Biology subjects

Ramjattun, K.

Publications and source records attributed to Ramjattun, K..

3 recordsLinked to original sources

bioRxiv2025

Characterizing spatial functional microniches with SpaceTravLR

The advent of spatial omics has revolutionized our understanding of tissue biology; however, these technologies remain largely descriptive and do not capture how changes in gene regulation propagate across spatial neighborhoods. While in-silico perturbation methods and foundation models aim to model the impact of genetic perturbations, these methods are limited to single-cell approaches that lack spatial resolution. Other studies can delineate morphological domains based on transcriptional similarity, but not spatial functional microniches. We address this major unmet need by developing SpaceTravLR (Spatially perturbing Transcription factors, Ligands and Receptors), a novel interpretable machine learning approach that generalizes across tissues and species, uncovering spatial features linked to functional outcomes, thereby capturing functional microniches with spatial resolution. SpaceTravLR infers how single or combinatorial genetic perturbations rewire signals across the tissue neighborhood, by propagating effects through underlying spatially resolved molecular networks, thereby modeling how perturbations can reshape both the targeted cell and its surrounding neighborhood. SpaceTravLR defines novel spatial microniches across a range of tissues at different scales of organization (niches, neighborhoods and tissues), disease and developmental contexts. SpaceTravLRs perturbation predictions are made solely from spatial omics data and closely align with experimental validation or known outcomes based on mechanistic studies. Critically, our approach enables the generation of mechanistic hypotheses underlying identified niches. We show SpaceTravLR discovered a novel mechanism for Ccr4 that drives the spatial location of a pathogenic population of allergen-specific T helper 2 (Th2) cells as they develop in the lymph node, which was experimentally validated in a murine model. Overall, SpaceTravLR provides a novel interpretable and experimentally validated framework for uncovering how genes act individually and combinatorially through cell-intrinsic and cell-extrinsic circuits to shape spatial tissue organization and function.

systems biology↗

bioRxiv2025

The Naïve Bayes Classifier as an Out-of-Distribution Detector of Novel Taxa

Detecting sequences from novel taxa remains a key challenge in metagenomic classification, as reference databases rarely capture the full extent of microbial diversity. We investigate the Naive Bayes Classifier++ (NBC++) as an out-of-distribution (OOD) detector by analyzing its log-likelihood scores across simulated and real metagenomic datasets. By partitioning reference databases and introducing taxonomic novelty, we derive thresholds that distinguish known from unknown reads at multiple taxonomic levels. These thresholds remain consistent across database sizes, indicating that once a lineage is represented, novelty detection performance stabilizes. Applied to a human gut metagenome, the thresholds reflect differences in database density and classification confidence. This work characterizes how NBC++ responds to novelty and illustrates its use in evaluating unclassified metagenomic reads.

bioinformatics↗

bioRxiv2022

COVID-19db linkage maps of cell surface proteins and transcription factors in immune cells

The highly contagious SARS-CoV-2 and its associated disease (COVID-19) are a threat to global public health and economies. To develop effective treatments for COVID-19, we must understand the host cell types, cell states and regulators associated with infection and pathogenesis such as dysregulated transcription factors (TFs) and surface proteins, including signalling receptors. To link cell surface proteins with TFs, we recently developed SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network) by integrating parallel single-cell proteomic and transcriptomic data based on Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and gene cis-regulatory information. We apply SPaRTAN to CITE-seq datasets from patients with varying degrees of COVID-19 severity and healthy controls to identify the associations between surface proteins and TFs in host immune cells. Here, we present COVID-19db of Immune Cell States (https://covid19db.streamlit.app/), a web server containing cell surface protein expression, SPaRTAN-inferred TF activities, and their associations with major host immune cell types. The data include four high-quality COVID-19 CITE-seq datasets with a toolset for userfriendly data analysis and visualization. We provide interactive surface protein and TF visualizations across major immune cell types for each dataset, allowing comparison between various patient severity groups for the discovery of potential therapeutic targets and diagnostic biomarkers.

bioinformatics↗