A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters

Multi-feature-Based Robust Cell Tracking

Cell tracking algorithms have been used to extract cell counts and motility information from time-lapse images of migrating cells. However, these algorithms often fail when the collected images have cells with spatially and temporally varying features, such as morphology, position, and signal-to-noise ratio. Consequently, state-of-the-art algorithms are not robust or reliable because they require manual inputs to overcome the cell feature changes. To address these issues, we present a fully automated, adaptive, and robust feature-based cell tracking algorithm for the accurate detection and tracking of cells in time-lapse images. Our algorithm tackles measurement limitations twofold. First, we use Hessian filtering and adaptive thresholding to detect the cells in images, overcoming spatial feature variations among the existing cells without manually changing the input thresholds. Second, cell feature parameters are measured, including position, diameter, mean intensity, area, and orientation, and these parameters are simultaneously used to accurately track the cells between subsequent frames, even under poor temporal resolution. Our technique achieved a minimum of 92% detection and tracking accuracy, compared to 16% from Mosaic and Trackmate. Our improved method allows for extended tracking and characterization of heterogeneous cell behavior that are of particular interest for intravital imaging users.

In vitro neutrophil migration is associated with inhaled corticosteroid treatment and serum cytokines in pediatric asthma

Background: Different asthma phenotypes are driven by molecular endotypes. A Th1-high phenotype is linked to severe, therapy-refractory asthma, subclinical infections and neutrophil inflammation. Previously, we found neutrophil granulocytes (NGs) from asthmatics exhibit decreased chemotaxis towards leukotriene B4 (LTB₄), a chemoattractant involved in inflammation response. We hypothesized that this pattern is driven by asthma in general and aggravated in a Th1-high phenotype.

Methods: NGs from asthmatic nd healthy children were stimulated with 10 nM LTB₄/100 nM N-formylmethionine-leucyl-phenylalanine and neutrophil migration was documented following our prior SiMA (simplified migration assay) workflow, capturing morphologic and dynamic parameters from single-cell tracking in the images. Demographic, clinical and serum cytokine data were determined in the ALLIANCE cohort.

Results: A reduced chemotactic response towards LTB₄ was confirmed in asthmatic donors regardless of inhaled corticosteroid (ICS) treatment. By contrast, only NGs from ICS-treated asthmatic children migrate similarly to controls with the exception of Th1-high donors, whose NGs presented a reduced and less directed migration towards the chemokines. ICS-treated and Th1-high asthmatic donors present an altered surface receptor profile, which partly correlates with migration.

Conclusions: Neutrophil migration in vitro may be affected by ICS-therapy or a Th1-high phenotype. This may be explained by alteration of receptor expression and could be used as a tool to monitor asthma treatment.

Method for simultaneous tracking of thousands of unlabeled cells within a transparent 3D matrix

Three-dimensional tracking of cells is one of the most powerful methods to investigate multicellular phenomena, such as ontogenesis, tumor formation or wound healing. However, 3D tracking in a biological environment usually requires fluorescent labeling of the cells and elaborate equipment, such as automated light sheet or confocal microscopy. Here we present a simple method for 3D tracking large numbers of unlabeled cells in a collagen matrix. Using a small lensless imaging setup, consisting of an LED and a photo sensor only, we were able to simultaneously track ~3000 human neutrophil granulocytes in a collagen droplet within an unusually large field of view (>50 mm²) at a time resolution of 4 seconds and a spatial resolution of ~1.5 μm in xy- and ~30 μm in z-direction. The setup, which is small enough to fit into any conventional incubator, was used to investigate chemotaxis towards interleukin-8 (IL-8 or CXCL8) and N-formylmethionyl-leucyl-phenylalanine (fMLP). The influence of varying stiffness and pore size of the embedding collagen matrix could also be quantified. Furthermore, we demonstrate our setup to be capable of telling apart healthy neutrophils from those where a condition of inflammation was (I) induced by exposure to lipopolysaccharide (LPS) and (II) caused by a pre-existing asthma condition. Over the course of our experiments we have tracked more than 420.000 cells. The large cell numbers increase statistical relevance to not only quantify cellular behavior in research, but to make it suitable for future diagnostic applications, too.

DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking

Accurate segmentation and tracking of cells in microscopy image sequences is extremely beneficial in clinical diagnostic applications and biomedical research. A continuing challenge is the segmentation of dense touching cells and deforming cells with indistinct boundaries, in low signal-to-noise-ratio images. In this paper, we present a dual-stream marker-guided network (DMNet) for segmentation of touching cells in microscopy videos of many cell types. DMNet uses an explicit cell marker-detection stream, with a separate mask-prediction stream using a distance map penalty function, which enables supervised training to focus attention on touching and nearby cells. For multi-object cell tracking we use M2Track tracking-by-detection approach with multi-step data association. Our M2Track with mask overlap includes short term track-to-cell association followed by track-to-track association to re-link tracklets with missing segmentation masks over a short sequence of frames. Our combined detection, segmentation and tracking algorithm has proven its potential on the IEEE ISBI 2021 6th Cell Tracking Challenge (CTC-6) where we achieved multiple top three rankings for diverse cell types. Our team name is MU-Ba-US, and the implementation of DMNet is available at, http://celltrackingchallenge.net/participants/MU-Ba-US/.

Cellpose: a generalist algorithm for cellular segmentation

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

Holographic Imaging of Insect Cell Cultures: Online Non-Invasive Monitoring of Adeno-Associated Virus Production and Cell Concentration

The insect cell-baculovirus vector system has become one of the favorite platforms for the expression of viral vectors for vaccination and gene therapy purposes. As it is a lytic system, it is essential to balance maximum recombinant product expression with harvest time, minimizing product exposure to detrimental proteases. With this purpose, new bioprocess monitoring solutions are needed to accurately estimate culture progression. Herein, we used online digital holographic microscopy (DHM) to monitor bioreactor cultures of Sf9 insect cells. Batches of baculovirus-infected Sf9 cells producing recombinant adeno-associated virus (AAV) and non-infected cells were used to evaluate DHM prediction capabilities for viable cell concentration, culture viability and AAV titer. Over 30 cell-related optical attributes were quantified using DHM, followed by a forward stepwise regression to select the most significant (p < 0.05) parameters for each variable. We then applied multiple linear regression to obtain models which were able to predict culture variables with root mean squared errors (RMSE) of 7 × 105 cells/mL, 3% for cell viability and 2 × 103 AAV/cell for 3-fold cross-validation. Overall, this work shows that DHM can be implemented for online monitoring of Sf9 concentration and viability, also permitting to monitor product titer, namely AAV, or culture progression in lytic systems, making it a valuable tool to support the time of harvest decision and for the establishment of controlled feeding strategies.

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel denoising algorithm, based on sparse coding, that can both enhance very faint and noisy nuclei signal but simultaneously detect nuclei position accurately. Furthermore our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. We also show that our denoising method coupled with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, we tested our method on two datasets from the cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96:96% recall for the C. elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99:3%).

Tumstatin fragment selectively inhibits neutrophil infiltration in experimental asthma exacerbation

BACKGROUND

Asthma is a chronic inflammatory disease with structural changes present. Burgess and colleagues recently found tumstatin markedly reduced in adult asthmatic lung tissue compared with nonasthmatics. ECM fragments such as tumstatin are named matrikines and act independently of the parent molecule. The role of Col IV matrikines in neutrophil inflammation (eg. exacerbation in asthma) has not been investigated to date. Severe adult asthma phenotypes are dominated by neutrophilic inflammation and show a high frequency of severe exacerbations.

OBJECTIVE

This study sought to investigate the role of a novel active region within tumstatin (CP17) and its implication in neutrophil inflammatory responses related to asthma exacerbation.

METHODS

For reactive oxygen production, isolated neutrophils were preincubated with peptides or vehicle for 1 hour and stimulated (PMA). Luminescence signal was recorded (integration over 10 seconds) for 1.5 hours. Neutrophil migration was performed according to the SiMA protocol. Mice were sensitized to OVA/Alumn by intraperitoneal (i.p.) injections. Mice were then treated with CP17, vehicle (PBS) or scrambled peptide (SP17) after OVA exposure (days 27 and 28, polyI:C stimulation). All animals were killed on day 29 with lung function measurement, histology and lavage.

RESULTS

CP17 decreased total ROS production rate to 52.44% (0.5 μmol/L, P < 0.05 vs SP17), reduced the in vitro directionality (vs SP17, P = 1 × 10^-6 ) and migration speed (5 μmol/L, P = 1 × 10^-3 ). In vivo application of CP17 decreased neutrophil inflammation ~1.8-fold (P < 0.001 vs SP17) and reduced numbers of mucus-producing cells (-29%, P < 0.05).

CONCLUSION

CP17 reduced the ROS production rate, migrational speed and selectively inhibited neutrophil accumulation in the lung interstitium and lumen.

CLINICAL RELEVANCE

CP17 may serve as a potential precursor for drug development to combat overwhelming neutrophil inflammation.

A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos

We present a novel computational framework for the analysis of high-throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made. Spatial, temporal, and cross-sectional variation of the analysed data are accommodated by two key contributions. First, time series analysis is exploited to estimate the temporal cell shape uncertainty in addition to cell trajectory. Second, a fast marching (FM) algorithm is used to integrate the inferred cell properties with the observed image measurements in order to obtain image likelihood for cell segmentation, and association. The proposed approach has been tested on eight different time-lapse microscopy data sets, some of which are high-throughput, demonstrating promising results for the detection, segmentation and association of planar cells. Our results surpass the state of the art for the Fluo-C2DL-MSC data set of the Cell Tracking Challenge (Maška et al., 2014).

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Here, we demonstrate that lens-free video microscopy enables us to simultaneously capture the kinetics of thousands of cells directly inside the incubator and that it is possible to monitor and quantify single cells along several cell cycles. We describe the full protocol used to monitor and quantify a HeLa cell culture for 2.7 days. First, cell culture acquisition is performed with a lens-free video microscope, and then the data is analyzed following a four-step process: multi-wavelength holographic reconstruction, cell-tracking, cell segmentation and cell division detection algorithms. As a result, we show that it is possible to gather a dataset featuring more than 10,000 cell cycle tracks and more than 2 x 10⁶ cell morphological measurements.

Quantitative label-free single cell tracking in 3D biomimetic matrices

Live cell imaging enables an observation of cell behavior over a period of time and is a growing field in modern cell biology. Quantitative analysis of the spatio-temporal dynamics of heterogeneous cell populations in three-dimensional (3D) microenvironments contributes a better understanding of cell-cell and cell-matrix interactions for many biomedical questions of physiological and pathological processes. However, current live cell imaging and analysis techniques are frequently limited by non-physiological 2D settings. Furthermore, they often rely on cell labelling by fluorescent dyes or expression of fluorescent proteins to enhance contrast of cells, which frequently affects cell viability and behavior of cells. In this work, we present a quantitative, label-free 3D single cell tracking technique using standard bright-field microscopy and affordable computational resources for data analysis. We demonstrate the efficacy of the automated method by studying migratory behavior of a large number of primary human macrophages over long time periods of several days in a biomimetic 3D microenvironment. The new technology provides a highly affordable platform for long-term studies of single cell behavior in 3D settings with minimal cell manipulation and can be implemented for various studies regarding cell-matrix interactions, cell-cell interactions as well as drug screening platform for primary and heterogeneous cell populations.

Novel therapeutic roles for surfactant-inositols and -phosphatidylglycerols in a neonatal piglet ARDS model: a translational study

The biological and immune-protective properties of surfactant-derived phospholipids and phospholipid subfractions in the context of neonatal inflammatory lung disease are widely unknown. Using a porcine neonatal triple-hit acute respiratory distress syndrome (ARDS) model (repeated airway lavage, overventilation, and LPS instillation into airways), we assessed whether the supplementation of surfactant (S; poractant alfa) with inositol derivatives [inositol 1,2,6-trisphosphate (IP3) or phosphatidylinositol 3,5-bisphosphate (PIP2)] or phosphatidylglycerol subfractions [16:0/18:1-palmitoyloleoyl-phosphatidylglycerol (POPG) or 18:1/18:1-dioleoyl-phosphatidylglycerol (DOPG)] would result in improved clinical parameters and sought to characterize changes in key inflammatory pathways behind these improvements. Within 72 h of mechanical ventilation, the oxygenation index (S+IP3, S+PIP2, and S+POPG), the ventilation efficiency index (S+IP3 and S+POPG), the compliance (S+IP3 and S+POPG) and resistance (S+POPG) of the respiratory system, and the extravascular lung water index (S+IP3 and S+POPG) significantly improved compared with S treatment alone. The inositol derivatives (mainly S+IP3) exerted their actions by suppressing acid sphingomyelinase activity and dependent ceramide production, linked with the suppression of the inflammasome nucleotide-binding domain, leucine-rich repeat-containing protein-3 (NLRP3)-apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC)-caspase-1 complex, and the profibrotic response represented by the cytokines transforming growth factor-β1 and IFN-γ, matrix metalloproteinase (MMP)-1/8, and elastin. In addition, IκB kinase activity was significantly reduced. S+POPG and S+DOPG treatment inhibited polymorphonuclear leukocyte activity (MMP-8 and myeloperoxidase) and the production of interleukin-6, maintained alveolar-capillary barrier functions, and reduced alveolar epithelial cell apoptosis, all of which resulted in reduced pulmonary edema. S+DOPG also limited the profibrotic response. We conclude that highly concentrated inositol derivatives and phosphatidylglycerol subfractions in surfactant preparations mitigate key inflammatory pathways in inflammatory lung disease and that their clinical application may be of interest for future treatment of the acute exudative phase of neonatal ARDS.

Tumstatin regulates the angiogenic and inflammatory potential of airway smooth muscle extracellular matrix

The extracellular matrix (ECM) creates the microenvironment of the tissue; an altered ECM in the asthmatic airway may be central in airway inflammation and remodelling. Tumstatin is a collagen IV‐derived matrikine reduced in the asthmatic airway wall that reverses airway inflammation and remodelling in small and large animal models of asthma. This study hypothesized that the mechanisms underlying the broad asthma‐resolving effects of tumstatin were due to autocrine remodelling of the ECM. Neutrophils and endothelial cells were seeded on decellularized ECM of non‐asthmatic (NA) or asthmatic (A) airway smooth muscle (ASM) cells previously exposed to tumstatin in the presence or absence of a broad matrix metalloproteinase inhibitor, Marimastat. Gene expression in NA and A ASM induced by tumstatin was assessed using RT‐PCR arrays. The presence of tumstatin during ECM deposition affected neutrophil and endothelial cell properties on both NA and A ASM‐derived matrices and this was only partly due to MMP activity. Gene expression patterns in response to tumstatin in NA and A ASM cells were different. Tumstatin may foster an anti‐inflammatory and anti‐angiogenic microenvironment by modifying ASM‐derived ECM. Further work is required to examine whether restoring tumstatin levels in the asthmatic airway represents a potential novel therapeutic approach.

Characterising live cell behaviour: Traditional label-free and quantitative phase imaging approaches

Label-free imaging uses inherent contrast mechanisms within cells to create image contrast without introducing dyes/labels, which may confound results. Quantitative phase imaging is label-free and offers higher content and contrast compared to traditional techniques. High-contrast images facilitate generation of individual cell metrics via more robust segmentation and tracking, enabling formation of a label-free dynamic phenotype describing cell-to-cell heterogeneity and temporal changes. Compared to population-level averages, individual cell-level dynamic phenotypes have greater power to differentiate between cellular responses to treatments, which has clinical relevance e.g. in the treatment of cancer. Furthermore, as the data is obtained label-free, the same cells can be used for further assays or expansion, of potential benefit for the fields of regenerative and personalised medicine.

Global linking of cell tracks using the Viterbi algorithm

Automated tracking of living cells in microscopy image sequences is an important and challenging problem. With this application in mind, we propose a global track linking algorithm, which links cell outlines generated by a segmentation algorithm into tracks. The algorithm adds tracks to the image sequence one at a time, in a way which uses information from the complete image sequence in every linking decision. This is achieved by finding the tracks which give the largest possible increases to a probabilistically motivated scoring function, using the Viterbi algorithm. We also present a novel way to alter previously created tracks when new tracks are created, thus mitigating the effects of error propagation. The algorithm can handle mitosis, apoptosis, and migration in and out of the imaged area, and can also deal with false positives, missed detections, and clusters of jointly segmented cells. The algorithm performance is demonstrated on two challenging datasets acquired using bright-field microscopy, but in principle, the algorithm can be used with any cell type and any imaging technique, presuming there is a suitable segmentation algorithm.

Graphical model for joint segmentation and tracking of multiple dividing cells

MOTIVATION

To gain fundamental insight into the development of embryos, biologists seek to understand the fate of each and every embryonic cell. For the generation of cell tracks in embryogenesis, so-called tracking-by-assignment methods are flexible approaches. However, as every two-stage approach, they suffer from irrevocable errors propagated from the first stage to the second stage, here from segmentation to tracking. It is therefore desirable to model segmentation and tracking in a joint holistic assignment framework allowing the two stages to maximally benefit from each other.

RESULTS

We propose a probabilistic graphical model, which both automatically selects the best segments from a time series of oversegmented images/volumes and links them across time. This is realized by introducing intra-frame and inter-frame constraints between conflicting segmentation and tracking hypotheses while at the same time allowing for cell division. We show the efficiency of our algorithm on a challenging 3D+t cell tracking dataset from Drosophila embryogenesis and on a 2D+t dataset of proliferating cells in a dense population with frequent overlaps. On the latter, we achieve results significantly better than state-of-the-art tracking methods.

AVAILABILITY AND IMPLEMENTATION

Source code and the 3D+t Drosophila dataset along with our manual annotations will be freely available on http://hci.iwr.uni-heidelberg.de/MIP/Research/tracking/

Quantitative characterization of cell behaviors through cell cycle progression via automated cell tracking

Cell behaviors are reflections of intracellular tension dynamics and play important roles in many cellular processes. In this study, temporal variations in cell geometry and cell motion through cell cycle progression were quantitatively characterized via automated cell tracking for MCF-10A non-transformed breast cells, MCF-7 non-invasive breast cancer cells, and MDA-MB-231 highly metastatic breast cancer cells. A new cell segmentation method, which combines the threshold method and our modified edge based active contour method, was applied to optimize cell boundary detection for all cells in the field-of-view. An automated cell-tracking program was implemented to conduct live cell tracking over 40 hours for the three cell lines. The cell boundary and location information was measured and aligned with cell cycle progression with constructed cell lineage trees. Cell behaviors were studied in terms of cell geometry and cell motion. For cell geometry, cell area and cell axis ratio were investigated. For cell motion, instantaneous migration speed, cell motion type, as well as cell motion range were analyzed. We applied a cell-based approach that allows us to examine and compare temporal variations of cell behavior along with cell cycle progression at a single cell level. Cell body geometry along with distribution of peripheral protrusion structures appears to be associated with cell motion features. Migration speed together with motion type and motion ranges are required to distinguish the three cell-lines examined. We found that cells dividing or overlapping vertically are unique features of cell malignancy for both MCF-7 and MDA-MB-231 cells, whereas abrupt changes in cell body geometry and cell motion during mitosis are unique to highly metastatic MDA-MB-231 cells. Taken together, our live cell tracking system serves as an invaluable tool to identify cell behaviors that are unique to malignant and/or highly metastatic breast cancer cells.

Heterogeneous structure of stem cells dynamics: statistical models and quantitative predictions

Understanding stem cell (SC) population dynamics is essential for developing models that can be used in basic science and medicine, to aid in predicting cells fate. These models can be used as tools e.g. in studying patho-physiological events at the cellular and tissue level, predicting (mal)functions along the developmental course, and personalized regenerative medicine. Using time-lapsed imaging and statistical tools, we show that the dynamics of SC populations involve a heterogeneous structure consisting of multiple sub-population behaviors. Using non-Gaussian statistical approaches, we identify the co-existence of fast and slow dividing subpopulations, and quiescent cells, in stem cells from three species. The mathematical analysis also shows that, instead of developing independently, SCs exhibit a time-dependent fractal behavior as they interact with each other through molecular and tactile signals. These findings suggest that more sophisticated models of SC dynamics should view SC populations as a collective and avoid the simplifying homogeneity assumption by accounting for the presence of more than one dividing sub-population, and their multi-fractal characteristics.

A benchmark for comparison of cell tracking algorithms

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark.

Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.

Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.

Contact:codesolorzano@unav.es

Supplementary information:Supplementary data are available at Bioinformatics online.

Labour-efficient in vitro lymphocyte population tracking and fate prediction using automation and manual review

Interest in cell heterogeneity and differentiation has recently led to increased use of time-lapse microscopy. Previous studies have shown that cell fate may be determined well in advance of the event. We used a mixture of automation and manual review of time-lapse live cell imaging to track the positions, contours, divisions, deaths and lineage of 44 B-lymphocyte founders and their 631 progeny in vitro over a period of 108 hours. Using this data to train a Support Vector Machine classifier, we were retrospectively able to predict the fates of individual lymphocytes with more than 90% accuracy, using only time-lapse imaging captured prior to mitosis or death of 90% of all cells. The motivation for this paper is to explore the impact of labour-efficient assistive software tools that allow larger and more ambitious live-cell time-lapse microscopy studies. After training on this data, we show that machine learning methods can be used for realtime prediction of individual cell fates. These techniques could lead to realtime cell culture segregation for purposes such as phenotype screening. We were able to produce a large volume of data with less effort than previously reported, due to the image processing, computer vision, tracking and human-computer interaction tools used. We describe the workflow of the software-assisted experiments and the graphical interfaces that were needed. To validate our results we used our methods to reproduce a variety of published data about lymphocyte populations and behaviour. We also make all our data publicly available, including a large quantity of lymphocyte spatio-temporal dynamics and related lineage information.

Selection and tuning of a fast and simple phase-contrast microscopy image segmentation algorithm for measuring myoblast growth kinetics in an automated manner

Acquiring and processing phase-contrast microscopy images in wide-field long-term live-cell imaging and high-throughput screening applications is still a challenge as the methodology and algorithms used must be fast, simple to use and tune, and as minimally intrusive as possible. In this paper, we developed a simple and fast algorithm to compute the cell-covered surface (degree of confluence) in phase-contrast microscopy images. This segmentation algorithm is based on a range filter of a specified size, a minimum range threshold, and a minimum object size threshold. These parameters were adjusted in order to maximize the F-measure function on a calibration set of 200 hand-segmented images, and its performance was compared with other algorithms proposed in the literature. A set of one million images from 37 myoblast cell cultures under different conditions were processed to obtain their cell-covered surface against time. The data were used to fit exponential and logistic models, and the analysis showed a linear relationship between the kinetic parameters and passage number and highlighted the effect of culture medium quality on cell growth kinetics. This algorithm could be used for real-time monitoring of cell cultures and for high-throughput screening experiments upon adequate tuning.

Improving active contour methods for tracking endothelial cells by the removal of low-confidence edge segments

Previously we developed an active contour method for segmenting and tracking cells in phase-contrast microscopy images. Our method is capable of fine-grained segmentation on noisy image sequences. In this paper, we improve the active contour segmentation model to provide better accuracy, by selectively identifying areas of the contour with low confidence and removing them. The method is applied to HMEC-1 cells (human microvascular endothelial cells). The segmentation provided by the method is quantitavely compared with manually-drawn contours, showing close fit and capability to 'lock' on to cell boundaries for hundreds of frames.

Advances in tracking hematopoiesis at the single-cell level

PURPOSE OF REVIEW

Studying heterogeneous populations, such as hematopoietic stem cells (HSCs), requires continuous long-term observation of living cells at the single-cell level. The purpose of this review is to discuss recent advances in technologies required for continuous single-cell analysis and the contribution of this approach to find answers in hematopoiesis research.

RECENT FINDINGS

Continuous long-term imaging at the single-cell level still requires custom-made hardware, software and manual in-depth analysis of large amounts of data. Despite these technical difficulties, continuous time-lapse imaging and single-cell tracking are increasingly used in hematopoiesis research. It has already contributed to answering decades-old questions.

SUMMARY

Continuous long-term single-cell analysis is indispensable for a comprehensive analysis of dynamic processes in heterogeneous cell populations. Despite many remaining technological hurdles, this approach is increasingly used in hematopoiesis research.

Morphology-based features for adaptive mitosis detection of in vitro stem cell tracking data

OBJECTIVES

The cultivation of adherently growing cell populations is a major task in the field of adult stem cell production used for drug discovery and in the field of regenerative medicine. To assessthe quality of a cell population, a crucial event is the mitotic cell division: the precise knowledge of these events enables the reconstruction of lineages and accurate proliferation curves as well as a detailed analysis of cell cycles. To serve in an autonomous cell farming framework, such a detector requires to work reliably and unsupervised.

METHODS

We introduce a mitosis detector that is using a maximum likelihood (ML) estimator based on morphological cell features (cell area, brightness, length, compactness). It adapts to the 3 phases of cell growth (lag, log and stationary phase). As a concurrent model, we compared ML with kernel SVMs using linear, quadratic and Gaussian kernel functions. All approaches are evaluated for their ability to distinguish between mitotic and non-mitotic events. The large, publicly available benchmark data CeTReS (reference data set A with >240,000 segmented cells, >2,000 mitotic events) is used for this evaluation.

RESULTS

The adaptive (unsupervised) ML approach clearly outperforms previously published non-adaptive approaches and the linear SVM. Furthermore, it robustly reaches a performance comparable to quadratic and Gaussian SVM.

CONCLUSIONS

The proposed simple and label free adaptive variant might be the method of choice when it comes to autonomous cell farming. Hereby, it is essential to have reliable and unsupervised mitosis detection that covers all phases of cell growth.

Photonic crystal slabs for surface contrast enhancement in microscopy of transparent objects

In optical microscopy the contrast of transparent objects achieved with conventional methods is often not satisfactory, for example for the automated recognition of cells. In this paper we present a nano-optical label-free approach for contrast enhancement based on photonic crystal slabs (PCS) as the specimen holder. Quasi-guided modes inside these structures cause an intrinsic color of the PCS, which strongly depends on the wavelength and the quality factor of the optical mode. Objects on the surface of the PCS experience a significant color and intensity contrast enhancement, as they change properties of the optical modes.