Fluorescent Labeling of Proteins in Whole Cell Extracts for Single-Molecule Imaging

Strategies for Overcoming the Single-Molecule Concentration Barrier

Fluorescence-based single-molecule approaches have helped revolutionize our understanding of chemical and biological mechanisms. Unfortunately, these methods are only suitable at low concentrations of fluorescent molecules so that single fluorescent species of interest can be successfully resolved beyond background signal. The application of these techniques has therefore been limited to high-affinity interactions despite most biological and chemical processes occurring at much higher reactant concentrations. Fortunately, recent methodological advances have demonstrated that this concentration barrier can indeed be broken, with techniques reaching concentrations as high as 1 mM. The goal of this Review is to discuss the challenges in performing single-molecule fluorescence techniques at high-concentration, offer applications in both biology and chemistry, and highlight the major milestones that shatter the concentration barrier. We also hope to inspire the widespread use of these techniques so we can begin exploring the new physical phenomena lying beyond this barrier.

The many faces of RNA-based RNase P, an RNA-world relic

RNase P is an essential enzyme that catalyzes removal of the 5' leader from precursor transfer RNAs. The ribonucleoprotein (RNP) form of RNase P is present in all domains of life and comprises a single catalytic RNA (ribozyme) and a variable number of protein cofactors. Recent cryo-electron microscopy structures of representative archaeal and eukaryotic (nuclear) RNase P holoenzymes bound to tRNA substrate/product provide high-resolution detail on subunit organization, topology, and substrate recognition in these large, multisubunit catalytic RNPs. These structures point to the challenges in understanding how proteins modulate the RNA functional repertoire and how the structure of an ancient RNA-based catalyst was reshaped during evolution by new macromolecular associations that were likely necessitated by functional/regulatory coupling.

Protein cofactors and substrate influence Mg2+-dependent structural changes in the catalytic RNA of archaeal RNase P

The ribonucleoprotein (RNP) form of archaeal RNase P comprises one catalytic RNA and five protein cofactors. To catalyze Mg²⁺-dependent cleavage of the 5′ leader from pre-tRNAs, the catalytic (C) and specificity (S) domains of the RNase P RNA (RPR) cooperate to recognize different parts of the pre-tRNA. While ∼250–500 mM Mg²⁺ renders the archaeal RPR active without RNase P proteins (RPPs), addition of all RPPs lowers the Mg²⁺ requirement to ∼10–20 mM and improves the rate and fidelity of cleavage. To understand the Mg²⁺- and RPP-dependent structural changes that increase activity, we used pre-tRNA cleavage and ensemble FRET assays to characterize inter-domain interactions in Pyrococcus furiosus (Pfu) RPR, either alone or with RPPs ± pre-tRNA. Following splint ligation to doubly label the RPR (Cy3-RPR_{C domain} and Cy5-RPR_{S domain}), we used native mass spectrometry to verify the final product. We found that FRET correlates closely with activity, the Pfu RPR and RNase P holoenzyme (RPR + 5 RPPs) traverse different Mg²⁺-dependent paths to converge on similar functional states, and binding of the pre-tRNA by the holoenzyme influences Mg²⁺ cooperativity. Our findings highlight how Mg²⁺ and proteins in multi-subunit RNPs together favor RNA conformations in a dynamic ensemble for functional gains.

The Benefits of Unnatural Amino Acid Incorporation as Protein Labels for Single Molecule Localization Microscopy

Single Molecule Localization Microscopy (SMLM) is an imaging method that allows for the visualization of structures smaller than the diffraction limit of light (~200 nm). This is achieved through techniques such as stochastic optical reconstruction microscopy (STORM) and photoactivated localization microscopy (PALM). A large part of obtaining ideal imaging of single molecules is the choice of the right fluorescent label. An upcoming field of protein labeling is incorporating unnatural amino acids (UAAs) with an attached fluorescent dye for precise localization and visualization of individual molecules. For this technique, fluorescent probes are conjugated to UAAs and are introduced into the protein of interest (POI) as a label. Here we contrast this labeling method with other commonly used protein-based labeling methods such as fluorescent proteins (FPs) or self-labeling tags such as Halotag, SNAP-tags, and CLIP-tags, and highlight the benefits and shortcomings of the site-specific incorporation of UAAs coupled with fluorescent dyes in SMLM.

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.

Sortase-mediated fluorescent labeling of CRISPR complexes

Fluorescent labeling of proteins is a critical requirement for single-molecule imaging studies. Many protein labeling strategies require harsh conditions or large epitopes that can inactivate the target protein, either by decreasing the protein's enzymatic activity or by blocking protein-protein interactions. Here, we provide a detailed protocol to efficiently label CRISPR-Cas complexes with a small fluorescent peptide via sortase-mediated transpeptidation. The sortase tag consists of just a few amino acids that are specifically recognized at either the N- or the C-terminus, making this strategy advantageous when the protein is part of a larger complex. Sortase is active at high ionic strength, 4°C, and with a broad range of organic fluorophores. We discuss the design, optimization, and single-molecule fluorescent imaging of CRISPR-Cas complexes on DNA curtains. Sortase-mediated transpeptidation is a versatile addition to the protein labeling toolkit.

Analysis of spliceosome dynamics by maximum likelihood fitting of dwell time distributions

Colocalization single-molecule methods can provide a wealth of information concerning the ordering and dynamics of biomolecule assembly. These have been used extensively to study the pathways of spliceosome assembly in vitro. Key to these experiments is the measurement of binding times-either the dwell times of a multi-molecular interaction or times in between binding events. By analyzing hundreds of these times, many new insights into the kinetic pathways governing spliceosome assembly have been obtained. Collections of binding times are often plotted as histograms and can be fit to kinetic models using a variety of methods. Here, we describe the use of maximum likelihood methods to fit dwell time distributions without binning. In addition, we discuss several aspects of analyzing these distributions with histograms and pitfalls that can be encountered if improperly binned histograms are used. We have automated several aspects of maximum likelihood fitting of dwell time distributions in the AGATHA software package.