Genetic screen identifies microRNA cluster 99b/let-7e/125a as a regulator of primitive hematopoietic cells.

Hematopoietic stem/progenitor cell (HSPC) traits differ between genetically distinct mouse strains. For example, DBA/2 mice have a higher HSPC frequency compared with C57BL/6 mice. We performed a genetic screen for micro-RNAs that are differentially expressed between LSK, LS(-)K(+), erythroid and myeloid cells isolated from C57BL/6 and DBA/2 mice. This analysis identified 131 micro-RNAs that were differentially expressed between cell types and 15 that were differentially expressed between mouse strains. Of special interest was an evolutionary conserved miR cluster located on chromosome 17 consisting of miR-99b, let-7e, and miR-125a. All cluster members were most highly expressed in LSKs and down-regulated upon differentiation. In addition, these microRNAs were higher expressed in DBA/2 cells compared with C57BL/6 cells, and thus correlated with HSPC frequency. To functionally characterize these microRNAs, we overexpressed the entire miR-cluster 99b/let-7e/125a and miR-125a alone in BM cells from C57BL/6 mice. Overexpression of the miR-cluster or miR-125a dramatically increased day-35 CAFC activity and caused severe hematopoietic phenotypes upon transplantation. We showed that a single member of the miR-cluster, namely miR-125a, is responsible for the majority of the observed miR-cluster overexpression effects. Finally, we performed genome-wide gene expression arrays and identified candidate target genes through which miR-125a may modulate HSPC fate.

Inferring combinatorial association logic networks in multimodal genome-wide screens.

MOTIVATION: We propose an efficient method to infer combinatorial association logic networks from multiple genome-wide measurements from the same sample. We demonstrate our method on a genetical genomics dataset, in which we search for Boolean combinations of multiple genetic loci that associate with transcript levels. RESULTS: Our method provably finds the global solution and is very efficient with runtimes of up to four orders of magnitude faster than the exhaustive search. This enables permutation procedures for determining accurate false positive rates and allows selection of the most parsimonious model. When applied to transcript levels measured in myeloid cells from 24 genotyped recombinant inbred mouse strains, we discovered that nine gene clusters are putatively modulated by a logical combination of trait loci rather than a single locus. A literature survey supports and further elucidates one of these findings. Due to our approach, optimal solutions for multi-locus logic models and accurate estimates of the associated false discovery rates become feasible. Our algorithm, therefore, offers a valuable alternative to approaches employing complex, albeit suboptimal optimization strategies to identify complex models. AVAILABILITY: The MATLAB code of the prototype implementation is available on: http://bioinformatics.tudelft.nl/ or http://bioinformatics.nki.nl/.

Cellular barcoding tool for clonal analysis in the hematopoietic system.

Clonal analysis is important for many areas of hematopoietic stem cell research, including in vitro cell expansion, gene therapy, and cancer progression and treatment. A common approach to measure clonality of retrovirally transduced cells is to perform integration site analysis using Southern blotting or polymerase chain reaction-based methods. Although these methods are useful in principle, they generally provide a low-resolution, biased, and incomplete assessment of clonality. To overcome those limitations, we labeled retroviral vectors with random sequence tags or "barcodes." On integration, each vector introduces a unique, identifiable, and heritable mark into the host cell genome, allowing the clonal progeny of each cell to be tracked over time. By coupling the barcoding method to a sequencing-based detection system, we could identify major and minor clones in 2 distinct cell culture systems in vitro and in a long-term transplantation setting. In addition, we demonstrate how clonal analysis can be complemented with transgene expression and integration site analysis. This cellular barcoding tool permits a simple, sensitive assessment of clonality and holds great promise for future gene therapy protocols in humans, and any other applications when clonal tracking is important.

Expression quantitative trait loci are highly sensitive to cellular differentiation state.

Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci (eQTLs). We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains. This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states. Although we identified a large number (365) of "static" eQTLs that were consistently active in all four cell types, we found a much larger number (1,283) of "dynamic" eQTLs showing cell-type-dependence. Of these, 140, 45, 531, and 295 were preferentially active in stem, progenitor, erythroid, and myeloid cells, respectively. A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene. We found no evidence for target genes that were regulated by distinct eQTLs in different cell types, suggesting that large-scale changes within functional regulatory networks are uncommon. Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study. Therefore, future genetical genomics studies should aim at studying multiple well-defined and highly purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible.

Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny.

Stem cells are unique in that they possess both the capacity to self-renew and thereby maintain their original pool as well as the capacity to differentiate into mature cells. In the past number of years, transcriptional profiling of enriched stem cell populations has been extensively performed in an attempt to identify a universal stem cell gene expression signature. While stem-cell-specific transcripts were identified in each case, this approach has thus far been insufficient to identify a universal group of core "stemness" genes ultimately responsible for self-renewal and multipotency. Similarly, in the hematopoietic system, comparisons of transcriptional profiles between different hematopoietic cell stages have had limited success in revealing core genes ultimately responsible for the initiation of differentiation and lineage specification. Here, we propose that the combined use of transcriptional profiling and genetic linkage analysis, an approach called "genetical genomics", can be a valuable tool to assist in the identification of genes and gene networks that specify "stemness" and cell fate decisions. We review past studies of hematopoietic cells that utilized transcriptional profiling and/or genetic linkage analysis, and discuss several potential future applications of genetical genomics.

Epigenetic control of hematopoietic stem cell aging the case of Ezh2.

Hematopoietic stem cells have potent, but not unlimited, selfrenewal potential. The mechanisms that restrict selfrenewal are likely to play a role during aging. Recent data suggest that the regulation of histone modifications by Polycomb group genes may be of crucial relevance to balance selfrenewal and aging. We provide evidence for the involvement of one of these Polycomb group genes, Ezh2, in aging of the hematopoietic stem cell system.

Modern genome-wide genetic approaches to reveal intrinsic properties of stem cells.

PURPOSE OF REVIEW: The clinical use of hematopoietic stem cells, which produce all mature blood cell lineages in the circulation, is continuously increasing. Identification of genes and gene networks specifying either stemness or commitment will not only be of major relevance for a fundamental understanding of developmental biology, but also for the emerging fields of tissue engineering and regenerative medicine. Our appreciation of the transcriptional machinery that distinguishes stem cells from their nonstem cell progeny is, however, rudimentary. State-of-the art genome-wide tools are now becoming available to elucidate intrinsic properties of stem cells. Here, we review recent progress that has been made in this field. RECENT FINDINGS: Approaches to study stem cell-specific genes and gene networks include genetical genomics, mRNA and microRNA expression profiling of carefully selected cells, proteomics, chromatin studies using 'CHIP-on-chip' tools, genome-wide binding site analyses for transcription factors and chromatin-remodeling proteins, and tools to study the three-dimensional organization of gene loci. It is promising to see that the combined application of these tools has resulted in the identification of multiple novel genes that regulate stem cell self-renewal. SUMMARY: Exploitation of the available technology and integrating the data by translation into a dynamic model of networks, operating in all four dimensions, will be essential to fully comprehend the elusive concept of 'stemness'. It is time to harvest.