Establishing a simple perfusion cell culture system for light-activated liposomes

Funding Information: The flow cytometry analysis was performed at the HiLife Flow Cytometry Unit, University of Helsinki. We thank the DDCB Faculty of Pharmacy Unit, hosted by the University of Helsinki and supported by HiLIFE and Biocenter Finland, for providing access to Varioskan LUX and Cytation 5. We also thank Sina Bahrpeyma and Joonatan Haapalainen for their technical assistance with the QuasiVivo system, and Shirin Tavakoli and Niklas Johansson for conjugating the DSPE-HA. Ti.L. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC CoG, grant agreement No 101001016). Ta.L. acknowledges funding from Phospholipid Research Center (#TLA-2019-068/1-1), Orion Research Foundation (#9-8214-9) and Academy of Finland (#330656). Open access funded by Helsinki University Library. The images were drawn and photographed by E.M. Funding Information: The flow cytometry analysis was performed at the HiLife Flow Cytometry Unit, University of Helsinki. We thank the DDCB Faculty of Pharmacy Unit, hosted by the University of Helsinki and supported by HiLIFE and Biocenter Finland, for providing access to Varioskan LUX and Cytation 5. We also thank Sina Bahrpeyma and Joonatan Haapalainen for their technical assistance with the QuasiVivo system, and Shirin Tavakoli and Niklas Johansson for conjugating the DSPE-HA. Ti.L. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC CoG, grant agreement No 101001016). Ta.L. acknowledges funding from Phospholipid Research Center (#TLA-2019-068/1-1), Orion Research Foundation (#9-8214-9) and Academy of Finland (#330656). Open access funded by Helsinki University Library. The images were drawn and photographed by E.M. Publisher Copyright: © 2023, The Author(s).The off-target effects of light-activated or targeted liposomes are difficult to distinguish in traditional well plate experiments. Additionally, the absence of fluid flow in traditional cell models can lead to overestimation of nanoparticle uptake. In this paper, we established a perfusion cell culture platform to study light-activated liposomes and determined the effect of flow on the liposomal cell uptake. The optimal cell culturing parameters for the A549 cells under flow conditions were determined by monitoring cell viability. To determine optimal liposome treatment times, particle uptake was measured with flow cytometry. The suitability of commercial QuasiVivo flow-chambers for near-infrared light activation was assessed with a calcein release study. The chamber material did not hinder the light activation and subsequent calcein release from the liposomes. Furthermore, our results show that the standard cell culturing techniques are not directly translatable to flow cultures. For non-coated liposomes, the uptake was hindered by flow. Interestingly, hyaluronic acid coating diminished the uptake differences between the flow and static conditions. The study demonstrates that flow affects the liposomal uptake by lung cancer cell line A549. The flow also complicates the cell attachment of A549 cells. Moreover, we show that the QuasiVivo platform is suitable for light-activation studies.Peer reviewe

Organic NIR-II dyes with ultralong circulation persistence for image-guided delivery and therapy

Acknowledgments This work was partially supported by grants from the National Key R&D Program of China (2020YFA0908800), NSFC (82111530209, 81773674, 91959103, 81573383, 21763002), Shenzhen Science and Technology Research Grant (JCYJ20190808152019182), the Applied Basic Research Program of Wuhan Municipal Bureau of Science and Technology (2019020701011429), Hubei Province Scientific and Technical Innovation Key Project (2020BAB058), the Local Development Funds of Science and Technology Department of Tibet (XZ202102YD0033C, XZ202001YD0028C), and the Fundamental Research Funds for the Central Universities.Peer reviewedPublisher PD

A perspective on nanomedicine: focus on cardiovascular medicine

Background: Nanomedicine refers to the application of nanotechnology to improve the diagnosis, monitoring and treatment of diseases. Although the primary application was originally in oncology, nanomedicine has witnessed substantial scientific interest and growth beyond chemotherapeutic drug development. Approach: Despite the widespread prevalence of cardiovascular diseases (CVDs), limitations remain in their clinical management regardless of the major technological advancement in diagnostic and therapeutic modalities available. In the present context, flourishing research in cardiovascular nanomedicine is expected to address the current challenges and bring about much sought for solutions to the identification and management of the progression of CVDs. Practical Implications: As the research portfolio of nanomedicine expands, it can have a significant impact on the management of CVDs, particularly atherosclerosis. Nanotechnology presents an opportunity to address the components of atherosclerotic plaque and enhance the therapeutic approaches to atherogenesis

Active Targeting of Cancer Cells by the Shape of Nanoparticles

The shape of nanoparticles has emerged as a potentially important factor in the design of drug carriers in vitro and in vivo. Changing the shape of the conventional nanospheres to high aspect ratio (length/diameter) nanoparticles such as rod-shaped and worm-like shapes results in a significant alteration in the amount of nanoparticles and drug that can be delivered to the nucleus (Nat Nanotechnol, 12 (1), 81-89 (2017)). This has been attributed to rod-shaped nanoparticles escaping endosomes and entering the nucleus more effectively in comparison to their spherical counterparts. However, it is unclear whether the shape of nanoparticles enables the targeting of cancer cells over healthy cells? This thesis seeks to show how a material property such as the shape can be used for active targeting of cancer cells over healthy cells rather than a surface receptor target such as antibody. The reason for this is that if an antibody modified nanoparticle contacts a healthy cell, it can still be taken up by the healthy cells. Also, the use of surface receptors confers so little advantage as indicated in the seminal Nature Reviews Materials 1 16014 (2016). Here materials morphology is explored as an approach to design a nanoparticle shape that is taken up by endocytosis mechanisms only occurring in cancer cells. Here it is found that nanorods internalise into cancer cells through the macropinocytosis pathway - a consequence of oncogenic alterations of cancer cells (most healthy cells are non-macropinocytic). Comparison of endocytic behaviour of cancer and healthy cells provides control over the uptake of nanorods by cancer cells and suppress the metabolism and endocytosis of nanorods in healthy cells. Secondly, the nanorods enable the delivery of the anticancer drug doxorubicin to the nucleus of cancer cells which selectively kills the cancer over the healthy cells in a monoculture or co-culture of the two mixed cell types. This study opens exciting possibilities for targeting cancer cells over healthy cells by adjusting the shape of nanoparticles

A New Representation for Spectral Data Applied to Raman Spectroscopy of Brain Cancer

Par sa nature infiltrative et son confinement derrière la barrière hémo-encéphalique, le cancer primaire du cerveau est l’une des néoplasies les plus difficiles à diagnostiquer et traiter. Son traitement repose sur la résection chirurgicale maximale. La spectroscopie Raman, capable d’identifier en temps réel des régions cancéreuses qui apparaîtraient normales à l’œil nu, promet d’améliorer considérablement le guidage neurochirurgical et maximiser la résection de la masse tumorale. Cependant, le signal Raman est très complexe à interpréter : les systèmes Raman peuvent maintenant capter des signaux de grande qualité que les méthodes analytiques actuelles ne parviennent pas à interpréter de manière reproductible. Ceci constitue une barrière importante à l’acceptation de la spectroscopie Raman par les médecins et les chercheurs œuvrant sur le cancer du cerveau. L’objectif de ce travail est de développer une méthode robuste d’ingénierie des variables (« Feature engineering ») qui permettrait d’identifier les processus moléculaires exploités par les systèmes Raman pour différentier les régions cancéreuses des régions saines lors de chirurgies cérébrales. Tout d’abord, nous avons identifié les régions Raman ayant une haute spécificité à notre problématique clinique par une revue systématique de la littérature. Un algorithme d’ajustement de courbe a été développé afin d’extraire la forme des pics Raman dans les régions sélectionnées. Puis, nous avons élaboré un modèle mathématique qui tient compte de l’interactivité entre les molécules de l’échantillon interrogé, ainsi qu’entre le signal Raman et l’âge du patient opéré. Pour valider le modèle, nous avons comparé sa capacité à compresser le signal avec celle de l’analyse en composante principale (ACP), le standard en spectroscopie Raman. Finalement, nous avons appliqué la méthode d’ingénierie des variables à des spectres Raman acquis en salle d’opération afin d’identifier quels processus moléculaires indiquaient la présence de cancer. Notre méthode a démontré une meilleure rétention d’information que l’ACP. En l’appliquant aux spectres Raman in vivo, les zones denses en cellules malignes démontrent une expression augmentée d’acides nucléiques ainsi que de certaines protéines, notamment le collagène, le tryptophan et la phénylalanine. De plus, l’âge des patients semble affecter l’impact qu’ont certaines protéines, lipides et acides nucléiques sur le spectre Raman. Nos travaux révèlent l’importance d’une modélisation statistique appropriée pour l’implémentation clinique de systèmes Raman chirurgicaux.----------ABSTRACT Because of its infiltrative nature and concealment behind the blood-brain barrier, primary brain cancer remains one of the most challenging oncological condition to diagnose and treat. The mainstay of treatment is maximal surgical resection. Raman spectroscopy has shown great promise to guide surgeons intraoperatively by identifying, in real-time, dense cancer regions that appear normal to the naked eye. The Raman signal of living tissue is, however, very challenging to interpret, and while most advances in Raman systems targeted the hardware, appropriate statistical modeling techniques are lacking. As a result, there is conflicting evidence as to which molecular processes are captured by Raman probes. This limitation hinders clinical translation and usage of the technology by the cancer-research community. This work focuses on the analytical aspect of Raman-based surgical systems. Its objective is to develop a robust data processing pipeline to confidently identify which molecular phenomena allow Raman systems to differentiate healthy brain and cancer during neurosurgeries. We first selected high-yield Raman regions based on previous literature on the subject, resulting in a list of reproducible Raman bands with high likelihood of brain-specific Raman signal. We then developed a peak-fitting algorithm to extract the shape (height and width) of the Raman signal at those specific bands. We described a mathematical model that accounted for all possible interactions between the selected Raman peaks, and the interaction between the peaks’ shape and the patient’s age. To validate the model, we compared its capacity to compress the signal while maintaining high information content against a Principal Component Analysis (PCA) of the Raman spectra, the fields’ standard. As a final step, we applied the feature engineering model to a dataset of intraoperative human Raman spectra to identify which molecular processes were indicative of brain cancer. Our method showed better information retention than PCA. Our analysis of in vivo Raman measurement showed that areas with high-density of malignant cells had increased expression of nucleic acids and protein compounds, notably collagen, tryptophan and phenylalanine. Patient age seemed to affect the impact of nucleic acids, proteins and lipids on the Raman spectra. Our work demonstrates the importance of appropriate statistical modeling in the implementation of Raman-based surgical devices