Natarajan Lab

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Single-cell & spatial regulatory networks

We combine single-cell and spatial multi-omics with gene regulatory network inference and machine learning to understand how cells make decisions, and how those decisions go wrong in chronic disease.

Our Research Get in Touch

Research Focus

What we work on

We study cells in their native tissue context, combining measurement, computation and disease biology to understand how cell identity is established, maintained and lost.

Electron micrograph of a cell in pale blue tones

Single-cell & spatial multi-omics

Mapping cell states, lineages and tissue niches at single-cell resolution, and placing them back into their spatial context within intact tissue.

Gene regulatory networks

Inferring and comparing regulatory networks across conditions to identify the transcription factors and circuits that drive cell fate and disease.

AI for biology

Deep generative and representation-learning models that extract biological structure from large single-cell and spatial datasets.

Disease modelling

Pulmonary fibrosis, neurodegeneration and autoimmunity (lupus), in close partnership with clinical collaborators.

Selected Publications

Highlights

2026

Inferring large networks with matrix factorisation to capture non-linear dependencies among genes using sparse single-cell profiles

Jha IP, Meshran AG, Kumar V, Natarajan KN, Kumar V Briefings in Bioinformatics (in press)
2023

A single-cell, time-resolved profiling of Xenopus mucociliary epithelium reveals nonhierarchical model of development

Lee J, Møller AF, Chae S, et al., Sedzinski J, Natarajan KN. Science Advances DOI
2021

TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data

Kim J, Jakobsen ST, Natarajan KN, Won KJ. Nucleic Acids Research DOI
2020

Predicting gene regulatory networks from cell atlases

Møller AF, Natarajan KN. Life Science Alliance DOI
2019

Comparative analysis of sequencing technologies for single-cell transcriptomics

Natarajan KN, et al. Genome Biology DOI
2017

Power analysis of single-cell RNA-sequencing experiments

Svensson V, Natarajan KN, et al. Nature Methods DOI
2015

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Buettner F, Natarajan KN, et al. Nature Biotechnology DOI

Full list on Google Scholar.

Get in Touch

Interested in joining?

We welcome enquiries from prospective students, postdocs and collaborators with interests in single-cell biology, spatial omics or machine learning.