Bridging the gap: Integrating cutting-edge techniques into biological imaging with deepImageJ.

This manuscript showcases the latest advancements in deepImageJ, a pivotal Fiji/ImageJ plugin for bioimage analysis in life sciences. The plugin, known for its user-friendly interface, facilitates the application of diverse pre-trained convolutional neural networks to custom data. The manuscript demonstrates several deepImageJ capabilities, particularly in deploying complex pipelines, three-dimensional (3D) image analysis, and processing large images.

Surpassing light inhomogeneities in structured-illumination microscopy with FlexSIM.

Super-resolution structured-illumination microscopy (SIM) is a powerful technique that allows one to surpass the diffraction limit by up to a factor two. Yet, its practical use is hampered by its sensitivity to imaging conditions which makes it prone to reconstruction artefacts. In this work, we present FlexSIM, a flexible SIM reconstruction method capable to handle highly challenging data.

Quantification of Mycobacterium tuberculosis Growth in Cell-Based Infection Assays by Time-Lapse Fluorescence Microscopy.

Quantification of Mycobacterium tuberculosis (Mtb) growth dynamics in cell-based in vitro infection models is traditionally carried out by measurement of colony forming units (CFU). However, Mtb being an extremely slow growing organism (16-24 h doubling time), this approach requires at least 3 weeks of incubation to obtain measurable readouts. In this chapter, we describe an alternative approach based on time-lapse microscopy and quantitative image analysis that allows faster quantification of Mtb growth dynamics in host cells.

Roadmap on Deep Learning for Microscopy.

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities.

A framework for evaluating the performance of SMLM cluster analysis algorithms.

Single-molecule localization microscopy (SMLM) generates data in the form of coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite a range of cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance.

Steer'n'Detect: fast 2D template detection with accurate orientation estimation.

Motivation: Rotated template matching is an efficient and versatile algorithm to analyze microscopy images, as it automates the detection of stereotypical structures, such as organelles that can appear at any orientation. Its performance however quickly degrades in noisy image data.

Results: We introduce Steer'n'Detect, an ImageJ plugin implementing a recently published algorithm to detect patterns of interest at any orientation with high accuracy from a single template in 2D images.

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ.

DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining.

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features.

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings.

In pancreatic islets from type 2 diabetes patients, the dampened circadian oscillators lead to reduced insulin and glucagon exocytosis.

Circadian clocks operative in pancreatic islets participate in the regulation of insulin secretion in humans and, if compromised, in the development of type 2 diabetes (T2D) in rodents. Here we demonstrate that human islet α- and β-cells that bear attenuated clocks exhibit strongly disrupted insulin and glucagon granule docking and exocytosis. To examine whether compromised clocks play a role in the pathogenesis of T2D in humans, we quantified parameters of molecular clocks operative in human T2D islets at population, single islet, and single islet cell levels.

Publisher Correction: Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

In the version of this paper originally published, Figure 4a contained errors that were introduced during typesetting. The bottom 11° ThunderSTORM image is an xz view but was incorrectly labeled as xy, and the low x-axis value in the four line profiles was incorrectly set as -60 instead of -50. These errors have been corrected in the PDF and HTML versions of the paper.

Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

With the widespread uptake of two-dimensional (2D) and three-dimensional (3D) single-molecule localization microscopy (SMLM), a large set of different data analysis packages have been developed to generate super-resolution images. In a large community effort, we designed a competition to extensively characterize and rank the performance of 2D and 3D SMLM software packages. We generated realistic simulated datasets for popular imaging modalities-2D, astigmatic 3D, biplane 3D and double-helix 3D-and evaluated 36 participant packages against these data.

Investigating Focal Adhesion Substructures by Localization Microscopy.

Cells rely on focal adhesions (FAs) to carry out a variety of important tasks, including motion, environmental sensing, and adhesion to the extracellular matrix. Although attaining a fundamental characterization of FAs is a compelling goal, their extensive complexity and small size, which can be below the diffraction limit, have hindered a full understanding. In this study we have used single-molecule localization microscopy (SMLM) to investigate integrin β3 and paxillin in rat embryonic fibroblasts growing on two different extracellular matrix-representing substrates (i.

Pancreatic α- and β-cellular clocks have distinct molecular properties and impact on islet hormone secretion and gene expression.

A critical role of circadian oscillators in orchestrating insulin secretion and islet gene transcription has been demonstrated recently. However, these studies focused on whole islets and did not explore the interplay between α-cell and β-cell clocks. We performed a parallel analysis of the molecular properties of α-cell and β-cell oscillators using a mouse model expressing three reporter genes: one labeling α cells, one specific for β cells, and a third monitoring circadian gene expression.

DeconvolutionLab2: An open-source software for deconvolution microscopy.

Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biological samples.

Transforms and Operators for Directional Bioimage Analysis: A Survey.

We give a methodology-oriented perspective on directional image analysis and rotation-invariant processing. We review the state of the art in the field and make connections with recent mathematical developments in functional analysis and wavelet theory. We unify our perspective within a common framework using operators.

Variational Phase Imaging Using the Transport-of-Intensity Equation.

We introduce a variational phase retrieval algorithm for the imaging of transparent objects. Our formalism is based on the transport-of-intensity equation (TIE), which relates the phase of an optical field to the variation of its intensity along the direction of propagation. TIE practically requires one to record a set of defocus images to measure the variation of intensity.

SpotCaliper: fast wavelet-based spot detection with accurate size estimation.

Motivation: SpotCaliper is a novel wavelet-based image-analysis software providing a fast automatic detection scheme for circular patterns (spots), combined with the precise estimation of their size. It is implemented as an ImageJ plugin with a friendly user interface. The user is allowed to edit the results by modifying the measurements (in a semi-automated way), extract data for further analysis.

Quantitative evaluation of software packages for single-molecule localization microscopy.

The quality of super-resolution images obtained by single-molecule localization microscopy (SMLM) depends largely on the software used to detect and accurately localize point sources. In this work, we focus on the computational aspects of super-resolution microscopy and present a comprehensive evaluation of localization software packages. Our philosophy is to evaluate each package as a whole, thus maintaining the integrity of the software.

Understanding and enhancing future infrastructure resiliency: a socio-ecological approach.

The resilience of any system, human or natural, centres on its capacity to adapt its structure, but not necessarily its function, to a new configuration in response to long-term socio-ecological change. In the long term, therefore, enhancing resilience involves more than simply improving a system's ability to resist an immediate threat or to recover to a stable past state. However, despite the prevalence of adaptive notions of resilience in academic discourse, it is apparent that infrastructure planners and policies largely continue to struggle to comprehend longer-term system adaptation in their understanding of resilience.

Analysis of S. pombe SIN protein association to the SPB reveals two genetically separable states of the SIN.

The Schizosaccharomyces pombe septation initiation network (SIN) regulates cytokinesis, and asymmetric association of SIN proteins with the mitotic spindle pole bodies (SPBs) is important for its regulation. Here, we have used semi-automated image analysis to study SIN proteins in large numbers of wild-type and mutant cells. Our principal conclusions are: first, that the association of Cdc7p with the SPBs in early mitosis is frequently asymmetric, with a bias in favour of the new SPB; second, that the early association of Cdc7p-GFP to the SPB depends on Plo1p but not Spg1p, and is unaffected by mutations that influence its asymmetry in anaphase; third, that Cdc7p asymmetry in anaphase B is delayed by Pom1p and by activation of the spindle assembly checkpoint, and is promoted by Rad24p; and fourth, that the length of the spindle, expressed as a fraction of the length of the cell, at which Cdc7p becomes asymmetric is similar in cells dividing at different sizes.