Rigorous and Reproducible Microscopy

Images generated by a microscope are never a perfect representation of the biological specimen. Microscopes and specimen preparation methods are prone to error and can impart images with unintended attributes that might be misconstrued as belonging to the biological specimen. In addition, our brains are wired to quickly interpret what we see, and with an unconscious bias toward that which makes the most sense to us based on our current understanding. Unaddressed errors in microscopy images combined with the bias we bring to visual interpretation of images can lead to false conclusions and irreproducible imaging data. Here we review important aspects of designing a rigorous light microscopy experiment: validation of methods used to prepare samples and of imaging system performance, identification and correction of errors, and strategies for avoiding bias in the acquisition and analysis of images.

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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. Here we provide a navigational guide for experimental biologists to understand quantitative bioimaging from sample preparation through 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.

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FPbase is a free and open-source, community-editable database for fluorescent proteins (FPs) and their properties. The primary objective is to aggregate structured and searchable FP data that is of interest to the imaging community and FP developers. Each protein in the database has a dedicated page showing amino acid sequence, accession IDs (e.g. GenBank, UniProt), evolution lineages and mutations, fluorescence attributes, structural data, references that introduced or characterized the protein, and more. Excerpts from primary literature can be entered to store key information about a protein that is otherwise difficult to capture within the current database schema. 

Link to FPbase

One of the challenges in modern fluorescence microscopy is to reconcile the conventional utilization of microscopes as exploratory instruments with their emerging and rapidly expanding role as a quantitative tools. The contribution of microscopy to observational biology will remain enormous owing to the improvements in acquisition speed, imaging depth, resolution and biocompatibility of modern imaging instruments. However, the use of fluorescence microscopy to facilitate the quantitative measurements necessary to challenge hypotheses is a relatively recent concept, made possible by advanced optics, functional imaging probes and rapidly increasing computational power. We argue here that to fully leverage the rapidly evolving application of microscopes in hypothesis-driven biology, we not only need to ensure that images are acquired quantitatively but must also re-evaluate how microscopy-based experiments are designed. In this Opinion, we present a reverse logic that guides the design of quantitative fluorescence microscopy experiments. This unique approach starts from identifying the results that would quantitatively inform the hypothesis and map the process backward to microscope selection. This ensures that the quantitative aspects of testing the hypothesis remain the central focus of the entire experimental design.

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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.

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Online Spectral Modeling & Analysis Tool to chose fluorophores based on filter and hardware specifications. 

Link to SearchLight 

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.

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This tool guides researchers through simple questions related to their imaging choices and dynamically generates a checklist of essential and optional metadata that can then be copied and pasted into a text editor or downloaded in pdf format. In addition to online use, by downloading and modifying the example text file, core facilities or laboratories are also able to create their own versions of MicCheck with custom metadata examples specific to their microscope systems.

Link to Original Version

Link to Version 2.0

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.

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Proper reporting of metadata is essential to reproduce microscopy experiments, interpret results and share images^1,2. The lack of methods reporting in microscopy is evident in that few research articles pass a test for the minimal information required to reproduce experiments¹ (about 17% of 240 articles containing 1,500 figures with images). The problem is compounded by the number and variety of microscope modalities, options and associated components. Automation has distanced researchers from the technical parameters, so it can be difficult for them to know what information needs to be reported. MethodsJ2 is an ImageJ/Fiji-based software tool that aims to improve reproducibility in microscopy by capturing image metadata from multiple sources, consolidating it and automatically generating methods text for publication.

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While the FAIR principles are well accepted in the scientific community, the implementation of appropriate metadata editing and transfer to ensure FAIR research data in practice is significantly lagging behind. On the one hand, it strongly depends on the availability of tools that efficiently support this step in research data management. On the other hand, it depends on the available standards regarding the interpretability of metadata. Here, we introduce a tool, MDEmic, for editing metadata of microscopic imaging data in an easy and comfortable way that provides high flexibility in terms of adjustment of metadata sets. This functionality was in great demand by many researchers applying microscopic techniques. MDEmic has already become a part of the standard installation package of the image database OMERO as OMERO.mde. This database helps to organize and visualize microscopic image data and keep track of their further processing and linkage to other data sets. For this reason, many imaging core facilities provide OMERO to their users. We present a use case scenario for the tailored application of OMERO.mde to imaging data of an institutional OMERO-based Membrane Dye Database, which requires specific experimental metadata. Similar to public image data repositories like the Image Data Resource, IDR, this database facilitates image data storage including rich metadata which enables data mining and re-use, one of the major goals of the FAIR principles.

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Recent technological advances have made microscopy indispensable in life science research. Its ubiquitous use, in turn, underscores the importance of ensuring that microscopy-based experiments are replicable and that the resulting data comparable. While there has been a wealth of review articles, practical guides and conferences devoted to the topic of maintaining standard instrument operating conditions, the paucity of attention dedicated to properly documenting microscopy experiments is undeniable. This lack of emphasis on accurate reporting extends beyond life science researchers themselves, to the review panels and editorial boards of many journals. Such oversight at the final step of communicating a scientific discovery can unfortunately negate the many valiant efforts made to ensure experimental quality control in the name of scientific reproducibility. This Review aims to enumerate the various parameters that should be reported in an imaging experiment by illustrating how their inconsistent application can lead to irreconcilable results.

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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 the obtained images and image analyses results, there are to date no unified guidelines. 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 image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality of microscopy data is in publications.

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Today, 25% of figures in biomedical publications contain images of various types, e.g. photos, light or electron microscopy images, x-rays, or even sketches or drawings. Despite being widely used, published images may be ineffective or illegible since details are not visible, information is missing or they have been inappropriately processed. The vast majority of such imperfect images can be attributed to the lack of experience of the authors as undergraduate and graduate curricula lack courses on image acquisition, ethical processing, and visualization. 
Here we present a step-by-step image processing workflow for effective and ethical image presentation. The workflow is aimed to allow novice users with little or no prior experience in image processing to implement the essential steps towards publishing images. The workflow is based on the open source software Fiji, but its principles can be applied with other software packages. All image processing steps discussed here, and complementary suggestions for image presentation, are shown in an accessible “cheat sheet”-style format, enabling wide distribution, use, and adoption to more specific needs.

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Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in 3 fields; plant sciences, cell biology, and physiology (n = 580 papers). Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.

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QC FOR MULTIPLEX MICROSCOPY

Although quality control (QC) methods have long been associated with analysis tools for single-cell genomics and transcriptomics research, analogous tools have lagged in the area of quantitative microscopy. There are now at least 9 different multiplex imaging platforms capable of routine acquisition of 20-40 channel microscopy data and each is sensitive to microscopy artifacts. Current tools for microscopy-based QC act on pixel-level data. CyLinter differs in that it allows users to work with both pixel-level and single-cell data to identify and remove cell segmentation instances corrupted by visual and image-processing artifacts that can significantly alter single-cell data quality.

Link to webpage

A Bio Image Analysis workflows benchmarking platform.
BIAFLOWS helps comparing bio image analysis workflows by benchmarking them on annotated datasets and simplifying their reproducible deployment.

https://biaflows-sandbox.neubias.org/#/

Multiple-choice microscopy pipeline

An end-to-end processing pipeline that transforms multi-channel whole-slide images into single-cell data. This website is a consolidated source of information for when, why, and how to use MCMICRO.

Link to Website

Believing is seeing - the deceptive influence of bias in quantitative microscopy. J Cell Sci. 2024 Jan 1;137(1):jcs261567. doi: 10.1242/jcs.261567. Epub 2024 Jan 10. PMID: 38197776. Lee RM, Eisenman LR, Khuon S, Aaron JS, Chew TL.

Engaging bioimaging scientists across North America by creating an inclusive and supportive community to share, advance and succeed together.

https://www.bioimagingnorthamerica.org/

Group of enthusiastic light microscopists from Academia and Industry all interested in improving quality assessment (QA) and quality control (QC) in light microscopy. 

https://quarep.org/

Global BioImaging is an international network of imaging infrastructures and communities, which was initiated in 2015 by a european (Horizon 2020) funded project.

Recognizing that scientific, technical and data challenges are universal rather than restricted by geographical boundaries, it brings together imaging facility operators and technical staff, scientists, managers and science policy officers from around the globe, to network, exchange experiences and build capacity internationally.

https://globalbioimaging.org/