Dye‐loaded polymer nanoparticles (NPs) emerge as a powerful tool for bioimaging applications, owing to their exceptional brightness and controlled small size. However, aggregation‐caused quenching (ACQ) and leakage of dyes at high loading remain important challenges of these nanomaterials. The use of bulky hydrophobic counterions has been recently proposed as an effective approach to minimize ACQ and dye leakage, but the role of counterion structure is still poorly understood. Here, a systematic study based on ten counterions, ranging from small hydrophilic perchlorate up to large hydrophobic tetraphenylborate derivatives, reveals how counterion nature can control encapsulation and emission of a cationic dye (rhodamine B octadecyl ester) in NPs prepared by nanoprecipitation of a biodegradable polymer, poly‐lactide‐co‐glycolide (PLGA). We found that increase in counterion hydrophobicity enhances dye encapsulation efficiency and prevents dye adsorption at the particle surface. Cellular imaging studies revealed that ≥95 % encapsulation efficiency, achieved with most hydrophobic counterions (fluorinated tetraphenylborates), is absolutely required because non‐encapsulated dye species at the surface of NPs are the origin of dye leakage and strong fluorescence background in cells. The size of counterions is found to be essential to prevent ACQ, where the largest species, serving as effective spacer between dyes, provide the highest fluorescence quantum yield. Moreover, we found that the most hydrophobic counterions favor dye–dye coupling inside NPs, leading to ON/OFF fluorescence switching of single particles. By contrast, less hydrophobic counterions tend to disperse dyes in the polymer matrix favoring stable emission of NPs. The obtained structure‐property relationships validate the counterion‐based approach as a mature concept to fight ACQ and dye leakage in the development of advanced polymeric nanomaterials with controlled optical properties.

International audienceFluorescent polymer nanoparticles for long-term labeling and tracking of living cells with any desired color code are developed. They are built from biodegradable poly(lactic-co-glycolic acid) polymer loaded with cyanine dyes (DiO, DiI, and DiD) with the help of bulky fluorinated counterions, which minimize aggregation-caused quenching. At the single particle level, these particles are ≈20-fold brighter than quantum dots of similar color. Due to their identical 40 nm size and surface properties, these nanoparticles are endocytosed equally well by living cells. Mixing nanoparticles of three colors in different proportions generates a homogeneous RGB (red, green, and blue) barcode in cells, which is transmitted through many cell generations. Cell barcoding is validated on 7 cell lines (HeLa, KB, embryonic kidney (293T), Chinese hamster ovary, rat basophilic leucemia, U97, and D2A1), 13 color codes, and it enables simultaneous tracking of co-cultured barcoded cell populations for >2 weeks. It is also applied to studying competition among drug-treated cell populations. This technology enabled six-color imaging in vivo for (1) tracking xenografted cancer cells and (2) monitoring morphogenesis after microinjection in zebrafish embryos. In addition to a robust method of multicolor cell labeling and tracking, this work suggests that multiple functions can be co-localized inside cells by combining structurally close nanoparticles carrying different functions

Rationale: The goal of imaging tumors at depth with high sensitivity and specificity represents a significant challenge in the field of biomedical optical imaging. 'Surface enhanced Raman scattering' (SERS) nanoparticles (NPs) have been employed as image contrast agents and can be used to specifically target cells in vivo. By tracking their unique “fingerprint” spectra, it becomes possible to determine their precise location. However, while the detection of SERS NPs is very sensitive and specific, conventional Raman spectroscopy imaging devices are limited in their inability to probe through tissue depths of more than a few millimetres, due to scattering and absorption of photons by biological tissues. Here, we combine the use of "Spatially Offset Raman spectroscopy" (SORS) with that of "surface-enhanced resonance Raman spectroscopy" (SERRS) in a technique known as "surface enhanced spatially offset resonance Raman spectroscopy" (SESO(R)RS) to image deep-seated glioblastoma multiforme (GBM) tumors in vivo in mice through the intact skull.Methods: A SORS imaging system was built in-house. Proof of concept SORS imaging was achieved using a PTFE-skull-tissue phantom. Imaging of GBMs in the RCAS-PDGF/N-tva transgenic mouse model was achieved through the use of gold nanostars functionalized with a resonant Raman reporter to create SERRS nanostars. These were then encapsulated in a thin silica shell and functionalized with a cyclic-RGDyK peptide to yield integrin-targeting SERRS nanostars. Non-invasive in vivo SORS image acquisition of the integrin-targeted nanostars was then performed in living mice under general anesthesia. Conventional non-SORS imaging was used as a direct comparison.Results: Using a low power density laser, GBMs were imaged via SESORRS in mice (n = 5) and confirmed using MRI and histopathology. The results demonstrate that via utilization of the SORS approach, it is possible to acquire clear and distinct Raman spectra from deep-seated GBMs in mice in vivo through the skull. SESORRS images generated using classical least squares outlined the tumors with high precision as confirmed via MRI and histology. Unlike SESORRS, conventional Raman imaging of the same areas did not provide a clear delineation of the tumor.Conclusion: To the best of our knowledge this is the first report of in vivo SESO(R)RS imaging. In a relevant brain tumor mouse model we demonstrate that this technique can overcome the limitations of conventional Raman imaging with regards to penetration depth. This work therefore represents a significant step forward in the potential clinical translation of SERRS nanoparticles for high precision cancer imaging.