Additional file 27: of The complex pattern of epigenomic variation between natural yeast strains at single-nucleosome resolution

Similar co-variation of histone marks at isolated vs. regional SNEPs. H3K4me3 SNEPs were termed ‘isolated’ when both flanking nucleosomes did not contain an H3K4me3 SNEP. All others were termed ‘regional’. The same definition was applied to SNEPs of other marks. On each set of nucleosomes (those corresponding to regional and those corresponding to isolated SNEPs for mark (1)), co-variation was quantified as in Fig. 8C, by computing the fraction of BY–RM isolated or regional SNEPs of mark (1) that showed synergistic and significant BY–RM differences in mark (2)

Principle of scPTL mapping.

<p>A cohort of multi-cellular individuals (or unicellular clones) with differing genotypes is used. For each individual (or clone), a cellular trait <i>X</i> is measured on a population of cells, and the observed distribution of <i>X</i> corresponds to the 'phenotype' of the corresponding individual. <b>A</b>) Kantorovich distances are computed for all pairs of individuals. The resulting distance matrix is used to place individuals in a multidimensional space. Proximity of individuals (grey and colored squares) in this space reflects comparable phenotypes (distributions in insets). <b>B</b>) Individuals are 'labeled' (blue <i>vs</i>. red) by their genotype at one genetic marker. <b>C</b>) A canonical discriminant analysis is performed to test if the genotype at the marker discriminates individuals in the phenotypic space. In the examples displayed, genetic linkage is significant at marker <i>2</i> but not at marker 1.</p

Test on simulations.

<p><b>A</b>) A model of gene expression with positive feedback was used to simulate data (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#sec015" target="_blank">methods</a>). For each of ~130 distinct individuals, parameters <i>α</i>_<i>0</i>, <i>α</i>_<i>1</i> and <i>K</i> were drawn from Gaussian distributions and then used to generate independent values of <i>X</i> in 10,000 cells of each individual. Mean values μ₀, μ₁ and μ_K depended on the genotype of individuals at a locus located in the middle of a 200cM genetic map. Other sources of inter-individual variability were modeled by the extrinsic noise strength η. <b>B</b>) Distributions obtained (one per individual) at various values of η. Color: genotype at the locus controlling μ₀, μ₁ and μ_K. <b>C</b>) QTL scans. For each individual, the mean (upper panels) or the variance (lower panels) of X were considered as quantitative traits and the map was scanned using interval mapping. Red dashed line: genome-wide significance threshold at 0.05. <b>D</b>) Coordinates of individuals (dots) in the phenotypic space obtained after computing Kantorovich distances and applying multi-dimensional scaling. Only the first two dimensions are shown. <b>E</b>) scPTL scan. At every marker position, linear discriminant analysis was performed. W score: -log₁₀(<i>Λ</i>), where <i>Λ</i> is the Wilks' lambda statistics of discrimination (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#sec015" target="_blank">methods</a>). Red dashed line: empirical genome-wide significance threshold at 0.05 (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#sec015" target="_blank">methods</a>).</p

Complementarity of scPTL and QTL mapping using experimental data.

<p>The data of Nogami et al. [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#pgen.1006213.ref022" target="_blank">22</a>] was used to perform genomic scans for QTL, cvQTL and scPTL. For scPTL mapping, we used both the first-axis only and multiple dimensions of the phenotypic space (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#sec015" target="_blank">methods</a>), and the results were pooled. <b>A</b>) Venn diagram showing the number of traits for which a significant locus was found in the genome by each method (each at FDR = 10%). <b>B</b>) Same representation as <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#pgen.1006213.g002" target="_blank">Fig 2B</a> showing the traits successfully mapped by each method. The traits that passed the heritability filter (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#sec015" target="_blank">methods</a>) and were considered for mapping are shown as triangles, and colored if mapping was successful.</p

The added value of scPTL mapping.

<p>A simple model is considered where a population of cells evolves, with a death rate that is constant and a division rate that depends on the intra-cellular concentration of a tumor-suppressor protein X (<b>A</b>). The population of cells is considered to be pathogenic if it exceeds 120,000 cells (over-proliferation). The expression of X follows the model described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#pgen.1006213.g004" target="_blank">Fig 4</a>, with extrinsic noise strength η = 0.16. <b>B</b>) Time-evolution of the number of cells in 124 simulations corresponding to 124 distinct individuals (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#sec015" target="_blank">methods</a>). Each line represents one individual. Color: same genotypic groups as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#pgen.1006213.g004" target="_blank">Fig 4</a>, which correspond to distinct sets of parameters for the regulation of X, which are linked to an scPTL located in the middle of a chromosome. Disease onset is earlier in group B (red curves). <b>C</b>) Using the macroscopic data (disease vs. healthy, 124 individuals) from panel B at age 23, the simulated disease-modifying locus was not detected by classical association mapping. Linkage was searched with an exact Fisher test. Dashed line: genome-wide 5% significance threshold determined by permutations. <b>D</b>) Using single-cell data (level of X in 10,000 cells, 124 individuals) from panel B at age 23, the locus was detected by scPTL mapping. Dashed line: genome-wide 5% significance threshold determined by permutations.</p

Concept and definitions.

<p><b>A)</b> A cellular trait is considered as a random variable <i>X</i> with density function <i>f</i>. The probability that one cell expresses <i>X</i> at a value comprised between <i>x</i>_<i>1</i> and <i>x</i>_<i>2</i> is given by the shaded area. <b>B</b>) <i>f</i> differs between individuals because of environmental and genetic factors.</p

Detection of a scPTL for the cellular response to galactose.

<p><b>A</b>) Time-course flow cytometry acquisitions of the response to galactose in strains BY and RM. Cells were cultivated in raffinose 2% and were shifted to a medium containing Raffinose 2% and Galactose 0.5%. After the indicated time, cultures were fixed with paraformaldehyde and analysed by flow cytometry. Histograms correspond to the fluorescent values obtained on cells gated for cell-size (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#sec015" target="_blank">methods</a>). <b>B</b>) Genome scan for scPTL affecting the response after 30 minutes induction. Data similar to panel A was generated for 60 segregants, and the histograms obtained at 30 min post-induction (shown in D), together with the genotypes from [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006213#pgen.1006213.ref066" target="_blank">66</a>] were used for scPTL mapping using the multi-dimensions method. The linkage profile (W score) obtained when retaining the first two dimensions is shown, colored by chromosome. Dotted line: significance threshold at genome-wide <i>p</i>-value < 0.005. Arrow: significant scPTL on chromosome 5. <b>C</b>) Two-dimensional coordinates of the 60 segregants in the phenotypic space (30 min induction time). color: genotype at the scPTL locus. <b>D</b>) Phenotypes (histograms of single-cell expression value) of the 60 segregants after 30 min induction, colored by the genotype of the segregants at the scPTL locus. a.u.: arbitrary unit. <b>E</b>) Boxplot summarizing the variance of histograms from panel D grouped by the genotype at the scPTL locus.</p

Additional file 2 of Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers

Additional file 2: Table S1. List of genes with predominant expression in testis, placenta and/or embryonic stem cells. Table S2. Frequencies of ectopic activations of the tissue-specific genes. Table S3. Results of the validation step in the biomarker discovery pipeline. Table S4. Datasets of normal tissues and breast cancers with corresponding sample sizes. Table S5. List of normal tissues and the corresponding sample sizes

Additional file 1 of Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers

Additional file 1: Fig. S1. (A) Heatmap showing the expression of 1882 genes in normal adult tissues with predominant expression in testis (male germinal), embryonic stem cells (ES cells) or placenta, and not expressed in normal breast (female genital). The expression levels of all genes are normalized by scaling each feature to a range between zero and one. The genes are ordered according their normalized expression levels in the tissues of interest (testis, placenta and ES cells, respectively). (B) Venn diagram showing the distribution of 1882 genes according the tissue of predominance: testis, embryonic stem cells and/or placenta. Fig. S2. Flow chart representing the main steps of the biomarker discovery pipeline. Fig. S3. Expression profiles in normal tissues of the five genes in the GEC panel DNMT3B, EXO1, MCM10, CENPF and CENPE based on RNA-seq data from GTEX and NCBI Sequence Read Archive. All five genes have a predominant expression profile in embryonic stem cells. They are also expressed in testis (male germinal) at lower levels. These genes are not expressed in normal breast and female genital tissues. Fig. S4. Kaplan-Meier individual survival curves of the genes DNMT3B, EXO1, MCM10, CENPF and CENPE in the training (TCGA-BRCA) and validation (GSE25066, GSE21653, GSE42568) datasets