Biology subjects

Kenney, M.

Publications and source records attributed to Kenney, M..

4 recordsLinked to original sources

bioRxiv2026

MERFISHEYES: Web-based Visualization and Sharing Platform for Single-cell and Single-molecule Spatial Transcriptomics

Interactive exploration and sharing of hybridization-based spatial transcriptomics datasets remain limited by large file sizes, specialized software, and computationally intensive preprocessing, restricting rapid access to publicly available resources such as the Brain Image Library. Here we present MERFISHEYES (https://merfisheyes.com), a browser-based platform for interactive visualization of single-cell and single-molecule spatial transcriptomics data across commercial and emerging high-throughput platforms. MERFISHEYES enables marker gene exploration, cross-dataset and cross-species comparison, visualization of subcellular RNA localization, and collaborative sharing through browser-accessible links generated directly from drag-and-drop datasets. We use MERFISHEYES to enable browser-based visualization of all 158 spatial transcriptomics datasets in the Brain Image Library and demonstrate its capabilities in handling high-throughput 3D data with genome-scale imputation of gene expression and DNA accessibility. MERFISHEYES provides a scalable framework for rapid exploration, sharing, and collaborative analysis of spatial transcriptomics data.

bioinformatics↗

bioRxiv2026

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases

The BRAIN Initiative Cell Atlas Network (BICAN) is generating large-scale multimodal datasets to profile cell types in the human, non-human primate, and mouse brain. The diversity of single-cell and spatial transcriptomic and epigenomic assays, combined with varied experimental contexts, multiple data-generating laboratories and distributed infrastructure, poses substantial challenges for data integration and reuse in BICAN. To address this, we implemented a standards framework that enables layered integration of these data into knowledge-ready products for interoperable brain cell atlases. This framework organizes data based on three progressively structured layers. First, we introduced an assay-agnostic modeling layer that unifies the representation of single-cell and spatial omics data using a common set of biological entities and processes assessed by diverse experimental techniques. Second, we implemented harmonized metadata standards that capture key experimental features linked to biospecimen provenance across heterogeneous tissue sources, species, and preparations, supporting integration and validation while minimizing burden on data contributors. Third, we present an extensible representation for data-driven cell type taxonomies that integrates molecular data with annotations, ontology mappings, and evidence. Together, these contributions represent an end-to-end framework that transforms heterogeneous datasets into structured, interoperable resources that support broad community reuse via mapping algorithms, annotation systems, and visualization platforms. This approach links biospecimen provenance with cell-level outputs and embeds these in a standardized taxonomy format, enabling downstream applications such as cross-dataset integration, reference mapping, and knowledge-driven analysis. More broadly, our work demonstrates a generalizable strategy for enabling an efficient data-to-knowledge pipeline in a large-scale consortium setting.

genomics↗

bioRxiv2025

Changes in gene regulation are associated with the evolution of resistance to a novel parasite

Host-parasite interactions are ubiquitous and are important drivers of diversification and evolution. Host immune systems in particular are frequent targets of parasite-driven selection. The resulting rapid evolution of immune genes is usually framed as an ongoing arms race between a co-evolving pair of host and parasite species. But, often immune evolution may be driven by the acquisition of a new and unfamiliar parasite. For instance, when marine populations of threespine stickleback (Gasterosteus aculeatus) colonized freshwater lakes [~]12,000 years ago, they encountered and evolved resistance to a freshwater-restricted cestode Schistocephalus solidus. We compared the transcriptomic responses of lab-reared stickleback from three populations with varying cestode susceptibility (naive marine, susceptible lake, and resistant lake), when exposed to several immune stimuli. The resulting changes in expression reveal strong evidence for shared and population-specific responses during evolution of defense against a new parasite. Our investigation highlights the roles of several key immunological processes in underlying a general physiological response to tissue damage (fibrosis), and the importance of regulation of this fibrosis as a necessary step for its co-option into parasite defense. Furthermore, we highlight changes in expression of fibrosis-associated genes which facilitate faster and more targeted deployment of this defense against parasites; fish from resistant populations have higher and more consistent expression of fibrosis genes, which experience strong negative feedback in response to cestode stimuli only. Combined, our results provide strong evidence that changes in gene regulation and increased negative feedback to mitigate immunopathology are essential steps in the evolution of novel parasite defense.

immunology↗

bioRxiv2023

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

neuroscience↗