Live-cell imaging powered by computation

Shroff, Hari; Testa, Ilaria; Jug, Florian; Manley, Suliana

doi:10.1038/s41580-024-00702-6

Cited by 15 publications

(2 citation statements)

References 127 publications

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“…For example, when coupled to artificial intelligence, the approach can have a big impact in the contribution of microscopy to pathology [41][42][43][44]. In basic biomedical research, the presented methodologies are now gaining an increasing space with event-driven acquisitions [40,[45][46][47]. The simultaneous development of new computational tools is now providing solutions to dramatically power up the statistical sampling in SMLM [35,36,48], and the possibility of retrieving highresolution sub-diffraction data from few acquired frames or even from a single snapshot [49] can offer new solutions to measure molecular dynamics in real time.…”

Section: Discussionmentioning

confidence: 99%

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction

Pelicci,

Furia,

Pelicci

et al. 2024

IJMS

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Surpassing the diffraction barrier revolutionized modern fluorescence microscopy. However, intrinsic limitations in statistical sampling, the number of simultaneously analyzable channels, hardware requirements, and sample preparation procedures still represent an obstacle to its widespread diffusion in applicative biomedical research. Here, we present a novel pipeline based on automated multimodal microscopy and super-resolution techniques employing easily available materials and instruments and completed with open-source image-analysis software developed in our laboratory. The results show the potential impact of single-molecule localization microscopy (SMLM) on the study of biomolecules’ interactions and the localization of macromolecular complexes. As a demonstrative application, we explored the basis of p53-53BP1 interactions, showing the formation of a putative macromolecular complex between the two proteins and the basal transcription machinery in situ, thus providing visual proof of the direct role of 53BP1 in sustaining p53 transactivation function. Moreover, high-content SMLM provided evidence of the presence of a 53BP1 complex on the cell cytoskeleton and in the mitochondrial space, thus suggesting the existence of novel alternative 53BP1 functions to support p53 activity.

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Section: Discussionmentioning

confidence: 99%

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction

Pelicci,

Furia,

Pelicci

et al. 2024

IJMS

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“…Fluorescence microscopy is an indispensable tool to directly observe cellular components and capture intra-cellular dynamics 1,2 . Limited by the optics, the photophysics of molecular probes, and sensor technology, fluorescence microscopic images are commonly degraded by intrinsic blurring and noise 3 .…”

Section: Introductionmentioning

confidence: 99%

Kernel learning enables fluorescence microscopic image deconvolution with enhanced performance and speed

Cheng,

Lu,

et al. 2024

Preprint

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The contrast and resolution of fluorescence microscopic images can be effectively improved by classic iterative Richardson-Lucy Deconvolution (RLD) algorithm, but this method is computationally expensive, particularly for three-dimensional data. Variants of RLD with manually designed unmatched backward projector can greatly accelerate deconvolution, however, they require careful parameter optimization to avoid introducing artifacts. Here, we develop Kernel Learning Deconvolution (KLD), which automatically learns forward/backward kernel in RLD from only one paired low-resolution and high-resolution images. The learned kernel reveals a similar pattern with handcrafted Wiener-Butterworth kernel but is more adaptive to data. Besides, it is robust to the signal-to-noise ratio and the number of training samples. KLD shows enhanced deconvolution performance and speed on different cellular structures and imaging modalities, including wide-field microscopy, confocal microscopy, and lattice light-sheet microscopy.

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Visualizing DNA/RNA, Proteins, and Small Molecule Metabolites within Live Cells

Jia,

Cui,

Ding

2024

Small

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Live cell imaging is essential for obtaining spatial and temporal insights into dynamic molecular events within heterogeneous individual cells, in situ intracellular networks, and in vivo organisms. Molecular tracking in live cells is also a critical and general requirement for studying dynamic physiological processes in cell biology, cancer, developmental biology, and neuroscience. Alongside this context, this review provides a comprehensive overview of recent research progress in live‐cell imaging of RNAs, DNAs, proteins, and small‐molecule metabolites, as well as their applications in molecular diagnosis, immunodiagnosis, and biochemical diagnosis. A series of advanced live‐cell imaging techniques have been introduced and summarized, including high‐precision live‐cell imaging, high‐resolution imaging, low‐abundance imaging, multidimensional imaging, multipath imaging, rapid imaging, and computationally driven live‐cell imaging methods, all of which offer valuable insights for disease prevention, diagnosis, and treatment. This review article also addresses the current challenges, potential solutions, and future development prospects in this field.

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Live-cell imaging powered by computation

Cited by 15 publications

References 127 publications

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction

Kernel learning enables fluorescence microscopic image deconvolution with enhanced performance and speed

Visualizing DNA/RNA, Proteins, and Small Molecule Metabolites within Live Cells

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