Superresolution mapping of energy landscape for single charge carriers in plastic semiconductors

Self-Reporting Conjugated Polymer Nanoparticles for Superoxide Generation and Detection

Conjugated polymer nanoparticles (CPNs or Pdots) have become increasingly popular fluorophores for multimodal applications that combine imaging with phototherapeutic effects. Reports of CPNs in photodynamic therapy applications typically focus on their ability to generate singlet oxygen. Alternatively, CPN excited states can interact with oxygen to form superoxide radical anion and a CPN-based hole polaron, both of which can have deleterious effects on fluorescence properties. Here, we demonstrate that CPNs prepared from the common conjugated polymer poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-co-(1,4-benzo-{2,1',3}-thiadiazole)] (PFBT, also known as F8BT) generate superoxide upon irradiation. We use the same CPNs to detect superoxide by doping them with a superoxide-responsive hydrocyanine dye developed by Murthy and co-workers. Superoxide induces off-to-on fluorescence switching by converting quenching hydrocyanine dyes to fluorescent cyanine dyes that act as fluorescence resonance energy transfer (FRET) acceptors for PFBT chromophores. Amplified FRET from the multichromophoric CPNs yields fluorescence signal intensities that are nearly 50 times greater than when the dye is excited directly or over 100 times greater when signal readout is from the CPN channel. The dye loading level governs the maximum amount of superoxide that induces a change in fluorescence properties and also influences the rate of superoxide generation by furnishing competitive excited state deactivation pathways. These results suggest that CPNs can be used to deliver superoxide in applications in which it is desirable and provide a caution for fluorescence-based CPN applications in which superoxide can damage fluorophores.

Hyperbranched Polymer Dots with Strong Absorption and High Fluorescence Quantum Yield for In Vivo NIR-II Imaging

In order to improve the fluorescence quantum yield (QY) of NIR-II-emitting nanoparticles, D-A-D fluorophores are typically linked to intramolecular rotatable units to reduce aggregation-induced quenching. However, incorporating such units often leads to a twisted molecular backbone, which affects the coupling within the D-A-D unit and, as a result, lowers the absorption. Here, we overcome this limitation by cross-linking the NIR-II fluorophores to form a 2D polymer network, which simultaneously achieves a high QY by well-controlled fluorophore separation and strong absorption by restricting intramolecular distortion. Using the strategy, we developed polymer dots with the highest NIR-II single-particle brightness among reported D-A-D-based nanoparticles and applied them for imaging of hindlimb vasculatures and tumors as well as fluorescence-guided tumor resection. The high brightness of the polymer dots offered exceptional image quality and excellent surgical results, showing a promising performance for these applications.

BODIPY-based near-infrared semiconducting polymer dot for selective yellow laser-excited cell imaging

Semiconducting polymer dots (Pdots) with both narrow-band absorption and emission are desirable for multiplexed bioassay applications, but such Pdots with absorption peaks beyond 400 nm are difficult to achieve. Here we describe a donor-energy transfer unit-acceptor (D-ETU-A) design strategy to produce a BODIPY-based Pdot that exhibits simultaneously narrow absorption and emission bands. A green BODIPY (GBDP) unit was employed as the main building block of the polymer backbone, conferring a strong, narrow-band absorption around 551 nm. An NIR720 acceptor provides narrow-band NIR emission. The small Stokes shift of the GBDP donor allows introduction of a benzofurazan-based ETU, resulting in a ternary Pdot with a fluorescence quantum yield of 23.2%, the most efficient yellow-laser excitable Pdot. Due to the strong absorbance band centered at 551 nm and weak absorbance at 405 nm and 488 nm, the Pdot showed high single-particle brightness when excited by a 561 nm (yellow) laser, and selective yellow laser excitation when used to label MCF cells, with much greater brightness when excited at 561 nm than at 405 nm or 488 nm.

High-Precision Mapping of Membrane Proteins on Synaptic Vesicles using Spectrally Encoded Super-Resolution Imaging

The spatial resolution of single-molecule localization microscopy is limited by the photon number of a single switching event because of the difficulty of correlating switching events dispersed in time. Here we overcome this limitation by developing a new class of photoswitching semiconducting polymer dots (Pdots) with structured and highly dispersed single-particle spectra. We imaged the Pdots at the first and the second vibronic emission peaks and used the ratio of peak intensities as a spectral coding. By correlating switching events using the spectral coding and performing 4-9 frame binning, we achieved a 2-3 fold experimental resolution improvement versus conventional superresolution imaging. We applied this method to count and map SV2 and proton ATPase proteins on synaptic vesicles (SVs). The results reveal that these proteins are trafficked and organized with high precision, showing unprecedented level of detail about the composition and structure of SVs.

Deep learning in single-molecule imaging and analysis: recent advances and prospects

Single-molecule microscopy is advantageous in characterizing heterogeneous dynamics at the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest from single-molecule data. As a new class of computer algorithms that simulate the human brain to extract data features, deep learning networks excel in task parallelism and model generalization, and are well-suited for handling nonlinear functions and extracting weak features, which provide a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advances in the application of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.

Sweet light o' mine: Photothermal and photodynamic inactivation of tenacious pathogens using conjugated polymers

Each year a rising number of infections can not be successfully treated owing to the increasing pandemic of antibiotic resistant pathogens. The global shortage of innovative antibiotics fuels the emergence and spread of drug resistant microbes. Basic research, development, and applications of alternative therapies are urgently needed. Since the 90´s, light-mediated therapies have promised to be the next frontier combating multidrug-resistance microbes. These platforms have demonstrated to be a reliable, rapid, and efficient alternative to eliminate tenacious pathogens while avoiding the emergence of resistance mechanisms. Among the materials showing antimicrobial activity triggered by light, conjugated polymers (CPs) have risen as the most promising option to tackle this complex situation. These materials present outstanding characteristics such as high absorption coefficients, great photostability, easy processability, low cytotoxicity, among others, turning them into a powerful class of photosensitizer (PS)/photothermal agent (PTA) materials. Herein, we summarize and discuss the advances in the field of CPs with applications in photodynamic inactivation and photothermal therapy towards bacteria elimination. Additionally, a section of current challenges and needs in terms of well-defined benchmark experiments and conditions to evaluate the efficiency of phototherapies is presented.

High-Throughput Counting and Superresolution Mapping of Tetraspanins on Exosomes Using a Single-Molecule Sensitive Flow Technique and Transistor-like Semiconducting Polymer Dots

A method for high-throughput counting and superresolution mapping of surface proteins on exosomes is described. The method combines a single-molecule sensitive flow technique and an adaptive superresolution imaging method. Exosomes stained with membrane dye and dye-conjugated antibodies were analyzed using a microfluidic platform at a flow rate of 100 exosome s-1 to determine size and protein copy number. Superresolution mapping was performed with exosomes labeled with novel transistor-like, semiconducting polymer dots (Pdots), which exhibit spontaneous blinking with <5 nm localization error and a broad range of optical-adjustable duty cycles. Based on the copy numbers extracted from the flow analysis, the switch-on frequency of the Pdots were finely adjusted so that structures of hundreds of exosomes were obtained within five minutes. The high throughput and high sensitivity of this method offer clear advantages for characterization of exosomes and similar biological vesicles.

Multimode Time-Resolved Superresolution Microscopy Revealing Chain Packing and Anisotropic Single Carrier Transport in Conjugated Polymer Nanowires

Here, we developed a novel, multimode superresolution method to perform full-scale structural mapping and measure the energy landscape for single carrier transport along conjugated polymer nanowires. Through quenching of the local emission, the motion of a single photogenerated hole was tracked using blinking-assisted localization microscopy. Then, utilizing binding and unbinding dynamics of quenchers onto the nanowires, local emission spectra were collected sequentially and assembled to create a superresolution map of emission sites throughout the structure. The hole polaron trajectories were overlaid with the superresolution maps to correlate structures with charge transport properties. Using this method, we compared the efficiency of inter- and intrachain hole transport inside the nanowires and for the first time directly measured the depth of carrier traps originated from torsional disorder and chemical defects.

Nanoscale Metal-Organic Frameworks as Fluorescence Sensors for Food Safety

Food safety has attracted attention worldwide, and how to detect various kinds of hazardous substances in an efficient way has always been a focus. Metal-Organic Frameworks (MOFs) are a class of hybrid porous materials formed by organic ligand and metal ions. Nanoscale MOFs (NMOFs) exhibit great potential in serving as fluorescence sensors for food safety due to their superior properties including high accuracy, great stability, fast response, etc. In this review, we focus on the recent development of NMOFs sensing for food safety. Several typical methods of NMOFs synthesis are presented. NMOFs-based fluorescence sensors for contaminants and adulterants, such as antibiotics, food additives, ions and mycotoxin etc. are summarized, and the sensing mechanisms are also presented. We explore these challenges in detail and provide suggestions about how they may be surmounted. This review could help the exploration of NMOFs sensors in food related work.

Dual-Mode Superresolution Imaging Using Charge Transfer Dynamics in Semiconducting Polymer Dots

In a conjugated polymer-based single-particle heterojunction, stochastic fluctuations of the photogenerated hole population lead to spontaneous fluorescence switching. We found that 405 nm irradiation can induce charge recombination and activate the single-particle emission. Based on these phenomena, we developed a novel class of semiconducting polymer dots that can operate in two superresolution imaging modes. The spontaneous switching mode offers efficient imaging of large areas, with <10 nm localization precision, while the photoactivation/deactivation mode offers slower imaging, with further improved localization precision (ca. 1 nm), showing advantages in resolving small structures that require high spatial resolution. Superresolution imaging of microtubules and clathrin-coated pits was demonstrated, under both modes. The excellent localization precision and versatile imaging options provided by these nanoparticles offer clear advantages for imaging of various biological systems.

Narrow-band polymer dots with pronounced fluorescence fluctuations for dual-color super-resolution imaging

Super-resolution optical fluctuation imaging (SOFI) produces fast, background-free, super-resolved images by analyzing the temporal fluorescence fluctuations of independent emitters. With sufficient brightness and fluctuations, a higher order of image processing affords a higher resolution and in principle the resolution enhancement is unbounded. However, it is practically challenging to find suitable probes for high-order SOFI. Herein, we report two types of BODIPY-based polymer dots (Pdots) with narrow-band emissions, pronounced fluctuations, and prominent photostability, thus enabling high-order, dual-color SOFI nanoscopy. Single-particle and subcellular SOFI analysis reveals the superior performance of the BODIPY Pdots as compared to conventional streptavidin-conjugated Alexa dyes. In contrast with wide-field images, the spatial resolution (∼57 nm) was enhanced by ∼6.0-fold in 8^th-order single-particle SOFI nanoscopy. A spatial resolution (61 nm) was obtained for single microtubules labeled by the BODIPY Pdots, while the majority of the subcellular structures were lost for those labeled by streptavidin-Alexa dyes in 8^th-order SOFI. This work indicates the unprecedented performance of Pdot probes for multi-color subcellular SOFI applications.