Biology subjects

Chidester, B.

Publications and source records attributed to Chidester, B..

3 recordsLinked to original sources

bioRxiv2025

POPARI: Modeling multisample variation in spatial transcriptomics

Integrating spatially-resolved transcriptomics (SRT) across biological samples is essential for understanding dynamic changes in tissue architecture and cell-cell interactions in situ. While tools exist for multisample single-cell RNA-seq, methods tailored to multisample SRT remain limited. Here, we introduce PO_SCPLOWOPARIC_SCPLOW, a probabilistic graphical model for factor-based decomposition of multisample SRT that captures condition-specific changes in spatial organization. PO_SCPLOWOPARIC_SCPLOW jointly learns spatial metagenes - linear gene expression programs - and their spatial affinities across samples. Its key innovations include a differential prior to regularize spatial accordance and spatial downsampling to enable multiresolution, hierarchical analysis. Simulations show PO_SCPLOWOPARIC_SCPLOW outperforms existing methods on multisample and multi-resolution spatial metrics. Applications to real datasets uncover spatial metagene dynamics, spatial accordance, and cell identities. In mouse brain (STARmap PLUS), PO_SCPLOWOPARIC_SCPLOW identifies spatial metagenes linked to AD; in thymus (Slide-TCR-seq), it captures increasing colocalization of V(D)J recombination and T cell proliferation; and in ovarian cancer (CosMx), it reveals sample-specific malignant-immune interactions. Overall, PO_SCPLOWOPARIC_SCPLOW provides a general, interpretable framework for analyzing variation in multisample SRT.

bioinformatics↗

bioRxiv2025

Learning single-cell spatial context through integrated spatial multiomics with CORAL

Cellular organization is central to tissue function and homeostasis, influencing development, disease progression, and therapeutic outcomes. The emergence of spatial omics technologies, including spatial transcriptomics and proteomics, has enabled the integration of molecular and histological features within tissues. Analyzing these multimodal data presents unique challenges, including variable resolutions, imperfect tissue alignment, and limited or variable spatial coverage. To address these issues, we introduce CORAL, a probabilistic deep generative model that leverages graph attention mechanisms to learn expressive, integrated representations of multimodal spatial omics data. CORAL deconvolves low-resolution spatial data into high-resolution single-cell profiles and detects functional spatial domains. It also characterizes cell-cell interactions and elucidates disease-relevant spatial features. Validated on synthetic data and experimental datasets, including Stero-CITE-seq data from mouse thymus, and paired CODEX and Visium data from hepatocellular carcinoma, CORAL demonstrates robustness and versatility. In hepatocellular carcinoma, CORAL uncovered key immune cell subsets that drive the failure of response to immunotherapy, highlighting its potential to advance spatial single-cell analyses and accelerate translational research.

bioinformatics↗

bioRxiv2020

SPICEMIX: Integrative single-cell spatial modeling for inferring cell identity

Spatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that can utilize the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here, we introduce SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW, a new interpretable method based on probabilistic, latent variable modeling for effective joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW markedly improves upon the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap, and Visium, we show that SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW can enhance the inference of complex cell identities, reveal interpretable spatial metagenes, and uncover differentiation trajectories. SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW is a generalizable framework for analyzing spatial transcriptome data to provide critical insights into the cell type composition and spatial organization of cells in complex tissues.

bioinformatics↗