Automated Cell Counting Method Research Articles

An automated approach for hemocytometer cell counting based on image-processing method

Evaluating cell viability plays an important role in routine cell culture, toxicology, and drug development. Trypan blue stain is one of the simplest assays that are used to determine the number of viable cells in a cell suspension. The stained cell suspension can then be counted under a microscope using a hemocytometer. Hemocytometers have a fixed volume, making it possible to calculate the concentration of cells in the sample and measure viability. Most of the related studies are conducted using manual counting methods. However, manual counting is time-consuming and laborious. While various automated cell counting devices have been proposed, they tend to be too expensive for widespread deployment. Although low-cost automated cell counting methods presented in the literatures provide high accuracy and cost-effective alternatives, only cells in the field of view of microscope with unknown volume are counted. This will make the concentration inestimable. Accordingly, the present study proposes a low-cost but accurate method for hemocytometer cell counting using an image-processing approach. The accuracy of the proposed method is demonstrated by comparing the cell counting results obtained for human cervical carcinoma (HeLa) cells with the manual counts. It is shown that the proposed approach has a mean absolute percentage error of less than 6.26 % for samples containing a mixture of live and dead cells. Moreover, the proposed system is equipped with a simple and intuitive human machine interface such that the counting task can be performed by individuals with no prior experience in the image-processing field.

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.

Validation of an automated cell counting method for cGMP manufacturing of human induced pluripotent stem cells

Human induced pluripotent stem cells (hiPSCs) must be manufactured as advanced therapy medicinal products (ATMPs) for innovative tissue replacement clinical applications. Yet, production of hiPSCs under current Good Manufacturing Practice (cGMP) presents many hurdles, such as the large-scale cell expansion needed to reach therapeutically-relevant hiPSC doses. For the monitoring of this phase, a fast and reliable cell counting method should be used. Conventional manual cell counting by the hemocytometer method is dependent on the operator's expertise and is time-consuming. Therefore, automation of sample preparation and analysis is needed to improve precision and rapidity of hiPSC cell counting. We investigated whether an automated cell counting method could be validated for use with hiPSCs, in comparison with a reference cell counting method included in the European Pharmacopeia, 10th edition. The proposed method was the fluorescence imaging-based NucleoCounter NC-100 system, whereas the reference method was manual cell counting using a Bürker hemocytometer. The validation strategy complied with EudraLex cGMP regulations for ATMP manufacturing and ICH Q2(R1) indications for validation of analytical methods. The use of the NucleoCounter NC-100 system for automated cell counting was validated, focusing on accuracy, specificity, intra- and inter-operator reproducibility, range and linearity, showing higher precision than the manual method. The automated method can be used more effectively than the manual one for hiPSC cell counting. Thus, this piece of work paves the way for all cGMP facilities that want to pursue hiPSC manufacturing for clinical use.

High-speed and high-precision fluorescence-based cell count and viability assays using the Cellaca&[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.

A robust and automated cell counting method in quantification of digital breast cancer immunohistochemistry images.

Quantitative analysis ofimmunohistochemically stained breast cancer specimens by cell counting is important for prognosis and treatment planning. This paper presents arobust, accurate, and novel method to label immunopositive and immunonegative cells automatically. During preprocessing, we developed anadaptive method to correct thecolour aberration caused by imaging conditions. Next, apixel-level segmentation was performed on preprocessed images using asupport vector machine with aradial basis function kernel in HSV colour space. Thesegmentation result was processed by mathematical morphology operations to correct error-segmented regions and extract themarker for each cell. Validation studies showed that theautomated cell-counting method had divergences varying from -5.05% to 3.99% compared with manual counting by apathologist, indicating considerable agreement ofthepresent automated cell counting method with manual counting. Thus, this method can free pathologists from laborious work and can potentially improve theaccuracy and thereproducibility ofdiagnosis.

Implementation of deep neural networks to count dopamine neurons in substantia nigra.

Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene‐function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and time‐consuming. The development of neural networks and deep learning has opened a new way to teach computers to count neurons. Implementation of a programming paradigm enables a computer to learn from the data and development of an automated cell counting method. The advantages of computerized counting are reproducibility, elimination of human error and fast high‐capacity analysis. We implemented whole‐slide digital imaging and deep convolutional neural networks (CNN) to count substantia nigra dopamine neurons. We compared the results of the developed method against independent manual counting by human observers and validated the CNN algorithm against previously published data in rats and mice, where tyrosine hydroxylase (TH)‐immunoreactive neurons were counted using unbiased stereology. The developed CNN algorithm and fully cloud‐embedded Aiforia™ platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.

Validation of NucleoCounter, an Automated Cell Counter

IntroductionThe most commonly used cell counting method is hemocytometer (HC) trypan dye exclusion due to its low cost and versatility. However, alternative cell counting methods are available for use when large numbers of samples are required to be analyzed rapidly.The NucleoCounter (NC) is an automated cell counter with an optimal counting range of 5E4–5E6 total cells/mL. It utilizes cassettes containing two different DNA‐intercalating fluorescent dyes to stain viable cells green and non‐viable cells blue.Developing a validation method is crucial to introducing an automated cell counter into industrial processes. ICH Q2 guidelines require accuracy and other parameters to be addressed when validating a new method. However, it is difficult to execute accuracy in the absence of a known reference standard. Therefore, a novel approach to augment accuracy determination was employed.While other reports have evaluated accuracy for trypan blue automated cell counters using bead suspensions, a suitable fluorescent bead reference standard was not available for the NC. Therefore, the addition of parallelism was considered an appropriate approach to augment the percent recovery calculations.Parallelism studies were carried out by spiking target cell numbers on top of an existing cell standard curve to create a new target cell standard curve. The two curves generated (original and spiked) would be expected to have similar slopes if the counting method is accurate across the range tested.MethodsPreliminary studies conducted compared HC cell counts to the NC across the 5E5–5E6 total cells/mL range.To execute parallelism, a stock single‐cell suspension of VTL C3A cells (a human hepatoblastoma‐derived cell line) was used to create a 5‐point (5.2E4–4E6 cells/mL) cell concentration standard curve and was counted on the NC. The measured standard curve concentrations and dilution factors were used to calculate the average target cell stock concentration. Next, a target spike of 0.75E6 cells/mL was added to each standard sample and the spiked curve was counted. The percent recovery (actual spiked/target spiked concentration) was calculated at each concentration. A similar parallelism approach was utilized to assess % viability accuracy, in which a non‐viable cell stock (created by treatment in 1% Saponin) was mixed with a viable cell stock in order to generate a reduced viability 5‐point (50–100% viability) standard curve.ResultsPreliminary studies comparing HC to NC cell counts demonstrated that NC exhibits better precision, linearity, and was slightly more accurate at higher concentration of cells.Comparison of the original and spiked standard curves displayed parallelism as evidenced by non‐statistically different slopes (p=0.694) (Figure 1). The % recovery across all concentrations was between 80–130%, which is within acceptable ranges for automated cell counting methods. The standard curves for % viability accuracy assessments (original and reduced viability) were also shown to be parallel to one another with similar slopes (p=0.338).ConclusionsThe results from these studies demonstrated that the NC automated cell counting instrument could be validated using the novel approach of parallelism to augment accuracy determinations in the absence of a reference standard.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Development and Efficacy Testing of a “Hollow Awl” That Leads to Patent Bone Marrow Channels and Greater Mesenchymal Stem Cell Mobilization During Bone Marrow Stimulation Cartilage Repair Surgery

To investigate whether microfracture with a cannulated hollow awl can yield more patent marrow channels and allow greater mobilization of the reparable cells to the defect compared to the conventional awl with blunt end in human knee joints. Patients who were planned for 1-stage bilateral total knee arthroplasty due to degenerative osteoarthritis with well-preserved lateral femoral condylar cartilage were retrospectively included. A 10-mm× 20-mm, rectangular, full-thickness chondral defect was made on the lateral femoral condyle on each knee joint. The 6-holed microfracture procedure, each at 9mm depth and 3mm separation of perforations, was followed using a hollow awl in one knee and using a conventional awl in the other knee, respectively. The bleeding through the microfracture holes was observed and collected using an absorbable gelatin sponge and was analyzed microscopically by colony forming unit-fibroblast assays and automated cell counting method. The representative 3 bony samples of the distal lateral femoral condyles obtained were also scanned with micro-computed tomography (micro-CT) for morphometric analysis (percent bone volume, trabecular separation, and total porosity) of subchondral bone microarchitecture of the microfracture holes. Twenty-two patients were enrolled, and the mean age was 70.8 ± 6.1 (58-83) years. Compared with the conventional awl group, the hollow awl group had a significantly greater amount of bleeding (1.8 ± 0.2g vs 1.1 ± 0.1g; P< .001) and a greater number of mesenchymal stem cells in the blood clot (21,474.0 ± 3,911.1 vs 13,329.7 ± 3,311.0; P= .004). The hollow awl group also showed overall more patent marrow channels around the adjacent subchondral bone of the microfracture hole, with greater trabecular separation on micro-CT analysis (P < .001). Compared to the conventional awl, microfracture with a cannulated hollow awl can yield more patent marrow channels at the adjacent subchondral bone of the microfracture hole and result in greater mobilization of the reparable cells to the defect in human knee joints. Level III, therapeutic case control.

Evaluation of a Methodology for Automated Cell Counting for Streak Mode Imaging Flow Cytometry

Identification of Circulating Tumor Cells (CTCs) has shown promising clinical applications, but since CTCs are found in very low concentration in blood large sample volumes are needed for meaningful enumeration. This issue impedes the analysis of CTCs using standard flow cytometry due to its low throughput. To address this issue, a high throughput microfluidic cytometer was recently developed using a wide field flow- flow cell instead of the conventional narrow hydrodynamic focusing cells (used in traditional flow cytometry) enabling analysis of large volumes at lower flow rate. This wide-field flow cytometer adopts a technique known as “streak photography” where exposure times and flow velocities are set such that the particles are imaged as short “streaks”. Since streaks are imaged with large number of pixels, they are easily distinguished from the noise which appears as “speckles” increasing the detection capabilities of the device, making it more suitable for analysis using current low sensitivity, high noise webcams or mobile phone cameras. The non-stationary nature of the high noisy background found in streak cytometry introduces additional challenges for automated cell counting methods using traditional cell detection techniques such TLC, CellProfiler, CellTracker and other tools based in traditional edge detection (e.g., Canny based filters) or manual thresholding. In order to address this issue, we developed a new automated enumeration approach that does not rely on edge detection or manual thresholding of individual cells, rather is based in image quantizing, morphological operations, 2D order-statistic filtering and decisions rules that take into account knowledge of the structure and expected location of the streaks in consecutive frames. We evaluated our approach comparing it with two current methods representing the major computational modalities for cell detection: CellTrack (based in edge detection) and MTrack2 (based in manual thresholding). Samples of 1 cell/mL nominal concentration were analyzed in batch size of 30 mL at flow rate of 10 mL/min and imaged at 4 frames per second (fps), the files were saved in uncompressed AVI format files. The cells were annotated and the signal to noise ratio (SNR) was calculated. For samples with average SNR greater than 4.4 dB, our method achieved a sensitivity of 91% compared to CellTrack (60%) and MTrack2 (71%). The True Positive Rate (TPR) of cells detected was 0.93 for our method compared with 0.80 for Mtrack2 and 0.29 for CellTrack. This demonstrated the ability of the algorithm to count rare cells with high accuracy for concentrations of 1 cell/mL with SNR greater than 4.4 dB. This cell counting capability will enable to automate low cost imaging flow cytometry based on CCD detector and the expansion of cell-based clinical diagnostics in resource-poor settings.

세포막 추출과 역추적 알고리즘 기반의 HeLa 세포 이미지 자동 셀 카운팅 기법

Cell counting is extensively used to analyze cell growth in biomedical research, and as a result automated cell counting methods have been developed to provide a more convenient and means to analyze cell growth. However, there are still many challenges to improving the accuracy of the cell counting for cells that proliferate abnormally, divide rapidly, and cluster easily, such as cancer cells. In this paper, we present an automated cell counting method for HeLa cells, which are used as reference for cancer research. We recognize and classify the morphological conditions of the cells by using a cell segmentation algorithm based on cell membrane extraction, and we then apply a cell back-tracking algorithm to improve the cell counting accuracy in cell clusters that have indistinct cell boundary lines. The experimental results indicate that our proposed segmentation method can identify each of the cells more accurately when compared to existing methods and, consequently, can improve the cell counting accuracy.

Fructooligosaccharides decreased the ability of probiotic Escherichia coli Nissle 1917 to adhere to co-cultures of human intestinal cell lines

In the present study, we investigated effects of prebiotic fructooligosaccharides (FOS) on the ability of Escherichia coli Nissle 1917 (EcN), a probiotic bacterial strain, to adhere to co-cultures of human intestinal cells (Caco-2 and HT29-MTX). For the bacterial adhesion assay, the co-cultures were seeded together at a ratio of 3:1 and then incubated EcN in the presence of various prebiotics including FOS. To eliminate possible bias, both manual and automated cell counting methods were utilized. In results, FOS significantly suppressed the adhesion abilities of probiotic EcN to the co-cultures. Taken altogether, we carefully suggest that an unbalanced supplementation of prebiotics may adversely impact on gut microflora; hence, further investigations are warranted in regards to such unexpected adverse effects and responsible mechanisms on colonization of probiotics in the gastrointestinal tract utilizing in vivo models.

Trypanosoma cruzi infectivity assessment in “in vitro” culture systems by automated cell counting

Chagas disease is an endemic, neglected tropical disease in Latin America that is caused by the protozoan parasite Trypanosoma cruzi. In vitro models constitute the first experimental approach to study the physiopathology of the disease and to assay potential new trypanocidal agents.Here, we report and describe clearly the use of commercial software (MATLAB®) to quantify T. cruzi amastigotes and infected mammalian cells (BeWo) and compared this analysis with the manual one. There was no statistically significant difference between the manual and the automatic quantification of the parasite; the two methods showed a correlation analysis r2 value of 0.9159. The most significant advantage of the automatic quantification was the efficiency of the analysis. The drawback of this automated cell counting method was that some parasites were assigned to the wrong BeWo cell, however this data did not exceed 5% when adequate experimental conditions were chosen.We conclude that this quantification method constitutes an excellent tool for evaluating the parasite load in cells and therefore constitutes an easy and reliable ways to study parasite infectivity.

Dye free automated cell counting and analysis

We have developed an automated cell counting method that uses images obtained at multiple focal heights to enumerate cells in confluent culture. By taking the derivative of image intensity with respect to focal height using two complementary images, we are able to count high-density monolayers of cells over a large image area. Our method resists errors arising from variability in the focal plane caused by flatness or tilt non-uniformities with a minimal amount of focal plane alignment, allowing the automated collection of images across a large area.

Accuracy and precision comparison of the hemocytometer and automated cell counting methods

The hemocytometer is the most widely used device for determining cell concentrations, requiring consistent criteria and tenacity to obtain measurements correctly and reproducibly. As an alternative, the Countess(tm) Automated Cell Counter employs digital imaging and an image analysis algorithm to identify and enumerate cells in a sample. To compare the performance of these two methods, the accuracy, precision, and effective ranges were determined for total cell count and percent viability. First, using a standardized bead solution, the accuracy and precision of glass and disposable hemocytometers were measured and found to be 30% and 5% different from the anticipated value, respectively. Subsequently, disposable hemocytometers and the automated counter were used to determine total cell numbers and percent viability for multiple cell types over a range of cell concentrations. Using a Student's t‐test, the results showed the Countess(tm) instrument measured cell concentrations and viability as accurately and precisely as the disposable hemocytometer. Additionally, the effective concentration range for the Countess(tm) instrument was two times greater than the hemocytometer, and the viability range matched the hemocytometer. The Countess(tm) Automated Cell Counter overall produces results much more rapidly without the problem of operator tedium and fatigue or compromised accuracy and precision.

Manual and automated leukocyte differentiation in bronchoalveolar lavage fluids from rodent models of pulmonary inflammation

Manual total and differential leukocyte counting in bronchoalveolar lavage fluids (BALF) by visual microscopy is a standard means of evaluating airway inflammation and the anti-inflammatory properties of therapeutics in various animal models of lung disease. The manual cell counting method derives total leukocyte counts from BALF with a hemocytometer, and cell differentials (mononuclear, neutrophil, eosinophil) are calculated from the percentage of each cell type taken from a count of at least 200 cells on a stained cytocentrifuge preparation of the BALF cells. These manual methods are time-consuming and have inherent error–variability. The ADVIA 120 Hematology System is an automated analyzer designed to perform total and differential leukocyte analysis of blood. With the light scattering, cell lysis resistance, and cytochemical staining data from a BALF sample processed by the ADIVA, a BALF total leukocyte count and differential analysis is provided in approximately 30 s. In order to correlate automated BALF leukocyte counting by the ADVIA 120 Hematology System with manual counting, we developed a manual red blood cell lysing and white blood cell staining technique for BALF cells similar to the process used by the ADVIA. Significant correlations for BALF white blood cells were obtained for the manual (microscopic analysis) and the automated (ADVIA) methods. Comparison of manual and automated cell counts also generates the same conclusions about anti-inflammatory drug efficacy. Both manual and automated cell counting methods agree that 3 mg/kg orally administered dexamethasone inhibited cigarette-smoke-induced total BALF cell counts by ∼65% in mice and 42 μg/kg fluticasone propionate delivered by nose-only inhalation inhibited allergen-induced total BALF cells by 77% in rats. The use of the ADVIA to perform total and differential leukocyte counts in BALF will save time spent manually counting cells and this instrument will standardize the analysis of white blood cells across the laboratories currently using various manual counting preparations and procedures.

Detecting Human CD34 + and CD34 - Hematopoietic Stem and Progenitor Cells Using a Sysmex Automated Hematology Analyzer

In clinical medicine, particularly in the newly developing stem cell therapies required to support the practice of regenerative medicine, the measurement of both CD34+ and CD34- hematopoietic stem cells (HSC)/hematopoietic progenitor cells (HPC) is important in obtaining more accurate information about the total HSC/HPC content in various stem/progenitor cell sources. We report the results of an investigation into methods of detecting CD34+ and CD34- HSC/HPC using the immature information (IMI) channel incorporated into the Sysmex XE-2100 and SE-9000 automated hematology analyzers. In this study, CD34+ and CD34- HSC/HPC were separated by immunologic methods and quantified by flow cytometry (FACScan) and IMI channel analysis. In addition, CD34-/CD133+ HSC were prepared by a sequential antibody-based positive selection strategy. These cells appeared in the same area as CD34+ cells in the IMI channel of the automated hematology analyzer. These findings confirmed that an automated hematology analyzer can be used to measure both CD34+ and CD34- HSC. These results may explain the difference in HSC/HPC counts sometimes observed between the automated hematology analyzer and flow cytometric methods for CD34+ measurement. The results of this study demonstrated the potential of automated cell counting methods for measuring HSC content in cellular products for both research and clinical applications.