Biology subjects

Vu, T. N.

Publications and source records attributed to Vu, T. N..

5 recordsLinked to original sources

bioRxiv2026

SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data

Spatial transcriptomics data are largely available with RNA expression alone, limiting the detection of cell states defined by surface protein abundance. The lack of multi-omics spatial data limits the ability to identify immune cells and their signaling in the tumor microenvironment, as most solid tumors are immunologically poor and exhibit protein-RNA abundance discordance in critical immune cell surface markers. Although emerging technologies enable spatial multi-omics profiling, technical and cost constraints remain a hurdle. We introduce SR2P, a stacking-based machine-learning framework for predicting spatial protein abundance from RNA expression. SR2P integrates 11 complementary predictive models and consistently outperforms existing methods across multiple spatial multi-omics benchmark. We showcased an application of SR2P recovered macrophage-enriched regions and identified potential immune markers associated with therapeutic response from head-and-neck squamous cell carcinoma patients. SR2P enables protein-abundance inference from RNA-only spatial data, extending the analytical capabilities of current spatial platforms for studies of tumor immunology.

bioinformatics↗

bioRxiv2024

Pathway activation model for personalized prediction of drug synergy

Targeted monotherapies for cancer often fail due to inherent or acquired drug resistance. By aiming at multiple targets simultaneously, drug combinations can produce synergistic interactions that increase drug effectiveness and reduce resistance. Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce DIPx, an algorithm for personalized prediction of drug synergy based on biologically motivated tumor- and drug-specific pathway activation scores (PASs). We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the models ability to handle novel combinations. The Spearman correlation coefficients between predicted and observed drug synergy were 0.50 (95% CI: 0.47-0.53) in Test Set 1 and 0.26 (95% CI: 0.22-0.30) in Test Set 2, compared to 0.38 (95% CI: 0.34-0.42) and 0.18 (95% CI: 0.16-0.20), respectively, for the best performing method in the Challenge. We show evidence that higher synergy is associated with higher functional interaction between the drug targets, and this functional interaction information is captured by PAS. We illustrate the use of PAS to provide a potential biological explanation in terms of activated pathways that mediate the synergistic effects of combined drugs. In summary, DIPx can be a useful tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.

bioinformatics↗

bioRxiv2022

A Glass Phase Plate for Wavelength SensitiveSuperresolution Microscopy

Multicolor localization microscopy typically relies on sequential imaging and bandpass filters to distinguish fluorescent tags, which introduces temporal delays during live imaging, and decreases photon yield. By engineering the point-spread function (PSF), different fluors can be imaged simultaneously and distinguished by their unique patterns, without discarding photons. Here, we insert a silicon-dioxide phase plate at the Fourier plane of the detection path of a wide-field fluorescence microscope to produce distinguishable PSFs (X-PSFs) at different wavelengths. We demonstrate that the resulting PSFs can be localized spatially and spectrally using a statistics-based computational algorithm and can be utilized for hyper-spectral super-resolution microscopy of biological samples. Single PSFs in fixed U2OS cells were acquired using dSTORM with simultaneous illumination of fluors without emission filters. The modified PSF achieves [~]21 nm lateral localization precision (FWHM), [~]17 nm axial precision (FWHM) with an average of 1,800 - 3,500 photons per PSF and a background as high as 130 - 400 photons per pixel. The modified PSF can distinguish up to three fluorescent probes with [~]80 nm peak-to-peak separation between consecutive spectra.

biophysics↗

bioRxiv2022

Syntaxin Habc is required to open Unc18 to template SNARE assembly

SNARE and Unc18 proteins form the core of the membrane fusion complex at synapses. The fusion machinery is evolutionarily ancient and mediates constitutive fusion in yeast. We demonstrate that the SNARE and Unc18 machinery in the nematode C. elegans can be replaced by yeast proteins and still carry out synaptic transmission. However, substitutions of individual components from yeast disrupts fusion. To understand the functional interactions within the core machinery we adopted an interspecies complementation approach using yeast. Synaptic transmission could be restored in chimeras when two key interfaces were present: a novel Habc-Unc18 contact site and an Unc18-SNARE motif contact site. An open form of Unc18 could bypass the requirement for the Habc-Unc18 interface. Together, these data suggest that the Habc domain of syntaxin is required for Unc18 to adopt an open conformation; open Unc18 then templates SNARE complex formation.

neuroscience↗

bioRxiv2017

speaq 2.0: A Wavelet-Based Workflow For High-Throughput 1D NMR Spectra Processing And Quantification

Nuclear Magnetic Resonance (NMR) spectroscopy is, together with liquid chromatography-mass spectrometry (LC-MS), the most established platform to perform metabolomics. In contrast to LC-MS however, NMR data is predominantly being processed with commercial software. This has the effect that its data processing remains tedious and dependent on user interventions. As a follow-up to speaq, a previously released workflow for NMR spectral alignment and quantitation, we present speaq 2.0. This completely revised framework to automatically analyze 1D NMR spectra uses wavelets to efficiently summarize the raw spectra with minimal information loss or user interaction. The tool offers a fast and easy workflow that starts with the common approach of peak-picking, followed by grouping. This yields a matrix consisting of features, samples and peak values that can be conveniently processed either by using included multivariate statistical functions or by using many other recently developed methods for NMR data analysis. speaq 2.0 facilitates robust and high-throughput metabolomics based on 1D NMR but is also compatible with other NMR frameworks or complementary LC-MS workflows. The methods are benchmarked using two publicly available datasets. speaq 2.0 is distributed through the existing speaq R package to provide a complete solution for NMR data processing. The package and the code for the presented case studies are freely available on CRAN (https://cran.r-project.org/package=speaq) and GitHub (https://github.com/beirnaert/speaq).\n\nAuthor summaryWe present speaq 2.0: a user friendly workflow for processing NMR spectra quickly and easily. By limiting the need for user interaction and allowing the construction of workflows by combining R functions, metabolomics data analysis becomes fully reproducible and shareable. Such advances are critical for the future of the metabolomics field as it needs to move towards a fully open-science approach. This is no trivial goal as many researchers are still using black-box commercial software that often requires manually doing several steps, thus hampering reproducibility. To encourage the shift towards open source, we deliberately made our method usable for anyone with the most basic of R experience, something that is easily acquired. speaq 2.0 allows a stand-alone analysis from spectra to statistical analysis. In addition, the package can be combined with existing tools to improve performance, as it provides a superior peak picking method compared to the standard binning approach.

bioinformatics↗