Cell dynamics revealed by microscopy advances

Abstract

Cell biology emerges from spatiotemporally coordinated molecular processes. Recent advances in live cell microscopy, fueled by a surge in optical, molecular, and computational technologies, have enabled dynamic observations from single molecules to whole organisms. Despite technological leaps, there is still an untapped opportunity to fully leverage their capabilities toward biological insight. We highlight how single molecule imaging has transformed our understanding of biological processes, with a focus on chromatin organization and transcription in the nucleus. We describe how this was enabled by the close integration of new imaging techniques with analysis tools and discuss the challenges to make a comparable impact at larger scales from organelles to organisms. By highlighting recent successful examples, we describe an outlook of ever-increasing data and the need for seamless integration between dataset visualization and quantification to realize the full potential warranted by advances in new imaging technologies.

Introduction

Since its inception, light microscopy has been a cornerstone of cell biology research[1,2]. However, through the 20th century, the development of biochemical and genetic methods offered a new molecular understanding of what occurred below the diffraction limit of traditional microscopes. The widespread adoption of fluorescent probes during the 1990’s marked a resurgence for light microscopy by providing protein-specific contrast in single living cells[3–5]. More recently, combined advances in optical physics, the development of sensitive and high speed cameras, and continual progress in computational power have dramatically increased the speed, sensitivity, and resolution of modern microscopes[6]. Single molecule imaging, in particular, has necessitated a fundamental re-examination of many biological processes, from gene regulation[7] to signal propagation[8,9].

In this review, we will examine the current state of live cell microscopy, with a particular focus on the subfield of single molecule imaging—a field we believe has made the most significant contributions to our understanding of biology. Despite the rapid technological progress in live cell imaging across nearly all spatial scales, we contend that translating technological progress to biological insight at cellular and organismal scales has proved more challenging. We identify and discuss notable exceptions to this trend, highlighting how their achievements combine novel imaging modalities with advanced computational and quantitative analyses. Furthermore, we will articulate our perspective on the necessary steps forward to ensure that technological advancements in microscopy can be effectively utilized to enable new biological discoveries.

Single molecule imaging in live cells

Some of the most transformative advancements in live cell imaging have come from the field of single molecule imaging. As an example, we focus on how these techniques have been applied to understand nuclear organization and function. However, it is important to recognize that the impact of single molecule imaging extends far beyond this area, significantly enhancing our understanding across diverse domains of cell biology and physiology[9–11].

Research spanning several decades has firmly established that while nearly all cells within an organism contain identical DNA sequences, their chromatin organization, mRNA output, and expression of trans-regulatory factors are highly cell type-specific[12–14]. This intricate orchestration ensures that each cell type maintains its unique functional characteristics, despite sharing a genetic blueprint. Imaging techniques like total internal reflection[15,16], highly inclined and laminated optical sheet[17,18], and light sheet microscopy[19–23] provide the low background and high sensitivity needed to image and track single fluorescent molecules in cells. In parallel, the development of self-labeling proteins like SNAP[24] and Halo-Tag[25] together with brighter, photostable, and cell permeable dyes[26,27] have revolutionized our ability to image and track single proteins in cells. These methods have exposed the dynamic, stochastic, and transient nature of molecular interactions within the nucleus, challenging the earlier, more static and deterministic models of nuclear function [13,28–30]

4D Chromatin organization

The integration of next-generation sequencing with chromatin capture techniques has revealed a non-random compartmentalization and enriched contact probabilities among key gene regulatory cis-elements[13]. These methods involve ligating DNA elements in close proximity and then sequencing them to generate a probability map of spatial contacts. However, the resulting map is a static and population-averaged snapshot of the genome structure in many cells. Dynamic live cell imaging studies focusing on DNA loop-regulating proteins such as CTCF and Rad21, a key component of the cohesion complex, indicate that these proteins bind to chromatin transiently, with durations that challenge the notion of stable chromatin compartments[29]. Additionally, comparisons between Hi-C maps and fixed cell fluorescent in-situ hybridization experiments suggest that 3D chromatin organization is more stochastic than previously thought[30,31].

Recent studies have combined genome engineering with cutting-edge live cell microscopy and quantitative analysis to test these implications more directly[32–34]. For instance, these approaches have shown that despite their frequent depiction in contact probability maps, CTCF/cohesin-mediated chromatin loops are rare[32]. Rather, these organizational features predominantly exist in dynamic, partially folded conformations, which starkly contrast with the static models previously inferred from genomic data[32]. Further studies observing the spatial organization of DNA gene regulatory elements indicate that direct interactions between enhancers and promoters do not necessarily correlate with contemporaneous gene transcription, challenging the long-held belief in their functional linkage and requiring new models that account for stochastic and dynamic interactions[33].

Gene Regulation and Formation of Higher Ordered Structures

Early investigations into gene regulation primarily relied on biochemical and genomic approaches, which portrayed a stable and well-organized interaction between trans and cis regulatory elements[28]. These studies supported a model where transcription factors (TFs) bind to enhancer DNA to initiate gene expression through structured, sequential assembly of macromolecular complexes that link distal cis-regulatory elements, ultimately leading to mRNA synthesis. However, advancements in live-cell imaging techniques have significantly revised our understanding of these processes, revealing that the behavior of nuclear proteins is more dynamic than previously thought[7,29,35,36].

Initial observations using live-cell imaging indicated that the duration for which TFs engage with DNA is typically very short[35,36]. It is now recognized that most TFs predominantly exist in a ‘free’ diffusing state, punctuated by brief non-specific interactions with DNA (<1 second), before they engage in what are considered stable binding events[7]. Intriguingly, even sequence-specific stable interactions typically last only seconds to minutes at most — far shorter than expected for stable complex formations envisioned in canonical models[7].

The application of advanced imaging techniques alongside molecular perturbation has further challenged our traditional understanding of gene regulatory complexes. Contrary to the structured and stoichiometric interactions described in vitro, live-cell studies suggest many molecular complexes are transient and less organized than anticipated. For instance, a combination of single-particle tracking and conditional knockouts has shown that the formation of the RNA polymerase II pre-initiation complex is more stochastic and less hierarchical than suggested by in vitro studies[37]. Moreover, single molecule imaging at active transcription sites reveals unexpectedly high concentrations of nuclear regulatory proteins, present in non-stoichiometric amounts[38]. Although still an active area of research, emerging evidence supports a model of gene regulation governed by transient, low-affinity interactions[7,39].

The synergistic development of microscopy and analytical tools

In many ways, the significant insights gleaned from single molecule imaging can be attributed to the concurrent development of microscopy advances and new analytical tools. User friendly software for the identification and temporal linking of single particles, such as TrackMate[40,41] and uTrack[42,43], and downstream analytical interpretation, such as SpotOn[44], have been instrumental in yielding real biological insight from complex, multidimensional data. One advantage is that, from an image analysis standpoint, a single fluorescently labeled transcription factor looks identical to any other fluorescently labeled protein in the cell. Thus, the same single particle tracking software can be easily generalized for a wide range of biological applications allowing for the rapid development of advanced quantitative analyses applicable to many biological problems. This is in stark contrast to pipelines for subcellular and organismal imaging which may require specialized analysis tools at different biological scales and custom solutions for unique computational tasks (e.g. tracking organelle contact sites and dynamics vs. quantifying changes in cell shape).

Live cell imaging at cellular and organismal scales

While single molecule imaging and single particle tracking have transformed our knowledge of protein dynamics and protein-protein complexes, other microscopy advances have been more challenging to leverage to their full potential. Unlike single molecule imaging where the datasets simply could not have been acquired without the new imaging approaches, imaging of protein dynamics at and above the diffraction limit has been possible since the discovery and implementation of fluorescent proteins[3]. Over the past decade, live cell compatible super-resolution approaches[45,46] and light sheet microscopy[19–22] have increased the resolution, speed, and dimensionality at which we can acquire data. However, decades of methodological inertia have shaped the experimental systems and analysis methods in cell biology around the limitations of conventional widefield and confocal microscopes. For example, many model cell lines are chosen for studies precisely because they are adherent to a coverslip, are flat, and can be imaged with low background under a standard epifluorescence microscope. Despite this, several recent works have demonstrated that, when combined with the development of new computational analyses, researchers can fully leverage the rich data provided by advanced correlative and volumetric microscopy techniques.

Microscopy advances at cellular and organismal length scales

Light sheet microscopy[19–22,47], hyperspectral imaging[48,49] and correlative live cell single molecule imaging[10,50–52] represent significant advances that are well poised to aid in the study of cellular dynamics. At the spatial scale of whole cells, 3D axially swept light sheet imaging and complex 3D shape analysis have been used to reveal a new role for blebbing in promoting cell survival in both tumorigenic and physiological contexts[53–55]. Specifically, usage of this implementation of light sheet imaging allowed visualization of micron-scale plasma membrane curvature and the recruitment of proteins to blebs at high frame rates over several minutes[53]. Lattice light sheet microscopy was similarly adapted to analyze subcellular signaling states of T-cells where high spatiotemporal imaging of signaling receptor complexes enabled the discrimination of distinct molecular states under varying stimulation conditions[56]. At the spatial scale of whole organisms, the creation of an adaptive light sheet microscope that follows the dynamic changes in size, shape, and optical properties of a mouse embryo allowed for direct observation of its development from gastrulation through organogenesis[57,58]. This impressive engineering feat is further enhanced by the generation of a computational framework to enable single cell tracking, monitoring of mitosis, and mapping of the various tissue development stages[59].

Beyond light sheet microscopy, hyperspectral imaging has revealed the complex and dynamic nature of multi-organelle contacts which was previously challenging due to the difficulties with imaging many fluorophores simultaneously[48]. Overcoming the issue of spectral overlap allowed unprecedented visualization of the interactome of organelle contacts and specifically identified distinct profiles for organelle interactions. Finally, correlative single particle tracking and total internal reflection fluorescence microscopy has been used to simultaneously examine the dynamic relationship of endoplasmic reticulum-mitochondrial contact sites and the trafficking vesicles to these locations. These contact sites have distinct subdomains, are highly dynamic, and are altered in response to stimuli[10].

Quantitative analysis tools are limited for complex microscopy data

A common theme among these examples is the development of analysis pipelines to fully leverage the capabilities of new microscopes. Tools such as Cellpose[60] for automated cell segmentation or Cell Profiler[61] for quantitative analysis of cellular dynamics have been instrumental in pushing quantification of microscopy data in 2D timelapses or 3D z-stacks. An important future goal will be to adapt these or similar tools to analyze 4- and 5D datasets of the size and complexity generated during experiments with advanced imaging techniques. Finally, visually interpreting images becomes substantially more challenging as the dimensionality increases. For example, we can easily watch a 2D slice over time and grasp dynamic changes or browse through focal planes in a single 3D time point to infer structure. But multidimensional (x, y, z, t, λ) datasets are substantially harder to navigate. This is especially true given the volume of data generated by modern microscopes. In this sense, the ultimate utility of advanced microscopes may rest with the development and dissemination of new analysis tools. While artificial intelligence and machine learning tools have been utilized for image deconvolution[62] and denoising[63], recent pioneering work has demonstrated machine learning can be used outside of data processing such as with smart microscopy where machine learning was used to automatically detect and image dynamic cellular events of interest without user input[64–67].We envision that similar approaches may also play an important role in not only operating microscopes but also interfacing and analyzing the data they generate. Lastly, machine learning has been demonstrated to be a valuable tool in connecting the dynamic relationship between biochemical activity and its functional output for cellular behavior such as contractile force generation[68].

To summarize, recent advances in imaging technology have been pivotal to our understanding of single molecule dynamics and new computational tools are allowing the instruments to have a similar impact on other areas of cell biology. Early adopters of these techniques at cell and organismal length scales are paving a path that may potentiate wider adoption, but it is currently limited by challenges in managing large data, developing specific and tailored analysis pipelines, and even accessing the data in a human interpretable way. The promise of integrating machine learning and artificial intelligence into common workflows has already revolutionized many industries and it is likely that biological imaging will be no different. The future of microscopy for cell biologists may look less like long hours in a darkened room and more like a conversation with the microscope or with the analysis software to acquire and analyze data of interest[69,70]. Ultimately, adoption of these tools in addition to a continued integration of math and computer science into biology curriculums may allow will allow researchers to directly engage with these large datasets and continue to push the boundaries of our understanding of cellular dynamics.

Figure 1:

Live cell dynamic imaging across length scales. Panel A depicts a representative single particle tracking experiment where single molecules are tracked in three-dimensional space. Common microscopy techniques include HiLo, light sheet, and TIRF microscopy while computational tools such as Trackmate and SpotOn are used to measure features of the tracked particles including diffusion kinetic modeling and circular histograms. Panel B shows subcellular imaging where HiLo, light sheet, and confocal microscopy are used to visualize organelle dynamics. Cellpose and Cellprofiler are commonly used to segment and track organelles which allows for measurements such as organelle contact frequency. Panel C is a representation of whole cell dynamic imaging which is often used to track various morphological features such as filopodial dynamics, blebs, or lamellipodial protrusions. Example analysis tools include uShape3D and LaMDA which can be used to generate measurements of protein localization along morphological features and characterize these features with metrics such as curvature. Finally, Panel D shows the common features of whole organism imaging which is represented by a zebrafish embryo. Lightsheet microscopy is often employed to generate high resolution 3D volumes over long periods of time which enables tracking of cells and their features with tools such as TGM 2.0 or TARDIS. By tracking nuclei movements, it is possible to perform lineage tracing of across developmental timescales.