FogBank: a single cell segmentation across multiple cell lines and image modalities

High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies

To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure.

Unbiased image segmentation assessment toolkit for quantitative differentiation of state-of-the-art algorithms and pipelines

BACKGROUND

Image segmentation pipelines are commonly used in microscopy to identify cellular compartments like nucleus and cytoplasm, but there are few standards for comparing segmentation accuracy across pipelines. The process of selecting a segmentation assessment pipeline can seem daunting to researchers due to the number and variety of metrics available for evaluating segmentation quality.

RESULTS

Here we present automated pipelines to obtain a comprehensive set of 69 metrics to evaluate segmented data and propose a selection methodology for models based on quantitative analysis, dimension reduction or unsupervised classification techniques and informed selection criteria.

CONCLUSION

We show that the metrics used here can often be reduced to a small number of metrics that give a more complete understanding of segmentation accuracy, with different groups of metrics providing sensitivity to different types of segmentation error. These tools are delivered as easy to use python libraries, command line tools, Common Workflow Language Tools, and as Web Image Processing Pipeline interactive plugins to ensure a wide range of users can access and use them. We also present how our evaluation methods can be used to observe the changes in segmentations across modern machine learning/deep learning workflows and use cases.

Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet

Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.

Topology-based fluorescence image analysis for automated cell identification and segmentation

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.

Dual role of Apolipoprotein D as long-term instructive factor and acute signal conditioning microglial secretory and phagocytic responses

Microglial cells are recognized as very dynamic brain cells, screening the environment and sensitive to signals from all other cell types in health and disease. Apolipoprotein D (ApoD), a lipid-binding protein of the Lipocalin family, is required for nervous system optimal function and proper development and maintenance of key neural structures. ApoD has a cell and state-dependent expression in the healthy nervous system, and increases its expression upon aging, damage or neurodegeneration. An extensive overlap exists between processes where ApoD is involved and those where microglia have an active role. However, no study has analyzed the role of ApoD in microglial responses. In this work, we test the hypothesis that ApoD, as an extracellular signal, participates in the intercellular crosstalk sensed by microglia and impacts their responses upon physiological aging or damaging conditions. We find that a significant proportion of ApoD-dependent aging transcriptome are microglia-specific genes, and show that lack of ApoD in vivo dysregulates microglial density in mouse hippocampus in an age-dependent manner. Murine BV2 and primary microglia do not express ApoD, but it can be internalized and targeted to lysosomes, where unlike other cell types it is transiently present. Cytokine secretion profiles and myelin phagocytosis reveal that ApoD has both long-term pre-conditioning effects on microglia as well as acute effects on these microglial immune functions, without significant modification of cell survival. ApoD-triggered cytokine signatures are stimuli (paraquat vs. Aβ oligomers) and sex-dependent. Acute exposure to ApoD induces microglia to switch from their resting state to a secretory and less phagocytic phenotype, while long-term absence of ApoD leads to attenuated cytokine induction and increased myelin uptake, supporting a role for ApoD as priming or immune training factor. This knowledge should help to advance our understanding of the complex responses of microglia during aging and neurodegeneration, where signals received along our lifespan are combined with damage-triggered acute signals, conditioning both beneficial roles and limitations of microglial functions.

Cellular and computational models reveal environmental and metabolic interactions in MMUT-type methylmalonic aciduria

Methylmalonyl-coenzyme A (CoA) mutase (MMUT)-type methylmalonic aciduria is a rare inherited metabolic disease caused by the loss of function of the MMUT enzyme. Patients develop symptoms resembling those of primary mitochondrial disorders, but the underlying causes of mitochondrial dysfunction remain unclear. Here, we examined environmental and genetic interactions in MMUT deficiency using a combination of computational modeling and cellular models to decipher pathways interacting with MMUT. Immortalized fibroblast (hTERT BJ5ta) MMUT-KO (MUTKO) clones displayed a mild mitochondrial impairment in standard glucose-based medium, but they did not to show increased reliance on respiratory metabolism nor reduced growth or viability. Consistently, our modeling predicted MUTKO specific growth phenotypes only for lower extracellular glutamine concentrations. Indeed, two of three MMUT-deficient BJ5ta cell lines showed a reduced viability in glutamine-free medium. Further, growth on 183 different carbon and nitrogen substrates identified increased NADH (nicotinamide adenine dinucleotide) metabolism of BJ5ta and HEK293 MUTKO cells compared with controls on purine- and glutamine-based substrates. With this knowledge, our modeling predicted 13 reactions interacting with MMUT that potentiate an effect on growth, primarily those of secondary oxidation of propionyl-CoA, oxidative phosphorylation and oxygen diffusion. Of these, we validated 3-hydroxyisobutytyl-CoA hydrolase (HIBCH) in the secondary propionyl-CoA oxidation pathway. Altogether, these results suggest compensation for the loss of MMUT function by increasing anaplerosis through glutamine or by diverting flux away from MMUT through the secondary propionyl-CoA oxidation pathway, which may have therapeutic relevance.

GEMA-An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices

Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

Efficient and Lightweight Framework for Real-Time Ore Image Segmentation Based on Deep Learning

Image segmentation approaches have been utilized to determine the particle size distribution of crushed ores in the past decades. It is not possible to deploy large and high-powered computing equipment due to the complex working environment, so existing algorithms are difficult to apply in practical engineering. This article presents a novel efficient and lightweight framework for ore image segmentation to discern full and independent ores. First, a lightweight backbone is introduced for feature extraction while reducing computational complexity. Then, we propose a compact pyramid network to process the data obtained from the backbone to reduce unnecessary semantic information and computation. Finally, an optimized detection head is proposed to obtain the feature to maintain accuracy. Extensive experimental results demonstrate the effectiveness of our method, which achieves 40 frames per second on our new ore image dataset with a very small model size. Meanwhile, our method maintains a high level of accuracy—67.68% in AP50box and 46.73% in AP50mask—compared with state-of-the-art approaches.

Autocrine signaling can explain the emergence of Allee effects in cancer cell populations

In many human cancers, the rate of cell growth depends crucially on the size of the tumor cell population. Low, zero, or negative growth at low population densities is known as the Allee effect; this effect has been studied extensively in ecology, but so far lacks a good explanation in the cancer setting. Here, we formulate and analyze an individual-based model of cancer, in which cell division rates are increased by the local concentration of an autocrine growth factor produced by the cancer cells themselves. We show, analytically and by simulation, that autocrine signaling suffices to cause both strong and weak Allee effects. Whether low cell densities lead to negative (strong effect) or reduced (weak effect) growth rate depends directly on the ratio of cell death to proliferation, and indirectly on cellular dispersal. Our model is consistent with experimental observations from three patient-derived brain tumor cell lines grown at different densities. We propose that further studying and quantifying population-wide feedback, impacting cell growth, will be central for advancing our understanding of cancer dynamics and treatment, potentially exploiting Allee effects for therapy.

A common feature of tumor growth is the production, by the cancer cells themselves, of hormones known as growth factors that increase the rate of cell division. This type of signalling makes the growth rate of the tumor depend on the population size in a non-linear manner, and the growth rate might become low or negative for small population sizes. This is known as the Allee effect which has been studied extensively in ecology. We have developed a computational model that can explain the Allee effect in terms of growth factor signalling, and show by mathematical analysis of the model that the magnitude of the Allee effect depends on the ratio of cell death to proliferation, as well as the properties of the growth factor. In addition we show that the model is consistent with experimental observations from three different cell lines derived from the brain tumor glioblastoma. Our findings indicate that the Allee effect can be exploited in order to improve the treatment of glioblastoma patients.

Robust optical flow algorithm for general single cell segmentation

Cell segmentation is crucial to the field of cell biology, as the accurate extraction of single-cell morphology, migration, and ultimately behavior from time-lapse live cell imagery are of paramount importance to elucidate and understand basic cellular processes. In an effort to increase available segmentation tools that can perform across research groups and platforms, we introduce a novel segmentation approach centered around optical flow and show that it achieves robust segmentation of single cells by validating it on multiple cell types, phenotypes, optical modalities, and in-vitro environments with or without labels. By leveraging cell movement in time-lapse imagery as a means to distinguish cells from their background and augmenting the output with machine vision operations, our algorithm reduces the number of adjustable parameters needed for manual optimization to two. We show that this approach offers the advantage of quicker processing times compared to contemporary machine learning based methods that require manual labeling for training, and in most cases achieves higher quality segmentation as well. This algorithm is packaged within MATLAB, offering an accessible means for general cell segmentation in a time-efficient manner.

Weakly supervised cell instance segmentation under various conditions

Cell instance segmentation is important in biomedical research. For living cell analysis, microscopy images are captured under various conditions (e.g., the type of microscopy and type of cell). Deep-learning-based methods can be used to perform instance segmentation if sufficient annotations of individual cell boundaries are prepared as training data. Generally, annotations are required for each condition, which is very time-consuming and labor-intensive. To reduce the annotation cost, we propose a weakly supervised cell instance segmentation method that can segment individual cell regions under various conditions by only using rough cell centroid positions as training data. This method dramatically reduces the annotation cost compared with the standard annotation method of supervised segmentation. We demonstrated the efficacy of our method on various cell images; it outperformed several of the conventional weakly-supervised methods on average. In addition, we demonstrated that our method can perform instance cell segmentation without any manual annotation by using pairs of phase contrast and fluorescence images in which cell nuclei are stained as training data.

Visual cohort comparison for spatial single-cell omics-data

Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities.

A multi-dimensional, time-lapse, high content screening platform applied to schistosomiasis drug discovery

Approximately 10% of the world's population is at risk of schistosomiasis, a disease of poverty caused by the Schistosoma parasite. To facilitate drug discovery for this complex flatworm, we developed an automated high-content screen to quantify the multidimensional responses of Schistosoma mansoni post-infective larvae (somules) to chemical insult. We describe an integrated platform to process worms at scale, collect time-lapsed, bright-field images, segment highly variable and touching worms, and then store, visualize, and query dynamic phenotypes. To demonstrate the methodology, we treated somules with seven drugs that generated diverse responses and evaluated 45 static and kinetic response descriptors relative to concentration and time. For compound screening, we used the Mahalanobis distance to compare multidimensional phenotypic effects induced by 1323 approved drugs. Overall, we characterize both known anti-schistosomals and identify new bioactives. Apart from facilitating drug discovery, the multidimensional quantification provided by this platform will allow mapping of chemistry to phenotype.

TASBE Image Analytics: A Processing Pipeline for Quantifying Cell Organization from Fluorescent Microscopy

Laboratory automation now commonly allows high-throughput sample preparation, culturing, and acquisition of microscopy images, but quantitative image analysis is often still a painstaking and subjective process. This is a problem especially significant for work on programmed morphogenesis, where the spatial organization of cells and cell types is of paramount importance. To address the challenges of quantitative analysis for such experiments, we have developed TASBE Image Analytics, a software pipeline for automatically segmenting collections of cells using the fluorescence channels of microscopy images. With TASBE Image Analytics, collections of cells can be grouped into spatially disjoint segments, the movement or development of these segments tracked over time, and rich statistical data output in a standardized format for analysis. Processing is readily configurable, rapid, and produces results that closely match hand annotation by humans for all but the smallest and dimmest segments. TASBE Image Analytics can thus provide the analysis necessary to complete the design-build-test-learn cycle for high-throughput experiments in programmed morphogenesis, as validated by our application of this pipeline to process experiments on shape formation with engineered CHO and HEK293 cells.

Ore image segmentation method using U-Net and Res_Unet convolutional networks

Image segmentation has been increasingly used to identify the particle size distribution of crushed ore; however, the adhesion of ore particles and dark areas in the images of blast heaps and conveyor belts usually results in lower segmentation accuracy. To overcome this issue, an image segmentation method UR based on deep learning U-Net and Res_Unet networks is proposed in this study. Gray-scale, median filter and adaptive histogram equalization techniques are used to preprocess the original ore images captured from an open pit mine to reduce noise and extract the target region. U-Net and Res_Unet are utilized to generate ore contour detection and optimization models, and the ore image segmentation result is illustrated by OpenCV. The efficiency and accuracy of the newly proposed UR method is demonstrated and validated by comparing with the existing image segmentation methods.

Image segmentation has been increasingly used to identify the particle size of crushed ore. How to accurately identify the ore particles in complex a environment is particularly important.

Comparison of Artificial Intelligence based approaches to cell function prediction

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels.

In Situ Single-Cell Western Blot on Adherent Cell Culture

Integrating 2D culture of adherent mammalian cells with single-cell western blotting (in situ scWB) uses microfluidic design to eliminate the requirement for trypsin release of cells to suspension, prior to single-cell isolation and protein analysis. To assay HeLa cells from an attached starting state, we culture adherent cells in fibronectin-functionalized microwells formed in a thin layer of polyacrylamide gel. To integrate the culture, lysis, and assay workflow, we introduce a one-step copolymerization process that creates protein-decorated microwells. After single-cell culture, we lyse each cell in the microwell and perform western blotting on each resultant lysate. We observe cell spreading after overnight microwell-based culture. scWB reports increased phosphorylation of MAP kinases (ERK1/2, p38) under hypertonic conditions. We validate the in situ scWB with slab-gel western blot, while revealing cell-to-cell heterogeneity in stress responses.

Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison

BACKGROUND

Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities.

RESULTS

We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online.

CONCLUSIONS

We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.

Learn to segment single cells with deep distance estimator and deep cell detector

Single cell segmentation is a critical and challenging step in cell imaging analysis. Traditional processing methods require time and labor to manually fine-tune parameters and lack parameter transferability between different situations. Recently, deep convolutional neural networks (CNN) treat segmentation as a pixel-wise classification problem and have become a general and efficient method for image segmentation. However, cell imaging data often possesses characteristics that adversely affect segmentation accuracy: absence of established training datasets, few pixels on cell boundaries, and ubiquitous blurry features. We developed a strategy that combines strengths of CNN and traditional watershed algorithm. First, we trained a CNN to learn Euclidean distance transform (EDT) of the mask corresponding to the input images (deep distance estimator). Next, we trained a faster R-CNN (Region with CNN) to detect individual cells in the EDT image (deep cell detector). Then, the watershed algorithm performed the final segmentation using the outputs of previous two steps. Tests on a library of fluorescence, phase contrast and differential interference contrast (DIC) images showed that both the combined method and various forms of the pixel-wise classification algorithm achieved similar pixel-wise accuracy. However, the combined method achieved significantly higher cell count accuracy than the pixel-wise classification algorithm did, with the latter performing poorly when separating connected cells, especially those connected by blurry boundaries. This difference is most obvious when applied to noisy images of densely packed cells. Furthermore, both deep distance estimator and deep cell detector converge fast and are easy to train.

Automatic Quantitative Segmentation of Myotubes Reveals Single-cell Dynamics of S6 Kinase Activation

Automatic cell segmentation is a powerful method for quantifying signaling dynamics at single-cell resolution in live cell fluorescence imaging. Segmentation methods for mononuclear and round shape cells have been developed extensively. However, a segmentation method for elongated polynuclear cells, such as differentiated C2C12 myotubes, has yet to be developed. In addition, myotubes are surrounded by undifferentiated reserve cells, making it difficult to identify background regions and subsequent quantification. Here we developed an automatic quantitative segmentation method for myotubes using watershed segmentation of summed binary images and a two-component Gaussian mixture model. We used time-lapse fluorescence images of differentiated C2C12 cells stably expressing Eevee-S6K, a fluorescence resonance energy transfer (FRET) biosensor of S6 kinase (S6K). Summation of binary images enhanced the contrast between myotubes and reserve cells, permitting detection of a myotube and a myotube center. Using a myotube center instead of a nucleus, individual myotubes could be detected automatically by watershed segmentation. In addition, a background correction using the two-component Gaussian mixture model permitted automatic signal intensity quantification in individual myotubes. Thus, we provide an automatic quantitative segmentation method by combining automatic myotube detection and background correction. Furthermore, this method allowed us to quantify S6K activity in individual myotubes, demonstrating that some of the temporal properties of S6K activity such as peak time and half-life of adaptation show different dose-dependent changes of insulin between cell population and individuals.Key words: time lapse images, cell segmentation, fluorescence resonance energy transfer, C2C12, myotube.

Automated Cell Segmentation for Quantitative Phase Microscopy

Automated cell segmentation and tracking is essential for dynamic studies of cellular morphology, movement, and interactions as well as other cellular behaviors. However, accurate, automated, and easy-to-use cell segmentation remains a challenge, especially in cases of high cell densities, where discrete boundaries are not easily discernable. Here, we present a fully automated segmentation algorithm that iteratively segments cells based on the observed distribution of optical cell volumes measured by quantitative phase microscopy. By fitting these distributions to known probability density functions, we are able to converge on volumetric thresholds that enable valid segmentation cuts. Since each threshold is determined from the observed data itself, virtually no input is needed from the user. We demonstrate the effectiveness of this approach over time using six cell types that display a range of morphologies, and evaluate these cultures over a range of confluencies. Facile dynamic measures of cell mobility and function revealed unique cellular behaviors that relate to tissue origins, state of differentiation, and real-time signaling. These will improve our understanding of multicellular communication and organization.

Phase contrast cell detection using multilevel classification

In this paper, we propose a fully automated learning-based approach for detecting cells in time-lapse phase contrast images. The proposed system combines 2 machine learning approaches to achieve bottom-up image segmentation. We apply pixel-wise classification using random forests (RF) classifiers to determine the potential location of the cells. Each pixel is classified into 4 categories (cell, mitotic cell, halo effect, and background noise). Various image features are extracted at different scales to train the RF classifier. The resulting probability map is partitioned using the k-means algorithm to form potential cell regions. These regions are expanded into the neighboring areas to recover some missing or broken cell regions. To validate the cell regions, another machine learning method based on the bag-of-features and spatial pyramid encoding is proposed. The result of the second classifier can be a validated cell, a merged cell, or a noncell. In the case that the cell region is classified as a merged cell, it is split by using the seeded watershed method. The proposed method is demonstrated on several phase contrast image datasets, ie, U2OS, HeLa, and NIH 3T3. In comparison to state-of-the-art cell detection techniques, the proposed method shows improved performance, particularly in dealing with noise interference and drastic shape variations.

Topological defects in epithelia govern cell death and extrusion

Epithelial tissues (epithelia) remove excess cells through extrusion, preventing the accumulation of unnecessary or pathological cells. The extrusion process can be triggered by apoptotic signalling, oncogenic transformation and overcrowding of cells. Despite the important linkage of cell extrusion to developmental, homeostatic and pathological processes such as cancer metastasis, its underlying mechanism and connections to the intrinsic mechanics of the epithelium are largely unexplored. We approach this problem by modelling the epithelium as an active nematic liquid crystal (that has a long range directional order), and comparing numerical simulations to strain rate and stress measurements within monolayers of MDCK (Madin Darby canine kidney) cells. Here we show that apoptotic cell extrusion is provoked by singularities in cell alignments in the form of comet-shaped topological defects. We find a universal correlation between extrusion sites and positions of nematic defects in the cell orientation field in different epithelium types. The results confirm the active nematic nature of epithelia, and demonstrate that defect-induced isotropic stresses are the primary precursors of mechanotransductive responses in cells, including YAP (Yes-associated protein) transcription factor activity, caspase-3-mediated cell death, and extrusions. Importantly, the defect-driven extrusion mechanism depends on intercellular junctions, because the weakening of cell-cell interactions in an α-catenin knockdown monolayer reduces the defect size and increases both the number of defects and extrusion rates, as is also predicted by our model. We further demonstrate the ability to control extrusion hotspots by geometrically inducing defects through microcontact printing of patterned monolayers. On the basis of these results, we propose a mechanism for apoptotic cell extrusion: spontaneously formed topological defects in epithelia govern cell fate. This will be important in predicting extrusion hotspots and dynamics in vivo, with potential applications to tissue regeneration and the suppression of metastasis. Moreover, we anticipate that the analogy between the epithelium and active nematic liquid crystals will trigger further investigations of the link between cellular processes and the material properties of epithelia.

Fundus Camera-Delivered Light-Induced Retinal Degeneration in Mice With the RPE65 Leu450Met Variant is Associated With Oxidative Stress and Apoptosis

Purpose

Oxidative stress, partly due to light, has an important role in many retinal diseases, including macular degeneration and retinal dystrophies. The Leu450Met variant of RPE65 is expressed in C57BL/6 and in many genetically modified mice. It confers significant resistance to light induced retinal degeneration (LIRD). Our goal was to develop an effective and efficient method to induce LIRD in resistant mice that would recapitulate mechanisms seen in known models of LIRD.

Methods

The retinas of C57BL/6J mice were exposed to light using a murine fundus camera. Two protocols (with and without intraperitoneal fluorescein) were used. Optical coherence tomography (OCT) helped determine the location and extent of retinal damage. Histology, TUNEL assay, quantitative (q) PCR, and immunohistochemistry were performed.

Results

Both protocols consistently generated LIRD in C57BL/6J mice. Optical coherence tomography and histology demonstrated that retinal damage starts at the level of the photoreceptor/outer retina and is more prominent in the superior retina. Fundus camera-delivered light-induced retinal degeneration (FCD-LIRD) is associated with apoptosis, subretinal microglia/macrophages, increased expression of oxidative stress response genes, and C3d deposition.

Conclusions

We characterize two new models of light-induced retinal degeneration that are effective in C57BL/6J mice, and can be modulated in terms of severity. We expect FCD-LIRD to be useful in exploring mechanisms of LIRD in resistant mice, which will be important in increasing our understanding of the retinal response to light damage and oxidative stress.

Generalizing cell segmentation and quantification

Background

In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work.

Results

To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods.

Conclusions

The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1604-1) contains supplementary material, which is available to authorized users.

Increased susceptibility to fundus camera-delivered light-induced retinal degeneration in mice deficient in oxidative stress response proteins

Oxidative stress is an important contributor to the pathogenesis of many retinal diseases including age-related macular degeneration and retinal dystrophies. Light-induced retinal degeneration (LIRD) can serve as a model in which to study the response of the retina to stress. Of note, many genetic mutant mice are in a C57BL/6 J background and are thus resistant to the usual LIRD models. We recently developed a new model of fundus camera-delivered light-induced retinal degeneration (FCD-LIRD) which is effective in strains of mice expressing the light-resistant variant of RPE65 (450Met), including C57BL/6 J. In this work we investigated whether FCD-LIRD would be useful as a model in which to test the effect of genetic mutations on the response of the retina to stress. Furthermore, we tested whether oxidative stress plays an important role in the setting of this new FCD-LIRD model. FCD-LIRD was applied to C57BL/6 J mice and to mice simultaneously deficient in three proteins that are important in the response of the retina to oxidative stress (SOD1, DJ-1 and Parkin). Using fundus photography, we found that retinal damage was dramatically increased in the SOD1/DJ-1/Parkin deficient mice compared to C57BL/6 J. Outer retinal OCT volume and RPE cell morphology analysis in ZO-1-stained flat mounts added support to these findings. Gene expression analysis confirmed a strong oxidative stress response after FCD-LIRD, which was differentially altered in the SOD1/DJ1/Parkin deficient mice. We conclude that FCD-LIRD is useful to study the effect of genetic mutations on the response of the retina to light stress in light-resistant strains of mice. Furthermore, oxidative stress seems to be an important component of FCD-LIRD. Finally, we have established protocols to quantify the effect of FCD-LIRD on the retina and RPE which will be useful for future studies. Further dissection of the mechanisms by which the retina responds to light-induced oxidative stress may result in new strategies to modulate this response, which could lead to a reduction in retinal and RPE damage.

Subcellular western blotting of single cells

Although immunoassays are the de facto standard for determining subcellular protein localization in individual cells, antibody probe cross-reactivity and fixation artifacts remain confounding factors. To enhance selectivity while providing single-cell resolution, we introduce a subcellular western blotting technique capable of separately assaying proteins in the 14 pL cytoplasm and 2 pL nucleus of individual cells. To confer precision fluidic control, we describe a passive multilayer microdevice that leverages the rapid transport times afforded by miniaturization. After isolating single cells in microwells, we apply single-cell differential detergent fractionation to lyse and western blot the cytoplasmic lysate, whereas the nucleus remains intact in the microwell. Subsequently, we lyse the intact nucleus and western blot the nuclear lysate. To index each protein analysis to the originating subcellular compartment, we utilize bi-directional electrophoresis, a multidimensional separation that assays the lysate from each compartment in a distinct region of the separation axis. Single-cell bi-directional electrophoresis eliminates the need for semi-subjective image segmentation algorithms required in immunocytochemistry. The subcellular, single-cell western blot is demonstrated for six targets per cell, and successfully localizes spliceosome-associated proteins solubilized from large protein and RNA complexes, even for closely sized proteins (a 7 kDa difference). Measurement of NF-κB translocation dynamics in unfixed cells at 15-min intervals demonstrates reduced technical variance compared with immunofluorescence. This chemical cytometry assay directly measures the nucleocytoplasmic protein distribution in individual unfixed cells, thus providing insight into protein signaling in heterogeneous cell populations.

Endothelial basement membrane laminin 511 is essential for shear stress response

Shear detection and mechanotransduction by arterial endothelium requires junctional complexes containing PECAM-1 and VE-cadherin, as well as firm anchorage to the underlying basement membrane. While considerable information is available for junctional complexes in these processes, gained largely from in vitro studies, little is known about the contribution of the endothelial basement membrane. Using resistance artery explants, we show that the integral endothelial basement membrane component, laminin 511 (laminin α5), is central to shear detection and mechanotransduction and its elimination at this site results in ablation of dilation in response to increased shear stress. Loss of endothelial laminin 511 correlates with reduced cortical stiffness of arterial endothelium in vivo, smaller integrin β1-positive/vinculin-positive focal adhesions, and reduced junctional association of actin-myosin II In vitro assays reveal that β1 integrin-mediated interaction with laminin 511 results in high strengths of adhesion, which promotes p120 catenin association with VE-cadherin, stabilizing it at cell junctions and increasing cell-cell adhesion strength. This highlights the importance of endothelial laminin 511 in shear response in the physiologically relevant context of resistance arteries.

Lineage mapper: A versatile cell and particle tracker

The ability to accurately track cells and particles from images is critical to many biomedical problems. To address this, we developed Lineage Mapper, an open-source tracker for time-lapse images of biological cells, colonies, and particles. Lineage Mapper tracks objects independently of the segmentation method, detects mitosis in confluence, separates cell clumps mistakenly segmented as a single cell, provides accuracy and scalability even on terabyte-sized datasets, and creates division and/or fusion lineages. Lineage Mapper has been tested and validated on multiple biological and simulated problems. The software is available in ImageJ and Matlab at isg.nist.gov.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BACKGROUND

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.

METHODS

We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.

RESULTS

The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.

CONCLUSIONS

The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.