High-fidelity structured illumination microscopy by point-spread-function engineering

Wen, Gang; Li, Simin; Wang, Linbo; Chen, Xiaohu; Sun, Zhenglong; Liang, Yong; Jin, Xin; Xing, Yifan; Jiu, Yaming; Tang, Yuguo; Li, Hui

doi:10.1038/s41377-021-00513-w

Cited by 80 publications

(87 citation statements)

References 44 publications

(87 reference statements)

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“…Peptide E3C16 undergoes self-assembly into twisted nanofibrils driven by the interdigitation of the C-terminal 54 Combining this with potential photobleaching leads to the dark defects along the nanofibers in the SIM images. 55 Therefore, these facts give rise to the challenges in obtaining an identical persistence length for the E3C16 nanofibers from the two independent studies. Prior to the study on the GSH-responsive self-sorting, we further investigated the GSH-responsive assembling properties of peptides E3C16E6 and EVM SeO (Figure 2).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…In addition, during the algorithmic reconstruction of the SIM images with thick objects, the fluorescence signals lying out of focus are pretty weak compared to the signals within focal layers . Combining this with potential photobleaching leads to the dark defects along the nanofibers in the SIM images . Therefore, these facts give rise to the challenges in obtaining an identical persistence length for the E3C16 nanofibers from the two independent studies.…”

Section: Resultsmentioning

confidence: 99%

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In Situ Self-Sorting Peptide Assemblies in Living Cells for Simultaneous Organelle Targeting

Liu

et al. 2022

J. Am. Chem. Soc.

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Self-sorting is a common phenomenon in eukaryotic cells and represents one of the versatile strategies for the formation of advanced functional materials; however, developing artificial self-sorting assemblies within living cells remains challenging. Here, we report on the GSHresponsive in situ self-sorting peptide assemblies within cancer cells for simultaneous organelle targeting to promote combinatorial organelle dysfunction and thereby cell death. The self-sorting system was created via the design of two peptides E3C16E6 and EVM SeO derived from lipidinspired peptide interdigitating amphiphiles and peptide bola-amphiphiles, respectively. The distinct organization patterns of the two peptides facilitate their GSH-induced self-sorting into isolated nanofibrils as a result of cleavage of disulfide-connected hydrophilic domains or reduction of selenoxide groups. The GSH-responsive in situ self-sorting in the peptide assemblies within HeLa cells was directly characterized by super-resolution structured illumination microscopy. Incorporation of the thiol and ER-targeting groups into the self-sorted assemblies endows their simultaneous targeting of endoplasmic reticulum and Golgi apparatus, thus leading to combinatorial organelle dysfunction and cell death. Our results demonstrate the establishment of the in situ self-sorting peptide assemblies within living cells, thus providing a unique platform for drug targeting delivery and an alternative strategy for modulating biological processes in the future.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

In Situ Self-Sorting Peptide Assemblies in Living Cells for Simultaneous Organelle Targeting

Liu

et al. 2022

J. Am. Chem. Soc.

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“…Secondly, the sample's scattering field intensity is collected straightly, circumventing an extra laser excitation process. Thirdly, the noise in this system may cause side lobes and artifacts in the reconstructed imaging, which can be further reduced through an optimization of the algorithm [32]. Besides, the influence of couplers' reflection and the higher harmonics on the grating can also be sufficiently suppressed to improve the imaging performance.…”

Section: Discussionmentioning

confidence: 99%

Terahertz non-label subwavelength imaging with composite photonics-plasmonics structured illumination

et al. 2021

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Inspired by the capability of structured illumination microscopy (SIM) in subwavelength imaging, many researchers devoted themselves to investigating this methodology. However, due to the free-propagating feature of the traditional structured illumination fields, the resolution can be only improved up to double times compared with the diffraction-limited microscopy. Besides, most of the previous studies, relying on incoherent illumination sources, are restricted to fluorescent samples. In this work, a subwavelength non-fluorescent imaging method is proposed based on the terahertz traveling wave and plasmonics illumination. Excited along with a metal grating, the spoof surface plasmons (SSPs) are employed as the plasmonics illumination. When the scattering waves with the SSPs illumination are captured, the sample's high-order spatial frequencies (SF) components are already encoded into the obtainable low-order ones. Then, a post-processing algorithm is summarized to shift the modulated SF components to their actual positions in the Fourier domain. In this manner, high-order SF components carrying the fine information are introduced to reconstruct the desired imaging, leading to an improvement of the resolution up to 0.12λ0. Encouragingly, the resolution can be further enhanced by tuning the working frequency of the SSPs. This method holds promise for some important applications in terahertz non-fluorescent microscopy and sample detection with weak scattering.

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“…These highfrequency pseudo-patterns caused by raw data intensity issues are commonly seen reconstruction artifacts in SIM 25,26 . Several algorithms have been proposed to address sidelobe artifacts and improve calculations in the frequency domain 27,28 . gmSRRF appeared to be functional for SIM artifacts in the spatial-temporal domain, filtering out the residual noise signal before it produced artifacts.…”

Section: Methodsmentioning

confidence: 99%

Achieving increased resolution and reconstructed image quality with gradient variance modified super-resolution radial fluctuations

Gong

Zhou

Yao

et al. 2021

Preprint

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Based on the calculation of the degree of gradient convergence, the super-resolution radial fluctuations (SRRF) algorithm can achieve higher resolution by combining temporal fluctuation analysis with localization microscopy methods. The algorithm is also capable of processing high-density fluorescence images. However, there are considerable artifacts due to high density, which lead to a loss of image resolution and low fidelity of images. This study demonstrates the use of fluorescence gradient fluctuations in super-resolution analysis and proposes gradient variance modified SRRF (gmSRRF) algorithm. The gmSRRF algorithm resolves finer structures and compensates for the loss of resolution caused by artifacts in SRRF images using relatively high-density stochastic optical reconstruction microscopy (STORM) data and conventional widefield, confocal, or structured illumination microscopy (SIM) imaging sequences. The effectiveness of this algorithm is proven by means of relevant simulations and experiments, which allow the reconstructed SRRF image to improve resolution and reduce artifacts and background noise.

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High-fidelity structured illumination microscopy by point-spread-function engineering

Cited by 80 publications

References 44 publications

In Situ Self-Sorting Peptide Assemblies in Living Cells for Simultaneous Organelle Targeting

In Situ Self-Sorting Peptide Assemblies in Living Cells for Simultaneous Organelle Targeting

Terahertz non-label subwavelength imaging with composite photonics-plasmonics structured illumination

Achieving increased resolution and reconstructed image quality with gradient variance modified super-resolution radial fluctuations

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