Single Cell Origin of Multilineage Colonies in Culture: Evidence That Differentiation of Multipotent Progenitors and Restriction of Proliferative Potential of Monopotent Progenitors Are Stochastic Processes

In this paper, we report analysis of differentiation in human hemopoietic colonies derived from a single cell. Cord blood mononulear cells and panned My-10 antigen-positive bone marrow and cord blood cells were plated in methylcellulose medium containing erythropoietin and conditioned medium. Initially, we performed mapping studies to identify candidate colony-forming cells. Subsequently, using a micromanipulator, we transferred single cells individually to 35-mm dishes for analysis of colony formation. Cellular composition of the colony was determined by identifying all of the cells in the May-Grunwald-Giemsa stained preparation. Of 150 single candidate cells replated, 63 produced colonies. The incidences of single lineage colonies included 19 erythroid, 17 monocyte-macrophage, and 9 eosinophil colonies. There were 18 mixed hemopoietic colonies consisting of cells in two, three, four, and five lineages in varying combinations. In some instances, we noted the predominance of one lineage and the presence of very small populations of cells in a second or third lineage. These results provide evidence for the single-cell origin of human multilineage hemopoietic colonies, and are consistent with the stochastic model of stem cell differentiation in man. They also indicate that restriction of the proliferative potential of committed progenitors is a stochastic process

Improved methods for the generation of human gene knockout and knockin cell lines

Recent studies have demonstrated the utility of recombinant adeno-associated viral (rAAV) vectors in the generation of human knockout cell lines. The efficiency with which such cell lines can be generated using rAAV, in comparison with more extensively described plasmid-based approaches, has not been directly tested. In this report, we demonstrate that targeting constructs delivered by rAAV vectors were nearly 25-fold more efficient than transfected plasmids that target the same exon. In addition, we describe a novel vector configuration which we term the synthetic exon promoter trap (SEPT). This targeting element further improved the efficiency of knockout generation and uniquely facilitated the generation of knockin alterations. An rAAV-based SEPT targeting construct was used to transfer a mutant CTNNB1 allele, encoding an oncogenic form of β-catenin, from one cell line to another. This versatile method was thus shown to facilitate the efficient integration of small, defined sequence alterations into the chromosomes of cultured human cells

Détermination of the frequency of leukemia stem cells in childhood precursor B cell acute lymphoblastic leukemias

LE KREMLIN-B.- PARIS 11-BU Méd (940432101) / SudocSudocFranceF

Inferring microRNA regulation of mRNA with partially ordered samples of paired expression data and exogenous prediction algorithms.

MicroRNAs (miRs) are known to play an important role in mRNA regulation, often by binding to complementary sequences in "target" mRNAs. Recently, several methods have been developed by which existing sequence-based target predictions can be combined with miR and mRNA expression data to infer true miR-mRNA targeting relationships. It has been shown that the combination of these two approaches gives more reliable results than either by itself. While a few such algorithms give excellent results, none fully addresses expression data sets with a natural ordering of the samples. If the samples in an experiment can be ordered or partially ordered by their expected similarity to one another, such as for time-series or studies of development processes, stages, or types, (e.g. cell type, disease, growth, aging), there are unique opportunities to infer miR-mRNA interactions that may be specific to the underlying processes, and existing methods do not exploit this. We propose an algorithm which specifically addresses [partially] ordered expression data and takes advantage of sample similarities based on the ordering structure. This is done within a Bayesian framework which specifies posterior distributions and therefore statistical significance for each model parameter and latent variable. We apply our model to a previously published expression data set of paired miR and mRNA arrays in five partially ordered conditions, with biological replicates, related to multiple myeloma, and we show how considering potential orderings can improve the inference of miR-mRNA interactions, as measured by existing knowledge about the involved transcripts

The top 10 interactions according to the <i>G–O</i> ordering.

<p>In the above diagram, we show the miRs (top row) and genes (bottom row) involved in the 10 most significant targeting interactions based on the <i>G–O</i> ordering from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051480#pone-0051480-g001" target="_blank">Figure 1</a>. In each case, the inferred interaction is negative, meaning that the miR inhibits the expression of the corresponding gene. A red line from an miR to an mRNA indicates that the interaction was predicted by <i>TargetScan</i> and a blue line indicates that the interaction was predicted by <i>miRanda</i>.</p

miRs with a significant estimate for the trend parameter.

<p>Shown are the most significant trend parameter estimates (). A “+” in the table denotes that the expression of the miR increased throughout the progression of the partial ordering by disease stage (<i>G–O</i>), and tended to be higher during later stages. Likewise, a “−” denotes that the expression of that miR tended to be higher in early stages and lower in later stages.</p