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Cellular Morphometry for Interrogating Nanomaterial–Cell Interactions
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DOI：10.1021/accountsmr.6c00077.png)
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Abstract
En 中文
ConspectusThe interactions between nanomaterials and cells are fundamental to a plethora of theranostic and nanomedicine applications. For instance, to induce targeted therapeutic effects, theranostic nanomaterials need to be selectively endocytosed by the target cells and at the same time avoid the nonspecific uptake by nontarget cells. The specific cellular uptake of nanomaterials is influenced predominantly by the extent of nanomaterial–cell interactions. These interactions further dictate downstream biological processes, such as intracellular trafficking, toxicological responses, and cellular clearance, ultimately determining therapeutic efficacy and biocompatibility. As such, active efforts have been geared toward formulating strategies to selectively modulate and interrogate nanomaterial-cell interactions.While many biomarkers can be leveraged to elucidate nanomaterial–cell interactions and the associated biological effects, one of the most direct ways to achieve this is by visualizing changes in the morphology of the nanomaterial-treated cells through microscopy imaging. In fact, changes in cellular morphology are often monitored along with variations in the expression of other biomarkers to determine the therapeutic performance and safety profiles of various nanomaterials. A wide range of microscopy modalities, including bright-field, phase-contrast, scanning probe, and fluorescence techniques, have enabled direct visualization of nanomaterial binding, uptake, and localization at both cellular and subcellular levels. Although substantial insights have been gained from examining cellular morphology, many studies have been limited to manual inspection and qualitative observation, rendering these interpretations highly subjective and potentially less reliable. Moreover, subtle yet biologically significant morphological variations may remain undetected by using conventional visual assessment alone.With the increasing implementation of advanced computational techniques, particularly those based on artificial intelligence (AI) and machine learning, to process and analyze microscopy images, it has become increasingly possible to probe cellular morphology quantitatively (i.e., cellular morphometry) to achieve more objective, reproducible, and reliable analyses and interpretations. By extracting numerical descriptors related to cell size, shape, texture, and intensity from microscopy images, cellular morphometry transforms qualitative visual data into multidimensional data sets that can be systematically analyzed to uncover hidden phenotypic patterns and structure–activity relationships. This advancement can potentially unveil previously unrecognized insights into nanomaterial–cell interactions and provide a more sensitive readout of cellular responses than conventional biochemical assays.Herein, in this Account, we describe our contributions to elucidating nanomaterial–cell interactions and our recent efforts in developing cellular morphometric approaches to quantitatively interrogate these interactions. We first highlight our use of both label-free and labeled microscopy techniques to visualize nanomaterial–cell interactions and derive key mechanistic insights. We next discuss the importance of quantitative evaluation of cellular morphology and recent advances in computational methods for quantitative morphological feature extraction and analysis. In particular, we emphasize the integration of AI-driven frameworks, including machine learning-based image segmentation and classification algorithms, to enhance the accuracy, scalability, and interpretability of cellular morphometric analysis. We conclude this Account by outlining current challenges and discussing emerging opportunities for advancing cellular morphometry as a powerful platform to interrogate nanomaterial–cell interactions.
Keywords:
Machine learning
Microscopy
Morphology
Nanomaterials
Nanoparticles
Journal
IF:
14.7
Papers:
634
Citations:
5.2K
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Cited Papers