Research Projects

Example 1

Gene expression in the fly embryo at single cell resolution (Karaiskos et al. Science 2017)

The lab has a long standing interest in the role of RNA in developmental biology. We established and optimized “drop-seq” in our lab (Alles et al. 2017). This microfluidics based method allows to rapidly capture mRNAs within thousands of individual cells in an hour on a desktop setup. After amplification and sequencing, based on barcode information individual reads can be assigned to individual molecules in individual cells. In collaboration with the Robert Zinzen lab, we applied this method to the dissociated fly embryo at stage 6. Each of the 6,000 cells of this embryo is distinct as it will give rise to a different part of the fly body. Therefore, it is important to know the molecular makeup of each individual cell. However, after sequencing individual cells, the problem is to identify the spatial position of this cell within the embryo. We designed a new method that solves this problem (“Distmap”) (Karaiskos et al., Science 2017).  As a result, we quantified gene expression for the vast majority of the embryo cells at a resolution of ~8,000 genes per cell. These data allowed us to create a “virtual embryo” where researchers can perform, online, virtual in situ hybridization for thousands of genes (www.devtex.org). We identified dozens of transcription factors and lncRNAs as likely novel regulatory factors in this system, and discovered that Hippo signaling is active in the embryo and probably responsible for inducing the exit of patches of cells from synchronized cell divisions, thus explaining observations that have been made decades ago.

Example 2

Very recently, we have established human brain organoids in the lab (Agnieszka Rybak-Wolf, unpublished). This system allows us to study, at single cell and subcellular level, functions of regulatory RNA. This system also allows us to study human brain diseases in individual patients since we can derive organoids by reprogramming cells from an individual into pluripotent stem cells. Using CRISP-CAS we can also edit the genome within organoids.

Example 3

We are studying regulation by RNA in solid tumors, muscle cells in human patients, and cardio-vascular systems in collaboration with many colleagues.

Example 4

In 2012/2013, we discovered that animals express a large number of different circular RNAs (circRNAs) in the cytoplasm of cells (Memczak et al. Nature 2013). This observation was also published independently by the Brown and Sharpless labs. However, we also showed that circRNA expression is tissue and developmental stage specific and identified a circRNA (CDR1as) that we proposed regulates gene expression by binding large numbers of a conserved miRNA (miR-7) (Memczak et al. Nature 2013; back-to-back with the Kjems lab). Our paper also contained several methods for detecting, imaging, and quantifying circRNA expression. We thus proposed that circRNAs can have regulatory roles. This is interesting since circRNAs are highly stable and therefore may perform specific tasks. In a serious of follow up papers, we (a) showed that circRNAs are usually produced by “backsplicing” (Ashwal-Fluss et al. Mol Cell 2014) (b) published a widely used public circRNA database where we curate published circRNAs (Glazar et al. RNA 2015) (c) showed that thousands of circRNAs are readily detected in human blood and may serve as biomarkers (d) discovered that a few hundred circRNAs are highly expressed in the mammalian brain and that this expression is well conserved,  developmental stage- and tissue specific, and enriched in synaptosomes  (Rybak-Wolf et al. Mol. Cell 2015).  Finally, we removed a the CDR1as locus from the mouse genome, showed that the expression of miRNAs that directly interact with CDR1as is specifically altered in the brain of KO mice, and found a behavior phenotype (“pre-pulse inhibition deificit”) that is indicative of neuropsychiatric disorders as well as an electrophysiological synaptic phenotype (Piwecka et al. Science 2017). We believe that these results argue that we discovered a regulatory layer of circRNAs and interacting molecules that have regulatory function in the mammalian brain.

Example 5

We wrote an “Analysis Review” about functions of competition effects in post-transcriptional gene regulation. In this review, we also proposed and tested one of the first quantitative models. (Jens & Rajewsky, Nature Reviews Genetics 2014).