Single Fluorogens and Orientation-Localization Microscopy for Quantifying Chemical and Biomolecular Dynamics at the Nanoscale

Graphical Abstract

Conspectus

Many chemical systems look uniform only because ensemble measurements average over their most interesting molecules. Electron-transfer rates vary across electrode surfaces; lipid membranes contain nanodomains with distinct packing and fluidity; peptide aggregates exhibit local polymorphism; and biomolecular condensates contain transient networks of interactions that are blurred in ensemble images. A central challenge in chemical imaging is not simply to see smaller structures, but to measure chemical variables such as polarity, redox potential, and molecular confinement at the single-molecule level. In this Account, we describe how fluorogens–molecules whose brightness, blinking, spectral shifts, orientation, rotational mobility, and motion are directly shaped by their surroundings–can be integrated with single-molecule orientation-localization microscopy (SMOLM) to produce multidimensional images of nanoscale chemistry.

The key idea is to treat a fluorophore as a molecular sensor instead of as a passive label. Its brightness and blinking can report local polarity or redox state; its position identifies where the event occurs; its orientation and rotational freedom reveal local order and confinement; and its trajectory reports transport or local viscoelasticity. By constructing single-molecule (SM) polarization-sensitive microscopes, we measure molecular orientation and wobble alongside nanoscale position and intensity. Thus, SMOLM provides the optical and computational machinery to extract multidimensional chemical observables from single fluorogens.

We first highlight SM electrochemical imaging, in which redox-sensitive fluorophores report local midpoint potentials, revealing spatial variations in electron-transfer behavior hidden by ensemble averaging and showing how redox mediators can homogenize and shift fluorophore reduction. We then turn to lipid membranes, where solvatochromic lipophilic probes such as Nile red and merocyanine 540 sense local packing, polarity, and fluidity. By measuring both rotational and translational dynamics, SMOLM shows that cholesterol-rich membrane regions can constrain molecular orientation while permitting heterogeneous, jump-like lateral motion and that cellular membrane curvature and composition generate spatially varying orientational order.

In peptide and amyloid assemblies, transiently binding fluorogens convert surface chemistry into nanoscale structural information. Nile red orientations report the alignment, disorder, and polymorphism of fibrillar architectures, distinguishing mature regions from newly deposited or disordered branches and revealing helical bilayer-like organization that is inaccessible to position-only super-resolution microscopy. Finally, we discuss biomolecular condensates, where environmentally sensitive fluorogens expose hidden nanoscale organization within droplets that often appear homogeneous by diffraction-limited imaging. SM trajectories identify hydrophobic hubs, speed-resolved maps reveal confined microphases, fluorogen mobility senses sequence variations in intrinsically disordered proteins, and SM tracking uncovers anisotropic diffusion along RNA condensate boundaries.

Together, these examples show that the richest information in fluorescence microscopy often lies not only in where a molecule is but also in how it behaves. The broader message is that fluorophores should be chosen not only for brightness or photostability but also for the chemical variables they can report. By uniting fluorogen chemistry, optical design, and computational imaging, single-fluorogen orientation-localization microscopy provides a route to image chemical environments, molecular organization, and nonequilibrium dynamics with nanoscale precision. Essentially, single fluorogens emerge as quantitative reporters of chemistry in action.

Introduction

The first step in high-precision chemical imaging is detecting a target molecule above background noise. Single molecules (SM) were first detected optically via absorption⁵ and fluorescence⁶ over 35 years ago. Room-temperature detection quickly followed,⁷ but a key challenge remained: How does one guarantee that the collected signal originates from one, and only one, molecule, especially in crowded living cells? Wave diffraction limits the minimum widths of both a focused excitation spot and the optical point-spread function (PSF), i.e., the image of an SM, to a diameter of approximately 0.61λ/NA ≈ 250 nm, where λ is the wavelength of light and NA is the numerical aperture of the microscope’s objective lens.⁸ Because fluorescence is incoherent, two noninteracting but closely spaced molecules are nearly indistinguishable from a single bright molecule⁹ unless single-photon coincidence analyses are performed.¹⁰ Soon, spectroscopists developed excitation and emission spectra as means to distinguish spatially overlapping molecules,^11,12 and today, super-resolved fluorescence microscopy typically modulates fluorophores’ emissive states over time to do the same.^7,13,14 In single-molecule localization microscopy (SMLM),^15–18 various mechanisms^19–24 serve as optical, chemical, or electrical “control knobs” to induce blinking.

Traditional labeling approaches treat fluorophores as passive labels. That is, rigid attachment to a biological target enables a fluorophore’s transition dipole moment to become a reporter of the target’s translational and rotational motions (Fig. 1A).^25,26 Building upon prior work,^27–29 our lab views single-molecule imaging through a different lens: a powerful, underexploited opportunity to treat a fluorophore as an active molecular sensor. For example, noncovalent interactions between a fluorophore and a lipid bilayer can cause it to align with its surroundings (Fig. 1B).³⁰ Fluorescence emissive states can also be modulated by charge-transfer interactions with both soluble and insoluble targets, opening a window into electrochemical processes (Fig. 1C).^1,31,32 Finally, Förster resonance energy transfer (smFRET) between donor and acceptor fluorophores can reveal interactions between and conformational states within biomolecules (Fig. 1D).^33,34 A central theme of this Account is that single fluorogens, when properly chosen and interpreted, act as multidimensional reporters of local chemistry.⁴

Figure 1:

Single-fluorogen sensing and single-molecule orientation-localization microscopy (SMOLM) for quantifying biomolecular dynamics. (A) Rigidly attached fluorophores enable their orientations to report rotations of their target. Green arrows: dipole emitters. (B) Transient binding of fluorogenic molecules activates their fluorescence and induces orientational alignment. Gray arrows: nonemissive (dark) molecules. (C) Charge-transfer reactions activate or deactivate ATTO 655 fluorescence. (D) Single-molecule Förster resonance energy transfer (smFRET) reports donor–acceptor interactions via distance-dependent (and orientation-dependent, not shown) energy transfer: (i) short distances yield the highest FRET efficiency (acceptor emission); (ii) intermediate distances with moderate efficiency; (iii) large distances with negligible coupling (large donor emission). (E) Simplified SMOLM schematic. Purple arrows: polarized illuminations. CAM1, CAM2: cameras. Inset: Example (left) pixOL⁵⁶ and (right) vortex^54,55 phase masks. Color bar: phase (rad). (F) Detection, estimation, and reconstruction of SMOLM data yield biomolecular insights. Left: Multiframe SMOLM image acquisition. Middle: Estimating molecular intensities, spatial positions, and orientations (with cones representing rotational mobility) in each frame. Right: Reconstructed biomolecular dynamics from multidimensional measurements: (i) Molecules exhibit translational motion but consistent orientations; (ii) Position-orientation heterogeneity along fibril-like structures, and molecular orientations vary systematically along each fibril; (iii) Intensity-position-orientation heterogeneity within a fibril network, where higher intensities and increased rotational freedom are observed at fibril intersections.

The second step in high-precision chemical imaging is estimation. The 20 years since the first demonstrations of SMLM^15–18 have brought tremendous advances beyond leaps in resolution.^35–37 Three-dimensional spatial information,³⁸ fluorescence emission spectra,^29,39,40 and excited state lifetimes^29,41 are all measurable in parallel with super-resolved images. Our laboratory is among the groups advancing single-molecule orientation-localization microscopy (SMOLM),^42,43 in which molecular orientations are measured simultaneously alongside their locations. Here, molecular orientation refers to the angular direction, e.g., (θ,ϕ) in spherical coordinates, of a fluorogen’s transition dipole moment, whereas rotational mobility or wobble (quantified as a solid angle Ω over the hemisphere) describes the range of angular motion captured during a single camera exposure.

One may infer molecular orientation by varying excitation polarizations (Fig. 1E); the emitted fluorescence intensity responds proportionally to absorption P_abs ∝ |E_ex · μ_abs|², where E_ex represents the excitation electric field and μ_abs represents the orientation of the molecule’s absorption dipole moment. Similarly, fluorescence emission is both polarized and anisotropic,⁴⁴ and dipole-spread function (DSF) engineering,⁴⁵ i.e., special phase masks (Fig. 1E inset), may be used to encode molecular orientation into the shapes of images produced by a polarization-sensitive microscope. Due to the limited number of photons available before photobleaching, both microscope hardware and image-analysis algorithms^42,46–48 must be engineered to minimize photon losses and handle severe Poisson shot noise; details of optimal SMOLM design are covered excellently elsewhere and are not within the scope of this Account.^{42,43,45,49–59}

What emerge are trajectories of SM brightnesses, positions, and orientations that reveal chemical and biomolecular dynamics (Fig. 1F) that cannot be detected via high-resolution imaging of static structures. In this Account, we highlight how single fluorogens and SMOLM provide unique insights into charge transfer with nanoporous electrodes and redox mediators,¹ the composition and fluidity of lipid membranes,^2,30,57 the dynamic architectures of peptide assemblies,^3,57,60,61 and the transient interaction networks within biomolecular condensates.^4,62,63 Each of these targets has unique chemical properties, and specific fluorogens and SMOLM designs have proven to be effective for studying each system (Table 1). Across these examples, we see a consistent picture: the richest information often lies not only in where molecules are but also in how they orient, move, and fluctuate in response to their environments.

Table 1:

Studies Utilizing Single-Fluorogen Sensing and/or Single-Molecule Orientation-Localization Microscopy (SMOLM) Highlighted in This Account

Fluorophores	Sensitivity	Microscopes	(Bio)chemical targets
ATTO 655, Alexa Fluor 647, ATTO 647N	electron transfer: oxidation, reduction	standard epifluorescence	nanoporous electrodes, phenazine methosulfate, riboflavin1
Nile red (NR) and derivatives, merocyanine 540 (MC540), DiI	polarity: membrane composition and fluidity	tri-spot,30,49 duo-spot,30 vortex,55 pixOL,56 radially and azimuthally polarized (raPol),2 radially and azimuthally polarized multi-view reflector (raMVR),57 POLCAM,48 4polar3D59	supported lipid bilayers,2,30,48,55–57,59 cellular membranes48,57
NR, Nile blue (NB), MC540	polarity: hydrophobicity	xy-polarized (xyPol),60 vortex,55 pixOL,56 raMVR,57 POLCAM48	amyloid-beta fibrils3,55,57,60,61 α-synuclein fibrils,48 KFE8 fibrils,3 heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) condensates4
Alexa Fluor 488, Alexa Fluor 568, Alexa Fluor 647, silicon rhodamine, PA-Janelia Fluor 549	high-affinity labeling or covalent conjugation	standard epifluorescence, POLCAM,48 4polar3D59	actin filaments,48,59 serine-rich splicing factor (SRSF1) microphases63
SYTOX orange, YO-PRO-1	DNA and RNA intercalation	standard epifluorescence, vortex54	λ-DNA,54 plectonemic supercoiled DNA,54 poly(rA) and poly(rC) condensates62

Charge Transfer

Studying electron transfer at the nanoscale has historically required scanning a small electrode across a sample of interest,⁶⁴ e.g., conductive probe atomic force microscopy.⁶⁵ However, thanks to their versatility, fluorogenic probes have enabled optical studies of chemical reactions, including catalysis and charge transfer, at the single-molecule level.^28,31,32,66 We recently reported single-molecule electrochemical (SMEC) imaging¹ of oxazine, cyanine, and rhodamine dyes on a nanoporous antimony-doped tin oxide (nATO)-coated indium tin oxide (ITO) coverslip. Critically, the nATO layer provides an electrical interface to the target fluorophores, while also immobilizing them (at least temporarily) so that they can be tracked using an epifluorescence microscope. Similar electrical interfaces have been utilized to modulate fluorophore blinking in SMLM.^23,24 The fluorescence of ATTO 655, Alexa Fluor 647, and ATTO 647N was reversibly controlled by applying various potentials to the nATO electrode; applying a reducing potential pushed the dyes into a dark, nonemissive state (Fig. 1C). The electrochemical current associated with the redox transformation of a single dye is too small to measure electronically, but SMEC imaging can measure the redox potential E° optically by counting the number of emitting molecules N_on within each frame and fitting data from a cyclic voltammetry scan to an appropriate sigmoid function.¹

Across the nATO surface, we observed spatial variations in the ATTO 655 potential map (Fig. 2A), especially during the forward scan toward negative potentials. If the electron-transfer process is unfavorable between the fluorophores and a certain region of the nATO electrode, then an extra voltage, called an overpotential, may be required beyond what is thermodynamically predicted to induce charge transfer to ATTO 655. Importantly, SMEC imaging resolves localized low midpoint potentials, which would otherwise be hidden by ensemble averaging. Adding phenazine methosulfate (PMS), a redox mediator, greatly promotes electron transfer and produces much more uniform dye reduction maps (Fig. 2B). Further, SMEC imaging directly quantifies a positive shift in the midpoint potential mediated by PMS (Fig. 2C), evidenced by counting the number N_on of emitting ATTO 655 molecules detected during the voltage scan (Fig. 2D). Thus, SMEC imaging is a powerful tool for elucidating electrochemical interactions in situ with SM sensitivity.

Figure 2:

Midpoint electrochemical potential maps calculated from forward and reverse potential scans of ATTO655 (A) without and (B) with 0.04 mM phenazine methosulfate (PMS). Bin size: 1 × 1 μm². Scale bar: 5 μm. (C) Line profiles indicate fluctuations of the midpoint potential along a 1 × 16 μm² region (white dotted line in A, B) on the nanoporous antimony-doped tin oxide (nATO) surface. Thick lines are midpoint potentials extracted from averaged N_on trajectories across three scan cycles and thin lines represent the data of each scan cycle. White arrows in A and black arrows in C indicate two representative regions exhibiting relatively low-midpoint potentials. (D) The raw N_on responses of two representative 1 × 1 μm² bins (blue dot for bin #1 and green dot for bin #2 in A, B) from the average of three forward scan cycles (blue profiles for bin #1 and green profiles for bin #2). The red dotted profiles represent fits to a sigmoid function, from which the midpoint potentials are extracted. Adapted with permission from ref. 1. Copyright 2024 The Royal Society of Chemistry.

Membrane Composition, Dynamics, and Fluidity

Super-resolved imaging of lipid membranes can be achieved without lipid modification by leveraging the points accumulation for imaging in nanoscale topography (PAINT) mechanism;¹⁸ solvatochromic dyes like NR and MC540⁶⁷ remain dark in aqueous solution until they collide and bind transiently to a lipid bilayer, causing them to emit fluorescence for ~100 ms, then diffuse back into solution and return to the dark state (Fig. 1B). Their transient binding and solvatochromism have been used extensively in SMLM to probe membrane properties by measuring emission spectral shifts^68–70 or changes in lateral diffusivity.^71,72

SMOLM provides unique insights into lipid membrane composition and nanoscale organization by resolving the orientation dynamics of individual fluorophores. We first demonstrated single-fluorophore orientation measurements in supported lipid bilayers (SLBs).³⁰ For example, the two long acyl chains of DiI insert into the nonpolar core of the bilayer formed by DPPC (1,2-Dipalmitoylsn-glycero-3-phosphocholine), while the chromophore headgroup resides in the charged polar region (Fig. 3A(i)). Thus, DiI’s transition dipole moment is predominantly parallel to the membrane surface (θ = 73.6 ± 21.2°, median±std) with limited wobble (Ω = 0.21π ± 0.41π sr) (Fig. 3B(i)). In contrast, MC540 in a more fluid membrane composed of DOPC (1,2-Dioleoyl-sn-glycero-3-phosphocholine) preferentially orients perpendicularly to the membrane (θ = 17.5 ± 14.2°) and displays a larger wobble (Ω = 0.71π ± 0.22π sr) (Figs. 3A(ii),B(ii)). We further observed that NR (Fig. 3A(iii)) exhibits strong sensitivity to cholesterol concentration in DPPC SLBs. Without cholesterol, NR shows a tilted out-of-plane orientation (θ = 26.0 ± 19.2°) with a relatively larger wobble (Ω = 0.96π ± 0.31π sr), but as cholesterol concentration increases (Fig. 3B(iii)), it becomes more perpendicular (θ = 8.7 ± 7.7°, 40% cholesterol) and exhibits reduced wobble (Ω = 0.26π ± 0.18π sr). Six-dimensional SMOLM, i.e., imaging 3D molecular positions and 3D orientations, shows that NR bound to spherical DPPC SLBs containing 40% cholesterol (Fig. 3A(iii)) orients perpendicularly to the sphere’s surface (Fig. 3C(i,ii)).⁵⁷

Figure 3:

SMOLM of lipid membranes. (A) Orientation and wobble of (i) DiI, (ii) merocyanine 540 (MC540), and (iii) Nile red (NR) within supported lipid bilayers (SLBs). (B) Polar angle θ and wobble (solid cone angle Ω) of single (i) DiI in DPPC, (ii) MC540 in DOPC, and (iii) NR in DPPC with varying cholesterol (chol) concentrations. Inset: median polar and solid angles across different cholesterol concentrations. Solid lines indicate the 1st/3rd quartiles, with their intersection denoting the median; dashed lines mark the 9th/91st percentiles. (C) (i) x-z view of NR localizations on a 2-μm-diameter spherical SLB; (ii) corresponding cross-sectional view of the dotted region in (i). Line segments indicate molecular orientation and are color-coded by (i) polar angle θ and (ii) azimuthal angle ϕ. (D,E) Representative NR trajectories showing (D) rotational and (E) translational diffusion within DPPC SLBs treated with various MβCD-chol concentrations. Circles and diamonds represent the first (t₀) and last frame (t_e), respectively. (F) Lateral displacements Δr accumulated over all NR trajectories; the purple shaded region represents localizations within cholesterol-rich regions of the SLB. Inset: mean-squared displacement (nm²) versus time lag Δt, with error bars representing the 33rd/67th percentile. Scale bars: (C) 500 nm, (E) 50 nm. Adapted with permission from ref. 30. Copyright 2020 Wiley-VCH. Adapted with permission from ref. 57. Copyright 2023 Springer Nature. Adapted with permission from ref. 2. Copyright 2022 American Chemical Society.

In another study, SMOLM was utilized to track fluidity changes within SLBs facilitated by methyl-β-cyclodextrin loaded with cholesterol (MβCD-chol).² Simultaneous tracking of NR positions and orientations reveals that NR’s rotational diffusion is constrained by cholesterol deposition (Fig. 3D), while its translational motions successively increase (Fig. 3E). Larger NR displacements occur predominantly within cholesterol-rich domains (Fig. 3F). The mean squared displacement (MSD) of NR trajectories scales sublinearly with time (Fig. 3F, inset), indicating inhomogeneous, largely confined diffusion interspersed with occasional long-range jumps. Therefore, combined position-orientation measurements reveal nanoscale chemical environments, molecular interactions, and dynamic heterogeneity within membranes that are difficult to sense using conventional SM imaging and tracking.

Beyond synthetic lipid systems, we extended SMOLM to imaging the membrane fluidity of fixed HEK-293T cells (Fig. 4).⁵⁷ MC540 exhibits highly constrained rotational mobility at the cell-coverslip interface, whereas larger wobble angles are observed at membrane protrusions, suggesting higher fluidity in curved membranes (Fig. 4D). The orientations of individual MC540 molecules exhibit spatial heterogeneity, including perpendicular (Fig. 4C, F(ii)), parallel (Fig. 4E, F(iii,iv)), and intermediate (Fig. 4F(i)) binding configurations relative to the membrane surface. These variations in orientation and rotational mobility demonstrate SMOLM’s power to characterize heterogeneity in lipid composition, packing, and membrane phase at the single-molecule level.

Figure 4:

6D SMOLM of HEK 293T cells. (A) Axial position z. Inset: diffraction-limited image from all raMVR imaging channels. Scale bar: 2 μm. (B) Azimuthal angle ϕ. (C) y-z view and (D,E) x-y views of boxed regions in (B). Lines are oriented and color-coded by (C) polar angle θ, (D) wobble angle Ω, and (E) azimuthal angle ϕ. Scale arrows, (A,B) 2 μm and (C-E) 500 nm. Inset in (D): wobble angle Ω within the yellow and white boxed areas. (F) MC540 tilt Δϕ relative to the membrane surface within the white boxed areas in (D,E). Adapted with permission from ref. 57. Copyright 2023 Springer Nature.

Peptide Assemblies, Fibrils, and Dynamics

SMOLM also provides a powerful approach for mapping the nanoscale organization and structural heterogeneity of peptides that self-assemble into cross-β protofilaments, fibrils, and higher-order structures, which are closely associated with neuronal toxicity and pathogenesis in neurodegenerative diseases.^73,74 Transiently binding, amyloidophilic fluorophores thioflavin T⁷⁵ and NR⁷⁶ have been employed in spectral^77,78 and polarization-resolved^79,80 measurements to map amyloid surface hydrophobicity. These fluorogens are dark in aqueous solution but fluoresce upon transiently binding to fibril surfaces.^81,82 After residence times of 10–60 ms, they unbind and return to a dark state.

SMOLM extends these techniques by measuring the orientations of individual fluorophores bound to fibril surfaces, thereby revealing their nanoscale architecture. For example, NR binds to amyloid-beta 42 (Aβ42) fibril networks with orientations predominantly parallel to the fibrils’ long axes (Figs. 1F(ii), 5A–C).⁶⁰ Furthermore, we observed local variations for short “branches” in the network (Fig. 5D). One branch shows a diverse range of orientations and large wobble angles, likely reflecting disordered binding sites associated with new depositions (Fig. 5D(i)), whereas another displays relatively well-ordered NR orientations (Fig. 5D(ii)), indicative of a more mature fibril branch.

Figure 5:

SMOLM of cross-β self-assemblies. (A) NR molecules bound to an Aβ42 fibril network, color-coded by mean azimuthal orientation ϕ. Inset: main binding mode of NR to β-sheets. (B-D) NR orientations in boxed regions of (A). Lines are oriented and color-coded by azimuthal angle ϕ. (E) 3D orientation distributions of NR molecules bound to (i) Aβ42, (ii) KFE8^L and (iii) KFE8^D fibrils in the u_x ≥ 0 hemisphere. (F) (i, iii) SMLM and (ii, iv) SMOLM images of KFE8^L and KFE8^D, respectively. (G) Fibril backbone tilt angle δ as measured by NR orientations (bin size: 1°). (H) Schematic of NR (red double-headed arrow) binding to a helical bilayer; (x, y, z) denote spatial coordinates; τ represents helical phase as winding around the long axis (x and u_x). Scale bars: (A) 1 μm, (B,C,D,F) 100 nm. Adapted with permission from ref. 60. Copyright 2020 Optical Society of America. Adapted with permission from ref. 3. Copyright 2024 American Chemical Society.

Importantly, SMOLM measures the full distribution of various NR behaviors as they bind to fibrils, yielding quantitative observations of fibrillar structures. For example, we analyzed the NR orientations relative to the fibrils’ long axes u_x, calling it the backbone tilt angle δ.³ For Aβ42, NR binds to fibrils with small δ angles (represented as a dense cluster near u_x in Fig. 5E(i)). In contrast, NR exhibited larger δ angles when binding to self-assembled fibrils of the engineered peptide KFE8 (Ac-FKFEFKFE-NH₂) (Fig. 5E,F).^83,84 Careful analyses of these data reveal two key insights. First, the larger δ angles for KFE8 directly show that its fibrils are more tightly wound, i.e., they exhibit a smaller pitch-to-diameter ratio, than those of Aβ42 (Fig. 5G). Further, the variation in δ is not explainable by measurement noise alone; rather, the NR orientations reveal that both KFE8 and Aβ42 adopt a double-layer helical ribbon architecture (Fig. 5H), and NR can bind to either layer. The presence of both layers causes larger variations in the δ than a single layer alone would. These structural features are not observable via conventional SMLM.

Transiently binding fluorogens enable longitudinal imaging of fibril dynamics over time scales ranging from hours to days.⁸⁵ Using NB, we monitored the dynamics of Aβ42 polymerization and decay.⁶¹ Remodeling was quantified using a localization-based metric η, allowing fibrillar segments to be categorized as decaying, stable, or growing (Fig. 6A). SMOLM further correlates these with molecular-scale orientations. NB shows well-ordered orientations (small standard deviation of azimuthal angle σ_ϕ and small wobble angles Ω) in stable Aβ42 assemblies (Fig. 6B(i)). In growing fibrils, NB orientations become increasingly uniform (decreasing σ_ϕ) (Fig. 6B(ii)), whereas we observed pronounced disorder (increasing σ_ϕ and large Ω) in a decaying fibril (Fig. 6B(iii)).

Figure 6:

SMOLM of Aβ42 fibril remodeling and dynamics. (A) SMLM images quantify morphological change η = (N_f − N_i)/(N_f + N_i), where N_i and N_f denote the initial and final number of NB localizations collected over time, respectively: (i) stable (η = −0.067), (ii) growth (η = 0.423), and (iii) decay (η = −0.479). (B) (Top) SMLM images, SMOLM maps of (middle) orientation (azimuthal angle ϕ) and (bottom) wobble (solid cone angle Ω) for (i) stable (η = 0.073), (ii) growth (η = 0.661), and (iii) decay (η = −0.69). Lines are color-coded by ϕ and scatter points by Ω. (C) y-z views of all NR localizations on lipid-coated spheres with and without Aβ42. Lines are oriented and color-coded by the measured polar angle θ. (D) Localizations within the z-slices marked in (C). Lines denote azimuthal angle ϕ and are color-coded by the wobble angle Ω. (E) Wobble angles Ω within the selected regions in (D). Scale bars: (A,B) 60 nm, (D) 500 nm. Adapted with permission from ref. 61. Copyright 2024 American Chemical Society. Adapted with permission from ref. 57. Copyright 2023 Springer Nature.

Another study characterized amyloid-lipid interaction using SMOLM (Fig. 6C–D). NR shows relatively small wobble within a pure lipid vesicle (Fig. 6E(i), Ω = 0.54π ± 0.30π sr) compared to larger wobble within Aβ42-disrupted bilayers (Fig. 6E(ii–iv), (ii) Ω = 0.95π ± 0.36π sr, (iii) Ω = 0.74π ± 0.38π sr, (iv) Ω = 0.67π ± 0.37π sr). We also observed a small Aβ42 aggregate attached to a spherical SLB (Fig. 6E(v)), suggesting that lipids may promote the formation of oligomers and protofibrils. However, NR remains aligned along the fibril axis and relatively rotationally fixed when bound to a mature fibril (Fig. 6E(vi), Ω = 0.22π ± 0.21π sr), consistent with other studies.^3,55,60 These discoveries further highlight the capabilities of transient binding and SMOLM for visualizing nanoscale structural heterogeneity within and dynamic remodeling of peptide assemblies.

Biomolecular Condensates

Biomolecular condensates are micrometer-sized, membraneless compartments formed via the phase separation of macromolecules, such as proteins and RNAs, and are critical for regulating spatiotemporal organization and cellular functions.^86–88 Studies of the driving forces of phase separation,^89,90 hierarchical organization,^91,92 and their links to condensate function^93,94 have clarified key biophysical and biochemical mechanisms underlying condensate formation and activity. Condensate properties depend strongly on molecular size, identity, and interactions; therefore, single-molecule techniques play a crucial role in connecting nanoscale molecular organization and dynamics to mesoscale features, providing mechanistic insights into condensate behaviors and functions.^95–99

As a first example, we studied nanoscale microphases of serine-rich splicing factors (SRSF1) that are enriched in nuclear speckles.⁶³ Microphases of SRSF1 are ~40 nm in diameter comprising tens of molecules. Short-range attractions drive the clustering of microphases into micrometer-sized assemblies, which are visible but appear to be mostly homogeneous using diffraction-limited confocal imaging (Fig. 7A), obscuring the underlying nanoscale structure of the microphases. We covalently labeled the SRSF1 protein with photoactivatable Janelia Fluor 549 (PA-JF-549), and tracked the translational displacements of longer-lived PA-JF-549 molecules that emitted for multiple camera frames. We then separated the data into slowly moving and quickly moving populations, thereby creating speed-resolved event maps (i.e., histograms) of entire microphases and their surroundings (Fig. 7B). Comparing the histograms for the two sets of displacements shows that slow molecules preferentially appear within confined local hotspots. Because we defined the displacements of slow-moving molecules to be less than 50 nm, the length scale of a single SRSF1 microphase, we conclude that these slow molecules were mostly confined within microphases.

Figure 7:

Single-molecule tracking reveals inhomogeneous molecular structure and dynamics in biomolecular condensates. (A) Confocal image of SRSF1 clusters stained with ANEPPS. Inset: schematic of SRSF1 microphases, clusters, and networks of clusters. (B) SMLM images of PA-JF-549 labeled SRSF1. (Slow) Displacements less than 50 nm per 20 ms frame. (Fast) Displacements greater than 50 nm per 20 ms frame. Insets: enlarged images of the boxed areas. Bin size: 25 × 25 nm². (C) Widefield and (D) SMLM images of hnRNPA1 condensates collected using NR. (C, inset) Intensity profile and (D, inset) localization histogram of the boxed regions. Bin size: 20 × 20 nm². (E) NR trajectories (yellow lines) exhibit both short (i) and long ((ii) and (iii)) burst durations, as well as high (ii) and low (iii) speeds. (F-G) SMLM images of NR with speeds (F) larger than 14.7 nm/ms and (G) shorter than 7.3 nm/ms. Bin size: 20 × 20 nm². (H) Speed distribution of NR measured between consecutive camera frames (10 ms exposure time). (I) Excess variance (variance/mean −1) measured over entire condensates, categorized by speed. Lines: excess variance of NR within each condensate. Adapted with permission from ref. 63. Copyright 2026 Elsevier. Adapted with permission from ref. 4. Copyright 2025 Springer Nature.

Switching from covalent labeling to environmentally sensitive fluorogens unlocks the power of this analysis. In another study, we characterized the molecular organization of condensates formed by the low-complexity domain of nuclear ribonucleoprotein A1 (hnRNPA1).⁴ Condensates formed by proteins containing intrinsically disordered regions (IDRs) are predicted to be network fluids, with a dynamic hub-and-spoke organization;^100,101 i.e., a small subset of molecules forms highly interconnected hubs, while the remaining molecules are connected to these hubs. Diffraction-limited, epifluorescence images of NR within hnRNPA1 condensates show pockets of enhanced local hydrophobicity; NR’s solvatochromism causes greater fluorescence in nonpolar environments (Fig. 7C). Consistent with the widefield images, these nanoscale hubs were further resolved using SMLM (Fig. 7D).

Similar to the SRSF1 system, we collected diffusive trajectories of NR within each condensate and quantified their burst durations and speeds (Fig. 7E). The burst durations represent the residence time of molecules in fluorescence-promoting environments, i.e., relatively nonpolar, hydrophobic regions. Molecular speed was calculated from displacements between 10 ms frames and reflects local viscoelasticity. Separating fast-moving probes (Fig. 7F) from slow-moving NR (Fig. 7G) reveals marked differences in spatial localization (all measured speeds are shown in Fig. 7H). Quickly moving molecules appeared uniformly throughout the condensate, while slow NRs were clustered into hotspots. Our data imply that hnRNPA1 condensates are more hydrophobic than coexisting dilute phases, thus yielding NR blinking events that exhibit high speeds, reflecting relatively weak binding. Upon encountering strong cross-links between aromatic residues, i.e., localized hubs, NR molecules become trapped temporarily, resulting in clustered localization patterns. Excess variance calculated from SMLM images across six independent condensates shows that only slow-moving molecules exhibit nonuniform spatial statistics, with a localization density variance much larger than the mean density across each condensate (the mean density is equal to its variance for a Poisson distribution, Fig. 7I). Overall, our observations support the theoretical prediction that nanoscale clusters within condensates are more hydrophobic than the surrounding regions.

To uncover how the number and type of aromatic residues affect condensate structure, we used NR to compare two variants of hnRNPA1 with increased (Aro+) and decreased (Aro−) numbers of aromatic residues in their sequences compared to those of wild type (WT). Though slight, NR burst durations do increase with an increased number of aromatic residues (Fig. 8A). However, diffusion is significantly slower in Aro+ condensates than in WT and Aro− (Fig. 8B), implying that NR is highly sensitive to the homotypic interaction strength. We further probed interfacial molecular alignment within hnRNPA1 condensates by imaging MC540 (Fig. 8C). Polarized, widefield imaging shows that MC540 has a significant polarization signature at condensate interfaces that increases with greater numbers of aromatic residues (Fig. 8D). In SMLM, MC540 exhibits more frequent transient binding to the interface with increasing aromaticity (Fig. 8E), and SMOLM imaging shows that MC540 orientations become increasingly perpendicular to the interface for Aro+ compared to those of Aro− and WT (Fig. 8C,F).

Figure 8:

Single fluorogens sense nanoscale dynamics within condensates. (A) Mean burst duration and (B) median speed of individual NR molecules within condensates formed by wild type (WT) and variants with increased (Aro+) and decreased (Aro−) numbers of aromatic residues of hnRNPA1. (C) Orientations of MC540 at the interfaces of Aro−, WT, and Aro+ condensates. The median angle δ between each MC540 orientation and the interface normal is computed for each individual condensate. (D) Linear dichroism (LD) of MC540 within Aro−, WT, and Aro+ condensates, measured by polarized fluorescence microscopy. (E) SMLM images of MC540. (F) Median orientation angles δ for MC540 measured using SMOLM. Bin size: 50 × 50 nm². *P < 0.05, **P < 0.01. Adapted with permission from ref. 4. Copyright 2025 Springer Nature.

Switching to RNA condensates, we explored interfacial diffusion at poly(rA) condensate interfaces using the RNA-intercalating dye YO-PRO-1.⁶² After measuring probe trajectories, we converted displacements into components parallel or perpendicular to the interface (Fig. 9A), enabling us to quantify both the diffusion speed and directionality relative to the interface simultaneously (Fig. 9B). Gathering all measured displacements between adjacent frames, we aligned them to a common origin (Fig. 9C) and fit them to a 2D Gaussian (Fig. 9D,E), thus yielding their means and standard deviations along the parallel and perpendicular directions. Importantly, the mean gives information on any preferred direction of motion (i.e., flow), while the standard deviations shed light on anisotropic motion at the interfaces (Fig. 9D) vs. interiors (Fig. 9E) of condensates. We find that parallel movements are significantly faster than perpendicular movements at the interface, and this dynamic anisotropy disappears as molecules progress into the interiors of the condensates (Fig. 9F). This phenomenon is consistent across various types of RNA condensates and their associated interfaces. These examples establish that when combined with fluorogenic molecules, SMOLM provides a versatile toolbox for sensing complex local biochemical environments within condensates.⁹⁹

Figure 9:

Single-molecule tracking of YO-PRO-1 near the RNA condensate interface shows anisotropic motion. (A) Converting 2D trajectories along x and y to displacements parallel (aqua) and perpendicular (red) to the condensate interface. (B) Trajectories of poly(rA) molecules in a poly(rA)-PEG condensate color-coded by their directions. (C) Displacements of the trajectories shown in (B) aligned to a common origin. (D-E) Displacements are fitted with 2D Gaussian distributions for trajectories (D) near interfaces (within 300 nm of the estimated interface) and (E) in the interior of the condensate. Bin size: 25 × 25 nm². Insets: scatters of YO-PRO-1 localizations (orange) at the interface and (gray) in the interior of a poly(rA) condensate. Inset scale bars: 500 nm. (F) The standard deviation in perpendicular and parallel motions for trajectories near interfaces: poly(rA) dense phase with dilute phase (rA-dil, orange), poly(rA) dense phase with poly(rC) adsorbed to the dilute phase (rA-rC-dil, yellow), and poly(rA) dense phase with poly(rC) dense phase (rA-rC, purple); and for interior trajectories: within poly(rA) condensates (interior rA, dark gray) and within poly(rA) condensates in touch with poly(rC) droplets (interior rA-rC, light gray). Adapted with permission from ref. 62. Copyright 2025 Springer Nature.

Discussion and Outlook

In our view, the emission and dynamics of fluorogens encode chemically meaningful information that is often richer than spatial localization alone. Specifically, fluorogens convert local chemical interactions into molecular orientation and rotational dynamics that we can detect via careful optical design and computational analysis. Importantly, these measurements do not require spatially resolved excitation or perturbative labeling strategies, making them broadly compatible with heterogeneous chemical and biological systems. In this Account, we showcase super-resolved mapping of electrochemical potentials and highlight the unique capabilities of SMOLM in various contexts (Table 1). More broadly, SMOLM represents one branch of multidimensional SM imaging techniques; others measure emission spectra,^102–104 lifetime,^41,105,106 and diffusivity.^71,107,108 With well-characterized fluorogen responses to their local environments, these methods become especially powerful and transform imaging observables into interpretable signatures of chemical interactions in space and time.

Despite these capabilities, we recognize the need for continued innovations. Many fluorophores lack sufficient sensitivity, specificity, brightness, or photostability for reliable multidimensional readout. Jointly optimizing fluorogen environmental sensitivity and photon efficiency is a promising direction for various chemical sensing applications.^23,109–112 In parallel, expanding the labeling toolkit with facile chemistries that rigidly attach fluorophores to target molecules¹¹³ will be essential for linking orientation measurements to biomolecular conformational dynamics.

One challenge for SMOLM is robustly estimating many SM parameters from noisy images.^114,115 Combining excitation-polarization modulation with DSF engineering provides additional orientation sensitivity^9,116,117 and could improve measurements of rotational dynamics. On the computational side, image processing frameworks that incorporate stochastic priors for molecular dynamics and realistic noise statistics,^118,119 together with deep learning,^47,120,121 could improve the speed and robustness of multiparameter estimation. Active-feedback tracking^122,123 and event-based acquisition^124,125 have increased the temporal resolution of SM microscopy and single-particle tracking but require careful adaptation for SMOLM.

We believe that by uniting fluorogen chemistry, optical design, and computational analysis, single-fluorogen orientation–localization microscopy provides a general strategy for imaging chemical environments, molecular organization, and nonequilibrium dynamics with nanoscale precision. Looking ahead, these advances should broaden SMOLM’s impact, particularly for studying nanoscale structural and dynamical heterogeneity in biomolecular condensates, antibody-antigen complexes, and electrochemical interfaces.^126–129

Biographies

Yiyang Chen was born in Zhengzhou, China, in 1999. He obtained his B.S. in physics from Nankai University in 2021. He is currently a Ph.D. student in imaging science supervised by Matthew D. Lew at Washington University in St. Louis. His research focuses on characterizing the fundamental limits of multidimensional single-molecule microscopy and developing advanced imaging techniques to resolve molecular organization and dynamics.

Yuanxin Qiu was born in Shangrao, China, in 1997. He obtained his B.S. in biomedical engineering from the Southern University of Science and Technology in 2019, followed by an M.S. in bioengineering from Temple University in 2021. He is currently pursuing a Ph.D. in imaging science at Washington University in St. Louis in Matthew D. Lew’s research group. His research interests lie in the field of optical system design, fundamental chemical and biomolecular sensing, and their application in investigating biomolecular systems.

Matthew D. Lew was born in San Antonio, TX, USA in 1986. He obtained a B.S. in electrical engineering from the California Institute of Technology in 2008, followed by an M.S. and Ph.D. in electrical engineering from Stanford University in 2010 and 2015, respectively. After a brief postdoctoral appointment in the Stanford University School of Medicine, he joined Washington University in St. Louis as an assistant professor in 2015. He is currently an associate professor and Associate Chair of the Preston M. Green Department of Electrical and Systems Engineering. He was recently elected to the 2026 Fellow Class of Optica (formerly the Optical Society of America). His major research interests integrate advanced optical design, computation, and biophysical chemistry to create multidimensional optical imaging systems that transcend conventional limits, thereby capturing molecular positions, orientations, chemical environments, and biomolecular interactions in unprecedented detail.