Subsecond lung cancer detection within a heterogeneous background of normal and benign tissue using single-point Raman spectroscopy.

Significance: Lung cancer is the most frequently diagnosed cancer overall and the deadliest cancer in North America. Early diagnosis through current bronchoscopy techniques is limited by poor diagnostic yield and low specificity, especially for lesions located in peripheral pulmonary locations. Even with the emergence of robotic-assisted platforms, bronchoscopy diagnostic yields remain below 80%. Aim: The aim of this study was to determine whether in situ single-point fingerprint (800 to 1700 cm-1) Raman spectroscopy coupled with machine learning could detect lung cancer within an otherwise heterogenous background composed of normal tissue and tissue associated with benign conditions, including emphysema and bronchiolitis. Approach: A Raman spectroscopy probe was used to measure the spectral fingerprint of normal, benign, and cancer lung tissue in 10 patients. Each interrogated specimen was characterized by histology to determine cancer type, i.e., small cell carcinoma or non-small cell carcinoma (adenocarcinoma and squamous cell carcinoma). Biomolecular information was extracted from the fingerprint spectra to identify biomolecular features that can be used for cancer detection. Results: Supervised machine learning models were trained using leave-one-patient-out cross-validation, showing lung cancer could be detected with a sensitivity of 94% and a specificity of 80%. Conclusions: This proof of concept demonstrates fingerprint Raman spectroscopy is a promising tool for the detection of lung cancer during diagnostic procedures and can capture biomolecular changes associated with the presence of cancer among a complex heterogeneous background within less than 1 s.

Recent advances in non-plasmonic surface-enhanced Raman spectroscopy nanostructures for biomedical applications.

Surface-enhanced Raman spectroscopy (SERS) is an emerging powerful vibrational technique offering unprecedented opportunities in biomedical science for the sensitive detection of biomarkers and the imaging and tracking of biological samples. Conventional SERS detection is based on the use of plasmonic substrates (e.g., Au and Ag nanostructures), which exhibit very high enhancement factors (EF = 1010 -1011 ) but suffers from serious limitations, including light-induced local heating effect due to ohmic loss and expensive price. These drawbacks may limit detection accuracy and large-scaled practical applications. In this review, we focus on alternative approaches based on plasmon-free SERS detection on low-cost nanostructures, such as carbons, oxides, chalcogenides, polymers, silicons, and so forth. The mechanism of non-plasmonic SERS detection has been attributed to interfacial charge transfer between the substrate and the adsorbed molecules, with no photothermal side-effects but usually less EF compared with plasmonic nanostructures. The strategies to improve Raman signal detection, through the tailoring of substrate composition, structure, and surface chemistry, is reviewed and discussed. The biomedical applications, for example, SERS cell characterization, biosensing, and bioimaging are also presented, highlighting the importance of substrate surface functionalization to achieve sensitive, accurate analysis, and excellent biocompatibility. This article is categorized under: Diagnostic Tools > Diagnostic Nanodevices Diagnostic Tools > Biosensing Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

SERS tags derived from silver nanoparticles and aryl diazonium salts for cell Raman imaging.

The surface functionalization of silver nanoparticles (NPs) by Raman reporters has stimulated a wide interest in recent years for the design of Surface-Enhanced Raman Spectroscopy (SERS) labels. However, silver NPs are prone to oxidation and aggregation, which strongly limits their applications. The design of stable SERS tags based on Ag NPs still represents a major challenge for Raman bioimaging. We address this issue herein by taking advantage of aryl diazonium salt chemistry to obtain stable Ag NPs functionalized by multifunctional polyaryl layers bearing different Raman reporters (-NO2, -CN, -CCH). The resulting SERS-encoded Ag NPs were characterized by UV-vis absorption, transmission electron microscopy (TEM) and SERS. The formation of multilayers at the surface of Ag NPs gives access to new spectrally distinguishable SERS codes thus broadening the library of available Raman tags. Proof-of-concept Raman imaging experiments were performed on cancer cells (HeLa) after NP uptake, highlighting the large potentials of diazonium salt chemistry to design Ag NPs-based SERS labels for Raman bioimaging.

[Therapeutic applications of extracellular vesicles]. / Applications thérapeutiques des vésicules extracellulaires.

Extracellular vesicles, secreted spontaneously or in response to stress by all cell types, are proposed as alternative biotherapies to cellular therapies and to synthetic nanomedicines. Their logistical advantages (storage, stability, availability, tolerance), their ability to cross biological barriers, to deliver their contents (proteins, lipids and nucleic acids) in order to modify their target cells, as well as their immunomodulatory and regenerative activities, are of growing interest for a very wide spectrum of diseases. Here we review the challenges to bring these biotherapies to the clinic and discuss some promising applications in cancer and regenerative medicine.

TITLE: Applications thérapeutiques des vésicules extracellulaires. ABSTRACT: Les vésicules extracellulaires, sécrétées spontanément ou en réponse à un stress par tous les types cellulaires, sont proposés comme des biothérapies alternatives aux thérapies cellulaires et aux nanomédicaments synthétiques. Leurs atouts logistiques (stockage, stabilité, disponibilité, tolérance), leur capacité à franchir les barrières biologiques, à délivrer leurs contenus (protéines, lipides et acides nucléiques) pour modifier leurs cellules cibles, ainsi que leurs activités immunomodulatrice et régénérative, suscitent un intérêt grandissant pour un très large spectre de maladies. Cette synthèse présente les défis qui restent à relever pour appliquer ces biothérapies en clinique. Quelques applications prometteuses dans les domaines du cancer et de la médecine régénérative seront proposées.

Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens.

SIGNIFICANCE: Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85 % detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8 % accuracy. AIM: To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. APPROACH: A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l'Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. RESULTS: Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4 % (+8 % ), +7 % (+9 % ), +2 % (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2 % (+1.7 % ), +4.5 % (+3.6 % ), +0 % (+0 % ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0 % (-2 % ), +0 % (-3 % ), +2 % (-2 % ), +4 (+3)], the AUC was improved in both testing sets. CONCLUSIONS: Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

Development of extracellular vesicle-based medicinal products: A position paper of the group "Extracellular Vesicle translatiOn to clinicaL perspectiVEs - EVOLVE France".

Extracellular vesicles (EV) are emergent therapeutic effectors that have reached clinical trial investigation. To translate EV-based therapeutic to clinic, the challenge is to demonstrate quality, safety, and efficacy, as required for any medicinal product. EV research translation into medicinal products is an exciting and challenging perspective. Recent papers, provide important guidance on regulatory aspects of pharmaceutical development, defining EVs for therapeutic applications and critical considerations for the development of potency tests. In addition, the ISEV Task Force on Regulatory Affairs and Clinical Use of EV-based Therapeutics as well as the Exosomes Committee from the ISCT are expected to contribute in an active way to the development of EV-based medicinal products by providing update on the scientific progress in EVs field, information to patients and expert resource network for regulatory bodies. The contribution of our work group "Extracellular Vesicle translatiOn to clinicaL perspectiVEs - EVOLVE France", created in 2020, can be positioned in complement to all these important initiatives. Based on complementary scientific, technical, and medical expertise, we provide EV-specific recommendations for manufacturing, quality control, analytics, non-clinical development, and clinical trials, according to current European legislation. We especially focus on early phase clinical trials concerning immediate needs in the field. The main contents of the investigational medicinal product dossier, marketing authorization applications, and critical guideline information are outlined for the transition from research to clinical development and ultimate market authorization.

Engineering and loading therapeutic extracellular vesicles for clinical translation: A data reporting frame for comparability.

Extracellular vesicles (EVs) have emerged as new drug delivery systems as well as a regenerative cell-free effectors going beyond academic research to reach industrial research and development (R&D). Many proof-of-concept studies are now published describing the delivery of drugs, nanoparticles or biologics among which nucleic acids, proteins, viruses, etc. Their main interests rely on their intrinsic biocompatibility, targeting capabilities and biological activities. The possibility of loading EVs with exogenous therapeutic drug/nanoparticles or imaging tracers opens up the perspectives to extend EV therapeutic properties and enable EV tracking. Clinical translation is still hampered by the difficulty to produce and load EVs with large scale, efficient and cGMP methods. In this review, we critically discuss important notions related to EV engineering and the methods available with a particular focus on technologies fitted for clinical translation. Besides, we provide a tentative data reporting frame in order to support comparability and standardization in the field.

Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation.

BACKGROUND: Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (RµS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories. METHODS AND FINDINGS: We used RµS to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l'Université de Montréal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Québec-Université Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%-69%), and pT3a (22%-49%) was more frequent than pT3b (9%-12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% ± 5%, 86% ± 6%, and 89% ± 8%, respectively, to differentiate PC from benign tissue, and 95% ± 2%, 96% ± 4%, and 94% ± 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets. CONCLUSIONS: In this study, we developed classification models for the analysis of RµS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RµS could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.

Integration of a Raman spectroscopy system to a robotic-assisted surgical system for real-time tissue characterization during radical prostatectomy procedures.

Surgical excision of the whole prostate through a radical prostatectomy procedure is part of the standard of care for prostate cancer. Positive surgical margins (cancer cells having spread into surrounding nonresected tissue) occur in as many as 1 in 5 cases and strongly correlate with disease recurrence and the requirement of adjuvant treatment. Margin assessment is currently only performed by pathologists hours to days following surgery and the integration of a real-time surgical readout would benefit current prostatectomy procedures. Raman spectroscopy is a promising technology to assess surgical margins: its in vivo use during radical prostatectomy could help insure the extent of resected prostate and cancerous tissue is maximized. We thus present the design and development of a dual excitation Raman spectroscopy system (680- and 785-nm excitations) integrated to the robotic da Vinci surgical platform for in vivo use. Following validation in phantoms, spectroscopic data from 20 whole human prostates immediately following radical prostatectomy are obtained using the system. With this dataset, we are able to distinguish prostate from extra prostatic tissue with an accuracy, sensitivity, and specificity of 91%, 90.5%, and 96%, respectively. Finally, the integrated Raman spectroscopy system is used to collect preliminary spectroscopic data at the surgical margin in vivo in four patients.

Mesoscopic characterization of prostate cancer using Raman spectroscopy: potential for diagnostics and therapeutics.

OBJECTIVE: To test if Raman spectroscopy (RS) is an appropriate tool for the diagnosis and possibly grading of prostate cancer (PCa). PATIENTS AND METHODS: Between 20 and 50 Raman spectra were acquired from 32 fresh and non-processed post-prostatectomy specimens using a macroscopic handheld RS probe. Each measured area was characterized and categorized according to histopathological criteria: tissue type (extraprostatic or prostatic); tissue malignancy (benign or malignant); cancer grade (Grade Groups [GGs] 1-5); and tissue glandular level. The data were analysed using machine-learning classification with neural network. RESULTS: The RS technique was able to distinguish prostate from extraprostatic tissue with a sensitivity of 82% and a specificity of 83% and benign from malignant tissue with a sensitivity of 87% and a specificity of 86%. In an exploratory fashion, RS differentiated benign from GG1 in 726/801 spectra (91%; sensitivity 80%, specificity 91%), from GG2 in 588/805 spectra (73%; sensitivity 76%, specificity 73%), from GG3 in 670/797 spectra (84%; sensitivity 86%, specificity 84%), from GG4 in 711/802 spectra (88%; sensitivity 77%, specificity 89%) and from GG5 in 729/818 spectra (89%; sensitivity 90%, specificity 89%). CONCLUSION: Current diagnostic approaches of PCa using needle biopsies have suboptimal cancer detection rates and a significant risk of infection. Standard non-targeted random sampling results in false-negative biopsies in 15-30% of patients, which affects clinical management. RS, a non-destructive tissue interrogation technique providing vibrational molecular information, resolved the highly complex architecture of the prostate and detect cancer with high accuracy using a fibre optic probe to interrogate radical prostatectomy (RP) specimens from 32 patients (947 spectra). This proof-of-principle paves the way for the development of in vivo tumour targeting spectroscopy tools for informed biopsy collection to address the clinical need for accurate PCa diagnosis and possibly to improve surgical resection during RP as a complement to histopathological analysis.

Combining high wavenumber and fingerprint Raman spectroscopy for the detection of prostate cancer during radical prostatectomy.

For prostate cancer (PCa) patients, radical prostatectomy (complete removal of the prostate) is the only curative surgical option. To date, there is no clinical technique allowing for real-time assessment of surgical margins to minimize the extent of residual cancer. Here, we present a tissue interrogation technique using a dual excitation wavelength Raman spectroscopy system capable of sequentially acquiring fingerprint (FP) and high wavenumber (HWN) Raman spectra. Results demonstrate the ability of the system to detect PCa in post-prostatectomy specimens. In total, 477 Raman spectra were collected from 18 human prostate slices. Each area measured with Raman spectroscopy was characterized as either normal or cancer based on histopathological analyses, and each spectrum was classified based on supervised learning using support vector machines (SVMs). Based on receiver operating characteristic (ROC) analysis, FP (area under the curve [AUC] = 0.89) had slightly superior cancer detection capabilities compared with HWN (AUC = 0.86). Optimal performance resulted from combining the spectral information from FP and HWN (AUC = 0.91), suggesting that the use of these two spectral regions may provide complementary molecular information for PCa detection. The use of leave-one-(spectrum)-out (LOO) or leave-one-patient-out (LOPO) cross-validation produced similar classification results when combining FP with HWN. Our findings suggest that the application of machine learning using multiple data points from the same patient does not result in biases necessarily impacting the reliability of the classification models.

Development and characterization of a handheld hyperspectral Raman imaging probe system for molecular characterization of tissue on mesoscopic scales.

PURPOSE: Raman spectroscopy is a promising cancer detection technique for surgical guidance applications. It can provide quantitative information relating to global tissue properties associated with structural, metabolic, immunological, and genetic biochemical phenomena in terms of molecular species including amino acids, lipids, proteins, and nucleic acid (DNA). To date in vivo Raman spectroscopy systems mostly included probes and biopsy needles typically limited to single-point tissue interrogation over a scale between 100 and 500 microns. The development of wider field handheld systems could improve tumor localization for a range of open surgery applications including brain, ovarian, and skin cancers. METHODS: Here we present a novel Raman spectroscopy implementation using a coherent imaging bundle of fibers to create a probe capable of reconstructing molecular images over mesoscopic fields of view. Detection is performed using linear scanning with a rotation mirror and an imaging spectrometer. Different slits widths were tested at the entrance of the spectrometer to optimize spatial and spectral resolution while preserving sufficient signal-to-noise ratios to detect the principal Raman tissue features. The nonbiological samples, calcite and polytetrafluoroethylene (PTFE), were used to characterize the performance of the system. The new wide-field probe was tested on ex vivo samples of calf brain and swine tissue. Raman spectral content of both tissue types were validated with data from the literature and compared with data acquired with a single-point Raman spectroscopy probe. The single-point probe was used as the gold standard against which the new instrument was benchmarked as it has already been thoroughly validated for biological tissue characterization. RESULT: We have developed and characterized a practical noncontact handheld Raman imager providing tissue information at a spatial resolution of 115 microns over a field of view >14 mm2 and a spectral resolution of 6 cm-1 over the whole fingerprint region. Typical integration time to acquire an entire Raman image over swine tissue was set to approximately 100 s. Spectra acquired with both probes (single-point and wide-field) showed good agreement, with a Pearson correlation factor >0.85 over different tissue categories. Protein and lipid content of imaged tissue were manifested into the measured spectra which correlated well with previous findings in the literature. An example of quantitative molecular map is presented for swine tissue and calf brain based on the ratio of protein-to-lipid content showing clear delineations between white and gray matter as well as between adipose and muscle tissue. CONCLUSION: We presented the development of a Raman imaging probe with a field of view of a few millimeters and a spatial resolution consistent with standard surgical imaging methods using an imaging bundle. Spectra acquired with the newly developed system on swine tissue and calf brain correlated well with an establish single-point probe and observed spectral features agreed with previous finding in the literature. The imaging probe has demonstrated its ability to reconstruct molecular images of soft tissues. The approach presented here has a lot of potential for the development of surgical Raman imaging probe to guide the surgeon during cancer surgery.

Impact of a mechanical shear stress on intracellular trafficking.

Intracellular trafficking mainly takes place along the microtubules, and its efficiency depends on the local architecture and organization of the cytoskeletal network. In this work, the cytoplasm of stem cells is subjected to mechanical vortexing at a frequency of up to 1 Hz, by using magnetic chains of endosomes embedded in the cell body, in order to locally perturb the network structure. The consequences are evaluated on the directionality and processivity of the spontaneous motion of endosomes. When the same chains are used both to shear the cell medium and to probe the intracellular traffic, a substantial decrease in transport efficiency is detected after applying the mechanical shear. Interestingly, when using different objects to apply the shear and to probe the spontaneous motion, no alteration of the transport efficiency can be detected. We conclude that shaking the vesicles mainly causes their unbinding from the cytoskeletal tracks, but has little influence on the integrity of the network itself. This is corroborated by active microrheology measurements, performed with chains actuated by a magnetic field, and showing that the mechanical compliance of the cytoplasm is similar before and after slow vortexing.

Highly Accurate Detection of Cancer In Situ with Intraoperative, Label-Free, Multimodal Optical Spectroscopy.

Effectiveness of surgery as a cancer treatment is reduced when all cancer cells are not detected during surgery, leading to recurrences that negatively impact survival. To maximize cancer cell detection during cancer surgery, we designed an in situ intraoperative, label-free, optical cancer detection system that combines intrinsic fluorescence spectroscopy, diffuse reflectance spectroscopy, and Raman spectroscopy. Using this multimodal optical cancer detection system, we found that brain, lung, colon, and skin cancers could be detected in situ during surgery with an accuracy, sensitivity, and specificity of 97%, 100%, and 93%, respectively. This highly sensitive optical molecular imaging approach can profoundly impact a wide range of surgical and noninvasive interventional oncology procedures by improving cancer detection capabilities, thereby reducing cancer burden and improving survival and quality of life. Cancer Res; 77(14); 3942-50. ©2017 AACR.

A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology.

There is an urgent need for improved techniques for disease detection. Optical spectroscopy and imaging technologies have potential for non- or minimally-invasive use in a wide range of clinical applications. The focus here, in vivo Raman spectroscopy (RS), measures inelastic light scattering based on interaction with the vibrational and rotational modes of common molecular bonds in cells and tissue. The Raman 'signature' can be used to assess physiological status and can also be altered by disease. This information can supplement existing diagnostic (e.g. radiological imaging) techniques for disease screening and diagnosis, in interventional guidance for identifying disease margins, and in monitoring treatment responses. Using fiberoptic-based light delivery and collection, RS is most easily performed on accessible tissue surfaces, either on the skin, in hollow organs or intra-operatively. The strength of RS lies in the high biochemical information content of the spectra, that characteristically show an array of very narrow peaks associated with specific chemical bonds. This results in high sensitivity and specificity, for example to distinguish malignant or premalignant from normal tissues. A critical issue is that the Raman signal is often very weak, limiting clinical use to point-by-point measurements. However, non-linear techniques using pulsed-laser sources have been developed to enable in vivo Raman imaging. Changes in Raman spectra with disease are often subtle and spectrally distributed, requiring full spectral scanning, together with the use of tissue classification algorithms that must be trained on large numbers of independent measurements. Recent advances in instrumentation and spectral analysis have substantially improved the clinical feasibility of RS, so that it is now being investigated with increased success in a wide range of cancer types and locations, as well as for non-oncological conditions. This review covers recent advances and continuing challenges, with emphasis on clinical translation.

Massive release of extracellular vesicles from cancer cells after photodynamic treatment or chemotherapy.

Photodynamic therapy is an emerging cancer treatment that is particularly adapted for localized malignant tumor. The phototherapeutic agent is generally injected in the bloodstream and circulates in the whole organism as a chemotherapeutic agent, but needs light triggering to induce localized therapeutic effects. We found that one of the responses of in vitro and in vivo cancer cells to photodynamic therapy was a massive production and emission of extracellular vesicles (EVs): only 1 hour after the photo-activation, thousands of vesicles per cell were emitted in the extracellular medium. A similar effect has been found after treatment with Doxorubicin (chemotherapy), but far less EVs were produced, even 24 hours after the treatment. Furthermore, we found that the released EVs could transfer extracellular membrane components, drugs and even large intracellular objects to naive target cells. In vivo, photodynamic treatment and chemotherapy increased the levels of circulating EVs several fold, confirming the vast induction of cancer cell vesiculation triggered by anti-cancer therapies.

Combining magnetic nanoparticles with cell derived microvesicles for drug loading and targeting.

Inspired by microvesicle-mediated intercellular communication, we propose a hybrid vector for magnetic drug delivery. It consists of macrophage-derived microvesicles engineered to enclose different therapeutic agents together with iron oxide nanoparticles. Here, we investigated in vitro how magnetic nanoparticles may influence the vector effectiveness in terms of drug uptake and targeting. Human macrophages were loaded with iron oxide nanoparticles and different therapeutic agents: a chemotherapeutic agent (doxorubicin), tissue-plasminogen activator (t-PA) and two photosensitizers (disulfonated tetraphenyl chlorin-TPCS2a and 5,10,15,20-tetra(m-hydroxyphenyl)chlorin-mTHPC). The hybrid cell microvesicles were magnetically responsive, readily manipulated by magnetic forces and MRI-detectable. Using photosensitizer-loaded vesicles, we showed that the uptake of microvesicles by cancer cells could be kinetically modulated and spatially controlled under magnetic field and that cancer cell death was enhanced by the magnetic targeting. From the clinical editor: In this article, the authors devised a biogenic method using macrophages to produce microvesicles containing both iron oxide and chemotherapeutic agents. They showed that the microvesicles could be manipulated by magnetic force for targeting and subsequent delivery of the drug payload against cancer cells. This smart method could provide a novel way for future fight against cancer.

Design of covalently functionalized carbon nanotubes filled with metal oxide nanoparticles for imaging, therapy, and magnetic manipulation.

Nanocomposites combining multiple functionalities in one single nano-object hold great promise for biomedical applications. In this work, carbon nanotubes (CNTs) were filled with ferrite nanoparticles (NPs) to develop the magnetic manipulation of the nanotubes and their theranostic applications. The challenges were both the filling of CNTs with a high amount of magnetic NPs and their functionalization to form biocompatible water suspensions. We propose here a filling process using CNTs as nanoreactors for high-yield in situ growth of ferrite NPs into the inner carbon cavity. At first, NPs were formed inside the nanotubes by thermal decomposition of an iron stearate precursor. A second filling step was then performed with iron or cobalt stearate precursors to enhance the encapsulation yield and block the formed NPs inside the tubes. Water suspensions were then obtained by addition of amino groups via the covalent functionalization of the external surface of the nanotubes. Microstructural and magnetic characterizations confirmed the confinement of NPs into the anisotropic structure of CNTs making them suitable for magnetic manipulations and MRI detection. Interactions of highly water-dispersible CNTs with tumor cells could be modulated by magnetic fields without toxicity, allowing control of their orientation within the cell and inducing submicron magnetic stirring. The magnetic properties were also used to quantify CNTs cellular uptake by measuring the cell magnetophoretic mobility. Finally, the photothermal ablation of tumor cells could be enhanced by magnetic stimulus, harnessing the hybrid properties of NP loaded-CNTs.

Magnetic and photoresponsive theranosomes: translating cell-released vesicles into smart nanovectors for cancer therapy.

Cell-released vesicles are natural carriers that circulate in body fluids and transport biological agents to distal cells. As nature uses vesicles in cell communication to promote tumor progression, we propose to harness their unique properties and exploit these biogenic carriers as Trojan horses to deliver therapeutic payloads to cancer cells. In a theranostic approach, cell-released vesicles were engineered by a top-down procedure from precursor cells, previously loaded with a photosensitizer and magnetic nanoparticles. The double exogenous cargo provided vesicles with magnetic and optical responsiveness allowing therapeutic and imaging functions. This new class of cell-derived smart nanovectors was named "theranosomes". Theranosomes enabled efficient photodynamic tumor therapy in a murine cancer model in vivo. Moreover the distribution of this biogenic vector could be monitored by dual-mode imaging, combining fluorescence and MRI. This study reports the first success in translating a cell communication mediator into a smart theranostic nanovector.

Impact of photosensitizers activation on intracellular trafficking and viscosity.

The intracellular microenvironment is essential for the efficiency of photo-induced therapies, as short-lived reactive oxygen species generated must diffuse through their intracellular surrounding medium to reach their cellular target. Here, by combining measurements of local cytoplasmic dissipation and active trafficking, we found that photosensitizers activation induced small changes in surrounding viscosity but a massive decrease in diffusion. These effects are the signature of a return to thermodynamic equilibrium of the system after photo-activation and correlated with depolymerization of the microtubule network, as shown in a reconstituted system. These mechanical measurements were performed with two intracellular photosensitizing chlorins having similar quantum yield of singlet oxygen production but different intracellular localizations (cytoplasmic for mTHPC, endosomal for TPCS2a). These two agents demonstrated different intracellular impact.