Biology subjects

Stephenson, W.

Publications and source records attributed to Stephenson, W..

10 recordsLinked to original sources

bioRxiv2026

Direct RNA Sequencing reveals epitranscriptomic regulation of brain cells and Alzheimer's Disease pathology

Alternative mRNA splicing and post-transcriptional RNA modification are key mechanisms that regulate transcript function; however, their role in neuronal activity and neurodegenerative disease remains poorly defined. In this study, we evaluated two nanopore-based long-read sequencing (LR-seq) formats: cDNA-PCR sequencing (CPS) and direct RNA sequencing (DRS). We then applied DRS to profile both full-length isoforms and RNA modifications in major brain cell types derived from induced pluripotent stem cells (iPSCs) and post-mortem Alzheimers disease (AD) brains. Relative to CPS, DRS achieved higher accuracy and sensitivity for transcript quantification, de novo transcript model construction, and open reading frame (ORF) annotation across neuropathological gene sets. Focusing on iPSC-derived neurons, we built a multi-omic atlas to connect transcriptional output with translational engagement and protein abundance, by integrating DRS-based mRNA abundance, N6-methyladenosine (m6A) status and poly(A) tail length with ribosome profiling (Ribo-seq) and mass spectrometry (MS). The combination of DRS and Ribo-seq data demonstrated synergism in predicting protein abundance. This analysis also uncovered a significant inverse relationship between m6A modification and mRNA abundance, which was dependent on the engagement of the ribosomal A-site. Lastly, we applied DRS to the epitranscriptomic analysis of AD brain samples, demonstrating that m6A profiles can be used to distinguish early-versus late-stage disease.

neuroscience↗

bioRxiv2024

Fibroblastic FLT3L supports lymph node dendritic cells in the interfollicular niche

Dendritic cell (DC) homeostasis is maintained in secondary lymphoid organs (SLOs) by Fms-like tyrosine kinase 3 ligand (FLT3L). The specific niche providing this DC growth factor within human and mouse SLOs is unclear. Here, we show that Gremlin1 (Grem1)-expressing lymph node fibroblastic reticular cells (FRCs) support DC homeostasis via provision of FLT3L. Grem1 FRCs co-localize with DCs and express FLT3L in human and mouse lymph nodes. Using a new genetic model, we provide evidence that FLT3L produced by GREM1 FRCs maintains lymph node preDCs, cDCs, and plasmacytoid DCs (pDCs). Spatial transcriptomics and cytofluorometry reveal that Grem1 FRC-derived FLT3L supports not only proliferation, but also survival of lymph node cDCs within the interfollicular zone (IFZ). Functionally, loss of Grem1 FRC-derived FLT3L impairs cDC priming of antigen-specific T cell responses. These findings provide key mechanistic insights underlying stromal cell support of DC homeostasis and function.

immunology↗

bioRxiv2023

Accurate long-read transcript discovery and quantification at single-cell resolution with Isosceles

Accurate detection and quantification of mRNA isoforms from nanopore long-read sequencing remains challenged by technical noise, particularly in single cells. To address this, we introduce Isosceles, a computational toolkit that outperforms other methods in isoform detection sensitivity and quantification accuracy across single-cell, pseudo-bulk and bulk resolution levels, as demonstrated using synthetic and biologically-derived datasets. Isosceles improves the fidelity of single-cell transcriptome quantification at the isoform-level, and enables flexible downstream analysis. As a case study, we apply Isosceles, uncovering coordinated splicing within and between neuronal differentiation lineages. Isosceles is suitable to be applied in diverse biological systems, facilitating studies of cellular heterogeneity across biomedical research applications.

bioinformatics↗

bioRxiv2023

Transcriptional dysregulation and impaired neuronal activity in FMR1 knock-out and Fragile X patients iPSC-derived models.

The lack of fragile X mental retardation protein (FMRP) protein, due to a repression of the FMR1 gene, causes Fragile X syndrome (FXS), one of the most prevalent forms of syndromic autisms. The FMR1 gene codes for an RNA binding protein involved in the regulation of gene expression through RNA processing, control of local translation, and protein-protein interactions; processes that are crucial for proper brain development. Taking advantage of induced pluripotent stem cells (iPSCs) and CRISPR-Cas9 genome editing technologies, we generated iPSC-derived cortical neural progenitors and cortical neurons from an FMR1 knock-out and patient cell line with the aim of identifying common phenotypes between the two cellular models. Using RNA sequencing, quantitative PCR and multielectrode array approaches, we assessed how the absence of the functional FMR1 gene affects the transcriptional profiles and the activities of iPSC-derived cortical neuronal progenitor cells (NPCs) and neurons with both models. We observed that FMR1 KO and FXS patient cells have a decrease in their mean firing rate; a cellular activity that can also be blocked by tetrodotoxin (TTX) application in wild-type active neurons. Relative to wild-type neurons, in FMR1 KO neurons, increased expression of presynaptic mRNA and transcription factors involved in the forebrain specification and decreased levels of mRNA coding AMPA and NMDA subunits were observed. Intriguingly, 40% of the differentially expressed genes were commonly deregulated between NPCs and differentiating neurons with significant enrichments in FMRP targets and Autism Related Genes found amongst downregulated genes. This implies that an absence of functional FMRP affects transcriptional profiles at the NPC stage, resulting in impaired activity and differentiation of the progenitors into mature neurons over time. These findings from the FMR1 KO lines were also shared with FXS patients iPSC-derived cells that also present with an impairment in activity and neuronal differentiation, illustrating the critical role of FMRP protein in neuronal development.

neuroscience↗

bioRxiv2023

Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we developed Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of unique transcripts per cell by 29-fold. We used scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we revealed associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. In addition, phasing of SNVs across transcripts facilitated measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.

genomics↗

bioRxiv2021

isoformant: A visual toolkit for reference-free long-read isoform analysis at single-read resolution

isoformant is an analytical toolkit for isoform characterization of Oxford Nanopore Technologies (ONT) long-transcript sequencing data (i.e. direct RNA and cDNA). Deployment of these tools using Jupyter Notebook enables interactive analysis of user-defined region-of-interest (ROI), typically a gene. The core module of isoformant clusters sequencing reads by k-mer density to generate isoform consensus sequences without the requirement for a reference genome or prior annotations. The inclusion of differential isoform usage hypothesis testing based on read distribution among clusters enables comparison across multiple samples. Here, as proof-of-principle, we demonstrate the utility of isoformant for analyzing isoform diversity of commercially-available isoform standard mixtures. isoformant is available here: https://github.com/danledinh/isoformant.

bioinformatics↗

bioRxiv2021

PySeq2500: An open source toolkit for repurposing HiSeq 2500 sequencing systems as versatile fluidics and imaging platforms.

Fluorescence microscopy is a key method in the life sciences. State of the art -omics methods combine fluorescence microscopy with complex protocols to visualize tens to thousands of features in each of millions of pixels across samples. These -omics methods require precise control of temperature, reagent application, and image acquisition parameters during iterative chemistry and imaging cycles conducted over the course of days or weeks. Automated execution of such methods enables robust and reproducible data generation. However, few commercial solutions exist for temperature controlled, fluidics coupled fluorescence imaging, and implementation of bespoke instrumentation requires specialized engineering expertise. Here we present PySeq2500, an open source Python code base and flow cell design that converts the Illumina HiSeq 2500 instrument into an open platform for programmable applications. Customizable PySeq2500 protocols enable experimental designs involving simultaneous 4-channel image acquisition, temperature control, reagent exchange, stable positioning, and sample integrity over extended experiments. To demonstrate accessible automation of complex, multi-day workflows, we use the PySeq2500 system for unattended execution of iterative indirect immunofluorescence imaging (4i). Our automated 4i method uses off-the-shelf antibodies over multiple cycles of staining, imaging, and antibody elution to build highly multiplexed maps of cell types and pathological features in mouse and postmortem human spinal cord sections. As demonstrated here, PySeq2500 enables non-specialists to develop and implement state of the art fluidics coupled imaging methods in a widely available benchtop system.

genomics↗

bioRxiv2020

Direct detection of RNA modifications and structure using single molecule nanopore sequencing

Many methods exist to detect RNA modifications by short-read sequencing, relying on either antibody enrichment of transcripts bearing modified bases or mutational profiling approaches which require conversion to cDNA. Endogenous modifications are present on several major classes of RNA including tRNA, rRNA and mRNA and can modulate diverse biological processes such as genetic recoding, mRNA export and RNA folding. In addition, exogenous modifications can be introduced to RNA molecules to reveal RNA structure and dynamics. Limitations on read length and library size inherent in short-read-based methods dissociate modifications from their native context, preventing single molecule analysis and modification phasing. Here we demonstrate direct RNA nanopore sequencing to detect endogenous and exogenous RNA modifications over long sequence distance at the single molecule level. We demonstrate comprehensive detection of endogenous modifications in E. coli and S. cerevisiae ribosomal RNA (rRNA) using current signal deviations. Notably 2-O-methyl (Nm) modifications generated a discernible shift in current signal and event level dwell times. We show that dwell times are mediated by the RNA motor protein which sits atop the nanopore. Further, we characterize a recently described small adduct-generating 2-O-acylation reagent, acetylimidazole (AcIm) for exogenously labeling flexible nucleotides in RNA. Finally, we demonstrate the utility of AcIm for single molecule RNA structural probing using nanopore sequencing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/126763v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1d13067org.highwire.dtl.DTLVardef@c0d0aaorg.highwire.dtl.DTLVardef@c362eeorg.highwire.dtl.DTLVardef@193ffdb_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

genomics↗

bioRxiv2017

Single-Cell RNA-Seq Of Rheumatoid Arthritis Synovial Tissue Using Low Cost Microfluidic Instrumentation

Droplet-based single cell RNA-seq has emerged as a powerful technique for massively parallel cellular profiling. While these approaches offer the exciting promise to deconvolute cellular heterogeneity in diseased tissues, the lack of cost-effective, reliable, and user-friendly instrumentation has hindered widespread adoption of droplet microfluidic techniques. To address this, we have developed a microfluidic control instrument that can be easily assembled from 3D printed parts and commercially available components costing approximately $540. We adapted this instrument for massively parallel scRNA-seq and deployed it in a clinical environment to perform single cell transcriptome profiling of disaggregated synovial tissue from a rheumatoid arthritis patient. We sequenced 8,716 single cells from a synovectomy, revealing 16 transcriptomically distinct clusters. These encompass a comprehensive and unbiased characterization of the autoimmune infiltrate, including inflammatory T and NK subsets that contribute to disease biology. Additionally, we identified fibroblast subpopulations that are demarcated via THY1 (CD90) and CD55 expression. Further experiments confirm that these represent synovial fibroblasts residing within the synovial intimal lining and subintimal lining, respectively, each under the influence of differing microenvironments. We envision that this instrument will have broad utility in basic and clinical settings, enabling low-cost and routine application of microfluidic techniques, and in particular single-cell transcriptome profiling.

genomics↗

bioRxiv2017

Large-scale simultaneous measurement of epitopes and transcriptomes in single cells.

Recent high-throughput single-cell sequencing approaches have been transformative for understanding complex cell populations, but are unable to provide additional phenotypic information, such as protein levels of cell-surface markers. Using oligonucleotide-labeled antibodies, we integrate measurements of cellular proteins and transcriptomes into an efficient, sequencing-based readout of single cells. This method is compatible with existing single-cell sequencing approaches and will readily scale as the throughput of these methods increase.

genomics↗