Biology subjects

Radens, C. M.

Publications and source records attributed to Radens, C. M..

6 recordsLinked to original sources

bioRxiv2026

Pan-cell-type prediction of splicing patterns from sequence and splicing factor expression

AO_SCPLOWBSTRACTC_SCPLOWAlternative splicing is a core determinant of cell-type-specific gene expression in humans, and its dysregulation contributes to many diseases including neurodegeneration, autoimmunity, and cancer. However, current deep learning models for predicting RNA expression from sequence are limited in how they handle cellular context dependence. These models typically achieve cell-type specificity by training separate models or heads for each tissue or cell-type, which assumes discrete, predefined cell types. This design not only prevents learning from pathological or experimentally perturbed transcriptomes, but also prevents generalization to new cellular contexts. Here we introduce PanExonNet, a deep learning framework that integrates cis- and trans-regulation by conditioning sequence-based splicing predictions on an inferred "splicing state" derived from the expression of RNA-binding proteins (RBPs) and spliceosome components. PanExonNet is trained on diploid, individual-specific gene sequences containing indels to predict splicing profiles derived from short-read RNA-seq, and it outputs multiple tracks at single-nucleotide resolution as well as donor-acceptor junction usage. Compared to multi-headed baselines such as Borzoi and Pangolin, PanExonNet exhibits substantially higher context specificity--as quantified by a {Delta}PSI correlation metric designed to isolate context-specific variation--and, crucially, generalizes to unseen cell types. Adding perturbational knockdown followed by RNA sequencing (KD-RNA-seq) data to the training set further improves generalization. This performance is enabled in part by our introduction of contextualizable convolutions, a modular layer that may broadly benefit genomic sequence modeling. This framework provides a scalable foundation for future DNA-to-RNA models that could improve variant effect prediction, the design of oligonucleotide therapeutics, and biomarker discovery across diverse cellular contexts.

genomics↗

bioRxiv2024

Machine learning-optimized targeted detection of alternative splicing

RNA-sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases which hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

genomics↗

bioRxiv2022

Alternative Splicing of Apoptosis Genes Promotes Human T Cell Survival

Alternative splicing occurs in the vast majority of human genes, giving rise to distinct mRNA and protein isoforms. We, and others, have previously identified hundreds of genes that change their isoform expression upon T cell activation via alternative splicing; however, how these changes link activation input with functional output remains largely unknown. Here we investigate how costimulation of T cells through the CD28 receptor impacts alternative splicing in T cells activated through the T cell receptor (CD3) and find that while CD28 signaling alone has minimal impact on splicing, it enhances the extent of change for up to 20% of TCR-induced alternative splicing events. Interestingly, a set of CD28-enhanced splicing events occur within genes encoding key components of the apoptotic signaling pathway; namely caspase-9, Bax and Bim. Using both CRISPR-edited cells and antisense oligos to force expression of specific isoforms, we show for all three of these genes that the isoform induced by CD3/CD28 costimulation promotes resistance to apoptosis, and that changes in all three genes together function combinatorially to further promote cell viability. Finally, we show that the JNK signaling pathway, induced downstream of CD3/CD28 costimulation, is required for each of these splicing events, further highlighting their co-regulation. Together these findings demonstrate that alternative splicing is a key mechanism by which costimulation of CD28 promotes viability of activated T cells.

molecular biology↗

bioRxiv2021

RNA splicing analysis using heterogeneous and large RNA-seq1datasets

The ubiquity of RNA-seq has led to many methods that use RNA-seq data to analyze variations in RNA splicing. However, available methods are not well suited for handling heterogeneous and large datasets. Such datasets scale to thousands of samples across dozens of experimental conditions, exhibit increased variability compared to biological replicates, and involve thousands of unannotated splice variants resulting in increased transcriptome complexity. We describe here a suite of algorithms and tools implemented in the MAJIQ v2 package to address challenges in detection, quantification, and visualization of splicing variations from such datasets. Using both large scale synthetic data and GTEx v8 as benchmark datasets, we demonstrate that the approaches in MAJIQ v2 outperform existing methods. We then apply MAJIQ v2 package to analyze differential splicing across 2,335 samples from 13 brain subregions, demonstrating its ability to offer new insights into brain subregion-specific splicing regulation.

bioinformatics↗

bioRxiv2020

MOCCASIN: A method for correcting for known and unknown confounders in RNA splicing analysis

While the effects of confounders on gene expression analysis have been extensively studied there is a lack of equivalent analysis and tools for RNA splicing analysis. Here we assess the effect of confounders in two large public RNA-Seq datasets (TARGET, ENCODE), develop a new method, MOCCASIN, to correct the effect of both known and unknown confounders on RNA splicing quantification, and demonstrate MOCCASINs effectiveness on both synthetic and real data.

genomics↗

bioRxiv2019

Meta-Analysis of Transcriptomic Variation in T cell Populations Reveals Novel Signatures of Gene Expression and Splicing

Distinct T cell subtypes are typically defined by the expression of distinct gene repertoires. However, there is variability between studies regarding the markers used to define each T cell subtype. Moreover, previous analysis of gene expression in T cell subsets has largely focused on gene expression rather than alternative splicing. Here we take a meta-analysis approach, comparing eleven independent RNA-Seq studies of human Th1, Th2, Th17 and/or Treg cells to identify transcriptomic features that correlate consistently with subtype. We find that known master-regulators are consistently enriched in the appropriate subtype, however, cytokines and other genes often used as markers are more variable. Importantly, we also identify previously unknown transcriptomic markers that consistently differentiate between subsets, including a few Treg-specific splicing patterns. Together this work highlights the heterogeneity in gene expression between isolates of the same subtype, but also suggests additional markers that can be used to define functional groupings.

immunology↗