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Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices.

Pancreatic cancer, ranking as the third factor in cancer‐related deaths, necessitates enhanced diagnostic measures through early detection. In response, SiMoT‐Single‐molecule with a large Transistor multiplexing array, achieving a Technology Readiness Level of 5, is proposed for a timely identification of pancreatic cancer precursor cysts and is benchmarked against the commercially available chemiluminescent immunoassay SIMOA (Single molecule array) SP‐X System. A cohort of 39 samples, comprising 33 cyst fluids and 6 blood plasma specimens, undergoes detailed examination with both technologies. The SiMoT array targets oncoproteins MUC1 and CD55, and oncogene KRAS, while the SIMOA SP‐X planar technology exclusively focuses on MUC1 and CD55. Employing Principal Component Analysis (PCA) for multivariate data processing, the SiMoT array demonstrates effective discrimination of malignant/pre‐invasive high‐grade or potentially malignant low‐grade pancreatic cysts from benign non‐mucinous cysts. Conversely, PCA analysis applied to SIMOA assay reveals less effective differentiation ability among the three cyst classes. Notably, SiMoT unique capability of concurrently analyzing protein and genetic markers with the threshold of one single molecule in 0.1 mL positions it as a comprehensive and reliable diagnostic tool. The electronic response generated by the SiMoT array facilitates direct digital data communication, suggesting potential applications in the development of field‐deployable liquid biopsy. SiMoT‐Single‐Molecule with Large Transistor technology simultaneously analyzes protein and genetic markers, achieving a one‐molecule threshold in 0.1 mL. Benchmarking against SIMOA chemiluminescent ultrasensitive assay, SiMoT outperforms SIMOA in speed and overall performance. Moreover, SiMoT provides an electronic response, enhancing its suitability for direct digital data communication.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents 1 – 5 . Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred 6 . In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures 7 . However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations 8 . Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine–reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health. Research published in Nature shows that surface-enhanced Raman spectroscopy carried out with colloids can quantify a range of molecules down to concentrations at the femtomolar level.

Cryo–electron microscopy, or simply cryo-EM, refers mainly to three very different yet closely related techniques: electron crystallography, single-particle cryo-EM, and electron cryotomography. In the past few years, single-particle cryo-EM in particular has triggered a revolution in structural biology and has become a newly dominant discipline. This Review examines the fascinating story of its start and evolution over the past 40-plus years, delves into how and why the recent technological advances have been so groundbreaking, and briefly considers where the technique may be headed in the future.

The three-dimensional positions of atoms in protein molecules define their structure and their roles in biological processes. The more precisely atomic coordinates are determined, the more chemical information can be derived and the more mechanistic insights into protein function may be inferred. Electron cryo-microscopy (cryo-EM) single-particle analysis has yielded protein structures with increasing levels of detail in recent years 1 , 2 . However, it has proved difficult to obtain cryo-EM reconstructions with sufficient resolution to visualize individual atoms in proteins. Here we use a new electron source, energy filter and camera to obtain a 1.7 Å resolution cryo-EM reconstruction for a human membrane protein, the β3 GABA A receptor homopentamer 3 . Such maps allow a detailed understanding of small-molecule coordination, visualization of solvent molecules and alternative conformations for multiple amino acids, and unambiguous building of ordered acidic side chains and glycans. Applied to mouse apoferritin, our strategy led to a 1.22 Å resolution reconstruction that offers a genuine atomic-resolution view of a protein molecule using single-particle cryo-EM. Moreover, the scattering potential from many hydrogen atoms can be visualized in difference maps, allowing a direct analysis of hydrogen-bonding networks. Our technological advances, combined with further approaches to accelerate data acquisition and improve sample quality, provide a route towards routine application of cryo-EM in high-throughput screening of small molecule modulators and structure-based drug discovery. Advances in electron cryo-microscopy hardware allow proteins to be studied at atomic resolution.

Plasmonic nanostructures have immense potentials for extreme concentration of light into deep‐subwavelength spaces with giant local field intensity, which can be exploited for novel applications including nanoscale optical trapping, biosensing, and enhanced spectroscopy. Herein, a succinct overview of the potentials of engineered plasmonic nanoarchitectures of various geometries including nanoparticles, nanodimers, nanoapertures, nanoporous surfaces, and picometer‐scale gap systems for single‐molecule analysis is provided. In particular, the potentials of single plasmonic nanoparticles for single‐molecule sensing, coupled plasmonic nanodimers for few‐molecule strong coupling, metallic nanocavities for optical manipulation of single molecules, nanoporous metasurfaces for enhanced single‐molecule spectroscopy, and plasmonic picocavities for single‐molecule optomechanics are extensively discussed. Finally, as an outlook, exploiting such novel nanoarchitectures for developing innovative biosensing platforms that will be able to trap and detect objects at the single‐molecule level is forwarded. A succinct overview of the potentials of various geometries of plasmonic nanoarchitectures for single‐molecule analysis is presented. Particular focus is given to exploiting individual nanoparticles, coupled nanodimers, nanoapertures, nanoporous metasurfaces, and picocavities for single‐molecule explorations. Finally, utilizing plasmonic nanopore geometry as a potential candidate for developing innovative biosensing platform that will be able to trap and detect single‐molecules is forwarded.

Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field—trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.

High‐sensitivity flow cytometers have been developed for multi‐parameter characterization of single extracellular vesicles (EVs), but performance varies among instruments and calibration methods. Here we compare the characterization of identical (split) EV samples derived from human colorectal cancer (DiFi) cells by three high‐sensitivity flow cytometers, two commercial instruments, CytoFLEX/CellStream, and a custom single‐molecule flow cytometer (SMFC). DiFi EVs were stained with the membrane dye di‐8‐ANEPPS and with PE‐conjugated anti‐EGFR or anti‐tetraspanin (CD9/CD63/CD81) antibodies for estimation of EV size and surface protein copy numbers. The limits of detection (LODs) for immunofluorescence and vesicle size based on calibration using cross‐calibrated, hard‐dyed beads were ∼10 PE/∼80 nm EV diameter for CytoFLEX and ∼10 PEs/∼67 nm for CellStream. For the SMFC, the LOD for immunofluorescence was 1 PE and ≤ 35 nm for size. The population of EVs detected by each system (di‐8‐ANEPPS+/PE+ particles) differed widely depending on the LOD of the system; for example, CellStream/CytoFLEX detected only 5.7% and 1.5% of the tetraspanin‐labelled EVs detected by SMFC, respectively, and median EV diameter and antibody copy numbers were much larger for CellStream/CytoFLEX than for SMFC as measured and validated using super‐resolution/single‐molecule TIRF microscopy. To obtain a dataset representing a common EV population analysed by all three platforms, we filtered out SMFC and CellStream measurements for EVs below the CytoFLEX LODs as determined by bead calibration (10 PE/80 nm). The inter‐platform agreement using this filtered dataset was significantly better than for the unfiltered dataset, but even better concordance between results was obtained by applying higher cutoffs (21 PE/120 nm) determined by threshold analysis using the SMFC data. The results demonstrate the impact of specifying LODs to define the EV population analysed on inter‐instrument reproducibility in EV flow cytometry studies, and the utility of threshold analysis of SMFC data for providing semi‐quantitative LOD values for other flow cytometers.

Single-molecule orientation-localization microscopy (SMOLM) builds upon super-resolved localization microscopy by imaging orientations and rotational dynamics of individual molecules in addition to their positions. This added dimensionality provides unparalleled insights into nanoscale biophysical and biochemical processes, including the organization of actin networks, movement of molecular motors, conformations of DNA strands, growth and remodeling of amyloid aggregates, and composition changes within lipid membranes. In this review, we discuss recent innovations in SMOLM and cover three key aspects: (1) biophysical insights enabled by labeling strategies that endow fluorescent probes to bind to targets with orientation specificity; (2) advanced imaging techniques that leverage the physics of light-matter interactions and estimation theory to encode orientation information with high fidelity into microscope images; and (3) computational methods that ensure accurate and precise data analysis and interpretation, even in the presence of severe shot noise. Additionally, we compare labeling approaches, imaging hardware, and publicly available analysis software to aid the community in choosing the best SMOLM implementation for their specific biophysical application. Finally, we highlight future directions for SMOLM, such as the development of probes with improved photostability and specificity, the design of “smart” adaptive hardware, and the use of advanced computational approaches to handle large, complex datasets. This review underscores the significant current and potential impact of SMOLM in deepening our understanding of molecular dynamics, paving the way for future breakthroughs in the fields of biophysics, biochemistry, and materials science.

Detecting single molecules, which represents the ultimate level of sensitivity, is typically achieved with research-grade equipment. Here we present a low-cost, portable smartphone-based fluorescence microscope capable of detecting single-molecule fluorescence directly, i.e., without the need for any signal amplification. The setup leverages the image sensors and data handling capacity of mass-produced smartphones, it is adaptable to different smartphones and capable of detecting single molecules across the visible spectral range. We showcase this capability through single-molecule measurements on DNA origami models and super-resolution microscopy of cells by single-molecule localization microscopy. Last, we illustrate its potential as a point-of-care (POC) device by implementing a single-molecule bioassay for RNA detection. This development paves the way for innovative applications of massively distributed or personalized assays with single-molecule sensitivity in various fields such as digital bioassays, POC diagnostics, field expeditions, STEM outreach, and life science education. Loretan and colleagues present a low-cost smartphone-based microscope capable of detecting single-molecule fluorescence. This approach opens doors to personalised and widely distributed applications in diagnostics, biosensing, and science education.