Joint tagging assisted fluctuation nanoscopy enables fast high‐density super‐resolution imaging

Zeng, Zhiping; Ma, Jing; Xi, Peng; Xu, Canhua

doi:10.1002/jbio.201800020

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Therefore, like other immuno-based technic PLA present a certain level of background, easily quantifiable by using single probe, secondary antibodies only, protein mutant and/or non-existing interaction detection. PLA can detect an interaction up to a 40 nm distance 36 and enables super high resolution immunofluorescence microscopy, which can distinguish molecules at a similar distance 37 . Although PLA cannot detect real-time protein-protein interaction, quantification of PLA signals of different GATA1 complexes in sequential stages of ES cell differentiation improves our understanding on the temporal changes of these complexes.…”

Section: Discussionmentioning

confidence: 99%

The dynamic emergence of GATA1 complexes identified in in vitro embryonic stem cell differentiation and in vivo mouse fetal liver

et al. 2019

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ATA1 is an essential transcriptional regulator of myeloid hematopoietic differentiation towards red blood cells. During erythroid differentiation, GATA1 forms different complexes with other transcription factors such as LDB1, TAL1, E2A and LMO2 ("the LDB1 complex") or with FOG1. The functions of GATA1 complexes have been studied extensively in definitive erythroid differentiation; however, the temporal and spatial formation of these complexes during erythroid development is unknown. We applied proximity ligation assay (PLA) to detect, localize and quantify individual interactions during embryonic stem cell differentiation and in mouse fetal liver (FL) tissue. We show that GATA1/LDB1 interactions appear before the proerythroblast stage and increase in a subset of the CD71 + /TER119cells to activate the terminal erythroid differentiation program in 12.5 day FL. Using Ldb1 and Gata1 knockdown FL cells, we studied the functional contribution of the GATA1/LDB1 complex during differentiation. This shows that the active LDB1 complex appears quite late at the proerythroblast stage of differentiation and confirms the power of PLA in studying the dynamic interaction of proteins in cell differentiation at the single cell level. We provide dynamic insight into the temporal and spatial formation of the GATA1 and LDB1 transcription factor complexes during hematopoietic development and differentiation.

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Section: Discussionmentioning

confidence: 99%

The dynamic emergence of GATA1 complexes identified in in vitro embryonic stem cell differentiation and in vivo mouse fetal liver

et al. 2019

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“…In conclusion, Betzig () has predicted that the optical properties of the emitters can be used to separate them to different dimensions. Previously, we have demonstrated that the inverse multiplexing in the spectral domain can improve the spatiotemporal resolution of intensity blinking‐based super‐resolution techniques (Zeng et al ., ; Zeng et al ., ). Herein, taking advantage of lifetime‐blinking property and wide‐field lifetime imaging technique, we demonstrated the possibility of achieving super‐resolution through lifetime blinking nature with numerical simulations.…”

Section: Resultsmentioning

confidence: 99%

Lifetime super‐resolution optical fluctuation imaging

Zeng

Chen³

et al. 2019

Journal of Microscopy

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Summary In this paper, we propose a promising super‐resolution imaging scheme in fluorescence lifetime domain (lifetime super‐resolution optical fluctuation imaging, ltSOFI). ltSOFI has the potential to obtain super‐resolution images by taking advantage of fluorescence lifetime blinking under wide‐field lifetime detection. The proof‐of‐concept for ltSOFI was demonstrated through numerical simulation of high‐order cumulant analysis on fluorescence lifetime blinking emitters. As a tentative experimental demonstration, we obtained super‐resolution lifetime imaging from time‐lapse FLIM recording of HeLa cells expressing a cAMP sensor using ltSOFI method. ltSOFI is expected to initiate a new dimension in the lifetime domain for blinking‐based super‐resolution microscopy. Lay Description We report on a promising super‐resolution imaging scheme in fluorescence lifetime domain (lifetime super‐resolution optical fluctuation imaging, ltSOFI). ltSOFI has the potential to obtain super‐resolution images by taking advantage of fluorescence lifetime blinking under wide‐field lifetime detection. Past advances in super‐resolution fluorescence microscopy primarily rely on the spatiotemporal modulation of the fluorescence intensity. Although the applications of the Q‐dot blinking have been discussed in the literature, most of the discussions have focused on the blinking of fluorescence intensity. Few studies have shown the possibility of super‐resolution imaging through fluorescence lifetime fluctuations. In this paper, we proposed the ltSOFI scheme that explored the possibility of super‐resolution reconstruction from the blinking of fluorescence lifetime. The proof‐of‐concept for ltSOFI was demonstrated through numerical simulation of high‐order cumulant analysis on fluorescence lifetime blinking emitters. As a tentative experimental demonstration, we obtained super‐resolution lifetime imaging from time‐lapse FLIM recording of HeLa cells expressing a cAMP sensor using ltSOFI method. The ltSOFI method is expected to initiate a new dimension in the lifetime domain for blinking‐based super‐resolution microscopy. Moreover, the existing fluorescence lifetime imaging microscopy and super‐resolution nanoscopy can benefit from the implementation of ltSOFI to significantly improve the imaging spatial resolution of fluorescence lifetime images. In addition, the proof‐of‐concept demonstration achieved by the numerical simulation and tentative experiment will provide a new perspective for obtaining fluorescence lifetime images with much finer details.

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“…Moreover, they are also notorious for their blinking [23]. However, by taking advantage of their blinking characteristic, the possibility of QDs being used in super-resolution imaging has been demonstrated, such as in stochastic optical reconstruction microscopy (STORM) [24], SOFI [25] and SRRF [26]. The FEAST method is based on fluorescent fluctuation, and therefore the blinking characteristic of QDs could be applied to FEAST.…”

Section: Feast Imaging Of Qd605 Labeled Samplementioning

confidence: 99%

Multicomposite super‐resolution microscopy: Enhanced Airyscan resolution with radial fluctuation and sample expansions

Wang

Yao

Jing

et al. 2020

Journal of Biophotonics

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Either modulated illumination or temporal fluctuation analysis can assist superresolution techniques in overcoming the diffraction limit of conventional optical microscopy. As they are not contradictory to each other, an effective combination of spatial and temporal super-resolution mechanisms would further improve the resolution of fluorescent images. Here, a super-resolution imaging method called fluctuation-enhanced Airyscan technology (FEAST) is proposed, which achieves~40 nm lateral imaging resolution and is useful for a range of fluorescent proteins and organic dyes. It was demonstrated not only to obtain different subcellular super-resolution images, but also to improve the accuracy of counting the average human epidermal growth factor receptor 2 (HER2) copy number for diagnosis in breast cancer. Furthermore, the combination of FEAST and sample expansion microscopy (Ex-FEAST) improves the lateral resolution to~26 nm. K E Y W O R D S

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Joint tagging assisted fluctuation nanoscopy enables fast high‐density super‐resolution imaging

Cited by 7 publications

References 17 publications

The dynamic emergence of GATA1 complexes identified in in vitro embryonic stem cell differentiation and in vivo mouse fetal liver

The dynamic emergence of GATA1 complexes identified in in vitro embryonic stem cell differentiation and in vivo mouse fetal liver

Lifetime super‐resolution optical fluctuation imaging

Multicomposite super‐resolution microscopy: Enhanced Airyscan resolution with radial fluctuation and sample expansions

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