In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.
Supplementary information: Supplementary data are available at Bioinformatics online.
In recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images.
We employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies.
For the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.
Chemical address tags can be defined as specific structural features shared by a set of bioimaging probes having a predictable influence on cell-associated visual signals obtained from these probes. Here, using a large image dataset acquired with a high content screening instrument, machine vision and cheminformatics analysis have been applied to reveal chemical address tags. With a combinatorial library of fluorescent molecules, fluorescence signal intensity, spectral, and spatial features characterizing each one of the probes' visual signals were extracted from images acquired with the three different excitation and emission channels of the imaging instrument. With multivariate regression, the additive contribution from each one of the different building blocks of the bioimaging probes towards each measured, cell-associated image-based feature was calculated. In this manner, variations in the chemical features of the molecules were associated with the resulting staining patterns, facilitating quantitative, objective analysis of chemical address tags. Hierarchical clustering and paired image-cheminformatics analysis revealed key structure-property relationships amongst many building blocks of the fluorescent molecules. The results point to different chemical modifications of the bioimaging probes that can exert similar (or different) effects on the probes' visual signals. Inspection of the clustered structures suggests intramolecular charge migration or partial charge distribution as potential mechanistic determinants of chemical address tag behavior.
Cheminformatics; machine vision; bioimaging; fluorescence; high content screening; image cytometry; combinatorial chemistry
This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target.
bioimage informatics; image pattern recognition; image processing
High-throughput microscopic screening instruments can generate huge collections of images of live cells incubated with combinatorial libraries of fluorescent molecules. Organizing and visualizing these images to discern biologically important patterns that link back to chemical structure is a challenge. We present an analysis and visualization methodology - Cheminformatic Assisted Image Array (CAIA) - that greatly facilitates data mining efforts. For illustration, we considered a collection of microscopic images acquired from cells incubated with each member of a combinatorial library of styryl molecules being screened for candidate bioimaging probes. By sorting CAIAs based on quantitative image features, the relative contribution of each combinatorial building block on probe intracellular distribution could be visually discerned. The results revealed trends hidden in the dataset: most interestingly, the building blocks of the styryl molecules appeared to behave as chemical address tags, additively and independently encoding spatial patterns of intracellular fluorescence. Translated into practice, CAIA facilitated discovery of several outstanding styryl molecules for live cell nuclear imaging applications.
Cheminformatics; high content screening; combinatorial library; styryl; fluorescence; bioimaging; chemical address tags; QSAR; CAIA
Intra-cellular and inter-cellular protein translocation can be observed by microscopic imaging of tissue sections prepared immunohistochemically. A manual densitometric analysis is time-consuming, subjective and error-prone. An automated quantification is faster, more reproducible, and should yield results comparable to manual evaluation. The automated method presented here was developed on rat liver tissue sections to study the translocation of bile salt transport proteins in hepatocytes. For validation, the cholestatic liver state was compared to the normal biological state.
An automated quantification method was developed to analyze the translocation of membrane proteins and evaluated in comparison to an established manual method. Firstly, regions of interest (membrane fragments) are identified in confocal microscopy images. Further, densitometric intensity profiles are extracted orthogonally to membrane fragments, following the direction from the plasma membrane to cytoplasm. Finally, several different quantitative descriptors were derived from the densitometric profiles and were compared regarding their statistical significance with respect to the transport protein distribution. Stable performance, robustness and reproducibility were tested using several independent experimental datasets. A fully automated workflow for the information extraction and statistical evaluation has been developed and produces robust results.
New descriptors for the intensity distribution profiles were found to be more discriminative, i.e. more significant, than those used in previous research publications for the translocation quantification. The slow manual calculation can be substituted by the fast and unbiased automated method.
With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For athis purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image feature analysis revealed how variation in one or the other building block of the styryl molecules led to variations in the molecules' visual signals. Across each pair of probes in the library, chemical similarity was significantly associated with spectral and total signal intensity similarity. However, chemical similarity was much less associated with similarity in subcellular probe fluorescence patterns. Quantitative analysis and visual inspection of pairs of images acquired from pairs of styryl isomers confirm that many closely-related probes exhibit different subcellular localization patterns. Therefore, idiosyncratic interactions between styryl molecules and specific cellular components greatly contribute to the subcellular distribution of the styryl probes' fluorescence signal. These results demonstrate how machine vision and cheminformatics can be combined to analyze the targeting properties of bioimaging probes, using large image data sets acquired with automated screening systems.
Cheminformatics; machine vision; bioimaging; fluorescence; styryl; high content screening; image cytometry; combinatorial chemistry
Electron multiplication charge-coupled devices (EMCCD) are widely used for photon counting experiments and measurements of low intensity light sources, and are extensively employed in biological fluorescence imaging applications. These devices have a complex statistical behaviour that is often not fully considered in the analysis of EMCCD data. Robust and optimal analysis of EMCCD images requires an understanding of their noise properties, in particular to exploit fully the advantages of Bayesian and maximum-likelihood analysis techniques, whose value is increasingly recognised in biological imaging for obtaining robust quantitative measurements from challenging data. To improve our own EMCCD analysis and as an effort to aid that of the wider bioimaging community, we present, explain and discuss a detailed physical model for EMCCD noise properties, giving a likelihood function for image counts in each pixel for a given incident intensity, and we explain how to measure the parameters for this model from various calibration images.
Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.
Genetically encoded fluorescent indicators for bioimaging are powerful tools for visualizing biological phenomena in specified cell types or cellular compartments. However, available gene promoters or localization sequences are not applicable for visualizing all expression events. Furthermore, a visualization technique focusing on single cells or cellular compartments is required for characterizing specific cellular properties including individuality of cells in the cell population. To address these limitations, we developed a genetically encoded caged Ca2+ indicator for which expression timing and location could be controlled. This indicator, PA-TNXL, comprises a Ca2+-binding protein and troponin between a photoactivatable FRET donor (PA-GFP) and a FRET quencher (dim variant of YFP). Ultraviolet irradiation activates the FRET Ca2+ indicator. Using this indicator, we successfully imaged Ca2+ dynamics in a given set of HeLa cells and cultured hippocampal neurons. This technology can be applied for developing other photoactivatable indicators, thereby opening a new area of biological research.
Two computerized restriction fragment length polymorphism pattern analysis systems, the BioImage system and the GelCompar system (Molecular Analyst Fingerprinting Plus in the United States), were compared. The two systems use different approaches to compare patterns from different gels. In GelCompar, a standard reference pattern in one gel is used to normalize subsequent gels containing lanes with the same reference pattern. In BioImage, the molecular sizes of the fragments are calculated from size standards present in each gel. The molecular size estimates obtained with the two systems for 12 restriction fragments of phage λ were between 97 and 101% of their actual sizes, with a standard deviation of less than 1% of the average estimated size for most fragments. At the window sizes used for analysis, the GelCompar system performed somewhat better than BioImage in identifying visually identical patterns generated by electrophoretic separation of HhaI-restricted DNA of Listeria monocytogenes. Both systems require the user to make critical decisions in the analysis. It is very important to visually verify that the systems are finding all bands in each lane and that no artifacts are being detected; both systems allow manual editing. It is also important to verify results obtained in the pattern matching or clustering portions of the analysis.
Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach.
visual analysis; multivariate analysis; dimension reduction; multivariate model construction
Over the last twenty years there have been great advances in light microscopy with the result that multi-dimensional imaging has driven a revolution in modern biology. The development of new approaches of data acquisition are reportedly frequently, and yet the significant data management and analysis challenges presented by these new complex datasets remains largely unsolved. Like the well-developed field of genome bioinformatics, central repositories are and will be key resources, but there is a critical need for informatics tools in individual laboratories to help manage, share, visualize, and analyze image data. In this article we present the recent efforts by the bioimage informatics community to tackle these challenges and discuss our own vision for future development of bioimage informatics solution.
microscopy; file formats; image management; image analysis; image processing
Modeling the local absorption and retention patterns of membrane-permeant small molecules in a cellular context could facilitate development of site-directed chemical agents for bioimaging or therapeutic applications. Here, we present an integrative approach to this problem, combining in silico computational models, in vitro cell based assays and in vivo biodistribution studies. To target small molecule probes to the epithelial cells of the upper airways, a multiscale computational model of the lung was first used as a screening tool, in silico. Following virtual screening, cell monolayers differentiated on microfabricated pore arrays and multilayer cultures of primary human bronchial epithelial cells differentiated in an air-liquid interface were used to test the local absorption and intracellular retention patterns of selected probes, in vitro. Lastly, experiments involving visualization of bioimaging probe distribution in the lungs after local and systemic administration were used to test the relevance of computational models and cell-based assays, in vivo. The results of in vivo experiments were consistent with the results of in silico simulations, indicating that mitochondrial accumulation of membrane permeant, hydrophilic cations can be used to maximize local exposure and retention, specifically in the upper airways after intratracheal administration.
We have developed an integrative, cell-based modeling approach to facilitate the design and discovery of chemical agents directed to specific sites of action within a living organism. Here, a computational, multiscale transport model of the lung was adapted to enable virtual screening of small molecules targeting the epithelial cells of the upper airways. In turn, the transport behaviors of selected candidate probes were evaluated to establish their degree of retention at a site of absorption, using computational simulations as well as two in vitro cell-based assay systems. Lastly, bioimaging experiments were performed to examine candidate molecules' distribution in the lungs of mice after local and systemic administration. Based on computational simulations, the higher mitochondrial density per unit absorption surface area is the key parameter determining the higher retention of small molecule hydrophilic cations in the upper airways, relative to lipophilic weak bases, specifically after intratracheal administration.
Molecular Breast Imaging (MBI) is a nuclear medicine technique used to image the breast. In this review we discuss our experience with this technique and clinical applications for the use of MBI.
Molecular Breast Imaging (MBI) depicts functional uptake of targeted radiotracers in the breast using dedicated gamma cameras.
MBI studies were performed under several institutional protocols evaluating the use of MBI in screening and diagnosis.
Using a single head system, sensitivity for breast cancer detection was 85% (57/67) overall and 29% for tumors ≤5mm in diameter. Sensitivity improved to 91% (117/128) overall and 69% for tumors ≤5mm.using a dual head system. In 650 high risk patients undergoing breast cancer screening, MBI detected 7 cancers, 5 which were missed on mammography. In 24/149 (16%) breast cancer patients MBI detected additional disease not seen on mammography. Sensitivity of MBI was 88% (83/94) for IDC, 79% (23/29) for ILC, 89% (25/28) for DCIS.
MBI can detect IDC, DCIS, and ILC. It has a promising role in evaluating extent of disease and multifocal disease in the breast for surgical treatment planning.
molecular breast imaging; scintimammography; breast cancer; detection
This article reviews the new physics and new applications of secondary ion mass spectrometry using cluster ion probes. These probes, particularly C60, exhibit enhanced molecular desorption with improved sensitivity owing to the unique nature of the energy-deposition process. In addition, these projectiles are capable of eroding molecular solids while retaining the molecular specificity of mass spectrometry. When the beams are microfocused to a spot on the sample, bioimaging experiments in two and three dimensions are feasible. We describe emerging theoretical models that allow the energy-deposition process to be understood on an atomic and molecular basis. Moreover, experiments on model systems are described that allow protocols for imaging on biological materials to be implemented. Finally, we present recent applications of imaging to biological tissue and single cells to illustrate the future directions of this methodology.
secondary ion mass spectrometry; bioimaging; molecular depth profiling; three-dimensional molecular imaging; C60; molecular dynamics
Multimodal nonlinear optical (NLO) imaging is poised to become a powerful tool in bioimaging given its ability to capitalize on the unique advantages possessed by different NLO imaging modalities. The integration of different imaging modalities such as two-photon-excited fluorescence, sum frequency generation, and coherent anti-Stokes Raman scattering on the same platform can facilitate simultaneous imaging of different biological structures. Parameters to be considered in constructing a multimodal NLO microscope are discussed with emphasis on achieving a compromise in these parameters for efficient signal generation with each imaging modality. As an example of biomedical applications, multimodal NLO imaging is utilized to investigate the central nervous system in healthy and diseased states.
Central nervous system imaging; multimodality; nonlinear optical (NLO) microscopy
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.
Innovations in biological and biomedical imaging produce complex high-content and multivariate image data. For decision-making and generation of hypotheses, scientists need novel information technology tools that enable them to visually explore and analyze the data and to discuss and communicate results or findings with collaborating experts from various places.
In this paper, we present a novel Web2.0 approach, BioIMAX, for the collaborative exploration and analysis of multivariate image data by combining the webs collaboration and distribution architecture with the interface interactivity and computation power of desktop applications, recently called rich internet application.
BioIMAX allows scientists to discuss and share data or results with collaborating experts and to visualize, annotate, and explore multivariate image data within one web-based platform from any location via a standard web browser requiring only a username and a password. BioIMAX can be accessed at http://ani.cebitec.uni-bielefeld.de/BioIMAX with the username "test" and the password "test1" for testing purposes.
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a powerful tool that enables the simultaneous detection and identification of biomolecules in analytes. MALDI-imaging mass spectrometry (MALDI-IMS) is a two-dimensional MALDI-mass spectrometric technique used to visualize the spatial distribution of biomolecules without extraction, purification, separation, or labeling of biological samples. MALDI-IMS has revealed the characteristic distribution of several biomolecules, including proteins, peptides, amino acids, lipids, carbohydrates, and nucleotides, in various tissues. The versatility of MALDI-IMS has opened a new frontier in several fields such as medicine, agriculture, biology, pharmacology, and pathology. MALDI-IMS has a great potential for discovery of unknown biomarkers. In this review, we describe the methodology and applications of MALDI-IMS for biological samples.
imaging mass spectrometry; matrix-assisted laser desorption/ionization mass spectrometry; biomarker; protein; lipids
Fluorescent probes, which allow visualization of cations such as Ca2+, Zn2+ etc., small biomolecules such as nitric oxide (NO) or enzyme activities in living cells by means of fluorescence microscopy, have become indispensable tools for clarifying functions in biological systems. This review deals with the general principles for the design of bioimaging fluorescent probes by modulating the fluorescence properties of fluorophores, employing mechanisms such as acceptor-excited Photoinduced electron Transfer (a-PeT), donor-excited Photoinduced electron Transfer (d-PeT), and spirocyclization, which have been established by our group. The a-PeT and d-PeT mechanisms are widely applicable for the design of bioimaging probes based on many fluorophores and the spirocyclization process is also expected to be useful as a fluorescence off/on switching mechanism. Fluorescence modulation mechanisms are essential for the rational design of novel fluorescence probes for target molecules. Based on these mechanisms, we have developed more than fifty bioimaging probes, of which fourteen are commercially available. The review also describes some applications of the probes developed by our group to in vitro and in vivo systems.
probe; bioimaging; photoinduced electron transfer; fluorescence; spirocyclization
The personalized approach in cancer treatment stimulates the search for new analytical techniques, including spectroscopic methods such as Raman spectroscopy, mass spectrometry MALDI (matrix-assisted laser desorption/ionization) imaging and high-resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR). The purpose of these studies is determination of metabolic profiles of cancer tissues, and their application in diagnostics and therapy of cancers. The review is mainly focused on application of HR MAS NMR technique. Qualitative and quantitative analysis of metabolites by means of this method is described for breast cancer tissues. In the near future HR MAS NMR in vitro studies of metabolic profiles combined with in vivo studies using MRI scanners may be applied as a new diagnostic tool.
tumor; metabolic profile; nuclear magnetic resonance; MALDI MS imaging; Raman spectroscopy
Quantification of callose deposits is a useful measure for the activities of plant immunity and pathogen growth by fluorescence imaging. For robust scoring of differences, this normally requires many technical and biological replicates and manual or automated quantification of the callose deposits. However, previously available software tools for quantifying callose deposits from bioimages were limited, making batch processing of callose image data problematic. In particular, it is challenging to perform large-scale analysis on images with high background noise and fused callose deposition signals.
We developed CalloseMeasurer, an easy-to-use application that quantifies callose deposition, a plant immune response triggered by potentially pathogenic microbes. Additionally, by tracking identified callose deposits between multiple images, the software can recognise patterns of how a given filamentous pathogen grows in plant leaves. The software has been evaluated with typical noisy experimental images and can be automatically executed without the need for user intervention. The automated analysis is achieved by using standard image analysis functions such as image enhancement, adaptive thresholding, and object segmentation, supplemented by several novel methods which filter background noise, split fused signals, perform edge-based detection, and construct networks and skeletons for extracting pathogen growth patterns. To efficiently batch process callose images, we implemented the algorithm in C/C++ within the Acapella™ framework. Using the tool we can robustly score significant differences between different plant genotypes when activating the immune response. We also provide examples for measuring the in planta hyphal growth of filamentous pathogens.
CalloseMeasurer is a new software solution for batch-processing large image data sets to quantify callose deposition in plants. We demonstrate its high accuracy and usefulness for two applications: 1) the quantification of callose deposition in different genotypes as a measure for the activity of plant immunity; and 2) the quantification and detection of spreading networks of callose deposition triggered by filamentous pathogens as a measure for growing pathogen hyphae. The software is an easy-to-use protocol which is executed within the Acapella software system without requiring any additional libraries. The source code of the software is freely available at https://sourceforge.net/projects/bioimage/files/Callose.
Callose deposition; Quantification; Immunity; Flagellin; Flg22; Bacteria; Defence response; Oomycete; Hyaloperonospora arabidopsidis; Encasements; Pathogen; Image analysis
Targeted molecular imaging with two-photon fluorescence microscopy (2PFM) is a powerful technique for chemical biology and, potentially, for non-invasive diagnosis and treatment of a number of diseases. The synthesis, photophysical studies, and bioimaging are reported for a versatile norbornene-based block copolymer multifunctional scaffold containing biocompatible (PEG), two-photon fluorescent dyes (fluorenyl), and targeting (cyclic-RGD peptide) moieties. The two bioconjugates, containing two different fluorenyl dyes and cRGDfK covalently attached to the polymer probe, formed a spherical micelle and self-assembled structure in water, for which size was analyzed by TEM and DLS. Cell-viability and 2PFM imaging of human epithelial U87MG cell lines that over express αvβ3 integrin was performed via incubation with the new probes, along with negative control studies using MCF-7 breast cancer cells and blocking experiments. 2PFM microscopy confirmed the high selectivity of the biocompatible probe in the integrin rich area in the U87MF cells while blocking as well as negative control MCF-7 experiments confirmed the integrin targeting ability of the new probes.
Water-soluble block copolymer probe; ROMP; two-photon bioimaging; integrin targeting
Significant technical advances in imaging, molecular biology and genomics have fueled a revolution in cell biology, in that the molecular and structural processes of the cell are now visualized and measured routinely. Driving much of this recent development has been the advent of computational tools for the acquisition, visualization, analysis and dissemination of these datasets. These tools collectively make up a new subfield of computational biology called bioimage informatics, which is facilitated by open source approaches. We discuss why open source tools for image informatics in cell biology are needed, some of the key general attributes of what make an open source imaging application successful, and point to opportunities for further operability that should greatly accelerate future cell biology discovery.