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

Abstract

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.

Polarity-JaM: An image analysis toolbox for cell polarity, junction and morphology quantification

“…However, this approach does not capture the complexity of protrusions such as filopodia or lamellipodia. The second group is computed from the relation of area and perimeter, such as, for example, circularity (19) or shape index (37), see Materials and Methods for more details. In particular, the shape index has been popular in soft matter physics to characterise mesoscale properties of tissues or cell monolayers as solid or fluid (38).…”

“…A major challenge in the creation of every image analysis pipeline is identifying and measuring informative features. This search has a large iterative component and is based on precise and accurate measurement of the relevant microscopy data (18). Several studies have proposed new meaningful measures such as Quantify Polarity (19), Junction Mapper (20) and Griottes (21) or reviewed existing ones (18).…”

“…This search has a large iterative component and is based on precise and accurate measurement of the relevant microscopy data (18). Several studies have proposed new meaningful measures such as Quantify Polarity (19), Junction Mapper (20) and Griottes (21) or reviewed existing ones (18). However, it is not yet possible to integrate these different aspects into a single pipeline and perform a multivariate analysis.…”

Imaging actin organisation and dynamics in 3D

“…To fully take advantage of optical microscopy as an experimental tool, its inherently quantitative nature should be exploited, as quantitative analysis of microscopy images can lead to unprecedented biological insights (Myers, 2012;Senft et al, 2023). All the considerations with respect to sample preparation and choosing the most suitable imaging modalities are, therefore, crucial when planning future analysis of the acquired imaging data, as all these major steps of an imaging experiment are tightly interconnected (Culley et al, 2023;Senft et al, 2023). Different analysis avenues for F-actin image analysis have been explored, covering cytoskeletal dynamics, architecture and mechanics.…”

“…Moreover, 3D acquisitions are often anisotropic in the z-axis (i.e. presenting lower spatial resolution in the z-axis compared to x-and y-axes), limiting the amount of information that can be extracted (Culley et al, 2023). The third dimension also adds to computational costs, both due to the requirement for more complex algorithms and the larger data file sizes.…”