scholarly journals Multiplexed whole-animal imaging with reversibly switchable optoacoustic proteins

2020 ◽  
Vol 6 (24) ◽  
pp. eaaz6293 ◽  
Author(s):  
Kanuj Mishra ◽  
Mariia Stankevych ◽  
Juan Pablo Fuenzalida-Werner ◽  
Simon Grassmann ◽  
Vipul Gujrati ◽  
...  
Keyword(s):  
Machine Learning ◽  
T Lymphocytes ◽  
Life Sciences ◽  
Imaging Systems ◽  
Temporal Domain ◽  
Animal Imaging ◽  
Highly Sensitive

We introduce two photochromic proteins for cell-specific in vivo optoacoustic (OA) imaging with signal unmixing in the temporal domain. We show highly sensitive, multiplexed visualization of T lymphocytes, bacteria, and tumors in the mouse body and brain. We developed machine learning–based software for commercial imaging systems for temporal unmixed OA imaging, enabling its routine use in life sciences.

scholarly journals Multiplexed whole animal imaging with reversibly switchable optoacoustic proteins

2020 ◽  
Author(s):  
Kanuj Mishra ◽  
Mariia Stankevych ◽  
Juan Pablo Fuenzalida-Werner ◽  
Simon Grassmann ◽  
Vipul Gujrati ◽  
...  
Keyword(s):  
Machine Learning ◽  
T Lymphocytes ◽  
Life Sciences ◽  
Imaging Systems ◽  
Temporal Domain ◽  
Animal Imaging ◽  
Highly Sensitive

We describe two photochromic proteins for cell-specific in vivo optoacoustic (OA) imaging with signal unmixing in the temporal domain. We show highly sensitive, multiplexed visualization of T lymphocytes, bacteria and tumors in the mouse body and brain. We developed machine learning-based software that allows commercial imaging systems to be used for temporal unmixed OA imaging, enabling its routine use in life sciences.

Synthesis and 31 P NMR characterization of new low toxic highly sensitive pH probes designed for in vivo acidic pH studies

2002 ◽  
Vol 10 (5) ◽  
pp. 1451-1458 ◽  
Author(s):  
Sophie Martel ◽  
Jean-Louis Clément ◽  
Agnès Muller ◽  
Marcel Culcasi ◽  
Sylvia Pietri
Keyword(s):  
Acidic Ph ◽  
Highly Sensitive ◽  
Nmr Characterization ◽  
Low Toxic ◽  
Ph Probes

scholarly journals Metabolic radiolabeling and in vivo PET imaging of cytotoxic T lymphocytes to guide combination adoptive cell transfer cancer therapy

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Dehua Lu ◽  
Yanpu Wang ◽  
Ting Zhang ◽  
Feng Wang ◽  
Kui Li ◽  
...  
Keyword(s):  
T Cells ◽  
T Lymphocytes ◽  
Solid Tumors ◽  
Cytotoxic T Lymphocytes ◽  
Pet Imaging ◽  
Stage Migration ◽  
Cell Transfer ◽  
Tumor Infiltration ◽  
Antitumor Effects

Abstract Background Adoptive T cell transfer-based immunotherapy yields unsatisfactory results in the treatment of solid tumors, partially owing to limited tumor infiltration and the immunosuppressive microenvironment in solid tumors. Therefore, strategies for the noninvasive tracking of adoptive T cells are critical for monitoring tumor infiltration and for guiding the development of novel combination therapies. Methods We developed a radiolabeling method for cytotoxic T lymphocytes (CTLs) that comprises metabolically labeling the cell surface glycans with azidosugars and then covalently conjugating them with 64Cu-1,4,7-triazacyclononanetriacetic acid-dibenzo-cyclooctyne (64Cu-NOTA-DBCO) using bioorthogonal chemistry. 64Cu-labeled control-CTLs and ovalbumin-specific CTLs (OVA-CTLs) were tracked using positron emission tomography (PET) in B16-OVA tumor-bearing mice. We also investigated the effects of focal adhesion kinase (FAK) inhibition on the antitumor efficacy of OVA-CTLs using a poly(lactic-co-glycolic) acid (PLGA)-encapsulated nanodrug (PLGA-FAKi). Results CTLs can be stably radiolabeled with 64Cu with a minimal effect on cell viability. PET imaging of 64Cu-OVA-CTLs enables noninvasive mapping of their in vivo behavior. Moreover, 64Cu-OVA-CTLs PET imaging revealed that PLGA-FAKi induced a significant increase in OVA-CTL infiltration into tumors, suggesting the potential for a combined therapy comprising OVA-CTLs and PLGA-FAKi. Further combination therapy studies confirmed that the PLGA-FAKi nanodrug markedly improved the antitumor effects of adoptive OVA-CTLs transfer by multiple mechanisms. Conclusion These findings demonstrated that metabolic radiolabeling followed by PET imaging can be used to sensitively profile the early-stage migration and tumor-targeting efficiency of adoptive T cells in vivo. This strategy presents opportunities for predicting the efficacy of cell-based adoptive therapies and for guiding combination regimens. Graphic Abstract

scholarly journals Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3827
Author(s):  
Gemma Urbanos ◽  
Alberto Martín ◽  
Guillermo Vázquez ◽  
Marta Villanueva ◽  
Manuel Villa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Vessel ◽  
Hyperspectral Imaging ◽  
Imaging Techniques ◽  
Venous Blood ◽  
Healthy Tissue ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Arterial Blood

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

scholarly journals Using the Microwell-mesh to culture microtissues in vitro and as a carrier to implant microtissues in vivo into mice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Melissa E. Monterosso ◽  
Kathryn Futrega ◽  
William B. Lott ◽  
Ian Vela ◽  
Elizabeth D. Williams ◽  
...  
Keyword(s):  
Hair Follicles ◽  
Live Animal ◽  
Nsg Mice ◽  
Animal Imaging ◽  
Organ Specific ◽  
Bioluminescence Signal ◽  
Serial Transplantation ◽  
Live Animal Imaging

AbstractProstate cancer (PCa) patient-derived xenografts (PDXs) are commonly propagated by serial transplantation of “pieces” of tumour in mice, but the cellular composition of pieces is not standardised. Herein, we optimised a microwell platform, the Microwell-mesh, to aggregate precise numbers of cells into arrays of microtissues, and then implanted the Microwell-mesh into NOD-scid IL2γ−/− (NSG) mice to study microtissue growth. First, mesh pore size was optimised using microtissues assembled from bone marrow-derived stromal cells, with mesh opening dimensions of 100×100 μm achieving superior microtissue vascularisation relative to mesh with 36×36 μm mesh openings. The optimised Microwell-mesh was used to assemble and implant PCa cell microtissue arrays (hereafter microtissues formed from cancer cells are referred to as microtumours) into mice. PCa cells were enriched from three different PDX lines, LuCaP35, LuCaP141, and BM18. 3D microtumours showed greater in vitro viability than 2D cultures, but neither proliferated. Microtumours were successfully established in mice 81% (57 of 70), 67% (4 of 6), 76% (19 of 25) for LuCaP35, LuCaP141, and BM18 PCa cells, respectively. Microtumour growth was tracked using live animal imaging for size or bioluminescence signal. If augmented with further imaging advances and cell bar coding, this microtumour model could enable greater resolution of PCa PDX drug response, and lead to the more efficient use of animals. The concept of microtissue assembly in the Microwell-mesh, and implantation in vivo may also have utility in implantation of islets, hair follicles or other organ-specific cells that self-assemble into 3D structures, providing an important bridge between in vitro assembly of mini-organs and in vivo implantation.

scholarly journals Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ossama Mahmoud ◽  
Mahmoud El-Sakka ◽  
Barry G. H. Janssen
Keyword(s):  
Machine Learning ◽  
Blood Flow ◽  
Human Error ◽  
Image Data ◽  
Video Microscopy ◽  
Microvascular Blood Flow ◽  
Image Processing Algorithm ◽  
Step Algorithm ◽  
3D Cnn

AbstractMicrovascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

scholarly journals Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Samira Sanami ◽  
Fatemeh Azadegan-Dehkordi ◽  
Mahmoud Rafieian-Kopaei ◽  
Majid Salehi ◽  
Maryam Ghasemi-Dehnoo ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
T Lymphocytes ◽  
Tertiary Structure ◽  
Therapeutic Vaccine ◽  
Epitope Prediction ◽  
Therapeutic Effects ◽  
Epitope Vaccine ◽  
In Vivo Experiments

AbstractCervical cancer, caused by human papillomavirus (HPV), is the fourth most common type of cancer among women worldwide. While HPV prophylactic vaccines are available, they have no therapeutic effects and do not clear up existing infections. This study aims to design a therapeutic vaccine against cervical cancer using reverse vaccinology. In this study, the E6 and E7 oncoproteins from HPV16 were chosen as the target antigens for epitope prediction. Cytotoxic T lymphocytes (CTL) and helper T lymphocytes (HTL) epitopes were predicted, and the best epitopes were selected based on antigenicity, allergenicity, and toxicity. The final vaccine construct was composed of the selected epitopes, along with the appropriate adjuvant and linkers. The multi-epitope vaccine was evaluated in terms of physicochemical properties, antigenicity, and allergenicity. The tertiary structure of the vaccine construct was predicted. Furthermore, several analyses were also carried out, including molecular docking, molecular dynamics (MD) simulation, and in silico cloning of the vaccine construct. The results showed that the final proposed vaccine could be considered an effective therapeutic vaccine for HPV; however, in vitro and in vivo experiments are required to validate the efficacy of this vaccine candidate.

scholarly journals O10: METFORMIN TREATMENT IMPROVES LYMPHOCYTE METABOLISM IN A MOUSE MODEL OF MAJOR SURGERY

2021 ◽  
Vol 108 (Supplement_1) ◽  
Author(s):  
TF Jones ◽  
A Gutierrez ◽  
del Arroyo ◽  
SM Henson ◽  
GL Ackland
Keyword(s):  
T Lymphocytes ◽  
Mouse Model ◽  
T Lymphocyte ◽  
Major Surgery ◽  
P Value ◽  
Free Access ◽  
Mitochondrial Bioenergetics ◽  
Metformin Treatment ◽  
Respiratory Capacity

Abstract Introduction Lymphopaenia is common after major surgery and associated with poor outcome. T-lymphocytes restrain damaging innate inflammation. Major surgery impairs T-lymphocyte metabolism in humans, which promotes lymphopaenia. Metformin is known to improve mitochondrial bioenergetics in models of inflammation. Firstly, we hypothesised that a mouse model of major surgery would demonstrate impaired T-lymphocyte metabolism and secondly, that metformin treatment in vivo would reverse the phenotype. Method Male C57Bl/6 mice aged between 8 and 12 weeks were housed in a specific pathogen free environment with free access to food and water. Animals were dosed with either vehicle (phosphate buffered saline, 20 ml/kg) or metformin (250 mg/kg) daily via intraperitoneal injection for four days prior to and after surgery. A partial hepatectomy was performed under isofluorane anaesthesia. Naive littermates were used as controls. All experiments were performed according to the Animals (Scientific Procedures) Act 1986. Splenic T-lymphocytes were isolated by negative selection using magnetic beads. Mitochondrial bioenergetics were measured using a Seahorse Extracellular Flux analyser. Parametric statistical analysis was performed and a p-value < 0.05 was chosen to represent significance. Result T-lymphocytes demonstrated reduced spare respiratory capacity (SRC, 285 vs 497 %, p=0.004) after surgery compared to naive controls. Metformin treatment in vivo reversed this observation and SRC was comparable to naive (437 vs 497 %, p=0.34). Metformin treatment in vitro increased spare respiratory capacity in T-lymphocytes from mice after surgery compared to naive (change from untreated, 187 vs 91 %, p=0.03). Conclusion Perioperative metformin treatment improved T-lymphocyte metabolism in a mouse model of major surgery. Take-home message Metformin is a potential treatment for the lymphocyte metabolic dysfunction observed after surgery.

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