Biology subjects

Ngai, J.

Publications and source records attributed to Ngai, J..

10 recordsLinked to original sources

bioRxiv2026

Evolving stem cell fate capacities and transcriptional priming in the developing olfactory epithelium

The olfactory epithelium of adult mammals contains two populations of stem cells that support its remarkable ability to regenerate neuronal and non-neuronal cell types throughout life. How olfactory epithelial cell types are established during development, however, is not well understood. Here, we use genetic lineage tracing and single-cell RNA sequencing of the perinatal mouse olfactory epithelium to construct a developmental trajectory consisting of multiple lineages. We identify transitional states and lineage relationships between individual cells and establish Ascl1+ cells as the primary multipotent progenitors in the perinatal olfactory epithelium. Further, Ascl1+ cells become progressively restricted in their cell fate capacity over developmental time and appear to be transcriptionally primed toward specific lineages. We also predict signaling pathways that may contribute to lineage plasticity and niche permissiveness. Together, these results contribute to our understanding of how cell-intrinsic and-extrinsic signals contribute to the establishment of a stem cell niche.

developmental biology↗

bioRxiv2024

Neocortical long-range inhibition promotes cortical synchrony and sleep

Sleep and wakefulness are associated with distinct cortical patterns of rhythmic activity. During low-arousal states such as slow wave sleep, synchronous low-frequency rhythms dominate activity across widespread cortical regions. Although inhibitory neurons are increasingly recognized as key players of cortical state, the in vivo circuit mechanisms coordinating synchronized activity across local and distant neocortical networks remain poorly understood. Here, we show that somatostatin and chondrolectin co-expressing cells (Sst-Chodl), a sparse and genetically distinct class of neocortical GABAergic inhibitory neurons, are selectively active during low-arousal states and largely silent during periods of high arousal. In contrast to most neocortical inhibitory neurons, Sst-Chodl cells, despite being extremely sparse, exert widespread influence across the neocortex via long-range axons that simultaneously target multiple regions. Selective activation of Sst-Chodl cells is sufficient to promote multi-region cortical synchronization characteristic of low-arousal states and to induce sleep. Together, these findings show that long-range Sst-Chodl inhibitory neurons not only track behavioral state but can actively promote sleep-like cortical activity and sleep behavior, highlighting an important contribution of cortical circuits to sleep regulation alongside established subcortical mechanisms.

neuroscience↗

bioRxiv2023

A Latent Activated Olfactory Stem Cell State Revealed by Single Cell Transcriptomic and Epigenomic Profiling

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify the transcriptional cascades and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around wound response and lineage-specific genes prior to their later expression in response to injury. Together these results provide evidence for a latent activated stem cell state, in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

genomics↗

bioRxiv2021

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects

Modern assays have enabled high-throughput studies of epigenetic regulation of gene expression using DNA sequencing. In particular, the assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of chromatin configuration for an entire genome. Despite the gain in popularity of the assay, there have been limited studies investigating the analytical challenges related to ATAC-seq data, and most studies leverage tools developed for bulk transcriptome sequencing (RNA-seq). Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample-specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins, and evaluate it together with eleven different normalization procedures on eight public ATAC-seq datasets. Our work clearly shows that accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, leading to improved accuracy and interpretability of the results. Using two case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

bioinformatics↗

bioRxiv2020

Generation of a DAT-Flp mouse line for intersectional genetic targeting of dopamine neuron subpopulations

Dopamine neurons project to diverse regions throughout the brain to modulate various brain processes and behaviors. It is increasingly appreciated that dopamine neurons are heterogeneous in their gene expression, circuitry, physiology, and function. Current approaches to target dopamine neurons are largely based on single gene drivers, which either label all dopamine neurons, or mark a sub-set but concurrently label non-dopaminergic neurons. Here we establish a novel mouse line in which Flp recombinase is knocked-in to the endogenous Slc6a3 (dopamine active transporter, DAT) locus. DAT-Flp mice can be used with various Cre-expressing mouse lines to efficiently and selectively label dopaminergic subpopulations using Cre/Flp-dependent intersectional strategies. We demonstrate the utility of this approach by crossing DAT-Flp mice with NEX-Cre mice, to specifically label Neurod6-expressing dopamine neurons that project to the nucleus accumbens medial shell. DAT-Flp mice represent a novel tool, which will help parse the diverse functions mediated by dopaminergic circuits.

neuroscience↗

bioRxiv2020

Transcriptome analysis of the zebrafish atoh7-/- mutant, lakritz, highlights Atoh7-dependent genetic networks with potential implications for human eye diseases

Expression of the bHLH transcription protein Atoh7 is a crucial factor conferring competence to retinal progenitor cells for the development of retinal ganglion cells. A number of studies have emerged establishing ATOH7 as a retinal disease gene. Remarkably, such studies uncovered ATOH7 variants associated with global eye defects including optic nerve hypoplasia, microphthalmia, retinal vascular disorders and glaucoma. The complex genetic networks and cellular decisions arising downstream of atoh7 expression, and how their dysregulation cause development of such disease traits remains unknown. To begin to understand such Atoh7-dependent events in vivo we performed transcriptome analysis of wild type and atoh7 mutant (lakritz) zebrafish embryos at the onset of retinal ganglion cell differentiation. We investigated in silico interplays of atoh7 and other disease-related genes and pathways. By network reconstruction analysis of differentially expressed genes we identified gene clusters enriched in retinal development, cell cycle, chromatin remodelling, stress response and Wnt pathways. By weighted gene coexpression network we identified coexpression modules affected by the mutation and enriched in retina development genes tightly connected to atoh7. We established the groundwork whereby Atoh7-linked cellular and molecular processes can be investigated in the dynamic multi-tissue environment of the developing normal and diseased vertebrate eye.

developmental biology↗

bioRxiv2020

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

Single-cell transcriptome sequencing (scRNA-Seq) has allowed many new types of investigations at unprecedented and unique levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into potentially novel cell types. Many approaches build on the existing clustering literature to develop tools specific to single-cell applications. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters identified. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist to select these tuning parameters, most of them are quite ad hoc. In general, there is little assurance that any given set of parameters will represent an optimal choice in the ever-present trade-off between cluster resolution and replicability. For instance, it may be the case that another set of parameters will result in more clusters that are also more replicable, or in fewer clusters that are also less replicable. Here, we propose a new method called Dune for optimizing the trade-off between the resolution of the clusters and their replicability across datasets. Our method takes as input a set of clustering results on a single dataset, derived from any set of clustering algorithms and associated tuning parameters, and iteratively merges clusters within partitions in order to maximize their concordance between partitions. As demonstrated on a variety of scRNA-Seq datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters. It provides an objective approach for identifying replicable consensus clusters most likely to represent common biological features across multiple datasets.

bioinformatics↗

bioRxiv2018

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells.\n\nIt is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperimentprovides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers.\n\nThe package clusterExperimentis publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.

bioinformatics↗

bioRxiv2017

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq

Systematic measurement biases make data normalization an essential preprocessing step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple, competing considerations behind the assessment of normalization performance, some of them study-specific. Because normalization can have a large impact on downstream results (e.g., clustering and differential expression), it is critically important that practitioners assess the performance of competing methods.\n\nWe have developed scone -- a flexible framework for assessing normalization performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes performance trade-offs and ranks large numbers of normalization methods by aggregate panel performance. The method is implemented in the open-source Bioconductor R software package scone. We demonstrate the effectiveness of scone on a collection of scRNA-seq datasets, generated with different protocols, including Fluidigm C1 and 10x platforms. We show that top-performing normalization methods lead to better agreement with independent validation data.

genomics↗

bioRxiv2017

Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics

Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. These methods can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a number of statistical and computational methods have been proposed for analyzing cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Here, we introduce a novel method, Slingshot, for inferring multiple developmental lineages from single-cell gene expression data. Slingshot is a uniquely robust and flexible tool for inferring developmental lineages and ordering cells to reflect continuous, branching processes.

bioinformatics↗