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Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data., Author summary Time-lapse microscopy can generate large image datasets which track single-cell properties like gene expression or growth rate over time. Deep learning tools are very useful for analyzing these data and can identify the location of cells and track their position. In this work, we introduce a new version of our Deep Learning for Time-lapse Analysis (DeLTA) software, which includes the ability to robustly segment and track bacteria that are growing in two dimensions, such as on agarose pads or within microfluidic environments. This capability is essential for experiments where spatial and positional effects are important, such as conditions with microbial co-cultures, cell-to-cell interactions, or spatial patterning. The software also tracks pole age and can be used to analyze replicative aging. These new features join other improvements, such as the ability to work directly with many common microscopy file formats. DeLTA 2.0 can reliably track hundreds of cells with low error rates, making it an ideal tool for high throughput analysis of microscopy data.

Oscillatory and sequential processes have been implicated in the spatial patterning of many embryonic tissues. For example, molecular clocks delimit segmental boundaries in vertebrates and insects and mediate lateral root formation in plants, whereas sequential gene activities are involved in the specification of regional identities of insect neuroblasts, vertebrate neural tube, vertebrate limb, and insect and vertebrate body axes. These processes take place in various tissues and organisms, and, hence, raise the question of what common themes and strategies they share. In this article, we review 2 processes that rely on the spatial regulation of periodic and sequential gene activities: segmentation and regionalization of the anterior–posterior (AP) axis of animal body plans. We study these processes in species that belong to 2 different phyla: vertebrates and insects. By contrasting 2 different processes (segmentation and regionalization) in species that belong to 2 distantly related phyla (arthropods and vertebrates), we elucidate the deep logic of patterning by oscillatory and sequential gene activities. Furthermore, in some of these organisms (e.g., the fruit fly Drosophila), a mode of AP patterning has evolved that seems not to overtly rely on oscillations or sequential gene activities, providing an opportunity to study the evolution of pattern formation mechanisms.

Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability can be challenged by low light conditions, mobilized sediment and scattering in murky waters. In this study, we introduce a novel observation system (called NepCon) which aims at reducing current limitations by combining an optimized image acquisition setup and tailored image analyses software. The NepCon system includes a high-contrast background to enhance the visibility of the target objects, a compact camera and an artificial light source. The image analysis software includes a machine learning algorithm which is evaluated here to test automatic detection and count of Norway lobster (Nephrops norvegicus). NepCon is specifically designed for applications in demersal trawls and this first phase aims at increasing the accuracy of N. norvegicus detection at the data acquisition level. To find the best contrasting background for the purpose we compared the output of four image segmentation methods applied to static images of N. norvegicus fixed in front of four test background colors. The background color with the best performance was then used to evaluate computer vision and deep learning approaches for automatic detection, tracking and counting of N. norvegicus in the videos. In this initial phase we tested the system in an experimental setting to understand the feasibility of the system for future implementation in real demersal fishing conditions. The N. norvegicus directed trawl fishery typically has no assistance from underwater observation technology and therefore are largely conducted blindly. The demonstrated perception system achieves 76% accuracy (F-score) in automatic detection and count of N. norvegicus, which provides a significant elevation of the current benchmark.

PLOS ONE 16(12), e0260509 (2021). doi:10.1371/journal.pone.0260509, Published by PLOS, San Francisco, California, US

Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that may be tightly packed is a major bottleneck in standard deep learning-based models. We report a Nuclear Segmentation Tool (NuSeT) based on deep learning that accurately segments nuclei across multiple types of fluorescence imaging data. Using a hybrid network consisting of U-Net and Region Proposal Networks (RPN), followed by a watershed step, we have achieved superior performance in detecting and delineating nuclear boundaries in 2D and 3D images of varying complexities. By using foreground normalization and additional training on synthetic images containing non-cellular artifacts, NuSeT improves nuclear detection and reduces false positives. NuSeT addresses common challenges in nuclear segmentation such as variability in nuclear signal and shape, limited training sample size, and sample preparation artifacts. Compared to other segmentation models, NuSeT consistently fares better in generating accurate segmentation masks and assigning boundaries for touching nuclei., Author summary Nuclear size and shape are essential indicators of cell cycle stage and cellular pathology. Efficient segmentation of nuclei in complex environments, especially for high-value yet low-quality samples is critical for detecting pathological states. In the majority of cases, biological features are still segmented using traditional segmentation methods requiring manual curation of segmentations, which is hugely time-consuming and does not achieve optimal performance. While a recent surge in deep learning tools has helped tremendously with the automation of segmentation tasks, existing platforms inefficiently segment nuclei in crowded cells with overlapping nuclear boundaries. NuSeT, assimilates the advantages of semantic segmentation (U-Net) and instance segmentation (Mask R-CNN), and consistently outperforms other start-of-the-art deep learning segmentation models in analyzing complex three-dimensional cell clusters and in tracking nuclei in crowded, dynamic environments. NuSeT can work with both fluorescent and histopathology image samples. We have also developed a graphic user interface for customized training and segmentation, that will aid considerably in the ease and accuracy of image segmentation in a wide range of image types.

Detection and segmentation of macrophage cells in fluorescence microscopy images is a challenging problem, mainly due to crowded cells, variation in shapes, and morphological complexity. We present a new deep learning approach for cell detection and segmentation that incorporates previously learned nucleus features. A novel fusion of feature pyramids for nucleus detection and segmentation with feature pyramids for cell detection and segmentation is used to improve performance on a microscopic image dataset created by us and provided for public use, containing both nucleus and cell signals. Our experimental results indicate that cell detection and segmentation performance significantly benefit from the fusion of previously learned nucleus features. The proposed feature pyramid fusion architecture clearly outperforms a state-of-the-art Mask R-CNN approach for cell detection and segmentation with relative mean average precision improvements of up to 23.88% and 23.17%, respectively., Author summary To analyze cell infection and changes in cell morphology in fluorescence microscopy images, proper cell detection and segmentation are required. In automated fluorescence microscopy image analysis, the separation of signals in close proximity is a challenging problem. High cell densities or cluster formations increase the probability of such situations on the cellular level. Another limitation is the detection of morphologically complex cells, such as macrophages or neurons. Their indefinite morphology causes identification issues when looking for slight variations of fixed shapes. Compared to merely segmenting cytoplasm, instance-based segmentation is a much harder task, since the assignment of a cell instance identity to every pixel of the image is required. We present a new deep learning approach for cell detection and segmentation that efficiently utilizes the nucleus channel for improving segmentation and detection performance of cells by incorporating previously learned nucleus features. This is achieved by a fusion of feature pyramids for nucleus detection and segmentation. The performance is evaluated on a microscopic image dataset, containing both nucleus and cell signals.

The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in images with a low signal-to-noise-ratio is still a challenging problem. In this paper, we present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. Furthermore, this representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types. For the prediction of the proposed neighbor distances, an adapted U-Net convolutional neural network (CNN) with two decoder paths is used. In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking. The adapted tracking algorithm includes a movement estimation in the cost function to re-link tracks with missing segmentation masks over a short sequence of frames. Our combined tracking by detection method has proven its potential in the IEEE ISBI 2020 Cell Tracking Challenge (http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE multiple top three rankings including two top performances using a single segmentation model for the diverse data sets., 25 pages, 14 figures, methods of the team KIT-Sch-GE for the IEEE ISBI 2020 Cell Tracking Challenge

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking, Author summary Automated microscopy experiments can generate massive data sets, allowing for detailed analysis of cell physiology and properties such as gene expression. In particular, dynamic measurements of gene expression with time-lapse microscopy have proved invaluable for understanding how gene regulatory networks operate. However, image processing remains a key bottleneck in the analysis pipeline, typically requiring human intervention and a posteriori processing. Recently, machine learning-based approaches have ushered in a new era of rapid, autonomous image analysis. In this work, we use and repurpose the U-Net deep learning algorithm to develop an image processing pipeline that can not only accurately identify the location of cells in an image, but also track them over time as they grow and divide. As an application, we focus on multi-hour time-lapse movies of bacteria growing in a microfluidic device. Our algorithm is accurate and fast, with error rates near 1% and requiring less than a second to analyze a typical movie frame. This increase in speed and fidelity has the potential to open new experimental avenues, e.g. where images are analyzed on-the-fly so that experimental conditions can be updated in real time.

The B cells in our body generate protective antibodies by introducing somatic hypermutations (SHM) into the variable region of immunoglobulin genes (IgVs). The mutations are generated by activation induced deaminase (AID) that converts cytosine to uracil in single stranded DNA (ssDNA) generated during transcription. Attempts have been made to correlate SHM with ssDNA using bisulfite to chemically convert cytosines that are accessible in the intact chromatin of mutating B cells. These studies have been complicated by using different definitions of “bisulfite accessible regions” (BARs). Recently, deep-sequencing has provided much larger datasets of such regions but computational methods are needed to enable this analysis. Here we leveraged the deep-sequencing approach with unique molecular identifiers and developed a novel Hidden Markov Model based Bayesian Segmentation algorithm to characterize the ssDNA regions in the IGHV4-34 gene of the human Ramos B cell line. Combining hierarchical clustering and our new Bayesian model, we identified recurrent BARs in certain subregions of both top and bottom strands of this gene. Using this new system, the average size of BARs is about 15 bp. We also identified potential G-quadruplex DNA structures in this gene and found that the BARs co-locate with G-quadruplex structures in the opposite strand. Using various correlation analyses, there is not a direct site-to-site relationship between the bisulfite accessible ssDNA and all sites of SHM but most of the highly AID mutated sites are within 15 bp of a BAR. In summary, we developed a novel platform to study single stranded DNA in chromatin at a base pair resolution that reveals potential relationships among BARs, SHM and G-quadruplexes. This platform could be applied to genome wide studies in the future., Author summary To make protective antibodies against various pathogens, the enzyme activation induced deaminase (AID) introduces mutations into single-stranded DNA (ssDNA) in the variable region of immunoglobulin genes (IGHVs) in B cells, as part of a process called somatic hypermutation (SHM). Here, a bisulfite assay, together with deep sequencing, was used to characterize the accessible ssDNA that represents the substrate of AID in B cells. To deal with issues such as noise in the data, we developed a novel algorithm to more accurately identify bisulfite accessible ssDNA regions (BARs) and applied it to the IGHV4–34 immunoglobulin gene in a human B cell line. Using the new algorithm, we found that location of these BARs recurred in certain subregions of the IGHV4–34 gene. The average size of the BARs is ∼15 bp, which is close to the size of a transcription bubble. We also found that some potential G-quadruplex DNA structures in the IGHV4–34 gene co-located with the BARs but on the opposite DNA strand. Furthermore, we found that, most of the AID induced mutations were near to, but not within, BARs suggesting alternative mechanisms for targeting somatic hypermutation.

Accurate RNA quantification at the single-cell level is critical for understanding the dynamics of gene expression and regulation across space and time. Single molecule FISH (smFISH), such as RNAscope, provides spatial and quantitative measurements of individual transcripts, therefore, can be used to explore differential gene expression among a heterogeneous cell population if combined with cell identify information. However, such analysis is not straightforward, and existing image analysis pipelines cannot integrate both RNA transcripts and cellular staining information to automatically output cell type-specific gene expression. We developed an efficient and customizable analysis method, Single-Molecule Automatic RNA Transcription Quantification (SMART-Q), to enable the analysis of gene transcripts in a cell type-specific manner. SMART-Q efficiently infers cell identity information from multiplexed immuno-staining and quantifies cell type-specific transcripts using a 3D Gaussian fitting algorithm. Furthermore, we have optimized SMART-Q for user experiences, such as flexible parameters specification, batch data outputs, and visualization of analysis results. SMART-Q meets the demands for efficient quantification of single-molecule RNA and can be widely used for cell type-specific RNA transcript analysis.

We investigated whether Tbx1, the gene for 22q11.2 deletion syndrome (22q11.2DS) and Foxi3, both required for segmentation of the pharyngeal apparatus (PA) to individual arches, genetically interact. We found that all Tbx1+/-;Foxi3+/- double heterozygous mouse embryos had thymus and parathyroid gland defects, similar to those in 22q11.2DS patients. We then examined Tbx1 and Foxi3 heterozygous, null as well as conditional Tbx1Cre and Sox172A-iCre/+ null mutant embryos. While Tbx1Cre/+;Foxi3f/f embryos had absent thymus and parathyroid glands, Foxi3-/- and Sox172A-iCre/+;Foxi3f/f endoderm conditional mutant embryos had in addition, interrupted aortic arch type B and retroesophageal origin of the right subclavian artery, which are all features of 22q11.2DS. Tbx1Cre/+;Foxi3f/f embryos had failed invagination of the third pharyngeal pouch with greatly reduced Gcm2 and Foxn1 expression, thereby explaining the absence of thymus and parathyroid glands. Immunofluorescence on tissue sections with E-cadherin and ZO-1 antibodies in wildtype mouse embryos at E8.5-E10.5, revealed that multilayers of epithelial cells form where cells are invaginating as a normal process. We noted that excessive multilayers formed in Foxi3-/-, Sox172A-iCre/+;Foxi3f/f as well as Tbx1 null mutant embryos where invagination should have occurred. Several genes expressed in the PA epithelia were downregulated in both Tbx1 and Foxi3 null mutant embryos including Notch pathway genes Jag1, Hes1, and Hey1, suggesting that they may, along with other genes, act downstream to explain the observed genetic interaction. We found Alcam and Fibronectin extracellular matrix proteins were reduced in expression in Foxi3 null but not Tbx1 null embryos, suggesting that some, but not all of the downstream mechanisms are shared., Author summary The mechanisms required for segmentation of the pharyngeal apparatus (PA) to individual arches are not precisely delineated in mammalian species. Using mouse models, we found that two transcription factor genes, Tbx1, the gene for 22q11.2 deletion syndrome and Foxi3, genetically interact in the third pharyngeal pouch endoderm during thymus and parathyroid gland development. When examining wildtype embryos, we found that each arch is surrounded by epithelial cells derived from the endoderm and ectoderm that undergo dynamic processes during PA segmentation. Invagination co-occurs with formation of multilayers of epithelia that become juxtaposed. The cells are then reorganized to form a dual-layer of tightly intercalated cells at the mature pouch-cleft junction. In Tbx1 and Foxi3 null mutant embryos, these processes are disrupted. Further, the endoderm cells form extensive multilayers in the region where cells should normally invaginate. When examining genes that may act downstream of Tbx1 and Foxi3 we found several including Notch pathway genes Jag1, Hes1, and Hey1 are downregulated in both mutant embryos. Together, we show that Tbx1 and Foxi3 are important for regulating PA segmentation through cellular and genetic mechanisms that may be critical in 22q11.2 deletion syndrome patients.

Genome segmentation approaches allow us to characterize regulatory states in a given cell type using combinatorial patterns of histone modifications and other regulatory signals. In order to analyze regulatory state differences across cell types, current genome segmentation approaches typically require that the same regulatory genomics assays have been performed in all analyzed cell types. This necessarily limits both the numbers of cell types that can be analyzed and the complexity of the resulting regulatory states, as only a small number of histone modifications have been profiled across many cell types. Data imputation approaches that aim to estimate missing regulatory signals have been applied before genome segmentation. However, this approach is computationally costly and propagates any errors in imputation to produce incorrect genome segmentation results downstream. We present an extension to the IDEAS genome segmentation platform which can perform genome segmentation on incomplete regulatory genomics dataset collections without using imputation. Instead of relying on imputed data, we use an expectation-maximization approach to estimate marginal density functions within each regulatory state. We demonstrate that our genome segmentation results compare favorably with approaches based on imputation or other strategies for handling missing data. We further show that our approach can accurately impute missing data after genome segmentation, reversing the typical order of imputation/genome segmentation pipelines. Finally, we present a new 2D genome segmentation analysis of 127 human cell types studied by the Roadmap Epigenomics Consortium. By using an expanded set of chromatin marks that have been profiled in subsets of these cell types, our new segmentation results capture a more complex picture of combinatorial regulatory patterns that appear on the human genome., Author summary Histone modifications and other gene regulatory signals can be profiled across the genome in a given cell type, and each type of regulatory signal correlates with the presence of specific gene regulatory activities. Genome segmentation methods look for patterns across combinations of regulatory signals to annotate more general “regulatory states” (e.g. enhancers, promoters, repressed regions, etc.) across the genome. To see how regulatory states change across cell types, we need to run genome segmentation in a consistent way across the analyzed cell types. However, due to experimental and cost limitations, we may not have profiled the same regulatory signals in all available cell types. Current approaches deal with this missing data problem by either limiting genome segmentation analysis to the subset of regulatory signals that have been profiled in all analyzed cell types (which limits the types of regulatory states that can be detected and/or the numbers of cell types that can be analyzed), or by predicting what the missing regulatory signals would have looked like. The latter “imputation” approach is computationally costly, and is not always accurate. The current manuscript introduces a third strategy to handling missing data in the genome segmentation problem. Our approach, based on the IDEAS genome segmentation platform, removes the need for data imputation by directly accounting for missing data within the algorithm. In cell types where some regulatory signals are missing, IDEAS can still provide accurate regulatory state annotations based on a combination of the regulatory signals that have been observed in that cell type, the regulatory states annotated at the same location in other cell types (which may be based on more complete regulatory signal information), and the regulatory states in surrounding regions.

The reproducibility of embryonic development is remarkable, although molecular processes are intrinsically stochastic at the single-cell level. How the multicellular system resists the inevitable noise to acquire developmental reproducibility constitutes a fundamental question in developmental biology. Toward this end, we focused on vertebrate somitogenesis as a representative system, because somites are repeatedly reproduced within a single embryo whereas such reproducibility is lost in segmentation clock gene-deficient embryos. However, the effect of noise on developmental reproducibility has not been fully investigated, because of the technical difficulty in manipulating the noise intensity in experiments. In this study, we developed a computational model of ERK-mediated somitogenesis, in which bistable ERK activity is regulated by an FGF gradient, cell-cell communication, and the segmentation clock, subject to the intrinsic noise. The model simulation generated our previous in vivo observation that the ERK activity was distributed in a step-like gradient in the presomitic mesoderm, and its boundary was posteriorly shifted by the clock in a stepwise manner, leading to regular somite formation. Here, we showed that this somite regularity was robustly maintained against the noise. Removing the clock from the model predicted that the stepwise shift of the ERK activity occurs at irregular timing with irregular distance owing to the noise, resulting in somite size variation. This model prediction was recently confirmed by live imaging of ERK activity in zebrafish embryos. Through theoretical analysis, we presented a mechanism by which the clock reduces the inherent somite irregularity observed in clock-deficient embryos. Therefore, this study indicates a novel role of the segmentation clock in noise-resistant developmental reproducibility., Author summary The segmentation clock has been widely considered vital for somite formation, because clock-deficient embryos display severe segmental defects. However, irregular somites are still formed, suggesting that the clock is not required for somite formation itself but rather endows it with developmental reproducibility. Thus, the following questions arose: How do irregular somites emerge in a clock-independent manner? How is the irregularity reduced in the presence of the clock? To address these questions, we developed a computational model of somitogenesis. We then clarified that the intrinsic noise induces spontaneous formation of irregular-sized somites in the absence of the clock, and that the clock plays an important role in suppressing the noise effect for reproducible somite formation.

Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However, current methods for segmenting nuclei in 3D tissues are not designed for situations in which nuclei are densely packed, nonspherical, or heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture. Here, we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree-structured ridge-tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computer’s memory. We consistently obtain accurate segmentation rates of >90%, even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation. We have also generated a live reporter of the NE that can be used to track live cells in 3 dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation. We provide these tools in an open-access user-friendly format., A new computational method allows researchers to measure the properties of individual nuclei in situations in which cells are tightly packed together, for example, during differentiation of stem cells in culture or during early postimplantation development.

Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is largely independent of the specific imaging method (bright-field/phase) and objective used (40X/63X). We validate our algorithm’s performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies.

Image segmentation, as a key step of microarray image processing, is crucial for obtaining the spot expressions simultaneously. However, state-of-art clustering-based segmentation algorithms are sensitive to noises. To solve this problem and improve the segmentation accuracy, in this article, several improvements are introduced into the fast and simple clustering methods (K-means and Fuzzy C means). Firstly, a contrast enhancement algorithm is implemented in image preprocessing to improve the gridding precision. Secondly, the data-driven means are proposed for cluster center initialization, instead of usual random setting. The third improvement is that the multi features, including intensity features, spatial features, and shape features, are implemented in feature selection to replace the sole pixel intensity feature used in the traditional clustering-based methods to avoid taking noises as spot pixels. Moreover, the principal component analysis is adopted for various feature extraction. Finally, an adaptive adjustment algorithm is proposed based on data mining and learning for further dealing with the missing spots or low contrast spots. Experiments on real and simulation data sets indicate that the proposed improvements made our proposed method obtains higher segmented precision than the traditional K-means and Fuzzy C means clustering methods.

Extensive apoptosis is often seen in patterning mutants, suggesting that tissues can detect and eliminate potentially harmful mis-specified cells. Here, we show that the pattern of apoptosis in the embryonic epidermis of Drosophila is not a response to fate mis-specification but can instead be explained by the limiting availability of prosurvival signaling molecules released from locations determined by patterning information. In wild-type embryos, the segmentation cascade elicits the segmental production of several epidermal growth factor receptor (EGFR) ligands, including the transforming growth factor Spitz (TGFα), and the neuregulin, Vein. This leads to an undulating pattern of signaling activity, which prevents expression of the proapoptotic gene head involution defective (hid) throughout the epidermis. In segmentation mutants, where specific peaks of EGFR ligands fail to form, gaps in signaling activity appear, leading to coincident hid up-regulation and subsequent cell death. These data provide a mechanistic understanding of how cell survival, and thus appropriate tissue size, is made contingent on correct patterning., Cells lacking essential patterning determinants are eliminated from developing tissues by apoptosis; this study shows that cell death in fruit fly segmentation mutants results from disruption of the pattern of EGFR signalling, which in normal embryos coordinates segment size with patterning information., Author summary In many tissues, defective cells are eliminated by a process called apoptosis. This process prevents the emergence of rogue cells, which could be detrimental to normal physiology. Apoptosis is particularly apparent in developing embryos that lack appropriate positional information, and it has been suggested that in the absence of clear positional instructions, cells are unable to acquire a defined fate and commit suicide as a result. Here, we have used mutant fruit fly embryos lacking essential segmental determinants to identify the molecular signals that trigger apoptosis in response to mispatterning. We found that cells do not trigger apoptosis in response to conflicting fate determinants. Instead, mispatterning disrupts a tissue size control system that removes excess cells in oversized segments. Specifically, correct patterning information leads to the segmentally repeated production of survival signals, which activate the epidermal growth factor receptor, and this system is disrupted in patterning mutants leading to reproducible patterns of apoptosis. We propose that a similar, though less obvious, process also occurs in normal embryos. In such embryos, each segment would initially comprise a slight excess of cells and would then be trimmed down to a size specified by the pattern of survival signal production and the range of these signals. We suggest that a similar regulatory logic could ensure the coordination of tissue patterning and size in a variety of developing tissues.

Assessing the cytoplasmic uptake of fluorescently-tagged drugs in heterogeneous cell types currently involves time-consuming manual segmentation of confocal microscopy images. We developed a set of methods that incorporate map algebra techniques to facilitate and expedite image segmentation, particularly of the parenchyma of intermediate cells in the stria vascularis of the inner ear. Map algebra is used to apply a convolution kernel to pixel neighborhoods to create a masking image to select pixels in the original image for further operations. Here, we describe the utility of integrated intensity-based, percentile-based, and local autocorrelation-based methods to automate segmentation of images into putative morphological regions for pixel intensity analysis. Integrated intensity-based methods are variants of watershed segmentation tools that determine morphological boundaries from rates of change in integrated pixel intensity. Percentile- and local autocorrelation-based methods evolved out of the process of developing map algebra- and integrated intensity-based tools. We identified several simplifications that are surprisingly effective for image segmentation and pixel intensity analysis. These methods were empirically validated on three levels: first, the algorithms were developed based on iterations of inspected results; second, algorithms were tested for various types of robustness; and third, developed algorithms were validated against results from manually-segmented images. We conclude the key to automated segmentation is supervision of output data.

Gene regulatory networks (GRNs) evolve as a result of the coevolutionary processes acting on transcription factors (TFs) and the cis-regulatory modules they bind. The zinc-finger TF zelda (zld) is essential for the maternal-to-zygotic transition (MZT) in Drosophila melanogaster, where it directly binds over thousand cis-regulatory modules to regulate chromatin accessibility. D. melanogaster displays a long germ type of embryonic development, where all segments are simultaneously generated along the whole egg. However, it remains unclear if zld is also involved in the MZT of short-germ insects (including those from basal lineages) or in other biological processes. Here we show that zld is an innovation of the Pancrustacea lineage, being absent in more distant arthropods (e.g. chelicerates) and other organisms. To better understand zld´s ancestral function, we thoroughly investigated its roles in a short-germ beetle, Tribolium castaneum, using molecular biology and computational approaches. Our results demonstrate roles for zld not only during the MZT, but also in posterior segmentation and patterning of imaginal disc derived structures. Further, we also demonstrate that zld is critical for posterior segmentation in the hemipteran Rhodnius prolixus, indicating this function predates the origin of holometabolous insects and was subsequently lost in long-germ insects. Our results unveil new roles of zld in different biological contexts and suggest that changes in expression of zld (and probably other major TFs) are critical in the evolution of insect GRNs., Author summary Pioneer transcription factors (TFs) are considered the first regulators of chromatin accessibility in fruit flies and vertebrates, modulating the expression of a large number of target genes. In fruit flies, zelda resembles a pioneer TF, being essential during early embryogenesis. However, the evolutionary origins and ancestral functions of zelda remain largely unknown. Through a number of gene silencing, microscopy and evolutionary analysis, the present work shows that zelda is an innovation of the Pancrustacea lineage, governing not only the MZT in the short-germ insect Tribolium castaneum, but also posterior segmentation and post-embryonic patterning of imaginal disc derived structures such as wings, legs and antennae. Further, zelda regulation of posterior segmentation predates the origin of insects with complete metamorphosis (holometabolous), as supported by gene silencing experiments in the kissing bug Rhodnius prolixus. We hypothesize that the emergence of zelda contributed to the evolution of gene regulatory networks and new morphological structures of insects.

Insects determine their body segments in two different ways. Short-germband insects, such as the flour beetle Tribolium castaneum, use a molecular clock to establish segments sequentially. In contrast, long-germband insects, such as the vinegar fly Drosophila melanogaster, determine all segments simultaneously through a hierarchical cascade of gene regulation. Gap genes constitute the first layer of the Drosophila segmentation gene hierarchy, downstream of maternal gradients such as that of Caudal (Cad). We use data-driven mathematical modelling and phase space analysis to show that shifting gap domains in the posterior half of the Drosophila embryo are an emergent property of a robust damped oscillator mechanism, suggesting that the regulatory dynamics underlying long- and short-germband segmentation are much more similar than previously thought. In Tribolium, Cad has been proposed to modulate the frequency of the segmentation oscillator. Surprisingly, our simulations and experiments show that the shift rate of posterior gap domains is independent of maternal Cad levels in Drosophila. Our results suggest a novel evolutionary scenario for the short- to long-germband transition and help explain why this transition occurred convergently multiple times during the radiation of the holometabolan insects., Author summary Different insect species exhibit one of two distinct modes of determining their body segments (known as segmentation) during development: they either use a molecular oscillator to position segments sequentially, or they generate segments simultaneously through a hierarchical gene-regulatory cascade. The sequential mode is ancestral, while the simultaneous mode has been derived from it independently several times during evolution. In this paper, we present evidence suggesting that simultaneous segmentation also involves an oscillator in the posterior end of the embryo of the vinegar fly, Drosophila melanogaster. This surprising result indicates that both modes of segment determination are much more similar than previously thought. Such similarity provides an important step towards our understanding of the frequent evolutionary transitions observed between sequential and simultaneous segmentation.

Most arthropods generate their posterior bodies by adding segments periodically, as the embryo grows, from a posteriorly located region called the segment addition zone. This mode of segmentation is shared with vertebrates and relies on oscillatory mechanisms, where the temporal periodicity of a clock is translated into repetitive spatial patterns. This ordered anterior-to-posterior pattern is achieved at the same time as the tissue elongates, opening the question of the functional coordination between the mechanisms of segmental patterning and posterior growth. The study of these processes in different arthropods has played an important role in unravelling some of the molecular mechanisms of segment formation. However, the behavior of cells during elongation and how cellular processes affect this segmental patterning has been poorly studied. Cell proliferation together with cell rearrangements are presumed to be the major forces driving axis elongation in the red flour beetle Tribolium castaneum. However, there still no strong evidence about the role and distribution of cell proliferation within the embryo. In this study, we propose to address these questions by using whole embryo cultures and pharmacological manipulation. We show that considerable cell proliferation occurs during germband elongation, measured by incorporation of the nucleoside analog of thymidine 5-Ethynyl-2'-deoxyuridine, EdU. Moreover, proliferating cells appeared to be spread along the elongating embryo with a posterior bias at early segmentation. In addition, when we blocked cell division, treated germbands were always shorter than controls and in some cases not able to fully elongate, even when control embryos already started to retract and leg buds are evident. Finally, we found that the absence of cell proliferation has no apparent effect on segmental patterning, as evidenced by Tc-engrailed (Tc-en) gene expression.