Biology subjects

Adey, A.

Publications and source records attributed to Adey, A..

10 recordsLinked to original sources

bioRxiv2025

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal

Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring. We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. This new functionality includes compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems.

bioinformatics↗

bioRxiv2025

Epigenetic and Transcriptomic Alterations Precede Amyloidosis in the Hippocampus of the Alzheimer's Disease AppNL-G-F Knock in Mouse Model

Detecting and understanding the early stages of Alzheimers disease (AD) is essential for uncovering initial mechanisms of neuropathology and devising effective interventions. In this study, we leveraged the humanized AppNL-G-F mouse which exhibits early-onset amyloid pathology with a predictable timeline, to investigate molecular changes in the hippocampus and blood before the onset of severe neuropathology and independent of aging. Employing a multi-omics approach, we identified alterations in chromatin accessibility, gene expression, and DNA methylation associated with early amyloidosis. Chromatin accessibility changes were prominent in excitatory neurons during early pathology, with a later shift to inhibitory neurons, potentially reflecting compensatory mechanisms to mitigate excitatory neuron dysregulation. Despite broadly comparable hippocampal cell composition, transcriptomic comparisons between wild-type and AppNL-G-F mice revealed major gene expression differences, particularly in pathways related to mitochondrial function and protein biosynthesis, preceding severe amyloid plaque deposition. In later stages, upregulation of immune and neuroinflammatory pathways was observed, aligning with established neuroinflammatory processes in AD. Additionally, we identified extensive DNA methylation differences in both the blood and hippocampus of AppNL-G-Fmice during early and late stages of pathology. Many differentially methylated regions in the blood, even at early pathology stages, were associated with cis-regulatory elements in the brain and were located near differentially expressed genes in the hippocampus. These regions were enriched in pathways associated with brain function, including neuron development and synaptic processes, highlighting a connection between blood methylation patterns and brain activity. This finding suggests the potential use of blood DNA methylation as a biomarker for the early detection of amyloidosis. Notably, we identified five candidate biomarker genes, including Rbfox1 and Camta1, with epigenetic dysregulation detectable in both the brain and blood prior to severe amyloid accumulation. Our study, leveraging a unique AD mouse model and a multi-omics approach, highlights epigenetic signatures of AD before the onset of clinical symptoms, providing a foundation for future research into early diagnosis and therapeutic strategies, as well as potential blood biomarkers.

molecular biology↗

bioRxiv2024

Atlas-scale Single-cell DNA Methylation Profiling with sciMETv3

Single-cell methods to assess DNA methylation have not yet achieved the same level of cell throughput compared to other modalities. Here, we describe sciMETv3, a combinatorial indexing-based technique that builds on our prior technology, sciMETv2. SciMETv3 achieves nearly a 100-fold improvement in cell throughput by increasing the index space while simultaneously reducing hands-on time and total costs per experiment. To reduce the sequencing burden of the assay, we demonstrate compatibility of sciMETv3 with capture techniques that enrich for regulatory regions, as well as the ability to leverage enzymatic conversion which can yield higher library diversity. We showcase the throughput of sciMETv3 by producing a >140k cell library from human middle frontal gyrus split across four multiplexed individuals using both Illumina and Ultima sequencing instrumentation. This library was prepared over two days by one individual and required no expensive equipment (e.g. a flow sorter, as required by sciMETv2). The same experiment produced an estimated 650k additional cells that were not sequenced, representing the power of sciMETv3 to meet the throughput needs of the most demanding atlas-scale projects. Finally, we demonstrate the compatibility of sciMETv3 with multimodal assays by introducing sciMET+ATAC, which will enable high- throughput exploration of the interplay between two layers of epigenetic regulation within the same cell, as well as the ability to directly integrate single-cell methylation datasets with existing single-cell ATAC-seq. HighlightsO_LIAtlas-scale production of single-cell DNA methylation libraries in a single experiment C_LIO_LIProtocols and evaluation using both Illumina and Ultima Genomics sequencing platforms C_LIO_LICompatibility of sciMETv3 with capture techniques to reduce sequencing burden C_LIO_LICompatibility of sciMETv3 with enzymatic conversion methods C_LIO_LIGeneration of an integrated >140,000 cell dataset from human middle frontal gyrus across four individuals C_LIO_LIAbility to profile both ATAC and genome-wide DNA methylation from the same cells and integration with datasets from each modality C_LIO_LIA novel implementation of the s3-ATAC technology that leverages a nanowell chip for increased throughput C_LI MotivationDNA methylation forms a basal layer of epigenomic regulatory control, shaping the genomic permissiveness of mammalian cells during lineage specification and development. Aberrant DNA methylation has been associated with myriad health conditions ranging from developmental disorders to cancer. The high cell type specificity necessitates analysis at the single-cell level, much like transcription or other epigenomic properties. However, robust and cost-effective techniques to produce atlas-scale datasets have not been realized for DNA methylation. Here, we directly meet this need by introducing sciMETv3, a high-throughput protocol capable of producing hundreds of thousands of single-cell DNA methylation profiles in a single experiment.

genetics↗

bioRxiv2023

Identification of Cellular Interactions in the Tumor Immune Microenvironment Underlying CD8 T Cell Exhaustion

While immune checkpoint inhibitors show success in treating a subset of patients with certain late-stage cancers, these treatments fail in many other patients as a result of mechanisms that have yet to be fully characterized. The process of CD8 T cell exhaustion, by which T cells become dysfunctional in response to prolonged antigen exposure, has been implicated in immunotherapy resistance. Single-cell RNA sequencing (scRNA-seq) produces an abundance of data to analyze this process; however, due to the complexity of the process, contributions of other cell types to a process within a single cell type cannot be simply inferred. We constructed an analysis framework to first rank human skin tumor samples by degree of exhaustion in tumor-infiltrating CD8 T cells and then identify immune cell type-specific gene-regulatory network patterns significantly associated with T cell exhaustion. Using this framework, we further analyzed scRNA-seq data from human tumor and chronic viral infection samples to compare the T cell exhaustion process between these two contexts. In doing so, we identified transcription factor activity in the macrophages of both tissue types associated with this process. Our framework can be applied beyond the tumor immune microenvironment to any system involving cell-cell communication, facilitating insights into key biological processes that underpin the effective treatment of cancer and other complicated diseases.

bioinformatics↗

bioRxiv2023

Multiplex imaging of localized prostate tumors reveals changes in mast cell type composition and spatial organization of AR-positive cells in the tumor microenvironment

Mapping spatial interactions of cancer, immune and stromal cells present novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate tumors, there is currently no understanding of how immune cell heterogeneity impacts spatial coordination between tumor and stromal cells in localized tumors. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of 699,461 single-cells that show epigenetic and molecular differences in distinct clinical grades. We report unique populations of mast cells that differentially express CD44, CD90 and Granzyme B (GZMB) and demonstrate GZMB+ mast cells are spatially associated with M2 macrophages in prostate tumors. Finally, we uncover recurrent neighborhoods that are primarily driven by androgen receptor positive (AR+) stromal cells and identify transcriptional networks active in AR+ prostate stroma.

cancer biology↗

bioRxiv2021

Single-cell analysis of localized low- and high-grade prostate cancers

Approximately, 30% of early-stage localized prostate cancer cases reoccur within 5 to 10 years [1, 2]. However, identifying precise molecular subtypes attributable to specific stages of prostate cancer has proven difficult due to high heterogeneity within localized tumors [3-5]. Bulk assays represent a population average, which is a result of the heterogeneity that exists at the individual prostate cancer cell level [6]. Here, we sequenced the accessible chromatin regions of 14,424 single-cells collected from 18 fresh-frozen prostate tumors using sci-ATAC-seq [7, 8]. We observed that shared chromatin features among low-grade prostate cancer epithelial cells were lost in high-grade tumors. Despite this loss, all high-grade tumors exhibited an enrichment for FOXA1, HOXB13 and CDX2 transcription factor binding sites within their accessible chromatin regions, indicating a shared trans-regulatory program. Single-cell analysis of the differentially accessible regions in high- versus low-grade prostate tumors identified two unique genes encoding neuronal adhesion molecules, NRXN1 and NLGN1. We found that NRXN1 and NLGN1 are expressed in the epithelial luminal, basal and neuroendocrine cells, as well as the immune, endothelial and neuronal cell types in all prostate tumors. Overall, these results provide a deeper understanding of the active gene regulatory networks in low- and high-grade prostate tumors at a striking resolution and provide critical insights for molecular stratification of the disease.

cancer biology↗

bioRxiv2020

Phenotype-guided subpopulation identification from single-cell sequencing data

Single-cell sequencing yields novel discoveries by distinguishing cell types, states and lineages within the context of heterogeneous tissues. However, interpreting complex single-cell data from highly heterogeneous cell populations remains challenging. Currently, most existing single-cell data analyses focus on cell type clusters defined by unsupervised clustering methods, which cannot directly link cell clusters with specific biological and clinical phenotypes. Here we present Scissor, a novel approach that utilizes disease phenotypes to identify cell subpopulations from single-cell data that most highly correlate with a given phenotype. This "phenotype-to-cell within a single step" strategy enables the utilization of a large amount of clinical information that has been collected for bulk assays to identify the most highly phenotype-associated cell subpopulations. When applied to a lung cancer single-cell RNA-seq (scRNA-seq) dataset, Scissor identified a subset of cells exhibiting high hypoxia activities, which predicted worse survival outcomes in lung cancer patients. Furthermore, in a melanoma scRNA-seq dataset, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expressions, which is associated with a favorable immunotherapy response. Thus, Scissor provides a novel framework to identify the biologically and clinically relevant cell subpopulations from single-cell assays by leveraging the wealth of phenotypes and bulk-omics datasets.

bioinformatics↗

bioRxiv2018

Chromosome Removal Via Cellular Fragmentation and Aneuploid Blastomere Exclusion in Primate Embryos

Aneuploidy that arises during meiosis and/or mitosis is a major contributor to early embryo loss. We previously demonstrated that human preimplantation embryos encapsulate mis-segregated chromosomes into micronuclei while undergoing cellular fragmentation and that fragments can contain chromosomal material, but the source of this DNA was unknown. Here, we leveraged the use of a non-human primate model and single-cell DNA-sequencing (scDNA-seq) to examine the chromosomal content of 471 individual samples comprising 254 blastomeres, 42 polar bodies, and 175 cellular fragments from a large number (N=50) of disassembled rhesus cleavage-stage embryos. Our analysis revealed that the frequency of aneuploidy and micronucleation is conserved between humans and macaques and that cellular fragments encapsulate whole and/or partial chromosomes lost from blastomeres. Single-cell/fragment genotyping demonstrated that these chromosome-containing cellular fragments (CCFs) can be either maternal or paternal in origin and display DNA damage via double-stranded breaks. Chromosome breakage and abnormal cytokinesis resulted in reciprocal losses/gains at the terminal ends of chromosome arms, uniparental genome segregation, and mixoploidy between blastomeres. Combining time-lapse imaging with scDNA-seq, we also determined that multipolar divisions at the zygote or 2-cell stage generated chaotic aneuploidy encompassing a complex mixture of maternal and paternal chromosomes. Despite frequent chromosomal mis-segregation at the cleavage-stage, we show that CCFs and non-dividing aneuploid blastomeres exhibiting extensive DNA damage are prevented from incorporation at the blastocyst stage. These findings suggest that embryos respond to chromosomal errors by encapsulation into micronuclei, elimination by cellular fragmentation, and selection against highly aneuploid blastomeres to overcome chromosome instability during preimplantation development.

developmental biology↗

bioRxiv2017

Chromatin accessibility dynamics of myogenesis at single cell resolution

Over a million DNA regulatory elements have been cataloged in the human genome, but linking these elements to the genes that they regulate remains challenging. We introduce Cicero, a statistical method that connects regulatory elements to target genes using single cell chromatin accessibility data. We apply Cicero to investigate how thousands of dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of co-accessible regulatory elements linked by Cicero meet criteria of \"chromatin hubs\", in that they are physically proximal, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of gene expression. Pseudotemporal analysis revealed a subset of elements bound by MYOD in myoblasts that exhibit early opening, potentially serving as the initial sites of recruitment of chromatin remodeling and histone-modifying enzymes. The methodological framework described here constitutes a powerful new approach for elucidating the architecture, grammar and mechanisms of cis-regulation on a genome-wide basis.

genomics↗

bioRxiv2017

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Conventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold \"shotgun cellular coverage\" of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

genomics↗