Research

Epigenetics, transcription and cellular dynamics

Our mission: to understand the fundamentals of epigenetic and transcriptional regulation in cellular dynamics — for early detection and treatment.

Primary areas of interest

Our research has two primary areas

AREA 01

Proteomics approaches to investigate transcription factor complexes

We developed RIME as a tool to assay endogenous protein complexes. RIME has been used to study complexes from cancer to embryonic stem cells. We are currently developing nanoparticle-based approaches to increase the sensitivity of the method and make it applicable to a wider range of biological samples.

Mohammed et al., Nature Protocols 2016 · Mohammed et al., Cell Reports 2013 · Nature 2016

AREA 02

Single-cell multiomics connecting genetic and epigenetic heterogeneity with hormone response

Using novel single-cell methods, including the multiomic NMT-seq approach, we profile tumor cell state at a transcriptional and epigenetic level. In cell lines and primary patient samples, we study how underlying genetic and epigenetic variations impact the function of hormones in cancer. On the technology side, we are adapting NMT-seq to spatial platforms and working to improve its throughput.

Mohammed*, Argelaguet* et al., Nature 2019 · Mohammed* et al., Cell Reports 2017 · Cell Systems 2018

In progress

What we're working on now

A look at active projects and the data behind them. Some pieces are interactive — hover and click to explore. Updated as the work develops.

Our technologies

Tools we've built

Methods developed by the lab and its members, openly available to the research community.

scNMT-seq

Single-cell nucleosome, methylation and transcription sequencing

A single-cell method for parallel transcriptome, chromatin accessibility, and DNA methylation profiling — three layers of regulation from one cell.

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RIME

Rapid immunoprecipitation mass spectrometry of endogenous proteins

Allows the study of protein complexes — in particular chromatin and transcription factor complexes — in a rapid and robust manner by mass spectrometry.

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TITAN

Topic inference of transcriptionally associated networks

A topic-modeling-based machine learning approach to identify transcriptional cell states in single-cell RNA-seq datasets.

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Dig into the details.

Every method and finding above is documented in our publication record — explore the full list.

All publications