Biology subjects

Shen, S. Y.

Publications and source records attributed to Shen, S. Y..

6 recordsLinked to original sources

bioRxiv2026

Benchmarking CUT&RUN analysis using motif enrichment

Background. Cleavage under targets and release using nuclease (CUT&RUN) maps the genome-wide locations of chromatin-associated proteins and provides an improved alternative to chromatin immunoprecipitation sequencing (ChIP-seq) for profiling sequence-specific transcription factor binding sites. Identifying these binding sites plays a critical role in understanding gene regulation, and transcription factors provide a useful setting for benchmarking because their well-defined sequence motifs serve as built-in controls for evaluating performance. Compared with ChIP-seq, CUT&RUN achieves higher resolution and lower background by avoiding cross-linking and bulk precipitation. Its distinct fragment length and cleavage characteristics, however, limit the direct transfer of existing computational tools, which primarily target ChIP-seq data. The performance of these tools on CUT&RUN can depend strongly on preprocessing choices. In this work, we investigate preprocessing strategies for transcription factor CUT&RUN, focusing on fragment length filtering and spike-in calibration. We aim to improve peak detection and provide practical guidance for analysis. Results. We designed a benchmarking method to evaluate peak-calling procedures for CUT&RUN data and the effects of preprocessing approaches, including fragment length filtering and spike-in calibration. We benchmarked the two most widely used peak callers, MACS2 and SEACR, by assessing motif enrichment---the degree to which identified peaks contain the expected transcription factor binding motifs. Filtering for fragments with a length [≤]120 bp generally improved target motif enrichment. Spike-in calibration using heterologous Saccharomyces cerevisiae DNA improved motif elucidation substantially for MACS2, with little benefit for SEACR. By contrast, using Escherichia coli DNA as a spike-in control often failed to produce valid results unless we could meticulously control E. coli contamination. MACS2 performed robustly across samples. SEACR performed especially well on clean, sparse-background datasets, but performed poorly on some datasets with denser background signal and often produced numerous apparent false positives. While MACS2 provided robust results under minor perturbations in fragment length filtering, SEACR exhibited greater sensitivity to such changes. Discussion. Our benchmarking highlights how both peak caller choice and preprocessing strategy shape the analysis of transcription factor CUT&RUN data. By comparing the robustness and limitations of two widely used peak callers, we provide practical guidance on fragment length filtering, spike-in calibration, and tool selection. These findings help improve the processing and interpretation of CUT&RUN data, allowing researchers to more rapidly and reliably utilize this new technology. We expect that our work will guide more informed choices in CUT&RUN analysis and support the development of improved computational methodologies.

bioinformatics↗

bioRxiv2026

Enrichment of methylated cell-free placental DNA

Abstract. Introduction: Preterm birth drives adverse perinatal maternal and infant health outcomes through heterogeneous symptoms, severity, and etiologies. Delivery prior to reaching 37 weeks of gestation may result from medically indicated intervention for pregnancy complications or spontaneously in the absence of prior symptoms. Placental tissue collected following preterm birth exhibits differential DNA methylation compared to full-term placentas and may indicate pregnancy health during gestation. Placental DNA currently has limited utility for assessing health of ongoing pregnancy, as sampling placental tissue during gestation increases the risk of infection and miscarriage. Risks associated with placental sampling during pregnancy limit the use of DNA methylation in clinical preterm birth prediction. Assessing preterm birth risk during gestation requires non-invasive methods for characterizing placental DNA methylation. Results: We quantified genome-wide DNA methylation patterns of hypermethylated cell-free DNA in pregnant (n = 99) and non-pregnant (n = 93) plasma using cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq). In each sample, we assessed DNA methylation status in 300-bp genomic windows, examining both sequencing read counts and calculated absolute molar DNA amount. Known hypermethylated placental regions, including RASSF1, STAT5A, and ERG promoters showed significantly increased odds of detection in pregnant samples, suggesting enrichment of cell-free placental DNA. Of the 536,444 300-bp windows examined, 173,071 (32%) showed significant enrichment in pregnant plasma. Linear modeling identified 107,505 differentially methylated regions (DMRs) associated with pregnancies later diagnosed with intrauterine growth restriction (IUGR) (n = 22). Alu elements showed increased representation in these DMRs than expected, while other repetitive elements exhibited underrepresentation. Discussion: These results demonstrate cfMeDIP-seq's ability to enrich for cell-free placental DNA and characterize cell-free DNA methylation signatures of pregnancies complicated by IUGR. Enrichment of cell-free placental DNA enables non-invasive profiling of placental DNA methylation from maternal plasma. Detectable epigenetic signatures in maternal plasma may identify pregnancies at elevated risk for preterm birth before clinical symptoms appear. Our findings further highlight the potential of cell-free placental DNA for monitoring pregnancy health.

genomics↗

bioRxiv2023

Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two age-related and fatal neurodegenerative disorders that lie on a shared disease spectrum. While both disorders involve complex interactions between neuronal and glial cells, the specific cell-type alterations and their contributions to disease pathophysiology remain incompletely understood. Here, we applied single-nucleus RNA sequencing of the orbitofrontal cortex, a region affected in ALS-FTLD, to map cell-type specific transcriptional signatures in C9orf72-related ALS (with and without FTLD) and sporadic ALS cases. Our findings reveal disease- and cell-type-specific transcriptional changes, with neurons exhibiting the most pronounced alterations, primarily affecting mitochondrial function, protein homeostasis, and chromatin remodeling. A comparison with independent datasets from different cortical regions of C9orf72 and sporadic ALS cases showed concordance in several pathways, with neuronal STMN2 and NEFL showing consistent up-regulation between brain regions and disease subtypes. We also interrogated alternative polyadenylation (APA) as an additional layer of transcriptional regulation, demonstrating that APA events are not correlated with identified gene expression changes. To interpret these events, we developed APA-Net, a deep learning model that integrates transcript sequences with RNA-binding protein expression profiles, revealing cell type-specific patterns of APA regulation. Our atlas illuminates cell type-specific pathomechanisms of ALS/FTLD, providing a valuable resource for further investigation.

neuroscience↗

bioRxiv2021

Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls

BackgroundCell-free methylated DNA immunoprecipitation-sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). This method allows for DNA methylation profiling of circulating tumour DNA in cancer patients blood samples. Such epigenetic profiling of circulating tumour DNA provides information about in which tissues tumour DNA originates, a key requirement of any test for early cancer detection. In addition, DNA methylation signatures provide prognostic information and can detect relapse. For robust quantitative comparisons between samples, immunoprecipitation enrichment methods like cfMeDIP-seq require normalization against common reference controls. MethodsTo provide a simple and inexpensive reference for quantitative normalization, we developed a set of synthetic spike-in DNA controls for cfMeDIP-seq. These controls account for technical variation in enrichment efficiency due to biophysical properties of DNA fragments. Specifically, we designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G+C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). We ensured that the spike-in synthetic DNA sequences do not align to the human genome. We integrated unique molecular indices (UMIs) into cfMeDIP-seq to control for differential amplification after enrichment. To assess enrichment bias according to distinct biophysical properties, we conducted cfMeDIP-seq solely on spike-in DNA fragments. To optimize the amount of spike-in DNA required, we added varying quantities of spike-in control DNA to sheared HCT116 colon cancer genomic DNA prior to cfMeDIP-seq. To assess batch effects, three separate labs conducted cfMeDIP-seq on peripheral blood plasma samples from acute myeloid leukemia (AML) patients. ResultsWe show that cfMeDIP-seq enriches for highly methylated regions, capturing [≥] 97% of methylated spike-in control fragments with [≤] 3% non-specific binding and preference for both high G+C content fragments and fragments with more CpGs. The use of 0.01 ng of spike-in control DNA in each sample provided sufficient sequencing reads to adjust for variance due to fragment length, G+C content, and CpG fraction. Using the known amount of each spiked-in fragment, we created a generalized linear model that absolutely quantifies molar amount from read counts across the genome, while adjusting for fragment length, G+C content, and CpG fraction. Employing our spike-in controls greatly mitigates batch effects, reducing batch-associated variance to [≤] 1% of the total variance within the data. DiscussionIncorporation of spike-in controls enables absolute quantification of methylated cfDNA generated from methylated DNA immunoprecipitation-sequencing (MeDIP-seq) experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases. We created an R package, spiky, to convert read counts to picomoles of DNA fragments, while adjusting for fragment properties that affect enrichment. The spiky package is available on Bioconductor (https://bioconductor.org/packages/spiky) and GitHub (https://github.com/trichelab/spiky). Contactmichael.hoffman@utoronto.ca

genomics↗

bioRxiv2020

Dynamics of the cell-free DNA methylome of metastatic castration resistant prostate cancer during androgen-targeting treatment

AimWe examined methylation changes in cell-free DNA (cfDNA) in metastatic castration resistant prostate cancer (mCRPC) during treatment. Materials and MethodsGenome-wide methylation analysis of sequentially collected cfDNA samples derived from mCRPC patients undergoing androgen-targeting therapy was performed. ResultsAlterations in methylation states previously implicated in prostate cancer progression were observed and patients that maintained methylation changes throughout therapy tended to have a longer time to clinical progression (TTP). Importantly, we also report that markers associated with a highly aggressive form of the disease, Neuroendocrine-CRPC, were associated with a faster TTP. ConclusionOur findings highlight the potential of monitoring cfDNA methylome during therapy in mCRPC, which may serve as predictive markers of response to androgen-targeting agents.

cancer biology↗

bioRxiv2017

DNA-Demethylating Agents enhance cytolytic activity of CD8+ T Cells and anti-tumor immunity

Recent studies have shown that DNA methyltransferase inhibitors (DNMTi) can induce IRF7 activation and Type I/III interferon signaling through dsRNA-mediated viral mimicry in cancer cells. By performing a large pan-cancer analysis using TCGA data, we determined that IRF7 activation is associated with higher CD8+ T cell tumor infiltration and higher cytolytic activity across multiple cancer types. Accordingly, we demonstrate that DNMTi treatment results in increased CD8+ T cell tumor infiltration, enhanced cytolytic activity and CD8+ T cell dependent tumor growth inhibition. Finally, we show that DNMTi triggers a process marked by the induction of viral mimicry directly on CD8+ T cells, leading to activation of dsRNA sensing pathway, and up-regulation of T cell activation markers, effector cytokines, and Granzyme B. Taken together, our findings suggest that dsRNA sensing pathway activation in the immune compartment, through pharmacological DNA demethylation, is a viable strategy for boosting anti-tumor immune response.

cancer biology↗