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.     

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of 5 steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5 minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns.     

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of five steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5-minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns.     

A database for management of gene expression data in situ

MOTIVATION: To create a spatiotemporal atlas of Drosophila segmentation gene expression at cellular resolution. RESULTS: The expression of segmentation genes plays a crucial role in the establishment of the Drosophila body plan. Using the IBM DB2 Relational Database Management System we have designed and implemented the FlyEx database. FlyEx contains 2832 images of 14 segmentation gene expression patterns obtained from 954 embryos and 2,073,662 quantitative data records. The averaged data is available for most of segmentation genes at eight time points. FlyEx supports operations on images of gene expression patterns. The database can be used to examine the quality of data, analyze the dynamics of formation of segmentation gene expression domains, as well as estimate the variability of gene expression patterns. We also provide the capability to download data of interest. AVAILABILITY: http://urchin.spbcas.ru/flyex, http://flyex.ams.sunysb.edu/flyex     

Temporal classification of Drosophila segmentation gene expression patterns by the multi-valued neural recognition method

In order to reconstruct the establishment of the body pattern over time in Drosophila embryos, we have developed automated methods for detecting the age of an embryo on the basis of knowledge about its gene expression patterns. In this paper we perform temporal classification of confocal images of expression patterns of genes controlling segmentation by means of a neural network based on multi-valued neurons (MVN). MVN are artificial neural processing elements with complex-valued weights and high functionality, which proved to be efficient for solving the image recognition problems. The results obtained by this method confirm its efficiency for image recognition and indicate that the method can detect characteristic features of expression patterns which mark their development over time.     

Dynamic filtration of the expression pattern variability of Drosophila zygotic segmentation genes

Analysis of the quantitative data obtained by processing the confocal images showed that the initial variability of the expression pattern of Drosophila zygotic segmentation genes was strongly reduced by the onset of gastrulation. The following variability components were studied: the range of gene expression intensity in different embryos, the time and succession of the formation of expression domain, types of formation, and domain positioning. At the level of zygotic genes, the positioning error proved to be dynamically filtered with time.     

GCPReg package for registration of the segmentation gene expression data in Drosophila

In modern functional genomics registration techniques are used to construct reference gene expression patterns and create a spatiotemporal atlas of the expression of all the genes in a network. In this paper we present a software package called GCPReg, which can be used to register the expression patterns of segmentation genes in the early Drosophila embryo. The key task which this package performs is the extraction of spatially localized characteristic features of expression patterns. To facilitate this task, we have developed an easy-to-use interactive graphical interface. We describe GCPReg usage and demonstrate how this package can be applied to register gene expression patterns in wild-type and mutants. GCPReg has been designed to operate on a UNIX platform and is freely available via the Internet at http://urchin.spbcas.ru/downloads/GCPReg/GCPReg.htm.     

Dynamic filtration of the variability of expression patterns of zygotic segmentation genes in Drosophila

An analysis of the quantitative data obtained by processing the confocal images showed that the early variability of expression patterns of zygotic segmentation genes in Drosophila drastically decreases by the time of the onset of gastrulation. The following components of variability were examined: the scatter of the levels of gene expression in different embryos, the time and sequence of the formation of expression domains, the type of their formation, and the domain positioning. It was found that the positioning error at the level of zygotic genes is dynamically filtered with time.     

Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

This protocol presents a method to perform quantitative, single-cell in situ analyses of protein expression to study lineage specificationin mouse preimplantation embryos. The procedures necessary for embryo collection, immunofluorescence, imaging on a confocal microscope, and image segmentation and analysis are described. This method allows quantitation of the expression of multiple nuclear markers and the spatial (XYZ) coordinates of all cells in the embryo. It takes advantage of MINS, an image segmentation software tool specifically developed for the analysis of confocal images of preimplantation embryos and embryonic stem cell (ESC) colonies. MINS carries out unsupervised nuclear segmentation across the X, Y and Z dimensions, and produces information on cell position in three-dimensional space, as well as nuclear fluorescence levels for all channels with minimal user input. While this protocol has been optimized for the analysis of images of preimplantation stage mouse embryos, it can easily be adapted to the analysis of any other samples exhibiting a good signal-to-noise ratio and where high nuclear density poses a hurdle to image segmentation (e.g., expression analysis of embryonic stem cell (ESC) colonies, differentiating cells in culture, embryos of other species or stages, etc.).     

Automated annotation of Drosophila gene expression patterns using a controlled vocabulary

MOTIVATION: Regulation of gene expression in space and time directs its localization to a specific subset of cells during development. Systematic determination of the spatiotemporal dynamics of gene expression plays an important role in understanding the regulatory networks driving development. An atlas for the gene expression patterns of fruit fly Drosophila melanogaster has been created by whole-mount in situ hybridization, and it documents the dynamic changes of gene expression pattern during Drosophila embryogenesis. The spatial and temporal patterns of gene expression are integrated by anatomical terms from a controlled vocabulary linking together intermediate tissues developed from one another. Currently, the terms are assigned to patterns manually. However, the number of patterns generated by high-throughput in situ hybridization is rapidly increasing. It is, therefore, tempting to approach this problem by employing computational methods. RESULTS: In this article, we present a novel computational framework for annotating gene expression patterns using a controlled vocabulary. In the currently available high-throughput data, annotation terms are assigned to groups of patterns rather than to individual images. We propose to extract invariant features from images, and construct pyramid match kernels to measure the similarity between sets of patterns. To exploit the complementary information conveyed by different features and incorporate the correlation among patterns sharing common structures, we propose efficient convex formulations to integrate the kernels derived from various features. The proposed framework is evaluated by comparing its annotation with that of human curators, and promising performance in terms of F1 score has been reported.     

Automated Detection of Soma Location and Morphology in Neuronal Network Cultures

Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma’s surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.     

Bayesian estimation of probabilistic atlas for tissue segmentation

Automatic anatomical brain image segmentation is still a challenge. In particular, algorithms have to address the partial volume effect (PVE) as well as the variability of the gray level of internal brain structures which may appear closer to gray matter (GM) than white matter (WM). Atlas based segmentation is one solution as it brings prior information. For such tasks, probabilistic atlases are very useful as they take account of the PVE information. In this paper, we provide a detailed analysis of a generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. The inputs are gray level data whereas our atlas is composed of both an estimation of the deformation metric and probability maps of each tissue (called class). This atlas is used to guide the tissue segmentation of new images. Experiments are shown on brain T1 MRI datasets. This method only requires approximate pre-registration, as the latter is done jointly with the segmentation. Note however that an approximate registration is a reasonable pre-requisite given the application.     

Segmentation of stained blood cell images measured at high scanning density with high magnification and high numerical aperture optics

In hematological morphology, it is necessary to resolve and analyze the smallest possible cellular details appearing in the light microscope. A prerequisite for computer-aided analysis of subtle morphological features is measuring the cells at a high scanning density with high magnification and high numerical aperture optics. Contrary to visual observations, the information content in a measured picture can be increased by setting the condensor's numerical aperture (NA) greater than the objective's NA. The complexity and heterogeneity of such cell images necessitate a new segmentation method that conserves the morphological information required in the subsequent image analysis, feature extraction, and cell classification. In our segmentation strategy, characteristic color difference thresholds for each nucleus and cytoplasm are combined with geometric operations, probability functions, and a cell model. All thresholds are repeatedly recalculated during the successive improvements of the image masks. None of the thresholds are fixed. This strategy segments blood cell images containing touching cells and large variations in staining, texture, size, and shape. Biological inconsistencies in the calculated cell masks are eliminated by comparing each mask with the cell model criteria integrated into the entire segmentation process. All 20,000 leukocyte images from 120 smears in our leukemia project were segmented with this method.     

A Digital Framework to Build,Visualize and Analyze a Gene Expression Atlas with Cellular Resolution in Zebrafish Early Embryogenesis

A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.     

An adjustable-threshold algorithm for the identification of objects in three-dimensional images

MOTIVATION:To develop a highly accurate, practical and fast automated segmentation algorithm for three-dimensional images containing biological objects. To test the algorithm on images of the Drosophila brain, and identify, count and determine the locations of neurons in the images. RESULTS: A new adjustable-threshold algorithm was developed to efficiently segment fluorescently labeled objects contained within three-dimensional images obtained from laser scanning confocal microscopy, or two-photon microscopy. The result of the test segmentation with Drosophila brain images showed that the algorithm is extremely accurate and provided detailed information about the locations of neurons in the Drosophila brain. Centroids of each object (nucleus of each neuron) were also recorded into an algebraic matrix that describes the locations of the neurons. AVAILABILITY: Interested parties should send their request for the NeuronMapper(TM) program with the segmentation algorithm to artemp@bcm.tmc.edu.     

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets

Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state of the art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted, which isolates segmentation errors due to under- or oversegmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end-to-end 3D and were originally designed for cell boundary detection, show high performance and offer clear advantages in terms of adaptability to new data.     

Registration of the expression patterns of Drosophila segmentation genes by two independent methods

MOTIVATION: To construct an integrated map of Drosophila segmentation gene expression from partial data taken from individual embryos. RESULTS: Spline and wavelet based registration techniques were developed to register Drosophila segmentation gene expression data. As ground control points for registration we used the locations of extrema on gene expression patterns, represented in 1D. The registration method was characterized by unprecedented high accuracy. A method for constructing the integrated pattern of gene expression at cellular resolution was designed. These patterns were constructed for 9 segmentation genes belonging to gap and pair-rule classes.     

Automatic annotation of spatial expression patterns via sparse Bayesian factor models

Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D-4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions.