Time-lapse Phase-contrast Microscopy Research Articles

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat.

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.

Contact Guidance Drives Upward Cellular Migration at the Mesoscopic Scale.

Cancer metastasis is associated with increased cancer incidence, recurrence, and mortality. The role of cell contact guidance behaviors in cancer metastasis has been recognized but has not been elucidated yet. The contact guidance behavior of cancer cells in response to topographical constraints is identified using microgrooved substrates with varying dimensions at the mesoscopic scale. Then, the cell morphology is determined to quantitatively analyze the effects of substrate dimensions on cells contact guidance. Cell density and migrate velocity signatures within the cellular population are determined using time-lapse phase-contrast microscopy. The effect of soluble factors concentration is determined by culturing cells upside down. Then, the effect of cell-substrate interaction on cell migration is investigated using traction force microscopy. With increasing depth and decreasing groove width, cell elongation and alignment are enhanced, while cell spreading is inhibited. Moreover, cells display preferential distribution on the ridges, which is found to be more pronounced with increasing depth and groove width. Determinations of cell density and migration velocity signatures reveal that the preferential distribution on ridges is caused by cell upward migration. Combined with traction force measurement, we find that migration toward ridges is governed by different cell-substrate interactions between grooves and ridges caused by geometrical constraints. Interestingly, the upward migration of cells at the mesoscopic scale is driven by entropic maximization. The mesoscopic cell contact guidance mechanism based on the entropic force driven theory provides basic support for the study of cell alignment and migration along healthy tissues with varying size, thereby aiding in the prediction of cancer metastasis. The online version contains supplementary material available at 10.1007/s12195-023-00766-y.

Analysis of Morphological Changes in the Nucleus and Vacuoles of Acanthamoeba castellanii following Giant Virus Infection.

Acanthamoeba castellanii medusavirus is a member of the phylum Nucleocytoviricota, also known as giant viruses, and has a unique strategy of infecting Acanthamoeba castellanii and replicating viral genes in the host nucleus. Here, we show time series changes in the intracellular morphology, including the nucleus, of host cells infected with four types of giant viruses, including medusavirus, using time-lapse phase-contrast microscopy and image analysis. We updated our phase-contrast-based kinetic analysis algorithm for amoebae (PKA3) to use multiple microscopic images with different focus positions to allow a more detailed analysis of their intracellular structures. Image analysis using PKA3 revealed that as medusavirus infection progressed, the host nucleus increased in size and the number of vacuoles decreased. In addition, infected host cells are known to become smaller and rounder at later stages of infection, but here they were found to be larger than uninfected cells at earlier stages. These results suggested that the propagation mechanism of medusavirus includes the formation of empty virus particles in the host cytoplasm, packaging of the viral genome replicated in the host nucleus, and then the release of viral particles. IMPORTANCE In this study, we quantitatively revealed how long the increase in host cell size or the increase in host nucleus size occurs after infection with giant viruses, especially medusavirus. To understand the underlying mechanism, we performed image analysis and determined that the host cell size increased at approximately 6 h postinfection (hpi) and the host nucleus enlarged at approximately 22 hpi, pointing to the importance of biochemical experiments. In addition, we showed that the intracellular structures could be quantitatively analyzed using multiple phase-contrast microscopy images with different focus positions at the same time point. Hence, morphological analyses of intracellular structures using phase-contrast microscopy, which have wide applications in live-cell observations, may be useful in studying various organisms that infect or are symbiotic with A. castellanii.

Spontaneous multi-scale void formation and closure in densifying epithelial and fibroblast monolayers from the sub-confluent state.

Using time-lapse phase contrast microscopy, the formation and closure of spontaneously generated voids in the densifying monolayers of isotropic epithelial cells (ECs) and elongated fibroblast cells (FCs) through proliferation from the sub-confluent state are investigated. It is found that, in both types of monolayers after forming a connected network composed of nematic patches with different orientations, numerous multi-scale voids can be spontaneously formed and gradually close with increasing time. The isotropic fluctuations of deformation and crawling of ECs and the anisotropic axial motion/alignment polarizations of FCs are the two keys leading to the following different generic dynamical behaviors. In EC monolayers, voids exhibit irregular boundary fluctuations and easier cell re-orientation of front layer cells (FLCs) surrounding void boundaries. Void closures are mainly through pinching the gap between the opposite fluctuating void boundaries, and the inward crawling of FLCs to reduce void area associated with topological rearrangement to reduce FLC number. In FC monolayers, large voids have piecewise smooth convex boundaries, and cusp-shaped concave boundaries with cells orienting toward the void at cusp tips. The extension of a thin cell bridge from the cusp tip can bisect a large void into smaller voids. For smaller FC voids dominated by convex boundaries, along which cell alignment prohibits inward crawling, the reduction of FLC number through successive outward squeezing of single FLCs by neighboring FLCs sliding along the void boundary plays an important role for topological rearrangement and void closure. Unlike those surrounding artificial wounds in dense EC monolayers, the absence of ring-like purse-strings surrounding EC and FC voids allows topological rearrangements for reducing void perimeter and void area.

Spatio-Temporal Mitosis Detection in Time-Lapse Phase-Contrast Microscopy Image Sequences: A Benchmark.

In this paper, we report the results of the first international contest on mitosis detection in phase-contrast microscopy image sequences (https://www.iti-tju.org/mitosisdetection), which was held at the workshop of computer vision for microscopy image analysis (CVMI) in CVPR 2019. This contest aims to promote research on spatiotemporal mitosis detection under microscopy images. In this contest, we released a large-scale time-lapse phase-contrast microscopy image dataset (C2C12-16) for the mitosis detection task. Compared with the previous popular datasets (e.g., C2C12, C3H10), C2C12-16 contains more annotated mitotic events and more diverse cell culture environments. A total of ten different mitosis detection methods were submitted in the contest and evaluated on the test sets of four different cell culture environments in C2C12-16. In this benchmark, we describe all methods and conduct a thorough analysis based on their performances and discuss a feasible direction for mitosis detection. To the best of our knowledge, this is the first benchmark for the mitosis detection problem using a time-lapse phase-contrast microscopy spatiotemporal image sequence model.

The importance of plasmin for the healing of the anterior cruciate ligament.

AimsThe anterior cruciate ligament (ACL) is known to have a poor wound healing capacity, whereas other ligaments outside of the knee joint capsule such as the medial collateral ligament (MCL) apparently heal more easily. Plasmin has been identified as a major component in the synovial fluid that varies among patients. The aim of this study was to test whether plasmin, a component of synovial fluid, could be a main factor responsible for the poor wound healing capacity of the ACL.MethodsThe effects of increasing concentrations of plasmin (0, 0.1, 1, 10, and 50 µg/ml) onto the wound closing speed (WCS) of primary ACL-derived ligamentocytes (ACL-LCs) were tested using wound scratch assay and time-lapse phase-contrast microscopy. Additionally, relative expression changes (quantitative PCR (qPCR)) of major LC-relevant genes and catabolic genes were investigated. The positive controls were 10% fetal calf serum (FCS) and platelet-derived growth factor (PDGF).ResultsWCS did not differ significantly among no plasmin versus each of the tested concentrations (six donors). The positive controls with PDGF and with FCS differed significantly from the negative controls. However, we found a trend demonstrating that higher plasmin concentrations up-regulate the expression of matrix metalloproteinase 13 (MMP13), 3 (MMP3), and tenomodulin (TNMD).ConclusionThe clinical relevance of this study is the possibility that it is not solely the plasmin, but also additional factors in the synovial fluid of the knee, that may be responsible for the poor healing capacity of the ACL.Cite this article: Bone Joint Res 2020;9(9):543–553.

Deep Reinforcement Learning-Based Progressive Sequence Saliency Discovery Network for Mitosis Detection In Time-Lapse Phase-Contrast Microscopy Images.

Mitosis detection plays an important role in the analysis of cell status and behavior and is therefore widely utilized in many biological research and medical applications. In this article, we propose a deep reinforcement learning-based progressive sequence saliency discovery network (PSSD)for mitosis detection in time-lapse phase contrast microscopy images. By discovering the salient frames when cell state changes in the sequence, PSSD can more effectively model the mitosis process for mitosis detection. We formulate the discovery of salient frames as a Markov Decision Process (MDP)that progressively adjusts the selection positions of salient frames in the sequence, and further leverage deep reinforcement learning to learn the policy in the salient frame discovery process. The proposed method consists of two parts: 1)the saliency discovery module that selects the salient frames from the input cell image sequence by progressively adjusting the selection positions of salient frames; 2)the mitosis identification module that takes a sequence of salient frames and performs temporal information fusion for mitotic sequence classification. Since the policy network of the saliency discovery module is trained under the guidance of the mitosis identification module, PSSD can comprehensively explore the salient frames that are beneficial for mitosis detection. To our knowledge, this is the first work to implement deep reinforcement learning to the mitosis detection problem. In the experiment, we evaluate the proposed method on the largest mitosis detection dataset, C2C12-16. Experiment results show that compared with the state-of-the-arts, the proposed method can achieve significant improvement for both mitosis identification and temporal localization on C2C12-16.

Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN.

Automated mitotic detection in time-lapse phase-contrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they do not consider the annotation gaps, which may occur since the appearances of mitosis cells are very similar before and after the annotated frame. In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating spatial-temporal likelihood map by 3DCNN. In this training, the loss gradually decreases with the gap size between ground-truth and estimation. This mitigates the annotation gaps. Our method outperformed the compared methods in terms of F1-score using challenging dataset that contains the data under four different conditions. Code is publicly available in https://github.com/naivete5656/MDMLM.

Cytotoxic, Antimicrobial, Antioxidant Properties and Effects on Cell Migration of Phenolic Compounds of Selected Transylvanian Medicinal Plants.

Medicinal plants are widely used in folk medicine but quite often their composition and biological effects are hardly known. Our study aimed to analyze the composition, cytotoxicity, antimicrobial, antioxidant activity and cellular migration effects of Anthyllis vulneraria, Fuchsia magellanica, Fuchsia triphylla and Lysimachia nummularia used in the Romanian ethnomedicine for wounds. Liquid chromatography with mass spectrometry (LC-MS/MS) was used to analyze 50% (v/v) ethanolic and aqueous extracts of the plants’ leaves. Antimicrobial activities were estimated with a standard microdilution method. The antioxidant properties were evaluated by validated chemical cell-free and biological cell-based assays. Cytotoxic effects were performed on mouse fibroblasts and human keratinocytes with a plate reader-based method assessing intracellular adenosine triphosphate (ATP), nucleic acid and protein contents and also by a flow cytometer-based assay detecting apoptotic–necrotic cell populations. Cell migration to cover cell-free areas was visualized by time-lapse phase-contrast microscopy using standard culture inserts. Fuchsia species showed the strongest cytotoxicity and the highest antioxidant and antimicrobial activity. However, their ethanolic extracts facilitated cell migration, most probably due to their various phenolic acid, flavonoid and anthocyanin derivatives. Our data might serve as a basis for further animal experiments to explore the complex action of Fuchsia species in wound healing assays.

Directed evolution by UV-C treatment of Bacillus cereus spores

Bacterial endospores are exposed to a broad variety of sublethal and lethal stresses in the food production chain. Generally, these stresses will not completely eliminate the existing spore populations, and thus constitute a selection pressure on the spores. One stress that is frequently used in the food production chains to disinfect (food) contact surfaces is UV-C. At a wavelength of 254 nm, UV-C has germicidal properties. The aim of this research is to investigate the impact of UV-C stress on the evolution of endospore recalcitrance and germination in B. cereus.A directed evolution experiment was set up in which B. cereus was repeatedly subjected to a cycle of sporulation, sporicidal UV-C treatment, germination and outgrowth. We show here that three independent lineages of UV-C cycled B. cereus spores reproducibly acquired a 30-fold or higher increase in UV-C resistance at 164 mJ/cm2. Surprisingly, the UV-C resistant spores of the clones isolated from each of the lineages also became significantly more sensitive to wet heat as a normally non-lethal heat treatment at 70 °C for 15 min resulted in an average 1.8 log cfu/mL reduction. From time-lapse phase contrast microscopy analysis, UV-C resistant mutant spores also showed a distinctive heterogeneity in refractility and a severe germination defect compared to the wild type. However, UV-C resistance of the corresponding vegetative cells was not altered.In conclusion, this work shows that UV-C resistance of endospores is an adaptive trait that can readily be improved, although at an apparent cost for heat resistance and germination efficiency. As such, these results provide novel insights in the evolvability of, and correlation between, some endospore properties.

Sequential Saliency Guided Deep Neural Network for Joint Mitosis Identification and Localization in Time-Lapse Phase Contrast Microscopy Images.

The analysis of cell mitotic behavior plays important role in many biomedical research and medical diagnostic applications. To improve the accuracy of mitosis detection in automated analysis systems, this paper proposes the sequential saliency guided deep neural network (SSG-DNN) to jointly identify and localize mitotic events in time-lapse phase contrast microscopy images. It consists of three key modules. First, the module of visual context learning extracts static visual feature and dynamic visual transition within individual volumetric cell regions. Secondly, with these information, the module of sequential saliency modeling aims to discover the saliency distribution over all successive frames in each volumetric region. Finally, the module of sequence structure modeling can leverage both visual context and saliency distribution for mitosis identification and localization. SSG-DNN can jointly realize visual feature learning and sequential structure modeling in the end-to-end framework. Moreover, the proposed method is independent of complicated preconditioning methods for mitotic candidate extraction and can be applied for mitosis detection in one-shot manner. To our knowledge, it is the first weakly supervised work to realize joint mitosis identification and localization only with sequence-wise labels. In our experiments, we evaluate its performances of both tasks on the popular C3H10 dataset and a novel and large-scale dataset, C2C12-16, which contains much more mitotic events and is more challenging owing to diverse cell culture conditions. Experimental results can demonstrate the superiority of the proposed method.

Cell mitosis event analysis in phase contrast microscopy images using deep learning.

In this paper, we solve the problem of mitosis event localization and its stage localization in time-lapse phase-contrast microscopy images. Our method contains three steps: first, we formulate a Low-Rank Matrix Recovery (LRMR) model to find salient regions from microscopy images and extract candidate patch sequences, which potentially contain mitosis events; second, we classify each candidate patch sequence by our proposed Hierarchical Convolution Neural Network (HCNN) with visual appearance and motion cues; third, for the detected mitosis sequences, we further segment them into four temporal stages by our proposed Two-stream Bidirectional Long-Short Term Memory (TS-BLSTM). In the experiments, we validate our system (LRMR, HCNN, and TS-BLSTM) and evaluate the mitosis event localization and stage localization performance. The proposed method outperforms state-of-the-arts by achieving 99.2% precision and 98.0% recall for mitosis event localization and 0.62 frame error on average for mitosis stage localization in five challenging image sequences.

Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research.

Phase classification of mitotic events using selective dictionary learning for stem cell populations

Nowadays, thanks to the use of advanced technological tools, stem cell studies which play an important role in regenerative medicine and cancer studies have increased considerably. In this study, selective dictionary learning method is presented for detecting mitotic event phases in stem cells using phase contrast time-lapse microscopy images. In the proposed method, three phases are defined for representation of mitotic events. Creating a dictionary that represents these phases with a single feature space restricts the success. For this reason, three dictionaries with different features are created. Although the multiplication of image alpha values with all generated dictionaries is quite suitable for determining the lowest error value, this process is time consuming. For this reason, a selective dictionary approach based on the automatic selection of the best values with a cooperation between the dictionaries has been proposed. In this way, the high success rate is maintained and the processing time is significantly reduced. The proposed method gives better results than other state-of-art studies in terms of computational efficiency and accuracy in experiments with C2C12 and BAEC datasets.

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro.

Vasculogenesis is a complex process by which endothelial stem and progenitor cells undergo de novo vessel formation. Quantitative assessment of vasculogenesis has become a central readout of endothelial progenitor cell functionality, and therefore, several attempts have been made to improve both in vitro and in vivo vasculogenesis models. However, standard methods are limited in scope, with static measurements failing to capture many aspects of this highly dynamic process. Therefore, the goal of developing this novel protocol was to assess the kinetics of in vitro vasculogenesis in order to quantitate rates of network formation and stabilization, as well as provide insight into potential mechanisms underlying vascular dysfunction. Application of this protocol is demonstrated using fetal endothelial colony forming cells (ECFCs) exposed to maternal diabetes mellitus. Fetal ECFCs were derived from umbilical cord blood following birth, cultured, and plated in slides containing basement membrane matrix, where they underwent vasculogenesis. Images of the entire slide wells were acquired using time-lapse phase contrast microscopy over 15 hours. Images were analyzed for derivation of quantitative data using an analysis software called Kinetic Analysis of Vasculogenesis (KAV). KAV uses image segmentation followed by skeletonization to analyze network components from stacks of multi-time point phase contrast images to derive ten parameters (9 measured, 1 calculated) of network structure including: closed networks, network areas, nodes, branches, total branch length, average branch length, triple-branched nodes, quad-branched nodes, network structures, and the branch to node ratio. Application of this protocol identified altered rates of vasculogenesis in ECFCs obtained from pregnancies complicated by diabetes mellitus. However, this technique has broad implications beyond the scope reported here. Implementation of this approach will enhance mechanistic assessment and improve functional readouts of vasculogenesis and other biologically important branching processes in numerous cell types or disease states.

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis <em>In Vitro</em>

Vasculogenesis is a complex process by which endothelial stem and progenitor cells undergo de novo vessel formation. Quantitative assessment of vasculogenesis has become a central readout of endothelial progenitor cell functionality, and therefore, several attempts have been made to improve both in vitro and in vivo vasculogenesis models. However, standard methods are limited in scope, with static measurements failing to capture many aspects of this highly dynamic process. Therefore, the goal of developing this novel protocol was to assess the kinetics of in vitro vasculogenesis in order to quantitate rates of network formation and stabilization, as well as provide insight into potential mechanisms underlying vascular dysfunction. Application of this protocol is demonstrated using fetal endothelial colony forming cells (ECFCs) exposed to maternal diabetes mellitus. Fetal ECFCs were derived from umbilical cord blood following birth, cultured, and plated in slides containing basement membrane matrix, where they underwent vasculogenesis. Images of the entire slide wells were acquired using time-lapse phase contrast microscopy over 15 hours. Images were analyzed for derivation of quantitative data using an analysis software called Kinetic Analysis of Vasculogenesis (KAV). KAV uses image segmentation followed by skeletonization to analyze network components from stacks of multi-time point phase contrast images to derive ten parameters (9 measured, 1 calculated) of network structure including: closed networks, network areas, nodes, branches, total branch length, average branch length, triple-branched nodes, quad-branched nodes, network structures, and the branch to node ratio. Application of this protocol identified altered rates of vasculogenesis in ECFCs obtained from pregnancies complicated by diabetes mellitus. However, this technique has broad implications beyond the scope reported here. Implementation of this approach will enhance mechanistic assessment and improve functional readouts of vasculogenesis and other biologically important branching processes in numerous cell types or disease states.

Pooled time series representation for mitosis event recognition

This paper proposes a new feature representation for mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. First, an imaging model-based microscopy image segmentation method is implemented for mitotic candidate extraction. Then, a new feature representation framework based on time series pooling is proposed for sequential events. At last, a support vector machine classifier is utilized for mitotic cell modeling and detection. Different from other feature representations including bag-of-visual-words when using identical underlying feature descriptors, this method can take advantage of temporal relations among frames, the idea is to keep track of how descriptor values are changing over time and summarize them to represent appearance in the cell sequence. The comparison experiments demonstrate the superiority of the proposed method.

Spatiotemporal Joint Mitosis Detection Using CNN-LSTM Network in Time-Lapse Phase Contrast Microscopy Images

We present an approach to jointly detect mitotic events spatially and temporally in time-lapse phase contrast microscopy images. In particular, we combine a convolutional neural network (CNN) and a long short-term memory (LSTM) network to detect mitotic events in patch sequences. The CNN-LSTM network can be trained end-to-end to simultaneously learn convolutional features within each frame and temporal dynamics between frames, without hand-crafted visual or temporal feature design. Owing to the LSTM layer, this approach is able to detect mitotic events in patch sequences of variable length, as well as making use of longer context information among frames in the sequences. To the best of our knowledge, this is the first work to detect mitosis using deep learning in both spatial and temporal domains. Experiments have shown that the CNN-LSTM network can be trained efficiently, and we evaluate this design by applying the network to original raw microscopy image sequences to locate mitotic events both spatially and temporally. The data with which we validate the proposed method include C3H10 mesenchymal and C2C12 myoblastic stem cell populations. Our approach achieved the F score of 98.72% on the C2C12 data set, and the F score of 96.5% on the C3H10 data set. The results on both data sets outperform the traditional graph model-based approaches by a large margin, both in terms of detection accuracy and frame localization accuracy. Furthermore, we have developed a framework to aid humans in annotating mitosis with high efficiency and accuracy in raw phase contrast microscopy images based on the joint detection results using the proposed method. Under this framework, expert level annotations can be obtained in raw phase contrast microscopy image sequences, and the annotations have shown to further improve the training performance of the CNN-LSTM network.

Inducing cells to disperse nickel nanowires via integrin-mediated responses

We present non-cytotoxic, magnetic, Arg-Gly-Asp (RGD)-functionalized nickel nanowires (RGD–nanowires) that trigger specific cellular responses via integrin transmembrane receptors, resulting in dispersal of the nanowires. Time-lapse fluorescence and phase contrast microscopy showed that dispersal of 3 μm long nanowire increased by a factor of 1.54 with functionalization by RGD, compared to polyethylene glycol (PEG), through integrin-specific binding, internalization and proliferation in osteosarcoma cells. Further, a 35.5% increase in cell density was observed in the presence of RGD–nanowires, compared to an increase of only 15.6% with PEG–nanowires. These results promise to advance applications of magnetic nanoparticles in drug delivery, hyperthermia, and cell separation where uniformity and high efficiency in cell targeting is desirable.