Visualization of actin filaments and monomers in somatic cell nuclei

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Molecular Biology of the Cell 24 (2013): 982-994, doi:10.1091/mbc.E12-09-0685.In addition to its long-studied presence in the cytoplasm, actin is also found in the nuclei of eukaryotic cells. The function and form (monomer, filament, or noncanonical oligomer) of nuclear actin are hotly debated, and its localization and dynamics are largely unknown. To determine the distribution of nuclear actin in live somatic cells and evaluate its potential functions, we constructed and validated fluorescent nuclear actin probes. Monomeric actin probes concentrate in nuclear speckles, suggesting an interaction of monomers with RNA-processing factors. Filamentous actin probes recognize discrete structures with submicron lengths that are excluded from chromatin-rich regions. In time-lapse movies, these actin filament structures exhibit one of two types of mobility: 1) diffusive, with an average diffusion coefficient of 0.06–0.08 μm2/s, or (2) subdiffusive, with a mobility coefficient of 0.015 μm2/s. Individual filament trajectories exhibit features of particles moving within a viscoelastic mesh. The small size of nuclear actin filaments is inconsistent with a role in micron-scale intranuclear transport, and their localization suggests that they do not participate directly in chromatin-based processes. Our results instead suggest that actin filaments form part of a large, viscoelastic structure in the nucleoplasm and may act as scaffolds that help organize nuclear contents.This bulk of this work was supported by a grant from the National Institutes of Health to R.D.M. (5R01GM061010-12). Additional support was provided by National Institutes of Health Grant R01 CA096840 (E.H.B.), a National Science Foundation Predoctoral Fellowship (B.B.), a National Institutes of Health Ruth L. Kirschstein Predoctoral Fellowship (B.B.), and a Genentech Fellowship (B.C.)

CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository with examples of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community.Comment: 17 pages, 2 figures, 1 tabl

Pseudo-Labeling Enhanced by Privileged Information and Its Application to In Situ Sequencing Images

Various strategies for label-scarce object detection have been explored by the computer vision research community. These strategies mainly rely on assumptions that are specific to natural images and not directly applicable to the biological and biomedical vision domains. For example, most semi-supervised learning strategies rely on a small set of labeled data as a confident source of ground truth. In many biological vision applications, however, the ground truth is unknown and indirect information might be available in the form of noisy estimations or orthogonal evidence. In this work, we frame a crucial problem in spatial transcriptomics - decoding barcodes from In-Situ-Sequencing (ISS) images - as a semi-supervised object detection (SSOD) problem. Our proposed framework incorporates additional available sources of information into a semi-supervised learning framework in the form of privileged information. The privileged information is incorporated into the teacher's pseudo-labeling in a teacher-student self-training iteration. Although the available privileged information could be data domain specific, we have introduced a general strategy of pseudo-labeling enhanced by privileged information (PLePI) and exemplified the concept using ISS images, as well on the COCO benchmark using extra evidence provided by CLIP.Comment: This paper has been accepted for publication at IJCAI 202

A biologist’s guide to planning and performing quantitative bioimaging experiments

Technological advancements in biology and microscopy have empowered a transition from bioimaging as an observational method to a quantitative one. However, as biologists are adopting quantitative bioimaging and these experiments become more complex, researchers need additional expertise to carry out this work in a rigorous and reproducible manner. This Essay provides a navigational guide for experimental biologists to aid understanding of quantitative bioimaging from sample preparation through to image acquisition, image analysis, and data interpretation. We discuss the interconnectedness of these steps, and for each, we provide general recommendations, key questions to consider, and links to high-quality open-access resources for further learning. This synthesis of information will empower biologists to plan and execute rigorous quantitative bioimaging experiments efficiently

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Preliminary development of an assay for detection of TERT expression, telomere length, and telomere elongation in single cells.

The telomerase enzyme enables unlimited proliferation of most human cancer cells by elongating telomeres and preventing replicative senescence. Despite the critical importance of telomerase in cancer biology, challenges detecting telomerase activity and expression in individual cells have hindered the ability to study patterns of telomerase expression and function across heterogeneous cell populations. While sensitive assays to ascertain telomerase expression and function exist, these approaches have proven difficult to implement at the single cell level. Here, we validate in situ RNAscope detection of the telomerase TERT mRNA and couple this assay with our recently described TSQ1 method for in situ detection of telomere elongation. This approach enables detection of TERT expression, telomere length, and telomere elongation within individual cells of the population. Using this assay, we show that the heterogeneous telomere elongation observed across a HeLa cell population is in part driven by variable expression of the TERT gene. Furthermore, we show that the absence of detectable telomere elongation in some TERT-positive cells is the result of inhibition by the telomeric shelterin complex. This combined assay provides a new approach for understanding the integrated expression, function, and regulation of telomerase at the single cell level

The NEUBIAS Gateway: a hub for bioimage analysis methods and materials: Editorial

We introduce the NEUBIAS Gateway, a new platform for publishing materials related to bioimage analysis, an interdisciplinary field bridging computer science and life sciences. This emerging field has been lacking a central place to share the efforts of the growing group of scientists addressing biological questions using image data. The Gateway welcomes a wide range of publication formats including articles, reviews, reports and training materials. We hope the Gateway further supports this important field to grow and helps more biologists and computational scientists learn about and contribute to these efforts.ISSN:2046-140