Glick, A. | bioRxiv Science

bioRxiv2025

Super-Resolved Spatial Transcriptomics Reveals Early Changes in RNA Localization in the 5xFAD Hippocampus

Cell-type-specific changes in gene expression and RNA localization are hallmarks of Alzheimers disease (AD) and other neurodegenerative disorders, yet spatial dysregulation in early disease stages remains poorly defined. Here, we applied Expansion Sequencing (ExSeq) to map the spatial distribution of 101 genes at super-resolution in the hippocampus of 4-week-old 5xFAD and wild-type (WT) mice, prior to overt pathology. We uncovered early alterations in RNA spatial organization and gene expression, including 23 genes showing altered localization without changes in abundance in the 5xFAD hippocampus. Using spatial expression analysis and single-cell neighborhood analysis, we identified cell-type- and region-specific molecular programs associated with synaptic function, neuroinflammation, and metabolic stress that differed between 5xFAD and WT mice. Spatial RNA velocity further revealed state differences influenced by local cell to cell interactions. Together, these results suggest that RNA positioning and transcriptional programs are perturbed at early disease stages. Finally, we provide the full super-resolution ExSeq dataset as an open resource for spatial and cell-type-specific analyses in early Alzheimers disease research. HighlightsO_LISuper-resolved transcriptomic profiling of the hippocampus at early disease stages C_LIO_LIIdentification of 23 genes with altered spatial localization without changes in abundance C_LIO_LIEarly alterations in single-cell neighborhood organization in the 5xFAD hippocampus C_LIO_LISpatial RNA velocity reveals cell-type-specific cell state differences shaped by cell-cell proximity C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/678295v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1b46cf2org.highwire.dtl.DTLVardef@5cc55eorg.highwire.dtl.DTLVardef@a1ade4org.highwire.dtl.DTLVardef@9f833e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2022

ICAT: A Novel Algorithm to Robustly Identify Cell States Following Perturbations in Single Cell Transcriptomes

MotivationThe detection of distinct cellular identities is central to the analysis of single-cell RNA sequencing experiments. However, in perturbation experiments, current methods typically fail to correctly match cell states between conditions or erroneously remove population substructure. Here we present the novel, unsupervised algorithm ICAT that employs self-supervised feature weighting and control-guided clustering to accurately resolve cell states across heterogeneous conditions. ResultsUsing simulated and real datasets, we show ICAT is superior in identifying and resolving cell states compared to current integration workflows. While requiring no a priori knowledge of extant cell states or discriminatory marker genes, ICAT is robust to low signal strength, high perturbation severity, and disparate cell type proportions. We empirically validate ICAT in a developmental model and find that only ICAT identifies a perturbation-unique cellular response. Taken together, our results demonstrate that ICAT offers a significant improvement in defining cellular responses to perturbation in single-cell RNA sequencing data. Availability and implementationhttps://github.com/BradhamLab/icat Supplemental Methods, Tables and Figures are available online.

bioinformatics↗