Additional file 1 of Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

Supplemental methods for the analysis of the olfactory epithelium data and supplemental figures 1-20. (ZIP 34910 kb

Protein Sialylation Regulates a Gene Expression Signature that Promotes Breast Cancer Cell Pathogenicity

Many mechanisms have been proposed for how heightened aerobic glycolytic metabolism fuels cancer pathogenicity, but there are still many unexplored pathways. Here, we have performed metabolomic profiling to map glucose incorporation into metabolic pathways upon transformation of mammary epithelial cells by 11 commonly mutated human oncogenes. We show that transformation of mammary epithelial cells by oncogenic stimuli commonly shunts glucose-derived carbons into synthesis of sialic acid, a hexosamine pathway metabolite that is converted to CMP-sialic acid by cytidine monophosphate <i>N</i>-acetylneuraminic acid synthase (CMAS) as a precursor to glycoprotein and glycolipid sialylation. We show that CMAS knockdown leads to elevations in intracellular sialic acid levels, a depletion of cellular sialylation, and alterations in the expression of many cancer-relevant genes to impair breast cancer pathogenicity. Our study reveals the heretofore unrecognized role of sialic acid metabolism and protein sialylation in regulating the expression of genes that maintain breast cancer pathogenicity

Molecular analysis of Met kinetic signature- mRNA and protein levels of selected genes in high and low Met expressing cells.

<p>(A) Total cellular RNA, was isolated from low (MCF7) and high Met (MDA231) cell cultures and mRNA expression of Met, Survivin, Pbk, Cyclin E1 and Ki67 was evaluated by quantitative real time PCR and compared mRNA levels of the housekeeping GAPDH gene. The primers used for the quantification of gene expression are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045969#pone.0045969.s011" target="_blank">Table S2</a>. A gray box denotes MCF7 cell line samples and a black box denotes MDA231 cell line samples (B) Samples from low (MCF7) and high Met (MDA231) cells were subjected to western blot (WB) analysis, before and 15 min and 60 min after treatment with HGF/SF, using antibodies against Met and activated Met (p-Met) and (C) antibodies against ERK K-23, p-ERK E-4, E-Cadherin, Survivin and Actin C4. (D, E) Subcellular localization of survivin in fluorescence (IF) analysis of Low (MCF7) and high Met (MDA231) cells after treatment with HGF/SF at 0 min, 10 min, 30 min and 24 h. The cells were Immunostained using anti-Survivin antibody. Immunofluorescence was examined using a 510 Meta Zeiss confocal laser scanning microscope (CLSM). Survivin quantification was performed on at least five confocal images per slide. Cell outline was defined based on Nomarski images; nuclei were defined based on the DAPI staining. Average pixel intensity was calculated separately for the nucleus and cytoplasm areas. (F) IF analysis of temporal kinetics of Survivin protein expression following treatment with HGF/SF.</p

Met Kinetic Signature correlation with clinical and histopathological data.

<p>Met Kinetic Signature correlation with clinical and histopathological data.</p

Met signature segmentation of cell line model and human breast cancer patients’ data sets.

<p>(A) Cells from six human breast cancer cell lines (MDA231, Hs578T, BT549, MCF10, MCF7 and T47D) were incubated with purified HGF/SF labeled with biotin by a protein biotinylation kit and allowed to bind for 30 min. Cells were then fixed with 4% Paraformaldehyde, permeablized, and stained with Streptavidin-coupled QDot585. Fluorescence levels calculated by image analysis using MICA image analysis software, p<0.0001. (B) Met canonical pathway score calculated by measuring the average mRNA levels of all Met canonical pathway genes (after normalization to average = 0, stdev = 1 per-gene) in high-Met (MDA231, Hs578T and BT549) as compared to the low-Met (MCF10, MCF7 and T47D) samples, p<0.0001. A gray box denotes high Met cell line samples and a black box denotes low Met cell line samples. (C) Hierarchical clustering division of breast cancer cell lines samples using Met kinetic signature genes.</p

Analysis of the association between High Met kinetic signature and basal-like tumors.

<p>Hierarchical clustering was used to divide three large breast cancer patient cohorts (Chang (A), GSE3165 (B) and GSE1456 (C)), according to Met kinetic signature genes. The resultant patient groups were analyzed for association with tumor molecular classification. A gray box denotes patients in the low Met activity group and a black box denotes patients in the high Met activity group.</p

Kaplan Meier survival analysis of Met kinetic signature’s segmentation of human breast cancer patient cohorts.

<p>Hierarchical clustering was used to divide six large breast cancer patient cohorts into high vs. low Met kinetic signature. Kaplan Meier analysis of overall survival (A,B,C,D,E) and metastasis-free survival (F,G) of the Chang (A, F, H, I), Miller (B), GSE1456 (C), GSE3165 (D), GSE11121 (E) and van ‘t veer (G) data sets. Kaplan Meier analysis of overall survival (H) and metastasis-free survival (I) of stage-I patients in the Chang data set. A red line denotes patients with high Met kinetic signature and a blue line denotes patients with low Met kinetic signature. In Chang data set, Met kinetic signature has a positive predictive value (PPV) and negative predictive value (NPV) of 41% and 82%, respectively.</p

ANAT derived pathways that correlate with Met activity and prognosis.

<p>p-values for differentiation between high and low Met samples in the cellular model and for differentiation between patients with good and poor prognosis in three large BC patient cohorts (Chang, Miller and van ‘t Veer) are provided. Reference for the association between the pathway genes, Met acivity and cancer progression are also provided.</p

Additional file 4: Table S3. of A Reproducibility-Based Computational Framework Identifies an Inducible, Enhanced Antiviral State in Dendritic Cells from HIV-1 Elite Controllers

DE Analysis. DE analysis results as described in “Methods,” including contrasts for c1 vs c3–5, c2 vs c3–5, c1 vs c2, and intra-c1 exposure differences. Numbers of cells in each comparison are printed above log-fold-change columns. Gene sets from Fig. 2c are included. (XLSX 3450 kb

Empirical flashover model of EHV post insulators based on ISP parameter in cold environments

The main objective of this contribution is to present an empirical model of ice-covered insulator flashover in accordance with an important flashover index, called icing stress product (ISP). The ISP as the product of the ice mass per centimeter of insulator length and the electrical conductivity of the melted ice accretion is used to establish an empirical model to determine the flashover voltage under icing conditions. To achieve this model, several tests were carried out on post station insulators typically used in Hydro-Quebec 735-kV substations, under DC and AC voltage, to determine the relationship between flashover stress and the ISP. The ISP-based method offers a good tool not only to select insulators for locations exposed to freezing conditions, but also to compare flashover results obtained under different test conditions. Moreover, to study the optimizing method of insulator flashover, the influence of air gaps on the flashover stress of ice-covered post insulators is investigated. The results reveal that the number of air gaps significantly affects the flashover stress. The test results are highly meaningful as they can be used as a reference for ranking several other insulator types and configurations in order to select the appropriate one for cold environments. Moreover, several mitigation options to improve insulator reliability in cold environments are provided