Biology subjects

Townsend, H. A.

Publications and source records attributed to Townsend, H. A..

6 recordsLinked to original sources

bioRxiv2026

Cross-assay RNA modeling reveals cancer biomarkers

The clinical heterogeneity of cancer poses a major challenge for precision medicine. Limited cohort sizes across evolving assay platforms impede reliable biomarker discovery. Here, we systematically evaluate how to integrate data from four transcriptomics platforms: bulk and single-cell (sc) RNA sequencing (RNA-seq), NanoString, and microarray for predictive modeling in cancer. We use high-grade serous carcinoma (HGSC) of tube-ovarian origin as a model system, as it is highly heterogeneous in both biology and assay data. We find that using fold-change of gene expression in patients with matched pre- and post-neoadjuvant chemotherapy samples reduces inter-patient and inter-assay variability but is insufficient to overcome platform-specific biases. Microarray and scRNA-seq data exhibit systematic biases, while RNA-seq and NanoString show the most promise for combination into a single training cohort. To mitigate inter-assay limitations, we generate a new data set of HGSC tumor samples profiled with both RNA-seq and NanoString, and use it to identify the limits of detection and optimal harmonization strategies. Our approaches enable integration of cohorts for separate and combined RNA-seq and NanoString predictive models of disease recurrence (test-set AUROCs > 0.8), validated in external microarray cohorts. We leverage single-cell and bulk RNA-seq network-based analyses to provide mechanistic context for genes in the predictive models. Our models indicate that GBP4 expression is a key predictor of recurrence and marks immune remodeling towards cytotoxicity. We provide an interactive web portal to facilitate exploration of data and results. These findings guide cross-assay harmonization of transcriptomic data and enable improved predictive modeling in heterogeneous cancers. Statement of SignificanceWe present a framework for integrating RNA-seq, NanoString, microarray, and single-cell transcriptomic data for predictive modeling, enabling robust biomarker discovery in heterogeneous cancers and identifying GBP4 as a marker of immune remodeling.

bioinformatics↗

bioRxiv2026

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data

In the treatment of high grade serous ovarian cancer (HGSC), patients initially diagnosed with unresectable tumors are first treated with neoadjuvant chemotherapy (NACT) to reduce tumor burden prior to surgery. Analysis of matched pre- and post-NACT samples from the same patients enables the investigation of chemotherapy impacts and the biomarkers of progression. Although the tumor immune microenvironment (TIME) has increasingly been recognized as critical in shaping the development and progression of HGSC, we lack a comprehensive understanding of how chemotherapy remodels the TIME. Previous studies have found evidence for a general inflammatory response post-NACT, despite inconsistencies regarding which differentially expressed genes and pathways are implicated. We combine matched NanoString gene expression data from multiple sources to create a large dataset of matched pre- and post- NACT samples (N=83, with 29 novel to this study) and investigate reproducibility. Further, we use machine learning methods to investigate whether patient progression-free survival (PFS) can be predicted from the observed impact of chemotherapy on the TIME as represented by the comprehensive set of NanoString features. We find overall low predictability of PFS from all NanoString features, suggesting that previous results may have been limited by small sample size effects and that larger datasets are needed to identify more generalizable and translatable findings. We identify a set of differential expression features that are the most important for predicting patient outcomes that can be validated in future computational and biological studies. Author summaryA subset of patients with high grade serous ovarian cancer are treated with chemotherapy before surgery to reduce tumor burden. We investigate a large dataset of samples taken before and after chemotherapy. These matched samples enable an investigation of how the environment around tumors, for example immune cell infiltration, reacts to chemotherapy, providing insights into biomarkers for treatment response and treatments that could complement chemotherapy. This larger dataset only partially replicates results from previous studies, while also providing new insights. Machine learning models designed to predict the time to patient recurrence from available biomarkers indicate that they are not strongly predictive of patient outcomes, in contrast to past studies. These results suggest that larger datasets are needed. We identify a set of genes that change with chemotherapy and are indicative of and potentially useful for predicting time to disease recurrence and can be further investigated.

cancer biology↗

bioRxiv2025

Improving confidence of differential transcription calls in enhancers

MotivationMost disease-associated genetic variants reside within transcribed regulatory elements (tREs). Patterns of differential transcription at tREs can be leveraged to identify upstream regulators and link enhancers to their target genes. But the low transcription levels and high variability in tREs makes identifying high confidence differentially transcribed elements challenging. ResultsWe present Mu Counts and TFEA-LE, two algorithms for robust identification of differentially transcribed tREs. The first step in accurate identification of differentially transcribed tREs is to obtain accurate RNA lengths and therefore counts over these regions. To this end we developed a method of accurate length inference (LIET-EMG) as wll as a rapid method for counting reads over tREs (Mu Counts). Armed with newly identified and quantified tREs, TFEA-LE then integrates motif information to simultaneously identify responsive tREs and their likely upstream regulators. We show improved precision and recall over general-purpose tools (e.g. DESeq2) in detecting p53-responsive tREs. We then clarify TF-specific responses within multi-TF perturbations in lung cells. Finally we show that the TFEA-LE approach improves TF activity inference, including in complex perturbations where many TFs respond. TFEALE is especially effective in technically challenging datasets, whether due to highly specific or broad responses, outliers, or high GC content. Ultimately, these 1 methods advance the systematic characterization of individual tREs, enabling their integration with regulators, target genes, and disease-associated variants for translational research. Availability and ImplementationTFEA-LE: https://github.com/Dowell-Lab/TFEA/tree/Lead_edge. Nextflow pipeline to run Mu Counts: https://github.com/Dowell-Lab/Bidir_Counting_Analysis. LIET (including modifications for tREs): https://github.com/Dowell-Lab/LIET/tree/LIET_EMGtoo. Source code for this work: https://github.com/Dowell-Lab/Improving_tRE_Analysis_Paper Contactrobin.dowell@colorado.edu

bioinformatics↗

bioRxiv2024

LIET Model: Capturing the kinetics of RNA polymerase from loading to termination

Transcription by RNA polymerases is an exquisitely regulated step of the central dogma. Transcription is the primary determinant of cell-state, and most cellular perturbations impact transcription by altering polymerase activity. Thus, detecting changes in polymerase activity yields insight into most cellular processes. Nascent run-on sequencing provides a direct readout of polymerase activity, but no tools exist to model this activity at genes. We focus on RNA polymerase II--responsible for transcribing protein-coding genes. We present the first model to capture the complete process of gene transcription. For individual genes, this model parameterizes each distinct stage of transcription--Loading, Initiation, Elongation, and Termination, hence LIET--in a biologically interpretable Bayesian mixture, which is applied to nascent run-on data. Our improved modeling of Loading /Initiation demonstrates these are characteristically different between sense and antisense strands. Applying LIET to 24 human cell-types, our analysis indicates the position of dissociation (the last step of Termination) appears to be highly consistent, indicative of a highly regulated process. Furthermore, applying LIET to perturbation experiments, we demonstrate its ability to detect specific changes in pausing (5'end), strand-bias, and dissociation location (3'end)--opening the door to differential assessment of transcription at individual stages of individual genes.

bioinformatics↗

bioRxiv2024

Single-cell based integrative analysis of transcriptomics and genetics reveals robust associations and complexities for inflammatory diseases

BackgroundUnderstanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible. MethodsWe present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of two recent algorithms, based on networks (scGWAS) or single-cell disease scores (scDRS), according to accuracy/sensitivity and interpretability (M. J. Zhang et al. 2022; Jia et al. 2022). While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,1,73 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC) (F. Zhang et al. 2023; Ishigaki et al. 2022; Smillie et al. 2019; de Lange et al. 2017). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls. ResultsWe first found that single-cell based tool scDRS called superior numbers of supported cell states, like MERTK+ myeloid cells in RA, which were overlooked by network-based scGWAS. While scGWAS was advantageous for gene exploration, scDRS captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5 scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohns disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.

bioinformatics↗

bioRxiv2023

Atlas of nascent RNA transcripts reveals enhancer to gene linkages

Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in unstable, non-coding bidirectional transcription. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as an SQL database which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.

genomics↗