Biology subjects

Bacher, R.

Publications and source records attributed to Bacher, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Exposure Duration Shapes the Hepatic Response to GenX: Divergent Acute and Chronic Transcriptomic Profiles Reveal Non-Monotonic Dose Effects and Increased Sensitivity in Human Liver Spheroids

Hexafluoropropylene oxide dimer acid (GenX), a replacement for legacy per- and polyfluoroalkyl substances (PFAS), is increasingly detected in the environment, yet its chronic toxicity remains poorly characterized. Current safety assessments rely largely on short-term, high-dose studies that may not capture the biological consequences of long-term, low-dose exposure. To address this gap, we employed 3D human liver (HepG2/C3A) spheroids cultured in a continuously rotating bioreactor system (ClinoStar) to systematically evaluate dose- and time-dependent mRNA changes in response to GenX under environmentally relevant conditions. Spheroids were exposed to GenX (0.08-50 M, spanning environmentally relevant to mechanistically informative concentrations) for acute (4 days) and chronic (4 weeks) durations, followed by genome-wide TempO-Seq transcriptomic profiling and benchmark dose (BMD) modeling. GenX elicited pronounced non-monotonic mRNA changes in acute exposure conditions, with the greatest number of differentially expressed genes (DEGs) observed at an intermediate concentration (0.4 M). In contrast, chronic exposure exhibited a generally concentration-dependent increase in DEGs, except for the 10 M condition, indicating a more consistent dose-response relationship than acute exposure. Notably, acute and chronic exposures elicited qualitatively distinct mRNA changes with low concordance across matched concentrations, demonstrating that exposure duration was a major determinant of mRNA changes. Acute low-dose GenX exposure preferentially modulated mRNA encoding components of cell cycle-related pathways, whereas acute higher dose exposures suppress mRNA levels of the constituents of lipid metabolic pathways and increase expression of mRNA encoding proteins involved in stress- and toxicity-associated signaling. Chronic exposure revealed a different pattern of changes in mRNA expression not observed under acute exposure conditions, including suppression of cellular components involved in lipid-related pathways at the lowest concentration tested. At higher concentrations, mRNA levels of components of multiple metabolic pathways were altered. Benchmark dose modeling identified a significantly lower transcriptomic point of departure (tPOD) for chronic exposure as compared to acute exposure, suggesting increased cellular sensitivity to prolonged GenX exposure and supporting the relevance of chronic models for human exposure assessment. Collectively, these findings demonstrate that GenX elicits time-dependent and non-monotonic changes in mRNA levels of human liver (HepG2/C3A) spheroids, with distinct responses depending on the exposure duration and dose. This study, therefore, highlights the importance of incorporating chronic, human-relevant in vitro models and transcriptomic endpoints into PFAS risk assessment and suggests that conventional short-term assays may underestimate the biological impact of sustained low-dose exposure. Key message (Impact of the study)This study provides systematic comparisons of short term (4 day) versus longer term (4 weeks), environmentally relevant GenX exposure in human liver spheroids, revealing non-monotonic, time-dependent changes in mRNA levels encoding cellular components of lipid metabolism-related pathways with potential implications for appropriate dose and time exposure parameters for use in New Approach Methods to be applied in risk assessment.

pharmacology and toxicology↗

bioRxiv2026

LLM-powered Functional Gene Set Summarization with genesetGPT

Transcriptomics datasets generated using next-generation sequencing techniques such as single cell RNA-sequencing (scRNA-seq) and spatially-resolved transcriptomics (SRT) allow researchers to study patterns in gene expression across celltypes, temporal processes, and spatial organization at ever-higher resolutions and depths. scRNA-seq analyses produce gene expression profiles and celltype-specific gene sets that require annotation to provide biological meaning, a process that has traditionally relied on the manual interpretations of clinical scientists. Similarly, SRT experiments typically require subjective, time-consuming annotation of spatial domains. Recent advances in large language model (LLM) methods offer opportunities to assist in the interpretation of such datasets. Many current LLM-based approaches aim to annotate transcriptomics-derived gene sets by integrating information from publicly available and online biological resources. While these approaches can be effective, they often struggle when presented with weakly-related or fully uncorrelated genes, sometimes inferring and justifying biological relationships that are not supported by existing literature. Additionally, the quality of LLM-generated interpretations is dependent on the provision of appropriate biological context and careful prompt design, both of which can present significant barriers to effective use. To address these limitations we propose genesetGPT, an efficient, LLM-based framework that emphasizes both curated biological context and iterative prompt construction, thus enabling realistic summarization of heterogeneous gene sets at scale. genesetGPT is implemented as an open-source Python package available for download at https://github.com/jr-leary7/genesetGPT.

genomics↗

bioRxiv2026

CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data

Copy number variations (CNVs) are major structural genomic variants that contribute to a wide range of human diseases. Accurate detection of CNVs from whole-exome sequencing (WES) data has been a long-sought goal for clinical and population genetic studies. Despite recent progress, existing WES-based CNV callers still suffer from high false-positive rates and reduced recall for short-length variants, and current deep learning methods have not fully used complementary information in region-level genomic features. Here we present CN-RNN, a deep learning-based CNV caller for WES data. The model combines a bidirectional long short-term memory (BiLSTM) branch that captures local depth changes and contextual dependencies across neighboring exons with a parallel multi-layer perceptron (MLP) branch that encodes region-level metadata such as GC content, mappability, and exon length. CN-RNN was trained on the Autism Sequencing Consortium (ASC) parent-child trio cohort using the Mendelian rule of inheritance to ensure high-quality training sets. It was evaluated across three independent datasets, in which we showed that CN-RNN outperformed existing WES-based CNV callers and deep learning methods. CN-RNN offers a scalable, accurate tool for CNV profiling in WES-based studies and supports broader application of CNV analysis in population and clinical research. CN-RNN is available at https://github.com/FeifeiXiao-lab/CN-RNN.

genetics↗

bioRxiv2026

simCRISPR: Modeling experimental complexity in pooled CRISPR screens

Pooled CRISPR screens are widely used to investigate gene function and uncover genetic interactions. However, benchmarking computational methods for detecting gene-by-environment (GxE) interactions remains difficult because ground truth is rarely available and existing simulation tools are not designed for GxE screening contexts. To address this, we developed simCRISPR, a flexible simulation framework for generating pooled CRISPR screen data under complex experimental designs. Using simulated datasets informed by empirical CRISPR screen designs, we evaluated commonly used analysis methods, comparing normalization strategies based on safe-harbor versus non-targeting sgRNAs and assessing empirical log2FC thresholds as an additional effect-size criterion. We found that safe-harbor-based normalization improved interaction detection when DNA damage-related effects were present, particularly when combined with empirical log2FC thresholding for DESeq2. Application of this workflow to a doxorubicin GxE screen further showed that safe-harbor-based normalization reduced bias in log2FC distributions and identified additional biologically relevant candidates. simCRISPR is available at https://github.com/bachergroup/simCRISPR.

bioinformatics↗

bioRxiv2026

scDEcrypter: Uncertainty-aware differential expression analysis for viral infection in scRNA-seq

Single-cell RNA-seq studies of viral infection are limited by sparse viral reads, under-labeled infected cells, and bystander responses that confound differential expression (DE) analysis. We introduce scDEcrypter, a penalized two-way mixture model that leverages partial labels for infection status and additional variables such as cell type. Our approach employs data-splitting to avoid double-dipping and enables fast, likelihood-based inference for DE analysis. Through simulations and applications on two different viral infection datasets, scDE-crypter demonstrated improved recovery of infected cell states and identified more biologically coherent infection-associated genes and enriched pathways.

genomics↗

bioRxiv2026

Divergent chromatin remodeling trajectories in CD66b⁺ MDSCs distinguishes recovery from chronic critical illness after sepsis

Sepsis survivors exhibit divergent clinical trajectories, including rapid recovery (RAP) or progression to chronic critical illness (CCI), yet how these outcomes are linked to epigenetic repression remains poorly defined. Here, we applied an optimized Omni-ATAC approach to profile chromatin accessibility in CD66b myeloid-derived suppressor cells (MDSCs) from healthy participants and longitudinally sampled sepsis cohorts stratified by outcome. RAP samples progressively restored healthy chromatin states, whereas CCI samples remained epigenetically fixed in aberrant configurations. Chromatin remodeling exhibited strong pathway specificity: promoters associated with MHC class II antigen presentation were coordinately repressed in CCI, while MHC class I antigen processing and presentation machinery remained preserved. Genome-wide analyses revealed extensive promoter remodeling during recovery in RAP, including immune regulatory loci such as ARG1, CD274, and S100A8/A9, contrasted with broad suppression of immune, metabolic, and chromatin regulatory programs in CCI. These findings define divergent epigenetic trajectories in post-sepsis MDSCs and implicate selective failure of antigen presentation as a mechanism of sepsis-induced immunoparalysis in CCI.

immunology↗

bioRxiv2025

Genome-wide CRISPR screens identified C18orf32 as a novel regulator of lipid metabolism that mediates PFOA-induced toxicity

Perfluorooctanoic acid (PFOA) remains a public health concern due to its persistence in the environment, despite global production bans. Epidemiological, animal, and in vitro studies have consistently linked PFOA exposure to hepatotoxicity characterized by lipid dysregulation; however, the molecular mechanisms underlying these adverse effects in humans remain poorly understood. To address this gap, we employed an integrated functional toxicogenomics framework combining CRISPR screening (for gene target identification), target validation, and assessment of target-derived cellular and molecular phenotypes. From genome-wide CRISPR screens in HepG2/C3A human liver cells, we identified 319 candidate genes (140 sensitive and 179 resistant) that modulate PFOA toxicity. Among these, C18orf32--encoding a lipid droplet (LD)-associated protein and the top resistant candidate--was selected for further mechanistic investigation based on its potential functional involvement in lipid metabolism. Targeted knockout (KO) of C18orf32 conferred marked cellular resistance to PFOA, accompanied by reduced LD and Triglyceride accumulation in cells under the exposure condition, suggesting a functional role of C18orf32p in lipid dysregulation. To elucidate molecular mechanisms underlying the cellular phenotypes, we conducted systematic transcriptomics profiling of wild-type (WT) and C18orf32 KO cells in the absence and presence of PFOA exposure. C18orf32 KO caused extensive gene expression reprogramming, with over one-third of coding genes differentially expressed. Notably, a range of lipid metabolism pathways including cholesterol metabolism, PPAR signaling, fatty acid metabolism, and peroxisome {beta}-oxidation--known mediators of PFOA-induced hepatotoxicity--were significantly downregulated in C18orf32 KO cells, contrasting to upregulation of these pathways in PFOA exposed WT cells. Collectively, our results identify C18orf32p as a previously unrecognized regulator of hepatic lipid metabolism and a key genetic determinant of PFOA-induced lipid dysregulation and hepatotoxicity in humans.

pharmacology and toxicology↗

bioRxiv2025

Identification of Functional Genetic Components Modulating Toxicity Response to PFOS using Genome-wide CRISPR Screens in HepG2/C3A cells

Perfluorooctane sulfonate (PFOS) poses significant health and environmental risks due to its persistence and widespread use and has been linked to various adverse outcomes, such as liver toxicity. Although the molecular responses and toxicity effects of PFOS exposure have been extensively studied, considerable uncertainty remains regarding the causal mechanisms leading to PFOS-associated adverse effects. To help bridge this gap, we conducted CRISPR screens in HepG2/C3A human liver cells exposed to IC25 (170 {micro}M) of PFOS to identify genes and pathways influencing PFOS-induced cytotoxicity. Using a genome-wide CRISPR knockout library targeting 18,819 genes, we identified 340 candidate genes that modulate PFOS-induced cytotoxicity when genetically disrupted (189 gene disruptions increased sensitivity and 151 gene disruptions increased resistance). From these candidate genes, we individually disrupted two candidate genes, SLC6A9 which encodes the glycine transporter GlyT1, and CPSF2, and confirmed increased resistance to PFOS exposure. Further, molecular docking analysis predicts that PFOS directly binds to GlyT1 and functional inhibition of GlyT1 also increases resistance to PFOS exposure. Gene-Disease outcome association analysis using the Comparative Toxicogenomics Database (CTD) indicated an enrichment of candidate genes associated with cancer-related and liver disease phenotypes. KEGG and STRING enrichment analyses found over representation of several biological pathways including DNA damage response and cell cycle. Lastly, cross-species conservation analysis using the top two validated gene targets found that their pathways were highly conserved in several environmentally relevant species. These findings provide new mechanistic and functional insights into PFOS-induced cytotoxicity, highlight potential molecular targets for toxicity mitigation, and establish a foundation for cross-species toxicogenomic modeling of PFOS health effects.

pharmacology and toxicology↗

bioRxiv2025

Characterizing common loss-of-function genes and their potential utility in assessing population variability and chemical susceptibility

Inter-individual and population variability in susceptibility to chemical exposures confounds determination of threshold exposure levels to protect the most vulnerable. Current risk assessment frameworks, in the absence of empiric chemical-specific data, generally recommend default or probabilistic adjustment factors to account for such variability. We present an experimental approach to incorporate common genetic variants potentially impacting population-level differences in toxicant susceptibility into human cell-based models for any cellular apical endpoint of interest. We focus on the genes with the most common aggregate loss-of-function (LoF) alleles in the gnomAD v3.0 data which we designated as the PopVarLoF set. Unexpectedly, enrichment analysis of these genes found significant overrepresentation of gene products playing important functional roles in toxicology. Interrogation of GWAS and PheWAS databases found that these genes are associated with diverse metabolic phenotypes consistent with the relevance of the PopVarLoF set in studying variability of toxicant response in human populations. We further characterized the PopVarLoF set by developing custom lentiviral CRISPR knockout libraries targeting the PopVarLoF genes to assess their functional essentiality in the HepG2/C3A cell line. Functional disruption of 14 of the PopVarLoF genes ([~]1 %) without toxicant exposure resulted in significant growth defects in this cell line, consistent with the majority of PopVarLoF gene products having non-essential roles. The development of human cell-based toxicity assays or other NAMs which include the empiric assessment of common genetic sources of population variability in susceptibility to chemical exposure could contribute to more robust risk assessment which protects vulnerable populations while reducing uncertainty. Impact statementWe characterize common loss of function genetic variants which could impact toxicant susceptibility and describe an approach to incorporate them into NAMs to enable empiric estimates of the contribution of genetic variability to diverse toxicity endpoints.

pharmacology and toxicology↗

bioRxiv2025

Physiologically Relevant 3D CRISPR Screening Enhances Mechanistic Insight into Chemical Toxicity Compared to 2D Screening

Many omics-based approaches in toxicology research primarily rely on correlative data, often lacking functional relationships or causal links between genotypes and phenotypes. CRISPR-based approaches can overcome this limitation by establishing direct causal connections between genes and toxicological phenotypes. Moreover, CRISPR screens enable scalable and systematic interrogation of gene function and associated mechanisms following chemical exposure, predominantly using in vitro models. In line with the paradigm of new approach methodologies (NAMs) in toxicology research, CRISPR screens hold promise to provide an in vitro cell-based functional toxicogenomics approach. One of the main limitations of conventional in vitro assays is their compromised physiological relevance to humans due to their inability to fully recapitulate in vivo physiology. To improve the functional and physiological relevance of the toxicogenomics approach, we developed a 3D CRISPR screening system using HepG2/C3A spheroids generated and cultivated in a continuously rotating bioreactor (ClinoStar). We first performed time-course 3D CRISPR screens to identify genes that confer growth disadvantage or advantage, influencing spheroid development compared to 2D cultures. We then applied this approach to a chemical toxicity study using doxorubicin, comparing the performance of the 3D and 2D systems in identifying chemical-specific mechanisms. The results showed that the 3D system captured more candidate genetic determinants and biological pathways related to DNA damage processes--a known toxicity mechanism of doxorubicin--demonstrating improved performance in identifying chemical-specific pathways over the 2D counterpart. In our screens, we employed custom CRISPR sgRNA libraries representing common human loss-of-function genetic variants (mean allele frequency > 0.1% in all individuals catalogued in the genome aggregation database), which potentially affect toxicity responses. By comparing our CRISPR screen results with previously reported genetic associations for doxorubicin response, we found that the 3D system identified more known associated genes than the 2D system. Together, the 3D CRISPR screening system demonstrated its feasibility and utility for physiologically relevant functional toxicogenomics. This platform enables in vitro NAMs, by providing a scalable and effective approach to identify causal genetic determinants and biological pathways that modulate chemical-induced toxicity.

pharmacology and toxicology↗

bioRxiv2025

Aberrant immune regulation and enrichment of stem-like CD8+ T cells in the pancreatic lymph node during type 1 diabetes development

Effector CD8+ T cells are key cellular drivers of type 1 diabetes (T1D) pathogenesis, yet questions remain regarding the molecular defects leading to altered cytotoxicity, their signature in peripheral tissues, and their receptor specificity. Thus, we analyzed human pancreatic lymph nodes (pLN) using mass cytometry and single cell RNA sequencing (scRNAseq) with combined proteomic and T cell receptor (TCR) profiling. Cytometric analysis revealed an enriched population of T stem-cell memory (TSCM)-like cells (CD8+CD45RA+CD27+CD28+CCR7+CXCR3+ T cells) in T1D pLNs. scRNAseq profiling indicated an elevated inflammatory cytokine gene signature (IFITM3, LTB) along with regulators of terminal differentiation (BCL6, BCL3), coupled with reduced expression of exhaustion-associated genes (DUSP2, NR4A2, TSC22D3) in CD8+ T cells in T1D pLN. Additionally, effector CD8+ T cells expressed features of progenitor exhausted cells (BCL2) in T1D pLN. Immune Response Enrichment Analysis (IREA) indicated IL-15 signaling as a significant driver of these phenotypes. Integrated TCR and transcriptomic analysis revealed a cluster of diverse naive-like CD8+ T cell clones in T1D pLN. When comparing pLN and pancreatic slice cellular isolates, we observed sharing of effector CD8+ T cells, with upregulation of terminal effector signatures detected within the pancreas relative to paired pLN samples. Multiplex imaging revealed differential localization of TCF1 and TOX expressing T cells in the pancreas, with TCF1+TOX+ cells located in closer proximity to the islets and displaying a mixture of activation and exhaustion-associated phenotypes. Thus, we provide multimodal cellular profiles enriched in T1D tissues for consideration in therapeutic targeting.

immunology↗

bioRxiv2023

Interpretable trajectory inference with single-cell Linear Adaptive Negative-binomial Expression (scLANE) testing

The rapid proliferation of trajectory inference methods for single-cell RNA-seq data has allowed researchers to investigate complex biological processes by examining underlying gene expression dynamics. After estimating a latent cell ordering, statistical models are used to determine which genes exhibit changes in expression that are significantly associated with progression through the biological trajectory. While a few techniques for performing trajectory differential expression exist, most rely on the flexibility of generalized additive models in order to account for the inherent nonlinearity of changes in gene expression. As such, the results can be difficult to interpret, and biological conclusions often rest on subjective visual inspections of the most dynamic genes. To address this challenge, we propose scLANE testing, which is built around an interpretable generalized linear model and handles nonlinearity with basis splines chosen empirically for each gene. In addition, extensions to estimating equations and mixed models allow for reliable trajectory testing under complex experimental designs. After validating the accuracy of scLANE under several different simulation scenarios, we apply it to a set of diverse biological datasets and display its ability to provide novel biological information when used downstream of both pseudotime and RNA velocity estimation methods. scLANE is freely available as an R package and is also accessible via a web server leveraging high performance computing resources at https://sclane.rc.ufl.edu/.

bioinformatics↗

bioRxiv2023

Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference

Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics, however researchers still encounter challenges in their analysis due to uncertainties in selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods performances are highly dataset-specific. To address these challenges, we developed Escort, a framework for evaluating a datasets suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort navigates single-cell trajectory analysis through data-driven assessments, reducing uncertainty and much of the decision burden associated with trajectory inference. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.

bioinformatics↗

bioRxiv2023

Multi-omics profiling reveals ethylene signalling as a key pathway underlying both genetic and epigenetic responses to low-dose ionizing radiation in Arabidopsis

There is increasing interest in the effects of low-dose ionizing radiation (IR) on plants as might occur during spaceflight, or as a consequence of human activities, such as nuclear power generation, that may result in the release of radioactive materials into the environment. High IR doses have long been used for the induction of mutations in plants with the goal of generating desirable traits for agribusiness. Less is known about the responses of plants to acute low doses of IR exposure. Here, we take a multi-omics approach to characterize the response to low dose IR in Arabidopsis thaliana. We adapt the Methyltransferase Accessibility Protocol for individual templates (MAPit) technique for use in plants allowing us to assay the epigenetic response to acute low-dose IR (10 cGy and 100 cGy) 72 hr after exposure, and, in parallel, use RNA sequencing to profile the transcription response at 1, 3, 24 and 72 hr after exposure. IR exposures as low as 10 cGy elicit robust genetic responses in A. thaliana detectable as early as 1 hr after exposure. Further examination revealed dose-dependent changes in gene expression, chromatin accessibility and DNA methylation that implicate the ethylene signalling pathway and abiotic stress response as underlying the transcriptional and epigenetic changes associated with IR. These changes are observable up to 72 hr post-exposure, suggesting that they are maintained well after the initial acute exposure. Our findings indicate that A. thaliana executes a multi-modal response to low-dose IR through induction and regulation of the ethylene response pathway.

genomics↗

bioRxiv2023

Human immune phenotyping reveals accelerated aging in type 1 diabetes

The composition of immune cells in peripheral blood is dramatically remodeled throughout the human lifespan, as environmental exposures shape the proportion and phenotype of cellular subsets. These dynamic shifts complicate efforts to identify disease-associated immune signatures in type 1 diabetes (T1D), which is variable in age of onset and rate of {beta}-cell decline. Herein, we conducted standardized flow cytometric immune profiling on peripheral blood from a cross-sectional cohort of T1D participants (n=240), their first-degree relatives (REL, n=310), those at increased risk with two or more islet autoantibodies (RSK, n=24), and autoantibody negative healthy controls (CTR, n=252). We constructed an immune-age predictive model in healthy subjects and developed an interactive data visualization portal (ImmScape; https://ufdiabetes.shinyapps.io/ImmScape/). When applied to the T1D cohort, this model revealed accelerated immune aging (p<0.001) as well as phenotypic signatures of disease after age correction. Of 192 investigated flow cytometry and complete blood count readouts, 46 were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with T1D after age-correction were predictive of T1D status (AUROC=82.3%). Phenotypes associated with accelerated aging in T1D included increased CXCR3+ and PD-1+ frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels (mean fluorescence intensity) on memory T cells. Additionally, quantitative trait locus analysis linked an increase in HLA-DR expression on monocytes with the T1D-associated HLA-DR4/DQ8 genotype, regardless of clinical group. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions. One Sentence SummaryPeripheral blood characterization reveals accelerated immune-age and age-adjusted proinflammatory immune phenotypes in type 1 diabetes.

immunology↗

bioRxiv2022

Flap-enabled next-generation capture (FENGC): precision targeted single-molecule profiling of epigenetic heterogeneity, chromatin dynamics, and genetic variation

Targeted sequencing is an increasingly sought technology. Available methods, however, are often costly and yield high proportions of off-target reads. Here, we present FENGC, a scalable, multiplexed method in which target sequences are assembled into 5' flaps for precise excision by flap endonuclease. Recovery of length-matched sequences, amplification with universal primers, and exonucleolytic removal of non-targeted genomic regions mitigate amplification biases and consistently yield [≥] 80% on-target sequencing. Furthermore, optimized sequential reagent addition and purifications minimize sample loss and facilitate rapid processing of sub-microgram quantities of DNA for detection of genetic variants and DNA methylation. Treatment of cultured human glioblastoma cells and primary murine monocytes with GC methyltransferase followed by FENGC and high-coverage enzymatic methyl sequencing provides single-molecule, long-read detection of differential endogenous CG methylation, dynamic nucleosome repositioning, and transcription factor binding. FENGC provides a versatile and cost-effective platform for targeted sequence enrichment for analysis of genetic and/or epigenetic heterogeneity.

cancer biology↗

bioRxiv2020

methylscaper: an R/Shiny app for joint visualization of DNA methylation and nucleosome occupancy in single-molecule and single-cell data

Differential DNA methylation and chromatin accessibility are associated with disease development, particularly cancer. Methods that allow profiling of these epigenetic mechanisms in the same reaction and at the single-molecule or single-cell level continue to emerge. However, a challenge lies in jointly visualizing and analyzing the heterogeneous nature of the data and extracting regulatory insight. Here, we developed methylscaper, a visualization framework for simultaneous analysis of DNA methylation and chromatin landscapes. Methylscaper implements a weighted principle component analysis that orders sequencing reads, each providing a record of the chromatin state of one epiallele, and reveals patterns of nucleosome positioning, transcription factor occupancy, and DNA methylation. We demonstrate methylscapers utility on a long-read, single-molecule methyltransferase accessibility protocol for individual templates (MAPit) dataset and a single-cell nucleosome, methylation, and transcription sequencing (scNMT-seq) dataset. In comparison to other procedures, methylscaper is able to readily identify chromatin features that are biologically relevant to transcriptional status while scaling to larger datasets. Availability and implementationMethylscaper, is available on GitHub at https://github.com/rhondabacher/methylscaper. Contactrbacher@ufl.edu

bioinformatics↗

bioRxiv2020

Normalization by distributional resampling of high throughput single-cell RNA-sequencing data

MotivationNormalization to remove technical or experimental artifacts is critical in the analysis of single-cell RNA-sequencing experiments, even those for which unique molecular identifiers (UMIs) are available. The majority of methods for normalizing single-cell RNA-sequencing data adjust average expression in sequencing depth, but allow the variance and other properties of the gene-specific expression distribution to be non-constant in depth, which often results in reduced power and increased false discoveries in downstream analyses. This problem is exacerbated by the high proportion of zeros present in most datasets. ResultsTo address this, we present Dino, a normalization method based on a flexible negative-binomial mixture model of gene expression. As demonstrated in both simulated and case study datasets, by normalizing the entire gene expression distribution, Dino is robust to shallow sequencing depth, sample heterogeneity, and varying zero proportions, leading to improved performance in downstream analyses in a number of settings. Availability and implementationThe R package, Dino, is available on GitHub at https://github.com/JBrownBiostat/Dino. Contactbrownj@biostat.wisc.edu, kendzior@biostat.wisc.edu

genomics↗