Biology subjects

Street, K.

Publications and source records attributed to Street, K..

12 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↗

bioRxiv2026

Annotation-Based Gene-Peak Links Improve Regulatory Network Prediction of Gene Expression in Human Kidney Multi-Omics

BackgroundLinking distal regulatory elements to their target genes is a central problem for interpreting chromatin accessibility and other non-coding genomic data. Proximity-based mapping is convenient but ignores three-dimensional enhancer-promoter architecture and can misassign long-range regulatory effects. Correlation-based approaches can also miss regulatory links because of limited statistical power and restrictive distance or significance thresholds. Single-cell and single-nucleus multi-omic datasets, such as 10x Multiome profiles that jointly measure chromatin accessibility and gene expression in the same cells or nuclei, now provide a way to evaluate gene-peak linkage strategies by testing how well linked accessibility features predict gene expression. Existing methods often focus on scoring individual enhancer-gene pairs. In this study, we proposed and constructed a fast annotation-based candidate gene-peak network and tested whether it improves downstream prediction of gene expression. MethodsWe first built a unified gene-peak regulatory network by integrating enhancer-based, promoter-based, and proximity-based linkage strategies. We then used single-cell multiome data from the Kidney Precision Medicine Project (KPMP) 10x Multiome cohort to evaluate whether the proposed links captured regulatory signals. We aggregated RNA expression and ATAC accessibility at the cell-type cluster level and trained predictive models to evaluate how well different linkage strategies could explain gene expression based on accessibility. Model performance was compared between annotation-based (including both enhancer- and promoter-based links) and proximity-based gene-peak links using testing R{superscript 2} and mean squared error (MSE) in a strict set of 1,704 genes and an adaptive set of 7,973 genes with more relaxed requirements. ResultsIn the strict regime (1,704 genes with [≥]20 peaks assigned by the nearest-TSS rule and [≥]10 annotated enhancer-based peaks), the annotation-based model consistently achieved higher testing R{superscript 2} and lower testing MSE than the proximity-based model. In the larger adaptive regime (7,973 genes with [≥]5 proximity-based and [≥]2 enhancer-based peaks), we defined for each gene a balanced number of selected peaks based on its available closest and enhancer links; the annotation-based model again showed globally higher testing R{superscript 2} and lower testing MSE. These improvements were observed over a broad range of candidate-linked peak numbers. ConclusionsUsing human kidney 10x Multiome data, we show that a fast annotation-based gene-peak linkage framework can improve prediction of gene expression from chromatin accessibility compared with conventional approaches. These results support the use of biologically informed enhancer and promoter annotations when constructing candidate gene regulatory networks. Our framework also showed concordance with the correlation-based Signac LinkPeaks method while providing broader coverage and greater computational efficiency. We have implemented these annotation-based linkage methods in the GPlinksR R package, providing a fast and scalable tool for constructing regulatory networks.

genomics↗

bioRxiv2026

VDJdive and ECLIPSE enhance single-cell TCR sequencing analysis through the probabilistic resolution of ambiguous clonotypes

Single-cell T cell receptor sequencing (scTCR-seq) has transformed our ability to track individual T cell clones and has been instrumental in advancing our understanding of human T cell differentiation. However, current computational pipelines for analysis, which require precise matching of CDR3 sequences from exactly 2 heterodimeric TCR chains to define the clonotype for each cell, are inherently limited because of the substantial proportion of cells possessing "ambiguous" clonotypes driven by missing (undetected from the technical issue of chain "dropout") or extra chains (present from either true biological expression or due to technical artifacts such as cellular doublets and ambient TCR contamination). As a result, clone sizes are artificially reduced, impeding the tracking of clones across conditions and differentiation states. Here we introduce VDJdive and ECLIPSE (Enhanced CLonotypic Inference via Prediction of Single-cell Expression), two computational methods that, together, resolve this clonal ambiguity by utilizing the expectation-maximization algorithm for the clonal prediction of ambiguous cells. These methods consider chain pairings across the sample, allowing for high-fidelity prediction of chains lost due to dropout and the discernment of biological expression of extra chains from technical artifacts. Consequently, clone sizes are augmented and cells without clonotype assignments are minimized. Our approach facilitates enhanced clonal tracking through these elevated clone sizes and is easily implementable, compatible with standard single-cell transcriptomic workflows, and broadly applicable across biological contexts and T cell subsets.

immunology↗

bioRxiv2025

Single-Cell Multiomic Profiling Uncovers Radiation Dosage-Sensitive, Cluster-Specific Regulatory Dynamics in Glioblastoma

Glioblastoma (GBM) is an aggressive brain tumor that inevitably recurs after chemoradiotherapy, resulting in poor patient outcomes. Given GBMs cellular heterogeneity, we hypothesized that radiation induces sub-population specific alterations to survive and adapt to radiation stress. We performed integrated single-cell RNA-seq and ATAC-seq analyses in Glioma Stem-Like Cultures three hours after they were exposed to clinically relevant radiation doses. Radiation reshaped the cellular landscape, altering both cell type composition and GBM subtype distribution. Cluster-specific and shared transcriptional programs were induced in a dose-dependent manner, with differentially expressed genes enriching distinct biological pathways. Chromatin accessibility analyses revealed parallel cluster-specific remodeling, with both opening and closing of regulatory elements linked to functional pathway shifts. Notably, 2 Gy and 6 Gy exposures elicited conserved transcriptional profiles in RNA clusters across different radiation doses. Together, these results highlight immediate radiation-induced transcriptional and chromatin remodeling programs in GBM at single-cell resolution and identify conserved cluster-specific adaptations that may underlie therapeutic resistance.

cancer biology↗

bioRxiv2024

Investigating Brain and Biological Development in Children and Their Relationship with Physical, Mental, and Academic Outcomes

Brain age and biological age, estimated using machine learning models with brain imaging and biological features, have emerged as promising biomarkers for predicting a broad range of health outcomes in adults. However, very few studies have examined the counterpart of brain age and biological age in children, that is Brain Development Index (BRDI) and Biological Development Index (BIDI). Existing studies on BRDI and BIDI are largely cross-sectional and do not provide adequate information on their temporal trajectory and predictive power for future health outcomes in children. Additionally, the interconnectedness of BRDI and BIDI across multiple health domains, especially child-specific developmental outcomes, remains underexplored. Our study utilized brain imaging features and blood-based biomarkers from the Adolescent Brain and Child Development (ABCD) study to assess the trajectory of BRDI and BIDI over multiple time points. We examined their relationships with physical, mental, and academic health outcomes. Lastly, we utilize Bayesian network analysis to examine the relationship between the two indexes and their subcomponents. We found that delayed BRDI and BIDI were significantly associated with adverse future health outcomes across several domains. In addition, Bayesian network analysis revealed BRDI and BIDI subcomponents influence one another across different organ systems. Additionally, males exhibited more advanced BRDI, while females showed more advanced BIDI, revealing important sex differences in adolescent development. This research provides the first comprehensive analysis of BRDI and BIDI trajectories, revealing their predictive power for future health outcomes and offering new insights into the interconnected development of brain and biological systems in children.

developmental biology↗

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↗

bioRxiv2022

Significance Analysis for Clustering with Single-Cell RNA-Sequencing Data

AO_SCPLOWBSTRACTC_SCPLOWUnsupervised clustering of single-cell RNA-sequencing data enables the identification and discovery of distinct cell populations. However, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty. Many popular pipelines use clustering stability methods to assess the algorithms output and decide on the number of clusters. However, we find that by not addressing known sources of variability in a statistically rigorous manner, these analyses lead to overconfidence in the discovery of novel cell-types. We extend a previous method for Gaussian data, Significance of Hierarchical Clustering (SHC), to propose a model-based hypothesis testing approach that incorporates significance analysis into the clustering algorithm and permits statistical evaluation of clusters as distinct cell populations. We also adapt this approach to permit statistical assessment on the clusters reported by any algorithm. We benchmarked our approach on real-world datasets against popular clustering workflows, demonstrating improved performance. To show its practical utility, we applied it to the Human Lung Cell Atlas and an atlas of the mouse cerebellar cortex. We identified several cases of over-clustering, leading to false discoveries, as well as under-clustering, resulting in the failure to identify new subpopulations that our method was able to detect.

bioinformatics↗

bioRxiv2021

A guide to trajectory inference and RNA velocity

Technological developments have led to an explosion of high-throughput single cell data, which are revealing unprecedented perspectives on cell identity. Recently, significant attention has focused on investigating, from single-cell RNA-sequencing (scRNA-seq) data, cellular dynamic processes, such as cell differentiation, cell cycle and cell (de)activation. Trajectory inference methods estimate a trajectory, a collection of differentiation paths of a dynamic system, by ordering cells along the paths of such a dynamic process. While trajectory inference tools typically work with gene expression levels, common scRNA-seq protocols allow the identification and quantification of unspliced pre-mRNAs and mature spliced mRNAs, for each gene. By exploiting the abundance of unspliced and spliced mRNA, one can infer the RNA velocity of individual cells, i.e., the time derivative of the gene expression state of cells. Whereas traditional trajectory inference methods reconstruct cellular dynamics given a population of cells of varying maturity, RNA velocity relies on a dynamical model describing splicing dynamics. Here, we initially discuss conceptual and theoretical aspects of both approaches, then illustrate how they can be combined together, and finally present an example use-case on real data.

bioinformatics↗

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

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↗

bioRxiv2019

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as the cell cycle, cell type differentiation, and cellular activation. Downstream of trajectory inference, it is vital to discover genes that are associated with the lineages in the trajectory to illuminate the underlying biological processes. Furthermore, genes that are differentially expressed between developmental/activational lineages might be highly relevant to further unravel the system under study. Current data analysis procedures, however, typically cluster cells and assess differential expression between the clusters, which fails to exploit the continuous resolution provided by trajectory inference to its full potential. The few available non-cluster-based methods only assess broad differences in gene expression between lineages, hence failing to pinpoint the exact types of divergence. We introduce a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of (i) within-lineage differential expression by detecting associations between gene expression and pseudotime over an entire lineage or by comparing gene expression between points/regions within the lineage and (ii) between-lineage differential expression by comparing gene expression between lineages over the entire lineages or at specific points/regions. By incorporating observation-level weights, the model additionally allows to account for zero inflation, commonly observed in single-cell RNA-seq data from full-length protocols. We evaluate the method on simulated and real datasets from droplet-based and full-length protocols, and show that the flexible inference framework is capable of yielding biological insights through a clear interpretation of the data.

bioinformatics↗

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↗