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Nuclear Localization Signals and Import

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of 2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Label-Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning.

Keivan Rahmani¹, Hamed Naghsh-Nilchi¹, Leah Sadr¹

¹Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, CA, 92093, USA.

Small (Weinheim an Der Bergstrasse, Germany)

|November 7, 2025

Summary

This study introduces a machine learning method to detect nuclear envelope (NE) poration by analyzing cell and nuclear shape changes. This AI approach enables efficient, label-free monitoring of NE disruption for improved nuclear delivery applications.

Keywords:

Cell Engineering Cell Therapy Gene Delivery Gene Therapy Machine Learning Nanopillar Nano‐Injection Nuclear delivery Nuclear envelope rupture nucleoporation

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Area of Science:

Biotechnology
Cell Biology
Artificial Intelligence

Background:

High-aspect-ratio nanostructures facilitate nuclear delivery via transient nuclear envelope (NE) disruption.
Sporadic nucleoporation events limit the efficiency of current nuclear delivery methods.

Purpose of the Study:

To develop a label-free, machine learning (ML) approach for detecting nucleoporation based on morphological cell and nuclear changes.
To establish a high-throughput, non-invasive method for monitoring NE disruption events.

Main Methods:

U2OS cells on silicon nanopillars were analyzed for NE disruption using Ku-80 mislocalization.
A custom algorithm quantified Ku-80/DAPI intensity profiles to establish ground truth for cell states (intact vs. porated).
An orientation-invariant variational autoencoder and support vector machine (SVM) were trained using cell/nuclear shape embeddings and morphological descriptors.

Main Results:

The ML model achieved an 87.0% area under the receiver operating characteristic curve and 82.9% test accuracy.
SHAP analysis identified nucleus-to-cell area ratio as the strongest predictor of nucleoporation.
Specific nuclear features (e.g., surface smoothness, bulging) and cell features (e.g., boundary complexity, elongation) significantly influenced nucleoporation probability.

Conclusions:

This AI workflow demonstrates a correlation between cell/nuclear morphology and nucleoporation events.
The developed method enables non-invasive, high-throughput monitoring of cellular events using relatively small datasets.
This approach holds potential for optimizing nuclear delivery strategies and investigating cellular phenotypes.