Methods for acquisition of quantitative from confocal images of gene expression in situ

In this review we summarize original methods for the extraction quantitative information from the confocal images of gene expression patterns. These methods include image segmentation, extraction of quantitative numerical data on gene expression, removal of background signal and spatial registration. Finally it is possible to construct a spatiotemporal atlas of gene expression form individual images obtained at each developmental stage. Initially all methods were developed to extract quantitative numerical information form confocal images of segmentation gene expression in Drosophila melanogaster. Application of these methods to Drosophila images makes it possible to reveal new mechanisms of formation of segmentation gene expression domains, as well as to construct the quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.     

Methods for acquisition of quantitative data from confocal images of gene expression in situ

In this review, we summarize original methods for the extraction of quantitative information from confocal images of gene-expression patterns. These methods include image segmentation, the extraction of quantitative numerical data on gene expression, and the removal of background signal and spatial registration. Finally, it is possible to construct a spatiotemporal atlas of gene expression from individual images recorded at each developmental stage. Initially all methods were developed to extract quantitative numerical information from confocal images of segmentation gene expression in Drosophila melanogaster. The application of these methods to Drosophila images makes it possible to reveal new mechanisms in the formation of segmentation gene expression domains, as well as to construct a quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.     

Reconstructing embryonic development

Novel approaches to bio-imaging and automated computational image processing allow the design of truly quantitative studies in developmental biology. Cell behavior, cell fate decisions, cell interactions during tissue morphogenesis, and gene expression dynamics can be analyzed in vivo for entire complex organisms and throughout embryonic development. We review state-of-the-art technology for live imaging, focusing on fluorescence light microscopy techniques for system-level investigations of animal development, and discuss computational approaches to image segmentation, cell tracking, automated data annotation, and biophysical modeling. We argue that the substantial increase in data complexity and size requires sophisticated new strategies to data analysis to exploit the enormous potential of these new resources.     

TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history

Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed.     

XMIPP: a new generation of an open-source image processing package for electron microscopy

X-windows based microscopy image processing package (Xmipp) is a specialized suit of image processing programs, primarily aimed at obtaining the 3D reconstruction of biological specimens from large sets of projection images acquired by transmission electron microscopy. This public-domain software package was introduced to the electron microscopy field eight years ago, and since then it has changed drastically. New methodologies for the analysis of single-particle projection images have been added to classification, contrast transfer function correction, angular assignment, 3D reconstruction, reconstruction of crystals, etc. In addition, the package has been extended with functionalities for 2D crystal and electron tomography data. Furthermore, its current implementation in C++, with a highly modular design of well-documented data structures and functions, offers a convenient environment for the development of novel algorithms. In this paper, we present a general overview of a new generation of Xmipp that has been re-engineered to maximize flexibility and modularity, potentially facilitating its integration in future standardization efforts in the field. Moreover, by focusing on those developments that distinguish Xmipp from other packages available, we illustrate its added value to the electron microscopy community.     

Molecular imaging of the embryonic heart: Fables and facts on 3D imaging of gene expression patterns

Molecular imaging, which is the three-dimensional (3D) visualization of gene expression patterns, is indispensable for the study of the function of genes in cardiac development. The instrumentation, as well as the development of specific contrast agents for molecular imaging, has shown spectacular advances in the last decade. In this review, the spatial resolutions, contrast agents, and applications of these imaging methods in the field of cardiac embryology are discussed. Apart from 3D reconstructions from histological sections, not many of these methods have been applied in embryological research. This review shows that, for most methods, neither the spatial resolutions nor the specificity and applicability of the contrast agents are adequate for the reliable imaging of specific gene expression at the microscopic resolution required for embryological studies of small organs like the developing heart. Although a 3D reconstruction from sections will always suffer from imperfections, the resulting reconstructions meet the aim of most biological studies, especially since the original microscopic images are linked. With respect to imaging of gene expression, only histological sections and laser scanning microscopy provide the required resolution and specificity at the tissue and cellular level. Episcopic fluorescence image capturing and optical projection tomography are being used for microscopic phenotyping and lineage analysis, and both show potential for detailed molecular imaging. Other methods can be used very efficiently in rapid evaluation of biological experiments and high-throughput screens of large-scale gene expression profiling efforts when high spatial resolution is not required.     

BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies

Background

The software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing.

Results

Here we describe BactImAS – a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree.

Conclusions

The presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. It will be of interest to the community of both end-users and developers of time-lapse microscopy software.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-251) contains supplementary material, which is available to authorized users.     

激光扫描共聚焦显微镜荧光探针的选择和应用

激光扫描共聚焦显微镜是检测生物荧光信号的最新技术手段。不仅广泛用于荧光定性、定量测量,还可用于活细胞动态荧光监测、组织细胞断层扫描、三维图象重建、共聚焦图象分析、荧光光漂白恢复、激光显微切割手术等。本文拟就激光扫描共聚焦显微镜常用的检测内容及其相关荧光探针的选择和应用做一简单的介绍。     

Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing <Emphasis Type="Italic">C. elegans</Emphasis> embryo

Background  

Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.     

Confocal scanning microscopy: three-dimensional biological imaging

Confocal scanning optical microscopy, arguably the most significant in biological light microscopy in this decade, enables one to obtain quantitative non-invasive optical sections through labelled biological specimens, virtually free from out-of-focus blur. A set of these optical sections collected at a series of focal levels through an object constitutes a three-dimensional image which may then be processed digitally for display as a computer reconstruction, a stereo pair or an animation sequence.     

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non‐invasive determination of these characteristics. We present an image‐processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source‐code for MATLAB and ImageJ is freely available under a permissive open‐source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc.     

Automated Characterization and Parameter-Free Classification of Cell Tracks Based on Local Migration Behavior

Cell migration is the driving force behind the dynamics of many diverse biological processes. Even though microscopy experiments are routinely performed today by which populations of cells are visualized in space and time, valuable information contained in image data is often disregarded because statistical analyses are performed at the level of cell populations rather than at the single-cell level. Image-based systems biology is a modern approach that aims at quantitatively analyzing and modeling biological processes by developing novel strategies and tools for the interpretation of image data. In this study, we take first steps towards a fully automated characterization and parameter-free classification of cell track data that can be generally applied to tracked objects as obtained from image data. The requirements to achieve this aim include: (i) combination of different measures for single cell tracks, such as the confinement ratio and the asphericity of the track volume, and (ii) computation of these measures in a staggered fashion to retrieve local information from all possible combinations of track segments. We demonstrate for a population of synthetic cell tracks as well as for in vitro neutrophil tracks obtained from microscopy experiment that the information contained in the track data is fully exploited in this way and does not require any prior knowledge, which keeps the analysis unbiased and general. The identification of cells that show the same type of migration behavior within the population of all cells is achieved via agglomerative hierarchical clustering of cell tracks in the parameter space of the staggered measures. The recognition of characteristic patterns is highly desired to advance our knowledge about the dynamics of biological processes.     

Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy

Quantitative single-cell time-lapse microscopy is a powerful method for analyzing gene circuit dynamics and heterogeneous cell behavior. We describe the application of this method to imaging bacteria by using an automated microscopy system. This protocol has been used to analyze sporulation and competence differentiation in Bacillus subtilis, and to quantify gene regulation and its fluctuations in individual Escherichia coli cells. The protocol involves seeding and growing bacteria on small agarose pads and imaging the resulting microcolonies. Images are then reviewed and analyzed using our laboratory's custom MATLAB analysis code, which segments and tracks cells in a frame-to-frame method. This process yields quantitative expression data on cell lineages, which can illustrate dynamic expression profiles and facilitate mathematical models of gene circuits. With fast-growing bacteria, such as E. coli or B. subtilis, image acquisition can be completed in 1 d, with an additional 1-2 d for progressing through the analysis procedure.     

Integrated genetic and computation methods for in planta cytometry

We present the coupled use of specifically localized fluorescent gene markers and image processing for automated quantitative analysis of cell growth and genetic activity across living plant tissues. We used fluorescent protein markers to identify cells, create seeds and boundaries for the automatic segmentation of cell geometries and ratiometrically measure gene expression cell by cell in Arabidopsis thaliana.     

三维电子显微镜方法进展

从生物样品（细胞或大分子）的电镜图象重组其三维结构的方法近年取得了重大进展,这是冷冻电镜样品制备、电镜设备、图象处理和分析方法等几方面进步的综合结果。三维电镜方法的进步和完善,使细胞和学家得以了解在复杂的细胞过程中各种相关细胞器之间的空间关系,而使分子生物学家不仅可以研究那些能够形成二维结晶的样品,并为分析具有重要生物功能但不能形成二维结晶的大分子或分子聚休物的结构提供了一种强大的手段。