Current developments in modelling the tumour microenvironment in vitro:Incorporation of biochemical and physical gradients

Tumour cell proliferation, metabolism and treatment response depend on the dynamic interaction of the tumour cells with other cellular components and physicochemical gradients present in the tumour microenvironment. Traditional experimental approaches used to investigate the dynamic tumour tissue face a number of limitations, such as lack of biological relevance for the tumour microenvironment and the difficulty to precisely control fluctuating internal conditions, for example in oxygen and nutrients. The arrival of advanced in vitro models represents an alternative approach for modelling the tumour microenvironment using cutting-edge technologies, such as microfabrication. Advanced model systems provide a promising platform for modelling the physiochemical conditions of the tumour microenvironment in a well-controlled manner. Amongst others, advanced in vitro models aim to recreate gradients of oxygen, nutrients and endogenous chemokines, and cell proliferation. Furthermore, the establishment of mechanical cues within such models, e.g., flow and extracellular matrix properties that influence cellular behaviour, are active research areas. These model systems aim to maintain tumour cells in an environment that resembles in vivo conditions. A prominent example of such a system is the microfluidic tumour-on-chip model, which aims to precisely control the local chemical and physical environment that surrounds the tumour cells. In addition, these models also have the potential to recapitulate environmental conditions in isolation or in combination. This enables the analysis of the dynamic interactions between different conditions and their potentially synergistic effects on tumour cells. In this review, we will discuss the various gradients present within the tumour microenvironment and the effects they exert on tumour cells. We will further highlight the challenges and limitations of traditional experimental models in modelling these gradients. We will outline recent achievements in advanced in vitro models with a particular focus on tumour-on-chip systems. We will also discuss the future of these models in cancer research and their contribution to developing more biologically relevant models for cancer research

Effects of meteorological factors on epidemic malaria in Ethiopia: a statistical modelling approach based on theoretical reasoning.

This study was conducted to quantify the association between meteorological variables and incidence of Plasmodium falciparum in areas with unstable malaria transmission in Ethiopia. We used morbidity data pertaining to microscopically confirmed cases reported from 35 sites throughout Ethiopia over a period of approximately 6-7 years. A model was developed reflecting biological relationships between meteorological and morbidity variables. A model that included rainfall 2 and 3 months earlier, mean minimum temperature of the previous month and P. falciparum case incidence during the previous month was fitted to morbidity data from the various areas. The model produced similar percentages of over-estimation (19.7% of predictions exceeded twice the observed values) and under-estimation (18.6%, were less than half the observed values). Inclusion of maximum temperature did not improve the model. The model performed better in areas with relatively high or low incidence (>85% of the total variance explained) than those with moderate incidence (55-85% of the total variance explained). The study indicated that a dynamic immunity mechanism is needed in a prediction model. The potential usefulness and drawbacks of the modelling approach in studying the weather-malaria relationship are discussed, including a need for mechanisms that can adequately handle temporal variations in immunity to malaria

18S is an appropriate housekeeping gene for in vitro hypoxia experiments

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Poisson transition rates from time-domain measurements with finite bandwidth

In time-domain measurements of a Poisson two-level system, the observed transition rates are always smaller than those of the actual system, a general consequence of finite measurement bandwidth in an experiment. This underestimation of the rates is significant even when the measurement and detection apparatus is ten times faster than the process under study. We derive here a quantitative form for this correction using a straightforward state-transition model that includes the detection apparatus, and provide a method for determining a system's actual transition rates from bandwidth-limited measurements. We support our results with computer simulations and experimental data from time-domain measurements of quasiparticle tunneling in a single-Cooper-pair transistor.Comment: 4 pages, 5 figure

Diphtheria antitoxin levels in the Netherlands: a population-based study.

In a population-based study in the Netherlands, diphtheria antitoxin antibodies were measured with a toxin-binding inhibition assay in 9, 134 sera from the general population and religious communities refusing vaccination. The Dutch immunization program appears to induce long-term protection against diphtheria. However, a substantial number of adults born before the program was introduced had no protective diphtheria antibody levels. Although herd immunity seems adequate, long-term population protection cannot be assured. As more than 60% of orthodox reformed persons have antibody levels lower than 0.01 IU/ml, introduction of diphtheria into religious communities refusing vaccination may constitute a danger of spread of the bacterium

High-throughput molecular imaging via deep-learning-enabled Raman spectroscopy.

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine

Image-guided Raman spectroscopy probe-tracking for tumor margin delineation

SIGNIFICANCE: Tumor detection and margin delineation are essential for successful tumor resection. However, postsurgical positive margin rates remain high for many cancers. Raman spectroscopy has shown promise as a highly accurate clinical spectroscopic diagnostic modality, but its margin delineation capabilities are severely limited by the need for pointwise application. AIM: We aim to extend Raman spectroscopic diagnostics and develop a multimodal computer vision-based diagnostic system capable of both the detection and identification of suspicious lesions and the precise delineation of disease margins. APPROACH: We first apply visual tracking of a Raman spectroscopic probe to achieve real-time tumor margin delineation. We then combine this system with protoporphyrin IX fluorescence imaging to achieve fluorescence-guided Raman spectroscopic margin delineation. RESULTS: Our system enables real-time Raman spectroscopic tumor margin delineation for both ex vivo human tumor biopsies and an in vivo tumor xenograft mouse model. We then further demonstrate that the addition of protoporphyrin IX fluorescence imaging enables fluorescence-guided Raman spectroscopic margin delineation in a tissue phantom model. CONCLUSIONS: Our image-guided Raman spectroscopic probe-tracking system enables tumor margin delineation and is compatible with both white light and fluorescence image guidance, demonstrating the potential for our system to be developed toward clinical tumor resection surgeries

Integrated photodynamic Raman theranostic system for cancer diagnosis, treatment, and post-treatment molecular monitoring

Theranostics, the combination of diagnosis and therapy, has long held promise as a means to achieving personalised precision cancer treatments. However, despite its potential, theranostics has yet to realise significant clinical translation, largely due the complexity and overriding toxicity concerns of existing theranostic nanoparticle strategies. / Methods: Here, we present an alternative nanoparticle-free theranostic approach based on simultaneous Raman spectroscopy and photodynamic therapy (PDT) in an integrated clinical platform for cancer theranostics. / Results: We detail the compatibility of Raman spectroscopy and PDT for cancer theranostics, whereby Raman spectroscopic diagnosis can be performed on PDT photosensitiser-positive cells and tissues without inadvertent photosensitiser activation/photobleaching or impaired diagnostic capacity. We further demonstrate that our theranostic platform enables in vivo tumour diagnosis, treatment, and post-treatment molecular monitoring in real-time. / Conclusion: This system thus achieves effective theranostic performance, providing a promising new avenue towards the clinical realisation of theranostics

Molecular imaging of extracellular vesicles in vitro via Raman metabolic labelling

Extracellular vesicles (EVs) are biologically-derived nanovectors important for intercellular communication and trafficking. As such, EVs show great promise as disease biomarkers and therapeutic drug delivery vehicles. However, despite the rapidly growing interest in EVs, understanding of the biological mechanisms that govern their biogenesis, secretion, and uptake remains poor. Advances in this field have been hampered by both the complex biological origins of EVs, which make them difficult to isolate and identify, and a lack of suitable imaging techniques to properly study their diverse biological roles. Here, we present a new strategy for simultaneous quantitative in vitro imaging and molecular characterisation of EVs in 2D and 3D based on Raman spectroscopy and metabolic labelling. Deuterium, in the form of deuterium oxide (D2O), deuterated choline chloride (d-Chol), or deuterated D-glucose (d-Gluc), is metabolically incorporated into EVs through the growth of parent cells on medium containing one of these compounds. Isolated EVs are thus labelled with deuterium, which acts as a bio-orthogonal Raman-active tag for direct Raman identification of EVs when introduced to unlabelled cell cultures. Metabolic deuterium incorporation demonstrates no apparent adverse effects on EV secretion, marker expression, morphology, or global composition, indicating its capacity for minimally obstructive EV labelling. As such, our metabolic labelling strategy could provide integral insights into EV biocomposition and trafficking. This approach has the potential to enable a deeper understanding of many of the biological mechanisms underpinning EVs, with profound implications for the design of EVs as therapeutic delivery vectors and applications as disease biomarkers

Improving Breast Cancer Treatment Specificity Using Aptamers Obtained by 3D Cell-SELEX

Three-dimensional spheroids of non-malignant MCF10A and malignant SKBR3 breast cells were used for subsequent 3D Cell-SELEX to generate aptamers for specific binding and treatment of breast cancer cells. Using 3D Cell-SELEX combined with Next-Generation Sequencing and bioinformatics, ten abundant aptamer families with specific structures were identified that selectively bind to SKBR3, and not to MCF10A cells. Multivalent aptamer polymers were synthesized by co-polymerization and analyzed for binding performance as well as therapeutic efficacy. Binding performance was determined by confocal fluorescence imaging and revealed specific binding and efficient internalization of aptamer polymers into SKBR3 spheroids. For therapeutic purposes, DNA sequences that intercalate the cytotoxic drug doxorubicin were co-polymerized into the aptamer polymers. Viability tests show that the drug-loaded polymers are specific and effective in killing SKBR3 breast cancer cells. Thus, the 3D-selected aptamers enhanced the specificity of doxorubicin against malignant over non-malignant breast cells. The innovative modular DNA aptamer platform based on 3D Cell SELEX and polymer multivalency holds great promise for diagnostics and treatment of breast cancer