Biology subjects

Chu, L.-F.

Publications and source records attributed to Chu, L.-F..

6 recordsLinked to original sources

bioRxiv2020

Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization

Considerable effort has been devoted to refining experimental protocols having reduced levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.

genomics↗

bioRxiv2020

Rapid changes in chromatin structure during dedifferentiation of primary hepatocytes in vitro

Primary hepatocytes are widely used in the pharmaceutical industry to screen drug candidates for hepatotoxicity, but isolated hepatocytes quickly dedifferentiate and lose their mature metabolic function in culture. Attempts have been made to better recapitulate the in vivo liver environment in culture, but the full spectrum of signals required to maintain hepatocyte function in vitro remains elusive. Here we studied the dedifferentiation process in detail through RNA-sequencing of hepatocytes cultured over eight days. We identified three distinct phases of dedifferentiation. An early phase, where mature hepatocyte genes are rapidly downregulated in a matter of hours. A middle phase, where fetal genes are activated, leading to hepatocytes with a fetal phenotype. A late phase, where initially rare contaminating non-parenchymal cells over-grow the culture as the hepatocytes gradually die. Using genetically tagged hepatocytes, we demonstrate that the cells reactivating fetal marker alpha-fetoprotein arise from cells previously expressing the mature hepatocyte marker albumin, and not from albumin negative precursor cells, proving that hepatocytes undergo true dedifferentiation. To better understand the signaling events that result in the rapid down-regulation of mature hepatocyte genes, we examined changes in chromatin accessibility of hepatocytes during the first 24h of culture using ATAC-seq. We find that drastic and rapid changes in chromatin accessibility occurs immediately upon start of culture. Using binding motif analysis of the areas of open chromatin sharing similar temporal profiles, we identify several candidate transcription factors potentially involved in the dedifferentiation of primary hepatocytes in culture.

cell biology↗

bioRxiv2019

Tradeoff between more cells and higher read depth for single-cell RNA-seq spatial ordering analysis of the liver lobule

As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of pseudo-spatial reconstruction. Overall, we find gene expression profiles after spatial-reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seqs higher sensitivity and read-depth allows for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses, and find that optimizing the balance between sequencing depth and number of cells within a protocol is important for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.

genomics↗

bioRxiv2019

Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data

Advances in single-cell transcriptomics enable measuring the gene expression of individual cells, allowing cells to be ordered by their state in a dynamic biological process. Many algorithms assign pseudotimes to each cell, representing the progress along the biological process. Ordering the expression data according to such pseudotimes can be valuable for understanding the underlying regulator-gene interactions in a biological process, such as differentiation. However, the distribution of cells sampled along a transitional process, and hence that of the pseudotimes assigned to them, is not uniform. This prevents using many standard mathematical methods for analyzing the ordered gene expression states. We present Single-cell Inference of Networks using Granger Ensembles (SINGE), an algorithm for gene regulatory network inference from single-cell gene expression data. Given ordered single-cell data, SINGE uses kernel-based Granger Causality regression, which smooths the irregular pseudotimes and missing expression values. It then aggregates the predictions from an ensemble of regression analyses with a modified Borda count to compile a ranked list of candidate interactions between transcriptional regulators and their target genes. In two mouse embryonic stem cell differentiation case studies, SINGE outperforms other contemporary algorithms for gene network reconstruction. However, a more detailed examination reveals caveats about transcriptional network reconstruction with single-cell RNA-seq data. Network inference methods, including SINGE, may have near random performance for predicting the targets of many individual regulators even if the overall performance is good. In addition, including uninformative pseudotime values can hurt the performance of network reconstruction methods. A MATLAB implementation of SINGE is available at https://github.com/gitter-lab/SINGE.

bioinformatics↗

bioRxiv2017

Trendy: Segmented regression analysis of expression dynamics for high-throughput ordered profiling experiments

AbstractHigh throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for profiling detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time-points. We present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each genes expression pattern and summarize overall dynamic activity in ordered condition experiments. For each gene, Trendy finds the optimal segmented regression model and provides the location and direction of dynamic changes in expression. We demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-seq datasets. Trendy is a flexible R package which characterizes gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions. Trendy is freely available as an R package with a full vignette at https://github.com/rhondabacher/Trendy.

genomics↗

bioRxiv2016

SCnorm: A quantile-regression based approach for robust normalization of single-cell RNA-seq data

Normalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data.

genomics↗