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

Background

Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.

Results

We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation.First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.

Conclusions

FogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0431-x) contains supplementary material, which is available to authorized users.     

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.     

A computational tool for the simulation and optimization of microbial strains accounting integrated metabolic/regulatory information

Background and scope

Recently, a number of methods and tools have been proposed to allow the use of genome-scale metabolic models for the phenotype simulation and optimization of microbial strains, within the field of Metabolic Engineering (ME). One of the limitations of most of these algorithms and tools is the fact that only metabolic information is taken into account, disregarding knowledge on regulatory events.

Implementation and performances

This work proposes a novel software tool that implements methods for the phenotype simulation and optimization of microbial strains using integrated models, encompassing both metabolic and regulatory information. This tool is developed as a plug-in that runs over OptFlux, a computational platform that aims to be a reference tool for the ME community.

Availability

The plug-in is made available in the OptFlux web site (www.optflux.org) together with examples and documentation.     

Personalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages

Background

Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. Cancer treatment platforms have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and image analysis software for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no bioinformatics solution that supports the integration of these heterogeneous datasets.

Results

We have developed a bioinformatics platform – Personalized Oncology Suite (POS) – that integrates clinical data, NGS data and whole-slide bioimages from tissue sections. POS is a web-based platform that is scalable, flexible and expandable. The underlying database is based on a data warehouse schema, which is used to integrate information from different sources. POS stores clinical data, genomic data (SNPs and INDELs identified from NGS analysis), and scanned whole-slide images. It features a genome browser as well as access to several instances of the bioimage management application Bisque. POS provides different visualization techniques and offers sophisticated upload and download possibilities. The modular architecture of POS allows the community to easily modify and extend the application.

Conclusions

The web-based integration of clinical, NGS, and imaging data represents a valuable resource for clinical researchers and future application in medical oncology. POS can be used not only in the context of cancer immunology but also in other studies in which NGS data and images of tissue sections are generated. The application is open-source and can be downloaded at http://www.icbi.at/POS.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-306) contains supplementary material, which is available to authorized users.     

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Light sheet fluorescence microscopy (LSFM) is gaining more and more popularity as a method to image embryonic development. The main advantages of LSFM compared to confocal systems are its low phototoxicity, gentle mounting strategies, fast acquisition with high signal to noise ratio and the possibility of imaging samples from various angles (views) for long periods of time. Imaging from multiple views unleashes the full potential of LSFM, but at the same time it can create terabyte-sized datasets. Processing such datasets is the biggest challenge of using LSFM. In this protocol we outline some solutions to this problem. Until recently, LSFM was mostly performed in laboratories that had the expertise to build and operate their own light sheet microscopes. However, in the last three years several commercial implementations of LSFM became available, which are multipurpose and easy to use for any developmental biologist. This article is primarily directed to those researchers, who are not LSFM technology developers, but want to employ LSFM as a tool to answer specific developmental biology questions. Here, we use imaging of zebrafish eye development as an example to introduce the reader to LSFM technology and we demonstrate applications of LSFM across multiple spatial and temporal scales. This article describes a complete experimental protocol starting with the mounting of zebrafish embryos for LSFM. We then outline the options for imaging using the commercially available light sheet microscope. Importantly, we also explain a pipeline for subsequent registration and fusion of multiview datasets using an open source solution implemented as a Fiji plugin. While this protocol focuses on imaging the developing zebrafish eye and processing data from a particular imaging setup, most of the insights and troubleshooting suggestions presented here are of general use and the protocol can be adapted to a variety of light sheet microscopy experiments.     

An integrated network visualization framework towards metabolic engineering applications

Background

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential.

Results

In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks.

Conclusions

The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0420-0) contains supplementary material, which is available to authorized users.     

An open-source algorithm for rapid unbiased determination of DNA fiber length

DNA fiber fluorography is widely employed to study the kinetics of DNA replication, but the usefulness of this approach has been limited by the lack of freely-available automated analysis tools. Quantification of DNA fibers usually relies on manual examination of immunofluorescence microscopy images, which is laborious and prone to inter- and intra-operator variability. To address this, we developed an unbiased, fully automated algorithm that quantifies length and color of DNA fibers from fluorescence microscopy images. Our fiber quantification method, termed FiberQ, is an open-source image processing tool based on edge detection and a novel segment splicing approach. Here, we describe the algorithm in detail, validate our results experimentally, and benchmark the analysis against manual assessments. Our implementation is offered free of charge to the scientific community under the General Public License.