Generative modeling of living cells with SO(3)-equivariant implicit neural representations.

Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these methods. This requires the synthesis of cell shapes as well as corresponding microscopy images using generative models. To synthesize realistic living cell shapes, the shape representation used by the generative model should be able to accurately represent fine details and changes in topology, which are common in cells. These requirements are not met by 3D voxel masks, which are restricted in resolution, and polygon meshes, which do not easily model processes like cell growth and mitosis. In this work, we propose to represent living cell shapes as level sets of signed distance functions (SDFs) which are estimated by neural networks. We optimize a fully-connected neural network to provide an implicit representation of the SDF value at any point in a 3D+time domain, conditioned on a learned latent code that is disentangled from the rotation of the cell shape. We demonstrate the effectiveness of this approach on cells that exhibit rapid deformations (Platynereis dumerilii), cells that grow and divide (C. elegans), and cells that have growing and branching filopodial protrusions (A549 human lung carcinoma cells). A quantitative evaluation using shape features and Dice similarity coefficients of real and synthetic cell shapes shows that our model can generate topologically plausible complex cell shapes in 3D+time with high similarity to real living cell shapes. Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.

CytoPacq: a web-interface for simulating multi-dimensional cell imaging.

MOTIVATION: Objective assessment of bioimage analysis methods is an essential step towards understanding their robustness and parameter sensitivity, calling for the availability of heterogeneous bioimage datasets accompanied by their reference annotations. Because manual annotations are known to be arduous, highly subjective and barely reproducible, numerous simulators have emerged over past decades, generating synthetic bioimage datasets complemented with inherent reference annotations. However, the installation and configuration of these tools generally constitutes a barrier to their widespread use. RESULTS: We present a modern, modular web-interface, CytoPacq, to facilitate the generation of synthetic benchmark datasets relevant for multi-dimensional cell imaging. CytoPacq poses a user-friendly graphical interface with contextual tooltips and currently allows a comfortable access to various cell simulation systems of fluorescence microscopy, which have already been recognized and used by the scientific community, in a straightforward and self-contained form. AVAILABILITY AND IMPLEMENTATION: CytoPacq is a publicly available online service running at https://cbia.fi.muni.cz/simulator. More information about it as well as examples of generated bioimage datasets are available directly through the web-interface. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Discovering cryptic species in the Aspiciliella intermutans complex (Megasporaceae, Ascomycota) - First results using gene concatenation and coalescent-based species tree approaches.

Taxonomic identifications in some groups of lichen-forming fungi have been challenge largely due to the scarcity of taxonomically relevant features and limitations of morphological and chemical characters traditionally used to distinguish closely related taxa. Delineating species boundaries in closely related species or species complexes often requires a range of multisource data sets and comprehensive analytical methods. Here we aim to examine species boundaries in a group of saxicolous lichen forming fungi, the Aspiciliella intermutans complex (Megasporaceae), widespread mainly in the Mediterranean. We gathered DNA sequences of the nuclear ribosomal internal transcribed spacer (nuITS), the nuclear large subunit (nuLSU), the mitochondrial small subunit (mtSSU) ribosomal DNA, and the DNA replication licensing factor MCM7 from 80 samples mostly from Iran, Caucasia, Greece and eastern Europe. We used a combination of phylogenetic strategies and a variety of empirical, sequence-based species delimitation approaches to infer species boundaries in this group. The latter included: the automatic barcode gap discovery (ABGD), the multispecies coalescent approach *BEAST and Bayesian Phylogenetics and Phylogeography (BPP) program. Different species delimitation scenarios were compared using Bayes factors species delimitation analysis. Furthermore, morphological, chemical, ecological and geographical features of the sampled specimens were examined. Our study uncovered cryptic species diversity in A. intermutans and showed that morphology-based taxonomy may be unreliable, underestimating species diversity in this group of lichens. We identified a total of six species-level lineages in the A. intermutans complex using inferences from multiple empirical operational criteria. We found little corroboration between morphological and ecological features with our proposed candidate species, while secondary metabolite data do not corroborate tree topology. The present study on the A. intermutans species-complex indicates that the genus Aspiciliella, as currently circumscribed, is more diverse in Eurasia than previously expected.

The Role of RNA Polymerase II Contiguity and Long-Range Interactions in the Regulation of Gene Expression in Human Pluripotent Stem Cells.

The eukaryotic nucleus is a highly complex structure that carries out multiple functions primarily needed for gene expression, and among them, transcription seems to be the most fundamental. Diverse approaches have demonstrated that transcription takes place at discrete sites known as transcription factories, wherein RNA polymerase II (RNAP II) is attached to the factory and immobilized while transcribing DNA. It has been proposed that transcription factories promote chromatin loop formation, creating long-range interactions in which relatively distant genes can be transcribed simultaneously. In this study, we examined long-range interactions between the POU5F1 gene and genes previously identified as being POU5F1 enhancer-interacting, namely, CDYL, TLE2, RARG, and MSX1 (all involved in transcriptional regulation), in human pluripotent stem cells (hPSCs) and their early differentiated counterparts. As a control gene, RUNX1 was used, which is expressed during hematopoietic differentiation and not associated with pluripotency. To reveal how these long-range interactions between POU5F1 and the selected genes change with the onset of differentiation and upon RNAP II inhibition, we performed three-dimensional fluorescence in situ hybridization (3D-FISH) followed by computational simulation analysis. Our analysis showed that the numbers of long-range interactions between specific genes decrease during differentiation, suggesting that the transcription of monitored genes is associated with pluripotency. In addition, we showed that upon inhibition of RNAP II, long-range associations do not disintegrate and remain constant. We also analyzed the distance distributions of these genes in the context of their positions in the nucleus and revealed that they tend to have similar patterns resembling normal distribution. Furthermore, we compared data created in vitro and in silico to assess the biological relevance of our results.

FiloGen: A Model-Based Generator of Synthetic 3-D Time-Lapse Sequences of Single Motile Cells With Growing and Branching Filopodia.

The existence of diverse image datasets accompanied by reference annotations is a crucial prerequisite for an objective benchmarking of bioimage analysis methods. Nevertheless, such a prerequisite is hard to satisfy for time lapse, multidimensional fluorescence microscopy image data, manual annotations of which are laborious and often impracticable. In this paper, we present a simulation system capable of generating 3-D time-lapse sequences of single motile cells with filopodial protrusions of user-controlled structural and temporal attributes, such as the number, thickness, length, level of branching, and lifetime of filopodia, accompanied by inherently generated reference annotations. The proposed simulation system involves three globally synchronized modules, each being responsible for a separate task: the evolution of filopodia on a molecular level, linear elastic deformation of the entire cell with filopodia, and the synthesis of realistic, time-coherent cell texture. Its flexibility is demonstrated by generating multiple synthetic 3-D time-lapse sequences of single lung cancer cells of two different phenotypes, qualitatively and quantitatively resembling their real counterparts acquired using a confocal fluorescence microscope.

An objective comparison of cell-tracking algorithms.

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy.

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.

Virtual cell imaging: A review on simulation methods employed in image cytometry.

The simulations of cells and microscope images thereof have been used to facilitate the development, selection, and validation of image analysis algorithms employed in cytometry as well as for modeling and understanding cell structure and dynamics beyond what is visible in the eyepiece. The simulation approaches vary from simple parametric models of specific cell components-especially shapes of cells and cell nuclei-to learning-based synthesis and multi-stage simulation models for complex scenes that simultaneously visualize multiple object types and incorporate various properties of the imaged objects and laws of image formation. This review covers advances in artificial digital cell generation at scales ranging from particles up to tissue synthesis and microscope image simulation methods, provides examples of the use of simulated images for various purposes ranging from subcellular object detection to cell tracking, and discusses how such simulators have been validated. Finally, the future possibilities and limitations of simulation-based validation are considered. © 2016 International Society for Advancement of Cytometry.

A benchmark for comparison of cell tracking algorithms.

MOTIVATION: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. RESULTS: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. AVAILABILITY AND IMPLEMENTATION: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.

Epiphytic lichen diversity in central European oak forests: assessment of the effects of natural environmental factors and human influences.

We investigated lichen diversity in temperate oak forests using standardized protocols. Forty-eight sites were sampled in the Czech Republic, Slovakia and Hungary. The effects of natural environmental predictors and human influences on lichen diversity (lichen diversity value, species richness) were analysed by means of correlation tests. We found that lichen diversity responded differently to environmental predictors between two regions with different human impact. In the industrial region, air pollution was the strongest factor. In the agricultural to highly forested regions, lichen diversity was strongly influenced by forest age and forest fragmentation. We found that several natural factors can in some cases obscure the effect of human influences. Thus, factors of natural gradient must be considered (both statistically and interpretively) when studying human impact on lichen diversity.

Generation of digital phantoms of cell nuclei and simulation of image formation in 3D image cytometry.

Image cytometry still faces the problem of the quality of cell image analysis results. Degradations caused by cell preparation, optics, and electronics considerably affect most 2D and 3D cell image data acquired using optical microscopy. That is why image processing algorithms applied to these data typically offer imprecise and unreliable results. As the ground truth for given image data is not available in most experiments, the outputs of different image analysis methods can be neither verified nor compared to each other. Some papers solve this problem partially with estimates of ground truth by experts in the field (biologists or physicians). However, in many cases, such a ground truth estimate is very subjective and strongly varies between different experts. To overcome these difficulties, we have created a toolbox that can generate 3D digital phantoms of specific cellular components along with their corresponding images degraded by specific optics and electronics. The user can then apply image analysis methods to such simulated image data. The analysis results (such as segmentation or measurement results) can be compared with ground truth derived from input object digital phantoms (or measurements on them). In this way, image analysis methods can be compared with each other and their quality (based on the difference from ground truth) can be computed. We have also evaluated the plausibility of the synthetic images, measured by their similarity to real image data. We have tested several similarity criteria such as visual comparison, intensity histograms, central moments, frequency analysis, entropy, and 3D Haralick features. The results indicate a high degree of similarity between real and simulated image data.