Single Molecule Cluster Analysis dissects splicing pathway conformational dynamics

Utilizing functional cell-free extracts to dissect ribonucleoprotein complex biology at single-molecule resolution

Cellular machineries that drive and regulate gene expression often rely on the coordinated assembly and interaction of a multitude of proteins and RNA together called ribonucleoprotein complexes (RNPs). As such, it is challenging to fully reconstitute these cellular machines recombinantly and gain mechanistic understanding of how they operate and are regulated within the complex environment that is the cell. One strategy for overcoming this challenge is to perform single molecule fluorescence microscopy studies within crude or recombinantly supplemented cell extracts. This strategy enables elucidation of the interaction and kinetic behavior of specific fluorescently labeled biomolecules within RNPs under conditions that approximate native cellular environments. In this review, we describe single molecule fluorescence microcopy approaches that dissect RNP-driven processes within cellular extracts, highlighting general strategies used in these methods. We further survey biological advances in the areas of pre-mRNA splicing and transcription regulation that have been facilitated through this approach. Finally, we conclude with a summary of practical considerations for the implementation of the featured approaches to facilitate their broader future implementation in dissecting the mechanisms of RNP-driven cellular processes. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

Single bacterial resolvases first exploit, then constrain intrinsic dynamics of the Holliday junction to direct recombination

Homologous recombination forms and resolves an entangled DNA Holliday Junction (HJ) crucial for achieving genetic reshuffling and genome repair. To maintain genomic integrity, specialized resolvase enzymes cleave the entangled DNA into two discrete DNA molecules. However, it is unclear how two similar stacking isomers are distinguished, and how a cognate sequence is found and recognized to achieve accurate recombination. We here use single-molecule fluorescence observation and cluster analysis to examine how prototypic bacterial resolvase RuvC singles out two of the four HJ strands and achieves sequence-specific cleavage. We find that RuvC first exploits, then constrains the dynamics of intrinsic HJ isomer exchange at a sampled branch position to direct cleavage toward the catalytically competent HJ conformation and sequence, thus controlling recombination output at minimal energetic cost. Our model of rapid DNA scanning followed by ‘snap-locking’ of a cognate sequence is strikingly consistent with the conformational proofreading of other DNA-modifying enzymes.

Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning

Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we use deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by hard thresholding. As a second application, the selector is used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. Finally, we show that AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.

Cooperative Analysis of Structural Dynamics in RNA-Protein Complexes by Single-Molecule Förster Resonance Energy Transfer Spectroscopy

RNA-protein complexes (RNPs) are essential components in a variety of cellular processes, and oftentimes exhibit complex structures and show mechanisms that are highly dynamic in conformation and structure. However, biochemical and structural biology approaches are mostly not able to fully elucidate the structurally and especially conformationally dynamic and heterogeneous nature of these RNPs, to which end single molecule Förster resonance energy transfer (smFRET) spectroscopy can be harnessed to fill this gap. Here we summarize the advantages of strategic smFRET studies to investigate RNP dynamics, complemented by structural and biochemical data. Focusing on recent smFRET studies of three essential biological systems, we demonstrate that investigation of RNPs on a single molecule level can answer important functional questions that remained elusive with structural or biochemical approaches alone: The complex structural rearrangements throughout the splicing cycle, unwinding dynamics of the G-quadruplex (G4) helicase RHAU, and aspects in telomere maintenance regulation and synthesis.

Protein unties the pseudoknot: S1-mediated unfolding of RNA higher order structure

Ribosomal protein S1 plays important roles in the translation initiation step of many Escherichia coli mRNAs, particularly those with weak Shine-Dalgarno sequences or structured 5′ UTRs, in addition to a variety of cellular processes beyond the ribosome. In all cases, the RNA-binding activity of S1 is a central feature of its function. While sequence determinants of S1 affinity and many elements of the interactions of S1 with simple secondary structures are known, mechanistic details of the protein's interactions with RNAs of more complex secondary and tertiary structure are less understood. Here, we investigate the interaction of S1 with the well-characterized H-type pseudoknot of a class-I translational preQ₁ riboswitch as a highly structured RNA model whose conformation and structural dynamics can be tuned by the addition of ligands of varying binding affinity, particularly preQ₁, guanine, and 2,6-diaminopurine. Combining biochemical and single molecule fluorescence approaches, we show that S1 preferentially interacts with the less folded form of the pseudoknot and promotes a dynamic, partially unfolded conformation. The ability of S1 to unfold the RNA is inversely correlated with the structural stability of the pseudoknot. These mechanistic insights delineate the scope and limitations of S1-chaperoned unfolding of structured RNAs.

The roles of structural dynamics in the cellular functions of RNAs

RNAs fold into 3D structures that range from simple helical elements to complex tertiary structures and quaternary ribonucleoprotein assemblies. The functions of many regulatory RNAs depend on how their 3D structure changes in response to a diverse array of cellular conditions. In this Review, we examine how the structural characterization of RNA as dynamic ensembles of conformations, which form with different probabilities and at different timescales, is improving our understanding of RNA function in cells. We discuss the mechanisms of gene regulation by microRNAs, riboswitches, ribozymes, post-transcriptional RNA modifications and RNA-binding proteins, and how the cellular environment and processes such as liquid-liquid phase separation may affect RNA folding and activity. The emerging RNA-ensemble-function paradigm is changing our perspective and understanding of RNA regulation, from in vitro to in vivo and from descriptive to predictive.

Structural dynamics of the N-terminal domain and the Switch loop of Prp8 during spliceosome assembly and activation

Precursor message RNA (pre-mRNA) splicing is executed by the spliceosome, a large ribonucleoprotein (RNP) machinery that is comparable to the ribosome. Driven by the rapid progress of cryo-electron microscopy (cryo-EM) technology, high resolution structures of the spliceosome in its different splicing stages have proliferated over the past three years, which has greatly facilitated the mechanistic understanding of pre-mRNA splicing. As the largest and most conserved protein in the spliceosome, Prp8 plays a pivotal role within this protein-directed ribozyme. Structure determination of different spliceosomal complexes has revealed intimate and dynamic interactions between Prp8 and catalytic RNAs as well as with other protein factors during splicing. Here we review the structural dynamics of two elements of Prp8, the N-terminal domain (N-domain) and the Switch loop, and delineate the dynamic organisation and underlying functional significance of these two elements during spliceosome assembly and activation. Further biochemical and structural dissections of idiographic splicing stages are much needed for a complete understanding of the spliceosome and pre-mRNA splicing.

SHAPE Probing Reveals Human rRNAs Are Largely Unfolded in Solution

Chemical probing experiments, coupled with empirically determined free energy change relationships, can enable accurate modeling of the secondary structures of diverse and complex RNAs. A current frontier lies in modeling large and structurally heterogeneous transcripts, including complex eukaryotic RNAs. To validate and improve on experimentally driven approaches for modeling large transcripts, we obtained high-quality SHAPE data for the protein-free human 18S and 28S ribosomal RNAs (rRNAs). To our surprise, SHAPE-directed structure models for the human rRNAs poorly matched accepted structures. Analysis of predicted rRNA structures based on low-SHAPE and low-entropy (lowSS) metrics revealed that, whereas ∼75% of Escherichia coli rRNA sequences form well-determined lowSS secondary structure, only ∼40% of the human rRNAs do. Critically, regions of the human rRNAs that specifically fold into well-determined lowSS structures were modeled to high accuracy using SHAPE data. This work reveals that eukaryotic rRNAs are more unfolded than are those of prokaryotic rRNAs and indeed are largely unfolded overall, likely reflecting increased protein dependence for eukaryotic ribosome structure. In addition, those regions and substructures that are well-determined can be identified de novo and successfully modeled by SHAPE-directed folding.

Bayesian-Estimated Hierarchical HMMs Enable Robust Analysis of Single-Molecule Kinetic Heterogeneity

Single-molecule kinetic experiments allow the reaction trajectories of individual biomolecules to be directly observed, eliminating the effects of population averaging and providing a powerful approach for elucidating the kinetic mechanisms of biomolecular processes. A major challenge to the analysis and interpretation of these experiments, however, is the kinetic heterogeneity that almost universally complicates the recorded single-molecule signal versus time trajectories (i.e., signal trajectories). Such heterogeneity manifests as changes and/or differences in the transition rates that are observed within individual signal trajectories or across a population of signal trajectories. Because characterizing kinetic heterogeneity can provide critical mechanistic information, we have developed a computational method that effectively and comprehensively enables such analysis. To this end, we have developed a computational algorithm and software program, hFRET, that uses the variational approximation for Bayesian inference to estimate the parameters of a hierarchical hidden Markov model, thereby enabling robust identification and characterization of kinetic heterogeneity. Using simulated signal trajectories, we demonstrate the ability of hFRET to accurately and precisely characterize kinetic heterogeneity. In addition, we use hFRET to analyze experimentally recorded signal trajectories reporting on the conformational dynamics of ribosomal pre-translocation (PRE) complexes. The results of our analyses demonstrate that PRE complexes exhibit kinetic heterogeneity, reveal the physical origins of this heterogeneity, and allow us to expand the current model of PRE complex dynamics. The methods described here can be applied to signal trajectories generated using any type of signal and can be easily extended to the analysis of signal trajectories exhibiting more complex kinetic behaviors. Moreover, variations of our approach can be easily developed to integrate kinetic data obtained from different experimental constructs and/or from molecular dynamics simulations of a biomolecule of interest.

Coming Together: RNAs and Proteins Assemble under the Single-Molecule Fluorescence Microscope

RNAs, across their numerous classes, often work in concert with proteins in RNA-protein complexes (RNPs) to execute critical cellular functions. Ensemble-averaging methods have been instrumental in revealing many important aspects of these RNA-protein interactions, yet are insufficiently sensitive to much of the dynamics at the heart of RNP function. Single-molecule fluorescence microscopy (SMFM) offers complementary, versatile tools to probe RNP conformational and compositional changes in detail. In this review, we first outline the basic principles of SMFM as applied to RNPs, describing key considerations for labeling, imaging, and quantitative analysis. We then sample applications of in vitro and in vivo single-molecule visualization using the case studies of pre-messenger RNA (mRNA) splicing and RNA silencing, respectively. After discussing specific insights single-molecule fluorescence methods have yielded, we briefly review recent developments in the field and highlight areas of anticipated growth.

An automated Bayesian pipeline for rapid analysis of single-molecule binding data

Single-molecule binding assays enable the study of how molecular machines assemble and function. Current algorithms can identify and locate individual molecules, but require tedious manual validation of each spot. Moreover, no solution for high-throughput analysis of single-molecule binding data exists. Here, we describe an automated pipeline to analyze single-molecule data over a wide range of experimental conditions. In addition, our method enables state estimation on multivariate Gaussian signals. We validate our approach using simulated data, and benchmark the pipeline by measuring the binding properties of the well-studied, DNA-guided DNA endonuclease, TtAgo, an Argonaute protein from the Eubacterium Thermus thermophilus. We also use the pipeline to extend our understanding of TtAgo by measuring the protein’s binding kinetics at physiological temperatures and for target DNAs containing multiple, adjacent binding sites.

Analysis of single-molecule binding assays still requires substantial manual user intervention. Here, the authors present a pipeline for rapid, automated analysis of co-localization single-molecule spectroscopy images, with a modular user interface that can be adjusted to a range of experimental conditions.

Life under the Microscope: Single-Molecule Fluorescence Highlights the RNA World

The emergence of single-molecule (SM) fluorescence techniques has opened up a vast new toolbox for exploring the molecular basis of life. The ability to monitor individual biomolecules in real time enables complex, dynamic folding pathways to be interrogated without the averaging effect of ensemble measurements. In parallel, modern biology has been revolutionized by our emerging understanding of the many functions of RNA. In this comprehensive review, we survey SM fluorescence approaches and discuss how the application of these tools to RNA and RNA-containing macromolecular complexes in vitro has yielded significant insights into the underlying biology. Topics covered include the three-dimensional folding landscapes of a plethora of isolated RNA molecules, their assembly and interactions in RNA-protein complexes, and the relation of these properties to their biological functions. In all of these examples, the use of SM fluorescence methods has revealed critical information beyond the reach of ensemble averages.

Assessing the relationship between toxicity and economic cost of oncological target agents: A systematic review of clinical trials

Target agents are peculiar oncological drugs which differ from the traditional therapies in their ability of recognizing specific molecules expressed by tumor cells and microenvironment. Thus, their toxicity is generally lower than that associated to chemotherapy, and they represent nowadays a new standard of care in a number of tumors. This paper deals with the relationship between economic costs and toxicity of target agents. At this aim, a cluster analysis-based exploration of the main features of a large collection of them is carried out, with a specific focus on the variables leading to the identification of their toxicity and related costs. The analysis of the toxicity is based on the Severe Adverse Events (SAE) and Discontinuation (D) rates of each target agent considering data published on PubMed from 1965 to 2016 in the phase II and III studies that have led to the approval of these drugs for cancer patients by US Food and Drug Administration. The construction of the dataset represents a key step of the research, and is grounded on the critical analysis of a wide set of clinical studies. In order to capture different evaluation strategies of the toxicity, clustering is performed according to three different criteria (including Voronoi tessellation). Our procedure allows us to identify 5 different groups of target agents pooled by similar SAE and D rates and, at the same time, 3 groups based on target agents' costs for 1 month and for the median whole duration of therapy. Results highlight several specific regularities for toxicity and costs. This study present several limitations, being realized starting from clinical trials and not from individual patients' data. However, a macroscopic perspective suggests that costs are rather heterogeneous, and they do not clearly follow the clustering based on SAE and D rates.

ICON: An Adaptation of Infinite HMMs for Time Traces with Drift

Bayesian nonparametric methods have recently transformed emerging areas within data science. One such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM that itself has become a workhorse in single molecule data analysis. The iHMM goes beyond the HMM by self-consistently learning all parameters learned by the HMM in addition to learning the number of states without recourse to any model selection steps. Despite its generality, simple features (such as drift), common to single molecule time traces, result in an overinterpretation of drift and the introduction of artifact states. Here we present an adaptation of the iHMM that can treat data with drift originating from one or many traces (e.g., Förster resonance energy transfer). Our fully Bayesian method couples the iHMM to a continuous control process (drift) self-consistently learned while learning all other quantities determined by the iHMM (including state numbers). A key advantage of this method is that all traces-regardless of drift or states visited across traces-may now be treated on an equal footing, thereby eliminating user-dependent trace selection (based on drift levels), preprocessing to remove drift, and postprocessing model selection based on state number.

An Introduction to Infinite HMMs for Single-Molecule Data Analysis

The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. Although the current literature on the iHMM is primarily intended for audiences in statistics, the idea is powerful and the iHMM's breadth in applicability outside machine learning and data science warrants a careful exposition. Here, we explain the key ideas underlying the iHMM, with a special emphasis on implementation, and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift.

Single-Molecule Analysis beyond Dwell Times: Demonstration and Assessment in and out of Equilibrium

We present a simple and robust technique for extracting kinetic rate models and thermodynamic quantities from single-molecule time traces. Single-molecule analysis of complex kinetic sequences (SMACKS) is a maximum-likelihood approach that resolves all statistically relevant rates and also their uncertainties. This is achieved by optimizing one global kinetic model based on the complete data set while allowing for experimental variations between individual trajectories. In contrast to dwell-time analysis, which is the current standard method, SMACKS includes every experimental data point, not only dwell times. As a result, it works as well for long trajectories as for an equivalent set of short ones. In addition, the previous systematic overestimation of fast over slow rates is solved. We demonstrate the power of SMACKS on the kinetics of the multidomain protein Hsp90 measured by single-molecule Förster resonance energy transfer. Experiments in and out of equilibrium are analyzed and compared to simulations, shedding new light on the role of Hsp90's ATPase function. SMACKS resolves accurate rate models even if states cause indistinguishable signals. Thereby, it pushes the boundaries of single-molecule kinetics beyond those of current methods.

RNA splicing process analysis for identifying antisense oligonucleotide inhibitors with padlock probe-based isothermal amplification

We report a highly sensitive method for quantifying the splicing products in different steps, enabling us to analyze the splicing process and identify ASO inhibitors.

RNA splicing, which mainly involves two transesterification steps, is a fundamental process of gene expression and its abnormal regulation contributes to serious genetic diseases. Antisense oligonucleotides (ASOs) are genetic control tools that can be used to specifically control genes through alteration of the RNA splicing pathway. Despite intensive research, how ASOs or various other factors influence the multiple processes of RNA splicing still remains obscure. This is largely due to an inability to analyze the splicing efficiency of each step in the RNA splicing process with high sensitivity. We addressed this limitation by introducing a padlock probe-based isothermal amplification assay to achieve quantification of the specific products in different splicing steps. With this amplified assay, the roles that ASOs play in RNA splicing inhibition in the first and second steps could be distinguished. We identified that 5′-ASO could block RNA splicing by inhibiting the first step, while 3′-ASO could block RNA splicing by inhibiting the second step. This method provides a versatile tool for assisting efficient ASO design and discovering new splicing modulators and therapeutic drugs.

Methodologies for studying the spliceosome's RNA dynamics with single-molecule FRET

The spliceosome is an extraordinarily dynamic molecular machine in which significant changes in composition as well as protein and RNA conformation are required for carrying out pre-mRNA splicing. Single-molecule fluorescence resonance energy transfer (smFRET) can be used to elucidate these dynamics both in well-characterized model systems and in entire spliceosomes. These types of single-molecule data provide novel information about spliceosome components and can be used to identify sub-populations of molecules with unique behaviors. When smFRET is combined with single-molecule fluorescence colocalization, conformational dynamics can be further linked to the presence or absence of a given spliceosome component. Here, we provide a description of experimental considerations, approaches, and workflows for smFRET with an emphasis on applications for the splicing machinery.

Discrimination Cascade Enabled Selective Detection of Single-Nucleotide Mutation

Owing to the significance of single nucleotide mutation (SNM) for personalized medicine, the detection of SNM with high accuracy has recently attracted considerable interest. Here, we present a kinetic method for selective detection of SNM based on a discrimination cascade constructed by combining the toehold strand displacement (TSD) and endonuclease IV (Endo IV) catalyzed hydrolysis. The single-nucleotide specificity of the two DNA reactions allows highly selective detection of all types of single nucleotide changes (including single-nucleotide insertion and deletion), achieving a high discrimination factor with a median of 491 which is comparable with recently reported methods. For the first time, the enzyme assisted nucleic acid assay was characterized by single molecule analysis on total internal reflection fluorescence microscope (TIRFM) suggesting that the two steps do not work independently and the rate of TSD can be tuned by Endo IV facilitated conformation selection. The effective discrimination of the point mutation of BRAF gene in cancer and normal cell line suggests that this method can be a prominent post-PCR genotyping assay.

Putting Humpty-Dumpty Together: Clustering the Functional Dynamics of Single Biomolecular Machines Such as the Spliceosome

The spliceosome is a biomolecular machine that, in all eukaryotes, accomplishes site-specific splicing of introns from precursor messenger RNAs (pre-mRNAs) with high fidelity. Operating at the nanometer scale, where inertia and friction have lost the dominant role they play in the macroscopic realm, the spliceosome is highly dynamic and assembles its active site around each pre-mRNA anew. To understand the structural dynamics underlying the molecular motors, clocks, and ratchets that achieve functional accuracy in the yeast spliceosome (a long-standing model system), we have developed single-molecule fluorescence resonance energy transfer (smFRET) approaches that report changes in intra- and intermolecular interactions in real time. Building on our work using hidden Markov models (HMMs) to extract kinetic and conformational state information from smFRET time trajectories, we recognized that HMM analysis of individual state transitions as independent stochastic events is insufficient for a biomolecular machine as complex as the spliceosome. In this chapter, we elaborate on the recently developed smFRET-based Single-Molecule Cluster Analysis (SiMCAn) that dissects the intricate conformational dynamics of a pre-mRNA through the splicing cycle in a model-free fashion. By leveraging hierarchical clustering techniques developed for Bioinformatics, SiMCAn efficiently analyzes large datasets to first identify common molecular behaviors. Through a second level of clustering based on the abundance of dynamic behaviors exhibited by defined functional intermediates that have been stalled by biochemical or genetic tools, SiMCAn then efficiently assigns pre-mRNA FRET states and transitions to specific splicing complexes, with the potential to find heretofore undescribed conformations. SiMCAn thus arises as a general tool to analyze dynamic cellular machines more broadly.

Lights, camera, action! Capturing the spliceosome and pre-mRNA splicing with single-molecule fluorescence microscopy

The process of removing intronic sequences from a precursor to messenger RNA (pre-mRNA) to yield a mature mRNA transcript via splicing is an integral step in eukaryotic gene expression. Splicing is carried out by a cellular nanomachine called the spliceosome that is composed of RNA components and dozens of proteins. Despite decades of study, many fundamentals of spliceosome function have remained elusive. Recent developments in single-molecule fluorescence microscopy have afforded new tools to better probe the spliceosome and the complex, dynamic process of splicing by direct observation of single molecules. These cutting-edge technologies enable investigators to monitor the dynamics of specific splicing components, whole spliceosomes, and even cotranscriptional splicing within living cells. WIREs RNA 2016, 7:683-701. doi: 10.1002/wrna.1358 For further resources related to this article, please visit the WIREs website.

Spliceosomal DEAH-Box ATPases Remodel Pre-mRNA to Activate Alternative Splice Sites

During pre-mRNA splicing, a central step in the expression and regulation of eukaryotic genes, the spliceosome selects splice sites for intron excision and exon ligation. In doing so, the spliceosome must distinguish optimal from suboptimal splice sites. At the catalytic stage of splicing, suboptimal splice sites are repressed by the DEAH-box ATPases Prp16 and Prp22. Here, using budding yeast, we show that these ATPases function further by enabling the spliceosome to search for and utilize alternative branch sites and 3' splice sites. The ATPases facilitate this search by remodeling the splicing substrate to disengage candidate splice sites. Our data support a mechanism involving 3' to 5' translocation of the ATPases along substrate RNA and toward a candidate site, but, surprisingly, not across the site. Thus, our data implicate DEAH-box ATPases in acting at a distance by pulling substrate RNA from the catalytic core of the spliceosome.

Facile creation of FRET systems from a pH-responsive AIE fluorescent vesicle

We demonstrate a facile approach to constructing aggregation induced emission (AIE) fluorescent vesicles for creation of FRET systems.

Single-Molecule Pull-Down FRET to Dissect the Mechanisms of Biomolecular Machines

Spliceosomes are multimegadalton RNA-protein complexes responsible for the faithful removal of noncoding segments (introns) from pre-messenger RNAs (pre-mRNAs), a process critical for the maturation of eukaryotic mRNAs for subsequent translation by the ribosome. Both the spliceosome and ribosome, as well as many other RNA and DNA processing machineries, contain central RNA components that endow biomolecular complexes with precise, sequence-specific nucleic acid recognition, and versatile structural dynamics. Single-molecule fluorescence (or Förster) resonance energy transfer (smFRET) microscopy is a powerful tool for the study of local and global conformational changes of both simple and complex biomolecular systems involving RNA. The integration of biochemical tools such as immunoprecipitation with advanced methods in smFRET microscopy and data analysis has opened up entirely new avenues toward studying the mechanisms of biomolecular machines isolated directly from complex biological specimens, such as cell extracts. Here, we detail the general steps for using prism-based total internal reflection fluorescence microscopy in exemplary single-molecule pull-down FRET studies of the yeast spliceosome and discuss the broad application potential of this technique.