Biology subjects

Dannenfelser, R.

Publications and source records attributed to Dannenfelser, R..

10 recordsLinked to original sources

bioRxiv2026

Reference-guided pseudotime inference across species and biological contexts

Cells collected at the same chronological age can vary substantially in biological age due to the heterogeneity in the timing of differentiation, speed of maturation, and degeneration. However, existing pseudotime inference methods either disregard chronological time information, or rely on accurate time-series labels within similar species or biological conditions of interest. As a result, both types of strategies often fail to faithfully order cells from biological contexts without reliable time labels, along the desired axis of interest such as human embryonic development or disease progression. Here, we propose Cavebear, a machine learning framework that enables pseudotime inference in a query species or condition guided by scRNA-seq time-series profiles from a reference species or condition. Cavebear achieves more accurate developmental pseudotime inference than existing methods and provides in vivo temporal mapping for in vitro experiments. Furthermore, we illustrate the potential of Cavebear to study cellular-level disease progression in human patients using mouse cancer development models as references. By transferring temporal information across species and conditions, Cavebear enables systematic investigation of biological variation in contexts where such annotations were previously unattainable.

bioinformatics↗

bioRxiv2026

Alternative splicing reshapes protein interaction networks through structured, directional rewiring across cancers

Alternative splicing generates extensive transcript diversity in cancer, but whether the resulting changes to protein-protein interaction (PPI) networks are structured across tumors or idiosyncratic to individual patients remains unresolved. Existing analyses catalog splicing events and their effects on individual interactions, but do not reconstruct patient-specific interaction networks at scale, leaving the systems-level architecture of splicing-driven rewiring uncharacterized. Here, focusing on exon skipping, the most prevalent form of alternative splicing in humans, we analyze 7,950 tumors across 28 cancer types to reconstruct patient-specific rewired PPI networks relative to matched normal tissues, distinguishing interaction gains from losses. We identify widespread and structured remodeling of protein connectivity, including a unique pattern of interaction gains in brain cancers, a conserved pan-cancer axis of interaction loss that converges on GTPase and Ras signaling, and a subset of genes that function as bidirectional switches, where rewiring direction varies across cancer types. At the patient level, tumors exhibiting extreme rewiring, especially pronounced interaction gains, are associated with significantly poorer survival, independent of tumor stage and mutational burden. These results delineate systems-level patterns through which alternative splicing reshapes protein interaction networks and contributes to tumor heterogeneity across cancers.

Cancer Biology↗

bioRxiv2025

Ontology-aware DNA methylation classification with a curated atlas ofhuman tissues and cell types

AO_SCPLOWBSTRACTC_SCPLOWDNA methylation (DNAm) is a core gene regulatory mechanism that captures cellular responses to short- and long-term stimuli such as environmental exposures, aging, and cellular differentiation. Although DNAm has proven valuable as a baseline biomarker for aging by enabling robust characterization of disease-associated methylation shifts associated with age, its potential to reveal analogous shifts in the context of tissue remains underex-plored. A major obstacle has been the absence of comprehensive, curated reference atlases spanning diverse normal human tissues, limiting most existing work to disease-subtype differentiation or localized tissue comparisons. To bridge this gap, we assemble the largest and most diverse atlas of exclusively healthy human tissue and cell samples profiled by 450K arrays, comprising of 16,959 samples across 86 tissues and cell types. Leveraging this resource, we introduce an ontology-aware classification framework that identifies robust CpG features associated with tissue and cell identity while integrating known anatomical and functional relationships (e.g., prefrontal cortex in the brain, leukocytes in blood). Our novel application of Minipatch learning distills a set of 190 CpG sites that can accurately support multi-label classification. We further validate our approach through an ontology-based label transfer task, demonstrating the effectiveness of ontology-informed learning to accurately predict relevant labels for 31 tissues and cell types not seen during training. These findings underscore the potential of our framework to enhance our understanding of healthy methylation landscapes and facilitate future applications in disease detection and personalized medicine.

bioinformatics↗

bioRxiv2025

Interpretable phenotype decoding from multi-condition sequencing data with ALPINE

As sequencing techniques advance in precision, affordability, and diversity, an abundance of heterogeneous sequencing data has become available, encompassing a wide range of phenotypic features and biological perturbations. Unfortunately, increased resolution comes with a cost of increased complexity of the biological search space, even at the individual study level, as perturbations are now often examined across many dimensions simultaneously, including different: donor phenotypes, anatomical regions and cell types, and time points. Furthermore, broad integration across studies promise unique opportunity to explore the molecular underpinnings of distinct healthy and disease states, larger than the original scope of the individual study. To fully realize the promise of both individual higher resolution studies and large cross-study integrations we need a robust methodology that can disentangle the influence of technical and non-relevant phenotypic factors, isolating relevant condition-specific signals from shared biological information while also providing interpretable insights into the genetic effects of these conditions. Current methods typically excel in only one of these areas. To address this gap, we developed ALPINE, a supervised non-negative matrix factorization (NMF) framework that effectively separates both technical and non-technical factors while simultaneously offering direct interpretability of condition-associated genes. Through simulations across 4 different scenarios, we demonstrate that ALPINE outperforms existing methods in both isolating the effect of different phenotypic conditions and prioritizing condition-associated genes. Furthermore, ALPINE has favorable performance in batch effect removal compared with state-of-the-art integration methods. When applied to real-world case studies, we showcase how ALPINE can be used to extract insights into the biological mechanisms that underlie differences between phenotypic conditions.

bioinformatics↗

bioRxiv2025

Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks

Accurate, data-driven representations of genes are critical for interpreting high-throughput biological data, yet no consensus exists on the most effective embedding strategy for common functional prediction tasks. Here, we present a systematic comparison of 38 gene embedding methods derived from amino acid sequences, gene expression profiles, protein-protein interaction networks, and biomedical literature. We benchmark each approach across three classes of tasks: predicting individual gene attributes, characterizing paired gene interactions, and assessing gene set relationships while trying to control for data leakage. Overall, we find that literature-based embeddings deliver superior performance across prediction tasks, sequence-based models excel in genetic interaction predictions, and expression-derived representations are well-suited for disease-related associations. Interestingly, network embeddings achieve similar performance to literature-based embeddings on most tasks despite using significantly smaller training sets. The type of training data has a greater influence on performance than the specific embedding construction method, with embedding dimensionality having only minimal impact. Our benchmarks clarify the strengths and limitations of current gene embeddings, providing practical guidance for selecting representations for downstream biological applications.

bioinformatics↗

bioRxiv2024

Disentangling associations between complex traits and cell types with seismic

AO_SCPLOWBSTRACTC_SCPLOWIntegrating single-cell RNA sequencing (scRNA-seq) with Genome-Wide Association Studies (GWAS) can help reveal GWAS-associated cell types, furthering our understanding of the cell-type-specific biological processes underlying complex traits and disease. However, current methods have technical limitations that hinder them from making systematic, scalable, interpretable disease-cell-type associations. In order to rapidly and accurately pinpoint associations, we develop a novel framework, seismic, which characterizes cell types using a new specificity score. We compare seismic with alternative methods across over 1,000 cell type characterizations at different granularities and 28 traits, demonstrating that seismic both corroborates findings and identifies trait-relevant cell groups which are not apparent through other methodologies. Furthermore, as part of the seismic framework, the specific genes driving cell type-trait associations can easily be accessed and analyzed, enabling further biological insights. The advantages of seismic are particularly salient in neurodegenerative diseases such as Parkinsons and Alzheimers, where disease pathology has not only cell-specific manifestations, but also brain region-specific differences. Interestingly, a case study of Alzheimers disease reveals the importance of considering GWAS endpoints, as studies relying on clinical diagnoses consistently identify microglial associations, while GWAS with a tau biomarker endpoint reveals neuronal associations. In general, seismic is a computationally efficient, powerful, and interpretable approach for identifying associations between complex traits and cell type-specific expression.

bioinformatics↗

bioRxiv2023

Enhancing gene set analysis in embedding spaces: a novel best-match approach

AO_SCPLOWBSTRACTC_SCPLOWEmbedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose ANDES, a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation-based and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multi-organism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.

bioinformatics↗

bioRxiv2023

Splitpea: uncovering cancer patient-specific protein interaction network rewiring

Protein-protein interactions play an essential role in nearly all biological processes, and it has become increasingly clear that in order to better understand the fundamental processes that underlie disease, we must develop a strong understanding of both their context specificity (e.g., tissue-specificity) as well as their dynamic nature (e.g., how they respond to environmental changes). While network-based approaches have found much initial success in the application of protein-protein interactions (PPIs) towards systems-level explorations of biology, they often overlook the fact that large numbers of proteins undergo alternative splicing. Alternative splicing has not only been shown to diversify protein function through the generation of multiple protein isoforms, but also remodel PPIs and affect a wide range diseases, including cancer. Isoform-specific interactions are not well characterized, so we develop a computational approach that uses domain-domain interactions in concert with differential exon usage data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx). Using this approach, we can characterize PPIs likely disrupted or possibly even increased due to splicing events for individual TCGA cancer patient samples relative to a matched GTEx normal tissue background.

systems biology↗

bioRxiv2022

Joint embedding of biological networks for cross-species functional alignment

Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions (PPIs) to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem. Here, we propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). More specifically, ETNA generates individual network embeddings based on network topological structures and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNAs embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.

bioinformatics↗

bioRxiv2021

Multi-antigen recognition circuits overcome challenges of specificity, heterogeneity, and durability in T cell therapy for glioblastoma

Treatment of solid cancers with chimeric antigen receptor (CAR) T cells is plagued by the lack of target antigens that are both tumor-specific and homogeneously expressed. We show that multiantigen prime-and-kill recognition circuits have the flexibility and precision to overcome these challenges in attacking glioblastoma. A synNotch receptor that recognizes a specific priming antigen - the heterogeneous glioblastoma neoantigen EGFRvIII or a brain tissue-specific antigen - is used to locally induce expression of a CAR, enabling thorough but controlled tumor killing by targeting of homogeneous antigens that are not absolutely tumor specific. Moreover, regulated CAR expression maintains a higher fraction of the T cells in the naive-like state which is associated with higher durability in vivo. In summary, using circuits that integrate recognition of multiple imperfect but complementary antigens, we improve the specificity and persistence of T cells directed against glioblastoma, providing a general recognition strategy applicable to other solid tumors.

immunology↗