Biology subjects

Pratapa, A.

Publications and source records attributed to Pratapa, A..

8 recordsLinked to original sources

bioRxiv2026

TP53-mediated bidirectional lineage plasticity drives alveolar epithelial cell extrusion and tissue remodeling

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.

Cell Biology↗

bioRxiv2025

Transforming Biological Foundation Model Representations for Out-of-Distribution Data

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 models 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 models 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.

bioinformatics↗

bioRxiv2025

SAME: Topology-flexible transforms enable robust integration of multimodal spatial omics

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.

bioinformatics↗

bioRxiv2024

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT

Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using 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 outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed 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.

bioinformatics↗

bioRxiv2023

Single-cell Spatial Metabolic and Immune Phenotyping of Head and Neck Cancer Tissues Identifies Tissue Signatures of Response and Resistance to Immunotherapy

Head and neck squamous cell carcinomas (HNSCC) are the seventh most common cancer and represent a global health burden. Immune checkpoint inhibitors (ICIs) have shown promise in treating recurrent/metastatic cases, with durable benefit in [~]30% of patients. Current biomarkers for head and neck tumors are limited in their dynamic ability to capture tumor microenvironment (TME) features, with an increasing need for deeper tissue characterization. Therefore, new biomarkers are needed to accurately stratify patients and predict responses to therapy. Here, we have optimized and applied an ultra-high plex, single-cell spatial protein analysis in HNSCC. Tissues were simultaneously analyzed with a panel of 101 antibodies that targeted biomarkers related to tumor immune, metabolic and stress microenvironments. Our data uncovered a high degree of intra-tumoral heterogeneity intrinsic to head and neck tumors and provided unique insights into the biology of the tumor. In particular, a cellular neighborhood analysis revealed the presence of 6 unique spatial tumor-immune neighborhoods enriched in functionally specialized immune cell subsets across the patient tissue. Additionally, functional phenotyping based on key metabolic and stress markers identified four distinct tumor regions with differential protein signatures. One tumor region was marked by infiltration of CD8+ cytotoxic T cells and overexpression of BAK, a proapoptotic regulator, suggesting strong immune activation and stress. Another adjacent region within the same tumor had high expression of G6PD and MMP9, known drivers of tumor resistance and invasion respectively. This dichotomy of immune activation-induced death and tumor progression in the same sample demonstrates the heterogenous niches and competing microenvironments that underpin clinical responses of therapeutic resistance. Our data integrate single-cell ultra-high plex spatial information with the functional state of the tumor microenvironment to provide insights into a partial response to immune checkpoint inhibitor therapy in HNSCC. We believe that the approach outlined in this study will pave the way towards a new understanding of TME features associated with response and sensitivity to ICI therapies.

cancer biology↗

bioRxiv2019

Multiple Myeloma DREAM Challenge Reveals Epigenetic Regulator PHF19 As Marker of Aggressive Disease

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients do not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involve assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post challenge analysis identified a novel expression-based risk marker and histone modifier, PHF19, which featured prominently in several independent models. Lastly, we show that a simple four feature predictor composed of age, International Staging System stage (ISS), and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.\n\nKey pointsO_LIMost comprehensive and unbiased assessment of prognostic biomarkers in MM resulting in a robust and parsimonious model.\nC_LIO_LIIdentification of PHF19 as the expression based biomarker most strongly associated with rapid progression in MM patients.\nC_LI

cancer biology↗

bioRxiv2019

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

We present a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. We develop a systematic framework called BEELINE for this purpose. We use synthetic networks with predictable cellular trajectories as well as curated Boolean models to serve as the ground truth for evaluating the accuracy of GRN inference algorithms. We develop a strategy to simulate single-cell gene expression data from these two types of networks that avoids the pitfalls of previously-used methods. We selected 12 representative GRN inference algorithms. We found that the accuracy of these methods (measured in terms of AUROC and AUPRC) was moderate, by and large, although the methods were better in recovering interactions in the synthetic networks than the Boolean models. Techniques that did not require pseudotime-ordered cells were more accurate, in general. The observation that the endpoints of many false positive edges were connected by paths of length two in the Boolean models suggested that indirect effects may be predominant in the outputs of the algorithms we tested. The predicted networks were considerably inconsistent with each other, indicating that combining GRN inference algorithms using ensembles is likely to be challenging. Based on the results, we present some recommendations to users of GRN inference algorithms, including suggestions on how to create simulated gene expression datasets for testing them. BEELINE, which is available at http://github.com/murali-group/BEELINE under an open-source license, will aid in the future development of GRN inference algorithms for single-cell transcriptomic data.

bioinformatics↗

bioRxiv2018

Efficient Synthesis of Mutants Using Genetic Crosses

The genetic cross is a fundamental, flexible, and widely-used experimental technique to create new mutant strains from existing ones. Surprisingly, the problem of how to efficiently compute a sequence of crosses that can make a desired target mutant from a set of source mutants has received scarce attention. In this paper, we make three contributions to this question.\n\nFirst, we formulate several natural problems related to efficient synthesis of a target mutant from source mutants. Our formulations capture experimentally-useful notions of verifiability (e.g the need to confirm that a mutant contains mutations in the desired genes) and permissibility (e.g., the requirement that no intermediate mutants in the synthesis be inviable).\n\nSecond, we develop combinatorial techniques to solve these problems. We prove that checking the existence of a verifiable, permissible synthesis is NP-complete in general. We complement this result with three polynomial time or fixed-parameter tractable algorithms for optimal synthesis of a target mutant for special cases of the problem that arise in practice.\n\nThird, we apply these algorithms to simulated data and to synthetic data. We use results from simulations of a mathematical model of the cell cycle to replicate realistic experimental scenarios where a biologist may be interested in creating several mutants in order to verify model predictions. Our results show that the consideration of permissible mutants can affect the existence of a synthesis or the number of crosses in an optimal one. Our algorithms gracefully handle the restrictions that permissible mutants impose. Results on synthetic data show that our algorithms scale well with increases in the size of the input and the fixed parameters.

systems biology↗