Platelet-biased stem cells reside at the apex of the haematopoietic stem-cell hierarchy

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Abstract

The blood system is maintained by a small pool of haematopoietic stem cells (HSCs), which are required and sufficient for replenishing all human blood cell lineages at millions of cells per second throughout life. Megakaryocytes in the bone marrow are responsible for the continuous production of platelets in the blood, crucial for preventing bleeding--a common and life-threatening side effect of many cancer therapies--and major efforts are focused at identifying the most suitable cellular and molecular targets to enhance platelet production after bone marrow transplantation or chemotherapy. Although it has become clear that distinct HSC subsets exist that are stably biased towards the generation of lymphoid or myeloid blood cells, we are yet to learn whether other types of lineage-biased HSC exist or understand their inter-relationships and how differently lineage-biased HSCs are generated and maintained. The functional relevance of notable phenotypic and molecular similarities between megakaryocytes and bone marrow cells with an HSC cell-surface phenotype remains unclear. Here we identify and prospectively isolate a molecularly and functionally distinct mouse HSC subset primed for platelet-specific gene expression, with enhanced propensity for short- and long-term reconstitution of platelets. Maintenance of platelet-biased HSCs crucially depends on thrombopoietin, the primary extrinsic regulator of platelet development. Platelet-primed HSCs also frequently have a long-term myeloid lineage bias, can self-renew and give rise to lymphoid-biased HSCs. These findings show that HSC subtypes can be organized into a cellular hierarchy, with platelet-primed HSCs at the apex. They also demonstrate that molecular and functional priming for platelet development initiates already in a distinct HSC population. The identification of a platelet-primed HSC population should enable the rational design of therapies enhancing platelet output.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

Gordon K. Smyth (2004)

The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

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Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

R. A. Irizarry (2003)

In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.