Biology subjects

Hu, Q.

Publications and source records attributed to Hu, Q..

133 records · Page 8Linked to original sources

bioRxiv2018

Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species.

New approaches are urgently needed to glean biological insights from the vast amounts of single cell RNA sequencing (scRNA-Seq) data now being generated. To this end, we propose that cell identity should map to a reduced set of factors which will describe both exclusive and shared biology of individual cells, and that the dimensions which contain these factors reflect biologically meaningful relationships across different platforms, tissues and species. To find a robust set of dependent factors in large-scale scRNA- Seq data, we developed a Bayesian non-negative matrix factorization (NMF) algorithm, scCoGAPS. Application of scCoGAPS to scRNA-Seq data obtained over the course of mouse retinal development identified gene expression signatures for factors associated with specific cell types and continuous biological processes. To test whether these signatures are shared across diverse cellular contexts, we developed projectR to map biologically disparate datasets into the factors learned by scCoGAPS. Because projecting these dimensions preserve relative distances between samples, biologically meaningful relationships/factors will stratify new data consistent with their underlying processes, allowing labels or information from one dataset to be used for annotation of the other--a machine learning concept called transfer learning. Using projectR, data from multiple datasets was used to annotate latent spaces and reveal novel parallels between developmental programs in other tissues, species and cellular assays. Using this approach we are able to transfer cell type and state designations across datasets to rapidly annotate cellular features in a new dataset without a priori knowledge of their type, identify a species-specific signature of microglial cells, and identify a previously undescribed subpopulation of neurosecretory cells within the lung. Together, these algorithms define biologically meaningful dimensions of cellular identity, state, and trajectories that persist across technologies, molecular features, and species.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC=\"FIGDIR/small/395004_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (81K):\norg.highwire.dtl.DTLVardef@dd1c07org.highwire.dtl.DTLVardef@5b1109org.highwire.dtl.DTLVardef@bb6714org.highwire.dtl.DTLVardef@16c66f0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

MultiPLIER: a transfer learning framework reveals systemic features of rare autoimmune disease

Unsupervised machine learning methods provide a promising means to analyze and interpret large datasets. However, most gene expression datasets generated by individual researchers remain too small to fully benefit from these methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. We trained a Pathway Level Information ExtractoR (PLIER) model using on a large public data compendium comprised of multiple experiments, tissues, and biological conditions. We then transferred the model to small rare disease datasets in an approach we term MultiPLIER. Models constructed from large, diverse public data i) included features that aligned well to important biological factors; ii) were more comprehensive than those constructed from individual datasets or conditions; iii) transferred to rare disease datasets where the models describe biological processes related to disease severity more effectively than models trained on specifically those datasets.

bioinformatics↗

bioRxiv2018

Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics

Single-cell RNA sequencing (scRNA-seq) is a powerful tool to profile the transcriptomes of a large number of individual cells at a high resolution. These data usually contain measurements of gene expression for many genes in thousands or tens of thousands of cells, though some datasets now reach the million-cell mark. Projecting high-dimensional scRNA-seq data into a low dimensional space aids downstream analysis and data visualization. Many recent preprints accomplish this using variational autoencoders (VAE), generative models that learn underlying structure of data by compress it into a constrained, low dimensional space. The low dimensional spaces generated by VAEs have revealed complex patterns and novel biological signals from large-scale gene expression data and drug response predictions. Here, we evaluate a simple VAE approach for gene expression data, Tybalt, by training and measuring its performance on sets of simulated scRNA-seq data. We find a number of counter-intuitive performance features: i.e., deeper neural networks can struggle when datasets contain more observations under some parameter configurations. We show that these methods are highly sensitive to parameter tuning: when tuned, the performance of the Tybalt model, which was not optimized for scRNA-seq data, outperforms other popular dimension reduction approaches - PCA, ZIFA, UMAP and t-SNE. On the other hand, without tuning performance can also be remarkably poor on the same data. Our results should discourage authors and reviewers from relying on self-reported performance comparisons to evaluate the relative value of contributions in this area at this time. Instead, we recommend that attempts to compare or benchmark autoencoder methods for scRNA-seq data be performed by disinterested third parties or by methods developers only on unseen benchmark data that are provided to all participants simultaneously because the potential for performance differences due to unequal parameter tuning is so high.

bioinformatics↗

bioRxiv2018

Specific histone modifications associate with alternative exon selection during mammalian development

Alternative splicing (AS) is frequent during early mouse embryonic development. Specific histone post-translational modifications (hPTMs) have been shown to regulate exon splicing by either directly recruiting splice machinery or indirectly modulating transcriptional elongation. In this study, we hypothesized that hPTMs regulate expression of alternatively spliced genes for specific processes during differentiation. To address this notion, we applied an innovative machine learning approach to relate global hPTM enrichment to AS regulation during mammalian tissue development. We found that specific histone modifications, H3K36me3 and H3K4me1, play a dominant role in skipped exon selection among all the tissues and developmental time points examined. In addition, we used iterative random forest model to identify interactions of several hPTMs that associated with skipped exon selection during tissue development. Collectively, our data demonstrated a link between hPTMs and alternative splicing which will drive further experimental studies on the functional relevance of these modifications to alternative splicing.

bioinformatics↗

bioRxiv2018

Recent mixing of Vibrio parahaemolyticus populations

BackgroundHumans have profoundly affected the ocean environment but little is known about anthropogenic effects on the distribution of microbes. Vibrio parahaemolyticus is found in warm coastal waters and causes gastroenteritis in humans and economically significant disease in shrimps.\n\nResultsBased on data from 1,103 genomes, we show that V. parahaemolyticus is divided into four diverse populations, VppUS1, VppUS2, VppX and VppAsia. The first two are largely restricted to the US and Northern Europe, while the others are found worldwide, with VppAsia making up the great majority of isolates in the seas around Asia. Patterns of diversity within and between the populations are consistent with them having arisen by progressive divergence via genetic drift during geographical isolation. However, we find that there is substantial overlap in their current distribution. These observations can be reconciled without requiring genetic barriers to exchange between populations if dispersal between oceans has increased dramatically in the recent past. We found that VppAsia isolates from the US have an average of 1.01% more shared ancestry with VppUS1 and VppUS2 isolates than VppAsia isolates from Asia itself. Based on time calibrated trees of divergence within epidemic lineages, we estimate that recombination affects about 0.017% of the genome per year, implying that the genetic mixture has taken place within the last few decades.\n\nConclusionsThese results suggest that human activity, such as shipping and aquatic products trade, are responsible for the change of distribution pattern of this marine species.

microbiology↗

bioRxiv2018

β-lactam Antibiotics Stimulate the Pathogenicity of Methicillin-resistant Staphylococcus aureus Via SarA-controlled Tandem Lipoprotein Expression

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of nosocomial infections worldwide. MRSA resists nearly all {beta}-lactam antibiotics that have a bactericidal activity and a signal inducer effect. However, studies have yet to clarify whether the inducer effect of empirically used {beta}-lactams stimulates MRSA pathogenicity in vivo. Here, we showed that a new cluster of tandem lipoprotein genes (tlpps) was upregulated in MRSA in response to the subinhibitory concentrations of {beta}-lactam induction. The increased Tlpps significantly altered immune responses by macrophages with high IL-6 and TNF levels. The deletion of the tlpps mutant (N315{Delta}tlpps) significantly decreased the proinflammatory cytokine levels in vitro and in vivo. The bacterial loads of N315{Delta}tlpps in the mouse kidney were also reduced compared with those of the wild type N315. The {beta}-lactam-treated MRSA exacerbated cutaneous infections with increased lesion size, extended illness, and flake-like abscess-formation compared with those of the nontreatment. The {beta}-lactam antibiotics that promoted the MRSA pathogenicity were SarA dependent, and the increasing expression of tlpps after {beta}-lactam treatment was directly controlled by the global regulator SarA. Overall, our findings suggested that {beta}-lactams should be used carefully because it might lead to a worse outcome of MRSA infection than inaction in the treatment.\n\nAuthor summary{beta}-lactams are widely used in practice to treat infectious diseases, however, {beta}-lactams worsening the outcome of a certain disease is poorly understood. In this study, we have identified a new cluster of tandem lipoprotein genes (tlpps) that is upregulated in the major clinically prevalent MRSA clones in response to the subinhibitory concentrations of {beta}-lactams induction. The major highlight in this work is that {beta}-lactams induce SarA expression, and then SarA directly binds to the tlpp cluster promoter region and upregulates the tlpp expression in MRSA. Moreover, the {beta}-lactam stimulated Tlpps are important virulence factors that enhance MRSA pathogenicity. The deletion of the tlpps mutant significantly decreases the proinflammatory cytokine levels in vitro and in vivo. The {beta}-lactam induced Tlpps enhance the host inflammatory responses by triggering the expression of IL-6 and TNF, thereby promoting bacterial colonization and abscess formation. These data elucidate that {beta}-lactams can worsen the outcome of MRSA infection through the induction of tlpps that are controlled by the global regulator SarA.

pathology↗

bioRxiv2017

Neural computations combine low- and high-order motion cues similarly, in dragonfly and monkey

Visual motion analysis is fundamental to survival across the animal kingdom. In insects, our understanding of the underlying computations has centered on the Hassenstein-Reichardt motion detector, which computes two-point cross-correlation via multiplication; in mammalian cortex, it is postulated that a similar signal is computed by comparing matched squaring operations. Both of these operations are difficult to implement biophysically in a precise fashion; moreover, they fail to detect the more complex multipoint local motion cues present in the visual environment. Here, via single-unit recordings in two visual specialists, dragonfly \"(Odonata)\" and macaque, and via model simulations, we show that neuronal computations are not simply approximations to idealized behaviors forced by biological constraints, but rather, are signatures of a common computational strategy to capture multiple local motion cues. The similarity of motion computations at the neuronal level in the brains of two extremely dissimilar animals, with evolutionary divergence of over 700 Myr1, suggests convergence on a common computational scheme for detecting visual motion.

neuroscience↗