Differential expression analysis of RNA-seq from CCLE was conducted using R. 2011), and levels of GSH and its rate-limiting metabolite cysteine have been shown to increase with tumor progression in patients (Hakimi et al., 2016). Furthermore, both primary and metastasized tumors have been shown to utilize the reducing factor nicotinamide adenine dinucleotide phosphate, reduced (NADPH) to Rabbit Polyclonal to RAD51L1 regenerate GSH stores and survive oxidative stress (Jiang et al., 2016; Piskounova et al., 2015). Blocking antioxidant production, including the synthesis of GSH, has long been viewed as a potential mechanism to treat cancers (Arrick et al., 1982; Hirono, 1961). Treatment of patients with l-buthionine-sulfoximine (BSO) (Griffith and Meister, 1979), an MSC1094308 inhibitor of GCLC, is well tolerated and has been used in combination with the alkylating agent melphalan in multiple Phase 1 clinical trials with mixed results (“type”:”clinical-trial”,”attrs”:”text”:”NCT00005835″,”term_id”:”NCT00005835″NCT00005835 and “type”:”clinical-trial”,”attrs”:”text”:”NCT00002730″,”term_id”:”NCT00002730″NCT00002730) (Bailey, 1998; Villablanca et al., 2016). Inhibition of GSH synthesis has been shown to prevent tumor initiation in multiple mouse models of spontaneous tumorigenesis; however, limited effects have been reported in established tumors (Harris et al., 2015). Another major antioxidant pathway, governed by the protein thioredoxin 1 (TXN), has been shown to support survival of cells upon GSH depletion. Treatment of thioredoxin reductase 1 (caused minimal effects on proliferation across cancer cell lines, as indicated by a essentiality score close to zero (Figure 1A). This score contrasted with those from other non-redundant metabolic genes such as those encoding phosphogluconate dehydrogenase (in the human breast cancer cell line HCC-1806 (a cell line with an essentiality score for above the ?0.6 threshold) (Figure 1B). Deletion of caused a drastic reduction in GSH levels without any effect on cellular proliferation (Figures 1C and 1D), mirroring the results observed in the published pooled CRISPR screens. To evaluate the differential sensitivity of cancer cell lines to glutathione depletion more quantitatively, we used an inhibitor of GCLC, L-buthionine-sulfoximine (BSO) (Griffith and Meister, 1979), to evaluate the effects of titratable depletion of GSH across a large panel of cancer cell lines (Figure 1E). The efficacy of BSO was confirmed by assessment of the reduction in GSH levels; BSO induced potent and rapid depletion of GSH within 48 hours (Figures 1F, 1G and S1A). Extending this analysis to a larger panel of breast cancer cell lines revealed near uniform kinetics of GSH depletion by BSO (Figure 1H). The effect of BSO on cell number after 72 hours was determined for 49 cell lines derived from breast cancer (both basal and luminal subtypes), lung cancer and ovarian cancer. Across all tumor types, the majority of cancer cell lines displayed no reduction in cell number after depletion of GSH by BSO (Figures 1I, 1J and S1B-1E). Interestingly, a minority of cell lines (six) was highly sensitive to BSO, with IC50 values ranging from 1 to 6 M (matching the MSC1094308 IC50 values for depletion of intracellular GSH). To identify candidate genes underlying sensitivity to GSH depletion, RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) was analyzed (Barretina et al., MSC1094308 2012; Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer, 2015). Fewer than 30 genes MSC1094308 were differentially expressed in the six highly sensitive cell lines relative to the other cancer cell lines (Table S1). These genes were not investigated further because the cell lines were derived from diverse tissues and it was not.
However, it requires considerable time to obtain principal or impartial components as the number of cells increases. characterize novel cell types and detect intra-population heterogeneity Tecarfarin sodium (Potter 2018). The amount of scRNA-seq data in the public domain has increased owing to technological development and the efforts to obtain large-scale transcriptomic profiling of cells (Han et al. 2018). Computational algorithms to process and analyze large-scale high-dimensional single-cell data are essential. To cluster high-dimensional scRNA-seq data, dimension-reduction algorithms such as principal component analysis (PCA) (Joliffe and Morgan 1992) or impartial component analysis (ICA) (Hyv?rinen and Oja 2000) have been successfully applied to process and to visualize high-dimensional scRNA-seq data. However, it requires considerable time to obtain principal or independent components as the number of cells increases. Dimension reduction decreases processing time at the cost of losing original cell-to-cell distances. For instance, t-distributed stochastic neighbor embedding (t-SNE) (van der Maaten 2014) effectively visualizes multidimensional data into a reduced-dimensional space. However, t-SNE distorts the distance between cells for its visualization. Besides, t-SNE requires considerable time for large-scale scRNA-seq data visualization and clustering. Random projection (RP) (Bingham and Mannila 2001) has been suggested as a powerful dimension-reduction method. Based on the JohnsonCLindenstrauss lemma (Johnson and Lindenstrauss 1984), RP reduces the dimension while the distances between the points are approximately preserved (Frankl and Maehara 1988). Theoretically, RP is very fast because it does not require calculation of pairwise cell-to-cell distances or theory components. To effectively handle very large-scale scRNA-seq data without excessive distortion of cell-to-cell distances, we developed SHARP (Supplemental Code), a hyperfast clustering algorithm based on ensemble RP (Methods) (Fig. 1A). RP (Bingham and Mannila 2001) projects the original for scRNA-seq data with cells and genes. Compared with it, a simple hierarchical clustering algorithm requires log( min(triangular part shows the scatter plots of the cell-to-cell distances, whereas the triangular part shows the Pearson’s correlation coefficient (PCC) of the corresponding two Tecarfarin sodium spaces. ((GCG), (INS), acinar (PRSS1), and (SST) cells (Supplemental Fig. S7). Clustering 1.3-million-cell data using SHARP Of note, SHARP provides an opportunity to study the million-cell-level data set. Previous analysis on the scRNA-seq data with 1,306,127 cells from embryonic mouse brains (10x Genomics 2017) was performed using rows corresponds to a gene (or transcript), and each of the columns corresponds to a single cell. The type of input data can be either fragments/reads per kilo base per million mapped reads (FPKM/RPKM), counts per million mapped reads (CPM), transcripts per million (TPM), or unique molecule identifiers (UMI). For consistency, FPKM/RPKM values are converted into TPM values, and UMI values are converted into CPM values. Data partition For a large-scale data set, SHARP performs data partition using a divide-and-conquer strategy. SHARP divides scRNA-seq data into blocks, where each block may contain different numbers of cells (i.e., is the minimum integer Tecarfarin sodium no less than in each block are as follows: If Tecarfarin sodium = 1, = = = 1; If = 2, 3, = log2( (0, 1] as suggested by the JohnsonCLindenstrauss lemma. Ensemble RP After RP, pairwise Pearson correlation coefficients between each pair of single cells were calculated using the dimension-reduced feature matrix. An agglomerative hierarchical clustering (hclust) with the ward.D (Ward 1963) method was used to cluster the correlation-based distance matrix. We first applied RP times to obtain RP-based dimension-reduced feature matrices and then further distance matrices. Each of the K matrices was clustered by a ward.D-based hclust. As a result, different Rabbit polyclonal to TPT1 clustering results were obtained, each from a RP-based distance matrix, that would be combined by a weighted-based metaclustering (wMetaC) algorithm (Ren et al. 2017) detailed in the next step. wMetaC Compared with the traditional cluster-based similarity partitioning algorithm (CSPA) (Strehl and Ghosh 2002) that treats each instance and each cluster equally important, wMetaC assigns different weights to different instances (or instance pairs) and different clusters to improve the clustering performance. wMetaC includes four steps: (1) calculating cell weights, (2) calculating weighted cluster-to-cluster pairwise similarity, (3) clustering on a weighted cluster-based similarity matrix, and (4) determining final results by a voting scheme. Note that wMetaC was applied to each block of single cells. The flowchart of the wMetaC ensemble clustering method is shown in Supplemental Figure S15. Specifically, for calculating cell weights, similar to the first several steps in CSPA, we first converted the individual RP-based clustering results into a colocation similarity matrix, S, whose element represents the similarity between the is the element in the = 1 (i.e., the = 0 (i.e., the reaches the minimum.