Biology subjects

Zou, L. S.

Publications and source records attributed to Zou, L. S..

4 recordsLinked to original sources

bioRxiv2021

Detection of allele-specific expression in spatial transcriptomics with spASE

Allele-specific expression (ASE), or the preferential expression of one allele, can be observed in transcriptomics data from early development throughout the lifespan. However, the prevalence of spatial and cell type-specific ASE variation remains unclear. Spatial transcriptomics technologies permit the study of spatial ASE patterns genome-wide at near-single-cell resolution. However, the data are highly sparse, and confounding between cell type and spatial location present further statistical challenges. Here, we introduce spASE (https://github.com/lulizou/spase), a computational framework for detecting spatial patterns in ASE within and across cell types from spatial transcriptomics data. To tackle the challenge presented by the low signal to noise ratio due to the sparsity of the data, we implement a spatial smoothing approach that greatly improves statistical power. We generated Slide-seqV2 data from the mouse hippocampus and detected ASE in X-chromosome genes, both within and across cell type, validating our ability to recover known ASE patterns. We demonstrate that our method can also identify cell type-specific effects, which we find can explain the majority of the spatial signal for autosomal genes. The findings facilitated by our method provide new insight into the uncharacterized landscape of spatial and cell type-specific ASE in the mouse hippocampus.

genomics↗

bioRxiv2020

Robust decomposition of cell type mixtures in spatial transcriptomics

Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in cell type assignment in Slide-seq data by accurately reproducing known cell type and subtype localization patterns in the cerebellum and hippocampus. We further show the advantages of RCTD by its ability to detect mixtures and identify cell types on an assessment dataset. Finally, we show how RCTDs recovery of cell type localization uniquely enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD has the potential to enable the definition of spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue.

genomics↗

bioRxiv2019

Single cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures

ObjectiveType 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome wide association studies (GWAS) have identified >400 independent signals that encode genetic predisposition. More than 90% of the associated single nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, and so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity.\n\nMethodsWe present genome-wide single cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell-combinatorial-indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on the U-Net architecture to accurately predict open chromatin peak calls in rare cell populations.\n\nResultsWe show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, we find that T2D GWAS SNPs are significantly enriched in beta cell-specific and cross cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep-learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals.\n\nConclusionsCollectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways.

genomics↗

bioRxiv2017

BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

BackgroundBisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to variability in depth of coverage. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features and motifs associated with predictive power.\n\nResultsHere we describe BoostMe, a novel method for imputation of DNA methylation within whole-genome bisulfite sequencing (WGBS) data based on a gradient boosting algorithm. Importantly, we designed a new feature that leverages information from multiple samples in the same tissue and disease state, enabling BoostMe to outperform existing imputation methods in speed and accuracy. We show that imputation improves WGBS concordance with the Infinium MethylationEPIC array at low WGBS sequencing depth, suggesting improvement in WGBS accuracy after imputation. Furthermore, we compare the ability of BoostMe and DeepCpG - a deep neural network method - to identify interesting features and motifs associated with methylation in three human tissues implicated in type 2 diabetes (T2D) etiology. We find that while BoostMe only identifies features important to general methylation levels across tissues, DeepCpG is able to learn differences in methylation-associated sequence motifs among different tissues and identify tissue-specific regulators of differentiation such as EBF1 in adipose, ASCL2 in muscle, and FOXA1, TCF12, and NRF1 in islets. Neither algorithm readily identified T2D-associated features.\n\nConclusionsOur findings demonstrate the current power and limitations of machine and deep learning algorithms to both improve the quality of, and infer biological meaning from, genome-wide DNA methylation data.

genomics↗