In Situ Visualization of Dynamic Cellular Effects of Phospholipid Nanoparticles via High-Speed Scanning Ion Conductance Microscopy

Exploring scanning electrochemical probe microscopy in single-entity analysis in biology: Past, present, and future

Scanning Electrochemical Probe Microscopy (SEPM) shows significant potential promise for analyzing localized electrochemical activity at biological interfaces of single entities. Utilizing various SEPM probe manipulations allows real-time monitoring of the morphology and physiological activities of single biological entities, offering vital electrochemical insights into biological processes. This review focuses on the application of five SEPM techniques in imaging single biological entities, highlighting their unique advantages in the observation and quantitative evaluation of biological morphology. Specifically, these techniques not only enable high-resolution imaging of single biological structures but also allow for quantitative analysis of their response behavior. Additionally, the integration of Artificial Intelligence (AI) is discussed to improve data processing and image analysis, potentially advancing SEPM technology towards automation. Although still in an early stage, AI integration opens new avenues for deeper single-entity analysis. This review aims to offer an interdisciplinary perspective and encourage advancements in SEPM-based imaging and analytical techniques, contributing to the bioanalytical field.

Machine-learning-guided quantitative delineation of cell morphological features and responses to nanomaterials

Delineation of cell morphological features is essential to decipher cell responses to external stimuli like theranostic nanomaterials. Conventional methods rely on labeled approaches, such as fluorescence imaging and flow cytometry, to assess cell responses. Besides potentially perturbing cell structure and morphology, these approaches are relatively complex, time-consuming, expensive, and may not be compatible with downstream analysis involving live cells. Herein, leveraging label-free phase-contrast or brightfield microscopy imaging and machine learning, the delineation of different cell types, phenotypes, and states for monitoring live cell responses is reported. Notably, pixel classification based on a supervised random forest classifier is used to distinguish between cells and backgrounds from the microscopy images, followed by cell segmentation and morphological feature extraction. Quantitative analysis shows that most of the compared cell groups have distinguishable size and shape features. Principal component analysis and unsupervised k-means clustering of morphological features reveal the possible existence of heterogenous cell subpopulations and treatment responses among the seemingly homogenous cell groups. This shows the merit of the reported approach in complementing conventional techniques for cell analysis. It is anticipated that the demonstrated method will further aid the implementation of machine learning to streamline the analysis of cell morphology and responses for early disease diagnosis and treatment response monitoring.

Characterizing Chemical, Environmental, and Stimulated Subcellular Physical Characteristics of Size-Fractionated PMs Down to PM0.1

Air pollution, especially particulate matter (PM), is a significant environmental pollution worldwide. Studying the chemical, environmental, and life-related cellular physical characteristics of size-fractionated PMs is important because of their different degrees of harmful effects on human respiratory tracts and organ systems, causing severe diseases. This study evaluates the chemical components of size-fractionated PMs down to PM0.1 collected during a biomass-burning episode, including elemental/organic carbon and trace elements. Single particle sizes and distributions of PM0.1, PM0.5-0.1, PM1.0-0.5, and PM2.5-1.0 were analyzed by scanning electron microscopy and Zeta sizer. Two commonly used cell lines, e.g., HeLa and Cos7 cells, and two respiratory-related cell lines including lung cancer/normal cells were utilized for cell cytotoxicity experiments, revealing the key effects of particle sizes and concentrations. A high-speed scanning ion conductance microscope explored particle-stimulated subcellular physical characteristics for all cell lines in dynamics, including surface roughness (SR) and elastic modulus (E). The statistical results of SR showed distinct features among different particle sizes and cell types while a E reduction was universally found. This work provides a comprehensive understanding of the chemical, environmental, and cellular physical characteristics of size-fractionated PMs and sheds light on the necessity of controlling small-sized PM exposures.

Shortwave-infrared-light-emitting probes for the in vivo tracking of cancer vaccines and the elicited immune responses

Tracking and imaging immune cells in vivo non-invasively would offer insights into the immune responses induced by vaccination. Here we report a cancer vaccine consisting of polymer-coated NaErF4/NaYF4 core-shell down-conversion nanoparticles emitting luminescence in the near-infrared spectral window IIb (1,500-1,700 nm in wavelength) and with surface-conjugated antigen (ovalbumin) and electrostatically complexed adjuvant (class-B cytosine-phosphate-guanine). Whole-body wide-field imaging of the subcutaneously injected vaccine in tumour-bearing mice revealed rapid migration of the nanoparticles to lymph nodes through lymphatic vessels, with two doses of the vaccine leading to the complete eradication of pre-existing tumours and to the prophylactic inhibition of tumour growth. The abundance of antigen-specific CD8+ T lymphocytes in the tumour microenvironment correlated with vaccine efficacy, as we show via continuous-wave imaging and lifetime imaging of two intravenously injected near-infrared-emitting probes (CD8+-T-cell-targeted NaYbF4/NaYF4 nanoparticles and H-2Kb/ovalbumin257-264 tetramer/PbS/CdS quantum dots) excited at different wavelengths, and by volumetrically visualizing the three nanoparticles via light-sheet microscopy with structured illumination. Nanoparticle-based vaccines and imaging probes emitting infrared light may facilitate the design and optimization of immunotherapies.

A novel nanoparticle fluorescent probe based on a water-soluble conjugated polymer for real-time monitoring of ATP fluctuation and configuration of the Golgi apparatus during the inhibition of glycolysis

BACKGROUND

Adenosine 5'-triphosphate (ATP) plays an important role in cell metabolism and has been regarded as an indicator of cell survival and damage. Golgi apparatus participates in the signal transduction processes of substance transport, ion homeostasis and stress when extracellular substances enter cells. Till now, there is no fluorescent probe for monitoring Golgi ATP level fluctuation and visualizing the configuration change of the Golgi apparatus during the inhibition of glycolysis.

RESULTS

Herein, we report the synthesis of a novel water-soluble cationic polythiophene derivative (PEMTEA) that can be employed as a fluorescent sensor for measuring ATP in the Golgi apparatus. PEMTEA self-assembles into PT-NP nanoparticles in aqueous solution with a diameter of approximately 2 nm. PT-NP displays high sensitivity and superb selectivity towards ATP with a detection limit of 90 nM and a linear detection range from 0 to 3.0 μM. The nanoparticles show low toxicity to HepG2 cells and good photostability in the Golgi apparatus. With the stimulation of Ca2+, PT-NP was practically applied to real-time monitor of endogenous ATP levels in the Golgi apparatus through fluorescence microscopy. Finally, we studied the relationship between the concentration of ATP and configuration of the Golgi apparatus during the inhibition of glycolysis using PT-NP.

SIGNIFICANCE

We have demonstrated that PT-NP can not only indicate the fluctuation and distribution of ATP in the Golgi apparatus, but also give the information of the configuration change of the Golgi apparatus at the single-cell level during the inhibition of glycolysis.

Microfluidic-assisted formulation of cell membrane-camouflaged anisotropic nanostructures

Anisotropic gold (Au) nanostructures have been widely explored for various nanomedicine applications. While these nanomaterials have shown great promise for disease theranostics, particularly for cancer diagnosis and treatment, the utilization and clinical translation of anisotropic Au nanostructures have been limited by their high phagocytic uptake and clearance and low cancer targeting specificity. Numerous efforts have thus been made toward mitigating these challenges. Many conventional strategies, however, rely on all-synthetic materials, involve complex chemical processes, or have low product throughput and reproducibility. Herein, by integrating cell membrane coating and microfluidic technologies, a high-throughput bioinspired approach for synthesizing biomimetic anisotropic Au nanostructures with minimized phagocytic uptake and improved cancer cell targeting is reported. Through continuous hydrodynamic flow focusing, mixing, and sonication, Au nanostructures are encapsulated within the macrophage and cancer cell membrane vesicles effectively. The fabricated nanostructures are uniform and highly stable in serum. Importantly, the macrophage membrane vesicle-encapsulated Au nanostructures can be preferentially internalized by breast cancer cells, but not by macrophages. Overall, this study has demonstrated the feasibility of employing an integrated microfluidic-sonication technique to formulate uniform and highly stable biomimetic anisotropic nanostructures for enhanced cancer theranostic applications.

Decoding Nanomaterial-Biosystem Interactions through Machine Learning

The interactions between biosystems and nanomaterials regulate most of their theranostic and nanomedicine applications. These nanomaterial-biosystem interactions are highly complex and influenced by a number of entangled factors, including but not limited to the physicochemical features of nanomaterials, the types and characteristics of the interacting biosystems, and the properties of the surrounding microenvironments. Over the years, different experimental approaches coupled with computational modeling have revealed important insights into these interactions, although many outstanding questions remain unanswered. The emergence of machine learning has provided a timely and unique opportunity to revisit nanomaterial-biosystem interactions and to further push the boundary of this field. This minireview highlights the development and use of machine learning to decode nanomaterial-biosystem interactions and provides our perspectives on the current challenges and potential opportunities in this field.

Zwitterionic materials for nucleic acid delivery and therapeutic applications

Nucleic acid therapeutics have demonstrated substantial potential in combating various diseases. However, challenges persist, particularly in the delivery of multifunctional nucleic acids. To address this issue, numerous gene delivery vectors have been developed to fully unlock the potential of gene therapy. The advancement of innovative materials with exceptional delivery properties is critical to propel the clinical translation of nucleic acid drugs. Cationic vector materials have received extensive attention, while zwitterionic materials remain relatively underappreciated in delivery. In this review, we outline a diverse range of zwitterionic material nucleic acid carriers, predominantly encompassing zwitterionic lipids, polymers and peptides. Their respective chemical structures, synthesis approaches, properties, advantages, and therapeutic applications are summarized and discussed. Furthermore, we highlight the challenges and future opportunities associated with the development of zwitterionic vector materials. This review will aid to understand the zwitterionic materials in aiding gene delivery, contributing to the continual progress of nucleic acid therapeutics.

Opto-SICM framework combines optogenetics with scanning ion conductance microscopy for probing cell-to-cell contacts

We present a novel framework, Opto-SICM, for studies of cellular interactions in live cells with high spatiotemporal resolution. The approach combines scanning ion conductance microscopy, SICM, and cell-type-specific optogenetic interrogation. Light-excitable cardiac fibroblasts (FB) and myofibroblasts (myoFB) were plated together with non-modified cardiomyocytes (CM) and then paced with periodic illumination. Opto-SICM reveals the extent of FB/myoFB-CM cell-cell contacts and the dynamic changes over time not visible by optical microscopy. FB-CM pairs have lower gap junctional expression of connexin-43 and higher contact dynamism compared to myoFB-CM pairs. The responsiveness of CM to pacing via FB/myoFB depends on the dynamics of the contact but not on the area. The non-responding pairs have higher net cell-cell movement at the contact. These findings are relevant to cardiac disease states, where adverse remodeling leads to abnormal electrical excitation of CM. The Opto-SICM framework can be deployed to offer new insights on cellular and subcellular interactions in various cell types, in real-time.

Mapping Nanomechanical Properties of Basal Surfaces in Metastatic Intestinal 3D Living Organoids with High-Speed Scanning Ion Conductance Microscopy

Studying mechanobiology is increasing of scientific interests in life science and nanotechnology since its impact on cell activities (e.g., adhesion, migration), physiology, and pathology. The role of apical surface (AS) and basal surface (BS) of cells played in mechanobiology is significant. The mechanical mapping and analysis of cells mainly focus on AS while little is known about BS. Here, high-speed scanning ion conductance microscope as a powerful tool is utilized to simultaneously reveal morphologies and local elastic modulus (E) of BS of genotype-defined metastatic intestinal organoids. A simple method is developed to prepare organoid samples allowing for long-term BS imaging. The multiple nano/microstructures, i.e., ridge-like, stress-fiber, and E distributions on BS are dynamically revealed. The statistic E analysis shows softness of BS derived from eight types of organoids following a ranking: malignant tumor cells > benign tumor cells > normal cells. Moreover, the correlation factor between morphology and E is demonstrated depending on cell types. This work as first example reveals the subcellular morphologies and E distributions of BS of cells. The results would provide a clue for correlating genotype of 3D cells to malignant phenotype reflected by E and offering a promising strategy for early-stage diagnosis of cancer.