Publications

Modeling memory T cell states at single-cell resolution identifies in vivo state-dependence of eQTLs influencing disease

Author(s): Nathan A, Asgari S, Ishigaki K, Amariuta T, Luo Y, Beynor JI, Baglaenko Y, Suliman S, Price A, Lecca L, Murray MB, Moody DB, Raychaudhuri S|Journal: bioRxiv| July 2021

Many non-coding genetic variants cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated…

Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease.

Author(s): Nathan A, Beynor JI, Baglaenko Y, Suliman S, Ishigaki K, Asgari S, Huang CC, Luo Y, Zhang Z, Lopez K, Lindestam Arlehamn CS, Ernst JD, Jimenez J, Calderón RI, Lecca L, Van Rhijn I, Moody DB, Murray MB, Raychaudhuri S|Journal: Nat Immunol|PMID: 34031617| May 2021

Multimodal T cell profiling can enable more precise characterization of elusive cell states underlying disease. Here, we integrated single-cell RNA…

Axes of inter-sample variability among transcriptional neighborhoods reveal disease associated cell states in single-cell data

Author(s): Reshef YA, Rumker L, Kang JB, Nathan A, Murray MB, Moody DB, Raychaudhuri S|Journal: bioRxiv| April 2021

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples…