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

Abstract

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.

Best practices and tools for reporting reproducible fluorescence microscopy methods

“…Rigorous and unbiased experimental design and analysis workflows are critical to provide accurate insight into the biological process under investigation 3,5,9,17 . Sample preparation, choice of instrument and related hardware, and image acquisition parameters (that is, metadata) have a profound effect on the image data validity and interpretation and therefore must be reported in the methods section of a published manuscript.…”

“…Box 2 describes some of the important validation steps that should be included in any experimental design and in the methods section, and Supplementary Table 1 lists selected resources for method validation. There are several outstanding publications that provide more information on method validation approaches and protocols 3,5,17 . Additionally, several initiatives in the microscopy field focus on the importance of quality control and instrument performance assessment to validate microscopy methods (Supplementary Table 1).…”

“…Sample preparation is critical in fluorescence microscopy and will greatly influence the quality of the final image and its quantification 29 . There are many publications that focus on troubleshooting sample preparation and appropriate controls required for the interpretation of microscopy data in both fixed and live specimens 3,5,9,17,[29][30][31][32][33] . Detailed sample preparation methods including fixation, permeabilization, labeling and mounting of the sample (Supplementary Exhibits 1-4) and validation steps should be Of particular importance to report is the specific fluorophore used in any given experiment.…”

“…Many publications focus on guidelines to successfully perform a microscopy experiment, including sample preparation, method validation and appropriate controls to ensure reproducibility [2][3][4][5] . However, there are still few resources to guide and assist researchers in writing rigorous and reproducible microscopy methods.…”

Data science in cell imaging

“…Although the boundaries of data science remain fluid, the field combines domain knowledge with techniques from mathematics, statistics, computer science and information sciences, such as machine learning, to identify patterns hidden in data and perform statistical hypothesis testing on large data sets. The data science toolbox enables the computation-first interpretation of cell images by allowing us to iteratively alternate computational analysis with the generation of biological hypotheses and visualization of the obtained results (Wait et al, 2020).…”