Method For Detection Of Cells Research Articles

Toward Sensitive and Reliable Immunoassays of Marine Biotoxins: From Rational Design to Food Analysis.

Marine biotoxins are metabolites produced by algae that can accumulate in shellfish or fish and enter organisms through the food chain, posing a serious threat to biological health. Therefore, accurate and rapid detection is an urgent requirement for food safety. Although various detection methods, including the mouse bioassay, liquid chromatography-mass spectrometry, and cell detection methods, and protein phosphatase inhibition assays have been developed in the past decades, the current detection methods cannot fully meet these demands. Among these methods, the outstanding immunoassay virtues of high sensitivity, reliability, and low cost are highly advantageous for marine biotoxin detection in complex samples. In this work, we review the recent 5-year progress in marine biotoxin immunodetection technologies such as optical immunoassays, electrochemical immunoassays, and piezoelectric immunoassays. With the assistance of immunoassays, the detection of food-related marine biotoxins can be implemented for ensuring public health and preventing food poisoning. In addition, the immunodetection technique platforms including lateral flow chips and microfluidic chips are also discussed. We carefully investigate the advantages and disadvantages for each immunoassay, which are compared to demonstrate the guidance for selecting appropriate immunoassays and platforms for the detection of marine biotoxins. It is expected that this review will provide insights for the further development of immunoassays and promote the rapid progress and successful translation of advanced immunoassays with food safety detection.

A Novel Voltage-Abnormal Cell Detection Method for Lithium-Ion Battery Mass Production Based on Data-Driven Model with Multi-Source Time Series Data

Before leaving the factory, lithium-ion battery (LIB) cells are screened to exclude voltage-abnormal cells, which can increase the fault rate, troubleshooting difficulty, and degrade pack performance. However, the time interval to obtain the detection results through the existing voltage-abnormal cell method is too long, which can seriously affect production efficiency and delay shipment, especially in the mass production of LIBs when facing a large number of time-critical orders. In this paper, we propose a data-driven voltage-abnormal cell detection method, using a fast model with simple architecture, which can detect voltage-abnormal cells based on the multi-source time series data of the LIB without a time interval. Firstly, our method transforms the different source data of a cell into a multi-source time series data representation and utilizes a recurrent-based data embedding to model the relation within it. Then, a simplified MobileNet is used to extract hidden feature from the embedded data. Finally, we detect the voltage-abnormal cells according to the hidden feature with a cell classification head. The experiment results show that the accuracy and average running time of our model on the voltage-abnormal cell detection task is 95.42% and 0.0509 ms per sample, which is a considerable improvement over existing methods.

Identification of De Novo Dividing Stem Cells.

The ability to alternate between quiescent and proliferating states is a remarkable feature of many types of somatic stem cells. The balance between quiescent and proliferating states is vital for maintenance of stem cells over the lifespan, and its disturbance may lead to premature depletion of the stem cell pool and loss of the tissue regenerative or renewal capacity at later stages of life. The question on how this balance is regulated is of critical importance in stem cell research and biology of aging. Assessment of the balance between quiescent and proliferating states has remained challenged until recently due to the lack of approaches for robust determination of the rate at which stem cells exit reversible cell cycle arrest. Here, we propose a simple method for detection of those stem cells that have entered the division cycle after a prolonged period of quiescence.The method combines cumulative and pulse labeling with thymidine analogues 5-bromo-2'-deoxyuridine (BrdU) and 5-ethynyl-2'-deoxyuridine (EdU). In the discussed labeling scheme, cells that have incorporated only the second label, EdU, are de novo dividing cells. The suggested double labeling method provides quantitative assessment of the rate at which stem cells exit the quiescent state and allows the fates of de novo dividing stem cells to be traced.

In vitro assessment of thyroid peroxidase inhibition by chemical exposure: comparison of cell models and detection methods.

Disruption of the thyroid hormone (TH) system is connected with diverse adverse health outcomes in wildlife and humans. It is crucial to develop and validate suitable in vitro assays capable of measuring the disruption of the thyroid hormone (TH) system. These assays are also essential to comply with the 3R principles, aiming to replace the ex vivo tests often utilised in the chemical assessment. We compared the two commonly used assays applicable for high throughput screening [Luminol and Amplex UltraRed (AUR)] for the assessment of inhibition of thyroid peroxidase (TPO, a crucial enzyme in TH synthesis) using several cell lines and 21 compounds from different use categories. As the investigated cell lines derived from human and rat thyroid showed low or undetectable TPO expression, we developed a series of novel cell lines overexpressing human TPO protein. The HEK-TPOA7 model was prioritised for further research based on the high and stable TPO gene and protein expression. Notably, the Luminol assay detected significant peroxidase activity and signal inhibition even in Nthy-ori 3-1 and HEK293T cell lines without TPO expression, revealing its lack of specificity. Conversely, the AUR assay was specific to TPO activity. Nevertheless, despite the different specificity, both assays identified similar peroxidation inhibitors. Over half of the tested chemicals with diverse structures and from different use groups caused TPO inhibition, including some widespread environmental contaminants suggesting a potential impact of environmental chemicals on TH synthesis. Furthermore, in silico SeqAPASS analysis confirmed the high similarity of human TPO across mammals and other vertebrate classes, suggesting the applicability of HEK-TPOA7 model findings to other vertebrates.

Multiple serous cavity effusion screening based on smear images using vision transformer

Serous cavity effusion is a prevalent pathological condition encountered in clinical settings. Fluid samples obtained from these effusions are vital for diagnostic and therapeutic purposes. Traditionally, cytological examination of smears is a common method for diagnosing serous cavity effusion, renowned for its convenience. However, this technique presents limitations that can compromise its efficiency and diagnostic accuracy. This study aims to overcome these challenges and introduce an improved method for the precise detection of malignant cells in serous cavity effusions. We have developed a transformer-based classification framework, specifically employing the vision transformer (ViT) model, to fulfill this objective. Our research involved collecting smear images and corresponding cytological reports from 161 patients who underwent serous cavity drainage. We meticulously annotated 4836 patches from these images, identifying regions with and without malignant cells, thus creating a unique dataset for smear image classification. The findings of our study reveal that deep learning models, particularly the ViT model, exhibit remarkable accuracy in classifying patches as malignant or non-malignant. The ViT model achieved an impressive area under the receiver operating characteristic curve (AUROC) of 0.99, surpassing the performance of the convolutional neural network (CNN) model, which recorded an AUROC of 0.86. Additionally, we validated our models using an external cohort of 127 patients. The ViT model sustained its high-level screening performance, achieving an AUROC of 0.98 at the patient level, compared to the CNN model’s AUROC of 0.84. The visualization of our ViT models confirmed their capability to precisely identify regions containing malignant cells in multiple serous cavity effusion smear images. In summary, our study demonstrates the potential of deep learning models, particularly the ViT model, in automating the screening process for serous cavity effusions. These models offer significant assistance to cytologists in enhancing diagnostic accuracy and efficiency. The ViT model stands out for its advanced self-attention mechanism, making it exceptionally suitable for tasks that necessitate detailed analysis of small, sparsely distributed targets like cellular clusters in serous cavity effusions.

Precursor exhausted CD8+T cells in colorectal cancer tissues associated with patient's survival and immunotherapy responsiveness.

Exhausted CD8+T cells represent a distinct cellular lineage that emerges during both chronic infections and cancers. Recent studies have shown that persistent antigen exposure can drive the differentiation of precursor exhausted CD8+T cells, termed Tpex cells, which are characterized as TCF-1+PD-1+CD8+T cells. Elevated Tpex cell frequencies in the tumor microenvironment (TME) are associated with improved overall survival (OS) in cancer patients and heightened responsiveness to anti-PD-1 therapy. In our present study, we utilized multi-color immunohistochemistry (mIHC) to determine the localization and clinical implications of tumor-infiltrating Tpex cells within the TME of human colorectal cancer (CRC) tissues. We also conducted a multi-omics integrative analysis using single-cell RNA sequencing (scRNA-seq) data derived from both the murine MC38 tumor model and human CRC tissues. This analysis helped delineate the transcriptional and functional attributes of Tpex cells within the CRC TME. Furthermore, we employed spatial transcriptome sequencing data from CRC patients to investigate the interactions between Tpex cells and other immune cell subsets within the TME. In conclusion, our study not only established a method for Tpex cell detection using mIHC technology but also confirmed that assessing Tpex cells within the CRC TME could be indicative of patients' survival. We further uncovered the transcriptional and functional characteristics of Tpex cells in the TME and ascertained their pivotal role in the efficacy of immunotherapy against CRC.

An Imbalanced Sequence Feature Extraction Approach for the Detection of LTE-R Cells with Degraded Communication Performance

Within the Shuo Huang Railway Company (Suning, China ) the long-term evolution for railways (LTE-R) network carries core wireless communication services for trains. The communication performance of LTE-R cells directly affects the operational safety of the trains. Therefore, this paper proposes a novel detection method for LTE-R cells with degraded communication performance. Considering that the number of LTE-R cells with degraded communication performance and that of normal cells are extremely imbalanced and that the communication performance indicator data for each cell are sequence data, we propose a feature extraction neural network structure for imbalanced sequences, based on shapelet transformation and a convolutional neural network (CNN). Then, to train the network, we set the optimization objective based on the Fisher criterion. Finally, using a two-stage training method, we obtain a neural network model that can distinguish LTE-R cells with degraded communication performance from normal cells at the feature level. Experiments on a real-world dataset show that the proposed method can realize the accurate detection of LTE-R cells with degraded communication performance and has high practical application value.

A microfluidic microalgae detection system for cellular physiological response based on an object detection algorithm.

The composition of species and the physiological status of microalgal cells serve as significant indicators for monitoring marine environments. Symbiotic with corals, Symbiodiniaceae are more sensitive to the environmental response. However, current methods for evaluating microalgae tend to be population-based indicators that cannot be focused on single-cell level, ignoring potentially heterogeneous cells as well as cell state transitions. In this study, we proposed a microalgal cell detection method based on computer vision and microfluidics, which combined microscopic image processing, microfluidic chip and convolutional neural network to achieve label-free, sheathless, automated and high-throughput microalgae identification and cell state assessment. By optimizing the data import, training process and model architecture, we solved the problem of identifying tiny objects at the micron scale, and the optimized model was able to perform the tasks of cell multi-classification and physiological state assessment with more than 95% mean average precision. We discovered a novel transition state and explored the thermal sensitivity of three clades of Symbiodiniaceae, and discovered the phenomenon of cellular heat shock at high temperatures. The evolution of the physiological state of Symbiodiniaceae cells is very important for directional cell evolution and early warning of coral ecosystem health.

Revolutionizing Senescence Detection: Advancements from Traditional Methods to Cutting-Edge Techniques.

The accumulation of senescent cells is an important factor in the complex progression of aging, with significant implications for the development of numerous diseases. Thus, understanding the fundamental mechanisms of senescence is paramount for advancing preventive and therapeutic approaches to age-related conditions. Important to this pursuit is the precise identification and examination of senescent cells, contingent upon the recognition of specific biomarkers. Historically, detection methods relied on assessing molecular protein and mRNA levels and various staining techniques. While these conventional approaches have contributed substantially to the field, they possess limitations in capturing the dynamic evolution of cellular aging in real time. The emergence of novel technologies has led to a paradigm shift in senescence research. Gene-edited mouse models and the application of advanced probes have revolutionized our ability to detect senescent cells. These cutting-edge methodologies provide a more detailed and accurate means of dynamically monitoring, characterizing and potentially eliminating senescent cells, thus enhancing our understanding of the complex mechanisms of aging. This review comprehensively explores both traditional and innovative senescent cell detection methods, elucidating their advantages, limitations and implications for future investigations and could serve as a comprehensive guide and catalyst for further advancements in the understanding of aging and associated pathologies.

Anomaly Detection Method for Lithium-Ion Battery Cells Based on Time Series Decomposition and Improved Manhattan Distance Algorithm.

Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in severe cases. Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles. First, time series decomposition is performed on the voltage data of all battery cells in the battery pack to obtain the voltage trend component of each cell. Then, the improved Manhattan distance algorithm is utilized to calculate and compare the Manhattan distance values between adjacent cell trend components, to determine the abnormal cells inside the battery pack. Furthermore, the Manhattan distance values at the same sampling moment are calculated within the data sequence to detect the specific time when the abnormal cells malfunction. The data analysis and experimental verification results based on actual vehicle operating conditions indicate that this method can accurately identify an abnormal cell within the battery pack and diagnose the specific moment of abnormality in the battery cell at an early stage of failure, with good robustness.

Multi-scale feature decoupling and similarity distillation for class-incremental defect detection of photovoltaic cells

Existing vision-based photovoltaic cell defect detection methods usually update models with all defect data of both old and new categories to adapt to new classes emerging in the dynamic data stream from realistic production lines. This model updating strategy wastes resources and sometimes is infeasible due to the confidentiality of historical data. In this paper, we propose a novel distillation-based model updating method, i.e., multi-scale Feature Decoupling and Similarity Distillation (mFDSD), which updates the model with only new data from the dynamic data stream but can effectively identify both old and new categories. In mFDSD, we design a mask-based feature decoupling distillation module and a similarity-regulated feature distillation module to adaptively regulate distillation losses assigned to important defective areas, less-important background areas, and discarded areas of feature maps. Experimental results demonstrate that our mFDSD outperforms the current state-of-the-art distillation-based model updating methods for the class-incremental defect detection of photovoltaic cells.

Spatial Mapping of Myeloma Bone Marrow Microenvironment Using a Deep Learning Approach

Background: Interactions between multiple myeloma (MM) cells and the bone marrow (BM) microenvironment influence disease evolution and drug resistance. Information is largely based on BM aspirates, which lack topological information and can be affected by haemodilution. To examine the spatial relationships between tumour and immune cells in the MM immune tumour microenvironment (iTME), we used multiplex immunohistochemistry (MIHC) coupled with deep learning (DL) image analysis for detailed visualization and unbiased automated analysis of BM components. Methods: BM trephines from patients (at a single centre) with monoclonal gammopathies of undetermined significance (n=9, MGUS) and newly diagnosed MM (n=10, NDMM) with paired baseline and day100 post autologous stem cell transplantation (ASCT) were stained with MIHC panels (BLIMP-1 for tumour, CD4, CD8, FOXP3). To dissect the unique BM landscape, we developed a superpixel based DL model (MoSaicNet) to distinguish blood, bone, fat and cellular tissue. We then developed a cell abundance aware DL method for cell detection and classification (AwareNet) on whole slide images. To tackle cell abundance bias, AwareNet assigned higher attention scores to rare cell types during model training. We used an independent validation cohort consisting of 9 NDMM with paired baseline and post-treatment BM samples to further evaluate the model's performance (n=18). Validation cohort samples were obtained from 7 different hospitals and were stained using a different autostainer of the same model. A colour normalization step was added before analysis. Results: MoSaicNet and AwareNet enabled us to evaluate densities and spatial proximity between cell types. To train, validate and test MoSaicNet, 269 regions of interest (69884 superpixels) from 19 BM samples were annotated by pathologists. For AwareNet, 8004 single-cell annotations were made on 11 samples. MoSaicNet achieved an overall classification area under the curve (AUC) of 0.99, 95% confidence interval (CI) [0.989, 0.991]. Similarly, AwareNet achieved a high overall AUC of 0.98 [0.977, 0.984]. AwareNet outperformed state of the art methods such as U-net and CONCORDe-Net in detecting both abundant and rare cell types. In addition, both algorithms achieved an overall AUC of >0.97 when applied to the validation cohort, despite different tissue processing and staining methods. Densities of FOXP3+CD4+, FOXP3-CD4+ and CD8+ cells did not differ significantly between NDMM and MGUS samples (p=0.32, p=0.81 and p=0.74 respectively), however, there were fewer BLIMP1+ cells in proximity to CD8+ cells in MGUS samples compared with NDMM samples. In NDMM patients, post-treatment samples revealed a reduction in densities of BLIMP1+ cells (p=0.013), CD8+ T cells (p=0.004) and FOXP3+CD4+ regulatory T cells (p=0.004) when compared with baseline. The number of BLIMP1+ cells in proximity to CD8+ and CD4+ cells significantly reduced post-treatment (Corrected for densities, p<0.05), indicating changes in iTME. Analysis of the validation cohort provided similar density and spatial results. Conclusion: We describe the use of a DL pipeline for tissue segmentation and automated cell annotation, enabling spatial mapping of the MM iTME to address topographical questions related to immune function and cell-to-cell interactions. The high accuracy in the validation cohort suggests that our model could be applied to other independent datasets after a normalization step.

A simple joint detection platform for high-throughput single-cell heterogeneity screening

Single cell heterogeneity plays an important role in many biological phenomena and distinguishing cells that exhibit certain mutation in sample could benefit clinical diagnose and drug screening. Typical single cell detection methods such as flow cytometry, in-situ hybridization, real-time amplification or sequencing test either protein or nucleic acid as target and usually require specialized instruments. Joint measurement of the both types of targets could be done by combining the above strategies precisely but also unwieldly. Methods for rapidly and parallelly screening single cells with target genotype and antigen is needed. In this study, we describe a gel plate platform to distinguish cell types based on their phenotypes on target gene and antigen with low equipment requirement. Integrated cell lysis and immobilization were done in the gel solidification step, after which antibody hybridization and real-time amplification were sequentially carried out without losing the original loci information of individual single cells so the three types of information of individual single cells could be combined to distinguished cells with expected genotype and phenotype. The easy-to-use gel platform has potential in point-of-care circumstances and single-cell stimulation response that have high requirements on efficiency and simplicity.

A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.

The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and differentiation occur, are notoriously difficult to detect due to changes in their appearance and number. Recently, convolutional neural network (CNN)-based methods have made significant progress in cell detection and tracking. However, these approaches require many manually annotated data for fully supervised training, which is time-consuming and often requires professional researchers. To alleviate such tiresome and labor-intensive costs, we propose a novel weakly supervised learning cell detection and tracking framework that trains the deep neural network using incomplete initial labels. Our approach uses incomplete cell markers obtained from fluorescent images for initial training on the Induced Pluripotent Stem (iPS) cell dataset, which is rarely studied for cell detection and tracking. During training, the incomplete initial labels were updated iteratively by combining detection and tracking results to obtain a model with better robustness. Our method was evaluated using two fields of the iPS cell dataset, along with the cell detection accuracy (DET) evaluation metric from the Cell Tracking Challenge (CTC) initiative, and it achieved 0.862 and 0.924 DET, respectively. The transferability of the developed model was tested using the public dataset FluoN2DH-GOWT1, which was taken from CTC; this contains two datasets with reference annotations. We randomly removed parts of the annotations in each labeled data to simulate the initial annotations on the public dataset. After training the model on the two datasets, with labels that comprise 10% cell markers, the DET improved from 0.130 to 0.903 and 0.116 to 0.877. When trained with labels that comprise 60% cell markers, the performance was better than the model trained using the supervised learning method. This outcome indicates that the model's performance improved as the quality of the labels used for training increased.

Optimization and validation of in vivo flow cytometry chimeric antigen receptor T cell detection method using CD19his indirect staining.

CD19-targeted chimeric antigen receptor T (CAR-T) cell therapy has shown unprecedented results in patients with B cell relapsed/refractory acute lymphoblastic leukemia (R/R-ALL) and B cell non-Hodgkin lymphomas where no other curative options are available. In vivo monitoring of CAR-T cell kinetics is fundamental to understand the correlation between CAR-T cells expansion and persistence with treatment response and toxicity development. The aim of this study was to define a robust, sensitive, and universal method for CAR-T cell detection using flow cytometry. We set up and compared with each other three assays for CD19 CAR-T cell detection, all based on commercially available reagents. All methods used a recombinant human CD19 protein fragment recognized by the single-chain variable fragment of the CAR construct. The two indirect staining assays (CD19his + APC-conjugated antihistidine antibody and CD19bio + APC-conjugated antibiotin antibody) showed better sensitivity and specificity compared with the direct staining with CD19-FITC, and CD19his had a better cost-effective profile. We validated CAR detection with CD19his with parallel quantitative real-time polymerase chain reaction data and we could demonstrate a strong positive correlation. We also showed that CD19his staining can be easily included in a multicolor flow cytometry panel to achieve additional information about the cell phenotype of CAR-T cell positive subpopulations. Finally, this method can be used for different anti-CD19 CAR-T cell products and for different sample sources. These data demonstrate that detection of CAR-T cells by CD19his flow cytometry staining is a reliable, robust, and broadly applicable tool for in vivo monitoring of CAR-T cells.

Simulation-based Reconstructed Diffusion unveils the effect of aging on protein diffusion in Escherichia coli.

We have developed Simulation-based Reconstructed Diffusion (SbRD) to determine diffusion coefficients corrected for confinement effects and for the bias introduced by two-dimensional models describing a three-dimensional motion. We validate the method on simulated diffusion data in three-dimensional cell-shaped compartments. We use SbRD, combined with a new cell detection method, to determine the diffusion coefficients of a set of native proteins in Escherichia coli. We observe slower diffusion at the cell poles than in the nucleoid region of exponentially growing cells, which is independent of the presence of polysomes. Furthermore, we show that the newly formed pole of dividing cells exhibits a faster diffusion than the old one. We hypothesize that the observed slowdown at the cell poles is caused by the accumulation of aggregated or damaged proteins, and that the effect is asymmetric due to cell aging.

A Key-Points Based Anchor-Free Cervical Cell Detector.

Cervical cell detection is crucial to cervical cytology screening at early stage. Currently most cervical cell detection methods use anchor-based pipeline to achieve the localization and classification of cells, e.g. faster R-CNN and YOLOv3. However, the anchors generally need to be pre-defined before training and the detection performance is inevitably sensitive to these pre-defined hyperparameters (e.g. number of anchors, anchor size and aspect ratios). More importantly, these preset anchors fail to conform to the cells with different morphology at inference phase. In this paper, we present a key-points based anchor-free cervical cell detector based on YOLOv3. Compared with the conventional YOLOv3, the proposed method applies a key-points based anchor-free strategy to represent the cells in the initial prediction phase instead of the preset anchors. Therefore, it can generate more desirable cell localization effect through refinement. Furthermore, PAFPN is applied to enhance the feature hierarchy. GIoU loss is also introduced to optimize the small cell localization in addition to focal loss and smooth L1 loss. Experimental results on cervical cytology ROI datasets demonstrate the effectiveness of our method for cervical cell detection and the robustness to different liquid-based preparation styles (i.e. drop-slide, membrane-based and sedimentation).

Flow Cytometry: The Next Revolution.

Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.

Persistent homological cell tracking technology

In this paper, we develop a cell tracking method based on persistent homological figure detection technology. We apply our tracking method to 9 different time-series cell images and extract several kinds of cell movements. Being able to analyze various images with a single method allows researchers to systematically understand and compare different tracking data. Persistent homological cell tracking technology’s 9 parameters all have clear meanings. Thus, researchers can decide the parameters not by black box trial-and-error but by the purpose of their analysis. We use model data with ground truth to see our method’s performance. We compare persistent homological figure detection and cell tracking technology with Image-Pro, sure-foreground in watershed method, and cell detection methods in previous studies. We see that there are some cases where Image-Pro’s tracking stops and requires manual plots, while our method does not require manual plots. We show that our technology includes sure-foreground and has more information, and can be applied to different types of data that previously needed different methods. We also show that our technology is powerful as a detection technology by applying the technology to 5 different types of cell images.

An Automated Cell Detection Method for TH-positive Dopaminergic Neurons in a Mouse Model of Parkinson's Disease Using Convolutional Neural Networks.

Quantification of tyrosine hydroxylase (TH)-positive neurons is essential for the preclinical study of Parkinson's disease (PD). However, manual analysis of immunohistochemical (IHC) images is labor-intensive and has less reproducibility due to the lack of objectivity. Therefore, several automated methods of IHC image analysis have been proposed, although they have limitations of low accuracy and difficulties in practical use. Here, we developed a convolutional neural network-based machine learning algorithm for TH+ cell counting. The developed analytical tool showed higher accuracy than the conventional methods and could be used under diverse experimental conditions of image staining intensity, brightness, and contrast. Our automated cell detection algorithm is available for free and has an intelligible graphical user interface for cell counting to assist practical applications. Overall, we expect that the proposed TH+ cell counting tool will promote preclinical PD research by saving time and enabling objective analysis of IHC images.