The imaging of rare events such as cell division remains to be a challenging endeavor. One way to circumvent the manual selection of rare events is to use machine learning approaches to identify cellular events of interest.
To fill this gap, Conrad et al. Pre-filtering at the image acquisition level can lead to a loss of valuable information; hence, the applicability of pre-filtering depends on the type of pre-filtering and biological questions asked. For example, pre-filtering removes information from prior time points but allows for increased throughput of downstream event-order analysis at selected regions of interest.
One primary goal of systems biology is to achieve a systems level of understanding of cellular genetics and physiology. The budding yeast, a simple and genetically tractable eukaryotic system, is a premier model organism for such functional genomic study [ 51 ].
Classical genomic screens in yeast have focused on specific morphological features such as cell size, cell shape, or bud site pattern [ 52 , 53 ]. In addition, the short lifespan of this model organism presents an interesting feature for research on aging.
However, yeast cells in liquid culture are suspension cells. Budding results in an exponential increase in the number of daughter cells. The classical analysis of aging in short-lived budding yeast by taking snapshots of a single yeast cell throughout its entire lifespan [ 54 , 55 ] involves laborious manual dissections of daughter cells from larger mother cells.
Recently, Lee et al. As a result, the combination of microfluidics with microscopy drastically improved the workflow for image-based analysis of aging.
Microscope-based cytometry is also a powerful tool with freely available software that quantifies fluorescence intensities in cellular resolution time series [ 57 ]. Similar to mammalian cell culture, yeast projects need to maximize the multiplicity of phenotypic readouts. The ready to use solution for multiparametric morphological analysis of yeast cells, CalMorph, is an image processing program that quantifies cell morphology parameters in triple-stained yeast cells [ 58 — 60 ].
The automated phenotyping of subcellular events has successfully been used to identify drug targets based on morphological phenotypes of a reference mutant panel [ 61 ]. A pure cell culture-based analysis of gene regulatory networks is not sufficient for understanding signal transduction pathways, which can involve multiple regulatory mechanisms at different scales of biological complexity. Compared to yeast, the worm Caenorhabditis elegans has the advantage of being a multicellular animal model with higher genetic homology to humans.
Furthermore, drug discovery screens with whole animals have the advantage of identifying compounds that modulate systemic phenotypes. Animal screens also have the potential to eliminate compounds with systemic toxicity earlier in the discovery process. The ability to conduct forward and reverse genetic screens in animal models such as C.
The potential to analyze large numbers of isogenic animals through high-throughput and HCS for accessing different aspects of human disease phenotypes will certainly ensure an important role for this model organism in future oriented translational research [ 62 — 67 ].
Finally, the possibility of flow sorting of worms by both size and fluorescence enables high-throughput experiments to be conducted [ 68 ]. One example of advanced image analysis in C. For classical locomotion analysis, each environment requires customized state-of-the-art image processing tools that rely on heuristic parameter tuning [ 69 — 80 ]. Sznitman et al. In this image analysis process, statistical models for the background environment and nematode appearance are explicitly learned from a single image, which includes a nematode in its environment, and are used to accurately segment target nematodes.
Locomotive movements and complex morphological structures of the worms are of interest for multiscale approaches in systems biology, which aim to connect molecular events and organic states. Complete organisms such as C. Green et al.
However, the relatively complex morphology presents a challenge for the comparative analysis of different worms. Image registration is a classical tool for resolving such problems. Recent developments in image processing can straighten C.
Advances in Bessel-beam technology and structured illumination microscopy promise even deeper insights, beyond the diffraction limit, into complex biological phenomena that require extended high-resolution time series in a multicellular context [ 7 , 84 ]. In contrast to C. The zebrafish system with its small transparent larva can be used in diverse screening assays, including the analysis of development and organ function in living animals. In addition, genetic and chemical perturbation methods are well established [ 85 , 86 ].
Zebrafish can be used in small molecule screening, genetic screens, drug discovery, drug lead identification, and target identification [ 87 , 88 ]. However, the throughput of such screens decreases with the age and size of the fish. The main strength of this model lies in developmental biology applications [ 89 ] rather than applications related to aging.
In the context of imaging the central nervous system, high-resolution images of brain cells need to be acquired.
Hence, blindly chosen fixed-field-views that lead to the potential omission of features of interest or low-resolution data of whole objects lacking cellular detail cannot fulfill this need. Stitching is an alternative of acquiring multiple fields of view at a high resolution for subsequent reassembling that can significantly increase imaging times and produce excessive and redundant data volumes.
The problem of untargeted image acquisition patterns is a widespread issue that generally limits the efficiency of HCS assays. However, custom algorithms can solve this problem by automatically identifying predefined regions such as the fish brain, and triggering targeted high-resolution captures [ 90 ]. However, for interpreting brain phenotypes, the data from single fish need to be mapped to a standard brain map to facilitate the statistical evaluation of replicate zebrafish brains.
This registration problem can be solved with the Virtual Brain Explorer ViBE-Z , which is a software tool that maps cellular gene expression data to a 3D standard larval zebrafish by using a fluorescent stain of cell nuclei for image registration [ 36 ]. Compared to previously described animal models, mouse models only enable moderate experimental throughput.
Due to the optical properties of mice, immunohistochemistry remains a gold standard method in this field. One common strategy for increasing the experimental throughput is the use of tissue arrays [ 91 ]. Notably, modern image analysis tools can assist in the evaluation of the resulting colored tissue images [ 92 , 93 ]. Recently, evolved imaging techniques and image analysis tools have enabled non-invasive experimental workflows providing statistically relevant amounts of data.
Near-infrared fluorescent optical imaging agents, which maximize the depth of tissue penetration, can be used for non-invasive whole mouse imaging, thus enabling the analysis of the presence and evolution of internal markers for disease progression [ 38 , 94 ]. Recent progress in image analysis has also been useful in the behavioral studies of mice; video tracking can be used to analyze the explorative behavior of mice [ 95 ].
For example, MiceProfiler is an open-source software that tracks and models the behavior of untagged mice [ 96 ]. The in vivo observation of live neurons is a useful approach because these cells perform their basic function of information processing by connecting with their neighbors. One way of monitoring the cellular dynamics of living neurons in mouse tissue is to use hippocampal slices of 5- to 7-day-old mice [ 97 , 98 ]. However, observing cellular dynamics in living mice is a more challenging endeavor.
Berning et al. This method was very invasive as optical access was provided by a glass-sealed hole in the skull of the anaesthetized and immobilized mouse. However, intravital microscopy is relevant for translational research, and significant technological progress has been made in recent years [ 99 — ]. Two major limitations of classical intravital microscopy are the limited optical penetration depth and immobilization of mice; however, these limitations can be overcome by using miniaturized implantable microscopes [ 37 , — ].
A central goal of image analysis is the conversion of microscopic images into biologically meaningful quantitative data. However, the amounts of image data produced using modern systems biology are very vast for manual analysis; hence, the development of automated image analysis tools is essential. Due to the complexity and size of modern imaging data, the computational analysis of biological imaging data has already become a vital emerging sub-discipline of bioinformatics and computer vision [ ].
Research using multiparametric imaging data relies heavily on computational approaches for image acquisition, data management, visualization, and correct data interpretation [ — ]. The typical functions of dedicated computer vision systems are data pre-processing, image segmentation, feature extraction, and decision making [ , ].
In this review, we focus on open-source solutions, which facilitate community-driven efforts in the development of image analysis. Examples of microscopy developments requiring custom computational workflows for image acquisition include structured-illumination microscopy [ ], super resolution microscopy [ — ], and Bessel-beam microscopy [ 5 ]. Some modern microscopes can produce up to 30 TiB of data per day [ ].
However, the volume of images generated in systems biology is growing rapidly. As a result, the scalability of storage solutions and awareness for the need of image repositories and common file formats for imaging projects are increasing. Research on image analysis has developed an entire ecosystem of image analysis tools. ImageJ [ — ], formerly known as NIH image, is a role model in the landscape of open-source tools for image analysis.
Since its beginnings it has always been free and it became the most popular and widespread multipurpose image analysis tool. ImageJ has become successful because the scientific community can freely use it to focus on image analysis rather than on application programming. The concept of software extensibility by adding plugins is also useful for developers and end users. Furthermore, this concept has been adopted by more recently evolved platforms such as Fiji [ ] and Icy [ , ].
The 2 main challenges in image analysis in systems biology are the analysis of complex high-level structures such as whole organisms and the rise of experiments with ever increasing throughput.
Imagery of large-scale biological systems such as embryos and brains requires state of the art algorithms for stitching, registration, and mapping to anatomical atlases. In addition to the extensible Vaa3D [ ] and Fiji software packages, which are both established in this field, new tools such as TeraStitcher that can handle TiB-scale datasets have now emerged [ ].
While the imaging of such high-level structures is typically conducted in a rather low throughput, partially automated workflows requiring a significant amount of user input are still quite common. In contrast, the amounts of images produced in high-throughput experiments are often increased by several orders of magnitude and cannot be manually analyzed. The challenge is to analyze data from HCS sets to a meaningful extent and in a reasonable amount of time. Several open-source packages for image analysis include functionality for machine learning-based cell classification.
Some of these packages are CellProfiler [ , ], CellClassifier [ ], and the R package EBImage [ ], which provide workflows for fixed cell images. CellProfiler can be used to address several application areas, including intensity and morphology measurements.
In contrast to tools designed for fixed objects, CellProfiler can perform two-dimensional 2D object tracking. Information about temporal coupling between cellular events is highly relevant for understanding the physiology of biological systems. Time-lapse imaging has emerged as a powerful tool for investigating dynamic cellular processes such as cell division or intracellular trafficking of labeled targets of interest.
However, for the analysis of such high-throughput cinematography, only a few tools are currently available. CellCognition [ 33 ] is a freely available software platform that includes high-throughput batch processing and annotation of complex cellular dynamics such as the progression of single cells through distinct cell division states. In this platform, temporal hidden Markov modeling is used to reduce classification noise at state transitions and to distinguish different states with similar morphology.
Briefly, CellCognition provides an analysis platform for live imaging-based screening with assays that directly score cellular dynamics [ 33 ]. As a result, BioImageXD, unlike CellProfiler and CellCognition, can offer options for 2D and 3D analyses by providing advanced batch-processing functions for multidimensional fluorescence image sets, including time series.
In addition to built-in tools for visualization, colocalization analysis, segmentation, and tracking, the graphical user interface of BioImageXD facilitates the assembly of custom image analysis pipelines. The open-source design of the project, as well as the use of Python and gold standard file formats such as OME-TIFF, should further facilitate the evolution of this project for the community working on spatio-temporally resolved data [ ]. An open-source software can foster productive collaborations between programming biologists and computer scientists interested in biology.
However, an important challenge is to ensure the availability of analysis tools to the entire community of microscope users. The timely public availability of professionally programmed, easy-to-use, open-source tools for image analysis will depend on career opportunities for talented image analysis code writers [ ], and the quality of these emerging tools will depend on good programming practices.
The microscope is important because biology mainly deals with the study of cells and their contents , genes, and all organisms. Some organisms are so small that they can only be seen by using magnifications of xx - xx , which can only be achieved by a microscope.
Cells are too small to be seen with the naked eye. Genetics is the study of variations in an organism generation after generation. Genetic engineering requires mixing of genes. Genes are even smaller than cells, which is why microscopes are essential to genetics. In microbiology, microscope is an optical instrument used for viewing very small objects, such as mineral samples or animal or plant cells, typically magnified several hundred times.
Light natural or artificial is transmitted through, or reflected from, the specimen and then passed through a system of lenses that produce a magnified image. It is also possible to measure the actual size of your specimen. A mini ruler called an eyepiece graticule that is placed in the eyepiece is needed. This is then calibrated using a special microscope slide called a stage micrometer. There are two types of microscope that are simple microscope and compound microscope.
Simple microscope is a single lens system that magnifies the object being viewed. Compound microscope consists of two lenses. The image produced by the first of the two lenses is remagnified by the second lens. The magnification is thus compounded. The microscope we used in the experiment is light microscope, it is an example of compound microscope. The compound microscope is the most familiar form of optical microscope. Show More. Read More.
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