Dominic Grün

Combinatorial microRNA target predictions

MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3′ untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTars ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals.

microRNA Target Predictions across Seven Drosophila Species and Comparison to Mammalian Targets

microRNAs are small noncoding genes that regulate the protein production of genes by binding to partially complementary sites in the mRNAs of targeted genes. Here, using our algorithm PicTar, we exploit cross-species comparisons to predict, on average, 54 targeted genes per microRNA above noise in Drosophila melanogaster. Analysis of the functional annotation of target genes furthermore suggests specific biological functions for many microRNAs. We also predict combinatorial targets for clustered microRNAs and find that some clustered microRNAs are likely to coordinately regulate target genes. Furthermore, we compare microRNA regulation between insects and vertebrates. We find that the widespread extent of gene regulation by microRNAs is comparable between flies and mammals but that certain microRNAs may function in clade-specific modes of gene regulation. One of these microRNAs (miR-210) is predicted to contribute to the regulation of fly oogenesis. We also list specific regulatory relationships that appear to be conserved between flies and mammals. Our findings provide the most extensive microRNA target predictions in Drosophila to date, suggest specific functional roles for most microRNAs, indicate the existence of coordinate gene regulation executed by clustered microRNAs, and shed light on the evolution of microRNA function across large evolutionary distances. All predictions are freely accessible at our searchable Web site http://pictar.bio.nyu.edu.

Single-cell messenger RNA sequencing reveals rare intestinal cell types

Understanding the development and function of an organ requires the characterization of all of its cell types. Traditional methods for visualizing and isolating subpopulations of cells are based on messenger RNA or protein expression of only a few known marker genes. The unequivocal identification of a specific marker gene, however, poses a major challenge, particularly if this cell type is rare. Identifying rare cell types, such as stem cells, short-lived progenitors, cancer stem cells, or circulating tumour cells, is crucial to acquire a better understanding of normal or diseased tissue biology. To address this challenge we first sequenced the transcriptome of hundreds of randomly selected cells from mouse intestinal organoids, cultured self-organizing epithelial structures that contain all cell lineages of the mammalian intestine. Organoid buds, like intestinal crypts, harbour stem cells that continuously differentiate into a variety of cell types, occurring at widely different abundances. Since available computational methods can only resolve more abundant cell types, we developed RaceID, an algorithm for rare cell type identification in complex populations of single cells. We demonstrate that this algorithm can resolve cell types represented by only a single cell in a population of randomly sampled organoid cells. We use this algorithm to identify Reg4 as a novel marker for enteroendocrine cells, a rare population of hormone-producing intestinal cells. Next, we use Reg4 expression to enrich for these rare cells and investigate the heterogeneity within this population. RaceID confirmed the existence of known enteroendocrine lineages, and moreover discovered novel subtypes, which we subsequently validated in vivo. Having validated RaceID we then applied the algorithm to ex vivo-isolated Lgr5-positive stem cells and their direct progeny. We find that Lgr5-positive cells represent a homogenous abundant population of stem cells mixed with a rare population of Lgr5-positive secretory cells. We envision broad applicability of our method for discovering rare cell types and the corresponding marker genes in healthy and diseased organs.

Validation of noise models for single-cell transcriptomics

Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition.

Design and Analysis of Single-Cell Sequencing Experiments

Recent advances in single-cell sequencing hold great potential for exploring biological systems with unprecedented resolution. Sequencing the genome of individual cells can reveal somatic mutations and allows the investigation of clonal dynamics. Single-cell transcriptome sequencing can elucidate the cell type composition of a sample. However, single-cell sequencing comes with major technical challenges and yields complex data output. In this Primer, we provide an overview of available methods and discuss experimental design and single-cell data analysis. We hope that these guidelines will enable a growing number of researchers to leverage the power of single-cell sequencing.

In vivo and transcriptome-wide identification of RNA binding protein target sites.

Animal mRNAs are regulated by hundreds of RNA binding proteins (RBPs). The identification of RBP targets is crucial for understanding their function. A recent method, PAR-CLIP, uses photoreactive nucleosides to crosslink RBPs to target RNAs in cells prior to immunoprecipitation. Here, we establish iPAR-CLIP (in vivo PAR-CLIP) to determine, at nucleotide resolution, transcriptome-wide binding sites of GLD-1, a conserved, germline-specific translational repressor in C. elegans. We identified 439 reproducible target mRNAs and demonstrate an excellent dynamic range of target detection by iPAR-CLIP. Upon GLD-1 knockdown, protein but not mRNA expression of the 439 targets was specifically upregulated, demonstrating functionality. Finally, we discovered strongly conserved GLD-1 binding sites near the start codon of target genes. These sites are functional in vitro and likely confer strong repression in vivo. We propose that GLD-1 interacts with the translation machinery near the start codon, a so-far-unknown mode of gene regulation in eukaryotes.

Gene expression of pluripotency determinants is conserved between mammalian and planarian stem cells

Freshwater planaria possess extreme regeneration capabilities mediated by abundant, pluripotent stem cells (neoblasts) in adult animals. Although planaria emerged as an attractive in vivo model system for stem cell biology, gene expression in neoblasts has not been profiled comprehensively and it is unknown how molecular mechanisms for pluripotency in neoblasts relate to those in mammalian embryonic stem cells (ESCs). We purified neoblasts and quantified mRNA and protein expression by sequencing and shotgun proteomics. We identified ∼4000 genes specifically expressed in neoblasts, including all ∼30 known neoblast markers. Genes important for pluripotency in ESCs, including regulators as well as targets of OCT4, were well conserved and upregulated in neoblasts. We found conserved expression of epigenetic regulators and demonstrated their requirement for planarian regeneration by knockdown experiments. Post‐transcriptional regulatory genes characteristic for germ cells were also enriched in neoblasts, suggesting the existence of a common ancestral state of germ cells and ESCs. We conclude that molecular determinants of pluripotency are conserved throughout evolution and that planaria are an informative model system for human stem cell biology.

De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data

Summary Adult mitotic tissues like the intestine, skin, and blood undergo constant turnover throughout the life of an organism. Knowing the identity of the stem cell is crucial to understanding tissue homeostasis and its aberrations upon disease. Here we present a computational method for the derivation of a lineage tree from single-cell transcriptome data. By exploiting the tree topology and the transcriptome composition, we establish StemID, an algorithm for identifying stem cells among all detectable cell types within a population. We demonstrate that StemID recovers two known adult stem cell populations, Lgr5+ cells in the small intestine and hematopoietic stem cells in the bone marrow. We apply StemID to predict candidate multipotent cell populations in the human pancreas, a tissue with largely uncharacterized turnover dynamics. We hope that StemID will accelerate the search for novel stem cells by providing concrete markers for biological follow-up and validation.

A Single-Cell Transcriptome Atlas of the Human Pancreas

Summary To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.

A new fate mapping system reveals context-dependent random or clonal expansion of microglia

Microglia constitute a highly specialized network of tissue-resident immune cells that is important for the control of tissue homeostasis and the resolution of diseases of the CNS. Little is known about how their spatial distribution is established and maintained in vivo. Here we establish a new multicolor fluorescence fate mapping system to monitor microglial dynamics during steady state and disease. Our findings suggest that microglia establish a dense network with regional differences, and the high regional turnover rates found challenge the universal concept of microglial longevity. Microglial self-renewal under steady state conditions constitutes a stochastic process. During pathology this randomness shifts to selected clonal microglial expansion. In the resolution phase, excess disease-associated microglia are removed by a dual mechanism of cell egress and apoptosis to re-establish the stable microglial network. This study unravels the dynamic yet discrete self-organization of mature microglia in the healthy and diseased CNS.