Biology subjects

Nguyen, L.

Publications and source records attributed to Nguyen, L..

7 recordsLinked to original sources

bioRxiv2018

Single-cell transcriptomics of the aged mouse brain reveals convergent, divergent and unique aging signatures

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how the brain is affected with aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide a comprehensive dataset of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell type specific manner, even at times in opposite directions. Thus, our data reveals that aging, rather than inducing a universal program drives a distinct transcriptional course in each cell population. These data provide an important resource for the aging community and highlight key molecular processes, including ribosomal biogenesis, underlying aging. We believe that this large-scale dataset, which is publicly accessible online (aging-mouse-brain), will facilitate additional discoveries directed towards understanding and modifying the aging process.

neuroscience↗

bioRxiv2018

Single-cell transcriptional dynamics and origins of neuronal diversity in the developing mouse neocortex

During cortical development, distinct subtypes of glutamatergic neurons are sequentially born and differentiate from dynamic populations of progenitors. The neurogenic competence of these progenitors progresses as corticogenesis proceeds; likewise, newborn neurons transit through sequential states as they differentiate. Here, we trace the developmental transcriptional trajectories of successive generations of apical progenitors (APs) and isochronic cohorts of their daughter neurons using parallel single-cell RNA sequencing between embryonic day (E) 12 and E15 in the mouse cerebral cortex. Our results identify the birthdate- and differentiation stage-related transcriptional dynamics at play during corticogenesis. As corticogenesis proceeds, APs transit through embryonic age-dependent molecular states, which are transmitted to their progeny to generate successive initial daughter cell identities. In neurons, essentially conserved post-mitotic differentiation programs are applied onto these distinct AP-derived ground states, allowing temporally-regulated sequential emergence of specialized neuronal cell types. Molecular temporal patterning of sequentially-born daughter neurons by their respective mother cell thus underlies emergence of neuronal diversity in the neocortex.\n\nOne Sentence SummaryDuring corticogenesis, temporally dynamic molecular birthmarks are transmitted from progenitors to their post-mitotic progeny to generate neuronal diversity.

neuroscience↗

bioRxiv2018

Machine learning models to predict in vivo drug response via optimal dimensionality reduction of tumour molecular profiles

Inter-tumour heterogeneity is one of cancers most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, lessons from the past indicate that single-gene markers of response are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. Results show that combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: Paclitaxel (breast cancer), Binimetinib (breast cancer) and Cetuximab (colorectal cancer). Interestingly, each of these ML models identify some responsive PDXs not harbouring the best actionable mutation for that case (such PDXs were missed by those single-gene markers). Moreover, ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive PDXs than the corresponding single-gene marker. As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if multiple ML algorithms were applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

bioinformatics↗

bioRxiv2017

Functional Compensation Between Hematopoietic Stem Cells In Vivo

In most organ systems, regeneration is a coordinated effort that involves many stem cells, but little is known about whether and how individual stem cells compensate for the functional deficiencies of other stem cells. Functional compensation between stem cells is critically important during disease progression and treatment. Here, we show how individual hematopoietic stem cells (HSCs) in a mouse heterogeneously compensate for the deficiencies of other HSCs during lymphopoiesis by increasing their clonal expansion at specific differentiation stages. This compensation rescues the overall blood supply and influences blood cell types outside of the deficient lineages in distinct patterns. We have identified the molecular regulators and signaling pathways in HSCs that are involved in this process. Our data demonstrate how stem cells interact with each other to constitute a coordinated network that is robust enough to withstand minor functional disruptions. Exploiting the innate compensation capacity of stem cell networks may improve the prognosis and treatment of many diseases.

systems biology↗

bioRxiv2017

Coarse-grained simulations of actomyosin rings point to a nodeless model involving both unipolar and bipolar myosins

Cytokinesis in most eukaryotic cells is orchestrated by a contractile actomyosin ring. While many of the proteins involved are known, the mechanism of constriction remains unclear. Informed by existing literature and new 3D molecular details from electron cryotomography, here we develop 3D coarse-grained models of actin filaments, unipolar and bipolar myosins, actin crosslinkers, and membranes and simulate their nteractions. Exploring a matrix of possible actomyosin configurations suggested that node-based architectures ike those presently described for ring assembly result in membrane puckers not seen in EM images of real cells. Instead, the model that best matches data from fluorescence microscopy, electron cryotomography, and biochemical experiments is one in which actin filaments transmit force to the membrane through evenly-distributed, membrane-attached, unipolar myosins, with bipolar myosins in the ring driving contraction. While at this point this model is only favored (not proven), the work highlights the power of coarse-grained biophysical simulations to compare complex mechanistic hypotheses.\n\nSignificance StatementIn most eukaryotes, a ring of actin and myosin drives cell division, but how the elements of the ring are arranged and constrict remain unclear. Here we use 3D coarse-grained simulations to explore various possibilities. Our simulations suggest that if actomyosin is arranged in nodes (as suggested by a popular model of ring assembly), the membrane distorts in ways not seen experimentally. Instead, actin and myosin are more ikely uniformly distributed around the ring. In the model that best fits experimental data, ring tension is generated by interactions between bipolar myosins and actin, and transmitted to the membrane via unipolar myosins. Technologically the study highlights how coarse-grained simulations can test specific mechanistic hypotheses by comparing their predicted outcomes to experimental results.

biophysics↗

bioRxiv2017

The polyploid state plays a tumor suppressive role in the liver

Most cells in the liver are polyploid, but the functional role of polyploidy is unknown. Polyploidization normally occurs through cytokinesis failure and endoreduplication around the time of weaning. To interrogate the function of polyploidy while avoiding irreversible manipulations of essential cell cycle genes, we developed multiple orthogonal mouse models to transiently and potently alter liver ploidy. Premature weaning, as well as in vivo knockdown of E2f8 or Anln, allowed us to toggle between diploid and polyploid states. While there was no impact of ploidy alterations on liver function, metabolism, or regeneration, hyperpolyploid mice suppressed and hyperdiploid mice accelerated tumorigenesis in mutagen and high fat induced models. Mechanistically, the diploid state was more susceptible to Cas9-mediated tumor suppressor loss but was similarly susceptible to MYC oncogene activation, indicating that ploidy differentially protected the liver from distinct genomic aberrations. Our work suggests that polyploidy evolved to prevent malignant outcomes of liver injury.

cancer biology↗

bioRxiv2016

Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data.\n\nHere we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC50 measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than K-fold cross-validation. Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG.

bioinformatics↗