in an SR model of HP. We measured cell concentrations and MUC5B expression in whole lung lavage (WLL) and quantified fibrosis using hydroxyproline assay and second harmonic generation. Muc5b expression in WLL fluid was significantly increased in SR-exposed WT mice compared with saline controls. WT mice challenged with SR developed more inflammation and lung fibrosis at 6 wk compared with 3 wk postexposure. Moreover, we found that 6 wk following challenge with SR, Muc5b-deficient mice had less lung inflammation and less lung fibrosis than Muc5b WT mice. Furthermore, Muc5b-deficient mice had significantly lower concentrations of TGF-β1 in the WLL compared with Muc5b WT mice at 6 wk of exposure. Muc5b appears to play a role in fibrosis in the animal model of HP and this may have implications for HP in humans.

Abstract 2748: High-speed and high-precision FL-based cell count and viability assays using the Cellaca™ MX high-throughput cell counter

Abstract With multiple FDA-approved cell therapy products available and others in pre-clinical and clinical trials, now, more than ever, it is critical to provide fast and accurate measurements of cell concentration and viability. For certain clinical products, cell concentration is synonymous with dosage, and proper cell viability is crucial to avoidance of potential harmful side effects. The complex nature of patient-derived samples makes legacy cell analysis methods such as the trypan blue exclusion assay difficult or impossible to perform. Nonspecific objects such as RBCs and cellular debris can significantly increase cell counting variation. Meanwhile, the need for cell-based products that are custom-tailored to each patient has resulted in the analysis of smaller and more numerous batches, leading to cell counting bottlenecks. With the challenges of messy samples and counting bottlenecks in mind, we investigate the use of the Cellaca™ MX, a cell counter that can increase throughput and employ fluorescence-based cell counting methods. The high-throughput cell counter can utilize 5 fluorescence imaging channels for cell count and viability analysis in less than 3 min for 24 samples. In addition, fluorescent cell-based assays such as apoptosis (Annexin V or Caspase 3/7), cell cycle, reactive oxygen species measurement can be performed to better characterize the target cell samples. In this work, we demonstrate the capabilities of Cellaca™ MX for high-throughput fluorescence-based cell counting. Comparison among multiple instruments using Jurkat cells and fluorescent beads reveals high consistency between replicate counts, between Cellaca consumable plates, and between instruments. We also compare cell counts made on the Cellaca™ MX with manual counts performed using a hemocytometer, as well as counts performed using other fluorescence-based automated cell counting methods. In addition, we illustrate the use of the ISO 20391-II cell counting standard to evaluate and compare the performance of cell counting methods. Finally, we include real-world primary T cell linearity results over a 4-log range of concentration. The results of the experiments confirm the suitability of the Cellaca™ MX for general fluorescence-based assays applicable to cell therapy-related workflows. Citation Format: Jordan Bell, Yongyang Huang, Sun Yung, Henry Qazi, Charles Hernandez, Bo Lin, Jean Qiu, Leo Chan. High-speed and high-precision FL-based cell count and viability assays using the Cellaca™ MX high-throughput cell counter [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2748.

E.coli biofilm formation and its susceptibility towards T4 bacteriophages studied in a continuously operating mixing - tubular bioreactor system.

SummaryA system consisting of a connected mixed and tubular bioreactor was designed to study bacterial biofilm formation and the effect of its exposure to bacteriophages under different experimental conditions. The bacterial biofilm inside silicone tubular bioreactor was formed during the continuous pumping of bacterial cells at a constant physiological state for 2 h and subsequent washing with a buffer for 24 h. Monitoring bacterial and bacteriophage concentration along the tubular bioreactor was performed via a piercing method. The presence of biofilm and planktonic cells was demonstrated by combining the piercing method, measurement of planktonic cell concentration at the tubular bioreactor outlet, and optical microscopy. The planktonic cell formation rate was found to be 8.95 × 10−3 h−1 and increased approximately four‐fold (4×) after biofilm exposure to an LB medium. Exposure of bacterial biofilm to bacteriophages in the LB medium resulted in a rapid decrease of biofilm and planktonic cell concentration, to below the detection limit within < 2 h. When bacteriophages were supplied in the buffer, only a moderate decrease in the concentration of both bacterial cell types was observed. After biofilm washing with buffer to remove unadsorbed bacteriophages, its exposure to the LB medium (without bacteriophages) resulted in a rapid decrease in bacterial concentration: again below the detection limit in < 2 h.

Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level

Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing HABs. Continuous monitoring based on grab sampling is time-consuming, costly, and labor-intensive. However, improving simulation performance remains a major challenge in modeling, and current methods are limited to simulating phytoplankton (e.g., Microcystis sp., Anabaena sp., Aulacoseira sp., Cyclotella sp., Pediastrum sp., and Eudorina sp.) and zooplankton (e.g., Cyclotella sp., Pediastrum sp., and Eudorina sp.) at the genus level. The traditional modeling approach is limited for evaluating the interactions between phytoplankton and zooplankton. Recently, deep learning (DL) models have been proposed for solving modeling problems because of their large data handling capabilities and model structure flexibilities. In this study, we evaluated the applicability of DL for simulating phytoplankton at the phylum/class and genus levels and zooplankton at the genus level. Our work was an explicit representation of the taxonomic and ecological hierarchy of the DL model structure. The prerequisite for this model design was the data collection at two taxonomic and hierarchical levels. Our model consisted of hierarchical DL with classification transformer (TF) and regression TF models. These DL models were hierarchically connected; the output of the phylum/class level model was transferred to the genus level simulation model, and the output of the genus level model was fed into the zooplankton simulation model. The classification TF model determined the phytoplankton occurrence initiation date, whereas the regression TF model quantified the cell concentration of plankton. The hierarchical DL showed potential to simulate phytoplankton at the phylum/class and genus levels by producing average R2, and root mean standard error values of 0.42 and 0.83 [log(cells mL−1)], respectively. All simulated plankton results closely matched the measured concentrations. Particularly, the simulated cyanobacteria showed good agreement with the measured cell concentration, with an R2 value of 0.72. In addition, our simulated result showed good agreement in peak concentration compared to observations. However, a limitation remained in following the temporal variation of Tintinnopsis sp. and Bosmia sp. Using an importance map from the TF model, water temperature, total phosphorus, and total nitrogen were identified as significant variables influencing phytoplankton and zooplankton blooms. Overall, our study demonstrated that DL can be used for modeling HABs at the phylum/class and genus levels.

Rapid in measurements of brown tide algae cell concentrations using fluorescence spectrometry and generalized regression neural network

The frequent occurrence of brown tide pollution in recent years has brought great losses to the economy of coastal areas. Therefore, accurate and efficient detection of the brown tide algae cell concentration is of great significance to the prevention of marine environmental pollution. In this paper, a combination of three-dimensional fluorescence spectroscopy and generalized regression neural network is used to detect the concentration of Aureococcus anophagefferens (A. anophagefferens). Firstly, the fluorescence spectrometer was used to collect spectra of A. anophagefferens with different growth cycles and different concentrations. In order to reduce the complexity of fluorescence spectral data and improve the efficiency of model calculation, the gradient boosting decision tree (GBDT) algorithm is used to rank the importance of spectral features, and select spectral features with great importance as input and concentration of algal cells as output. In view of the nonlinear relationship between input and output, a generalized regression neural network model optimized by the improved sparrow search algorithm (FASSA-GRNN) was established to predict the concentration of algae cells, The model results show that MSE is 0.0046, MAE is 0.0569, and R2 is 0.9955. In addition, the FASSA-GRNN model is compared with the prediction results of the SSA-GRNN, GWO-GRNN, and GRNN models. The results show that the prediction accuracy of FASSA-GRNN is better than other models, and the improved sparrow search algorithm (FASSA) can reach the global optimum faster during the training process. This research provides a new method for predicting the concentration of algae cells.

Colony Forming Potential and Protein Composition of Commercial Umbilical Cord Allograft Products in Comparison With Autologous Orthobiologics

Background: Umbilical cord (UC) connective tissues contain plastic-adherent, colony forming unit–fibroblasts (CFU-Fs) amenable to culture expansion for potential therapeutic use. Recently, UC-derived allograft products have been made available to practitioners in orthopaedics and other specialties, by companies purporting “stem cell”–based healing. However, such marketing claims conflict with existing regulations for these human tissues, generating questions over the cellular and protein composition of current commercially available UC allograft products. Purpose: To evaluate commercial UC allograft products for viable cells, CFU-Fs, and protein makeup. Study Design: Descriptive laboratory study. Methods: Five commercial UC allograft products claiming to contain viable, undescribed “stem cells,” 2 obtained from UC blood (UCB) and 3 from UC tissue (UCT), were analyzed. Image-based methods were used to measure cell concentration and viability, a traditional CFU-F assay was used to evaluate in vitro behavior indicative of a connective tissue progenitor cell phenotype often referred to as mesenchymal stem/stromal cells, and quantitative immunoassay arrays were used to measure a combination of cytokines and growth factors. Bone marrow concentrate (BMC) and plasma derived from the blood and bone marrow of middle-aged individuals served as comparative controls for cell culture and protein analyses, respectively. Results: Viable cells were identified within all 5 UC allograft products, with those derived from UCB having greater percentages of living cells (40%-59%) than those from UCT (1%-22%). Compared with autologous BMC (>95% viability and >300 million living cells), no CFU-Fs were observed within any UC allograft product (<15 million living cells). Moreover, a substantial number of proteins, particularly those within UCB allograft products, were undetectable or present at lower concentrations compared with blood and bone marrow plasma controls. Interestingly, several important growth factors and cytokines, including basic fibroblast growth factor, hepatocyte growth factor, interleukin-1 receptor antagonist, and osteoprotegerin, were most prevalent in 1 or more UCT allograft products as compared with blood and bone marrow plasma. Conclusion: CFU-Fs, often referred to as stem cells, were not found within any of the commercial UC allograft products analyzed, and clinicians should remain wary of marketing claims stating otherwise. Clinical Relevance: Any therapeutic benefit of current UC allograft products in orthopaedic medicine is more likely to be attributed to their protein composition (UCT > UCB) or inclusion of cells without colony forming potential (UCB > UCT).

Effect of nitrogen, phosphorus, and sulfur on the start-up of a biological 1,4-dioxane removal process using Pseudonocardia sp. D17

Nitrogen (N), phosphorus (P), and sulfur (S) are the major essential nutrients required for stable 1,4-dioxane treatment by the cylindrical-type bioreactor using gel-entrapped Pseudonocardia sp. D17. In this paper, the minimum concentrations of N, P, and S were determined in a series of continuous feeding start-up tests. When the influent 1,4-dioxane concentration was 20 mg L−1, concentrations of greater than 2.0 mg-N L−1, 0.4 mg-P L−1, and 0.1 mg-S L−1 were required to smoothly start the bioreactor to maintain the effluent 1,4-dioxane level lower than 0.5 mg L−1 (i.e., the Japanese effluent standard level). These essential concentrations indicate that the feed of the essential nutrients requires the ratio of 1,4-dioxane: N: P: S = 20: 2.0: 0.4: 0.1 for efficient treatment. The bioreactor could be successfully started with higher concentrations of 1,4-dioxane (i.e., up to 100 mg L−1) when the essential nutrients were fed at this ratio. The measurement of cell concentration in the gel beads indicated that 7.3 × 107 CFU (mL-beads)−1 is the reference value to achieve efficient 1,4-dioxane removal. The bioreactor started with sufficient nutrient levels demonstrated stable treatment performance even when severe nutrient limitations were provided after the start-up.

Multi-volume hemacytometer

Cell counting has become an essential method for monitoring the viability and proliferation of cells. A hemacytometer is the standard device used to measure cell numbers in most laboratories which are typically automated to increase throughput. The principle of both manual and automated hemacytometers is to calculate cell numbers with a fixed volume within a set measurement range (105 ~ 106 cells/ml). If the cell concentration of the unknown sample is outside the range of the hemacytometer, the sample must be prepared again by increasing or decreasing the cell concentration. We have developed a new hemacytometer that has a multi-volume chamber with 4 different depths containing different volumes (0.1, 0.2, 0.4, 0.8 µl respectively). A multi-volume hemacytometer can measure cell concentration with a maximum of 106 cells/ml to a minimum of 5 × 103 cells/ml. Compared to a typical hemacytometer with a fixed volume of 0.1 µl, the minimum measurable cell concentration of 5 × 103 cells/ml on the multi-volume hemacytometer is twenty times lower. Additionally, the Multi-Volume Cell Counting model (cell concentration calculation with the slope value of cell number in multi-chambers) showed a wide measurement range (5 × 103 ~ 1 × 106 cells/ml) while reducing total cell counting numbers by 62.5% compared to a large volume (0.8 µl-chamber) hemacytometer.

Analysis of methods for quantifying yeast cell concentration in complex lignocellulosic fermentation processes

Cell mass and viability are tightly linked to the productivity of fermentation processes. In 2nd generation lignocellulose-based media quantitative measurement of cell concentration is challenging because of particles, auto-fluorescence, and intrinsic colour and turbidity of the media. We systematically evaluated several methods for quantifying total and viable yeast cell concentrations to validate their use in lignocellulosic media. Several automated cell counting systems and stain-based viability tests had very limited applicability in such samples. In contrast, manual cell enumeration in a hemocytometer, plating and enumeration of colony forming units, qPCR, and in situ dielectric spectroscopy were further investigated. Parameter optimization to measurements in synthetic lignocellulosic media, which mimicked typical lignocellulosic fermentation conditions, resulted in statistically significant calibration models with good predictive capacity for these four methods. Manual enumeration of cells in a hemocytometer and of CFU were further validated for quantitative assessment of cell numbers in simultaneous saccharification and fermentation experiments on steam-exploded wheat straw. Furthermore, quantitative correlations could be established between these variables and in situ permittivity. In contrast, qPCR quantification suffered from inconsistent DNA extraction from the lignocellulosic slurries. Development of reliable and validated cell quantification methods and understanding their strengths and limitations in lignocellulosic contexts, will enable further development, optimization, and control of lignocellulose-based fermentation processes.

High-speed and high-precision fluorescence-based cell count and viability assays using the Cellaca&amp;[trade] MX high-throughput cell counter

Abstract With multiple FDA-approved cell therapy products available and others in pre-clinical and clinical trials, now, more than ever, it is critical to provide fast and accurate measurements of cell concentration and viability. For certain clinical products, cell concentration is synonymous with dosage, and accurate cell viability is crucial for avoidance of harmful side effects. The complex nature of patient-derived samples makes legacy cell analysis methods such as the trypan blue dye exclusion assay difficult to perform. Nonspecific objects such as RBCs and cellular debris can significantly increase cell counting variation. Meanwhile, the need for cell-based products that are custom-tailored to each patient has increased the number of samples requiring analysis, leading to cell counting bottlenecks. With these challenges in mind, we investigate the performance of the Cellaca™ MX. This high-throughput cell counter captures images in up to 5 FL colors for cell count and viability analysis in under 3 min for 24 samples. Comparison among multiple instruments using Jurkat cells and fluorescent beads reveals high consistency between replicate counts, between plates, and between instruments. We also compare cell counts conducted on the Cellaca™ MX with counts performed using other FL-based automated cell counting methods, as well as manual counting. In addition, we illustrate the use of the new ISO cell counting standards to evaluate and compare the performance of cell counting methods. Finally, we include real-world primary T cell linearity results over a 4-log range of concentration. The results of the experiments confirm the suitability of the Cellaca™ MX for general FL-based assays applicable to cell therapy-related workflows.

Development of Digital Image Processing as an Innovative Method for Activated Sludge Biomass Quantification

Activated sludge process is the most common method for biological treatment of industrial and municipal wastewater. One of the most important parameters in performance of activated sludge systems is quantitative monitoring of biomass to keep the cell concentration in an optimum range. In this study, a novel method for activated sludge quantification based on image processing and RGB analysis is proposed. According to the results, the intensity of blue color in the macroscopic image of activated sludge culture can be a very accurate index for cell concentration measurement and R2 coefficient, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) which are 0.990, 2.000, 0.323, and 13.848, respectively, prove this claim. Besides, in order to avoid the difficulties of working in the three-parameter space of RGB, converting to grayscale space has been applied which can estimate cell concentration with R2 = 0.99. Ultimately, an exponential correlation between RGB values and cell concentrations in lower amounts of biomass has been proposed based on Beer-Lambert law which can estimate activated sludge biomass concentration with R2 = 0.97 based on B index.