Biology subjects

Chung, D.

Publications and source records attributed to Chung, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Circular Data Analysis for Spatial Omics

Many biological quantities in omics are inherently periodic or directional, including circadian phase, cell-cycle position, and cellular orientation. Treating such quantities as ordinary linear variables can introduce artificial discontinuities and obscure biological interpretation. Circular methods are well developed across directional statistics, bioinformatics, and spatial statistics, but practical guidance for omics applications remains limited. We present key considerations for circular data analysis in single-cell and spatial omics, including the provenance and observational unit of the circular quantity, the inferential or predictive objective, the distinction between tissue composition and residual spatial dependence, and the choice of appropriate models. We describe relevant approaches for circular representation, inference, clustering, regression, machine learning, and spatial analysis, together with representative R software. Their practical use is illustrated through spatial transcriptomic case studies of derived circadian peak phase in a mouse model of Alzheimer's disease and latent cell-cycle position in human ovarian cancer. These examples demonstrate how the definition of the angle, its observational unit, and tissue heterogeneity shape appropriate analysis and interpretation, including when spatial modeling is not warranted. We conclude with practical recommendations and directions for future methodological development.

bioinformatics↗

bioRxiv2026

spaCraft: calibrated power analysis and sample-size planning for multi-sample spatial transcriptomics

Comparative spatial transcriptomics is now routine, yet the number of tissue sections per group is rarely determined by formal power analysis. Power depends jointly on between sample variation and domains recovered by clustering, a combination not represented by existing tools. We present spaCraft, which converts a replicated pilot into endpoint-specific per-group sample-size recommendations. It fits models of spatial expression, domain geometry and composition, then estimates power through a generate, recover, test loop that reestimates domains in every synthetic sample, allowing clustering uncertainty to enter the recommendation. Its differential-expression and composition endpoints are tested on recovered rather than assumed domains, yielding calibrated power rather than detection rates. We applied spaCraft to four cohorts spanning Visium, Stereo-seq, and Visium HD. In held-out validation, three-sample pilots predicted sample-size requirements in independent real samples, supporting the full chain from pilot fitting through domain recovery to endpoint testing. Sample size thereby becomes an explicit, reproducible property of the planned analysis rather than an informal guess.

bioinformatics↗

bioRxiv2026

MSGPCA: Multi-Slice Graph PCA for replicate-aware Spatial Omics analysis

As spatial transcriptomics (ST) and spatial proteomics (SP) technologies mature, experimental designs are increasingly moving beyond single-slice analyses toward multi-slice studies involving one or more donors and experimental conditions. Although these designs enable the identification of reproducible spatial signals, they also introduce substantial biological heterogeneity, particularly when integrating non-serial slices or anatomically distinct regions. If not modeled carefully, such variation can blur slice-specific tissue structure, mask conserved molecular patterns, and limit the discovery of biologically relevant latent structure. Although dimension reduction is essential for representing high-dimensional molecular data in a lower-dimensional space, existing multi-slice methods typically enforce a globally shared representation that inadequately accommodates slice-level heterogeneity. To address this limitation, we propose Multi-Slice Graph Principal Component Analysis (MSGPCA), which decomposes molecular variation into shared spatial factors conserved across slices and slice-specific factors that capture local tissue microarchitecture. In downstream analyses, MSGPCA-derived representations recover spatial tissue structure, denoise molecular profiles, and reveal biologically interpretable metafeatures associated with shared and slice-specific biology. In a mass spectrometry imaging dataset comprising nonserial slices of ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC), the shared factors captured broad biological differences across tissue regions, whereas the slice-specific factors revealed intratumoral spatial variation within the IBC microenvironment. In human dorsolateral prefrontal cortex ST data, MSGPCA recovered laminar cortical architecture across adjacent slices, closely aligning with expert pathologist annotations. Together, these findings demonstrate that MSGPCA resolves shared tissue architecture while preserving local microenvironmental variation in complex multi-slice spatial omics datasets.

bioinformatics↗

bioRxiv2026

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of actionable panels for targeted validation and downstream translation. Existing pipelines rely largely on univariate differential-expression analyses, which ignore joint molecular structure and provide limited predictive insight. Multivariate machine-learning methods, including random forest,XGBoost, elastic net, and specialized single-cell panel-selection approaches, can capture predictive patterns but typically lack explicit spatial modeling and probabilistic feature selection, relying instead on model-specific importance scores or user-specified panel sizes. We develop rapid kernel machine regression (RKMR), a scalable framework for spatial-omics marker discovery that integrates nonlinear kernel modeling, spike-and-slab variable selection, and spatial dependence. RKMR uses automatic relevance determination (ARD) kernels and sparsity-inducing priors to capture nonlinear marker-outcome relationships and implicit feature interactions while producing approximate posterior inclusion probabilities (PIPs) that quantify model-based uncertainty in feature inclusion. To scale inference to large spatial datasets, RKMR combines low-rank kernel approximations with stochastic variational optimization. In simulations, RKMR consistently achieves higher AUPRC than competing methods across a range of molecular-signal and spatial-effect settings. Across spatial transcriptomics and scRNA-seq datasets, RKMR identifies parsimonious marker sets that recover reported cell-type signatures and reproducible tissue-layer markers. These results establish RKMR as a scalable and uncertainty-aware framework for translating high-dimensional spatial omics data into robust, experimentally actionable marker panels.

bioinformatics↗

bioRxiv2026

Skiaviridae: a large DNA virus family that persistently infects thraustochytrid protists

Genomics and metagenomics have transformed our understanding of the diversity of large DNA viruses infecting eukaryotic microorganisms. Using long-read sequencing, we discovered ubiquitous, co-culturing nucleocytoviruses in several thraustochytrid protists. These viruses are closely related to SmDNAV, an 'orphan' virus of the thraustochytrid Sicyoidochytrium minutum. Transcriptomics, proteomics and electron microscopy demonstrate viral gene expression and particle formation in these cultures without obvious defects in culture growth. We also found SmDNAV-type viruses associated with previously sequenced thraustochytrids. Phylogenetic analysis reveals that the SmDNAV and SmDNAV-like genomes belong to a hitherto unrecognized order of Nucleocytoviricota, here named "Skiavirales". All fifteen identified skiavirus genomes lack numerous viral hallmark genes including DNA-dependent RNA polymerase; all but three also lack DNA polymerase family B (PolB). PolB phylogeny suggests a specific relationship between Skiavirales and the recently discovered mirusviruses, which belong to a different viral realm. Gene exchange between skiaviruses, co-occurring mirusviruses, and host nuclear genomes shows that persistent infection by diverse large DNA viruses provides an opportunity for virus-virus and virus-host co-evolution in thraustochytrids and, perhaps, other microbial eukaryotes.

evolutionary biology↗

bioRxiv2026

CRISPR/Cas9 gene editing in the heterotrophic protist Acanthamoeba castellanii

Members of the amoebozoan genus Acanthamoeba are unicellular heterotrophic protists that inhabit a wide range of terrestrial and aquatic environments. They are opportunistic pathogens known to host disease-causing bacteria as well large DNA viruses. While Acanthamoeba is commonly used to study cellular locomotion, phagocytosis, and disease, genetic tools for precise editing of the Acanthamoeba genome are still in their infancy. Here we have developed CRISPR/Cas9 tools for genetically engineering the model acanthamoebid Acanthamoeba castellanii strain Neff. In proof-of-principle experiments, we targeted the myosin-II heavy chain gene. Evidence for editing was found using reverse transcriptase PCR, microscopy, and DNA sequencing, which also identified unique alleles for the target locus. Collectively, we demonstrate that CRISPR can be employed to edit Acanthamoeba genes using a knock-in approach. This work serves as a foundation to further develop A. castellanii as a model system with which to study diverse questions in cell and molecular biology, biochemistry and evolution.

molecular biology↗

bioRxiv2026

FusedFCR: A Fused Forward Continuation-Ratio model for marker selection along cell-fate trajectories

Time-course single-cell RNA sequencing (scRNA-seq) data collected across ordered stages provide population-level snapshots of differentiation, disease progression, and aging. Supervised pseudotime methods use observed stage labels to reconstruct continuous progression but generally do not identify marker genes associated with changes from one stage to the next. Unsupervised pseudotime-based marker selection methods infer latent trajectories directly from expression data and identify trajectory-associated genes, but do not explicitly link these associations to the observed stages. We propose FusedFCR, a regularized forward continuation-ratio model that represents cellular progression through a sequence of conditional transitions across ordered stages. FusedFCR combines a lasso penalty for gene selection with a fusion penalty that encourages similar effects across adjacent transitions while allowing transient and direction-changing associations. The resulting transition-specific coefficients support interpretable gene selection and a continuous pseudotime-like projection anchored to the observed developmental stages. In simulations, FusedFCR accurately recovers gene-effect trajectories and improved predictive performance relative to alternative methods. Applied to one mouse and three human datasets (mouse pancreatic beta-cells, human extravillous trophoblast, human induced pluripotent stem cell derived astrocytes, and human endometrial cells during the secretory phase), FusedFCR identifies biologically interpretable genes associated with distinct developmental transitions. Gene set enrichment analysis further reveals stage-specific pathway activity consistent with known developmental biology, while held-out stage-classification accuracy was competitive or superior across both datasets. Together, these results show that FusedFCR complements pseudotemporal ordering by identifying which molecular programs change and when those changes emerge along the developmental trajectory. An accompanying R package is available on GitHub.

bioinformatics↗

bioRxiv2026

CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data

Copy number variations (CNVs) are major structural genomic variants that contribute to a wide range of human diseases. Accurate detection of CNVs from whole-exome sequencing (WES) data has been a long-sought goal for clinical and population genetic studies. Despite recent progress, existing WES-based CNV callers still suffer from high false-positive rates and reduced recall for short-length variants, and current deep learning methods have not fully used complementary information in region-level genomic features. Here we present CN-RNN, a deep learning-based CNV caller for WES data. The model combines a bidirectional long short-term memory (BiLSTM) branch that captures local depth changes and contextual dependencies across neighboring exons with a parallel multi-layer perceptron (MLP) branch that encodes region-level metadata such as GC content, mappability, and exon length. CN-RNN was trained on the Autism Sequencing Consortium (ASC) parent-child trio cohort using the Mendelian rule of inheritance to ensure high-quality training sets. It was evaluated across three independent datasets, in which we showed that CN-RNN outperformed existing WES-based CNV callers and deep learning methods. CN-RNN offers a scalable, accurate tool for CNV profiling in WES-based studies and supports broader application of CNV analysis in population and clinical research. CN-RNN is available at https://github.com/FeifeiXiao-lab/CN-RNN.

genetics↗

bioRxiv2026

Phylogeny and morphology of novel Labyrinthulomycetes in Nova Scotia, Canada

Labyrinthulomycetes are ubiquitous marine decomposers and include the thraustochytrids, a lineage known for their extensive production of polyunsaturated fatty acids and biotechnology applications. Despite historical taxonomic confusion, environmental surveys have begun to expand the known diversity within labyrinthulomycetes. Here we present 18S ribosomal DNA (rDNA) sequences from 17 novel labyrinthulomycetes strains established as axenic cultures from natural samples taken from three locations in Nova Scotia, Canada. We also present light and transmission electron microscopic analysis for three of these isolates. The combination of phylogenetic and morphological analysis suggests that these isolates belong to three different genera, Thraustochytrium, Ulkenia and Oblongichytrium. Our results expand the known genetic diversity of labyrinthulomycetes in Atlantic Canadian waters, contributing new strains to understudied genera for future comparative genomic investigations, including Ulkenia and Oblongichytrium for which very few genomic resources are available.

evolutionary biology↗

bioRxiv2026

Microstate-specific functional connectivity in alcohol use disorder using resting-state EEG

Alcohol Use Disorder (AUD) is often described as a "disconnection syndrome", reflecting neural dysfunction associated with chronic alcohol use. However, previous studies on functional connectivity of resting-state EEG (rsEEG) in AUD have reported inconsistent findings--some showing hyperconnectivity, others hypoconnectivity or no significant differences--possibly due to overlooking the brains dynamic nature even at its resting state. Here, we examined rsEEG in 26 young adults with AUD and 35 healthy controls (HC), using microstate analysis to characterize transient brain states and to estimate functional connectivity within each state. We showed that microstate map topologies were comparable between the two groups. However, the temporal dynamics in the AUD group were biased toward microstates C and D, characterized by anterior-to-posterior configurations, within which between-channel functional connectivity differed from that of HC. Particularly within microstate C, the AUD group showed significantly reduced functional connectivity compared to the HC group in the majority of channel pairs. Moreover, compared with conventional functional connectivity estimates, microstate-specific functional connectivity yielded superior classification performance in distinguishing individuals with AUD from HC. Subsequent computation of graph-theoretical measures revealed that individuals with AUD showed less stable hub-like properties and reduced small-worldness in microstate C, indicating diminished network efficiency. Our findings suggest that transient EEG properties, exemplified by microstate-specific functional connectivity, provide informative neural markers for characterizing the impact of alcohol use on the brain. HighlightsO_LIResting-state EEG microstates reveal altered brain dynamics in alcohol use disorder C_LIO_LIMicrostate C exhibits widespread reductions in functional connectivity in AUD C_LIO_LIMicrostate-specific connectivity outperforms conventional measures in AUD classification C_LIO_LIGraph analysis reveals reduced network efficiency and unstable hubs in AUD C_LI

neuroscience↗

bioRxiv2025

The urinary pathobiont Actinobaculum massiliense generates androgens via the dirAB pathway

While overlooked during the Human Microbiome Project, characterizing the urinary microbiota in health and disease is a new frontier in microbiome science. Recent studies have associated differential abundance of bacterial taxa including Propionimicrobium lymphophilum and Actinobaculum/Actinotignum spp. with prostate cancer. In this study, we collected urine from subjects prior to prostate biopsy and applied a novel Human Sterolbiome Discovery High-throughput (HSDH) assay to identify culturable urinary bacteria with the ability to generate androgens. Application of the HSDH assay to urine samples led to the isolation of eight P. lymphophilum strains positive for cortisol side-chain cleavage (steroid-17,20-desmolase), 17{beta}-HSDH activity, or both. In addition, we isolated three strains of Actinobaculum massiliense that encode DHEA isomerase reductase (dir) genes. The dirA gene encodes a novel 3{beta}/17{beta}-hydroxysteroid dehydrogenase/{Delta}4,5-isomerase and the dirB gene encodes a novel 17{beta}-hydroxysteroid dehydrogenase isoform. Structural prediction and molecular dynamics reveal probable catalytic mechanisms based on the shared catalytic triad but distinct binding pocket geometries of the DirA and DirB that describe their respective reactions. Phylogenetic analysis of DirA and DirB revealed homologs in urinary tract commensals as well as bacteria associated with steroid degradation found in aquatic and terrestrial environments. Taken together, the development of the HSDH assay and the identification of the dir pathway genes is a significant advance in microbial endocrinology, laying the methodological foundation and providing the molecular basis for understanding the role of urinary tract bacteria in host endocrine physiology.

microbiology↗

bioRxiv2025

Annotation-Free Prediction of Cancer Cells and Glands and Spatial Analysis of Immune Cells.

Prostate cancer is classified as "immune-cold" due to limited infiltration of immune cells and no clear correlation between immune cells and clinical outcomes. However, immune cells are found in prostate cancers and the spatial relationships between these immune cells and cancer cells/glands have not been investigated, partly due to a lack of automated tools that classify both cancerous cells/glands. In this paper, we have developed an end-to-end tool (TOPAZ: Tissue Organization identification using sPAtial proteomics) that combines multiplexed single-cell protein data with histology images to: 1) predict cancerous versus non-cancerous epithelial cells using a Gaussian-mixture model; 2) predict cancerous/non-cancerous gland type using a principal curve estimation. Using TOPAZ to assign cancer and non-cancerous labels to cells and glands, we extracted multiscale spatial features from the classification results--including immune dense-region geometrical features and cell-to-gland distances-- and correlated the features with risk of biochemical recurrence and cancer grade. Tissue-microarrays containing 753 cores from 217 prostate cancer patients underwent multiplexed immunofluorescent imaging (Cell DIVE, Leica) for epithelial cell markers (panCK26, S6, NaKATPase), basal cell markers (p63, CK5), a cancer cell marker (AMACR), and T cell markers (CD3, CD4, CD8, FOXP3, CD68). Cancerous/non-cancerous cell classification from TOPAZ achieved 82% sensitivity and 99% specificity against expert annotation, and the pipeline further predicted cancerous/non-cancerous glands without manual threshold tuning. Regulatory-T-cell and helper-T-cell percentages decreased, and macrophage percentage increased with grade increase (P < 0.05). When the median distance from cancerous gland centroids to the nearest regulatory or helper T-cell exceeded approximately 50 {micro}m, the hazard of biochemical recurrence doubled (log-rank P < 0.01). The open-source Shiny app TOPAZ (https://chunglab.bmi.osumc.edu/TOPAZ) packages the workflow, predicting individual cell types and gland shapes. By combining probabilistic cell typing with gland-shape modeling, TOPAZ yields interpretable multiscale spatial features linked to prognosis and is released as an open web app for unrestricted use. Author SummarySpatial distribution of cancerous cells/glands and immune cell distribution has not been considered as a prognosticator in prostate cancer. In addition, automated tools that can quantify and integrate these distributions are lacking. We combined high-dimensional single-cell protein measurements with histology images to map gland structure in prostate cancer tissue. Our web-based tool, TOPAZ (https://chunglab.bmi.osumc.edu/TOPAZ) predicts whether each epithelial cell and gland in virtual H&E image is cancerous or not. Once the predictions are made, a spatial analysis workflow helps quantify spatial features of immune cells relative to the glands and correlate with recurrence risk and grades. Across 753 tissue cores from 217 prostate cancer patients, helper and regulatory-T-cells located more than about 50 {micro}m away from cancerous epithelial glands were associated with a higher risk of biochemical recurrence. The pipeline provides new insights for researchers and pathologists into prostate cancer progression and biochemical recurrence through integration of spatial location of cancer glands and immune cells.

cancer biology↗

bioRxiv2025

Discovery of a potent anti-Zika virus benzamide series targeting the viral protein NS4B

1.Zika virus (ZIKV), a member of the Flaviviridae family, causes significant public health concerns through congenital Zika syndrome and Guillain-Barre syndrome, yet no effective anti-ZIKV drugs or vaccines are available. To address this critical need, we conducted phenotypic, cytopathic effect-based, high-throughput screening followed by medicinal chemistry optimization and discovered novel benzamide anti-ZIKV leads. Current best compounds demonstrated superior potency (EC50 values 40-400 nM, CC50 > 50 {micro}M) compared to NITD-008, the most potent known anti-ZIKV agent. Time-of-addition assays, resistant virus selection studies, and biophysical binding experiments confirmed that NS4B interference constitutes the primary antiviral mechanism. Notably, resistance mutations mapped to the C-terminus of NS4B, distinct from other flavivirus NS4B inhibitors targeting dengue or yellow fever viruses, revealing novel insights into a critical function of the region. These findings establish NS4B as an Achilles heel for flaviviruses and support the development of pan-flavivirus therapeutics targeting this essential viral protein. 2. AUTHOR SUMMARYSince its pandemic spread in 2015-2016, Zika virus infection remains a significant public health threat worldwide. The virus can cause severe brain damage in developing babies and serious neurological complications like Guillain-Barre syndrome in adults. Despite these devastating consequences, we currently lack effective medicines or vaccines to prevent the virus from spreading through communities or from mothers to their unborn children. To address this critical gap, we conducted a large-scale screening of chemical compounds and discovered a promising new class of molecules that can effectively stop Zika virus from replication. Using medicinal chemistry techniques, we were able to make these compounds even more potent against the virus. In follow-up studies, we found that our compounds work by interfering with a specific viral protein called NS4B, which the virus needs to replicate its genome within the cell. Remarkably, other research teams studying related other flaviviruses (e.g., dengue and yellow fever virus) have independently discovered that this same protein is a vulnerable target. Our findings suggest that NS4B represents a universal weakness across the entire flavivirus family, making it an attractive target for developing broad-spectrum antiviral treatments.

microbiology↗

bioRxiv2025

Adaptive generalization and efficient learning under uncertainty

People often use recognizable features to infer the value of novel consumables. This "generalization" strategy is known to be beneficial in stable environments, such that individuals can use previously learned rules and values in efficiently exploring new situations. However, it remains unclear whether and how individuals adjust their generalization strategy in volatile environments where previously learned information becomes obsolete. We hypothesized that individuals adaptively use generalization by continuously updating their beliefs about the credibility of the feature-based reward generalization model at each state. Our data showed that participants used generalization more when the novel environment remained consistent with the previously learned monotonic association between feature and reward, suggesting efficient utilization of prior knowledge. Against other accounts, we found that individuals incorporated an arbitration mechanism between feature-based value generalization and model-based learning based on volatility tracking. Notably, our suggested model captured differential impacts of generalization dependent on the context-volatility, such that individuals who were biased the most toward generalization showed the lowest learning errors when the value of stimuli are generalized along the recognizable feature, but showed the highest errors in a volatile environment. This work provides novel insights into the adaptive usage of generalization, orchestrating two distinctive learning mechanisms through monitoring their credibility, and highlights the potential adverse effects of overgeneralization in volatile contexts.

neuroscience↗

bioRxiv2025

An Intrinsic-hoc Framework for Heterogeneous Cellular Senescence Elucidation Using Deep Graph Representation Learning and Experimental Validation

Cellular senescence is a primordial driver of tissue and organ aging, and the accumulation of senescent cells (SnCs) has been implicated in numerous age-related diseases. A major barrier to studying senescence is the rarity and heterogeneity of SnCs, which are not a uniform population but instead comprise diverse senotypes shaped by cell-of-origin and microenvironmental context. Such heterogeneity exceeds what classical senescence hallmarks can resolve at single-cell resolution, motivating the need for computational frameworks that can capture senotype-level diversity intrinsically. Here, we introduce DeepSAS, a deep graph representation learning framework that robustly identifies cell-type-specific SnCs and their senescence-associated genes (SnGs). DeepSAS incorporates a heterogeneous graph that integrates intracellular transcriptional states with intercellular communication cues, enabling the joint inference of senescent cells and senescence-linked genes through attention-based contrastive learning. Applied to public healthy eye and lung atlases, DeepSAS identified SnCs whose proportions positively correlate with aging. From in-house idiopathic pulmonary fibrosis (IPF) patient scRNA-seq data, DeepSAS detected 1,678 SnCs (out of 24,125 cells) and 263 SnGs across 26 cell types, including 43 SnGs that are uniquely associated with a single cell type. We generated high-resolution Xenium spatial transcriptomics data to further validate SnGs in IPF, revealing NFE2L2 as a SnG specifically enriched in CTHRC1+ fibroblasts. Notably, the ex vivo bleomycin-induced senescence in human precision-cut lung slice (hPCLS) samples similarly identified NFE2L2 as an SnG in CTHRC1+ fibroblasts, albeit with stronger transcriptional signals, suggesting mechanistic differences in senescence cells associated with chronic and acute injury. Overall, DeepSAS uncovers distinct senescence programs and infers cell-type-specific SnGs that are difficult to resolve using existing marker-based approaches. We believe it offers a generalizable and translationally relevant strategy for advancing senescence biology and therapeutic development.

bioinformatics↗

bioRxiv2025

Deconstructing the Cancer Epigenome using Reduced Representation Bisulfite Sequencing (RRBS)

Reduced Representation Bisulfite Sequencing (RRBS) represents a relatively inexpensive methodology for investigating the influence of DNA methylation on the development and progression of diseases including cancer. RRBS is a targeted approach which combines the use of restriction enzyme selection, bisulfite conversion, and high throughput sequencing technologies. The computational processing and analysis of RRBS data therefore requires modified approaches compared to typical DNA sequencing, due to deviations from standard laboratory protocols. This chapter presents a step-by-step pipeline for the processing of raw RRBS reads including quality control, trimming, alignment, methylation extraction, differential methylation analysis, annotation, and downstream analysis. This pipeline is designed to ensure optimal processing for accurate results and to provide downstream analysis tools that can extract biological insights from the data.

bioinformatics↗

bioRxiv2025

A Gentle Introduction to Spatial Transcriptomic Analysis with 10X Visium Data

Spatial transcriptomics (ST) combines single-cell RNA-seq gene expression data with spatial coordinates to provide an accurate, 2D picture of gene expression across a tissue sample. With this technology, we can discover detailed RNA localization, study development, investigate the tumor microenvironment, and create a tissue atlas. Full ST analysis requires several steps, however, as this protocol aims to be a simple introduction to the analysis process, only the foundational steps of clustering, spatially variable gene discovery, and cell-cell communication are presented, focusing on data obtained from the 10X Genomics Visium platform. An expanded protocol with full code is available at https://github.com/j-gillespie-dna/STanalysis.git

bioinformatics↗

bioRxiv2025

Zinc Finger Repressors mediate widespread PRNP lowering in the nonhuman primate brain and profoundly extend survival in prion disease mice

Prion disease is a rapidly progressing and invariably fatal neurodegenerative disorder with no approved treatment. The disease is caused by the self-templated misfolding of the prion protein (PrP) into toxic species, ultimately leading to neurodegeneration and death. We evaluated a novel epigenetic regulation approach using Zinc Finger Repressors (ZFRs) to ablate PrP expression at the transcriptional level. When delivered using adeno-associated virus (AAV), ZFRs potently and specifically reduced prion mRNA expression by >95% in vitro and to near undetectable levels within single neurons in vivo. In wildtype mice, ZFRs stably lowered neuronal PrP expression throughout the central nervous system for at least 17 months. In mice inoculated with misfolded PrP, AAV-ZFRs given at either early or late disease stages profoundly extended lifespan, significantly reduced PrP in the brain, and improved an array of molecular, histological, biomarker, and behavioral readouts. Finally, we delivered a ZFR targeting the human prion gene (PRNP) to cynomolgus monkeys using a novel blood-brain-barrier penetrant AAV capsid. Extensive bulk and single-cell assessments revealed widespread ZFR expression and PRNP repression in all 35 brain regions assessed, providing the first demonstration of epigenetic regulation across the nonhuman primate neuraxis following a single intravenous (IV) dose. These results highlight the potential of a one-time IV administered ZFR treatment for prion disease and other neurological disorders.

neuroscience↗