Informatics and quantitative analysis in biological imaging

A practical guide to bioimaging research data management in core facilities

Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data-driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well-established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging - Society for Microscopy and Image Analysis (GerBI-GMB), cross-institutional working groups and third-party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging-core-facility-centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.

Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

Building a FAIR image data ecosystem for microscopy communities

Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis, and management practices, which are currently hampering the full potential of image data being realized. Here, we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimaging data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.

A standard for sharing spatial transcriptomics data

Spatial transcriptomic technologies have the potential to reveal critical relationships between the function of genes and cells and their spatial organization. Here, we provide a sharing model for spatial transcriptomics data with the aim of establishing a set of primary data and metadata needed to reproduce analyses and facilitate computational methods development.

Bioimage informatics: Investing in software usability is essential

In 2018, PLOS Biology announced CellProfiler 3.0, which has become one of the most used pieces of image analysis software in biology. The rapid adoption of this software speaks to the importance of user experience to disseminate new methods of bioimage informatics.

High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers

Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using hematoxylin and eosin (H&E)-stained tissue (not genomics) remains the primary diagnostic method in cancer. Recently developed highly multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially resolved single-cell data. Here, we describe the 'Orion' platform for collecting H&E and high-plex immunofluorescence images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that immunofluorescence and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a 10- to 20-fold discrimination between rapid and slow (or no) progression, demonstrating the ability of multimodal tissue imaging to generate high-performance biomarkers.

Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology

The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.

Multiphoton intravital microscopy of rodents

Tissues are heterogeneous with respect to cellular and non-cellular components and in the dynamic interactions between these elements. To study the behaviour and fate of individual cells in these complex tissues, intravital microscopy (IVM) techniques such as multiphoton microscopy have been developed to visualize intact and live tissues at cellular and subcellular resolution. IVM experiments have revealed unique insights into the dynamic interplay between different cell types and their local environment, and how this drives morphogenesis and homeostasis of tissues, inflammation and immune responses, and the development of various diseases. This Primer introduces researchers to IVM technologies, with a focus on multiphoton microscopy of rodents, and discusses challenges, solutions and practical tips on how to perform IVM. To illustrate the unique potential of IVM, several examples of results are highlighted. Finally, we discuss data reproducibility and how to handle big imaging data sets.

Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data

Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.

MITI minimum information guidelines for highly multiplexed tissue images

The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.

Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model

Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications

For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.

Apparent calcium spark properties and fast-scanning 2D confocal imaging modalities

Ca²⁺ sparks are instrumental to understand physiological and pathological Ca²⁺ signaling in the heart. High-speed two spatially dimensional (2D) confocal imaging (>120 Hz) enables acquisition of sparks with high-content information, however, owing to a wide variety of different acquisition modalities the question arises: how much they reflect the "true" Ca²⁺ spark properties. To address this issue, we compared a fast point and a 2D-array scanner equipped with a range of different detectors. As a quasi-standard biological sample, we employed Ca²⁺ sparks in permeabilized and intact mouse ventricular myocytes and utilized an unbiased, automatic Ca²⁺ spark analysis tool, iSpark. Data from the point scanner suffered from low pixel photon fluxes (PPF) concomitant with high Poissonian noise. Images from the 2D-array scanner displayed substantially increased PPF, lower Poissonian noise and almost 3-fold increased sign-to-noise ratios. Noteworthy, data from the 2D scanner suffered from considerable inter-pinhole crosstalk evident for the permeabilized cells. Spark properties, such as frequency, amplitude, decay time and spatial spread were distinctly different for any scanner/detector combination. Our study reveals that the apparent Ca²⁺ spark properties differ dependent on the particular recording modality and set-up employed, quantitatively.

An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population

AIM

To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010.

METHODS

Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment.

RESULTS

An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects.

CONCLUSIONS

The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.

Imaging methods are vastly underreported in biomedical research

A variety of microscopy techniques are used by researchers in the life and biomedical sciences. As these techniques become more powerful and more complex, it is vital that scientific articles containing images obtained with advanced microscopes include full details about how each image was obtained. To explore the reporting of such details we examined 240 original research articles published in eight journals. We found that the quality of reporting was poor, with some articles containing no information about how images were obtained, and many articles lacking important basic details. Efforts by researchers, funding agencies, journals, equipment manufacturers and staff at shared imaging facilities are required to improve the reporting of experiments that rely on microscopy techniques.

Intravital imaging of megakaryocytes

The dynamics of platelet formation could only be investigated since the development of two-photon microscopy in combination with suitable fluorescent labeling strategies. In this review paper, we give an overview of recent advances in fluorescence imaging of the bone marrow that have contributed to our understanding of platelet biogenesis during the last decade. We make a brief survey through the perspectives and limitations of today's intravital imaging, but also discuss complementary methods that may help to piece together the puzzle of megakaryopoiesis and platelet formation.

Quantitative profiling of integrin αvβ3 on single cells with quantum dot labeling to reveal the phenotypic heterogeneity of glioblastoma

The distribution, localization and density of individual molecules (e.g. drug-specific receptors) on single cells can offer profound information about cell phenotypes. Profiling this information is a new research direction within the field of single cell biology, but it remains technically challenging. Through the combined use of quantum dot labeling, structured illumination microscopy (SIM) and computer-aided local surface reconstruction, we acquired a 3D imaging map of a drug target molecule, integrin αvβ3, on glioblastoma cells at the single cell level. The results revealed that integrin αvβ3 exhibits discrete distribution on the surface of glioblastoma cells, with its density differing significantly among cell lines. The density is illustrated as the approximate number of target molecules per μm2 on the irregular cell surface, ranging from 0 to 1.6. Functional studies revealed that the sensitivity of glioblastoma cells to inhibitor molecules depends on the density of the target molecules. After inhibitor treatment, the viability and invasion ability of different glioblastoma cells were highly correlated with the density of integrin αvβ3 on their surfaces. This study not only provides a novel protocol for the quantitative analysis of surface proteins from irregular single cells, but also offers a clue for understanding the heterogeneity of tumor cells on the basis of molecular phenotypes. Thus, this work has potential significance in guiding targeted therapies for cancers.

Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging

Multiplexed tissue imaging enables precise, spatially resolved enumeration and characterization of cell types and states in human resection specimens. A growing number of methods applicable to formalin-fixed, paraffin-embedded (FFPE) tissue sections have been described, the majority of which rely on antibodies for antigen detection and mapping. This protocol provides step-by-step procedures for confirming the selectivity and specificity of antibodies used in fluorescence-based tissue imaging and for the construction and validation of antibody panels. Although the protocol is implemented using tissue-based cyclic immunofluorescence (t-CyCIF) as an imaging platform, these antibody-testing methods are broadly applicable. We demonstrate assembly of a 16-antibody panel for enumerating and localizing T cells and B cells, macrophages, and cells expressing immune checkpoint regulators. The protocol is accessible to individuals with experience in microscopy and immunofluorescence; some experience in computation is required for data analysis. A typical 30-antibody dataset for 20 FFPE slides can be generated within 2 weeks.

Open Source Tools for Biological Image Analysis

Visiting the Bio Imaging Search Engine (BISE) (Bio, BISE, Engine, http://biii.eu/, Imaging, Search) website at the time of writing this article, almost 1200 open source assets (components, workflows, collections) were found. This overwhelming range of offer difficults the fact of making a reasonable choice, especially to newcomers. In the following chapter, we briefly sketch the advantages of the open source software (OSS) particularly used for image analysis in the field of life sciences. We introduce both the general OSS idea as well as some programs used for image analysis. Even more, we outline the history of ImageJ as it has served as a role model for the development of more recent software packages. We focus on the programs that are, to our knowledge, the most relevant and widely used in the field of light microscopy, as well as the most commonly used within our facility. In addition, we briefly discuss recent efforts and approaches aimed to share and compare algorithms and introduce software and data sharing good practices as a promising strategy to facilitate reproducibility, software understanding, and optimal software choice for a given scientific problem in the future.

Functionalized chitosan with self-assembly induced and subcellular localization-dependent fluorescence ‘switch on’ property

A chitosan-based probe was developed that offers a self-assembly-induced and subcellular localization-dependent fluorescence ‘switch on’ property.

Smooth 2D manifold extraction from 3D image stack

Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading interpretation. Here we propose smooth manifold extraction, an algorithm that produces a continuous focused 2D extraction from a 3D volume, hence preserving local spatial relationships. We demonstrate the usefulness of our approach by applying it to various biological applications using confocal and wide-field microscopy 3D image stacks. We provide a parameter-free ImageJ/Fiji plugin that allows 2D visualization and interpretation of 3D image stacks with maximum accuracy.

Maximum Intensity Projection is a common tool to represent 3D biological imaging data in a 2D space, but it creates artefacts. Here the authors develop Smooth Manifold Extraction, an ImageJ/Fiji plugin, to preserve local spatial relationships when extracting the content of a 3D volume to a 2D space.

A Scalable Cyberinfrastructure for Interactive Visualization of Terascale Microscopy Data

The goal of the recently emerged field of connectomics is to generate a wiring diagram of the brain at different scales. To identify brain circuitry, neuroscientists use specialized microscopes to perform multichannel imaging of labeled neurons at a very high resolution. CLARITY tissue clearing allows imaging labeled circuits through entire tissue blocks, without the need for tissue sectioning and section-to-section alignment. Imaging the large and complex non-human primate brain with sufficient resolution to identify and disambiguate between axons, in particular, produces massive data, creating great computational challenges to the study of neural circuits. Researchers require novel software capabilities for compiling, stitching, and visualizing large imagery. In this work, we detail the image acquisition process and a hierarchical streaming platform, ViSUS, that enables interactive visualization of these massive multi-volume datasets using a standard desktop computer. The ViSUS visualization framework has previously been shown to be suitable for 3D combustion simulation, climate simulation and visualization of large scale panoramic images. The platform is organized around a hierarchical cache oblivious data layout, called the IDX file format, which enables interactive visualization and exploration in ViSUS, scaling to the largest 3D images. In this paper we showcase the VISUS framework used in an interactive setting with the microscopy data.

Robust Hit Identification by Quality Assurance and Multivariate Data Analysis of a High-Content, Cell-Based Assay

Recent technological advances in high-content screening instrumentation have increased its ease of use and throughput, expanding the application of high-content screening to the early stages of drug discovery. However, high-content screens produce complex data sets, presenting a challenge for both extraction and interpretation of meaningful information. This shifts the high-content screening process bottleneck from the experimental to the analytical stage. In this article, the authors discuss different approaches of data analysis, using a phenotypic neurite outgrowth screen as an example. Distance measurements and hierarchical clustering methods lead to a profound understanding of different high-content screening readouts. In addition, the authors introduce a hit selection procedure based on machine learning methods and demonstrate that this method increases the hit verification rate significantly (up to a factor of 5), compared to conventional hit selection based on single readouts only. ( Journal of Biomolecular Screening 2007:1042-1049)

BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models

BioSig3D is a computational platform for high-content screening of three-dimensional (3D) cell culture models that are imaged in full 3D volume. It provides an end-to-end solution for designing high content screening assays, based on colony organization that is derived from segmentation of nuclei in each colony. BioSig3D also enables visualization of raw and processed 3D volumetric data for quality control, and integrates advanced bioinformatics analysis. The system consists of multiple computational and annotation modules that are coupled together with a strong use of controlled vocabularies to reduce ambiguities between different users. It is a web-based system that allows users to: design an experiment by defining experimental variables, upload a large set of volumetric images into the system, analyze and visualize the dataset, and either display computed indices as a heatmap, or phenotypic subtypes for heterogeneity analysis, or download computed indices for statistical analysis or integrative biology. BioSig3D has been used to profile baseline colony formations with two experiments: (i) morphogenesis of a panel of human mammary epithelial cell lines (HMEC), and (ii) heterogeneity in colony formation using an immortalized non-transformed cell line. These experiments reveal intrinsic growth properties of well-characterized cell lines that are routinely used for biological studies. BioSig3D is being released with seed datasets and video-based documentation.

Metadata management for high content screening in OMERO

High content screening (HCS) experiments create a classic data management challenge-multiple, large sets of heterogeneous structured and unstructured data, that must be integrated and linked to produce a set of "final" results. These different data include images, reagents, protocols, analytic output, and phenotypes, all of which must be stored, linked and made accessible for users, scientists, collaborators and where appropriate the wider community. The OME Consortium has built several open source tools for managing, linking and sharing these different types of data. The OME Data Model is a metadata specification that supports the image data and metadata recorded in HCS experiments. Bio-Formats is a Java library that reads recorded image data and metadata and includes support for several HCS screening systems. OMERO is an enterprise data management application that integrates image data, experimental and analytic metadata and makes them accessible for visualization, mining, sharing and downstream analysis. We discuss how Bio-Formats and OMERO handle these different data types, and how they can be used to integrate, link and share HCS experiments in facilities and public data repositories. OME specifications and software are open source and are available at https://www.openmicroscopy.org.

Aggregation induced emission controlled by a temperature-sensitive organic–inorganic hybrid polymer with a particular LCST

The fluorescence intensity change of TPE encapsulated in POSS–PNIPAM with a particular LCST (37.5 °C) with the temperature change.

Cell membrane permeable fluorescent perylene bisimide derivatives for cell lysosome imaging

The cellular uptake of Lyso-APBIprobes is improved by PEG chains, and the double morpholine moieties make Lyso-APBI probes have higher acid activation ratio and better cell lysosome specificity.

Bioimage Informatics for Big Data

Bioimage informatics is a field wherein high-throughput image informatics methods are used to solve challenging scientific problems related to biology and medicine. When the image datasets become larger and more complicated, many conventional image analysis approaches are no longer applicable. Here, we discuss two critical challenges of large-scale bioimage informatics applications, namely, data accessibility and adaptive data analysis. We highlight case studies to show that these challenges can be tackled based on distributed image computing as well as machine learning of image examples in a multidimensional environment.

Quantitative Analysis of Rat Dorsal Root Ganglion Neurons Cultured on Microelectrode Arrays Based on Fluorescence Microscopy Image Processing

Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BACKGROUND

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.

METHODS

We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.

RESULTS

The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.

CONCLUSIONS

The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

Blobs and curves: object-based colocalisation for plant cells

Blobs and curves occur everywhere in plant bioimaging: from signals of fluorescence-labelled proteins, through cytoskeletal structures, nuclei staining and cell extensions such as root hairs. Here we look at the problem of colocalisation of blobs with blobs (protein-protein colocalisation) and blobs with curves (organelle-cytoskeleton colocalisation). This article demonstrates a clear quantitative alternative to pixel-based colocalisation methods and, using object-based methods, can quantify not only the level of colocalisation but also the distance between objects. Included in this report are computational algorithms, biological experiments and guidance for those looking to increase their use of computationally-based and quantified analysis of bioimages.

Temperature-sensitive fluorescent organic nanoparticles with aggregation-induced emission for long-term cellular tracing

Temperature-sensitive organic nanoparticles with AIE effect were assembled in water from tetraphenylethene-based poly(N-isopropylacrylamide) (TPE-PNIPAM), which was synthesized by ATRP using TPE derivative as initiator. The size and fluorescence of TPE-PNIPAM nanoparticles can be tuned by varying the temperature. These nanoparticles can be internalized readily by HeLa cells and can be used as long-term tracer in live cells to be retained for as long as seven passages.

Confocal microscopy on the Internet

In a few short years, the Internet (in terms of the World Wide Web) has become a powerful informational resource for the original scientific literature pertaining to biological investigations using the laser scanning confocal microscope. However, there still remains an obvious void in the development of educational Web sites targeted at beginning students and novices in the field. Furthermore, many of the commercial aftermarket manufacturers (for example, those offering live-cell imaging chambers) have Web sites that are not adequately represented in published compilations, and are therefore somewhat difficult to locate. In order to address this issue, several educational sites dedicated to optical microscopy and digital imaging that are being constructed and hosted at The Florida State University are currently turning their attention to the increasing application of confocal microscopy in the biological and materials sciences. The primary focus of this effort is to create new sections on the existing sites that address the important educational issues in confocal microscopy, as well as creating indices of links to both the confocal scientific literature and the Web sites of manufacturers who supply useful accessories.

Laser scanning confocal microscopy: history, applications, and related optical sectioning techniques

Confocal microscopy is an established light microscopical technique for imaging fluorescently labeled specimens with significant three-dimensional structure. Applications of confocal microscopy in the biomedical sciences include the imaging of the spatial distribution of macromolecules in either fixed or living cells, the automated collection of 3D data, the imaging of multiple labeled specimens and the measurement of physiological events in living cells. The laser scanning confocal microscope continues to be chosen for most routine work although a number of instruments have been developed for more specific applications. Significant improvements have been made to all areas of the confocal approach, not only to the instruments themselves, but also to the protocols of specimen preparation, to the analysis, the display, the reproduction, sharing and management of confocal images using bioinformatics techniques.

Analysis of cellular events using CellCard™ System in cell-based high-content multiplexed assays

High-content screening technologies utilize assays that monitor and quantify multiple cellular events. These assays are typically performed on a single cell type with automated microscopy and image analysis. However, in order to better understand the selectivity of a compound across multiple cell lines, these types of assay must be run serially, which is time consuming. The CellCard System developed by Vitra Bioscience enables multiple cell types to be assayed within a single microtiter well, thereby enabling the simultaneous determination of cellular responses across ten cell types. This multiplexed approach could address the demand for assay capacity, increase the quality of the biologic data, reduce timelines, and improve cost-effectiveness in hit identification and lead evaluation. The authors have carried out an in-depth evaluation of this technology platform using ten cancer cell lines and a library of compounds that affect cellular growth through different mechanisms. Multiple assays were used to investigate the compound effects on membrane integrity, cell cycle progression and apoptosis. In this technology review, the authors discuss personal experience with assay validation, data analysis, results such as cell type-specific compound effects, and the potential application of the CellCard System in drug discovery.

Extensible visualization and analysis for multidimensional images using Vaa3D

Open-Source 3D Visualization-Assisted Analysis (Vaa3D) is a software platform for the visualization and analysis of large-scale multidimensional images. In this protocol we describe how to use several popular features of Vaa3D, including (i) multidimensional image visualization, (ii) 3D image object generation and quantitative measurement, (iii) 3D image comparison, fusion and management, (iv) visualization of heterogeneous images and respective surface objects and (v) extension of Vaa3D functions using its plug-in interface. We also briefly demonstrate how to integrate these functions for complicated applications of microscopic image visualization and quantitative analysis using three exemplar pipelines, including an automated pipeline for image filtering, segmentation and surface generation; an automated pipeline for 3D image stitching; and an automated pipeline for neuron morphology reconstruction, quantification and comparison. Once a user is familiar with Vaa3D, visualization usually runs in real time and analysis takes less than a few minutes for a simple data set.

Using 2-photon microscopy to understand albuminuria

Intravital 2-photon microscopy, along with the development of fluorescent probes and innovative software, has rapidly advanced the study of intracellular and intercellular processes at the organ level. Researchers can quantify the distribution, behavior, and dynamic interactions of up to four labeled chemical probes and proteins simultaneously and repeatedly in four dimensions (3D + time) with subcellular resolution in real time. Transgenic fluorescently labeled proteins, delivery of plasmids, and photo-activatable probes enhance these possibilities. Thus, multi-photon microscopy has greatly extended our ability to understand cell biology intra-vitally at cellular and subcellular levels. For example, evaluation of rat surface glomeruli and accompanying proximal tubules has shown the long held paradigm regarding limited albumin filtration under physiologic conditions is to be questioned. Furthermore, the role of proximal tubules in determining albuminuria under physiologic and disease conditions was supported by direct visualization and quantitative analysis.

In situ study of the impact of inter- and intra-reader variability on region of interest (ROI) analysis in preclinical molecular imaging

We estimated reader-dependent variability of region of interest (ROI) analysis and evaluated its impact on preclinical quantitative molecular imaging. To estimate reader variability, we used five independent image datasets acquired each using microPET and multispectral fluorescence imaging (MSFI). We also selected ten experienced researchers who utilize molecular imaging in the same environment that they typically perform their own studies. Nine investigators blinded to the data type completed the ROI analysis by drawing ROIs manually that delineate the tumor regions to the best of their knowledge and repeated the measurements three times, non-consecutively. Extracted mean intensities of voxels within each ROI are used to compute the coefficient of variation (CV) and characterize the inter- and intra-reader variability. The impact of variability was assessed through random samples iterated from normal distributions for control and experimental groups on hypothesis testing and computing statistical power by varying subject size, measured difference between groups and CV. The results indicate that inter-reader variability was 22.5% for microPET and 72.2% for MSFI. Additionally, mean intra-reader variability was 10.1% for microPET and 26.4% for MSFI. Repeated statistical testing showed that a total variability of CV < 50% may be needed to detect differences < 50% between experimental and control groups when six subjects (n = 6) or more are used and statistical power is adequate (80%). Surprisingly high variability has been observed mainly due to differences in the ROI placement and geometry drawn between readers, which may adversely affect statistical power and erroneously lead to negative study outcomes.

Using Cell-ID 1.4 with R for microscope-based cytometry

This unit describes a method for quantifying various cellular features (e.g., volume, total and subcellular fluorescence localization) from sets of microscope images of individual cells. It includes procedures for tracking cells over time. One purposely defocused transmission image (sometimes referred to as bright-field or BF) is acquired to segment the image and locate each cell. Fluorescence images (one for each of the color channels to be analyzed) are then acquired by conventional wide-field epifluorescence or confocal microscopy. This method uses the image-processing capabilities of Cell-ID and data analysis by the statistical programming framework R, which is supplemented with a package of routines for analyzing Cell-ID output. Both Cell-ID and the analysis package are open-source.