Quantitative image based apoptotic index measurement using multispectral imaging flow cytometry: a comparison with standard photometric methods

Morphological characterization by microscopy remains the gold standard for accurately identifying apoptotic cells using characteristics such as nuclear condensation, nuclear fragmentation, and membrane blebbing. However, quantitative measurement of apoptotic morphology using microscopy can be time consuming and can lack objectivity and reproducibility, making it difficult to identify subtle changes in large populations. Thus the apoptotic index of a sample is commonly measured by flow cytometry using a variety of fluorescence intensity based (photometric) assays which target hallmarks of apoptosis with secondary markers such as the TUNEL (Terminal Deoxynucleotide Transferase dUTP Nick End Labeling) assay for detection of DNA fragmentation, the Annexin V assay for surface phosphatidylserine (PS) exposure, and fluorogenic caspase substrates to detect caspase activation. Here a novel method is presented for accurate quantitation of apoptosis based on nuclear condensation, nuclear fragmentation, and membrane blebbing using automated image analysis on large numbers of images collected in flow by the ImageStream multispectral imaging cytometer. Additionally the measurement of nuclear fragmentation correlates with the secondary methods of detection of apoptosis over time, indicating that it is also an early marker for apoptosis. False-positive and false-negative events associated with each photometric flow cytometry based method are quantitated and can be automatically removed/included where appropriate. Acquisition of multi-spectral imagery on large numbers of cells couples the quantitative advantage of flow cytometry with the accuracy of morphology-based algorithms allowing more complete and robust analysis of apoptosis.     

A Random Motility Assay Based on Image Correlation Spectroscopy

We demonstrate the random motility (RAMOT) assay based on image correlation spectroscopy for the automated, label-free, high-throughput characterization of random cell migration. The approach is complementary to traditional migration assays, which determine only the collective net motility in a particular direction. The RAMOT assay is less demanding on image quality compared to single-cell tracking, does not require cell identification or trajectory reconstruction, and performs well on live-cell, time-lapse, phase contrast video microscopy of hundreds of cells in parallel. Effective diffusion coefficients derived from the RAMOT analysis are in quantitative agreement with Monte Carlo simulations and allowed for the detection of pharmacological effects on macrophage-like cells migrating on a planar collagen matrix. These results expand the application range of image correlation spectroscopy to multicellular systems and demonstrate a novel, to our knowledge, migration assay with little preparative effort.     

Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

Background  

High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells.     

Automated detection and tracking of individual and clustered cell surface low density lipoprotein receptor molecules.

We have developed a technique to detect, recognize, and track each individual low density lipoprotein receptor (LDL-R) molecule and small receptor clusters on the surface of human skin fibroblasts. Molecular recognition and high precision (30 nm) simultaneous automatic tracking of all of the individual receptors in the cell surface population utilize quantitative time-lapse low light level digital video fluorescence microscopy analyzed by purpose-designed algorithms executed on an image processing work station. The LDL-Rs are labeled with the biologically active, fluorescent LDL derivative dil-LDL. Individual LDL-Rs and unresolved small clusters are identified by measuring the fluorescence power radiated by the sub-resolution fluorescent spots in the image; identification of single particles is ascertained by four independent techniques. An automated tracking routine was developed to track simultaneously, and without user intervention, a multitude of fluorescent particles through a sequence of hundreds of time-lapse image frames. The limitations on tracking precision were found to depend on the signal-to-noise ratio of the tracked particle image and mechanical drift of the microscope system. We describe the methods involved in (i) time-lapse acquisition of the low-light level images, (ii) simultaneous automated tracking of the fluorescent diffraction limited punctate images, (iii) localizing particles with high precision and limitations, and (iv) detecting and identifying single and clustered LDL-Rs. These methods are generally applicable and provide a powerful tool to visualize and measure dynamics and interactions of individual integral membrane proteins on living cell surfaces.     

High-throughput RNAi screening by time-lapse imaging of live human cells

RNA interference (RNAi) is a powerful tool to study gene function in cultured cells. Transfected cell microarrays in principle allow high-throughput phenotypic analysis after gene knockdown by microscopy. But bottlenecks in imaging and data analysis have limited such high-content screens to endpoint assays in fixed cells and determination of global parameters such as viability. Here we have overcome these limitations and developed an automated platform for high-content RNAi screening by time-lapse fluorescence microscopy of live HeLa cells expressing histone-GFP to report on chromosome segregation and structure. We automated all steps, including printing transfection-ready small interfering RNA (siRNA) microarrays, fluorescence imaging and computational phenotyping of digital images, in a high-throughput workflow. We validated this method in a pilot screen assaying cell division and delivered a sensitive, time-resolved phenoprint for each of the 49 endogenous genes we suppressed. This modular platform is scalable and makes the power of time-lapse microscopy available for genome-wide RNAi screens.     

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.     

A Quantitative Method to Track Protein Translocation between Intracellular Compartments in Real-Time in Live Cells Using Weighted Local Variance Image Analysis

The genetic expression of cloned fluorescent proteins coupled to time-lapse fluorescence microscopy has opened the door to the direct visualization of a wide range of molecular interactions in living cells. In particular, the dynamic translocation of proteins can now be explored in real time at the single-cell level. Here we propose a reliable, easy-to-implement, quantitative image processing method to assess protein translocation in living cells based on the computation of spatial variance maps of time-lapse images. The method is first illustrated and validated on simulated images of a fluorescently-labeled protein translocating from mitochondria to cytoplasm, and then applied to experimental data obtained with fluorescently-labeled hexokinase 2 in different cell types imaged by regular or confocal microscopy. The method was found to be robust with respect to cell morphology changes and mitochondrial dynamics (fusion, fission, movement) during the time-lapse imaging. Its ease of implementation should facilitate its application to a broad spectrum of time-lapse imaging studies.     

SuperSegger: robust image segmentation,analysis and lineage tracking of bacterial cells

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame‐to‐frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB‐based image processing package well‐suited to quantitative analysis of high‐throughput live‐cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine‐learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame‐to‐frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell‐cycle dynamics in bacteria as well as cell‐contact mediated phenomena. This package has a range of built‐in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution.     

BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies

Background

The software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing.

Results

Here we describe BactImAS – a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree.

Conclusions

The presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. It will be of interest to the community of both end-users and developers of time-lapse microscopy software.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-251) contains supplementary material, which is available to authorized users.     

Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues

To study the process of morphogenesis, one often needs to collect and segment time-lapse images of living tissues to accurately track changing cellular morphology. This task typically involves segmenting and tracking tens to hundreds of individual cells over hundreds of image frames, a scale that would certainly benefit from automated routines; however, any automated routine would need to reliably handle a large number of sporadic, and yet typical problems (e.g., illumination inconsistency, photobleaching, rapid cell motions, and drift of focus or of cells moving through the imaging plane). Here, we present a segmentation and cell tracking approach based on the premise that users know their data best-interpreting and using image features that are not accounted for in any a priori algorithm design. We have developed a program, SeedWater Segmenter, that combines a parameter-less and fast automated watershed algorithm with a suite of manual intervention tools that enables users with little to no specialized knowledge of image processing to efficiently segment images with near-perfect accuracy based on simple user interactions.     

The Yeast Resource Center Public Image Repository: A large database of fluorescence microscopy images

Background  

There is increasing interest in the development of computational methods to analyze fluorescent microscopy images and enable automated large-scale analysis of the subcellular localization of proteins. Determining the subcellular localization is an integral part of identifying a protein's function, and the application of bioinformatics to this problem provides a valuable tool for the annotation of proteomes. Training and validating algorithms used in image analysis research typically rely on large sets of image data, and would benefit from a large, well-annotated and highly-available database of images and associated metadata.     

Context based mixture model for cell phase identification in automated fluorescence microscopy

Background  

Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task.     

Enhancement of Apoptotic and Autophagic Induction by a Novel Synthetic C-1 Analogue of 7-deoxypancratistatin in Human Breast Adenocarcinoma and Neuroblastoma Cells with Tamoxifen

Breast cancer is one of the most common cancers amongst women in North America. Many current anti-cancer treatments, including ionizing radiation, induce apoptosis via DNA damage. Unfortunately, such treatments are non-selective to cancer cells and produce similar toxicity in normal cells. We have reported selective induction of apoptosis in cancer cells by the natural compound pancratistatin (PST). Recently, a novel PST analogue, a C-1 acetoxymethyl derivative of 7-deoxypancratistatin (JCTH-4), was produced by de novo synthesis and it exhibits comparable selective apoptosis inducing activity in several cancer cell lines. Recently, autophagy has been implicated in malignancies as both pro-survival and pro-death mechanisms in response to chemotherapy. Tamoxifen (TAM) has invariably demonstrated induction of pro-survival autophagy in numerous cancers. In this study, the efficacy of JCTH-4 alone and in combination with TAM to induce cell death in human breast cancer (MCF7) and neuroblastoma (SH-SY5Y) cells was evaluated. TAM alone induced autophagy, but insignificant cell death whereas JCTH-4 alone caused significant induction of apoptosis with some induction of autophagy. Interestingly, the combinatory treatment yielded a drastic increase in apoptotic and autophagic induction. We monitored time-dependent morphological changes in MCF7 cells undergoing TAM-induced autophagy, JCTH-4-induced apoptosis and autophagy, and accelerated cell death with combinatorial treatment using time-lapse microscopy. We have demonstrated these compounds to induce apoptosis/autophagy by mitochondrial targeting in these cancer cells. Importantly, these treatments did not affect the survival of noncancerous human fibroblasts. Thus, these results indicate that JCTH-4 in combination with TAM could be used as a safe and very potent anti-cancer therapy against breast cancer and neuroblastoma cells.     

Quantification of Dynamic Morphological Drug Responses in 3D Organotypic Cell Cultures by Automated Image Analysis

Glandular epithelial cells differentiate into complex multicellular or acinar structures, when embedded in three-dimensional (3D) extracellular matrix. The spectrum of different multicellular morphologies formed in 3D is a sensitive indicator for the differentiation potential of normal, non-transformed cells compared to different stages of malignant progression. In addition, single cells or cell aggregates may actively invade the matrix, utilizing epithelial, mesenchymal or mixed modes of motility. Dynamic phenotypic changes involved in 3D tumor cell invasion are sensitive to specific small-molecule inhibitors that target the actin cytoskeleton. We have used a panel of inhibitors to demonstrate the power of automated image analysis as a phenotypic or morphometric readout in cell-based assays. We introduce a streamlined stand-alone software solution that supports large-scale high-content screens, based on complex and organotypic cultures. AMIDA (Automated Morphometric Image Data Analysis) allows quantitative measurements of large numbers of images and structures, with a multitude of different spheroid shapes, sizes, and textures. AMIDA supports an automated workflow, and can be combined with quality control and statistical tools for data interpretation and visualization. We have used a representative panel of 12 prostate and breast cancer lines that display a broad spectrum of different spheroid morphologies and modes of invasion, challenged by a library of 19 direct or indirect modulators of the actin cytoskeleton which induce systematic changes in spheroid morphology and differentiation versus invasion. These results were independently validated by 2D proliferation, apoptosis and cell motility assays. We identified three drugs that primarily attenuated the invasion and formation of invasive processes in 3D, without affecting proliferation or apoptosis. Two of these compounds block Rac signalling, one affects cellular cAMP/cGMP accumulation. Our approach supports the growing needs for user-friendly, straightforward solutions that facilitate large-scale, cell-based 3D assays in basic research, drug discovery, and target validation.     

Quantification of Local Morphodynamics and Local GTPase Activity by Edge Evolution Tracking

Advances in time-lapse fluorescence microscopy have enabled us to directly observe dynamic cellular phenomena. Although the techniques themselves have promoted the understanding of dynamic cellular functions, the vast number of images acquired has generated a need for automated processing tools to extract statistical information. A problem underlying the analysis of time-lapse cell images is the lack of rigorous methods to extract morphodynamic properties. Here, we propose an algorithm called edge evolution tracking (EET) to quantify the relationship between local morphological changes and local fluorescence intensities around a cell edge using time-lapse microscopy images. This algorithm enables us to trace the local edge extension and contraction by defining subdivided edges and their corresponding positions in successive frames. Thus, this algorithm enables the investigation of cross-correlations between local morphological changes and local intensity of fluorescent signals by considering the time shifts. By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity. The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6–8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities. Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship. Thus, this algorithm extends the value of time-lapse imaging data to better understand dynamics of cellular function.     

A digital image processing system for quantitating dynamic morphology in cultured mammalian cells

An automated, video-driven system has been developed which can quantitate dynamic cell morphology in cultured mammalian cells. This system is based upon the Personal Image Analysis System and is assisted by a video-enhanced contrast microscopy with a computer-aided digital image processing unit and a time-lapse video technique. Various parameters for cell motility including locomotion (vectorial translation) and accompanying shape changes can be simultaneously analyzed. Here, we describe this system and demonstrate its application in Balb/c 3T3 cell culture. This system represents a new tool for exploring subtleties of mammalian cell behavior.     

Preferential Fas-mediated apoptotic execution at G1 phase: the resistance of mitotic cells to the cell death

Apoptosis is induced by various stresses generated from the extracellular and intracellular environments. The fidelity of the cell cycle is monitored by surveillance mechanisms that arrest its further progression if any crucial process has not been completed or damages are sustained, and then the cells with problems undergo apoptosis. Although the molecular mechanisms involved in the regulation of the cell cycle and that of apoptosis have been elucidated, the links between them are not clear, especially that between cell cycle and death receptor-mediated apoptosis. By using the HeLa.S-Fucci (fluorescent ubiquitination-based cell cycle indicator) cells, we investigated the relationship between the cell cycle progression and apoptotic execution. To monitor apoptotic execution during cell cycle progression, we observed the cells after induction of apoptosis with time-lapse fluorescent microscopy. About 70% of Fas-mediated apoptotic cells were present at G₁ phase and about 20% of cells died immediately after cytokinesis, whereas more than 60% of etoposide-induced apoptotic cells were at S/G₂ phases in random culture of the cells. These results were confirmed by using synchronized culture of the cells. Furthermore, mitotic cells showed the resistance to Fas-mediated apoptosis. In conclusion, these findings suggest that apoptotic execution is dependent on cell cycle phase and Fas-mediated apoptosis preferentially occurs at G₁ phase.     

Computer‐automated bird detection and counts in high‐resolution aerial images: a review

Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time‐consuming if images must be analyzed manually. Recent advances in digital cameras and image‐analysis software offer unprecedented potential for computer‐automated bird detection and counts in high‐resolution aerial images. We review the literature on this subject and provide an overview of the main image‐analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic image‐analysis software. However, the sophisticated analysis capabilities of modern object‐based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal‐infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true‐color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife‐monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer‐automated aerial photographic census techniques.