Advanced optical imaging for the rational design of nanomedicines

Ortiz-Perez, Ana; Zhang, Miao; Fitzpatrick, Laurence W.; Izquierdo-Lozano, Cristina; Albertazzi, Lorenzo

doi:10.1016/j.addr.2023.115138

Cited by 7 publications

(3 citation statements)

References 205 publications

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“…As opposed to pixel-based images generated by conventional microscopy, SMLM produces an event list of localizations, also known as point cloud, highly suited to the application of computational imaging, network modeling, and machine learning based 3D pattern analysis tools. The combination of super-resolution microscopy with computational and machine learning tools have been proposed to be promising tools for advancing drug development and nanomedicines [13, 14].…”

Section: Introductionmentioning

confidence: 99%

Super-resolution single molecule network analysis (SuperResNET) detects changes to clathrin structure by small molecule inhibitors

Wong,

Khater,

Hallgrimson

et al. 2024

Preprint

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Specificity of small molecules for their target molecule in the cell is critical to determine their effective use as biologics and therapeutics. Small molecule inhibitors of clathrin endocytosis, Pitstop 2, and the dynamin inhibitor Dynasore, have off-target effects and their specificity has been challenged. Here, we used SuperResNET to apply network analysis to 20 nm resolution dSTORM single-molecule localization microscopy (SMLM) to test whether Pitstop 2 and Dynasore alter the morphology of clathrin coated pits in intact cells. SuperResNET analysis of dSTORM data from HeLa and Cos7 cells identifies three classes of clathrin structures: small oligomers (Class I); pits and vesicles (Class II); and larger clusters corresponding to fused clathrin pits and clathrin plaques (Class III). SuperResNET analysis of high resolution MinFlux imaging identifies Class 1 oligomers as well as Class 2 structures including morphologically identifiable clathrin pits and vesicles. SuperResNET feature analysis of dSTORM data shows that Pitstop 2 and Dynasore induce the formation of distinct homogenous populations of clathrin structures in HeLa cells. Pitstop 2 blobs are smaller and more elongated than those induced by Dynasore, indicating that these two clathrin inhibitors arrest clathrin endocytosis at distinct stages. Pitstop 2 and Dynasore are not impacting clathrin structure via actin depolymerization as the actin depolymerizing agent latrunculin A (LatA) induced larger heterogeneous clathrin structures. Ternary analysis of SuperResNET shape features presents a distinct profile for Pitstop 2 Class II structures. The most representative Pitstop blobs align with and resemble MinFlux clathrin pits while control structures resemble Minflux clathrin vesicles. SuperResNET analysis of SMLM data is therefore a highly sensitive approach to detect the effect of small molecules on target molecule structure in situ in the cell.

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Section: Introductionmentioning

confidence: 99%

Super-resolution single molecule network analysis (SuperResNET) detects changes to clathrin structure by small molecule inhibitors

Wong,

Khater,

Hallgrimson

et al. 2024

Preprint

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show abstract

“…Recent application of AI to reconstruction methods has the potential to reduce noise, improve spatial and temporal resolution, and automate high-throughput super-resolution and live imaging ( Fu et al, 2023 ; Priessner et al, 2024 ; Qiao et al, 2023 ). The application of AI and machine learning to SRM has been proposed to be the next step in advancing nanomedicine development, drug discovery, and antiviral research ( Li et al, 2024a ; Ortiz-Perez et al, 2024 ; Petkidis et al, 2023 ). In this Perspective, we focus on the specific application of AI to acquire semantic insight, new biology, from SRM.…”

Section: Introductionmentioning

confidence: 99%

AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth

Nabi,

Cardoen,

Khater

et al. 2024

Journal of Cell Biology

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Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, offers tremendous potential for the discovery of new biology, that, by definition, is not known and lacks ground truth. Herein, we describe the application of weakly supervised paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles.

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“…According to current definitions, nanomedicines are nanoscale tools utilized for disease diagnosis, prevention, and treatment [1,2]. They have the capacity to facilitate early disease detection and prevention, direct a bioactive molecule to the intended site of action, and regulate the release of the molecule to guarantee an ideal concentration at the target of therapy for the intended duration [3,4].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine

Huang,

Zheng,

et al. 2023

Bioengineering

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In recent years, nanomedicines prepared using supercritical technology have garnered widespread research attention due to their inherent attributes, including structural stability, high bioavailability, and commendable safety profiles. The preparation of these nanomedicines relies upon drug solubility and mixing efficiency within supercritical fluids (SCFs). Solubility is closely intertwined with operational parameters such as temperature and pressure while mixing efficiency is influenced not only by operational conditions but also by the shape and dimensions of the nozzle. Due to the special conditions of supercriticality, these parameters are difficult to measure directly, thus presenting significant challenges for the preparation and optimization of nanomedicines. Mathematical models can, to a certain extent, prognosticate solubility, while simulation models can visualize mixing efficiency during experimental procedures, offering novel avenues for advancing supercritical nanomedicines. Consequently, within the framework of this endeavor, we embark on an extensive review encompassing the application of mathematical models, artificial intelligence (AI) methodologies, and computational fluid dynamics (CFD) techniques within the medical domain of supercritical technology. We undertake the synthesis and discourse of methodologies for calculating drug solubility in SCFs, as well as the influence of operational conditions and experimental apparatus upon the outcomes of nanomedicine preparation using supercritical technology. Through this comprehensive review, we elucidate the implementation procedures and commonly employed models of diverse methodologies, juxtaposing the merits and demerits of these models. Furthermore, we assert the dependability of employing models to compute drug solubility in SCFs and simulate the experimental processes, with the capability to serve as valuable tools for aiding and optimizing experiments, as well as providing guidance in the selection of appropriate operational conditions. This, in turn, fosters innovative avenues for the development of supercritical pharmaceuticals.

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Advanced optical imaging for the rational design of nanomedicines

Cited by 7 publications

References 205 publications

Super-resolution single molecule network analysis (SuperResNET) detects changes to clathrin structure by small molecule inhibitors

Super-resolution single molecule network analysis (SuperResNET) detects changes to clathrin structure by small molecule inhibitors

AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth

Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine

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