The Friedländer group applies state-of-the-art computational and genomic methods to address fundamental questions in RNA biology. The focus is on quantitatively describing and functionally characterizing mammalian transcriptomes, and methods include next-generation sequencing of single and pooled cells, as well as development of source code, databases and wet-lab protocols.

Of particular interest to us are microRNAs: 22 nucleotide long RNA molecules that regulate the levels of protein-coding genes in all animals. Since they confer regulation on the majority of human genes, it is not surprising that microRNAs are involved in numerous biological processes, including diseases such as cardiovascular, immunological, neurodegenerative, and psychiatric disorders and cancer. Even though miRNAs have been systematically studied for more than fifteen years, fundamental questions regarding their evolution, biogenesis and function remain unanswered.

We study microRNA function by profiling these regulators and their gene targets in the single cells where the interactions between them occur. From the measurements we infer copy-per-cell numbers for the transcripts, and we develop mathematical models to describe the kinetics of regulation. For this purpose, we apply single-cell sequencing methods and single-molecule FISH. To study microRNA biogenesis, we have developed a method to measure processing of thousands of RNA structures simultaneously in mammalian cells. To address evolutionary aspects of microRNAs, we compare microRNAs between representatives of all animal groups, using our highly curated reference database.

The group is part of the Department of Molecular Biosciences, Wenner-Gren Institute of Stockholm University, but is located at the SciLifeLab genomics institute. The group consists of computational and wet-lab biologists, working in close collaboration to pursue individual projects.

Key words

Computational biology, genomics, single-cell transcriptomics, miRNA biogenesis and function

Selected Publications

Kang W; Eldfjell Y; Fromm B; Estivill X; Biryukova I; Friedländer MR†, 2018. miRTrace reveals the organismal origins of microRNA sequencing data. Genome Biol 19(1):213.

Bonath F; Domingo-Prim J; Tarbier M; Friedländer MR†; Visa N†, 2018. Next-generation sequencing reveals two populations of damage-induced small RNAs at endogenous DNA double-strand breaks.  Nucleic Acids Research

Kang W; Bang-Berthelsen CH; Holm A; Houben AJ; Müller AH; Thymann T; Pociot F; Estivill X; Friedländer MR†, 2017. Survey of 800+ data sets from human tissue and body fluid reveals xenomiRs are likely artifacts.  RNA 23(4):433-445

Lappalainen T; Sammeth M; Friedländer MR; ‘t Hoen PA; Monlong J; Rivas MA; Gonzàlez-Porta M; Kurbatova N; Griebel T; Ferreira PG; Barann M; Wieland T; Greger L; van Iterson M; Almlöf J; Ribeca P; Pulyakhina I; Esser D; Giger T; Tikhonov A; Sultan M; Bertier G; MacArthur DG; Lek M; Lizano E; Buermans HP; Padioleau I; Schwarzmayr T; Karlberg O; Ongen H; Kilpinen H; Beltran S; Gut M; Kahlem K; Amstislavskiy V; Stegle O; Pirinen M; Montgomery SB; Donnelly P; McCarthy MI; Flicek P; Strom TM; Lehrach H; Schreiber S; Sudbrak R; Carracedo A; Antonarakis SE; Häsler R; Syvänen AC; van Ommen GJ; Brazma A; Meitinger T; Rosenstiel P; Guigó R; Gut IG; Estivill X; Dermitzakis ET, 2013. Transcriptome and genome sequencing uncovers functional variation in humans.  Nature 501(7468):506-11

Friedländer MR; Chen W; Adamidi C; Maaskola J; Einspanier R; Knespel S; Rajewsky N, 2008. Discovering microRNAs from deep sequencing data using miRDeep.  Nat Biotechnol 26(4):407-15

† corresponding author