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
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
To study the chemical determinants of small molecule transport inside cells, it is crucial to visualize relationships between the chemical structure of small molecules and their associated subcellular distribution patterns. For this purpose, we experimented with cells incubated with a synthetic combinatorial library of fluorescent, membrane-permeant small molecule chemical agents. With an automated high content screening instrument, the intracellular distribution patterns of these chemical agents were microscopically captured in image data sets, and analyzed off-line with machine vision and cheminformatics algorithms. Nevertheless, it remained challenging to interpret correlations linking the structure and properties of chemical agents to their subcellular localization patterns in large numbers of cells, captured across large number of images.
To address this challenge, we constructed a Multidimensional Online Virtual Image Display (MOVID) visualization platform using off-the-shelf hardware and software components. For analysis, the image data set acquired from cells incubated with a combinatorial library of fluorescent molecular probes was sorted based on quantitative relationships between the chemical structures, physicochemical properties or predicted subcellular distribution patterns. MOVID enabled visual inspection of the sorted, multidimensional image arrays: Using a multipanel desktop liquid crystal display (LCD) and an avatar as a graphical user interface, the resolution of the images was automatically adjusted to the avatar’s distance, allowing the viewer to rapidly navigate through high resolution image arrays, zooming in and out of the images to inspect and annotate individual cells exhibiting interesting staining patterns. In this manner, MOVID facilitated visualization and interpretation of quantitative structure-localization relationship studies. MOVID also facilitated direct, intuitive exploration of the relationship between the chemical structures of the probes and their microscopic, subcellular staining patterns.
MOVID can provide a practical, graphical user interface and computer-assisted image data visualization platform to facilitate bioimage data mining and cheminformatics analysis of high content, phenotypic screening experiments.
Machine vision; Cheminformatics; Virtual reality; Data mining; Optical probes; Multivariate analysis; Human-computer interaction; Graphical user interface
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.
Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application.
Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE's applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material).
Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/; Login: whidetestuser; Password: whidetest.
Supplementary data are available at Bioinformatics online.
High-throughput, image-based screens of cellular responses to genetic or chemical perturbations generate huge numbers of cell images. Automated analysis is required to quantify and compare the effects of these perturbations. However, few of the current freely-available bioimage analysis software tools are optimized for efficient handling of these images. Even fewer of them are designed to transform the phenotypic features measured from these images into discriminative profiles that can reveal biologically meaningful associations among the tested perturbations.
We present a fast and user-friendly software platform called "cellXpress" to segment cells, measure quantitative features of cellular phenotypes, construct discriminative profiles, and visualize the resulting cell masks and feature values. We have also developed a suite of library functions to load the extracted features for further customizable analysis and visualization under the R computing environment. We systematically compared the processing speed, cell segmentation accuracy, and phenotypic-profile clustering performance of cellXpress to other existing bioimage analysis software packages or algorithms. We found that cellXpress outperforms these existing tools on three different bioimage datasets. We estimate that cellXpress could finish processing a genome-wide gene knockdown image dataset in less than a day on a modern personal desktop computer.
The cellXpress platform is designed to make fast and efficient high-throughput phenotypic profiling more accessible to the wider biological research community. The cellXpress installation packages for 64-bit Windows and Linux, user manual, installation guide, and datasets used in this analysis can be downloaded freely from http://www.cellXpress.org.
The super-resolution microscopy called RESOLFT relying on fluorophore switching between longlived states, stands out by its coordinate-targeted sequential sample interrogation using low light levels. While RESOLFT has been shown to discern nanostructures in living cells, the reversibly photoswitchable green fluorescent protein (rsEGFP) employed in these experiments was switched rather slowly and recording lasted tens of minutes. We now report on the generation of rsEGFP2 providing faster switching and the use of this protein to demonstrate 25–250 times faster recordings.
For decades it was assumed that the diffraction of light meant that optical microscopy could not resolve features that were smaller than about the half the wavelength of the light being used to create an image. However, various ‘super-resolution’ methods have allowed researchers to overcome this diffraction limit for fluorescence imaging, which is the most popular form of microscopy used in the life sciences. This approach involves tagging the biomolecules of interest with fluorescent molecules, such as green fluorescent protein (GFP), so that they can be identified in cells. An excitation laser then drives the fluorescent molecule, which is also known as a fluorophore, into an excited state: after a short time, the fluorophore can return to its ground state by releasing a fluorescence photon. Images of the sample are built up by detecting these photons.
In STED super-resolution microscopy a second laser is used to instantly send the molecules from their excited or ‘on’ states back to their ground or ‘off’ states before any fluorescence can occur. The second laser beam is usually shaped like a doughnut, with a small region of low light intensity surrounded by a region of much higher intensity. STED microscopy is able to beat the diffraction limit because the second laser turns all the fluorophores ‘off’ except those in the small sub-wavelength region at the centre of the doughnut. The image is build up by scanning both lasers over the sample so that the small region in which the fluorophores are ‘on’ probes the entire cell.
RESOLFT is a similar technique that employs fluorescent molecules with ‘on’ and ‘off’ times that are much longer than those used in STED microscopy. In particular, RESOLFT uses fluorescent molecules that can be rapidly switched back and forth between long-lived ‘on’ and ‘off’ states many times by the two lasers. The fact that both these states are long-lived states means that RESOLFT requires much lower laser intensities than STED, which makes it attractive for imaging biological samples over large areas or long times.
RESOLFT demonstrated its suitability for bioimaging for the first time last year, with a protein called rsEGFP (reversibly switchable enhanced GFP) being employed as the fluorophore. However, the time needed to switch this protein between the ‘on state’ and the ‘off state’ was relatively long, and it took about an hour to record a typical image. Now, Grotjohann et al. have modified this protein to make a new fluorophore called rsEGFP2 with a shorter switching time, and have used it to image various structures—including Vimentin, a protein that forms part of the cytoskeleton in many cells, and organelles called peroxisomes—inside live mammalian cells. They were able to record these images some 25–250 times faster than would have been possible with previous RESOLFT approaches. The combination of RESOLFT and rsEGFP2 should allow researchers to image a wide variety of structures and processes in living cells that have not been imaged before.
confocal microscopy; fluorescent probes; GFP; nanoscopy; superresolution; live-cell imaging; None
To build on the last century's tremendous strides in understanding the workings of individual proteins in the test tube, we now face the challenge of understanding how macromolecular machines, signaling pathways, and other biological networks operate in the complex environment of the living cell. The fluorescent proteins (FPs) revolutionized our ability to study protein function directly in the cell by enabling individual proteins to be selectively labeled through genetic encoding of a fluorescent tag. Although FPs continue to be invaluable tools for cell biology, they show limitations in the face of the increasingly sophisticated dynamic measurements of protein interactions now called for to unravel cellular mechanisms. Therefore, just as chemical methods for selectively labeling proteins in the test tube significantly impacted in vitro biophysics in the last century, chemical tagging technologies are now poised to provide a breakthrough to meet this century's challenge of understanding protein function in the living cell.
With chemical tags, the protein of interest is attached to a polypeptide rather than an FP. The polypeptide is subsequently modified with an organic fluorophore or another probe. The FlAsH peptide tag was first reported in 1998. Since then, more refined protein tags, exemplified by the TMP- and SNAP-tag, have improved selectivity and enabled imaging of intracellular proteins with high signal-to-noise ratios. Further improvement is still required to achieve direct incorporation of powerful fluorophores, but enzyme-mediated chemical tags show promise for overcoming the difficulty of selectively labeling a short peptide tag.
In this Account, we focus on the development and application of chemical tags for studying protein function within living cells. Thus, in our overview of different chemical tagging strategies and technologies, we emphasize the challenge of rendering the labeling reaction sufficiently selective and the fluorophore probe sufficiently well behaved to image intracellular proteins with high signal-to-noise ratios. We highlight recent applications in which the chemical tags have enabled sophisticated biophysical measurements that would be difficult or even impossible with FPs. Finally, we conclude by looking forward to (i) the development of high-photon-output chemical tags compatible with living cells to enable high-resolution imaging, (ii) the realization of the potential of the chemical tags to significantly reduce tag size, and (iii) the exploitation of the modular chemical tag label to go beyond fluorescent imaging.
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.
Recent technological advances in flow cytometry provide a versatile platform for high throughput screening of compound libraries coupled with high-content biological testing and drug discovery. The G protein-coupled receptors (GPCRs) constitute the largest class of signaling molecules in the human genome with frequent roles in disease pathogenesis, yet many examples of orphan receptors with unknown ligands remain. The complex biology and potential for drug discovery within this class provide strong incentives for chemical biology approaches seeking to develop small molecule probes to facilitate elucidation of mechanistic pathways and enable specific manipulation of the activity of individual receptors. We have initiated small molecule probe development projects targeting two distinct families of GPCRs: the formylpeptide receptors (FPR/FPRL1) and G protein-coupled estrogen receptor (GPR30). In each case the assay for compound screening involved the development of an appropriate small molecule fluorescent probe, and the flow cytometry platform provided inherently biological rich assays that enhanced the process of identification and optimization of novel antagonists. The contributions of cheminformatics analysis tools, virtual screening, and synthetic chemistry in synergy with the biomolecular screening program have yielded valuable new chemical probes with high binding affinity, selectivity for the targeted receptor, and potent antagonist activity. This review describes the discovery of novel small molecule antagonists of FPR and FPRL1, and GPR30, and the associated characterization process involving secondary assays, cell based and in vivo studies to define the selectivity and activity of the resulting chemical probes
flow cytometry; fluorescent; GPCR; formylpeptide receptor; inflammation; GPR30; GPER; estrogen; nongenomic; cancer; antidepressant
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
Mesoscopic simulation studies the structure, dynamics and properties of large molecular ensembles with millions of atoms: Its basic interacting units (beads) are no longer the nuclei and electrons of quantum chemical ab-initio calculations or the atom types of molecular mechanics but molecular fragments, molecules or even larger molecular entities. For its simulation setup and output a mesoscopic simulation kernel software uses abstract matrix (array) representations for bead topology and connectivity. Therefore a pure kernel-based mesoscopic simulation task is a tedious, time-consuming and error-prone venture that limits its practical use and application. A consequent cheminformatics approach tackles these problems and provides solutions for a considerably enhanced accessibility. This study aims at outlining a complete cheminformatics roadmap that frames a mesoscopic Molecular Fragment Dynamics (MFD) simulation kernel to allow its efficient use and practical application.
The molecular fragment cheminformatics roadmap consists of four consecutive building blocks: An adequate fragment structure representation (1), defined operations on these fragment structures (2), the description of compartments with defined compositions and structural alignments (3), and the graphical setup and analysis of a whole simulation box (4). The basis of the cheminformatics approach (i.e. building block 1) is a SMILES-like line notation (denoted fSMILES) with connected molecular fragments to represent a molecular structure. The fSMILES notation and the following concepts and methods for building blocks 2-4 are outlined with examples and practical usage scenarios. It is shown that the requirements of the roadmap may be partly covered by already existing open-source cheminformatics software.
Mesoscopic simulation techniques like MFD may be considerably alleviated and broadened for practical use with a consequent cheminformatics layer that successfully tackles its setup subtleties and conceptual usage hurdles. Molecular Fragment Cheminformatics may be regarded as a crucial accelerator to propagate MFD and similar mesoscopic simulation techniques in the molecular sciences.
Graphical abstractA molecular fragment cheminformatics roadmap for mesoscopic simulation.
Dissipative particle dynamics; Computer simulation; Molecular fragmentation; fSmiles; Fragment smiles; Molecular fragment cheminformatics; Molecular fragment dynamics; Mesoscopic simulation; Peptide representation; Protein representation
Analysis and visualization of large collections of molecules is one of the most frequent challenges cheminformatics experts in pharmaceutical industry are facing. Various sophisticated methods are available to perform this task, including clustering, dimensionality reduction or scaffold frequency analysis. In any case, however, viewing and analyzing large tables with molecular structures is necessary. We present a new visualization technique, providing basic information about the composition of molecular data sets at a single glance.
A method is presented here allowing visual representation of the most common structural features of chemical databases in a form of a cloud diagram. The frequency of molecules containing particular substructure is indicated by the size of respective structural image. The method is useful to quickly perceive the most prominent structural features present in the data set. This approach was inspired by popular word cloud diagrams that are used to visualize textual information in a compact form. Therefore we call this approach “Molecule Cloud”. The method also supports visualization of additional information, for example biological activity of molecules containing this scaffold or the protein target class typical for particular scaffolds, by color coding. Detailed description of the algorithm is provided, allowing easy implementation of the method by any cheminformatics toolkit. The layout algorithm is available as open source Java code.
Visualization of large molecular data sets using the Molecule Cloud approach allows scientists to get information about the composition of molecular databases and their most frequent structural features easily. The method may be used in the areas where analysis of large molecular collections is needed, for example processing of high throughput screening results, virtual screening or compound purchasing. Several example visualizations of large data sets, including PubChem, ChEMBL and ZINC databases using the Molecule Cloud diagrams are provided.
Molecule cloud; Visualization; Scaffold analysis; Chemical databases; Open source
Visualization in biology has been greatly facilitated by the use of fluorescent proteins as in-cell probes. The genes coding for these wavelength-tunable proteins can be readily fused with the DNA coding for a protein of interest, which enables direct monitoring of natural proteins in real time inside living cells. Despite their success, however, fluorescent proteins have limitations that have only begun to be addressed in the past decade through the development of bioorthogonal chemistry. In this approach, a very small bioorthogonal tag is embedded within the basic building blocks of the cell, and then a variety of external molecules can be selectively conjugated to these pre-tagged biomolecules. The result is a veritable palette of biophysical probes for the researcher to choose from.
In this Account, we review our progress in developing a photoinducible, bioorthogonal tetrazole–alkene cycloaddition reaction (“photoclick chemistry”) and applying it to probe protein dynamics and function in live cells. The work described here summarizes the synthesis, structure, and reactivity studies of tetrazoles, including their optimization for applications in biology. Building on key insights from earlier reports, our initial studies of the reaction have revealed full water compatibility, high photoactivation quantum yield, tunable photoactivation wavelength, and broad substrate scope; an added benefit is the formation of fluorescent cycloadducts. Subsequent studies have shown fast reaction kinetics (up to 11.0 M−1 s−1), with the rate depending on the HOMO energy of the nitrile imine dipole as well as the LUMO energy of the alkene dipolarophile. Moreover, through the use of photocrystallography, we have observed that the photogenerated nitrile imine adopts a bent geometry in the solid state. This observation has led to the synthesis of reactive, macrocyclic tetrazoles that contain a short “bridge” between two flanking phenyl rings.
This photoclick chemistry has been used to label proteins rapidly (within ~1 minute) both in vitro and in E. coli. To create an effective interface with biology, we have identified both a metabolically incorporable alkene amino acid, homoallylglycine, and a genetically encodable tetrazole amino acid, p-(2-tetrazole)phenylalanine. We demonstrate the utility of these two moieties, respectively, in spatiotemporally controlled imaging of newly synthesized proteins and in site-specific labeling of proteins. Additionally, we demonstrate the use of the photoclick chemistry to perturb the localization of a fluorescent protein in mammalian cells.
Imaging the behavior of RNA in a living cell is a powerful means for understanding RNA functions and acquiring spatiotemporal information in a single cell. For more distinct RNA imaging in a living cell, a more effective chemical method to fluorescently label RNA is now required. In addition, development of the technology labeling with different colors for different RNA would make it easier to analyze plural RNA strands expressing in a cell.
Tag technology for RNA imaging in a living cell has been developed based on the unique chemical functions of exciton-controlled hybridization-sensitive oligonucleotide (ECHO) probes. Repetitions of selected 18-nucleotide RNA tags were incorporated into the mRNA 3′-UTR. Pairs with complementary ECHO probes exhibited hybridization-sensitive fluorescence emission for the mRNA expressed in a living cell. The mRNA in a nucleus was detected clearly as fluorescent puncta, and the images of the expression of two mRNAs were obtained independently and simultaneously with two orthogonal tag–probe pairs.
A compact and repeated label has been developed for RNA imaging in a living cell, based on the photochemistry of ECHO probes. The pairs of an 18-nt RNA tag and the complementary ECHO probes are highly thermostable, sequence-specifically emissive, and orthogonal to each other. The nucleotide length necessary for one tag sequence is much shorter compared with conventional tag technologies, resulting in easy preparation of the tag sequences with a larger number of repeats for more distinct RNA imaging.
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.
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.
A two-photon absorbing (2PA) and aggregation-enhanced near infrared (NIR) emitting pyran derivative, encapsulated in and stabilized by silica nanoparticles (SiNPs), is reported as a nanoprobe for two-photon fluorescence microscopy (2PFM) bioimaging that overcomes fluorescence quenching associated with high chromophore loading. The new SiNP probe exhibited aggregate-enhanced emission producing nearly twice as strong signal as the unaggregated dye, a three-fold increase in two-photon absorption relative to the DFP in solution, and approx. four-fold increase in photostability. The surface of the nanoparticles was functionalized with a folic acid (FA) derivative for folate-mediated delivery of the nanoprobe for 2PFM bioimaging. Surface modification of SiNPs with the FA derivative was supported by zeta potential variation and 1H NMR spectral characterization of the SiNPs as a function of surface modification. In vitro studies using HeLa cells expressing folate receptor (FR) indicated specific cellular uptake of the functionalized nanoparticles. The nanoprobe was demonstrated for FRtargeted one-photon in vivo imaging of HeLa tumor xenograft in mice upon intravenous injection of the probe. The FR-targeting nanoprobe not only exhibited highly selective tumor targeting but also readily extravasated from tumor vessels, penetrated into the tumor parenchyma, and was internalized by the tumor cells. Two-photon fluorescence microscopy bioimaging provided three-dimensional (3D) cellular-level resolution imaging up to 350 µm deep in the HeLa tumor.
Aggregation enhanced emission; near infrared emission; folate receptor targeting; two-photon absorption; two-photon fluorescence microscopy; silica nanoparticles; HeLa tumor; in vivo imaging; ex vivo imaging
Quantum-confined nanostructures are considered ‘artificial atoms’ because the wavefunctions of their charge carriers resemble those of atomic orbitals. For multiple-domain heterostructures, however, carrier wavefunctions are more complex and still not well understood. We have prepared a unique series of cation-exchanged HgxCd1−xTe quantum dots (QDs) and seven epitaxial core–shell QDs and measured their first and second exciton peak oscillator strengths as a function of size and chemical composition. A major finding is that carrier locations can be quantitatively mapped and visualized during shell growth or cation exchange simply using absorption transition strengths. These results reveal that a broad range of quantum heterostructures with different internal structures and band alignments exhibit distinct carrier localization patterns that can be used to further improve the performance of optoelectronic devices and enhance the brightness of QD probes for bioimaging.
The confinement of electrical charges in quantum dots makes them of interest for applications in imaging and photovoltaics. Here, the authors demonstrate that based on optical absorption measurements and theoretical modelling it is possible to derive the charge carrier distribution in quantum dots.
The computational processing and analysis of small molecules is at heart of cheminformatics and structural bioinformatics and their application in e.g. metabolomics or drug discovery. Pipelining or workflow tools allow for the Lego™-like, graphical assembly of I/O modules and algorithms into a complex workflow which can be easily deployed, modified and tested without the hassle of implementing it into a monolithic application. The CDK-Taverna project aims at building a free open-source cheminformatics pipelining solution through combination of different open-source projects such as Taverna, the Chemistry Development Kit (CDK) or the Waikato Environment for Knowledge Analysis (WEKA). A first integrated version 1.0 of CDK-Taverna was recently released to the public.
The CDK-Taverna project was migrated to the most up-to-date versions of its foundational software libraries with a complete re-engineering of its worker's architecture (version 2.0). 64-bit computing and multi-core usage by paralleled threads are now supported to allow for fast in-memory processing and analysis of large sets of molecules. Earlier deficiencies like workarounds for iterative data reading are removed. The combinatorial chemistry related reaction enumeration features are considerably enhanced. Additional functionality for calculating a natural product likeness score for small molecules is implemented to identify possible drug candidates. Finally the data analysis capabilities are extended with new workers that provide access to the open-source WEKA library for clustering and machine learning as well as training and test set partitioning. The new features are outlined with usage scenarios.
CDK-Taverna 2.0 as an open-source cheminformatics workflow solution matured to become a freely available and increasingly powerful tool for the biosciences. The combination of the new CDK-Taverna worker family with the already available workflows developed by a lively Taverna community and published on myexperiment.org enables molecular scientists to quickly calculate, process and analyse molecular data as typically found in e.g. today's systems biology scenarios.
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
Fluorescence is a mainstay of bioanalytical methods, offering sensitive and quantitative reporting, often in multiplexed or multiparameter assays. Perhaps the best example of the latter is flow cytometry, where instruments equipped with multiple lasers and detectors allow measurement of 15 or more different fluorophores simultaneously, but increases beyond this number are limited by the relatively broad emission spectra. Surface enhanced Raman scattering (SERS) from metal nanoparticles can produce signal intensities that rival fluorescence, but with narrower spectral features that allow a greater degree of multiplexing. We are developing nanoparticle SERS tags as well as Raman flow cytometers for multiparameter single cell analysis of suspension or adherent cells. SERS tags are based on plasmonically active nanoparticles (gold nanorods) whose plasmon resonance can be tuned to give optimal SERS signals at a desired excitation wavelength. Raman resonant compounds are adsorbed on the nanoparticles to confer a unique spectral fingerprint on each SERS tag, which are then encapsulated in a polymer coating for conjugation to antibodies or other targeting molecules. Raman flow cytometry employs a high resolution spectral flow cytometer capable of measuring the complete SERS spectra, as well as conventional flow cytometry measurements, from thousands of individual cells per minute. Automated spectral unmixing algorithms extract the contributions of each SERS tag from each cell to generate high content, multiparameter single cell population data. SERS-based cytometry is a powerful complement to conventional fluorescence-based cytometry. The narrow spectral features of the SERS signal enables more distinct probes to be measured in a smaller region of the optical spectrum with a single laser and detector, allowing for higher levels of multiplexing and multiparameter analysis.
Nanoparticle; Multiplex; Multiparameter; Plasmonics; Spectroscopy; Probe
To explore the extent to which current knowledge about the organelle-targeting features of small molecules may be applicable towards controlling the accumulation and distribution of exogenous chemical agents inside cells, molecules with known subcellular localization properties (as reported in the scientific literature) were compiled into a single data set. This data set was compared to a reference data set of approved drug molecules derived from the DrugBank database, and to a reference data set of random organic molecules derived from the PubChem database. Cheminformatic analysis revealed that molecules with reported subcellular localizations were comparably diverse. However, the calculated physicochemical properties of molecules reported to accumulate in different organelles were markedly overlapping. In relation to the reference sets of Drug Bank and Pubchem molecules, molecules with reported subcellular localizations were biased towards larger, more complex chemical structures possessing multiple ionizable functional groups and higher lipophilicity. Stratifying molecules based on molecular weight revealed that many physicochemical properties trends associated with specific organelles were reversed in smaller vs. larger molecules. Most likely, these reversed trends are due to the different transport mechanisms determining the subcellular localization of molecules of different sizes. Molecular weight can be dramatically altered by tagging molecules with fluorophores or by incorporating organelle targeting motifs. Generally, in order to better exploit structure-localization relationships, subcellular targeting strategies would benefit from analysis of the biodistribution effects resulting from variations in the size of the molecules.
drug transport; pharmacokinetics; biodistribution; drug targeting; databases; mathematical modeling; drug delivery; cheminformatics
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.
Near-infrared (NIR) imaging technology has been widely used for biomedical research and applications, since it can achieve deep penetration in biological tissues due to less absorption and scattering of NIR light. In our research, polymer nanoparticles with NIR fluorophores doped were synthesized. The morphology, absorption/emission features and chemical stability of the fluorescent nanoparticles were characterized, separately. NIR fluorescent nanoparticles were then utilized as bright optical probes for macro in vivo imaging of mice, including sentinel lymph node (SLN) mapping, as well as distribution and excretion monitoring of nanoparticles in animal body. Furthermore, we applied the NIR fluorescent nanoparticles in in vivo microscopic bioimaging via a confocal microscope. Under the 635 nm-CW excitation, the blood vessel architecture in the ear and the brain of mice, which were administered with nanoparticles, was visualized very clearly. The imaging depth of our one-photon microscopy, which was assisted with NIR fluorescent nanoprobes, can reach as deep as 500 μm. Our experiments show that NIR fluorescent nanoparticles have great potentials in various deep-tissue imaging applications.
(160.2540) Fluorescent and luminescent materials; (160.4236) Nanomaterials; (300.6170) Spectra; (170.3880) Medical and biological imaging; (170.2655) Functional monitoring and imaging; (180.1790) Confocal microscopy