Biology subjects

Nielsen, A. J.

Publications and source records attributed to Nielsen, A. J..

3 recordsLinked to original sources

bioRxiv2024

Diverse Intracellular Trafficking of Insulin Analogs byMachine Learning-based Colocalization and DiffusionAnalysis

Insulin signaling is crucial for maintaining cellular function and systemic homeostasis, with its dysregulation leading to metabolic disorders in particular diabetes. While insulin analogs are essential in type 1 diabetes treatment, their intracellular trafficking and sorting compared to endogenous insulin remains largely unresolved at the molecular level. Understanding these are important in improving therapeutics and guiding future drug development. However, current methods rely on static imaging and bulk receptor assays conducted in non-physiological conditions, which often disrupt native insulin signaling and provide only snapshots that fail to capture the temporal dynamics of insulin trafficking and characteristic sorting pathways. Here, we directly observe insulin trafficking and intracellular sorting and compare the characteristics of Atto655 labeled recombinant human insulin (HI655) with insulin aspart (IAsp655), a clinically approved, rapid-acting insulin analog. We developed a combined approach integrating Colocalization Fingerprinting, a machine learning framework for reliable, time-resolved colocalization analysis, together with our recently developed deep learning-assisted single-particle diffusional analysis (DeepSPT). Our analysis revealed subtle, yet significant differences in intracellular behavior between IAsp655 and HI655, particularly in diffusional behavior and lysosomal colocalization, highlighting the potential of our approach to decipher subtle differences in intracellular trafficking and sorting characteristics. In addition to contributing to a more detailed understanding of biology of insulin analogs and intracellular sorting, we provide a reliable machine-learning methodology to study intricate cellular processes.

biophysics↗

bioRxiv2024

Defect-Engineered Metal-Organic Frameworks as Nanocarriers for Pharmacotherapy: Insights into Intracellular Dynamics at The Single Particle Level

NanoMOFs are widely implemented in a host of assays involving drug delivery, biosensing catalysis, and bioimaging. Despite their wide use, the cell entry pathways and cell fate remain poorly understood. Here we have synthesized a new fluorescent nanoMOF integrating ATTO 655 into surface defects of colloidal nano UiO-66 that allowed us to track the spatiotemporal localization of Single nanoMOF in live cells. Density Functional Theory(DFT) reveals the stronger binding of ATTO 655 to the uncoordinated saturated Zr6 cluster nodes compared with phosphate and Alendronate Sodium (AL). Parallelized tracking of the spatiotemporal localization of tens of thousands of nanoMOFs and analysis using machine learning platforms revealed whether nanoMOFs remain outside as well as their cellular internalization pathways. To quantitatively assess their colocalization with endo/lysosomal compartments, we developed a colocalization proxy approach relying on the nanoMOF detection of particles in one channel to the signal in the corresponding endo/lysosomal compartments channel, considering signal vs local background intensity ratio (S/B) and signal-to-noise ratio (SNR). This strategy effectively mitigates the potential inflation of colocalization values arising from the heightened expression of signals originating from endo/lysosomal compartments, it also overcomes limitations of low SNRs in the endo/lysosomal compartments marker channel, which incapacitates any trajectory-trajectory colocalization assessment. The results accurately measure the amount of nanoMOFs colocalization in real-time from early (EE) to late endosomes(LE) and lysosomes(LY) and emphasize the importance of understanding their intracellular dynamics based on single-particle tracking (SPT) for optimal and safe drug delivery.

biophysics↗

bioRxiv2023

Deep learning assisted single particle tracking for automated correlation between diffusion and function

Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone indicates that besides structure, motion encodes function at the molecular and subcellular level.

biophysics↗