Aditya Pratapa

Cell extrusion contributes to epithelial homeostasis, but its dysregulation can lead to tumorigenesis or degeneration. A fine balance in this process is therefore essential for tissue integrity. Yet the cell types and states vulnerable to extrusion, and the mechanisms that drive it, remain elusive. Here, using spatial maps of cell states in human idiopathic pulmonary fibrosis (IPF) we find that aberrant TP53 activation in alveolar epithelial cells drives cell extrusion. Genetic modulation of TP53 specifically in alveolar epithelial type 1 cells (AT1) was sufficient to induce plasticity and subsequent extrusion as demonstrated by lineage tracing and live imaging. Strikingly, single cell and bulk transcriptome profiling revealed aberrant TP53 drives AT1 cells to acquire a transitional state mirroring AT2-derived regeneration associated intermediate states. Critically, loss of AT1 derived transitional state triggers a compensatory AT2-derived regenerative response, establishing a bidirectional transitional state that activates myofibroblasts and remodels the alveolus. Together, our study implicates AT1 plasticity and their reversion as an unrecognized driver of epithelial cell loss and establishes bidirectional transitional state as a central mechanism underlying progression of fibrotic remodeling.

Foundation models pre-trained on certain biological data modalities exhibit systematic representational biases when encountering out-of-distribution (OOD) data from new assays. The embedding drift largely arises from instrumentation and protocol-related artifacts rather than true biological variation in cell states or tissue morphology. These drifts are distinct from conventional batch effects and cannot be remedied by retraining as sample sizes are often insufficient, and modifying existing embeddings breaks downstream tools that depend on stable representations. We introduce USHER, an adaptable framework to learn simple transforms that return OOD embeddings to a foundation model’s reference space. USHER enables embedding transformation via an expectation maximization-style procedure. Given a reference in-distribution sample, USHER first estimates a Fused Gromov-Wasserstein coupling that aligns unpaired OOD (source) and reference (target) embeddings by minimizing transport distance while preserving local structure. To make optimal transport couplings more useful for down-stream tasks, we introduce the concept of entropic filtering to retain only high-confidence correspondences. In the second step, USHER learns a low-complexity transformation that reliably restores the model’s representation space for OOD data. We demonstrate this learned transformation generalizes to other OOD data from similar experimental conditions. We applied USHER to correct platform-specific biases seen when running scGPT on Xenium transcript counts: USHER maps Xenium embeddings back to the native scRNA-seq representation space, improving cell type clustering and cross-platform integration. Histopathology foundation models trained on H&E images fail on MALDI metabolite-profiled tissue images due to data-acquisition artifacts. USHER corrects these, enabling cell-type classification and protein abundance imputation. USHER offers a generalizable framework to make biological foundation models portable across a rapidly-evolving experimental landscape.

Spatial omics technologies provide complementary and layered molecular insights that span proteins, transcripts, and metabolites. However, aligning and integrating these modalities across serial tissue sections remains a computational challenge. Existing alignment methods are primarily unimodal and assume preserved topology, often failing with tissue distortions like tears, folds, or anatomical changes. Here, we present SAME (Spatial Alignment of Multimodal Expression) that introduces space-tearing transforms, a framework for controlling localized topological disruptions during cross-sectional alignment. Using integer linear programming to maximize cell type matches across the modalities, we enhance cell-type alignment accuracy by 20% compared to existing methods while preserving biologically meaningful spatial relationships. Applied to protein-RNA integration in healthy tongue tissue and lung adenocarcinoma, SAME revealed cryptic immune subpopulations that were otherwise missed by RNA-only or protein-only classification. In a separate lung adenocarcinoma study, we assayed and integrated protein and metabolomic profiles, uncovering localized mevalonic acid upregulation specifically within tumor-macrophage spatial niches and identifying targeted metabolic crosstalk invisible to single-modality approaches. SAME enables unprecedented experimental designs that leverage each modality independently while computationally recovering cross-modal spatial structure, unlocking multimodal discoveries in complex tissues.

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we develop TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000 cells; 51 cell types) from three niches (brain, intestine, gland), TACIT outperforms existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types reveals new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

Supplemental Figure S5. Top differentially expressed genes by expression level and statistical significance.

Supplemental Figure S1. Sample clustering by RNAseq and top differentially expressed genes by treatment group.

Supplemental Figure S4. GO Activated Immune Response Genes.

Supplemental Figure S6. Spatial proximity between immune cells and tumor cells.

Supplemental Figure S2. Differential expression of androgen-related genes with or without degarelix.

Supplemental Figure S3. Top differentially activated GO pathways with ADT.