Biology subjects

Vu, T.

Publications and source records attributed to Vu, T..

28 records · Page 2Linked to original sources

bioRxiv2021

Lipid phase separation in vesicles enhances TRAIL-mediated cytotoxicity

Ligand spatial presentation and density play important roles in many signaling pathways mediated by cell receptors and are critical parameters when designing protein-conjugated therapeutic nanoparticles. Currently, Janus particles are most often used to spatially control ligand conjugation, but the technological challenge of manufacturing Janus particles limits adoption for translational applications. Here, we demonstrate that lipid phase separation can be used to spatially control protein presentation onto lipid vesicles. We used this system to study the density dependence of TNF-related apoptosis inducing ligand (TRAIL), a model therapeutic protein that exhibits greater cytotoxicity to cancer cells when conjugated onto a vesicle surface than when administered as a soluble protein. Using assays for apoptosis and caspase activity, we show that phase separated TRAIL vesicles induced higher cytotoxicity to Jurkat cancer cells than uniformly-conjugated TRAIL vesicles, and enhanced cytotoxicity was dependent on the TRAIL domain density. We then assessed this relationship in other cancer cell lines and demonstrated that phase separated TRAIL vesicles only enhanced cytotoxicity through one TRAIL receptor, DR5, while another TRAIL receptor, DR4, was unaffected by the TRAIL density. These results indicate unique signaling requirements for each TRAIL receptor and how TRAIL therapy could be tailored depending on the relative levels of expression for cancer receptors of interest. Overall, this work demonstrates a readily adoptable method to control protein conjugation and density on bilayer vesicles that can be easily adopted to other therapeutic nanoparticle systems to improve receptor signaling of nanoparticles targeted to cancer and diseased cells.

bioengineering↗

bioRxiv2021

SPF: A Spatial and Functional Data Analytic Approach to cell Imaging data

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subjects clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available Multiplexed Ion beam Imaging (MIBI) triple-negative breast cancer dataset.

bioinformatics↗

bioRxiv2021

DenVar: Density-based Variation analysis of multiplex imaging data

Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) platforms have become increasingly popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions, often known as functional markers, in the TME becomes extremely crucial for subject-specific assessment of risks, such as risk of recurrence and risk of death. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular functional marker, and the other to classify the subjects based on the proportion of the positive cells. The selection of the thresholds has a large impact on the results and an arbitrary selection can lead to an incomprehensible conclusion. In light of this problem, we present a threshold-free distance between the subjects based on the probability densities of the functional markers. The distance can be used to classify the subjects into meaningful groups or can be used in a linear mixed model setup for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling an easier but interpretable analysis of these types of data. With the proposed method, we analyze a lung cancer dataset from an mIHC platform, finding the difference in the density of functional marker HLA-DR to be significantly associated with the overall survival. The approach is also applied on an MIBI triple-negative breast cancer dataset to analyze effects of multiple functional markers. Finally, we demonstrate the reliability of our method through extensive simulation studies.

bioinformatics↗

bioRxiv2021

Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis

Multiplexed mRNA profiling in the spatial context provides important new information enabling basic research and clinical applications. Unfortunately, most existing spatial transcriptomics methods are limited due to either low multiplexing or assay complexity. Here, we introduce a new spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based target decoding. This technology is the first application combining the biophotonic techniques, Spectral and Fluorescence Lifetime Imaging and Microscopy (FLIM), to the field of spatial transcriptomics. By integrating the time dimension with conventional spectrum-based measurements, MOSAICA enables direct and highly-multiplexed in situ spatial biomarker profiling in a single round of staining and imaging while providing error correction removal of background autofluorescence. We demonstrate mRNA encoding using combinatorial spectral and lifetime labeling and target decoding and quantification using a phasor-based image segmentation and machine learning clustering technique. We then showcase MOSAICAs multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of only five fluorophores with facile error-correction and removal of autofluorescent moieties. MOSAICAs analysis is strongly correlated with sequencing data (Pearsons r = 0.9) and was further benchmarked using RNAscopeand LGC Stellaris. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues that have a high degree of tissue scattering and intrinsic autofluorescence, demonstrating the robustness of the approach. We then demonstrate simultaneous co-detection of protein and mRNA in colorectal cancer cells. MOSAICA represents a simple, versatile, and scalable tool for targeted spatial transcriptomics analysis that can find broad utility in constructing human cell atlases, elucidating biological and disease processes in the spatial context, and serving as companion diagnostics for stratified patient care.

bioengineering↗

bioRxiv2021

Phylomedicine of mutational processes in somatic cancer cell populations

Mutational processes in somatic cancer cell populations are constantly changing, leaving their signatures in the accumulated genomic variation in tumors. The inference of mutational signatures from the observed genetic variation enables spatiotemporal tracking of tumor mutational processes that evolve due to cellular environmental changes, mutations, and treatment regimes. Ultimately, mutational patterns illuminate the mechanistic understanding of their evolution in cancer progression. We show that the integration of cancer cell phylogeny with mutational signature deconvolution enables higher-resolution detection of gain and loss of mutational processes within the phylogeny. This approach to analyzing somatic genomic variations in 61 lung cancer patients revealed a high turn-over of mutational processes over time and closely related clonal lineages. Some mutational signatures (e.g., smoking-related) showed a higher propensity to be lost, whereas others (e.g., AID/APOBEC) were gained during lung tumors evolution. These observations shed light on the evolution of mutational processes in somatic cell evolution.

evolutionary biology↗

bioRxiv2020

The Past, Present and Future of Elephant Landscapes in Asia

Habitat loss drives species declines worldwide, but is seldom quantified over centennial timescales. We constructed ecological niche models for Asian elephants based on land-use change between 850-2015, and predictions under six different climate/socioeconomic scenarios from 2015-2099. We find that over 64% of suitable natural habitat across diverse ecosystems was lost over the past three centuries. Average patch size dropped 83% from approximately 99,000 km2 to 16,000 km2 and the area occupied by the largest patch decreased 83% from ~ 4 million km2 (45% of area) to 54,000 km2 (~7.5% of area). Over half of current elephant range appears unsuitable. Habitat availability is predicted to decline further this century across all scenarios. The most severe losses occur under RCP3.4-SSP4, representing mid-range emissions but high regional inequities. We conclude that climate change mitigation measures must include policies to ensure inter-regional socioeconomic equity to safeguard landscapes for elephants, humans, and other species.

ecology↗

bioRxiv2019

Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data

BackgroundTumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions of a cancer patient. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples. These clone phylogenies are used to infer mutation order and clone origin times during tumor progression, rendering the selection of the appropriate clonal deconvolution method quite critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies have not been consistently assessed.\n\nMethodsWe evaluated the performance of seven computational methods in producing clone phylogenies for simulated datasets in which clones were sampled from multiple sectors of a primary tumor (multi-region) or primary and metastatic tumors in a patient (multi-site). We assessed the accuracy of tested methods metrics in determining the order of mutations and the branching pattern within the reconstructed clone phylogenies.\n\nResultsThe accuracy of the reconstructed mutation order varied extensively among methods (9% - 44% error). Methods also varied significantly in reconstructing the topologies of clone phylogenies, as 24% - 58% of the inferred clone groupings were incorrect. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The occurrence of multiple seeding events among tumor sites during metastatic tumor evolution hindered deconvolution of clones for all tested methods.\n\nConclusionsOverall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets and conditions.

cancer biology↗

bioRxiv2019

A human ex vivo dengue virus neutralization assay identifies priority antibodies and epitopes for vaccines and therapeutics.

BackgroundDengue is the most prevalent arboviral disease, for which neither effective vaccines nor antivirals are available. Clinical trials with Dengvaxia, the first licensed dengue vaccine, show the conventional in vitro plaque reduction neutralization test (PRNT) failed to discriminate between neutralizing and non-neutralizing antibodies. A number of human monoclonal antibodies (mAbs) were characterized by PRNT as being neutralizers of virus infectivity for mammalian cells.\n\nMethodolody/Principle findingsWe developed a neutralization assay and tested the capacity of 12 mAbs to neutralize the infectiousness of dengue patient viremic blood in mosquitoes. We identified minimum concentrations of a subset of mAbs required to achieve dengue virus neutralization, and modelled the impact of a therapeutic mAb candidate on viremia.\n\nFive of the 12 mAbs (14c10, 2D22, 1L12, 747(4)B7, 753(3)C10), all of which target quaternary epitopes, potently inhibited dengue virus infection of Ae. aegypti. The potency of several mAbs was compromised in the context of patients with secondary serological profiles, possibly reflecting competition between the exogenously-added mAbs and the patients own antibody responses at or near the target epitopes. The minimum concentrations that mAbs neutralized DENV ranged from 0.1 - 5 {micro}g/mL. An Fc-disabled variant of mAb (14c10-LALA) was as potent as its parent mAb. Within-host mathematical modelling suggests infusion of 14c10-LALA could bring about rapid acceleration of viremia resolution in a typical patient.\n\nConclusions/SignificanceThese data delivered a unique assessment of anti-viral potency of a panel of human mAbs. Results support the advancement of dengue virus neutralization assays, and the development of therapeutics against flaviviruses, to which dengue virus and Zika virus belong.\n\nAuthor summaryDengue is the most prevalent arboviral disease affecting humans. There are no therapeutics for the disease. Antibody-mediated immunity against dengue is also not well-understood, as shown by the failure of the conventional neutralization assay used to predict the efficacy of Dengvaxia, the first licensed vaccine for the disease. It is likely that the neutralization assay targets non-neutralizing antibodies, but there are no validation assays available. To this end, we developed a novel virus neutralization assay, employing Aedes aegypti mosquitoes and viremic blood from dengue patients, to examine the virus-neutralizing potency of 12 human-derived monoclonal antibodies (mAbs). While all of these mAbs neutralized dengue virus using the conventional assay, seven of them failed to block dengue virus infections of mosquitoes using our assay. The remaining five mAbs neutralized at least one serotype of dengue virus and the minimum neutralizing concentrations of range from 0.1 - 5 {micro}g/mL. Using the minimum neutralizing concentration of a therapeutic mAb candidate, we investigated the impact of the mAb on viremia using a mathematical model and found the mAb accelerated the reduction of viremia. The results support the advancement of dengue virus neutralization assays, and the development of therapeutics for dengue.

immunology↗

bioRxiv2018

Predicting clone genotypes from tumor bulk sequencing of multiple samples

MotivationAnalyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known.\n\nResultsWe have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP, and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by Phy-loWGS, and BayClone2, Canopy, and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets.\n\nConclusionsDeconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference.\n\nAvailability and ImplementationCloneFinder is implemented in Python and is available from https://github.com/gstecher/CloneFinderAPI.\n\nContacts.kumar@temple.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online

cancer biology↗

bioRxiv2018

Computational enhancement of single-cell sequences for inferring tumor evolution

Motivation: Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing the assessment of single nucleotide variation (SNV) at the highest cellular resolution. However, state-of-the-art single-cell sequencing technologies produce data with many missing bases (MBs) and incorrect base designations that lead to false-positive (FP) and false-negative (FN) detection of somatic mutations. While computational methods are available to make biological inferences in the presence of these errors, the accuracy of the imputed MBs and corrected FPs and FNs remains unknown.\n\nResults: Using computer simulated datasets, we assessed the robustness performance of four existing methods (OncoNEM, SCG, SCITE, and SiFit) and one new method (BEAM). BEAM is a Bayesian evolution-aware method that improves the quality of single-cell sequences by using the intrinsic evolutionary information in the single-cell data in a molecular phylogenetic framework. Overall, BEAM and SCITE performed the best. Most of the methods imputed MBs with high accuracy, but effective detection and correction of FPs and FNs require sampling a large number of SNVs. Analysis of an empirical dataset shows that computational methods can improve both the quality of tumor single-cell sequences and their utility for biological inference.\n\nConclusions: Tumor cells descend from pre-existing cells, which creates evolutionary continuity in single-cell sequencing datasets. This information enables BEAM and other methods to correctly impute missing data and incorrect base assignments, but correction of FPs and FNs remains challenging when the number of SNVs sampled is small relative to the number of cells sequenced.\n\nAvailability: BEAM is available on the web at https://github.com/SayakaMiura/BEAM.\n\nContact: s.kumar@temple.edu

cancer biology↗