Automated Detection and Tracking of Cell Clusters in Time-Lapse Fluorescence Microscopy Images

Similarity Metrics for Subcellular Analysis of FRET Microscopy Videos

Understanding the heterogeneity of molecular environments within cells is an outstanding challenge of great fundamental and technological interest. Cells are organized into specialized compartments, each with distinct functions. These compartments exhibit dynamic heterogeneity under high-resolution microscopy, which reflects fluctuations in molecular populations, concentrations, and spatial distributions. To enhance our comprehension of the spatial relationships among molecules within cells, it is crucial to analyze images of high-resolution microscopy by clustering individual pixels according to their visible spatial properties and their temporal evolution. Here, we evaluate the effectiveness of similarity metrics based on their ability to facilitate fast and accurate data analysis in time and space. We discuss the capability of these metrics to differentiate subcellular localization, kinetics, and structures of protein-RNA interactions in Forster resonance energy transfer (FRET) microscopy videos, illustrated by a practical example from recent literature. Our results suggest that using the correlation similarity metric to cluster pixels of high-resolution microscopy data should improve the analysis of high-dimensional microscopy data in a wide range of applications.

Visualization and quantitative analyses for mouse embryonic stem cell tracking by manipulating hierarchical data structures using time-lapse confocal microscopy images

We present a cell tracking method for time-lapse confocal microscopy (3D) images that uses dynamic hierarchical data structures to assist cell and colony segmentation and tracking. During the segmentation, the cell and colony numbers and their geometric data are recorded for each 3D image set. In tracking, the colony correspondences between neighboring frames of time-lapse 3D images are first computed using the recorded colony centers. Then, cell correspondences in the correspondent colonies are computed using the recorded cell centers. The examples show the proposed cell tracking method can achieve high tracking accuracy for time-lapse 3D images of undifferentiated but self-renewing mouse embryonic stem (mES) cells where the number and mobility of ES cells in a cell colony may change suddenly by a colony merging or splitting, and cell proliferation or death. The geometric data in the hierarchical data structures also help the visualization and quantitation of the cell shapes and mobility.

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6^th edition of the Cell Tracking Challenge.

Prediction for Morphology and States of Stem Cell Colonies using a LSTM Network with Progressive Training Microscopy Images

We present a new LSTM (P-LSTM: Progressive LSTM) network, aiming to predict morphology and states of cell colonies from time-lapse microscopy images. Apparent short-term changes occur in some types of time-lapse cell images. Therefore, long-term-memory dependent LSTM networks may not predict accurately. The P-LSTM network incorporates the images newly generated from cell imaging progressively into LSTM training to emphasize the LSTM short-term memory and thus improve the prediction accuracy. The new images are input into a buffer to be selected for batch training. For real-time processing, parallel computation is introduced to implement concurrent training and prediction on partitioned images.Two types of stem cell images were used to show effectiveness of the P-LSTM network. One is for tracking of ES cell colonies. The actual and predicted ES cell images possess similar colony areas and the same transitions of colony states (moving, merging or morphology changing), although the predicted colony mergers may delay in several time-steps. The other is for prediction of iPS cell reprogramming from the CD34+ human cord blood cells. The actual and predicted iPS cell images possess high similarity evaluated by the PSNR and SSIM similarity evaluation metrics, indicating the reprogramming iPS cell colony features and morphology can be accurately predicted.

Automatic three-dimensional segmentation of mouse embryonic stem cell nuclei by utilising multiple channels of confocal fluorescence images

Time-lapse confocal fluorescence microscopy images from mouse embryonic stem cells (ESCs) carrying reporter genes, histone H2B-mCherry and Mvh-Venus, have been used to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. Accurate cell nucleus segmentation is required to analyse the ESC dynamics and differentiation at a single cell resolution. Several methods used concavities on nucleus contours to segment overlapping cell nuclei. Our proposed method evaluates not only the concavities but also the size and shape of every 2D nucleus region to determine if any of the strait, extrusion, convexity and large diameter criteria is satisfied to segment overlapping nuclei inside the region. We then use a 3D segmentation method to reconstruct simple, convex, and reasonably sized 3D nuclei along the image stacking direction using the radius and centre of every segmented region in respective microscopy images. To avoid false concavities on nucleus boundaries, fluorescence images of the H2B-mCherry reporter are used for localisation of cell nuclei and Venus fluorescence images are used for determining the cell colony ranges. We use a series of image preprocessing procedures to remove noise outside and inside cell colonies, and in respective nuclei, and to smooth nucleus boundaries based on the colony ranges.  We propose dynamic data structures to record every segmented nucleus region and solid in sets (volumes) of 3D confocal images. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei on microscopy images and that the segmentation method effectively segment out every nucleus with a reasonable size and shape. All 3D nuclei in a set (volume) of confocal microscopy images can be accessed by the dynamic data structures for 3D reconstruction. The 3D nuclei in time-lapse confocal microscopy images can be tracked to calculate cell movement and proliferation in consecutive volumes for understanding the dynamics of the differentiation characteristics about ESCs. LAY DESCRIPTION: Embryonic stem cells (ESCs) are considered as an ideal source for basic cell biology study and producing medically useful cells in vitro. This study uses time-lapse confocal fluorescence microscopy images from mouse ESCs carrying reporter gene to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. To automate analyses of ESC differentiation behaviours, accurate cell nucleus segmentation to distinguish respective cells are required. A series of image preprocessing procedures are implemented to remove noise in live-cell fluorescence images but yield overlapping cell nuclei. A segmentation method that evaluates boundary concavities and the size and shape of every nucleus is then used to determine if any of the strait, extrusion, convexity, large and local minimum diameter criteria satisfied to segment overlapping nuclei. We propose a dynamic data structure to record every newly segmented nucleus. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei and that the segmentation method effectively detects overlapping nuclei. All segmented nuclei in confocal images can be accessed using the dynamic data structures to be visualised and manipulated for quantitative analyses of the ESC differentiation behaviours. The manipulation can be tracking of segmented 3D cell nuclei in time-lapse images to calculate their dynamics of differentiation characteristics.

An Application of Photoactivatable Substrate for the Evaluation of Epithelial-mesenchymal Transition Inhibitors

Epithelial-mesenchymal transition (EMT), phenotypic changes in cell adhesion and migration, is involved in cancer invasion and metastasis, hence becoming a target for anti-cancer drugs. In this study, we report a method for the evaluation of EMT inhibitors by using a photoactivatable gold substrate, which changes from non-cell-adhesive to cell-adhesive in response to light. The method is based on the geometrical confinement of cell clusters and the subsequent migration induction by controlled photoirradiation of the substrate. As a proof-of-concept experiment, a known EMT inhibitor was successfully evaluated in terms of the changes in cluster area or leader cell appearance, in response to biochemically and mechanically induced EMT. Furthermore, an application of the present method for microbial secondary metabolites identified nanaomycin H as an EMT inhibitor, potentially killing EMTed cells in disseminated conditions. These results demonstrate the potential of the present method for screening new EMT inhibitors.