Single-molecule tracking technologies for quantifying the dynamics of gene regulation in cells, tissue and embryos

Automated live-cell single-molecule tracking in enteroid monolayers reveals transcription factor dynamics probing lineage-determining function

Lineage transcription factors (TFs) provide one regulatory level of differentiation crucial for the generation and maintenance of healthy tissues. To probe TF function by measuring their dynamics during adult intestinal homeostasis, we established HILO-illumination-based live-cell single-molecule tracking (SMT) in mouse small intestinal enteroid monolayers recapitulating tissue differentiation hierarchies in vitro. To increase the throughput, capture cellular features, and correlate morphological characteristics with diffusion parameters, we developed an automated imaging and analysis pipeline, broadly applicable to two-dimensional culture systems. Studying two absorptive lineage-determining TFs, we found an expression level-independent contrasting diffusive behavior: while Hes1, key determinant of absorptive lineage commitment, displays a large cell-to-cell variability and an average fraction of DNA-bound molecules of ∼32%, Hnf4g, conferring enterocyte identity, exhibits more uniform dynamics and a bound fraction of ∼56%. Our results suggest that TF diffusive behavior could indicate the progression of differentiation and modulate early versus late differentiation within a lineage.

A fine kinetic balance of interactions directs transcription factor hubs to genes

Eukaryotic gene regulation relies on the binding of sequence-specific transcription factors (TFs). TFs bind chromatin transiently yet occupy their target sites by forming high-local concentration microenvironments (hubs and condensates) that increase the frequency of binding. Despite their ubiquity, such microenvironments are difficult to study in endogenous contexts due to technical limitations. Here, we use live embryo light-sheet imaging, single-molecule tracking, and genomics to overcome these limitations and investigate how hubs are localized to target genes to drive TF occupancy and transcription. By examining mutants of a hub-forming TF, Zelda, in Drosophila embryos, we find that hub formation propensity, spatial distributions, and temporal stabilities are differentially regulated by DNA binding and disordered protein domains. We show that hub localization to genomic targets is driven by a finely-tuned kinetic balance of interactions between proteins and chromatin, and hubs can be redirected to new genomic sites when this balance is perturbed.

Transcription dynamics and genome organization in the mammalian nucleus: Recent advances

Single-molecule tracking (SMT) has emerged as the dominant technology to investigate the dynamics of chromatin-transcription factor (TF) interactions. How long a TF needs to bind to a regulatory site to elicit a transcriptional response is a fundamentally important question. However, highly divergent estimates of TF binding have been presented in the literature, stemming from differences in photobleaching correction and data analysis. TF movement is often interpreted as specific or non-specific association with chromatin, yet the dynamic nature of the chromatin polymer is often overlooked. In this perspective, we highlight how recent SMT studies have reshaped our understanding of TF dynamics, chromatin mobility, and genome organization in the mammalian nucleus, focusing on the technical details and biological implications of these approaches. In a remarkable convergence of fixed and live-cell imaging, we show how super-resolution and SMT studies of chromatin have dovetailed to provide a convincing nanoscale view of genome organization.

The method in the madness: Transcriptional control from stochastic action at the single-molecule scale

Cell states result from the ordered activation of gene expression by transcription factors. Transcription factors face opposing design constraints: they need to be dynamic to trigger rapid cell state transitions, but also stable enough to maintain terminal cell identities indefinitely. Recent progress in live-cell single-molecule microscopy has helped define the biophysical principles underlying this paradox. Beyond transcription factor activity, single-molecule experiments have revealed that at nearly every level of transcription regulation, control emerges from multiple short-lived stochastic interactions, rather than deterministic, stable interactions typical of other biochemical pathways. This architecture generates consistent outcomes that can be rapidly choreographed. Here, we highlight recent results that demonstrate how order in transcription regulation emerges from the apparent molecular-scale chaos and discuss remaining conceptual challenges.

Single-Macromolecule Studies of Eukaryotic Genomic Maintenance

Genomes are self-organized and self-maintained as long, complex macromolecules of chromatin. The inherent heterogeneity, stochasticity, phase separation, and chromatin dynamics of genome operation make it challenging to study genomes using ensemble methods. Various single-molecule force-, fluorescent-, and sequencing-based techniques rooted in different disciplines have been developed to fill critical gaps in the capabilities of bulk measurements, each providing unique, otherwise inaccessible, insights into the structure and maintenance of the genome. Capable of capturing molecular-level details about the organization, conformational changes, and packaging of genetic material, as well as processive and stochastic movements of maintenance factors, a single-molecule toolbox provides an excellent opportunity for collaborative research to understand how genetic material functions in health and malfunctions in disease. In this review, we discuss novel insights brought to genomic sciences by single-molecule techniques and their potential to continue to revolutionize the field-one molecule at a time.

Surprising Features of Nuclear Receptor Interaction Networks Revealed by Live Cell Single Molecule Imaging

Type 2 Nuclear Receptors (T2NRs) require heterodimerization with a common partner, the Retinoid X Receptor (RXR), to bind cognate DNA recognition sites in chromatin. Based on previous biochemical and over-expression studies, binding of T2NRs to chromatin is proposed to be regulated by competition for a limiting pool of the core RXR subunit. However, this mechanism has not yet been tested for endogenous proteins in live cells. Using single molecule tracking (SMT) and proximity-assisted photoactivation (PAPA), we monitored interactions between endogenously tagged retinoid X receptor (RXR) and retinoic acid receptor (RAR) in live cells. Unexpectedly, we find that higher expression of RAR, but not RXR increases heterodimerization and chromatin binding in U2OS cells. This surprising finding indicates the limiting factor is not RXR but likely its cadre of obligate dimer binding partners. SMT and PAPA thus provide a direct way to probe which components are functionally limiting within a complex TF interaction network providing new insights into mechanisms of gene regulation in vivo with implications for drug development targeting nuclear receptors.

Using single molecule imaging to explore intracellular heterogeneity

Despite more than 100 years of study, it is unclear if the movement of proteins inside the cell is best described as a mosh pit or an exquisitely choreographed dance. Recent studies suggest the latter. Local interactions induce molecular condensates such as liquid-liquid phase separations (LLPSs) or non-liquid, functionally significant molecular aggregates, including synaptic densities, nucleoli, and Amyloid fibrils. Molecular condensates trigger intracellular signaling and drive processes ranging from gene expression to cell division. However, the descriptions of condensates tend to be qualitative and correlative. Here, we indicate how single-molecule imaging and analyses can be applied to quantify condensates. We discuss the pros and cons of different techniques for measuring differences between transient molecular behaviors inside and outside condensates. Finally, we offer suggestions for how imaging and analyses from different time and space regimes can be combined to identify molecular behaviors indicative of condensates within the dynamic high-density intracellular environment.

Gene expression model inference from snapshot RNA data using Bayesian non-parametrics

Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational frameworks do not perform simultaneous Bayesian inference of gene expression models and parameters from such data. Rather, gene expression models-composed of gene states, their connectivities and associated parameters-are currently deduced by pre-specifying gene state numbers and connectivity before learning associated rate parameters. Here we propose a method to learn full distributions over gene states, state connectivities and associated rate parameters, simultaneously and self-consistently from single-molecule RNA counts. We propagate noise from fluctuating RNA counts over models by treating models themselves as random variables. We achieve this within a Bayesian non-parametric paradigm. We demonstrate our method on the Escherichia coli lacZ pathway and the Saccharomyces cerevisiae STL1 pathway, and verify its robustness on synthetic data.

Single nucleosome tracking to study chromatin plasticity

The dynamic spatial organization of chromatin within the nucleus is emerging as a key regulator of gene activity and cell phenotype. This review will focus on single molecule tracking as an enabling tool to study chromatin dynamics at the level of individual nucleosomes.

Triple-color single-molecule imaging for analysis of the role of receptor oligomers in signal transduction

Membrane receptors provide interfaces of various extracellular stimuli to transduce the signal into the cell. Receptors are required to possess such conflicting properties as high sensitivity and noise reduction for the cell to keep its homeostasis and appropriate responses. To understand the mechanisms by which these functions are achieved, single-molecule monitoring of the motilities of receptors and signaling molecules on the plasma membrane is one of the most direct approaches. This review article introduces several recent single-molecule imaging studies of receptors, including the author’s recent work on triple-color single-molecule imaging of G protein-coupled receptors. Based on these researches, advantages and perspectives of the single-molecule imaging approach to solving the mechanisms of receptor functions are illustrated.

Advances in live imaging early mouse development: exploring the researcher's interdisciplinary toolkit

Live imaging is an important part of the developmental biologist's armoury of methods. In the case of the mouse embryo, recent advances in several disciplines including embryo culture, microscopy hardware and computational analysis have all contributed to our ability to probe dynamic events during early development. Together, these advances have provided us with a versatile and powerful 'toolkit', enabling us not only to image events during mouse embryogenesis, but also to intervene with them. In this short Spotlight article, we summarise advances and challenges in using live imaging specifically for understanding early mouse embryogenesis.

Deep learning for bioimage analysis in developmental biology

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.