At the core of molecular biology lies the intricate interplay between sequence, structure, and function. Single-molecule techniques provide in-depth dynamic insights into structure and function, but laborious assays impede functional screening of large sequence libraries. We introduce high-throughput Single-molecule Parallel Analysis for Rapid eXploration of Sequence space (SPARXS), integrating single-molecule fluorescence with next-generation sequencing. We applied SPARXS to study the sequence-dependent kinetics of the Holliday junction, a critical intermediate in homologous recombination. By examining the dynamics of millions of Holliday junctions, covering thousands of distinct sequences, we demonstrated the ability of SPARXS to uncover sequence patterns, evaluate sequence motifs, and construct thermodynamic models. SPARXS emerges as a versatile tool for untangling the mechanisms that underlie sequence-specific processes at the molecular scale.@en

Climate and justice are interconnected. However, simply raising ethical issues associated with the links between climate change, technology, and health is insufficient. Rather, policies and practices need to consider ethics ahead of time. If it is only added “after the fact,” policy will be less efficient and opportunities for carbon minimization will be lost. This will require the cooperation of people at many levels and can be guided by two essential ethical principles: distributive justice and environmental sustainability.@en

Argonaute proteins are the central effectors of RNA-guided RNA silencing pathways in eukaryotes, playing crucial roles in gene repression and defense against viruses and transposons. Eukaryotic Argonautes are subdivided into two clades: AGOs generally facilitate miRNA- or siRNA-mediated silencing, while PIWIs generally facilitate piRNA-mediated silencing. It is currently unclear when and how Argonaute-based RNA silencing mechanisms arose and diverged during the emergence and early evolution of eukaryotes. Here, we show that in Asgard archaea, the closest prokaryotic relatives of eukaryotes, an evolutionary expansion of Argonaute proteins took place. In particular, a deep-branching PIWI protein (HrAgo1) encoded by the genome of the Lokiarchaeon ‘Candidatus Harpocratesius repetitus’ shares a common origin with eukaryotic PIWI proteins. Contrasting known prokaryotic Argonautes that use single-stranded DNA as guides and/or targets, HrAgo1 mediates RNA-guided RNA cleavage, and facilitates gene silencing when expressed in human cells and supplied with miRNA precursors. A cryo-EM structure of HrAgo1, combined with quantitative single-molecule experiments, reveals that the protein displays structural features and target-binding modes that are a mix of those of eukaryotic AGO and PIWI proteins. Thus, this deep-branching archaeal PIWI may have retained an ancestral molecular architecture that preceded the functional and mechanistic divergence of eukaryotic AGOs and PIWIs.

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Proteins are the primary functional actors of the cell. While proteoform diversity is known to be highly biologically relevant, current protein analysis methods are of limited use for distinguishing proteoforms. Mass spectrometric methods, in particular, often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate fluorescence resonance energy transfer (FRET)-based single-molecule protein fingerprinting to map the location of individual amino acids and post-translational modifications within single full-length protein molecules. Our data show that both intrinsically disordered proteins and folded globular proteins can be fingerprinted with a subnanometer resolution, achieved by probing the amino acids one by one using single-molecule FRET via DNA exchange. This capability was demonstrated through the analysis of alpha-synuclein, an intrinsically disordered protein, by accurately quantifying isoforms in mixtures using a machine learning classifier, and by determining the locations of two O-GlcNAc moieties. Furthermore, we demonstrate fingerprinting of the globular proteins Bcl-2-like protein 1, procalcitonin and S100A9. We anticipate that our ability to perform proteoform identification with the ultimate sensitivity may unlock exciting new venues in proteomics research and biomarker-based diagnosis@en

At the core of molecular biology lies the intricate interplay between sequence, structure, and function. Single-molecule techniques provide in-depth dynamic insights into structure and function, but laborious assays impede functional screening of large sequence libraries. We introduce high-throughput Single-molecule Parallel Analysis for Rapid eXploration of Sequence space (SPARXS), integrating single-molecule fluorescence with next-generation sequencing. We applied SPARXS to study the sequence-dependent kinetics of the Holliday junction, a critical intermediate in homologous recombination. By examining the dynamics of millions of Holliday junctions, covering thousands of distinct sequences, we demonstrated the ability of SPARXS to uncover sequence patterns, evaluate sequence motifs, and construct thermodynamic models. SPARXS emerges as a versatile tool for untangling the mechanisms that underlie sequence-specific processes at the molecular scale.@en

Understanding the structure of biomolecules is vital for deciphering their roles in biological systems. Single-molecule techniques have emerged as alternatives to conventional ensemble structure analysis methods for uncovering new biology in molecular dynamics and interaction studies, yet only limited structural information could be obtained experimentally. Here, we address this challenge by introducing iMAX FRET, a one-pot method that allows ab initio 3D profiling of individual molecules using two-color FRET measurements. Through the stochastic exchange of fluorescent weak binders, iMAX FRET simultaneously assesses multiple distances on a biomolecule within a few minutes, which can then be used to reconstruct the coordinates of up to four points in each molecule, allowing structure-based inference. We demonstrate the 3D reconstruction of DNA nanostructures, protein quaternary structures, and conformational changes in proteins. With iMAX FRET, we provide a powerful approach to advance the understanding of biomolecular structure by expanding conventional FRET analysis to three dimensions.

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In a recent publication in Nature, Motone et al. report the development of a protein sequencing method using nanopores that enables the reading of long protein strands. This method allows for multi-pass re-reading and can detect single amino acid substitutions as well as post-translational modifications (PTMs).@en

The inherent properties of 2D materials—light mass, high out-of-plane flexibility, and large surface area—promise great potential for precise and accurate nanomechanical mass sensing, but their application is often hampered by surface contamination. Here we demonstrate a tri-layer graphene nanomechanical resonant mass sensor with sub-attogram resolution at room temperature, fabricated by a bottom-up process. We found that Joule-heating is effective in cleaning the graphene membrane surface, which results in a large improvement in the stability of the resonance frequency. We characterized the sensor by depositing Cr metal using a stencil mask and found a mass-resolution that is sufficient to weigh very small particles, like large proteins and protein complexes, with potential applications in the fields of nanobiology and medicine.

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Fluorescence resonance energy transfer (FRET) is a photophysical phenomenon that has been repurposed as a biophysical tool to measure nanometer distances. With FRET by DNA eXchange, or FRET X, many points of interest (POIs) in a single object can be probed, overcoming a major limitation of conventional single-molecule FRET. In FRET X, short fluorescently labeled DNA imager strands specifically and transiently bind their complementary docking strands on a target molecule, such that at most a single FRET pair is formed at each point in time and multiple POIs on a single molecule can be readily probed. Here, we describe the sample preparation, image acquisition, and data analysis for structural analysis of DNA nanostructures with FRET X.

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Next-generation sequencing techniques have led to a new quantitative dimension in the biological sciences. In particular, integrating sequencing techniques with biophysical tools allows sequence-dependent mechanistic studies. Using the millions of DNA clusters that are generated during sequencing to perform high-throughput binding affinity and kinetics measurements enabled the construction of energy landscapes in sequence space, uncovering relationships between sequence, structure, and function. Here, we review the approaches to perform ensemble fluorescence experiments on next-generation sequencing chips for variations of DNA, RNA, and protein sequences. As the next step, we anticipate that these fluorescence experiments will be pushed to the single-molecule level, which can directly uncover kinetics and molecular heterogeneity in an unprecedented high-throughput fashion. Molecular biophysics in sequence space, both at the ensemble and single-molecule level, leads to new mechanistic insights. The wide spectrum of applications in biology and medicine ranges from the fundamental understanding of evolutionary pathways to the development of new therapeutics.

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Nanomechanical resonator devices are widely used as ultrasensitive mass detectors for fundamental studies and practical applications. The resonance frequency of the resonators shifts when a mass is loaded, which is used to estimate the mass. However, the shift signal is often blurred by the thermal noise, which interferes with accurate mass detection. Here, we demonstrate the reduction of the noise interference in mass detection in suspended graphene-based nanomechanical resonators, by using applied machine learning. Featurization is divided into image and sequential datasets, and those datasets are trained and classified using 2D and 1D convolutional neural networks (CNNs). The 2D CNN learning-based classification shows a performance with f1-score over 99% when the resonance frequency shift is more than 2.5% of the amplitude of the thermal noise range.

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Förster resonance energy transfer (FRET) is a useful phenomenon in biomolecular investigations, as it can be leveraged for nanoscale measurements. The optical signals produced by such experiments can be analyzed by fitting a statistical model. Several software tools exist to fit such models in an unsupervised manner but lack the flexibility to adapt to different experimental setups and require local installations. Here, we propose to fit models to optical signals more intuitively by adopting a semisupervised approach, in which the user interactively guides the model to fit a given data set, and introduce FRETboard, a web tool that allows users to provide such guidance. We show that our approach is able to closely reproduce ground truth FRET statistics in a wide range of simulated single-molecule scenarios and correctly estimate parameters for up to 11 states. On in vitro data, we retrieve parameters identical to those obtained by laborious manual classification in a fraction of the required time. Moreover, we designed FRETboard to be easily extendable to other models, allowing it to adapt to future developments in FRET measurement and analysis.

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Single-molecule protein identification is an unrealized concept with potentially ground-breaking applications in biological research. We propose a method called FRET X (Förster Resonance Energy Transfer via DNA eXchange) fingerprinting, in which the FRET efficiency is read out between exchangeable dyes on protein-bound DNA docking strands and accumulated FRET efficiencies constitute the fingerprint for a protein. To evaluate the feasibility of this approach, we simulated fingerprints for hundreds of proteins using a coarse-grained lattice model and experimentally demonstrated FRET X fingerprinting on model peptides. Measured fingerprints are in agreement with our simulations, corroborating the validity of our modeling approach. In a simulated complex mixture of >300 human proteins of which only cysteines, lysines, and arginines were labeled, a support vector machine was able to identify constituents with 95% accuracy. We anticipate that our FRET X fingerprinting approach will form the basis of an analysis tool for targeted proteomics.

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In recent years, single molecule technology has experienced a rapid growth, with exciting developments in fundamental research and real-world applications. Detecting and studying biological phenomena on the
single molecule level requires a unique synergy between researchers working on instrumentation, physics, and the life sciences. In the iScience special issue ‘‘Single Molecule Technology – From Biotechnology to
Biomedical Applications’’, guest edited by Amit Meller and Chirlmin Joo (Figure 1), we are highlighting a variety of research on nanopore technology, single molecule fluorescence, and a selection of other ultra-
sensitive detection methods. More content in the special issue can be found here: https://www.sciencedirect.com/journal/iscience/special-issue/10PGSBV55N0. The guest editors in this backstory share their thoughts on what is currently exciting in the field, and the advances they think will make an impact in the near future.@en

Single-cell profiling methods have had a profound impact on the understanding of cellular heterogeneity. While genomes and transcriptomes can be explored at the single-cell level, single-cell profiling of proteomes is not yet established. Here we describe new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell profiling. These technologies will in turn facilitate biological discovery and open new avenues for ultrasensitive disease diagnostics.

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Single-molecule FRET is a versatile tool to study nucleic acids and proteins at the nanometer scale. However, currently, only a couple of FRET pairs can be reliably measured on a single object, which makes it difficult to apply single-molecule FRET for structural analysis of biomolecules. Here, we present an approach that allows for the determination of multiple distances between FRET pairs in a single object. We use programmable, transient binding between short DNA strands to resolve the FRET efficiency of multiple fluorophore pairs. By allowing only a single FRET pair to be formed at a time, we can determine the pair distance with subnanometer precision. The distance between other pairs are determined by sequentially exchanging DNA strands. We name this multiplexing approach FRET X for FRET via DNA eXchange. Our FRET X technology will be a tool for the high-resolution analysis of biomolecules and nanostructures.

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Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step detection method, AutoStepfinder, that determines steps in large datasets without requiring prior knowledge on the noise contributions and location of steps. The analysis is based on a series of partition events that minimize the difference between the data and the fit. A dual-pass strategy determines the optimal fit and allows AutoStepfinder to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of AutoStepfinder provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.

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In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).

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Single-molecule localization microscopy (SMLM) is a potent tool to examine biological systems with unprecedented resolution, enabling the investigation of increasingly smaller structures. At the forefront of these developments is DNA-based point accumulation for imaging in nanoscale topography (DNA-PAINT), which exploits the stochastic and transient binding of fluorescently labeled DNA probes. In its early stages the implementation of DNA-PAINT was burdened by low-throughput, excessive acquisition time, and difficult integration with live-cell imaging. However, recent advances are addressing these challenges and expanding the range of applications of DNA-PAINT. We review the current state of the art of DNA-PAINT in light of these advances and contemplate what further developments remain indispensable to realize live-cell imaging.

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