Hypothesis-driven quantitative fluorescence microscopy - the importance of reverse-thinking in experimental design

De-risking transformative microscopy technologies for broad adoption

The past 20 years have seen a paradigm-shifting explosion of new optical microscopy technologies aimed at uncovering fundamental biological insights. Yet only a small portion 'cross the finish line' into wide adoption by the life science community. We contend that this is not primarily due to a lack of technical prowess or utility. Rather, many risks can conspire to derail the adoption of potentially disruptive technologies. One way to address these challenges is to de-risk paradigm-shifting inventions within open-access technology incubators. Here we detail the framework needed to shepherd innovative microscopy techniques through the often-treacherous adoption landscape to enable transformative scientific output.

Practical considerations for data exploration in quantitative cell biology

Data exploration is an essential step in quantitative cell biology, bridging raw data and scientific insights. Unlike polished, published figures, effective data exploration requires a flexible, hands-on approach that reveals trends, identifies outliers and refines hypotheses. This Opinion offers simple, practical advice for building a structured data exploration workflow, drawing on the authors' personal experience in analyzing bioimage datasets. In addition, the increasing availability of generative artificial intelligence and large language models makes coding and improving data workflows easier than ever before. By embracing these practices, researchers can streamline their workflows, produce more reliable conclusions and foster a collaborative, transparent approach to data analysis in cell biology.

PunctaFinder: An algorithm for automated spot detection in fluorescence microscopy images

Fluorescence microscopy has revolutionized biological research by enabling the visualization of subcellular structures at high resolution. With the increasing complexity and volume of microscopy data, there is a growing need for automated image analysis to ensure efficient and consistent interpretation. In this study, we introduce PunctaFinder, a novel Python-based algorithm designed to detect puncta, small bright spots, in raw fluorescence microscopy images without image denoising or signal enhancement steps. Furthermore, unlike other available spot detectors, PunctaFinder not only detects puncta, but also defines the cytoplasmic region, making it a valuable tool to quantify target molecule localization in cellular contexts. PunctaFinder is a widely applicable punctum detector and size estimator, as evidenced by its successful detection of Atg9-positive vesicles, lipid droplets, aggregates of a destabilized luciferase mutant, and the nuclear pore complex. Notably, PunctaFinder excels in detecting puncta in images with a relatively low resolution and signal-to-noise ratio, demonstrating its capability to identify dim puncta and puncta of dynamic target molecules. PunctaFinder reliably detects puncta in fluorescence microscopy images where automated analysis was not possible before, providing researchers with an efficient and robust method for punctum quantification in fluorescence microscopy images.

Made to measure: An introduction to quantifying microscopy data in the life sciences

Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement 'good' is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments.

Staying on track - Keeping things running in a high-end scientific imaging core facility

Modern life science research is a collaborative effort. Few research groups can single-handedly support the necessary equipment, expertise and personnel needed for the ever-expanding portfolio of technologies that are required across multiple disciplines in today's life science endeavours. Thus, research institutes are increasingly setting up scientific core facilities to provide access and specialised support for cutting-edge technologies. Maintaining the momentum needed to carry out leading research while ensuring high-quality daily operations is an ongoing challenge, regardless of the resources allocated to establish such facilities. Here, we outline and discuss the range of activities required to keep things running once a scientific imaging core facility has been established. These include managing a wide range of equipment and users, handling repairs and service contracts, planning for equipment upgrades, renewals, or decommissioning, and continuously upskilling while balancing innovation and consolidation.

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.

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.

Successful 3D imaging of cleared biological samples with light sheet fluorescence microscopy

In parallel with the development of tissue-clearing methods, over the last decade, light sheet fluorescence microscopy has contributed to major advances in various fields, such as cell and developmental biology and neuroscience. While biologists are increasingly integrating three-dimensional imaging into their research projects, their experience with the technique is not always up to their expectations. In response to a survey of specific challenges associated with sample clearing and labeling, image acquisition, and data analysis, we have critically assessed the recent literature to characterize the difficulties inherent to light sheet fluorescence microscopy applied to cleared biological samples and to propose solutions to overcome them. This review aims to provide biologists interested in light sheet fluorescence microscopy with a primer for the development of their imaging pipeline, from sample preparation to image analysis. Importantly, we believe that issues could be avoided with better anticipation of image analysis requirements, which should be kept in mind while optimizing sample preparation and acquisition parameters.

Imagining the future of optical microscopy: everything, everywhere, all at once

The optical microscope has revolutionized biology since at least the 17th Century. Since then, it has progressed from a largely observational tool to a powerful bioanalytical platform. However, realizing its full potential to study live specimens is hindered by a daunting array of technical challenges. Here, we delve into the current state of live imaging to explore the barriers that must be overcome and the possibilities that lie ahead. We venture to envision a future where we can visualize and study everything, everywhere, all at once - from the intricate inner workings of a single cell to the dynamic interplay across entire organisms, and a world where scientists could access the necessary microscopy technologies anywhere.

Glia Cell Morphology Analysis Using the Fiji GliaMorph Toolkit

Glial cells are the support cells of the nervous system. Glial cells typically have elaborate morphologies that facilitate close contacts with neighboring neurons, synapses, and the vasculature. In the retina, Müller glia (MG) are the principal glial cell type that supports neuronal function by providing a myriad of supportive functions via intricate cell morphologies and precise contacts. Thus, complex glial morphology is critical for glial function, but remains challenging to resolve at a sub-cellular level or reproducibly quantify in complex tissues. To address this issue, we developed GliaMorph as a Fiji-based macro toolkit that allows 3D glial cell morphology analysis in the developing and mature retina. As GliaMorph is implemented in a modular fashion, here we present guides to (a) setup of GliaMorph, (b) data understanding in 3D, including z-axis intensity decay and signal-to-noise ratio, (c) pre-processing data to enhance image quality, (d) performing and examining image segmentation, and (e) 3D quantification of MG features, including apicobasal texture analysis. To allow easier application, GliaMorph tools are supported with graphical user interfaces where appropriate, and example data are publicly available to facilitate adoption. Further, GliaMorph can be modified to meet users' morphological analysis needs for other glial or neuronal shapes. Finally, this article provides users with an in-depth understanding of data requirements and the workflow of GliaMorph. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Download and installation of GliaMorph components including example data Basic Protocol 2: Understanding data properties and quality 3D-essential for subsequent analysis and capturing data property issues early Basic Protocol 3: Pre-processing AiryScan microscopy data for analysis Alternate Protocol: Pre-processing confocal microscopy data for analysis Basic Protocol 4: Segmentation of glial cells Basic Protocol 5: 3D quantification of glial cell morphology.

Multi-Photon Microscopy

In this series of papers on light microscopy imaging, we have covered the fundamentals of microscopy, super-resolution microscopy, and lightsheet microscopy. This last review covers multi-photon microscopy with a brief reference to intravital imaging and Brainbow labeling. Multi-photon microscopy is often referred to as two-photon microscopy. Indeed, using two-photon microscopy is by far the most common way of imaging thick tissues; however, it is theoretically possible to use a higher number of photons, and three-photon microscopy is possible. Therefore, this review is titled "multi-photon microscopy." Another term for describing multi-photon microscopy is "non-linear" microscopy because fluorescence intensity at the focal spot depends upon the average squared intensity rather than the squared average intensity; hence, non-linear optics (NLO) is an alternative name for multi-photon microscopy. It is this non-linear relationship (or third exponential power in the case of three-photon excitation) that determines the axial optical sectioning capability of multi-photon imaging. In this paper, the necessity for two-photon or multi-photon imaging is explained, and the method of optical sectioning by multi-photon microscopy is described. Advice is also given on what fluorescent markers to use and other practical aspects of imaging thick tissues. The technique of Brainbow imaging is discussed. The review concludes with a description of intravital imaging of the mouse. © 2023 Wiley Periodicals LLC.

Practical considerations for quantitative light sheet fluorescence microscopy

Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.

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.

Combining multiple fluorescence imaging techniques in biology: when one microscope is not enough

While fluorescence microscopy has proven to be an exceedingly useful tool in bioscience, it is difficult to offer simultaneous high resolution, fast speed, large volume, and good biocompatibility in a single imaging technique. Thus, when determining the image data required to quantitatively test a complex biological hypothesis, it often becomes evident that multiple imaging techniques are necessary. Recent years have seen an explosion in development of novel fluorescence microscopy techniques, each of which features a unique suite of capabilities. In this Technical Perspective, we highlight recent studies to illustrate the benefits, and often the necessity, of combining multiple fluorescence microscopy modalities. We provide guidance in choosing optimal technique combinations to effectively address a biological question. Ultimately, we aim to promote a more well-rounded approach in designing fluorescence microscopy experiments, leading to more robust quantitative insight.

Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration

With an increase in subject knowledge expertise required to solve specific biological questions, experts from different fields need to collaborate to address increasingly complex issues. To successfully collaborate, everyone involved in the collaboration must take steps to “meet in the middle.” We thus present a guide on truly cross-disciplinary work using bioimage analysis as a showcase, where it is required that the expertise of biologists, microscopists, data analysts, clinicians, engineers, and physicists meet. We discuss considerations and best practices from the perspective of both users and technology developers, while offering suggestions for working together productively and how this can be supported by institutes and funders. Although this guide uses bioimage analysis as an example, the guiding principles of these perspectives are widely applicable to other cross-disciplinary work.

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.

Quantifiable Intravital Light Sheet Microscopy

Live imaging of zebrafish embryos that maintains normal development can be difficult to achieve due to a combination of sample mounting, immobilization, and phototoxicity issues that, once overcome, often still results in image quality sufficiently poor that computer-aided analysis or even manual analysis is not possible. Here, we describe our mounting strategy for imaging the zebrafish midbrain-hindbrain boundary (MHB) with light sheet fluorescence microscopy (LSFM) and pilot experiments to create a study-specific set of parameters for semiautomatically tracking cellular movements in the embryonic midbrain primordium during zebrafish segmentation.

Single-Molecule Localization Microscopy of Subcellular Protein Distribution in Neurons

Over the past years several forms of superresolution fluorescence microscopy have been developed that offer the possibility to study cellular structures and protein distribution at a resolution well below the diffraction limit of conventional fluorescence microscopy (<200 nm). A particularly powerful superresolution technique is single-molecule localization microscopy (SMLM). SMLM enables the quantitative investigation of subcellular protein distribution at a spatial resolution up to tenfold higher than conventional imaging, even in live cells. Not surprisingly, SMLM has therefore been used in many applications in biology, including neuroscience. This chapter provides a step-by-step SMLM protocol to visualize the nanoscale organization of endogenous proteins in dissociated neurons but can be extended to image other adherent cultured cells. We outline a number of methods to visualize endogenous proteins in neurons for live-cell and fixed application, including immunolabeling, the use of intrabodies for live-cell SMLM, and endogenous tagging using CRISPR/Cas9.

Spatial determinates of effector and memory CD8+ T cell fates

The lymph node plays a critical role in mounting an adaptive immune response to infection, clearance of foreign pathogens, and cancer immunosurveillance. Within this complex structure, intranodal migration is vital for CD8⁺ T cell activation and differentiation. Combining tissue clearing and volumetric light sheet fluorescent microscopy of intact lymph nodes has allowed us to explore the spatial regulation of T cell fates. This has determined that short-lived effector (T_SLEC ) are imprinted in peripheral lymph node interfollicular regions, due to CXCR3 migration. In contrast, stem-like memory cell (T_SCM ) differentiation is determined in the T cell paracortex. Here, we detail the inflammatory and chemokine regulators of spatially restricted T cell differentiation, with a focus on how to promote T_SCM . We propose a default pathway for T_SCM differentiation due to CCR7-directed segregation of precursors away from the inflammatory effector niche. Although volumetric imaging has revealed the consequences of intranodal migration, we still lack knowledge of how this is orchestrated within a complex chemokine environment. Toward this goal, we highlight the potential of combining microfluidic chambers with pre-determined complexity and subcellular resolution microscopy.

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.

What is the structure of our infrastructure? -A Review of UK Light Microscopy Facilities

Core Facilities and Technology Platforms are increasingly important components of the science research landscape. However, data on facility operations and staff careers are lacking to inform their development. Here we have surveyed 114 people working in 46 Light Microscopy (LM) facilities within the United Kingdom. Our survey explores issues around Career Progression, Facility Operations, and Funding. The data show that facilities are substantial repositories of equipment and knowledge which adapt to meet the needs of their local environments. Our report highlights the challenges faced by facility managers, institutions, and funders in evaluating facility performance and devising strategies to maximize the return on research funding investment. This article is protected by copyright. All rights reserved.

Visualizing the Invisible: Advanced Optical Microscopy as a Tool to Measure Biomechanical Forces

The importance of mechanical force in biology is evident across diverse length scales, ranging from tissue morphogenesis during embryo development to mechanotransduction across single adhesion proteins at the cell surface. Consequently, many force measurement techniques rely on optical microscopy to measure forces being applied by cells on their environment, to visualize specimen deformations due to external forces, or even to directly apply a physical perturbation to the sample via photoablation or optogenetic tools. Recent developments in advanced microscopy offer improved approaches to enhance spatiotemporal resolution, imaging depth, and sample viability. These advances can be coupled with already existing force measurement methods to improve sensitivity, duration and speed, amongst other parameters. However, gaining access to advanced microscopy instrumentation and the expertise necessary to extract meaningful insights from these techniques is an unavoidable hurdle. In this Live Cell Imaging special issue Review, we survey common microscopy-based force measurement techniques and examine how they can be bolstered by emerging microscopy methods. We further explore challenges related to the accompanying data analysis in biomechanical studies and discuss the various resources available to tackle the global issue of technology dissemination, an important avenue for biologists to gain access to pre-commercial instruments that can be leveraged for biomechanical studies.

Super-Resolution Fluorescence Microscopy Methods for Assessing Mouse Biology

Super-resolution (diffraction unlimited) microscopy was developed 15 years ago; the developers were awarded the Nobel Prize in Chemistry in recognition of their work in 2014. Super-resolution microscopy is increasingly being applied to diverse scientific fields, from single molecules to cell organelles, viruses, bacteria, plants, and animals, especially the mammalian model organism Mus musculus. In this review, we explain how super-resolution microscopy, along with fluorescence microscopy from which it grew, has aided the renaissance of the light microscope. We cover experiment planning and specimen preparation and explain structured illumination microscopy, super-resolution radial fluctuations, stimulated emission depletion microscopy, single-molecule localization microscopy, and super-resolution imaging by pixel reassignment. The final section of this review discusses the strengths and weaknesses of each super-resolution technique and how to choose the best approach for your research. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC.

Data science in cell imaging

Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools - democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

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

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.