A guide to accurate reporting in digital image acquisition - can anyone replicate your microscopy data?

A practical guide to bioimaging research data management in core facilities

Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data-driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well-established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging - Society for Microscopy and Image Analysis (GerBI-GMB), cross-institutional working groups and third-party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging-core-facility-centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.

Improving light microscopy training routines with evidence-based education

The low reproducibility of scientific data published in articles has recently become a cause of concern in many scientific fields. Data involving light microscopy is no exception. The low awareness of researchers of the technologies they use in their research has been identified as one of the main causes of the problem. Potential solutions have hinted at the need to improve technological and methodological education within research. Despite the pivotal role of microscopy core facilities in the education of researchers being well documented, facility staff (FS) often learn their trade on the job, without receiving themselves any structured education about the technology they teach others to use. Additionally, despite endorsing an important role at the highest level of education, most FS never receive any training in pedagogy, the field of research on teaching and learning methods. In this article, we argue that the low level of awareness that researchers have of microscopy stems from a knowledge gap formed between them and microscopy FS during training routines. On the one hand, FS consider that their teaching task is to explain what is needed to produce reliable data. On the other, despite understanding what is being taught, researchers fail to learn the most challenging aspects of microscopy, those involving their judgement and reasoning. We suggest that the misunderstanding between FS and researchers is due to FS not being educated in pedagogy and thus often confusing understanding and learning. To bridge this knowledge gap and improve the quality of the microscopy education available to researchers, we propose a paradigm shift where training staff at technological core facilities be acknowledged as full-fledged teachers and offered structured education not only in the technology they teach but also in pedagogy. We then suggest that training routines at facilities be upgraded to follow the principles of the Constructive Alignment pedagogical method. We give an example of how this can be applied to existing microscopy training routines. We also describe a model to define where the responsibility of FS in training researchers begins and ends. This involves a major structural change where university staff involved in teaching research technologies themselves receive appropriate education. For this to be achieved, we advocate that funding agencies, universities, microscopy and core facility organisations mobilise resources of time and funding. Such changes may involve funding the creation and development of 'Train-the-trainer' type of courses and giving incentives for FS to upgrade their technological and pedagogical knowledge, for example by including them in career paths. We believe that this paradigm shift is necessary to improve the level of microscopy education and ultimately the reproducibility of published data.

Community-developed checklists for publishing images and image analyses

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data.

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

Believing is seeing - the deceptive influence of bias in quantitative microscopy

The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.

The analysis of protein recruitment to laser microirradiation-induced DNA damage in live cells: Best practices for data analysis

Laser microirradiation coupled with live-cell fluorescence microscopy is a powerful technique that has been used widely in studying the recruitment and retention of proteins at sites of DNA damage. Results obtained from this technique can be found in published works by both seasoned and infrequent users of microscopy. However, like many other microscopy-based techniques, the presentation of data from laser microirradiation experiments is inconsistent; papers report a wide assortment of analytic techniques, not all of which result in accurate and/or appropriate representation of the data. In addition to the varied methods of analysis, experimental and analytical details are commonly under-reported. Consequently, publications reporting data from laser microirradiation coupled with fluorescence microscopy experiments need to be carefully and critically assessed by readers. Here, we undertake a systematic investigation of commonly reported corrections used in the analysis of laser microirradiation data. We validate the critical need to correct data for photobleaching and we identify key experimental parameters that must be accounted for when presenting data from laser microirradiation experiments. Furthermore, we propose a straightforward, four-step analytical protocol that can readily be applied across platforms and that aims to improve the quality of data reporting in the DNA damage field.

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images

Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.

Reporting reproducible imaging protocols

A reproducible imaging protocol should include four main detailed sections. The first should describe the sample preparation and include details about the tissue and/or cell culture preparation, the staining procedure, the optical grade of the coverslip, and the type of mounting media used to mount the sample. The second section should describe the configuration and components of the microscope and include the type of stand, stage, illumination, and detector, as well as the emission (EM) and excitation (EX) filters, objective, and immersion medium specifications. Specialized microscopes may have other important components in the optical path to include. The third section should describe the settings used to acquire an image like the exposure and/or dwell time, final magnification and optical resolution, the pixel and field of view (FOV) sizes, time intervals for any time lapse, total power at the objective (i.e., directed at your sample) and number of planes and step size used to collect a 3-dimensional image, and order of operations used in multi-dimensional image acquisitions. The final section should include details about the image analysis workflow such as the image processing steps, segmentation and measurement methods used to extract information from the image, data size, and necessary computing hardware and networking requirements if data sets are >1 GB, as well as citations and versions for the software and code used to perform any of these steps. Every effort should be made to make an example dataset with accurate metadata available online. Finally, specifics about the type of replicates included in the experiment and details about the statistical analysis conducted are also necessary.

Multifaceted mirror array illuminator for fluorescence excitation-scanning spectral imaging microscopy

SIGNIFICANCE

Hyperspectral imaging (HSI) technologies offer great potential in fluorescence microscopy for multiplexed imaging, autofluorescence removal, and analysis of autofluorescent molecules. However, there are also associated trade-offs when implementing HSI in fluorescence microscopy systems, such as decreased acquisition speed, resolution, or field-of-view due to the need to acquire spectral information in addition to spatial information. The vast majority of HSI fluorescence microscopy systems provide spectral discrimination by filtering or dispersing the fluorescence emission, which may result in loss of emitted fluorescence signal due to optical filters, dispersive optics, or supporting optics, such as slits and collimators. Technologies that scan the fluorescence excitation spectrum may offer an approach to mitigate some of these trade-offs by decreasing the complexity of the emission light path.

AIM

We describe the development of an optical technique for hyperspectral imaging fluorescence excitation-scanning (HIFEX) on a microscope system.

APPROACH

The approach is based on the design of an array of wavelength-dependent light emitting diodes (LEDs) and a unique beam combining system that uses a multifurcated mirror. The system was modeled and optimized using optical ray trace simulations, and a prototype was built and coupled to an inverted microscope platform. The prototype system was calibrated, and initial feasibility testing was performed by imaging multilabel slide preparations.

RESULTS

We present results from optical ray trace simulations, prototyping, calibration, and feasibility testing of the system. Results indicate that the system can discriminate between at least six fluorescent labels and autofluorescence and that the approach can provide decreased wavelength switching times, in comparison with mechanically tuned filters.

CONCLUSIONS

We anticipate that LED-based HIFEX microscopy may provide improved performance for time-dependent and photosensitive assays.

A misadventure of the correlation coefficient

The correlation coefficient gauges linear association between two variables. However, interpreting its value depends on the question at hand. This article argues that relying on the correlation coefficient may be irrelevant for many neuroscience research tasks. When the experimental dataset is contextually suitable for binning-averaging, other indicators of statistical association could prove more suitable.

Comparative neuroprotective effects of Cerebrolysin, dexamethasone, and ascorbic acid on sciatic nerve injury model: Behavioral and histopathological study

Background

The majority of the suggested experimental modalities for peripheral nerve injury (PNI) result in varying degrees of recovery in animal models; however, there are not many reliable clinical pharmacological treatment models available. To alleviate PNI complications, research on approaches to accelerate peripheral nerve regeneration is encouraged. Cerebrolysin, dexamethasone, and ascorbic acid (vitamin C) drug models were selected in our study because of their reported curative effects of different mechanisms of action.

Methodology

A total of 40 adult male albino rats were used in this study. Sciatic nerve crush injury was induced in 32 rats, which were divided equally into four groups (model, Cerebrolysin, dexamethasone, and vitamin C groups) and compared to the sham group (n = 8). The sciatic nerve sensory and motor function regeneration after crushing together with gastrocnemius muscle histopathological changes were evaluated by the sciatic function index, the hot plate test, gastrocnemius muscle mass ratio, and immune expression of S100 and apoptosis cascade (BAX, BCL2, and BAX/BCL2 ratio).

Results

Significant improvement of the behavioral status and histopathological assessment scores occurred after the use of Cerebrolysin (as a neurotrophic factor), dexamethasone (as an anti-inflammatory), and vitamin C (as an antioxidant). Despite these seemingly concomitant, robust behavioral and pathological changes, vitamin C appeared to have the best results among the three main outcome measures. There was a positive correlation between motor and sensory improvement and also between behavioral and histopathological changes, boosting the effectiveness, and implication of the sciatic function index as a mirror for changes occurring on the tissue level.

Conclusion

Vitamin C is a promising therapeutic in the treatment of PNI. The sciatic function index (SFI) test is a reliable accurate method for assessing sciatic nerve integrity after both partial disruption and regrowth.

PAC1, VPAC1, and VPAC2 Receptor Expression in Rat and Human Trigeminal Ganglia: Characterization of PACAP-Responsive Receptor Antibodies

Pituitary adenylate cyclase-activating peptide (PACAP) is a neuropeptide expressed in the trigeminal ganglia (TG). The TG conducts nociceptive signals in the head and may play roles in migraine. PACAP infusion provokes headaches in healthy individuals and migraine-like attacks in patients; however, it is not clear whether targeting this system could be therapeutically efficacious. To effectively target the PACAP system, an understanding of PACAP receptor distribution is required. Therefore, this study aimed to characterize commercially available antibodies and use these to detect PACAP-responsive receptors in the TG. Antibodies were initially validated in receptor transfected cell models and then used to explore receptor expression in rat and human TG. Antibodies were identified that could detect PACAP-responsive receptors, including the first antibody to differentiate between the PAC_1n and PAC_1s receptor splice variants. PAC₁, VPAC₁, and VPAC₂ receptor-like immunoreactivity were observed in subpopulations of both neuronal and glial-like cells in the TG. In this study, PAC₁, VPAC₁, and VPAC₂ receptors were detected in the TG, suggesting they are all potential targets to treat migraine. These antibodies may be useful tools to help elucidate PACAP-responsive receptor expression in tissues. However, most antibodies exhibited limitations, requiring the use of multiple methodologies and the careful inclusion of controls.

Quality assessment in light microscopy for routine use through simple tools and robust metrics

Although there is a need to demonstrate reproducibility in light microscopy acquisitions, the lack of standardized guidelines monitoring microscope health status over time has so far impaired the widespread use of quality control (QC) measurements. As scientists from 10 imaging core facilities who encounter various types of projects, we provide affordable hardware and open source software tools, rigorous protocols, and define reference values to assess QC metrics for the most common fluorescence light microscopy modalities. Seven protocols specify metrics on the microscope resolution, field illumination flatness, chromatic aberrations, illumination power stability, stage drift, positioning repeatability, and spatial-temporal noise of camera sensors. We designed the MetroloJ_QC ImageJ/Fiji Java plugin to incorporate the metrics and automate analysis. Measurements allow us to propose an extensive characterization of the QC procedures that can be used by any seasoned microscope user, from research biologists with a specialized interest in fluorescence light microscopy through to core facility staff, to ensure reproducible and quantifiable microscopy results.

Mathematical model and genomics construction of developmental biology patterns using digital image technology

Biological pattern formation ensures that tissues and organs develop in the correct place and orientation within the body. A great deal has been learned about cell and tissue staining techniques, and today’s microscopes can capture digital images. A light microscope is an essential tool in biology and medicine. Analyzing the generated images will involve the creation of unique analytical techniques. Digital images of the material before and after deformation can be compared to assess how much strain and displacement the material responds. Furthermore, this article proposes Development Biology Patterns using Digital Image Technology (DBP-DIT) to cell image data in 2D, 3D, and time sequences. Engineered materials with high stiffness may now be characterized via digital image correlation. The proposed method of analyzing the mechanical characteristics of skin under various situations, such as one direction of stress and temperatures in the hundreds of degrees Celsius, is achievable using digital image correlation. A DBP-DIT approach to biological tissue modeling is based on digital image correlation (DIC) measurements to forecast the displacement field under unknown loading scenarios without presupposing a particular constitutive model form or owning knowledge of the material microstructure. A data-driven approach to modeling biological materials can be more successful than classical constitutive modeling if adequate data coverage and advice from partial physics constraints are available. The proposed procedures include a wide range of biological objectives, experimental designs, and laboratory preferences. The experimental results show that the proposed DBP-DIT achieves a high accuracy ratio of 99,3%, a sensitivity ratio of 98.7%, a specificity ratio of 98.6%, a probability index of 97.8%, a balanced classification ratio of 97.5%, and a low error rate of 38.6%.

Reproductive Apparatus, Gonadic Maturation, and Allometry of Cyclocephala barrerai Martínez (Coleoptera: Melolonthidae: Dynastinae)

Simple Summary

Cyclocephala barrerai larvae feed on the roots of ornamental and commercial grasses. Most of their biology is still unknown, and there are no tools to manage their populations. The objective of this work was to study the reproductive system, gonadic maturation, and allometry of C. barrerai. This is the first report into the Melolonthidae family that characterizes the reproductive apparatus, gonadic maturation of both sexes, and allometric relationships of virgin F1 specimens. Adults do not feed, so gonadic maturation depends on larvae reserves. Females present sclerotized accessory glands and a genital chamber with muscular fibers and bacteria. The morphology of the parameres and the antennae are under sexual selection. This is the first report of two main differences among sexes: females are heavier while males have longer antennae. Males and females exhibit allometric relationships that can be used to predict the bodyweight of field-collected specimens.

Abstract

The Order Coleoptera provides good examples of morphological specializations in the reproductive apparatus, gonadic maturation, and allometry differing between the sexes. The female and male reproductive apparatus has been modified to ensure reproduction between individuals of the same species. The genus Cyclocephala has more than 500 species distributed in America, and Cyclocephala barrerai Martínez is an economically important species in the central part of Mexico. The objective of this work was to study the reproductive system, gonadic maturation, and allometry of C. barrerai. We used light, scanning electron, and laser scanning confocal microscopy to describe the reproductive apparatus and gonadic maturation of females and males. The relationship between adult weight and different parts of the body was established by linear regression. Regardless, the reproductive apparatuses of C. barrerai are like those of other Melolonthidae: the genital chamber, the type II accessory glands, and the ventral plaques of the female and the ejaculator bulb and genital capsule of the males are specific to C. barrerai. The gonads are fully developed when 18 d old. The weight of adult C. barrerai has a positive linear relationship with distinct parts of its body, while the antennae of males are larger than those of the females.

Live cell microscopy: From image to insight

Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.

When light meets biology - how the specimen affects quantitative microscopy

Fluorescence microscopy images should not be treated as perfect representations of biology. Many factors within the biospecimen itself can drastically affect quantitative microscopy data. Whereas some sample-specific considerations, such as photobleaching and autofluorescence, are more commonly discussed, a holistic discussion of sample-related issues (which includes less-routine topics such as quenching, scattering and biological anisotropy) is required to appropriately guide life scientists through the subtleties inherent to bioimaging. Here, we consider how the interplay between light and a sample can cause common experimental pitfalls and unanticipated errors when drawing biological conclusions. Although some of these discrepancies can be minimized or controlled for, others require more pragmatic considerations when interpreting image data. Ultimately, the power lies in the hands of the experimenter. The goal of this Review is therefore to survey how biological samples can skew quantification and interpretation of microscopy data. Furthermore, we offer a perspective on how to manage many of these potential pitfalls.

Quantifying Human Natural Killer Cell Migration by Imaging and Image Analysis

Migration is an important function for natural killer cells. Cell motility has implications in development, tissue infiltration, and cytotoxicity, and measuring the properties of natural killer (NK) cell migration using in vitro assays can be highly informative. Many researchers have an interest in studying properties of NK cell migration in the context of genetic mutation, disease, or in specific tissues and microenvironments. Motility assays can also provide information on the localization of proteins during different phases of cell migration. These assays can be performed on different surfaces for migration or coupled with chemoattractants and/or target cells to test functional outcomes or characterize cell migration speeds and phenotypes. NK cells undergo migration during differentiation in tissue, and these conditions can be modeled by culturing NK cells on a confluent bed of stromal cells on glass and imaging cell migration. Alternatively, fibronectin- or ICAM-1-coated surfaces promote NK cell migration and can be used as substrates. Here, we will describe techniques for the experimental setup and analysis of NK cell motility assays by confocal microscopy or in-incubator imaging using commercially available systems. Finally, we describe open-source software for analyzing cell migration using manual tracking or automated approaches and discuss considerations for the implementation of each of these methods.

Validation of Excitation-Scan Hyperspectral Multi-faceted Mirror Array Prototype System Advancements to Hyperspectral Imaging Applications

Hyperspectral imaging technologies (HSI) have undergone rapid development since their beginning stages. While original applications were in remote sensing, other uses include agriculture, food safety and medicine. HSI has shown great utility in fluorescence microscopy for detecting signatures from many fluorescent molecules; however, acquisitions speeds have been slow due to light losses associated with spectral filtering. Therefore, we designed a novel light emitting diode (LED)-based rapid excitation scanning hyperspectral imaging platform allowing users to obtain simultaneous measurements of fluorescent labels without compromising acquisition speeds. Previously, we reported our results of the optical ray trace simulations and the geometrical capability of designing a multifaceted mirror imaging system as an initial approach to combine light at many wavelengths. The design utilized LEDs and a multifaceted mirror array to combine light sources into a liquid light guide. The computational model was constructed using Monte Carlo optical ray software (TracePro, Lambda Research Corp.). Recent prototype validation results show that when compared to a commercial emission scanning spectral confocal microscope (Zeiss-LSM-980), the novel LED-based excitation scanning HSI prototype successfully detected and separated six fluorescent labels from a custom 6-label African green monkey kidney epithelial cells. We report on the prototype's ability to overcome limitations of acquisition speeds, sensitivity, and specificity present in conventional systems. Future work will evaluate prototype's light losses to determine latent design modifications needed to demonstrate the system's feasibility as a promising solution for overcoming HSI acquisition speeds. This work was supported by NSF award MRI1725937.

A Systematic, Open-Science Framework for Quantification of Cell-Types in Mouse Brain Sections Using Fluorescence Microscopy

The ever-expanding availability and evolution of microscopy tools has enabled ground-breaking discoveries in neurobiology, particularly with respect to the analysis of cell-type density and distribution. Widespread implementation of many of the elegant image processing tools available continues to be impeded by the lack of complete workflows that span from experimental design, labeling techniques, and analysis workflows, to statistical methods and data presentation. Additionally, it is important to consider open science principles (e.g., open-source software and tools, user-friendliness, simplicity, and accessibility). In the present methodological article, we provide a compendium of resources and a FIJI-ImageJ-based workflow aimed at improving the quantification of cell density in mouse brain samples using semi-automated open-science-based methods. Our proposed framework spans from principles and best practices of experimental design, histological and immunofluorescence staining, and microscopy imaging to recommendations for statistical analysis and data presentation. To validate our approach, we quantified neuronal density in the mouse barrel cortex using antibodies against pan-neuronal and interneuron markers. This framework is intended to be simple and yet flexible, such that it can be adapted to suit distinct project needs. The guidelines, tips, and proposed methodology outlined here, will support researchers of wide-ranging experience levels and areas of focus in neuroscience research.

Best practices and tools for reporting reproducible fluorescence microscopy methods

Although fluorescence microscopy is ubiquitous in biomedical research, microscopy methods reporting is inconsistent and perhaps undervalued. We emphasize the importance of appropriate microscopy methods reporting and seek to educate researchers about how microscopy metadata impact data interpretation. We provide comprehensive guidelines and resources to enable accurate reporting for the most common fluorescence light microscopy modalities. We aim to improve microscopy reporting, thus improving the quality, rigor and reproducibility of image-based science.

Towards community-driven metadata standards for light microscopy: tiered specifications extending the OME model

Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

Avoiding a replication crisis in deep-learning-based bioimage analysis

Deep learning algorithms are powerful tools to analyse, restore and transform bioimaging data, increasingly used in life sciences research. These approaches now outperform most other algorithms for a broad range of image analysis tasks. In particular, one of the promises of deep learning is the possibility to provide parameter-free, one-click data analysis achieving expert-level performances in a fraction of the time previously required. However, as with most new and upcoming technologies, the potential for inappropriate use is raising concerns among the biomedical research community. This perspective aims to provide a short overview of key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. These comments are based on our own experience gained while optimising various deep learning tools for bioimage analysis and discussions with colleagues from both the developer and user community. In particular, we focus on describing how results obtained using deep learning can be validated and discuss what should, in our views, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis would need to be reported in publications to describe the use of such tools to guarantee that the work can be reproduced. We hope this perspective will foster further discussion between developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure that this transformative technology is used appropriately.

A guide to accurate reporting in digital image processing - can anyone reproduce your quantitative analysis?

Considerable attention has been recently paid to improving replicability and reproducibility in life science research. This has resulted in commendable efforts to standardize a variety of reagents, assays, cell lines and other resources. However, given that microscopy is a dominant tool for biologists, comparatively little discussion has been offered regarding how the proper reporting and documentation of microscopy relevant details should be handled. Image processing is a critical step of almost any microscopy-based experiment; however, improper, or incomplete reporting of its use in the literature is pervasive. The chosen details of an image processing workflow can dramatically determine the outcome of subsequent analyses, and indeed, the overall conclusions of a study. This Review aims to illustrate how proper reporting of image processing methodology improves scientific reproducibility and strengthens the biological conclusions derived from the results.

Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications

For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.