Quantifying and visualising the nuances of cellular dynamics in vivo using intravital imaging

Intravital imaging is a powerful technology used to quantify and track dynamic changes in live cells and tissues within an intact environment. The ability to watch cell biology in real-time ‘as it happens’ has provided novel insight into tissue homeostasis, as well as disease initiation, progression and response to treatment. In this minireview, we highlight recent advances in the field of intravital microscopy, touching upon advances in awake versus anaesthesia-based approaches, as well as the integration of biosensors into intravital imaging. We also discuss current challenges that, in our opinion, need to be overcome to further advance the field of intravital imaging at the single-cell, subcellular and molecular resolution to reveal nuances of cell behaviour that can be targeted in complex disease settings.     

Automated laser scanning cytometry: a powerful tool for multi-parameter analysis of drug-induced apoptosis.

BACKGROUND: Simultaneous analysis of multiple intracellular events is critical for assessing the effect of biological response modifiers, including the efficacy of chemotherapy. Here we used the automated laser scanning cytometry (LSC) for multi-parameter analysis of drug-induced tumor cell apoptosis. MATERIALS: Using 2-mercaptopyridine-N-oxide-hydrate sodium salt, or the commonly used chemotherapeutic agents etoposide and camptothecin, we performed simultaneous analyses of apoptosis-related morphological features as well as fluorescence-based biochemical changes in a 96-well format. RESULTS: We demonstrate the scope of LSC as a platform for comparing multiple variables between different cell populations, distinguishing unique events at a single cell level within a sample population, and enabling simultaneous screenings in a single assay at multiple dosages and time-points. CONCLUSION: These data underscore the power of LSC for simultaneous multi-parameter analysis, which could have implications for screening or assessing the efficacy of drug responses in heterogeneous cell populations and at the single cell level.     

Toward high-content/high-throughput imaging and analysis of embryonic morphogenesis

In vivo study of embryonic morphogenesis tremendously benefits from recent advances in live microscopy and computational analyses. Quantitative and automated investigation of morphogenetic processes opens the field to high-content and high-throughput strategies. Following experimental workflow currently developed in cell biology, we identify the key challenges for applying such strategies in developmental biology. We review the recent progress in embryo preparation and manipulation, live imaging, data registration, image segmentation, feature computation, and data mining dedicated to the study of embryonic morphogenesis. We discuss a selection of pioneering studies that tackled the current methodological bottlenecks and illustrated the investigation of morphogenetic processes in vivo using quantitative and automated imaging and analysis of hundreds or thousands of cells simultaneously, paving the way for high-content/high-throughput strategies and systems analysis of embryonic morphogenesis.     

Fully-automated image processing software to analyze calcium traces in populations of single cells

Advances in fluorescence live cell imaging over the last decade have revolutionized cell biology by providing access to single-cell information in space and time. One current limitation of live-cell imaging is the lack of automated procedures to analyze single-cell data in large cell populations. Most commercially available image processing softwares do not have built-in image segmentation tools that can automatically and accurately extract single-cell data in a time series. Consequently, individual cells are usually identified manually, a process which is time consuming and inherently low-throughput. We have developed a MATLAB-based image segmentation algorithm that reliably detects individual cells in dense populations and measures their fluorescence intensity over time. To demonstrate the value of this algorithm, we measured store-operated calcium entry (SOCE) in hundreds of individual cells. Rapid access to single-cell calcium signals in large populations allowed us to precisely determine the relationship between SOCE activity and STIM1 levels, a key component of SOCE. Our image processing tool can in principle be applied to a wide range of live-cell imaging modalities and cell-based drug screening platforms.     

Imaging Circulating Tumor Cells in Freely Moving Awake Small Animals Using a Miniaturized Intravital Microscope

Metastasis, the cause for 90% of cancer mortality, is a complex and poorly understood process involving the invasion of circulating tumor cells (CTCs) into blood vessels. These cells have potential prognostic value as biomarkers for early metastatic risk. But their rarity and the lack of specificity and sensitivity in measuring them render their interrogation by current techniques very challenging. How and when these cells are circulating in the blood, on their way to potentially give rise to metastasis, is a question that remains largely unanswered. In order to provide an insight into this "black box" using non-invasive imaging, we developed a novel miniature intravital microscopy (mIVM) strategy capable of real-time long-term monitoring of CTCs in awake small animals. We established an experimental 4T1-GL mouse model of metastatic breast cancer, in which tumor cells express both fluorescent and bioluminescent reporter genes to enable both single cell and whole body tumor imaging. Using mIVM, we monitored blood vessels of different diameters in awake mice in an experimental model of metastasis. Using an in-house software algorithm we developed, we demonstrated in vivo CTC enumeration and computation of CTC trajectory and speed. These data represent the first reported use we know of for a miniature mountable intravital microscopy setup for in vivo imaging of CTCs in awake animals.     

Fluorescent multiplexing of 3D spheroids: Analysis of biomarkers using automated immunohistochemistry staining platform and multispectral imaging

In preclinical cancer studies, three-dimensional (3D) cell spheroids and aggregates are preferred over monolayer cell cultures due to their architectural and functional similarity to solid tumors. We performed a proof-of-concept study to generate physiologically relevant and predictive preclinical models using non–small cell lung adenocarcinoma, and colon and colorectal adenocarcinoma cell line-derived 3D spheroids and aggregates. Distinct panels were designed to determine the expression profiles of frequently studied biomarkers of the two cancer subtypes. The lung adenocarcinoma panel included ALK, EGFR, TTF-1, and CK7 biomarkers, and the colon and colorectal adenocarcinoma panel included BRAF V600E, MSH2, MSH6, and CK20. Recent advances in immunofluorescence (IF) multiplexing and imaging technology enable simultaneous detection and quantification of multiple biomarkers on a single slide. In this study, we performed IF staining of multiple biomarkers per section on formalin-fixed paraffin-embedded 3D spheroids and aggregates. We optimized protocol parameters for automated IF and demonstrated staining concordance with automated chromogenic immunohistochemistry performed with validated protocols. Next, post-acquisition spectral unmixing of the captured fluorescent signals were utilized to delineate four differently stained biomarkers within a single multiplex IF image, followed by automated quantification of the expressed markers. This workflow has the potential to be adapted to preclinical high-throughput screening and drug efficacy studies utilizing 3D spheroids from cancer cell lines and patient-derived organoids. The process allows for cost, time, and resource savings through concurrent staining of several biomarkers on a single slide, the ability to study the interactions of multiple expressed proteins within a single region of interest, and enable quantitative assessment of biomarkers in cancer cells.     

Miniaturized embryo array for automated trapping, immobilization and microperfusion of zebrafish embryos

Zebrafish (Danio rerio) has recently emerged as a powerful experimental model in drug discovery and environmental toxicology. Drug discovery screens performed on zebrafish embryos mirror with a high level of accuracy the tests usually performed on mammalian animal models, and fish embryo toxicity assay (FET) is one of the most promising alternative approaches to acute ecotoxicity testing with adult fish. Notwithstanding this, automated in-situ analysis of zebrafish embryos is still deeply in its infancy. This is mostly due to the inherent limitations of conventional techniques and the fact that metazoan organisms are not easily susceptible to laboratory automation. In this work, we describe the development of an innovative miniaturized chip-based device for the in-situ analysis of zebrafish embryos. We present evidence that automatic, hydrodynamic positioning, trapping and long-term immobilization of single embryos inside the microfluidic chips can be combined with time-lapse imaging to provide real-time developmental analysis. Our platform, fabricated using biocompatible polymer molding technology, enables rapid trapping of embryos in low shear stress zones, uniform drug microperfusion and high-resolution imaging without the need of manual embryo handling at various developmental stages. The device provides a highly controllable fluidic microenvironment and post-analysis eleuthero-embryo stage recovery. Throughout the incubation, the position of individual embryos is registered. Importantly, we also for first time show that microfluidic embryo array technology can be effectively used for the analysis of anti-angiogenic compounds using transgenic zebrafish line (fli1a:EGFP). The work provides a new rationale for rapid and automated manipulation and analysis of developing zebrafish embryos at a large scale.     

CentTracker: a trainable,machine-learning–based tool for large-scale analyses of Caenorhabditis elegans germline stem cell mitosis

Investigating the complex interactions between stem cells and their native environment requires an efficient means to image them in situ. Caenorhabditis elegans germline stem cells (GSCs) are distinctly accessible for intravital imaging; however, long-term image acquisition and analysis of dividing GSCs can be technically challenging. Here we present a systematic investigation into the technical factors impacting GSC physiology during live imaging and provide an optimized method for monitoring GSC mitosis under minimally disruptive conditions. We describe CentTracker, an automated and generalizable image analysis tool that uses machine learning to pair mitotic centrosomes and that can extract a variety of mitotic parameters rapidly from large-scale data sets. We employ CentTracker to assess a range of mitotic features in a large GSC data set. We observe spatial clustering of mitoses within the germline tissue but no evidence that subpopulations with distinct mitotic profiles exist within the stem cell pool. We further find biases in GSC spindle orientation relative to the germline’s distal–proximal axis and thus the niche. The technical and analytical tools provided herein pave the way for large-scale screening studies of multiple mitotic processes in GSCs dividing in situ, in an intact tissue, in a living animal, under seemingly physiological conditions.     

Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization

MOTIVATION: Determining locations of protein expression is essential to understand protein function. Advances in green fluorescence protein (GFP) fusion proteins and automated fluorescence microscopy allow for rapid acquisition of large collections of protein localization images. Recognition of these cell images requires an automated image analysis system. Approaches taken by previous work concentrated on designing a set of optimal features and then applying standard machine-learning algorithms. In fact, trends of recent advances in machine learning and computer vision can be applied to improve the performance. One trend is the advances in multiclass learning with error-correcting output codes (ECOC). Another trend is the use of a large number of weak detectors with boosting for detecting objects in images of real-world scenes. RESULTS: We take advantage of these advances to propose a new learning algorithm, AdaBoost.ERC, coupled with weak and strong detectors, to improve the performance of automatic recognition of protein subcellular locations in cell images. We prepared two image data sets of CHO and Vero cells and downloaded a HeLa cell image data set in the public domain to evaluate our new method. We show that AdaBoost.ERC outperforms other AdaBoost extensions. We demonstrate the benefit of weak detectors by showing significant performance improvements over classifiers using only strong detectors. We also empirically test our method's capability of generalizing to heterogeneous image collections. Compared with previous work, our method performs reasonably well for the HeLa cell images. AVAILABILITY: CHO and Vero cell images, their corresponding feature sets (SSLF and WSLF), our new learning algorithm, AdaBoost.ERC, and Supplementary Material are available at http://aiia.iis.sinica.edu.tw/     

Apoptosis detection via automated algorithms to analyze biomarker translocation in reporter cells

Rapid, efficient, and robust quantitative analyses of dynamic apoptotic events are essential in a high-throughput screening workflow. Currently used methods have several bottlenecks, specifically, limitations in available fluorophores for downstream assays and misinterpretation of statistical image data analysis. In this study, we developed cytochrome-C (Cyt-C) and caspase-3/-8 reporter cell lines using lung (PC9) and breast (T47D) cancer cells, and characterized them from the response to apoptotic stimuli. In these two reporter cell lines, the spatial fluorescent signal translocation patterns served as reporters of activations of apoptotic events, such as Cyt-C release and caspase-3/-8 activation. We also developed a vision-based, tunable, automated algorithm in MATLAB to implement the robust and accurate analysis of signal translocation in single or multiple cells. Construction of the reporter cell lines allows live monitoring of apoptotic events without the need for any other dyes or fixatives. Our algorithmic implementation forgoes the use of simple image statistics for more robust analytics. Our optimized algorithm can achieve a precision greater than 90% and a sensitivity higher than 85%. Combining our automated algorithm with reporter cells bearing a single-color dye/fluorophore, we expect our approach to become an integral component in the high-throughput drug screening workflow.     

Intravital microscopy: a novel tool to study cell biology in living animals

Intravital microscopy encompasses various optical microscopy techniques aimed at visualizing biological processes in live animals. In the last decade, the development of non-linear optical microscopy resulted in an enormous increase of in vivo studies, which have addressed key biological questions in fields such as neurobiology, immunology and tumor biology. Recently, few studies have shown that subcellular processes can be imaged dynamically in the live animal at a resolution comparable to that achieved in cell cultures, providing new opportunities to study cell biology under physiological conditions. The overall aim of this review is to give the reader a general idea of the potential applications of intravital microscopy with a particular emphasis on subcellular imaging. An overview of some of the most exciting studies in this field will be presented using resolution as a main organizing criterion. Indeed, first we will focus on those studies in which organs were imaged at the tissue level, then on those focusing on single cells imaging, and finally on those imaging subcellular organelles and structures.     

An Innovative Method for Obtaining Consistent Images and Quantification of Histochemically Stained Specimens

Obtaining digital images of color brightfield microscopy is an important aspect of biomedical research and the clinical practice of diagnostic pathology. Although the field of digital pathology has had tremendous advances in whole-slide imaging systems, little effort has been directed toward standardizing color brightfield digital imaging to maintain image-to-image consistency and tonal linearity. Using a single camera and microscope to obtain digital images of three stains, we show that microscope and camera systems inherently produce image-to-image variation. Moreover, we demonstrate that post-processing with a widely used raster graphics editor software program does not completely correct for session-to-session inconsistency. We introduce a reliable method for creating consistent images with a hardware/software solution (ChromaCal™; Datacolor Inc., NJ) along with its features for creating color standardization, preserving linear tonal levels, providing automated white balancing and setting automated brightness to consistent levels. The resulting image consistency using this method will also streamline mean density and morphometry measurements, as images are easily segmented and single thresholds can be used. We suggest that this is a superior method for color brightfield imaging, which can be used for quantification and can be readily incorporated into workflows.     

Automated live cell imaging systems reveal dynamic cell behavior

Automated time‐lapsed microscopy provides unique research opportunities to visualize cells and subcellular components in experiments with time‐dependent parameters. As accessibility to these systems is increasing, we review here their use in cell science with a focus on stem cell research. Although the use of time‐lapsed imaging to answer biological questions dates back nearly 150 years, only recently have the use of an environmentally controlled chamber and robotic stage controllers allowed for high‐throughput continuous imaging over long periods at the cell and subcellular levels. Numerous automated imaging systems are now available from both companies that specialize in live cell imaging and from major microscope manufacturers. We discuss the key components of robots used for time‐lapsed live microscopic imaging, and the unique data that can be obtained from image analysis. We show how automated features enhance experimentation by providing examples of uniquely quantified proliferation and migration live cell imaging data. In addition to providing an efficient system that drastically reduces man‐hours and consumes fewer laboratory resources, this technology greatly enhances cell science by providing a unique dataset of temporal changes in cell activity. © 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2011     

Real‐time interactive two‐photon photoconversion of recirculating lymphocytes for discontinuous cell tracking in live adult mice

The potential usefulness of intravital two‐photon microscopy for fate mapping is limited by its inability to track cells beyond the confines of the imaging volume. Therefore, we have developed and validated a novel method for in vivo photolabelling of spatially‐restricted cells expressing the Kaede optical highlighter by two‐photon excitation. This has allowed us to optically mark a cohort of follicular B cells and track their dissemination from the original imaging volume in the lymph node to the spleen and contralateral lymph node. We also present the first demonstration, to our knowledge, of in vivo photoconversion of a freely moving single cell in a live adult animal. This method of `discontinuous' cell tracking therefore significantly extends the fate mapping capabilities of two‐photon microscopy to delineate the spatiotemporal dynamics of cellular processes that span multiple anatomical sites at the single cell level. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multi-resolution correlative focused ion beam scanning electron microscopy: Applications to cell biology

Efficient correlative imaging of small targets within large fields is a central problem in cell biology. Here, we demonstrate a series of technical advances in focused ion beam scanning electron microscopy (FIB–SEM) to address this issue. We report increases in the speed, robustness and automation of the process, and achieve consistent z slice thickness of ∼3 nm. We introduce “keyframe imaging” as a new approach to simultaneously image large fields of view and obtain high-resolution 3D images of targeted sub-volumes. We demonstrate application of these advances to image post-fusion cytoplasmic intermediates of the HIV core. Using fluorescently labeled cell membranes, proteins and HIV cores, we first produce a “target map” of an HIV infected cell by fluorescence microscopy. We then generate a correlated 3D EM volume of the entire cell as well as high-resolution 3D images of individual HIV cores, achieving correlative imaging across a volume scale of 10⁹ in a single automated experimental run.     

Stretching the timescale of intravital imaging in tumors

Since the time it was pioneered in 1992, intravital imaging of tumors at cellular resolution has offered us the extremely important opportunity of “seeing biology”. However, until now, most studies were monitoring tumor cell behavior in the same animal over short times, requiring the combining of acquired data into a hypothesis via statistical analysis. In the last several months, different groups have independently developed techniques to extend the time scale of intravital imaging to several days. This improvement allows one to address the connection between tumor cell behavior and the microenvironment which surrounds them. We can now assess dynamics of the cell-cell interactions in tumors, analyze tumor cell fate and changes in the tumor extracellular matrix which accompany tumor progression.     

Selective single cell isolation for genomics using microraft arrays

Genomic methods are used increasingly to interrogate the individual cells that compose specific tissues. However, current methods for single cell isolation struggle to phenotypically differentiate specific cells in a heterogeneous population and rely primarily on the use of fluorescent markers. Many cellular phenotypes of interest are too complex to be measured by this approach, making it difficult to connect genotype and phenotype at the level of individual cells. Here we demonstrate that microraft arrays, which are arrays containing thousands of individual cell culture sites, can be used to select single cells based on a variety of phenotypes, such as cell surface markers, cell proliferation and drug response. We then show that a common genomic procedure, RNA-seq, can be readily adapted to the single cells isolated from these rafts. We show that data generated using microrafts and our modified RNA-seq protocol compared favorably with the Fluidigm C1. We then used microraft arrays to select pancreatic cancer cells that proliferate in spite of cytotoxic drug treatment. Our single cell RNA-seq data identified several expected and novel gene expression changes associated with early drug resistance.