Open source tools for fluorescent imaging

Recent Advances in High-Content Imaging and Analysis in iPSC-Based Modelling of Neurodegenerative Diseases

In the field of neurodegenerative pathologies, the platforms for disease modelling based on patient-derived induced pluripotent stem cells (iPSCs) represent a valuable molecular diagnostic/prognostic tool. Indeed, they paved the way for the in vitro recapitulation of the pathological mechanisms underlying neurodegeneration and for characterizing the molecular heterogeneity of disease manifestations, also enabling drug screening approaches for new therapeutic candidates. A major challenge is related to the choice and optimization of the morpho-functional study designs in human iPSC-derived neurons to deeply detail the cell phenotypes as markers of neurodegeneration. In recent years, the specific combination of high-throughput screening with subcellular resolution microscopy for cell-based high-content imaging (HCI) screening allowed in-depth analyses of cell morphology and neurite trafficking in iPSC-derived neuronal cells by using specific cutting-edge microscopes and automated computational assays. The present work aims to describe the main recent protocols and advances achieved with the HCI analysis in iPSC-based modelling of neurodegenerative diseases, highlighting technical and bioinformatics tips and tricks for further uses and research. To this end, microscopy requirements and the latest computational pipelines to analyze imaging data will be explored, while also providing an overview of the available open-source high-throughput automated platforms.

Dermal type I collagen assessment by digital image analysis

Type I collagen is the main dermal component, and its evaluation is relevant to quantitative studies in dermatopathology. However, visual gradation (0 to 4+) has low precision and high subjectivity levels. This study aimed to develop and validate a digital morphometric analysis technique to estimate type I collagen levels in the papillary dermis. Four evaluators visually quantified (0 to 4+) the density of type I collagen in 63 images of forearm skin biopsies marked by immunohistochemistry and two evaluators analyzed the same images using digital morphometric techniques (RGB split colors (I) and color deconvolution (II)). Automated type I collagen density estimation in the papillary dermis (two techniques) were correlated with visual evaluations (Spearman's rho coefficients of 0.48 and 0.62 (p<0.01)). With regard to the inter-observer repeatability, the four evaluators who used visual classification had an intraclass correlation coefficient (for absolute agreement) of 0.53, while the other two evaluators who used digital analysis (algorithm II) had an intraclass correlation coefficient of 0.97.

Microfluidics for High-Throughput Quantitative Studies of Early Development

Developmental biology has traditionally relied on qualitative analyses; recently, however, as in other fields of biology, researchers have become increasingly interested in acquiring quantitative knowledge about embryogenesis. Advances in fluorescence microscopy are enabling high-content imaging in live specimens. At the same time, microfluidics and automation technologies are increasing experimental throughput for studies of multicellular models of development. Furthermore, computer vision methods for processing and analyzing bioimage data are now leading the way toward quantitative biology. Here, we review advances in the areas of fluorescence microscopy, microfluidics, and data analysis that are instrumental to performing high-content, high-throughput studies in biology and specifically in development. We discuss a case study of how these techniques have allowed quantitative analysis and modeling of pattern formation in the Drosophila embryo.

MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells

We introduce here MATtrack, an open source MATLAB-based computational platform developed to process multi-Tiff files produced by a photo-conversion time lapse protocol for live cell fluorescent microscopy. MATtrack automatically performs a series of steps required for image processing, including extraction and import of numerical values from Multi-Tiff files, red/green image classification using gating parameters, noise filtering, background extraction, contrast stretching and temporal smoothing. MATtrack also integrates a series of algorithms for quantitative image analysis enabling the construction of mean and standard deviation images, clustering and classification of subcellular regions and injection point approximation. In addition, MATtrack features a simple user interface, which enables monitoring of Fluorescent Signal Intensity in multiple Regions of Interest, over time. The latter encapsulates a region growing method to automatically delineate the contours of Regions of Interest selected by the user, and performs background and regional Average Fluorescence Tracking, and automatic plotting. Finally, MATtrack computes convenient visualization and exploration tools including a migration map, which provides an overview of the protein intracellular trajectories and accumulation areas. In conclusion, MATtrack is an open source MATLAB-based software package tailored to facilitate the analysis and visualization of large data files derived from real-time live cell fluorescent microscopy using photoconvertible proteins. It is flexible, user friendly, compatible with Windows, Mac, and Linux, and a wide range of data acquisition software. MATtrack is freely available for download at eleceng.dit.ie/courtney/MATtrack.zip.

Nanoinformatics: emerging databases and available tools

Nanotechnology has arisen as a key player in the field of nanomedicine. Although the use of engineered nanoparticles is rapidly increasing, safety assessment is also important for the beneficial use of new nanomaterials. Considering that the experimental assessment of new nanomaterials is costly and laborious, in silico approaches hold promise. Several major challenges in nanotechnology indicate a need for nanoinformatics. New database initiatives such as ISA-TAB-Nano, caNanoLab, and Nanomaterial Registry will help in data sharing and developing data standards, and, as the amount of nanomaterials data grows, will provide a way to develop methods and tools specific to the nanolevel. In this review, we describe emerging databases and tools that should aid in the progress of nanotechnology research.

A versatile, bar-coded nuclear marker/reporter for live cell fluorescent and multiplexed high content imaging

The screening of large numbers of compounds or siRNAs is a mainstay of both academic and pharmaceutical research. Most screens test those interventions against a single biochemical or cellular output whereas recording multiple complementary outputs may be more biologically relevant. High throughput, multi-channel fluorescence microscopy permits multiple outputs to be quantified in specific cellular subcompartments. However, the number of distinct fluorescent outputs available remains limited. Here, we describe a cellular bar-code technology in which multiple cell-based assays are combined in one well after which each assay is distinguished by fluorescence microscopy. The technology uses the unique fluorescent properties of assay-specific markers comprised of distinct combinations of different 'red' fluorescent proteins sandwiched around a nuclear localization signal. The bar-code markers are excited by a common wavelength of light but distinguished ratiometrically by their differing relative fluorescence in two emission channels. Targeting the bar-code to cell nuclei enables individual cells expressing distinguishable markers to be readily separated by standard image analysis programs. We validated the method by showing that the unique responses of different cell-based assays to specific drugs are retained when three assays are co-plated and separated by the bar-code. Based upon those studies, we discuss a roadmap in which even more assays may be combined in a well. The ability to analyze multiple assays simultaneously will enable screens that better identify, characterize and distinguish hits according to multiple biologically or clinically relevant criteria. These capabilities also enable the re-creation of complex mixtures of cell types that is emerging as a central area of interest in many fields.

Quantitative fluorescence imaging reveals point of release for lipoproteins during LDLR-dependent uptake

The LDL receptor (LDLR) supports efficient uptake of both LDL and VLDL remnants by binding lipoprotein at the cell surface, internalizing lipoprotein through coated pits, and releasing lipoprotein in endocytic compartments before returning to the surface for further rounds of uptake. While many aspects of lipoprotein binding and receptor entry are well understood, it is less clear where, when, and how the LDLR releases lipoprotein. To address these questions, the current study employed quantitative fluorescence imaging to visualize the uptake and endosomal processing of LDL and the VLDL remnant β-VLDL. We find that lipoprotein release is rapid, with most release occurring prior to entry of lipoprotein into early endosomes. Published biochemical studies have identified two mechanisms of lipoprotein release: one that involves the β-propeller module of the LDLR and a second that is independent of this module. Quantitative imaging comparing uptake supported by the normal LDLR or by an LDLR variant incapable of β-propeller-dependent release shows that the β-propeller-independent process is sufficient for release for both lipoproteins but that the β-propeller process accelerates both LDL and β-VLDL release. Together these findings define where, when, and how lipoprotein release occurs and provide a generalizable methodology for visualizing endocytic handling in situ.