Phase Contrast Images Of Cells Research Articles

Disulfiram Inhibits Opsonin-Independent Phagocytosis and Migration of Human Long-Lived In Vitro Cultured Phagocytes from Multiple Inflammatory Diseases.

Disulfiram (DSF), an anti-alcoholism medicine, exerts treatment effects in patients suffering from persistent Borreliosis and also exhibits anti-cancer effects through its copper chelating derivatives and induction of oxidative stress in mitochondria. Since chronic/persistent borreliosis is characterized by increased amounts of pro-inflammatory macrophages, this study investigated opsonin-independent phagocytosis, migration, and surface marker expression of in vivo activated and in vitro cultured human monocyte-derived phagocytes (macrophages and dendritic cells) with and without DSF treatment. Phagocytosis of non-opsonized Dynabeads® M-450 and migration of macrophages and dendritic cells were monitored using live cell analyzer Juli™ Br for 24 h, imaging every 3.5 min. To simultaneously monitor phagocyte function, results were analyzed by a newly developed software based on the differential phase contrast images of cells before and after ingestion of Dynabeads. DSF decreased the phagocytic capacities exhibited by in vitro enriched and long-lived phagocytes. Although no chemotactic gradient was applied to the test system, vigorous spontaneous migration was observed. We therefore set up an algorithm to monitor and quantify both phagocytosis and migration simultaneously. DSF not only reduced phagocytosis in a majority of these long-lived phagocytes but also impaired their migration. Despite these selective effects by DSF, we found that DSF reduced the expression densities of surface antigens CD45 and CD14 in all of our long-lived phagocytes. In cells with a high metabolic activity and high mitochondrial contents, DSF led to cell death corresponding to mitochondrial oxidative stress, whereas metabolically inactive phagocytes survived our DSF treatment protocol. In conclusion, DSF affects the viability of metabolically active phagocytes by inducing mitochondrial stress and secondly attenuates phagocytosis and migration in some long-lived phagocytes.

Automated tracking of cell migration in phase contrast images with CellTraxx

The ability of cells to move and migrate is required during development, but also in the adult in processes such as wound healing and immune responses. In addition, cancer cells exploit the cells’ ability to migrate and invade to spread into nearby tissue and eventually metastasize. The majority of cancer deaths are caused by metastasis and the process of cell migration is therefore intensively studied. A common way to study cell migration is to observe cells through an optical microscope and record their movements over time. However, segmenting and tracking moving cells in phase contrast time-lapse video sequences is a challenging task. Several tools to track the velocity of migrating cells have been developed. Unfortunately, most of the automated tools are made for fluorescence images even though unlabelled cells are often preferred to avoid phototoxicity. Consequently, researchers are constrained with laborious manual tracking tools using ImageJ or similar software. We have therefore developed a freely available, user-friendly, automated tracking tool called CellTraxx. This software makes it easy to measure the velocity and directness of migrating cells in phase contrast images. Here, we demonstrate that our tool efficiently recognizes and tracks unlabelled cells of different morphologies and sizes (HeLa, RPE1, MDA-MB-231, HT1080, U2OS, PC-3) in several types of cell migration assays (random migration, wound healing and cells embedded in collagen). We also provide a detailed protocol and download instructions for CellTraxx.

Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy

A variety of biological and pharmaceutical studies, such as for anti-cancer drugs, require the quantification of cell responses over long periods of time. This is performed with time-lapse video microscopy that gives a long sequence of frames. For this purpose, phase contrast imaging is commonly used since it is minimally invasive. The cell responses of interest in this study are the mitotic cell divisions. Their manual measurements are tedious, subjective, and restrictive. This study introduces an automated method for these measurements. The method starts with preprocessing for restoration and reconstruction of the phase contrast time-lapse sequences. The data are first restored from intensity non-uniformities. Subsequently, the circular symmetry of the contour of the mitotic cells in phase contrast images is used by applying a Circle Hough Transform (CHT) to reconstruct the entire cells. The CHT is also enhanced with the ability to “vote” exclusively towards the center of curvature. The CHT image sequence is then registered for misplacements between successive frames. The sequence is subsequently processed to detect cell centroids in individual frames and use them as starting points to form spatiotemporal trajectories of cells along the positive as well as along the negative time directions, that is, anti-causally. The connectivities of different trajectories enhanced by the symmetry of the trajectories of the daughter cells provide as topological by-products the events of cell divisions together with the corresponding entries into mitoses as well as exits from cytokineses. The experiments use several experimental video sequences from three different cell lines with many cells undergoing mitoses and divisions. The quantitative validations of the results of the processing demonstrate the high performance and efficiency of the method.

Development of convolutional neural networks for recognition of tenogenic differentiation based on cellular morphology

Background and objective: The use of automated systems for image recognition is highly preferred for regenerative medicine applications to evaluate stem cell differentiation early in the culturing state with non-invasive methodologies instead of invasive counterparts. Bone marrow-derived mesenchymal stem cells (BMSCs) are able to differentiate into desired cell phenotypes, and thereby promise a proper cell source for tendon regeneration. The therapeutic success of stem cell therapy requires cellular characterization prior to the implantation of cells. The foremost problem is that traditional characterization techniques require cellular material which would be more useful for cell therapy, complex laboratory procedures, and human expertise. Convolutional neural networks (CNNs), a class of deep neural networks, have recently made great improvements in image-based classifications, recognition, and detection tasks. We, therefore, aim to develop a potential CNN model in order to recognize differentiated stem cells by learning features directly from image data of unlabelled cells.Methods: The differentiation of bone marrow mesenchymal stem cells (BMSCs) into tenocytes was induced with the treatment of bone morphogenetic protein-12 (BMP-12). Following the treatment and incubation step, the phase-contrast images of cells were obtained and immunofluorescence staining has been applied to characterize the differentiated state of BMSCs. CNN models were developed and trained with the phase-contrast cell images. The comparison of CNN models was performed with respect to prediction performance and training time. Moreover, we have evaluated the effect of image enhancement method, data augmentation, and fine-tuning training strategy to increase classification accuracy of CNN models. The best model was integrated into a mobile application.Results: All the CNN models can fit the biological data extracted from immunofluorescence characterization. CNN models enable the cell classification with satisfactory accuracies. The best result in terms of accuracy and training time is achieved by the model proposed based on Inception-ResNet V2 trained from scratch using image enhancement and data augmentation strategies (96.80%, 434.55 sec).Conclusion: Our study reveals that the CNN models show good performance by identifying stem cell differentiation. Importantly this technique provides a faster and real-time tool in comparison to traditional methods enabling the adjustment of culture conditions during cultivation to improve the yield of therapeutic stem cells.

Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform.

Single-cell delivery platforms like microinjection and nanoprobe electroporation enable unparalleled control over cell manipulation tasks but are generally limited in throughput. Here, we present an automated single-cell electroporation system capable of automatically detecting cells with artificial intelligence (AI) software and delivering exogenous cargoes of different sizes with uniform dosage. We implemented a fully convolutional network (FCN) architecture to precisely locate the nuclei and cytosol of six cell types with various shapes and sizes, using phase contrast microscopy. Nuclear staining or reporter fluorescence was used along with phase contrast images of cells within the same field of view to facilitate the manual annotation process. Furthermore, we leveraged the near-human inference capabilities of the FCN network in detecting stained nuclei to automatically generate ground-truth labels of thousands of cells within seconds, and observed no statistically significant difference in performance compared to training with manual annotations. The average detection sensitivity and precision of the FCN network were 95±1.7% and 90±1.8%, respectively, outperforming a traditional image-processing algorithm (72±7.2% and 72±5.5%) used for comparison. To test the platform, we delivered fluorescent-labeled proteins into adhered cells and measured a delivery efficiency of 90%. As a demonstration, we used the automated single-cell electroporation platform to deliver Cas9-guide RNA (gRNA) complexes into an induced pluripotent stem cell (iPSC) line to knock out a green fluorescent protein-encoding gene in a population of ~200 cells. The results demonstrate that automated single-cell delivery is a useful cell manipulation tool for applications that demand throughput, control, and precision.

Recognition of tenogenic differentiation using convolutional neural network

Abstract Methodologies to assess stem cell differentiation in the culturing state are needed for regenerative medicine and tissue engineering techniques. In recent years, convolutional neural networks (CNNs), a class of deep neural networks, have made impressive advancements in image-based classification, recognition and detection tasks. CNNs have been introduced as a non-invasive cell characterization method by learning features directly from image data of unlabeled cells. Furthermore, this approach serves as a rapid and inexpensive methodology with high performance compared to traditional techniques that require complex laboratory procedures including antibody staining and gene expression analysis. Here, we studied the potential of the CNNs approach to recognize stem cell differentiation based on cell morphology utilizing phasecontrast microscopy images.We have examined the differentiation potential of bone marrow mesenchymal stem cells (BMSCs) into tenocytes, with the treatment of bone morphogenetic protein-12 (BMP-12). After treatment, the phase-contrast images of cells were obtained directly from cell culture flasks to train CNN and the differentiated phenotype of stem cells was characterized by immunostaining. CNN was able to classify the cells into three groups including non-stem cells (chondrocytes), stem cells (BMSCs) and differentiated stem cells (tenocytes) based on their morphology with 92.2 % accuracy. The presented study revealed that CNN performed faster and non-invasive cell classification task compared to traditional methodologies.

A multi-stage random forest classifier for phase contrast cell segmentation.

We present a machine learning based approach to automatically detect and segment cells in phase contrast images. The proposed method consists of a multi-stage classification scheme based on random forest (RF) classifier. Both low level and mid level image features are used to determine meaningful cell regions. Pixel-wise RF classification is first carried out to categorize pixels into 4 classes (dark cell, bright cell, halo artifact, and background) and generate a probability map for cell regions. K-means clustering is then applied on the probability map to group similar pixels into candidate cell regions. Finally, cell validation is performed by another RF to verify the candidate cell regions. The proposed method has been tested on U2-OS human osteosarcoma phase contrast images. The experimental results show better performance of the proposed method with precision 92.96% and recall 96.63% compared to a state-of-the-art segmentation technique.

Prediction of neural differentiation fate of rat mesenchymal stem cells by quantitative morphological analyses using image processing techniques.

Differentiation of bone marrow mesenchymal stem cells (BMSCs) into neural cells has received significant attention in recent years. However, there is still no practical method to evaluate differentiation process non-invasively and practically. The cellular quality evaluation method is still limited to conventional techniques, which are based on extracting genes or proteins from the cells. These techniques are invasive, costly, time consuming, and should be performed by relevant experts in equipped laboratories. Moreover, they cannot anticipate the future status of cells. Recently, cell morphology has been introduced as a feasible way of monitoring cell behavior because of its relationship with cell proliferation, functions and differentiation. In this study, rat BMSCs were induced to differentiate into neurons. Subsequently, phase contrast images of cells taken at certain intervals were subjected to a series of image processing steps and cell morphology features were calculated. In order to validate the viability of applying image-based approaches for estimating the quality of differentiation process, neural-specific markers were measured experimentally throughout the induction. The strong correlation between quantitative imaging metrics and experimental outcomes revealed the capability of the proposed approach as an auxiliary method of assessing cell behavior during differentiation.

3D Culture of rat embryonic stem cells and rat induced pluripotent stem cells in a novel PGmatrix hydrogel (LB20)

Expanding embryonic stem cells (ESCs) in a synthetic 3D microenvironment in vitro while maintaining the undifferentiated state has been challenging. It would be advantageous to identify a substrate which could be modified such as by changing ligands that interact with cell surface receptors or by changing the substrate’s stiffness. We developed a synthetic PGmatrix hydrogel to include these design specifications and determined whether it could support the expansion of undifferentiated ESCs in a 3D microenvironment. Rat ESCs (rESCs) and rat induced pluripotent stem cells (riPSCs) were used for evaluation. The cells were grown both in 2D and 3D in PGmatrix hydrogel containing 2 inhibitors. Dynamic rheological tests of PGmatrix hydrogel in 2i medium showed a rapid increase of storage moduli (G’ = 600 Pa for 3mM in 1 hr) indicating gelation after mixing. The mechanical strength of hydrogel was full recovered after shear‐thinning into liquid phase.Also, the hydrogel reached a self‐supporting strength within 1000sec which is a reasonable rate to ensure cells were suspended in the hydrogel matrix before they reached the bottom of the plate. Both cell lines were cultured for more than 30 passages using regular trypsinization method to produce single cells at passage prior to plating. We harvested cells from the hydrogel for passaging by shearing the gel to liberate the cells. Phase contrast images of cells grown in 3D showed similar morphology which had rounded phase‐bright borders compared to cells grown on murine embryonic fibroblast (MEF) feeder layer. They retained pluripotent stem cell properties and showed positive staining for alkaline phosphatase, Oct‐4, Nanog, Sox‐2 and SSEA‐1. Karyotypes of pluripotent stem cells cultured in 3D were normal after 20 passages. Embryoid bodies (EBs) could be formed by hanging drop after the cells were trypsinized from 3D cultures. We plan to compare the gene expression profiles of 2D and 3D cells and determine if the 3D cultured cells can undergo differentiation into all the 3 germ layers and form teratomas. Growing ESCs in a fully synthetic 3D culture can significant advantages for directing differentiation and examining the interactions with surface receptors. Such knowledge will enable 3D culture for their use in drug development or for encapsulation of cells for clinical use.

Novel Automated Tracking Analysis of Particles Subjected to Shear Flow: Kindlin-3 Role in B Cells

Shear flow assays are used to mimic the influence of physiological shear force in diverse situations such as leukocyte rolling and arrest on the vasculature, capture of nanoparticles, and bacterial adhesion. Analysis of such assays usually involves manual counting, is labor-intensive, and is subject to bias. We have developed the Leukotrack program that incorporates a novel (to our knowledge) segmentation routine capable of reliable detection of cells in phase contrast images. The program also automatically tracks rolling cells in addition to those that are more firmly attached and migrating in random directions. We demonstrate its use in the analysis of lymphocyte arrest mediated by one or more active conformations of the integrin LFA-1. Activation of LFA-1 is a multistep process that depends on several proteins including kindlin-3, the protein that is mutated in leukocyte adhesion deficiency-III patients. We find that the very first stage of LFA-1-mediated attaching is unable to proceed in the absence of kindlin-3. Our evidence indicates that kindlin-3-mediated high-affinity LFA-1 controls both the early transient integrin-dependent adhesions in addition to the final stable adhesions made under flow conditions.

Segmentation of HeLa cells in phase-contrast images with and without DAPI stained cell nuclei

Microscopy is an essential tool for the observation of microbial effectors in host cells. Manual evaluation of the acquired image data is elaborate and error prone. Thus, in order to increase objectivity and reproducibility of results, an automated evaluation of the experiment is performed. The present study evaluates methods to delineate HeLa cells depicted in phase-contrast (PC) micrographs, with and without the additional use of the DAPI stained cell nuclei. For initial cell detection a variance filter is applied to the PC micrographs. Based on this foreground cells are segmented with an intensity based k-means clustering. In a second step, the foreground areas are refined to match the cell boarders by region growing on the background. These foreground areas are used to study the performance of cell separation methods, namely seeded watershed (SW) and graph cuts (GC). Either the maxima of the distance transform of the foreground regions in the PC images (“dist”) or alternatively regions of the cell nuclei (“nuc”) obtained from the DAPI channel are utilized to initialize the cell separation methods. For the detailed evaluation of segmentation quality the cells were categorized to four classes, namely isolated cells, touching cells, overlapping and overlaying cells. With regards the rating of the obtained performance using the Jaccard overlap measure, an intra-observer variance was determined as the median of performance values of all cells with p=0.79. The performance using seeds of type “dist” resulted in pSW(dist)=0.59 for watershed transform and pGC(dist)=0.60 for graph cuts. Switching seeds to type “nuc” performance improved to pSW(nuc)=0.72 for watershed transform and pGC(nuc)=0.69 for graph cuts.

Electroporation-induced changes in normal immature rat myoblasts (H9C2)

Application of a high electric field causes an electric shock to the heart. This is utilized in defibrillation to reestablish normal contraction rhythms during dangerous arrhythmias or in cardiac arrest. If shock-induced transmembrane potentials are large enough, they can cause tissue destruction due to irreversible electroporation (EP). Also electrochemotherapy of nearby tissues may have an adverse effect on the heart. Herein, we present experimental data on effects of electroporation in culture of cardiac cells (H9C2). The electric field was applied in short pulses of 25-3250 V/cm, 50 µs each. The viability of cells was tested by MTT assay after 24 hours. For detection of DNA fragmentation, associated with apoptosis, alkaline and neutral comet assays were performed after EP. Additionally phase contrast images of cells obtained directly after EP were analyzed. Although cell images indicated disruption of cell membranes after EP with high intensities, only a few percent of apoptotic cells and no necrotic effects in the cell nucleus could be observed in comet assay tests performed 2 hours post EP. MTT viability test showed that pulse intensities above 375 V/cm are destructive for myocytes viability.

Quantitative cell imaging using single beam phase retrieval method

Quantitative three-dimensional imaging of cells can provide important information about their morphology as well as their dynamics, which will be useful in studying their behavior under various conditions. There are several microscopic techniques to image unstained, semi-transparent specimens, by converting the phase information into intensity information. But most of the quantitative phase contrast imaging techniques is realized either by using interference of the object wavefront with a known reference beam or using phase shifting interferometry. A two-beam interferometric method is challenging to implement especially with low coherent sources and it also requires a fine adjustment of beams to achieve high contrast fringes. In this letter, the development of a single beam phase retrieval microscopy technique for quantitative phase contrast imaging of cells using multiple intensity samplings of a volume speckle field in the axial direction is described. Single beam illumination with multiple intensity samplings provides fast convergence and a unique solution of the object wavefront. Three-dimensional thickness profiles of different cells such as red blood cells and onion skin cells were reconstructed using this technique with an axial resolution of the order of several nanometers.

Cell counting tool parameters optimization approach for electroporation efficiency determination of attached cells in phase contrast images

In this paper a novel parameter optimization approach for cell detection tool and counting cells procedure in phase contrast images are presented. Manual counting of the attached cells in phase contrast images is time-consuming and subjective. For evaluation of electroporation efficiency of attached cells, we often perform manual counting of the cells which is needed to determine the percentage of electroporated cells under different experimental conditions. Here we present an automated cell counting procedure based on novel artificial neural network optimization of Image-based Tool for Counting Nuclei algorithm parameters to fit the training image set based on counts from an expert. Comparing the results of automated cell counting to user manual counting a 90,31% average agreement was achieved which is reasonably good especially taking into account inter-person error which can be up to 10%. Even more, our procedure can also be used for fluorescent cell images with similar counting accuracy (>90%) enabling us to determine electroporation efficiency. In our experiments, the electroporation efficiency determined by manual cell counting was virtually the same as the one obtained by the automated procedure.

Sphingosine 1-Phosphate Modulates Spinal Nociceptive Processing

Sphingosine 1-Phosphate (S1P) modulates various cellular functions such as apoptosis, cell differentiation, and migration. Although S1P is an abundant signaling molecule in the central nervous system, very little is known about its influence on neuronal functions. We found that S1P concentrations were selectively decreased in the cerebrospinal fluid of adult rats in an acute and an inflammatory pain model. Pharmacological inhibition of sphingosine kinases (SPHK) decreased basal pain thresholds and SphK2 knock-out mice, but not SphK1 knock-out mice, had a significant decrease in withdrawal latency. Intrathecal application of S1P or sphinganine 1-phosphate (dihydro-S1P) reduced the pain-related (nociceptive) behavior in the formalin assay. S1P and dihydro-S1P inhibited cyclic AMP (cAMP) synthesis, a key second messenger of spinal nociceptive processing, in spinal cord neurons. By combining fluorescence resonance energy transfer (FRET)-based cAMP measurements with Multi Epitope Ligand Cartography (MELC), we showed that S1P decreased cAMP synthesis in excitatory dorsal horn neurons. Accordingly, intrathecal application of dihydro-S1P abolished the cAMP-dependent phosphorylation of NMDA receptors in the outer laminae of the spinal cord. Taken together, the data show that S1P modulates spinal nociceptive processing through inhibition of neuronal cAMP synthesis.

Towards rapid cervical cancer diagnosis: automated detection and classification of pathologic cells in phase-contrast images

In this paper, a combination of two methods based on texture analysis, contour grouping, and pattern recognition techniques is presented to detect and classify pathologic cells in cervical vaginal smears using the phase-contrast microscopy. The first method applies statistical geometrical features to detect image regions that contain epithelial cells and hide those regions with medium and contamination. Sequential forward floating selection was used to identify the most representative features. A shape of cells was identified by applying an active contour model supported by some postprocessing techniques. The second method applies edge detection, ridge following, contour grouping, and Fisher linear discriminant to detect abnormal nuclei. Evaluation of the algorithms' performance and comparison with alternative approaches show that both methods are reliable and, when combined, improve the classification. By presenting only images or their parts that are diagnostically important, the method unburdens a physician from massive and messy data. It also indicates abnormalities marking atypical nuclei and, in that sense, supports diagnosis of cervical cancer.