In the case of epithelial cells, mature cell—cell adhesions subsequently form including specialized cell—cell adhesion complexes, such as AJs and TJs. Simultaneously, apicobasal polarization establishes. After the formation of mature cell—cell adhesions, cells cease migration and proliferation. This process is called mesenchymal—epithelial transition MET. During MET, cells use many of the molecular components of cell adhesion and signaling systems.
In addition, afadin, which directly interacts with nectins at AJs, is also localized at a leading edge of migrating cells and regulates directional cell migration including formation of leading-edge structures, as mentioned in Section 3.
Furthermore, when cells collide, Necl-5 and nectins trans- interact at contact sites. This interaction enhances the nectin—afadin interaction, which in turn enhances the nectin—nectin interaction. Thus, the Necl-5—nectin, nectin—nectin, and nectin—afadin interactions cooperatively increase the clustering of the nectin—afadin complex at the primordial cell—cell contact sites, promoting the formation of the mature nectin-based cell—cell adhesion structures. Edwin L. Cooper, in NeuroImmune Biology , Cell migration is a key event in natural invertebrate and vertebrate immunity .
Microbial products like lipopolysaccharide LPS and formyl-methyl-leucyl-phenylalanine fMLP promote cell recruitment to infection sites. In mammals, complement activation by factors such as zymosan induces C5a production, and that in turn influences leukocyte migration. The endogenous factor hyaluronic acid HA , an extracellular matrix component, also promotes cell migration through its receptor CD Using microchemotaxis chambers, leukocytes coelomocytes from the sipunculan worm Themiste petricola migrate towards exogenously and endogenously derived chemoattractants [LPS, fMLP, or zymosan treated plasma ZTP ].
HA mediated effects are blocked by the monoclonal antibody IM7 directed to mouse CD44, suggesting that a CDlike cross-reactive antigen might play a role in HA mediated coelomocyte locomotion. Jamie A. Davies, in Mechanisms of Morphogenesis Second Edition , Cell migration of one sort or another is an almost universal attribute of sexual reproduction, for the obvious reason that two gametes produced in different places, and usually in different individuals, have to unite.
In these cases, the gametes share the task of finding each other. This does not always involve cell migration; in the filamentous alga Spyrogyra , for example, gametes remain in the organism that produces them and find each other simply by extending a cell process towards the gametes of an adjacent individual 1 Figure 7. In most cases, though, adults are not present at a high enough density for this to be feasible, and gametes have to migrate by crawling or swimming.
In both plants and animals, migration has favoured the loss of isogamy and the specialization of gametes either to be nutrient-rich and relatively immobile ova , or numerous, small and specialized for migration sperm.
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Mechanisms for bringing gametes together. The upper conjugation is seen at a late stage, after union of gametes, while the lower one is just beginning. Anders I. Persson PhD, William A. Weiss PhD, in Neurobiology of Disease , Cellular migration is dependent on adhesion to the surrounding matrix. Extracellular matrix ECM proteins fibronectin, laminin, collagen, vitronectin, and tenascin in the brain are localized to the perivascular space.
These proteins interact with adhesion molecules such as integrins and cadherins. Altered levels of the ECM proteins in the tumor cells and surrounding tissue contribute to tumor cell migration. One of the most important hallmarks of malignant gliomas is their invasive behavior.
Cell migration and proliferation during the in vitro wound repair of the respiratory epithelium.
Glioma migration and invasion are influenced by overexpression of matrix metalloproteinases MMPs in glioma. Invasion of tumor cells is regulated by several proteins. Rho proteins are proteins that cycle between GTP- and GDP-bound states and therefore have a multitude of functions, including effects on the migratory behavior of glioma cells. Inhibition or overexpression of these proteins has been shown to alter migration of glioma cells. Some studies suggest that radiotherapy reduces glioma invasion in xenografts and cultures.
Samples for which the difference of the two highest class probabilities was below a threshold of 0. To automatically correct remaining classification errors and ensure consistent class sequences we exploit the temporal correspondences determined by our tracking approach using a state transition model. Note that previous approaches for correction of classification errors e.
However, in our application almost all class transitions are valid given the relatively low number of classes except transitions from apoptosis to interphase or mitosis. Consequently, we developed an approach for correction of classification errors which additionally includes information from the tracking approach, i.
For example, in case mitosis has not been detected by the classifier it might have been detected by tracking due to the appearance of a new object. Thus, our approach incorporates additional information on changes of the object topology. With regard to the high importance of mitosis for our biological application we identified three major criteria which needed to be enforced by our approach for error correction: 1 avoiding false negative mitosis, 2 avoiding false positive mitosis, and 3 avoiding false positive apoptosis.
Criterion 1 is achieved by assigning at each split of a trajectory the class mitosis to the mother cell as well as to all direct daughter cells Fig. Criterion 2 requires checking all cell nuclei which have been classified as mitosis by the classifier. A nucleus is confirmed as being a real mitosis if it is involved in a trajectory splitting event mother or daughter cell, Fig.
A nucleus is also confirmed as being mitotic if the successor is classified as cluster because a split would not be detectable in a cluster , and if the direct successor is a valid apoptosis case see criterion 3 for apoptosis check, Fig. The latter case represents segregation problems with a potential mitotic delay, where a cell starts performing mitosis but is not able to split and subsequently goes into apoptosis. If none of these conditions holds, the class of the respective nucleus is corrected to interphase. For criterion 3 we check for each nucleus of class apoptosis, first, whether it is directly followed by at least one other nucleus of class apoptosis, and second, we determine the percentage of apoptotic nuclei in the whole sequence of its successors i.
Otherwise, the nucleus is corrected to interphase. This correction scheme can also be formulated in terms of a deterministic finite state transition model and its corresponding accepted regular language see, e. The corresponding finite state machine is depicted in Figure 4 e. If an input sequence is not accepted by the finite state machine, that is, it is not part of the corresponding regular language, the respective symbols are corrected accordingly as described above.
In our application we used our image analysis approach for analyzing the screening data but also for verification of the experimental settings as well as for improvement of the subsequent statistical analysis. Therefore, we systematically analyzed the computed trajectories with respect to global motion and proliferation properties. In the first step, we determined the average cell motility for each spot, that is, we computed the mean displacements considering all cells and all time steps per spot.
Next, we developed a measure to estimate the fraction of cell nuclei remaining in a spot over the whole observation period. Finally, we extracted rare events, that is, cell divisions into more than two daughter cells. Note that these events are very hard to identify manually due to the large number of cells within the field of view and the very rare occurrence of these events. The fraction of cells that were seeded in the spot area and are still located there at the evaluation time point e.
To estimate the fraction of those cells we analyzed the trajectories w. For this analysis, we define a border region in the images having the width of the maximum possible displacement d max Fig. Trajectories starting within this border region in frames later than the first frame are assumed to be cells entering the spot region. However, only if the trajectories of entering cells are ending within the inner image region i. Correspondingly, trajectories that are ending within the border region before the last frame are identified as cells leaving the spot region and are counted as n leave not including n enter,leave , Fig.
For leaving cells, we check whether there exists an entering cell very close to the leaving position in the subsequent frame. We can also determine the number of cells at the final time point n end , representing all trajectories ending in the spot region at the evaluation time point. Note that the number of such cells cannot be directly measured but these cells are included in the determined number of leaving cells n l eave together with those cells that definitely leave the spot n leave,final Eq.
This leads to the following equations, where n orig is the total number of cells originating from the spot region Eq. Left: Sketch of a cell array with multiple spots, the arrows represent potential cell migration patterns. Right: Types of trajectories of leaving and entering cells; Image border region blue and image inner region gray. Solving this system of equations with the three unknowns n orig , n leave,final , and n leave,enter allows us to determine the fraction of cells remaining in the spot region with respect to the evaluation time point by using. Multipolar divisions i.
However, multipolar divisions are rare and difficult to identify manually in a large data set as a human observer would need to go through all movies, each with around frames and showing a large number of nuclei. Also, the observer would need to look at the same movie many times examining different regions in the field of view since generally one can focus on only a few neighboring cells at a time. To support the evaluation of multipolar divisions, we developed an automatic approach for detecting such events.
The basis for this approach is our tracking approach which allows not only tracking mitotic cells with two daughter cells, but also allows tracking mitotic cells with more than two daughter cells using the same criteria as described above. This enables coping with abnormal cell divisions. To determine multipolar divisions, we analyze all cell lineage trees and determine the number of daughter cell nuclei. We also developed a tool for visualizing the resulting multipolar divisions, which provides the coordinates and the time frame of detected potential multipolar divisions in the videos.
Rapid quantification of cellular proliferation and migration using ImageJ | BioTechniques
Thus, an observer can directly inspect the candidates and only needs to check them without tedious searching. We have applied our approach to a large data set of a screen using two neuroblastoma cell lines i. The data set comprises 4, image sequences with to time steps total number of images: , , see also above in section Materials and Methods. We used multiple computers of a computing cluster and performed the computation in parallel. For each step of our approach the performance has been quantitatively evaluated based on manually determined ground truth as described below.
There, the performance of six different methods was quantified based on a large spectrum of different types of cell microscopy images. It turned out that our approach was among the two best approaches overall, and, in particular, for the images most similar to the images used here i. First, the Dice coefficient was computed to determine the similarity between the area of the automatic segmentation result and the manually determined ground truth segmentation.
The Dice coefficient provides a measure for the normalized overlap of two segmented images: where A auto and A man represent the area in pixels of the automatic and manually determined segmentation, respectively. We randomly selected two manually segmented images from different cell arrays comprising a total number of cell nuclei and obtained a high overall Dice coefficient of 0. A difference image of both segmentations showed that the discrepancy was due to generally smaller cell areas in the automatic segmentation.
It turned out that our segmentation approach yielded a high accuracy of The tracking accuracy was determined by quantifying the number of correct correspondences, trajectory breaks, and wrong correspondences i. To this end, we manually checked complete trajectories including 14, correspondences see Table 2.
The overall percentage of correct correspondences turned out to be We obtained 1. The wrong correspondences resulted from errors during correspondence finding e. We studied which feature normalization strategy is most appropriate for our data. For the three normalized feature sets the transformation parameters were determined 1 based on the training set, 2 based on a data set including images from all spots of all cell arrays, and 3 based on each single image.
The classification performance was evaluated using an independent, manually annotated test set see section Materials and Methods , and the obtained confusion matrices are given in Table 3 , left. To compare the classification performance we used the following measures based on the number of true positives TP , false positives FP , true negatives TN , and false negatives FN for each class see Table 3 :.
It turned out that for the independent test set the highest average values for all four measures i. From the performance values, it turns out that the normalization generally reduced the classification accuracy. Standard statistical tests for normality could not be used because of the very large sample number , samples.
The sample set was generated by selecting 4 to 5 time steps i. This resulted in high classification accuracies of However, applying the trained classifier to other data from the screen and checking example results showed that the accuracy was typically lower.
Thus, to determine a more realistic estimate of the classification accuracy, an independent test set was generated by systematically drawing samples from the complete data set as described in section Materials and Methods. It turned out that the accuracies using the independent test set were lower compared with the training set, that is, Only the sensitivity was slightly decreased from However, the number of false positive mitosis samples as well as the number of confusions between mitosis and apoptosis was significantly decreased.
A low number of false positive mitosis samples i. We systematically analyzed the automatically extracted trajectories to optimize the experimental settings and support the statistical analysis and hit detection. First, we analyzed the cell motility for each spot, and second, we evaluated the global proliferation behavior of the cells.
By analyzing the cell motility e. The global migration behavior of cells is caused, for example, by a gradient of growth medium.
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Figure 6 a shows the average displacement for each spot of a cell array averaged over eight cell arrays with similar layout as a heatmap. The average cell motility turned out to be generally higher in the border regions of the cell array than in the center region. This might be caused by cells seeded outside of the spotting region which migrate to the center where more growth medium is available. Furthermore, we determined the percentage of cells remaining in the spot where they have been seeded and from which they took up the siRNA.
In Figure 6 c, the fraction of cells remaining in the spot region has been determined at four different time points of the total observation period and plotted as a time curve. The plot shows that the percentage of cells originating from the respective spot decreases over time. This value provides an estimate of the confidence of the experimental observations w. The confidence per spot i. Moreover, the specific spatial pattern characterized by higher confidence values in the middle of the cell array and lower confidence values in rows and columns close to the border of the cell array helps us to adequately preprocess our data before the final hit detection is performed.
Mechanisms for bringing gametes together. The upper conjugation is seen at a late stage, after union of gametes, while the lower one is just beginning. Anders I. Persson PhD, William A. Weiss PhD, in Neurobiology of Disease , Cellular migration is dependent on adhesion to the surrounding matrix. Extracellular matrix ECM proteins fibronectin, laminin, collagen, vitronectin, and tenascin in the brain are localized to the perivascular space. These proteins interact with adhesion molecules such as integrins and cadherins. Altered levels of the ECM proteins in the tumor cells and surrounding tissue contribute to tumor cell migration.
Proliferation and Migration of Label-Retaining Cells of the Kidney Papilla
One of the most important hallmarks of malignant gliomas is their invasive behavior. Glioma migration and invasion are influenced by overexpression of matrix metalloproteinases MMPs in glioma. Invasion of tumor cells is regulated by several proteins. Rho proteins are proteins that cycle between GTP- and GDP-bound states and therefore have a multitude of functions, including effects on the migratory behavior of glioma cells. Inhibition or overexpression of these proteins has been shown to alter migration of glioma cells. Some studies suggest that radiotherapy reduces glioma invasion in xenografts and cultures.
However, clinical trials to date have not demonstrated reduced invasion of tumor cells in patients. Sandip Biswal, in Arthritis in Color , Cell trafficking of specific cell populations to inflamed joints is a common strategy to image RA and related autoimmune diseases. This concept is the underlying premise for many long-established radionuclide-based techniques such as the gallium 67 Ga scan and the WBC scan.
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Barriga, Roberto Mayor, in Current Topics in Developmental Biology , Abstract Cell migration is essential for morphogenesis, adult tissue remodeling, wound healing, and cancer cell migration. Gagnon, in Current Topics in Membranes , 3. Natural Immunity Edwin L. Cooper, in NeuroImmune Biology , 2. Cell Migration in Development Jamie A. Davies, in Mechanisms of Morphogenesis Second Edition , Cell migration of one sort or another is an almost universal attribute of sexual reproduction, for the obvious reason that two gametes produced in different places, and usually in different individuals, have to unite.
Glioma Anders I. Mechanisms of Glioma Cell Migration and Invasion Cellular migration is dependent on adhesion to the surrounding matrix.