Biology subjects

Jensen, D. M.

Publications and source records attributed to Jensen, D. M..

7 recordsLinked to original sources

bioRxiv2026

Donor-specific assemblies enhance somatic structural variant detection in complex genomic regions

Structural variants (SVs) contribute substantially to genomic variation and disease, but detecting somatic SVs (sSVs) remains difficult due to reference bias, mosaicism, and enrichment in repetitive regions. Linear reference genomes, like GRCh38 and CHM13, do not fully capture individual genomic structure, which can obscure true somatic variation. Donor-specific assemblies (DSAs) generated from the same genome where sSVs are being assayed provide a personalized alternative, yet their performance for sSV detection has not been systematically assessed. As part of the Somatic Mosaicism across Human Tissues (SMaHT) Network, we benchmark a DSA for sSV discovery in the COLO829 melanoma cell line with a matched normal sample from the same individual. We compare sSV detection across GRCh38, CHM13, and the COLO829BL_DSA using three different sSV callers (Delly, Severus, and Sniffles2) and sequence data from multiple long-read platforms. The COLO829BL_DSA identifies 1.8-fold more manually validated sSVs than linear references, in regions both shared with GRCh38 and CHM13 and unique to the COLO829BL_DSA. Variants detected only with the COLO829BL_DSA are often found in satellite and other repeat-rich regions that are difficult to resolve using standard references. In addition, several COLO829BL_DSA-specific sSVs are located in genes, some of which are associated with cancer. Overall, these results underscore the utility of DSAs in improving sSV detection.

genomics↗

bioRxiv2025

Comprehensive benchmarking of somatic single-nucleotide variant and indel detection at ultra-low allele fractions using short- and long-read data

Mosaic mutations in normal tissues occur at low variant allele fractions (VAFs), complicating detection. To benchmark strategies, the SMaHT Network created a cell-line mixture (1:49) and produced ultra-deep whole-genome sequencing using short and long reads (five centers, 180-500x each). We assembled a reference of 44,008 mosaic SNVs and 2,059 Indels, cross-validation between platforms to expose limits of short-read analysis. We also partitioned the genome by mappability to examine the impact of genomic context, added a negative reference set, and accounted for culture-derived mutations. When seven institutions applied eleven algorithms to mixture data, call sets were largely discordant across tools and replicates, partly reflecting stochastic presence of low-VAF mutations in biological replicants. For >2% VAF SNVs, sensitivity and precision approached [~]80% at [≥]300x, with little gain from additional sequencing. This work provides a comprehensive framework for reliable detection of low-VAF mutations in non-cancer tissues and a valuable resource for the community.

bioinformatics↗

bioRxiv2025

A telomere-to-telomere map of somatic mutation burden and functional impact in cancer

Oncogenesis involves widespread genetic and epigenetic alterations, yet the full spectrum of somatic variation genome-wide remains unresolved. We generated a near-telomere-to-telomere (T2T) diploid assembly of a donor paired with deep short- and long-read sequencing of their melanoma. This revealed that 16% of somatic variants occur in sequences absent from GRCh38, with satellite repeats acting as hotspots for UV-induced damage due to sequence-intrinsic mutability and inefficient repair. Centromere kinetochore domains emerged as focal sites of structural, genetic, and epigenetic variation, leading to remodeling of centromere kinetochore binding domains during tumor evolution. Single-molecule telomere reconstructions uncovered cycles of attrition, deletion, and telomerase-mediated extension that shape cancer telomeres. Finally, diploid chromatin maps exposed that copy number alterations and epimutations, rather than point mutations, predominate in rewiring cancer regulatory programs. These findings define the full landscape of a cancers somatic variation and their functional impact, establishing a blueprint for T2T studies of mosaicism.

genomics↗

bioRxiv2025

Deep Learning-Enhanced Light Sheet Microscopy Unveils Semaglutide Impact on Cardiac Fibrosis

BackgroundExtensive preclinical research aims to develop novel therapeutics for myocardial fibrosis (MF), a condition marked by collagen accumulation that impairs cardiac function. MF is particularly relevant in heart failure with preserved ejection fraction (HFpEF), a growing clinical challenge with limited treatment options. However, current methods for quantifying MF in mouse models struggle to accurately capture its heterogeneous regional distribution, creating a significant barrier to reliably assessing the efficacy of therapeutics. PurposeTo develop a whole-heart fibrosis imaging and deep learning (DL)-based quantification method and validate the workflow by assessing the efficacy of a glucagon-like peptide-1 receptor (GLP-1R) agonist in mouse HFpEF model. Experimental ApproachBy utilizing a fluorescent collagen-labelling dye, tissue clearing and 3D light sheet microscopy, we developed a high-throughput imaging platform for MF. We established DL framework to quantify perivascular and replacement fibrosis, as well as hypertrophy, in 17 left ventricular (LV) segments. The antifibrotic effects of the GLP-1R agonist semaglutide were evaluated in the db/db UNx-ReninAAV mouse model, which exhibits diabetes, kidney failure, obesity, and hypertension. Key ResultsWhole-heart 3D light sheet microscopy, combined with artificial intelligence, enables micrometer-resolution analysis of MF distribution in rodents. This approach allows for detailed characterization of distinct regional fibrosis patterns. Chronic semaglutide treatment significantly reduced LV hypertrophy and perivascular fibrosis but had no significant effect on replacement fibrosis. Conclusions and ImplicationsThe established 3D imaging and quantification approach provides a powerful tool for evaluating the therapeutic efficacy of antifibrotic compounds and studying the cellular and pathological mechanisms underlying cardiovascular diseases.

pathology↗

bioRxiv2025

Altered Functional Network Energy Across Multiscale Brain Networks in Preterm vs. Full-Term Subjects: Insights from the Adolescent Brain Cognitive Development (ABCD) Study

Infants born prematurely, or preterm, can experience altered brain connectivity, due in part to incomplete brain development at the time of parturition. Research has also shown structural and functional differences in the brain that persist in these individuals as they enter adolescence when compared to peers who were fully mature at birth. In this study, we examined functional network energy across multiscale functional connectivity in approximately 4600 adolescents from the Adolescent Brain Cognitive Development (ABCD) study who were either preterm or full term at birth. We identified three key brain networks that show significant differences in network energy between preterm and full-term subjects. These networks include the visual network (comprising the occipitotemporal and occipital subnetworks), the sensorimotor network, and the high cognitive network (including the temporoparietal and frontal subnetworks). Additionally, it was demonstrated that full-term subjects exhibit greater instability, leading to more dynamic reconfiguration of functional brain information and increased flexibility across the three identified canonical brain networks compared to preterm subjects. In contrast, those born prematurely show more stable networks but less dynamic and flexible organization of functional brain information within these key canonical networks. In summary, measuring multiscale functional network energy offered insights into the stability of canonical brain networks associated with subjects born prematurely. These findings enhance our understanding of how early birth impacts brain development.

neuroscience↗

bioRxiv2022

The OSUMMER lines: a series of ultraviolet-accelerated NRAS-mutant mouse melanoma cell lines syngeneic to C57BL/6

An increasing number of cancer subtypes are treated with front-line immunotherapy. However, approaches to overcome primary and acquired resistance remain limited. Pre-clinical mouse models are often used to investigate resistance mechanisms, novel drug combinations, and delivery methods; yet most of these models lack the genetic diversity and mutational patterns observed in human tumors. Here we describe a series of thirteen C57BL/6J melanoma cell lines to address this gap in the field. The Ohio State University-Moffitt Melanoma Exposed to Radiation (OSUMMER) cell lines are derived from mice expressing endogenous, melanocyte-specific, and clinically relevant Nras driver mutations (Q61R, Q61K, or Q61L). Exposure of these animals to a single, non-burning dose of ultraviolet B accelerates the onset of spontaneous melanomas with mutational patterns akin to human disease. Furthermore, in vivo irradiation selects against potent tumor antigens, which could prevent the outgrowth of syngeneic cell transfers. Each OSUMMER cell line possesses distinct in vitro growth properties, trametinib sensitivity, mutational signatures, and predicted antigenicity. Analysis of OSUMMER allografts shows a correlation between strong, predicted antigenicity and poor tumor outgrowth. These data suggest that the OSUMMER lines will be a valuable tool for modeling the heterogeneous responses of human melanomas to targeted and immune-based therapies. SIGNIFICANCENRAS-activating mutations are the second most common genetic driver event in cutaneous melanoma, occurring in 15% to 25% of cases. With few therapeutic options beyond immunotherapy, patients with NRAS-mutant melanoma have a poorer prognosis. Pre-clinical mouse models that mimic the high mutational burden of human NRAS-mutant melanomas are lacking in the field. Here, we describe a series of NRAS-mutant melanoma cell lines, derived from ultraviolet (UV)-induced, spontaneous tumors. These lines permit the study of targeted, NRAS mutant-specific, immune, and combination therapies in C57BL/6J mice. With the release of this resource, we hope to catalyze new therapeutic approaches for NRAS-mutant melanoma.

cancer biology↗

bioRxiv2020

Impact of Model Order Choice on the Results of Parallel Independent Component Analysis

Parallel independent component analysis (pICA) is a data-driven method that identifies the maximally independent components of multiple imaging modalities while simultaneously investigating the strength of their correlations. Researchers using pICA are given the option to use the suggested model order calculated by the minimum descriptive length (MDL) algorithm, or they can choose their own model order. To date, there are no suggested guidelines for this choice. To test the sensitivity of pICA to the selection of model order, we applied it to a well-researched brain disorder, schizophrenia, looking at the correlations between patterns of grey matter volume (GM) volume and white matter integrity, measured using fractional anisotropy (FA). We varied model orders from low to high, and tested the sensitivity to disorder effects (cases vs controls), similarity of spatial maps identified across model orders, consolidation or distribution effects related to model order selection, and the performance of the minimum descriptive length (MDL) algorithm. The pICA results (multimodal analysis) were also compared to the ICA (unimodal analysis) for each imaging modality. Across model orders, there was consistent sensitivity to disorder effects, and clustered patterns of spatial maps for both the GM and FA reflecting those differences. The MDL-estimated model order captured the majority, but not all, of the spatial patterns present in the GM and FA. There was not the expected consolidation of spatial maps at lower model orders, nor the distribution of spatial maps at higher model orders. The spatial patterns identified in the ICA closely resemble those found in the pICA, although lacking the benefit of the optimization algorithm, were not as highly correlated. This offers some insight and guidance for researchers interested in using pICA with regard to selecting model order for their particular analysis of multiple imaging modalities.

neuroscience↗