Surpassing the Diffraction Limit in Label-Free Optical Microscopy

Abstract

Super-resolution optical microscopy has enhanced our ability to visualize biological structures on the nanoscale. Fluorescence-based techniques are today irreplaceable in exploring the structure and dynamics of biological matter with high specificity and resolution. However, the fluorescence labeling concept narrows the range of observed interactions and fundamentally limits the spatiotemporal resolution. In contrast, emerging label-free imaging methods are not inherently limited by speed and have the potential to capture the entirety of complex biological processes and dynamics. While pushing a complex unlabeled microscopy image beyond the diffraction limit to single-molecule resolution and capturing dynamic processes at biomolecular time scales is widely regarded as unachievable, recent experimental strides suggest that elements of this vision might be already in place. These techniques derive signals directly from the sample using inherent optical phenomena, such as elastic and inelastic scattering, thereby enabling the measurement of additional properties, such as molecular mass, orientation, or chemical composition. This perspective aims to identify the cornerstones of future label-free super-resolution imaging techniques, discuss their practical applications and theoretical challenges, and explore directions that promise to enhance our understanding of complex biological systems through innovative optical advancements. Drawing on both traditional and emerging techniques, label-free super-resolution microscopy is evolving to offer detailed and dynamic imaging of living cells, surpassing the capabilities of conventional methods for visualizing biological complexities without the use of labels.

Figure 1

A scheme illustrating super-resolution microscopy techniques categorized into three distinct groups based on the approach used to overcome the diffraction limit. The generalized mechanisms and representative examples of the fluorescence-based techniques are shown on the left, while the corresponding label-free approaches are listed on the right.

Figure 2

Schematic representation of energy diagrams and relative radiation wavelengths in (a) coherent anti-Stokes Raman scattering (CARS), (b) coherent Stokes Raman scattering (CSRS), (c) stimulated Raman scattering–stimulated Raman loss (SRL) and stimulated Raman gain (SRG), and (d) stimulated Raman excited fluorescence microscopy (SREF).

Figure 3

Chemical microscopy images of cells with an enhanced resolution. (a) Stimulated Raman scattering (human lung-cancer). Reprinted with permission from ref (82). Copyright 2008 AAAS (reprinted with permission from AAAS). (b, c) CARS and six-wave mixing CARS, respectively (HeLa). Reprinted with permission from ref (60) Copyright 2020 SNCSC (reproduced with permission from SNCSC). (d) Saturated stimulated Raman scattering (HeLa). Reprinted with permission from ref (85). Copyright 2019 American Physical Society (copyright (2019) by the American Physical Society). (e) Vibrational imaging of swelled tissues and analysis (HeLa, reprinted and adapted with permission under the Creative Commons CC-BY 4.0 from ref (91)). (f) Molecule anchorable gel-enabled nanoscale imaging (HeLa). Adapted with permission from ref (92). Copyright 2022 Wiley. (g) Vibrational photothermal relaxation localization (chondroblast). Reproduced with permission from ref (59). Copyright 2023 SNCSC (reproduced with permission from SNCSC). (h) Stimulated Raman scattering with a deconvolution algorithm (HeLa). Reproduced with permission from ref (86). Copyright 2023 SNCSC (reproduced with permission from SNCSC).

Figure 4

Principle of label-free super-resolution imaging based on molecular turnover and interferometric scattering microscopy. (a) Subwavelength particle and optical fields involved in the interferometric detection of the light scattered by it and (b) optical layout of the microscope with interferometric detection of scattering. Examples of the detected interferometric images corresponding to (c) the binding of single protein to and (d) unbinding of protein oligomer from the imaged sample. Reconstruction of a super-resolved image of a disassembled microtubule. (e) Diffraction-limited interferometric image obtained by an iSCAT microscope during the microtubule disassembly, (f) two examples of incremental images highlighting the localization of two distinct unbinding events, and (g) super-resolved image obtained by overlaying localized positions of detected and localized unbinding events. Scale bars in (e)–(g) correspond to 1 μm. (a, b) Illustration by the author adapted with permission from ref (12). Copyright 2014 Springer Nature. (c–g) Adapted with permission from the published data in ref (14) by the author.

Figure 5

Computationally enhanced feature detection. (a) Raw image of GFP-tagged microtubules in HeLa cells and the CARE network restored image with cropped details of the fine structure, reproduced with permission from ref(144). Copyright 2018 SNCSC. (b and d) Nanofluidic-scattering-microscopy kymographs of one-dimensional trajectories of single (b) thyroglobulin and (d) albumin (BSA) confined in a nanochannel; (c and e) corresponding single biomolecule trajectory identified using a neural network. Kindly adapted with permission from the published data in ref (100) by the author. Detection of a 9 kDa protein in (f) fluorescence and, simultaneously, in elastic scattering using a (g) differential rolling average algorithm and (h) results of the isolation forest classification based on probability maps. Color bar: binary classification. (f–h) Scale bars, 1.5 μm. Reprinted and adapted with permission under the Creative Commons CC-BY 4.0 from ref (140).

Figure 6

Illustration portrays the diversity of optical imaging techniques used to analyze dynamic biological entities. Illustration by Pavel Trávníček.