Biology subjects

Kidder, B. L.

Publications and source records attributed to Kidder, B. L..

11 recordsLinked to original sources

bioRxiv2026

KDM4 inhibition alters H3K9me3 distribution and developmental responses in SETDB1-deficient embryonic stem cells

SETDB1 contributes to histone H3 lysine 9 methylation, whereas KDM4 demethylases act on H3K9 and, for KDM4A-C, H3K36. We examined whether Setdb1 status modifies responses to the KDM4-family inhibitor QC6352 in embryonic stem cells (ESCs) and embryoid bodies (EBs). QC6352 shifted Setdb1-depleted ESC colonies toward more compact morphology and increased alkaline-phosphatase (AP) staining, but also affected Setdb1-intact cells. Separate Kdm4a, Kdm4b, and Kdm4c knockdowns identified overlapping and member-specific transcriptional responses. Factorial RNA-seq revealed broadly concordant transcriptional responses to KDM4 inhibition in Setdb1-intact and depleted ESCs, alongside a smaller subset of genes with genotype-dependent treatment effects. Within this subset, two gene modules showed attenuation or reversal of Setdb1-associated expression changes following KDM4 inhibition. ChIP-seq revealed changes in the relative distribution of H3K9me3, H3K36me3, and H3K4me3 between promoters and gene bodies following KDM4 inhibition. KDM4 inhibition altered H3K9me3 distributions across repeat families in both Setdb1-intact and depleted ESCs. QC6352 also altered EB morphology and produced transcriptional responses that differed between ESCs and EBs. These findings show that KDM4 inhibition remodels chromatin and transcription in both Setdb1-intact and depleted ESCs, with Setdb1 status influencing a subset of transcriptional responses.

molecular biology↗

bioRxiv2026

Early-onset spontaneous murine mammary tumors show increased lymphocyte representation and reduced MHC antigen presentation

Simple Summary Not all breast tumors behave the same way, and part of the reason may be inherited rather than caused by the tumor itself. Using mice that carry the same cancer-causing HER2/neu gene but differ in genetic background, we found that introduction of the PWK/PhJ genetic background was associated with tumors that appeared about a month earlier. These early tumors contained a larger proportion of immune cells, including T and B cells, but the T-cell population showed reduced expression of genes associated with cytotoxic activity. At the same time, the tumors had lower levels of proteins and genes that let the immune system recognize tumor cells, alongside higher levels of structural proteins that can make tissue stiffer and easier for cancer to invade. These findings suggest that genetic background shapes not only how quickly breast tumors arise but also how well the immune system can recognize them, a distinction that may matter for future immune-based treatments. Background/Objectives: Genetic background modifies cancer risk and phenotype in humans, but its influence on the tumor immune microenvironment and antigen-presentation machinery in ErbB2 (HER2/neu)-driven breast cancer is not well defined. We used a genetically diverse mouse cross to test whether the wild-derived PWK/PhJ background alters immune representation and antigen presentation in a NeuT model of HER2/neu-positive breast cancer. Methods: Previously, quantitative trait locus (QTL) mapping using Diversity Outbred mice and r/qtl2 identified candidate loci driving tumor onset and growth. The wild-derived PWK/PhJ genetic background was predicted to harbor genes that regulated early onset of HER2/neu-driven tumors. BALB NeuT and (BALBxPWK) F1 NeuT female mice were monitored twice weekly for spontaneous mammary tumor onset and growth. Tumor immune composition was profiled by single-cell RNA sequencing. Protein abundance was measured by data-independent acquisition proteomics in formalin-fixed, paraffin-embedded tumor tissue (n=5 per genotype). Single-nucleus multiome (RNA + ATAC), bulk RNA-seq, and H3K27ac datasets from the same strain comparison were analyzed for focused MHC II cross-layer analyses. Results: As predicted, (BALBxPWK) F1 NeuT mice developed tumors significantly earlier than the parental BALB NeuT mice (10 vs. 15 weeks of age; p < 0.0001). In the scRNA-seq dataset, F1 tumors showed greater representation of Cd3e-expressing T cells and a Cd79a-expressing B-cell population not detected in BALB NeuT tumors, and a distinct neutrophil cluster; F1 T cells also showed lower expression of the cytotoxicity-associated genes Tox, Ltk, Nkg7, and Gzmb relative to BALB NeuT tumor-infiltrating T cells. Proteomics identified 46 differentially abundant proteins (25 higher, 21 lower in F1), with coordinate down-regulation of Stat1, Tapbp, Parp9, and Dtx3l alongside MHC class I and II proteins and up-regulation of extracellular-matrix and elastic-fiber proteins. Focused cross-layer analysis showed lower whole-tumor expression of a Stat1/MHC II-associated gene set together with reduced epithelial chromatin accessibility at selected regions near Cd74 and H2-Aa. Conclusions: The PWK/PhJ genetic background is associated with earlier HER2-driven tumor onset, greater lymphocyte proportion, and reduced activity of a STAT1-associated antigen-presentation program despite similar post-onset tumor growth kinetics.

cancer biology↗

bioRxiv2026

PWK genetic background reprograms heterochromatin and malignant epithelial states in Neu-driven mammary tumors

Genetic background can alter the latency and behavior of cancers initiated by the same oncogenic driver, but how inherited variation reshapes the regulatory landscape that permits tumor development remains poorly understood. The PWK genetic background accelerates mammary tumor onset in the NeuT model. We compared PWK NeuT and BALB NeuT tumors using tumor-level RNA-seq, histone ChIP-seq, and single-cell multiome profiling. PWK tumors exhibited broad transcriptional reprogramming characterized by reduced immune-associated expression and increased epithelial differentiation and neuronal-like programs. Chromatin remodeling was spatially redistributed rather than uniformly increased or decreased. PWK tumors showed expansion of H3K9me3- and H4K20me3-associated chromatin, while both marks were depleted across many expressed genes and concentrated within selected intergenic and repeat-rich territories, revealing coordinated redistribution of constitutive heterochromatin. Single-cell multiome profiling showed that broad epithelial representation remained similar between strains, yet specific malignant states were strongly strain biased. Integrated RNA and accessibility analyses identified concordant strain-selective regulatory programs and candidate loci, while trajectory analysis revealed distinct epithelial state transitions between PWK NeuT and BALB NeuT tumors. Together, these findings indicate that inherited genetic background reshapes transcriptional programs, heterochromatin organization, and malignant epithelial state trajectories in NeuT mammary tumors, providing a regulatory framework for understanding strain-dependent differences in tumor onset.

cancer biology↗

bioRxiv2026

ORBIT: Annotation-Aware Empirical Enrichment and Semantic Reranking for Interpretable Functional-Class Recovery

Gene-set interpretation workflows are widely used to summarize transcriptomic and proteomic experiments, yet standard enrichment tools often return long, redundant result tables that require substantial manual consolidation. We developed ORBIT (Ontology-Ranked Biological Interpretation Tool), an annotation-aware interpretation workflow that combines empirical enrichment, semantic reranking, and redundancy-aware representative-term selection to prioritize interpretable functional summaries from gene sets. We evaluated ORBIT on a curated tiered benchmark of human functional-class gene sets spanning clean reference sets, size-ladder variants, and mixed-difficulty cases. On the 45-set core benchmark, ORBIT semantic achieved higher expected-class recovery than Enrichr and PANTHER Gene Ontology molecular-function baselines, with a mean reciprocal rank of 0.916 and top-1 recovery of 0.889. Bootstrap confidence intervals and paired permutation testing supported the robustness of this advantage, and supplemental analyses extended the comparison to g:Profiler. In a GPCR mixed-function case study, ORBIT compressed redundant enriched terms into semantic representative neighborhoods, illustrating how long enrichment outputs can be converted into reviewable biological summaries. We applied ORBIT across complementary transcriptomic analyses spanning single-cell immune-cell markers, interferon-beta stimulation, and breast-cancer subtype biology. In these settings, ORBIT condensed marker and differential-expression signatures into prioritized functional summaries with explicit links to the genes, cell types, and annotation classes supporting each result. Together, these applications show how ORBIT can move from ranked enrichment terms to structured biological interpretation, preserving the evidence trail from input genes to functional summaries.

bioinformatics↗

bioRxiv2026

PixelDeck: A local-first media library manager for biomedical imaging

Modern biomedical imaging workflows generate large volumes of derived images and short videos that must be reviewed, compared, curated, and reused following primary acquisition and analysis. In practice, these assets are often dispersed across nested filesystem hierarchies on local drives, external media, or network storage, limiting efficient retrieval, deduplication, and figure assembly. We present PixelDeck, an open-source, local-first browser application for organizing and interactively browsing large biomedical image and video libraries on commodity workstations. PixelDeck integrates recursive folder import, SHA-256-based duplicate detection, metadata extraction, thumbnail and preview generation, full-text search, and asynchronous export within a responsive interface, supported by a modular ingestion pipeline, managed storage layer, and interactive browsing environment optimized for high-volume media collections. The system is implemented using a Next.js and React frontend, a SQLite metadata store accessed via Prisma, managed local media storage, and a background worker that executes import and export tasks asynchronously, enabling scalable processing on standard hardware. To evaluate performance, we conducted structured benchmark imports using public histopathology images curated from PanopTILs, SICAPv2, and PanNuke datasets, where dataset-specific import behavior, duplicate detection, and ingestion metrics were recorded as reproducible outputs. Embedding-based analysis further demonstrates dataset-level separation consistent with underlying image characteristics. These results show that PixelDeck provides an efficient, scalable local curation layer for heterogeneous biomedical imaging collections, enabling streamlined dataset exploration and preparation for downstream analysis.

bioinformatics↗

bioRxiv2026

A Query-to-Dashboard Framework for Reproducible PubMed-Scale Bibliometrics and Trend Intelligence

The rapid expansion of biomedical literature necessitates computational approaches for systematic analysis of publication patterns, identification of emerging scientific themes, and characterization of field evolution. We present PubMed Atlas, an integrated command-line and web-based platform for conducting topic-specific bibliometric analyses through programmatic access to PubMed E-utilities. This workflow retrieves PubMed identifiers matching user-defined queries, downloads comprehensive metadata in batch mode, extracts structured information including titles, abstracts, author affiliations, Medical Subject Headings, publication classifications, funding acknowledgments, and digital object identifiers, then organizes these data within a local SQLite relational database optimized for rapid queries and visualization. An accompanying Streamlit-based interactive dashboard enables exploration of temporal publication patterns, journal distribution profiles, MeSH term frequencies, geographic author distributions, and direct linking to recent publications. We demonstrate the application of PubMed Atlas to cancer stem cell biology and stem cell transcriptional regulatory network research, providing a framework for reproducible bibliometric investigation and systematic identification of research gaps within dynamically evolving scientific domains.

bioinformatics↗

bioRxiv2026

Multimodal gene embeddings for drug-target prediction and lineage reconstruction

Understanding how gene function emerges across molecular, cellular, and pharmacologic contexts remains a central challenge in systems biology and drug discovery. Conventional computational models typically operate within a single modality, such as expression, ontology, or interaction networks, limiting their ability to capture the multidimensional nature of gene function. Here, we present NEWT (Neural Embeddings for Wide-spectrum Targeting), a multimodal deep learning framework that integrates heterogeneous biological knowledge into a unified and interpretable representation space. By combining functional annotations, large-scale co-expression data, pathway information, lineage programs, transcriptional regulons, and protein-protein interaction features through an attention-guided fusion architecture, NEWT learns cross-modal dependencies that reflect both global functional hierarchies and context-specific regulatory relationships. Applied to L1000 perturbational transcriptomes, NEWT achieves higher compound-target prediction accuracy than prior embedding models and reconstructs pharmacological networks that reveal mechanistic and repurposing opportunities. When extended to single-cell RNA-seq data, NEWT preserves developmental trajectories and enhances the resolution of lineage hierarchies. Together, these results demonstrate that multimodal gene embeddings can bridge pharmacogenomic and single-cell transcriptomic analyses within a common functional geometry, establishing a scalable foundation for integrative target discovery and systems-level modeling of cellular identity.

systems biology↗

bioRxiv2025

De novo protein design enables targeting of intractable oncogenic interfaces

Protein-protein interactions (PPIs) involving oncogenic drivers remain among the most intractable targets in cancer biology due to their dynamic conformations and limited accessibility to conventional small molecules. Although antibodies and indirect inhibitors have achieved clinical success against targets such as PD-1/PD-L1 and MYC, challenges persist related to tissue penetration, intracellular delivery, resistance, and incomplete blockade of key interface hotspots. Here, we present DesignForge, an integrated de novo protein design framework that combines deep-learning-based structure generation, sequence optimization, and energetic hotspot mapping to create compact miniprotein binders for PPIs. Using this approach, we engineered PD-1 mimetics predicted to disrupt the PD-1/PD-L1 immune checkpoint, designed scaffolds targeting the MYC/MAX dimerization interface, and generated KRAS binders in a manner predicted to occlude RAF interaction. The top designs showed high structural confidence by AlphaFold2, favorable stability metrics, and consistent hotspot engagement identified through MOE-based analyses. Collectively, these results establish DesignForge as a generalizable in silico platform for rational design of therapeutic protein binders that extend beyond antibody and small-molecule modalities to systematically target intractable oncogenic PPIs.

bioinformatics↗

bioRxiv2025

Stable Maintenance of Two-Cell-Like Cells from Embryonic Stem Cells Reveals Chromatin and Super Enhancer Regulation of MERVL Elements

Mouse embryonic stem cells (ESCs) occasionally transit into a rare two-cell-like (2C) state characterized by transient activation of endogenous retroviruses such as MERVL and expression of 2C-specific genes including the Zscan4 cluster. These 2C-like cells (2CLCs) resemble early blastomeres and display expanded developmental potential, but their unstable and sporadic nature has hindered mechanistic studies. Here, we establish stable 2CLCs (s2CLCs) that maintain persistent MERVL expression and homogeneous 2C gene activation. Live-cell imaging revealed uniform and sustained MERVL activity in s2CLCs, contrasting with the heterogeneous and transient expression observed in conventional ESCs. Transcriptome profiling demonstrated robust induction of 2C-specific regulatory networks, and embryoid body differentiation combined with machine learning uncovered increased lineage variability and expanded developmental trajectories. Epigenomic profiling further revealed unique chromatin states, distinctive super enhancer landscapes, and active enhancer marking at MERVL loci. Together, these findings demonstrate that stable maintenance of the 2C-like state is achievable in vitro, providing a powerful model to dissect ERV-driven transcriptional regulation, epigenomic remodeling, and totipotent-like developmental potential.

developmental biology↗

bioRxiv2025

Automated Image-Based Profiling of Pluripotent Stem Cell Colonies

Quantitative image analysis is essential for advancing stem cell biology, developmental studies, and drug discovery, yet most workflows still rely on manual or semi-quantitative scoring that is slow, subjective, and poorly scalable. A major challenge is converting complex colony morphologies into reproducible, high-dimensional datasets. To address this gap, we developed ColonyQuant, an open-source platform that integrates automated colony segmentation, alkaline phosphatase (AP) intensity quantification, morphometric profiling, and statistical classification into a single workflow. ColonyQuant computes per-colony functional readouts alongside comprehensive shape descriptors, capturing both staining intensity and structural features in a unified framework. Applied to embryonic stem cells (ESCs) treated with a selective KDM4 histone-demethylase inhibitor, ColonyQuant revealed dose-dependent reductions in colony area and integrated AP signal, together with systematic remodeling of morphometric metrics. Multivariate analyses robustly stratified treatment groups and identified intensity and solidity as principal features capturing dose-dependent colony responses. By transforming subjective scoring into objective, scalable, and biologically interpretable phenotyping, ColonyQuant provides a reproducible platform for stem cell research and high-content screening.

developmental biology↗

bioRxiv2023

Advanced image generation for cancer using diffusion models

Deep neural networks have significantly advanced medical image analysis, yet their full potential is often limited by the relatively small dataset sizes. Generative modeling has stimulated attention for its potential applications in the synthesis of medical images. Recent advancements in diffusion models have exhibited a remarkable capacity for producing photorealistic images. Despite this promising development, the application of such models in the generation of medical images remains underexplored. In this study, we explored the potential of using diffusion models to generate medical images, with a particular emphasis on producing brain magnetic resonance imaging (MRI) scans, such as those depicting low-grade gliomas. Additionally, we examined the generation of contrast enhanced spectral mammography (CESM) images, as well as chest and lung X-ray images. Utilizing the Dreambooth platform, we trained stable diffusion models based on text prompts, class and instance images, subsequently prompting the trained models to produce medical images. The generation of medical imaging data presents a viable approach for preserving the anonymity of medical images, effectively reducing the likelihood of patient re-identification during the exchange of data for research. The findings of this study reveal that the application of diffusion models in generating images successfully captures attributes specific to oncology within imaging modalities. Consequently, this research establishes a framework that harnesses the power of artificial intelligence for the generation of cancer medical imagery.

cancer biology↗