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Biology subjects

Nguyen, T. N. H.

Publications and source records attributed to Nguyen, T. N. H..

3 recordsLinked to original sources

Microfluidic Modulation of Microvasculature in Microdissected Tumors

The microvasculature within the tumor microenvironment (TME) plays an essential role in cancer signaling beyond nutrient delivery. However, it has been challenging to control the generation and/or maintenance of microvasculature in ex vivo systems, a critical step for establishing cancer models of high clinical biomimicry. There have been great successes in engineering tissues incorporating microvasculature de novo (e.g., organoids and organs-on-chip), but these reconstituted tissues are formed with non-native cellular and molecular components that can skew certain outcomes such as drug efficacy. Microdissected tumors, on the other hand, show promise in preserving the TME, which is key for creating cancer models that can bridge the gap between bench and bedside. However, microdissected tumors are challenging to perfuse. Here, we developed a microfluidic platform that allows for perfusing the microvasculature of microdissected tumors. We demonstrate that, compared to diffusive transport, microfluidically perfused tissues feature larger and longer microvascular structures, with a better expression of CD31, a marker for endothelial cells, as analyzed by 3D imaging. This study also explores the effects of nitric oxide pathway-related drugs on endothelial cells, which are sensitive to shear stress and can activate endothelial nitric oxide synthase, producing nitric oxide. Our findings highlight the critical role of controlled perfusion and biochemical modulation in preserving tumor microvasculature, offering valuable insights for developing more effective cancer treatments.

bioengineering↗

Label-Free, Real-Time Monitoring of Cytochrome C Responses to Drugs in Microdissected Tumor Biopsies with a Multi-Well Aptasensor Platform

Functional assays on intact tumor biopsies can potentially complement and extend genomics-based approaches for precision oncology, drug testing, and organs-on-chips cancer disease models by capturing key determinants of therapeutic response, such as tissue architecture, tumor heterogeneity, and the tumor microenvironment. Currently, most of these assays rely on fluorescent labeling, a semi-quantitative method best suited to be a single-time-point terminal assay or labor-intensive terminal immunostaining analysis. Here, we report integrated aptamer electrochemical sensors for on-chip, real-time monitoring of increases of cytochrome C, a cell death indicator, from intact microdissected tissues with high affinity and specificity. The platform features a multi-well sensor layout and a multiplexed electronic setup. The aptasensors measure increases in cytochrome C in the supernatant of mouse or human microdissected tumors after exposure to various drug treatments. Since the aptamer probe can be easily exchanged to recognize different targets, the platform could be adapted for multiplexed monitoring of various biomarkers, providing critical information on the tumor and its microenvironment. This approach could not only help develop more advanced cancer disease models but also apply to other complex in vitro disease models, such as organs-on-chips and organoids.

bioengineering↗

KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction

SummaryKaMRaT is a program for processing large k-mer count tables extracted from high throughput sequencing data. Major functions include scoring k-mers based on count statistics, merging overlapping k-mers into longer contigs and selecting k-mers based on their presence in certain samples. KaMRaT s main application is the reference-free analysis of multi-sample and multi-condition datasets from RNA-seq, as well as ChiP-seq or ribo-seq experiments. KaMRaT enables the identification of condition-specific or differential sequences, irrespective of any gene or transcript annotation. Implementation and availabilityKaMRaT is implemented in C++. Source code and documentation are available via https://github.com/Transipedia/KaMRaT. Container images are available via https://hub.docker.com/r/xuehl/kamrat.

bioinformatics↗