Controlling particle-level heterogeneity arising from postformation modification of biomolecular coronas remains a central challenge in bionano interaction studies. Variability in corona modifications can alter the biological identity of individual nanoparticles (NPs), compromising the accuracy, reproducibility, and interpretability of both in vivo and in vitro experiments. Despite its significant impact, particle-level uniformity is rarely measured directly. This issue is particularly critical for corona fluorescent labeling, where bulk fluorescence readouts can suggest efficient modification while masking substantial heterogeneity among individual particles. Here, we present a particle-resolved framework for optimizing fluorescent labeling of biomolecular coronas. Using a controlled core-shell superparamagnetic iron oxide NP (SPION) platform, we labeled corona-associated proteins with Alexa Fluor 405 NHS ester while systematically varying critical reaction parameters, i.e., interparticle distance, dye-to-NP molar ratio, and mixing conditions. High-sensitivity nanoscale flow cytometry revealed that corona labeling is not simply an ensemble chemical reaction, but a heterogeneity-generating process shaped by local reaction environments and intrinsic NP structural variability. Conditions that appeared comparable by bulk fluorescence produced distinct particle-level distributions. We further show that the same design logic is applicable to structurally distinct biogenic NPs, milk extracellular vesicles (MEVs), and Alexa Fluor 647 as an additional dye molecular scaffold. Overall, this work underscores the need to shift from ensemble-averaged to single-particle characterization in biomolecular corona research and establishes transferable design principles for reliable optimization of corona labeling across diverse NP systems, while recognizing that system- and dye-specific refinement remains necessary.

Nanomaterial-protein interactions (NPI) are pivotal to realizing the therapeutic and diagnostic potential of nanomaterials. Although AI promises to accelerate mechanistic understanding and enable rational nanomaterial design, robust generalization to unseen nanomaterials or proteins remains unresolved. Here, we present CuMMI (curriculum-guided multimodal interaction model), a generalizable, explainable, and transferable model designed to infer NPI across complex biological settings. CuMMI leverages a self-constructed million-scale NPI dataset and adopts a multi-stage curriculum centered on human plasma, with progressively broader biofluid exposure to enhance data coverage and generalizability. By integrating protein sequence, structure, and a text-encoded experimental context of 37 features, CuMMI captures complementary material-specific, biochemical, and environmental information. Sample-level quality weights are assigned to ensure full utilization of available data while mitigating low-confidence and sparsely recorded entries. Ablation studies highlight the most influential tabular features, clarifying their contribution to the prediction. Through rigorous external validation across independence-preserving temporal, nanomaterial-held-out, and protein-held-out evaluations, our framework consistently achieves good performance (mean of five classification metrics exceeding 0.75), highlighting its robustness and generalizability to unseen data. Furthermore, fine-tuning on independent gold-nanoparticle data and a held-out protein subset further delivers better performance than training from scratch with substantially fewer samples. Together, our approach enables generalizable and transferable NPI prediction and may accelerate in vitro research and applications of nanomaterials.

Membraneless RNA granules are essential for posttranscriptional gene regulation, influencing cellular functions and contributing to neurodegenerative diseases. However, a comprehensive understanding of their compositions and organization has been challenging due to their complex nature. In this study, we develop robust machine learning models to reliably identify RNA granule proteomes within the human proteome, capturing central RNA granule characteristics despite the heterogeneity across diverse in vitro conditions. Furthermore, we uncover protein-protein interaction (PPI) community grammars within the RNA granule proteome, highlighting PPIs as key stabilizers of RNA granule structure and function. Dense PPI clusters serve as stable "cores," forming key functional subunits across heterogeneous RNA granules. We introduce a state-of-the-art framework for understanding RNA granule biology and underscore the critical role of PPIs in maintaining RNA granule integrity.

A new integrated tunable microfluidic particle synthesis and shape population analysis workflow allows us to study the immunological readouts for even highly complex shaped nanoparticles. Using this approach, we demonstrate that some gold nanoparticles, when injected parenterally, are taken up by axillary and brachial lymph nodes. We then show that specific nanoparticle shapes influence the primary structure of the T cell receptor, inducing changes in hypervariable complementary-determining regions (CDRs) and increasing the clonal diversity of the T cell receptor repertoires. These same particles were previously found to modify cellular epigenomes and elevate the level of autoantibodies. Our results are consistent with other emerging reports that precisely controlled nanoarchitectural features are recognized and captured in multiple tiers of biology, with potential implications for vaccine adjuvant design. Our conclusions may also be relevant to an extensive legacy of poorly understood epidemiological studies, suggesting links between some pollutant particulates and complex forms of immune dysregulation and autoimmune diseases.

RNA granules are dynamic compartments within cells that play a crucial role in posttranscriptional regulation of gene expression. They are associated with a variety of human neurodegenerative diseases. While RNA granules play vital roles in cellular functions, the comprehension of their assembly has remained elusive. In this study, we employed robust machine learning models combining residue content and physicochemical features to accurately identify potential RNA granule ( i.e., stress granule and P-body) proteome within the human proteome. Our models achieved good performance with high areas under the receiver operating characteristic curve of up to 0.88, outperforming previous liquid-liquid phase separation models. Intriguingly, the predicted RNA granule proteome reveals a significant enrichment in biological functions and domains associated with RNA granule-related processes, mirroring findings from observed high-confidence RNA granule protein datasets. Furthermore, our analysis unveils critical physicochemical attributes, notably hydrophobicity, influencing the formation of RNA granules. Using the constructed model, we uncovered the central roles of RNA granule proteins with high propensities within the comprehensive RNA granule protein-protein interaction (PPI) network and their commonality in diverse RNA granules. Furthermore, we identified prominent clusters with dense PPIs, significantly contributing to critical biological processes within diverse RNA granules, including translation, mRNA decay, rRNA processing, and mRNA splicing. This analysis proposes a hypothesis: dense PPI clusters are integral functional subunits, constituting relatively stable ‘cores’ within diverse RNA granules. In conclusion, this study provides a comprehensive molecular and community-based foundation for understanding the importance of PPIs in the stability of RNA granule formation and functionality. This analysis contributes to a deeper and more comprehensive understanding of the intricate nature of RNA granules and opens avenues for future research and therapeutic interventions targeting RNA granule- related diseases. ### Competing Interest Statement The authors have declared no competing interest.

In this Account, we describe our research into ultrasmall nanoparticles, including their unique properties, and outline some of the new opportunities they offer. We will summarize our perspective on the current state of the field and highlight what we see as key questions that remain to be solved. First, there are several nanostructure size-scale regimes, with qualitatively distinct functional biological attributes. Broadly generalized, larger particles (e.g., larger than 300 nm) tend to be more efficiently swept away by the first line of the immune system (for example macrophages). In the "middle-sized" regime (20-300 nm), nanoparticle surfaces and shapes can be recognized by energy-dependent cellular reorganizations, then organized locally in a spatial and temporally coherent way. That energy is gated and made available by specific cellular recognition processes. The relationship between particle surface design, endogenously derived nonspecific biomolecular corona, and architectural features recognized by the cell is complex and only purposefully and very precisely designed nanoparticle architectures are able to navigate to specific targets. At sufficiently small sizes (<10 nm including the ligand shell, associated with a core diameter of a few nm at most) we enter the "quasi-molecular regime" in which the endogenous biomolecular environment exchanges so rapidly with the ultrasmall particle surface that larger scale cellular and immune recognition events are often greatly simplified. As an example, ultrasmall particles can penetrate cellular and biological barriers within tissue architectures via passive diffusion, in much the same way as small molecule drugs do. An intriguing question arises: what happens at the interface of cellular recognition and ultrasmall quasi-molecular size regimes? Succinctly put, ultrasmall conjugates can evade defense mechanisms driven by larger scale cellular nanoscale recognition, enabling them to flexibly exploit molecular interaction motifs to interact with specific targets. Numerous advances in control of architecture that take advantage of these phenomena have taken place or are underway. For instance, syntheses can now be sufficiently controlled that it is possible to make nanoparticles of a few hundreds of atoms or metalloid clusters of several tens of atoms that can be characterized by single crystal X-ray structure analysis. While the synthesis of atomically precise clusters in organic solvents presents challenges, water-based syntheses of ultrasmall nanoparticles can be upscaled and lead to well-defined particle populations. The surface of ultrasmall nanoparticles can be covalently modified with a wide variety of ligands to control the interactions of these particles with biosystems, as well as drugs and fluorophores. And, in contrast to larger particles, many advanced molecular analytical and separation tools can be applied to understand their structure. For example, NMR spectroscopy allows us to obtain a detailed image of the particle surface and the attached ligands. These are considerable advantages that allow further elaboration of the level of architectural control and characterization of the ultrasmall structures required to access novel functional regimes and outcomes. The ultrasmall nanoparticle regime has a unique status and provides a potentially very interesting direction for development.

Conjugation of biomolecules on the surface of nanoparticles (NPs) to achieve active targeting is widely investigated within the scientific community. However, while a basic framework of the physicochemical processes underpinning bionanoparticle recognition is now emerging, the precise evaluation of the interactions between engineered NPs and biological targets remains underdeveloped. Here, we show how the adaptation of a method currently used to evaluate molecular ligand-receptor interactions by quartz crystal microbalance (QCM) can be used to obtain concrete insights into interactions between different NP architectures and assemblies of receptors. Using a model bionanoparticle grafted with oriented apolipoprotein E (ApoE) fragments, we examine key aspects of bionanoparticle engineering for effective interactions with target receptors. We show that the QCM technique can be used to rapidly measure construct-receptor interactions across biologically relevant exchange times. We contrast random adsorption of the ligand at the surface of the NPs, resulting in no measurable interaction with target receptors, to grafted oriented constructs, which are strongly recognized even at lower graft densities. The effects of other basic parameters impacting the interaction such as ligand graft density, receptor immobilization density, and linker length were also efficiently evaluated with this technique. Dramatic changes in interaction outcomes with subtle alterations in these parameters highlight the general importance of measuring the interactions between engineered NPs and target receptors ex situ early on in the construct development process for the rational design of bionanoparticles.

Nanoparticles-based drug delivery systems have attracted significant attention in biomedical fields because they can deliver loaded cargoes to the target site in a controlled manner. However, tremendous challenges must still be overcome to reach the expected targeting and therapeutic efficacy in vivo. These challenges mainly arise because the interaction between nanoparticles and biological systems is complex and dynamic and is influenced by the physicochemical properties of the nanoparticles and the heterogeneity of biological systems. Importantly, once the nanoparticles are injected into the blood, a protein corona will inevitably form on the surface. The protein corona creates a new biological identity which plays a vital role in mediating the bio-nano interaction and determining the ultimate results. Thus, it is essential to understand how the protein corona affects the delivery journey of nanoparticles in vivo and what we can do to exploit the protein corona for better delivery efficiency. In this review, we first summarize the fundamental impact of the protein corona on the delivery journey of nanoparticles. Next, we emphasize the strategies that have been developed for tailoring and exploiting the protein corona to improve the transportation behavior of nanoparticles in vivo. Finally, we highlight what we need to do as a next step towards better understanding and exploitation of the protein corona. We hope these insights into the "Yin and Yang" effect of the protein corona will have profound implications for understanding the role of the protein corona in a wide range of nanoparticles.

Endocytosis, as one of the main ways for nanostructures enter cells, is affected by several aspects, and shape is an especially critical aspect during the endocytosis of nanostructures. However, it has remained challenging to capture the dynamic internalization behaviors of rod-shaped nanostructures while also probing the mechanical aspects of the internalization. Here, using the atomic force microscopy-based force tracing technique, transmission electron microscopy, and molecular dynamic simulation, we mapped the detailed internalization behaviors of rod-shaped nanostructures with different aspect ratios at the single-particle level. We found that the gold nanorod is endocytosed in a noncontinuous and force-rebound rotation manner, herein named "intermittent rotation". The force tracing test indicated that the internalization force (∼81 pN, ∼108 pN, and ∼157 pN) and time (∼0.56 s, ∼0.66 s, and ∼1.14 s for a 12.10 nm × 11.96 nm gold nanosphere and 26.15 nm × 13.05 nm and 48.71 nm × 12.45 nm gold nanorods, respectively) are positively correlated with the aspect ratios. However, internalization speed is negatively correlated with internalization time, irrespective of the aspect ratio. Further, the energy analysis suggested that intermittent rotation from the horizontal to vertical direction can reduce energy dissipation during the internalization process. Thus, to overcome the energy barrier of internalization, the number and angle of rotation increases with aspect ratios. Our findings provide critical missing evidence of rod-shaped nanostructure's internalization, which is essential for fundamentally understanding the internalization mechanism in living cells.

Recent observations suggest a role for complex nanoscale particulate shape in the regulation of specific immune-related cellular and in vivo processes. We suspect that cellular recognition of nanostructure architecture could involve nonmolecular inputs, including cellular transduction of nanoscale spatially resolved stresses induced by complex shape. Here, we report nanoscale shape-dependent control of the cellular epigenome. Interpretation of ChIP-Seq sequencing suggests that differential marking of H3K27me3 may be linked to sensory and synapse-recognition of nanoscale forces induced by complex shape. The observations raise significant questions on the role of particle-shape-induced immune regulation and memory, with potential consequences in both causes and treatment of immune-related disease.

The progress achieved over the last three decades in the field of bioconjugation has enabled the preparation of sophisticated nanomaterial–biomolecule conjugates, referred to herein as bionanoconstructs, for a multitude of applications including biosensing, diagnostics, and therapeutics. However, the development of bionanoconstructs for the active targeting of cells and cellular compartments, both in vitro and in vivo, is challenged by the lack of understanding of the mechanisms governing nanoscale recognition. In this review, we highlight fundamental obstacles in designing a successful bionanoconstruct, considering findings in the field of bionanointeractions. We argue that the biological recognition of bionanoconstructs is modulated not only by their molecular composition but also by the collective architecture presented upon their surface, and we discuss fundamental aspects of this surface architecture that are central to successful recognition, such as the mode of biomolecule conjugation and nanomaterial passivation. We also emphasize the need for thorough characterization of engineered bionanoconstructs and highlight the significance of population heterogeneity, which too presents a significant challenge in the interpretation of in vitro and in vivo results. Consideration of such issues together will better define the arena in which bioconjugation, in the future, will deliver functional and clinically relevant bionanoconstructs.

Since it is now possible to make, in a controlled fashion, an almost unlimited variety of nanostructure shapes, it is of increasing interest to understand the forms of biological control that nanoscale shape allows. However, a priori rational investigation of such a vast universe of shapes appears to present intractable fundamental and practical challenges. This has limited the useful systematic investigation of their biological interactions and the development of innovative nanoscale shape-dependent therapies. Here, we introduce a concept of biologically relevant inductive nanoscale shape discovery and evaluation that is ideally suited to, and will ultimately become, a vehicle for machine learning discovery. Combining the reproducibility and tunability of microfluidic flow nanochemistry syntheses, quantitative computational shape analysis, and iterative feedback from biological responses in vitro and in vivo, we show that these challenges can be mastered, allowing shape biology to be explored within accepted scientific and biomedical research paradigms. Early applications identify significant forms of shape-induced biological and adjuvant-like immunological control.

Automatized approaches for nanoparticle synthesis and characterization represent a great asset to their applicability in the biomedical field by improving reproducibility and standardization, which help to meet the selection criteria of regulatory authorities. The scaled-up production of nanoparticles with carefully defined characteristics, including intrinsic morphological features, minimal intra-batch, batch-to-batch, and operator variability, is an urgent requirement to elevate nanotechnology towards more trustable biological and technological applications. In this work, microfluidic approaches were employed to achieve fast mixing and good reproducibility in synthesizing a variety of gold nanostructures. The microfluidic setup allowed exploiting spatial resolution to investigate the growth evolution of the complex nanoarchitectures. By physically isolating intermediate reaction fractions, we performed an advanced characterization of the shape properties during their growth, not possible with routine characterization methods. Employing an in-house developed method to assign a specific identity to shapes, we followed the particle growth/deformation process and identified key reaction parameters for more precise control of the generated morphologies. Besides, this investigation led to the optimization of a one-pot multi-size and multi-shape synthesis of a variety of gold nanoparticles. In summary, we describe an optimized platform for highly controlled synthesis and a novel approach for the mechanistic study of shape-evolving nanomaterials.

There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.

The successful mRNA vaccines against COVID-19 contain polyethylene glycol (PEG) to stabilize the lipid nanoparticles. Recent data show that PEG-specific antibodies can be induced or boosted by mRNA vaccination. Further research is needed to study the potential links between PEG-specific antibodies, vaccine reactogenicity and enhanced clearance of other PEG-containing medicines.

Throughout the evolution of biological species on Earth, cells and organs have developed many complex structures and processes to ensure their interactions with individual chemical molecules (small and macromolecular) and nanoscale objects result in no harm. These evolutionary mechanisms complicate our attempts to use modern nanoscale science to develop effective and efficient treatments for disease or other biological dysfunctions. Here we describe the complexity of biology on the nanoscale and the implications for the success of recently-discovered nanoscience, which has resulted in an almost infinite number of potential nanomaterials of unknown efficacy. We discuss how tools to categorize nanomaterials on the basis of structure, properties and interactions can provide insights on promising directions.

While checking the Python code used in the original version of this Article for calculating principal components based on nanoparticle contour coordinates, we realized that the code contained a bug which was causing an indexing error related to the ordering of the projection matrix.This affected the final projection of the data onto the base set of the principal components.Figure panels 3b, 3c, 3h and 3i have now been replaced with the corrected plots in the PDF and HTML versions of the Article.

Silica nanoparticles (SiNP) trigger a range of innate immune responses in relevant essential organs, such as the liver and the lungs. Inflammatory reactions, including NLRP3 inflammasome activation, have been linked to particulate materials; however, the molecular mechanisms and key actors remain elusive. Although many receptors, including several scavenger receptors, were suggested to participate in SiNP cellular uptake, mechanistic evidence of their role on innate immunity is lacking. Here we present an atomic force microscopy-based approach to physico-mechanically map the specific interaction occurring between nanoparticles and scavenger receptor A1 (SRA1) in vitro on living lung epithelial cells. We find that SiNP recognition by SRA1 on human macrophages plays a key role in mediating NLRP3 inflammasome activation, and we identify cellular mechanical changes as clear indicators of inflammasome activation in human macrophages, greatly advancing our knowledge on the interplay among nanomaterials and innate immunity.