Results of transcript sequencing for AtT-20FlpIn cells. mRNA was isolated from AtT-20FlpIn cells using standard procedures, next generation sequencing was performed by Macrogen (https://dna.macrogen.com/). A report ourtlining the workflow and data analysis methods is available from the Authors by request.

Deposited data is in an Excel file, which includes the gene symbol, transcript ID from the reference mouse genome, protein ID and transcript abundance. The AtT-20FlpIn cells were generated by Dr Santiago, and have been used as the 'wild type' cells for generating cell lines stably expressing GPCR and ion channels for most of the molecular pharmacology projects in the Molecular Pharmacodynamics group.

Subcellular methods

The subcellular resource of the Human Protein Atlas provides high-resolution insights into the expression and spatiotemporal distribution of proteins encoded by 13603 genes (67% of the human protein-coding genes), as well as predictions for an additional 3459 secreted- or membrane proteins, covering a total of 17062 genes (85% of the human protein-coding genes). For each gene, the subcellular distribution of the protein has been investigated by immunofluorescence (ICC-IF) and confocal microscopy in up to three different standard cell lines, selected from a panel of 42 cell lines used in the subcellular resource. For some genes, the protein has also been stained in up to three ciliated cell lines, induced pluripotent stem cells (iPSCs) and/or in human sperm cells. Upon image analysis, the subcellular localization of the protein has been classified into one or more of 49 different organelles and subcellular structures. In addition, the resource includes an annotation of genes that display single-cell variation in protein expression levels and/or subcellular distribution, as well as an extended analysis of cell cycle dependency of such variations. 

The subcellular resource offers a database for detailed exploration of individual genes and proteins of interest, as well as for systematic analysis of proteomes in a broader context. More information about the content of the resouce, as well as the generation and analysis of the data, can be found in the Methods summary. Learn about:

The subcellular distribution of proteins in standard human cell lines, including ciliated cells and iPSCs. The subcellular distribution of proteins in human sperm. The proteomes of different organelles and subcellular structures. Single-cell variability in the expression levels and/or localizations of proteins.

The Cell Atlas provides high-resolution insights into the expression and spatio-temporal distribution of proteins within human cells. Using a panel of 64 cell lines to represent various cell populations in different organs and tissues of the human body, the mRNA expression of all human genes are characterized by deep RNA-sequencing. The subcellular distribution of each protein is investigated in a subset of cell lines selected based on corresponding gene expression. The protein localization data is derived from antibody-based profiling by immunofluorescence confocal microscopy, and classified into 32 different organelles and fine subcellular structures. The Cell Atlas currently covers 12390 genes (63%) for which there are available antibodies. It offers a database for exploring details of individual genes and proteins of interest, as well as systematically analyzing transcriptomes and proteomes in broader contexts, in order to increase our understanding of human cells.

DLD-1 and MOLT-4 cell lines were cultured in a Rotating cell culture system to simulate microgravity and mRNA expression profile was observed in comparison to Static controls. Cells were grown in 10mL rotating vessels in an RCCS and in 60mm Petri dishes (test control respectively).Two replicates of test (Microgravity) and control (static) each from DLD-1 and MOLT-4 were analyzed by microarray. Simulated microgravity affected the solid tumor cell line DLD-1 markedly which showed a higher percentage of dysregulated genes compared to the hematological tumor cell line MOLT-4. Microgravity affects the cell cycle of DLD-1 cells and disturbs expression of cell cycle regulatory gene networks. Multiple microRNA host genes were dysregulated and significantly mir-22 tumor suppressor microRNA is highly upregulated in DLD-1.

The SUM human breast cancer cell lines have been used by many labs around the world to develop extensive data sets derived from comparative genomic hybridization analysis, gene expression profiling, whole exome sequencing, and reverse phase protein array analysis. In a previous study, the authors of this paper performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. In this study, the authors have developed the SUM Breast Cancer Cell Line Knowledge Base, to make all of these omics data sets available to users of the SUM lines, and to allow users to mine the data and analyse them with respect to biological pathways enriched by the data in each cell line.Data access: All the datasets supporting the findings of this study are publicly available in the SLKBase platform here: https://sumlineknowledgebase.com/. RPPA data, drug sensitivity data, apelisib response data, and data on dose response, are also part of this figshare data record (https://doi.org/10.6084/m9.figshare.12497630).Study aims and methodology: This web-based knowledge base provides users with data and information on the derivation of each of the cell lines, provides narrative summaries of the genomics and cell biology of each breast cancer cell line, and provides protocols for the proper maintenance of the cells. The database includes a series of data mining tools that allow rapid identification of the functional oncogene signatures for each line, the enrichment of any KEGG pathway with screen hit and gene expression data for each of the lines, and a rapid analysis of protein and phospho-protein expression for the cell lines. A gene search tool that returns all of the functional genome and functional druggable data for any gene for the entire cell line panel, is included. Additionally, the authors have expanded the database to include functional genomic data for an additional 29 commonly used breast cancer cell lines. The three overarching goals in the original development of the SLKBase are: 1) to provide a rich source of information for anyone working with any of the SUM breast cancer cell lines, 2) to give researchers ready access to the large genomic data sets that have been developed with these cells, and 3) to allow researchers to perform orthogonal analyses of the various genomics data sets that we and others have obtained from the SUM lines. For more information on the development and contents of the database, please read the related article.Datasets supporting the paper:The data mining tools accessed the following datasets to generate the figures and tables, and these datasets are downloadable from the Data Download centre on the SLKBase: Exome sequencing data: SLKBase.exome_.seq_.sum_.xlsxGene amplification and expression data for the SUM cell lines: SUM44amplificationdata.xlsSUM52.xlsSUM149.xlsSUM159.xlsSUM185.xlsSUM190.xlsSUM225.xlsSUM229.xlsSUM1315.xlsCellecta shRNA screen data for the SUM cell lines:SUM44Celectadata.csvSUM52Cellectadata.csvSUM102Cellectadata.csvSUM149Cellectadata.csvSUM159Cellectadata.csvSUM185Cellectadata.csvSUM190Cellectadata.csvSUM225Cellectadata.csvSUM229Cellectadata.csvSUM1315hits.hit.csvMCF10A.hits_.csvBreast cancer cell line data included in this data record (these datasets were used to generate figures 1, 2 and 7 in the article):Proteomics data from the Reverse Phase Protein Array (RPPA) assay analysis: Ethier.SUMline.RPPA.xlsxDrug sensitivity data: NAVITOCLAX.drugsensitivity.Zscores.xlsxApelisib response data: Apelisib all lines (2).xlsxDose response data: 092614 Dose Response CP 52s.11.15.xlsxAll the files are either in .xlsx or .csv file format.

This data package contains expression profiles for proteins in normal and cancer tissues. It also contains data on sequence based RNA levels in human tissue and cell line.

Data accompanying the manuscript describing MIX-Seq, a method for transcriptional profiling of mixtures of cancer cell lines treated with small molecule and genetic perturbations (McFarland and Paolella et al., Nat Commun, 2020). Data consists of single-cell RNA-sequencing (UMI count matrices), and associated drug sensitivity and genomic features of the cancer cell lines.See README file for more information on dataset contents.

Summary from the GEO: "We studied transcriptional changes by Affymetrix human microarrays in DLBCL cell lines as a result of treatment with GSK126, a potent, highly-selective, SAM-competitive, small molecule inhibitor of EZH2
In eukaryotes, epigenetic post-translational modification of histones is critical for regulation of chromatin structure and gene expression. EZH2 is the catalytic subunit of the Polycomb Repressive Complex 2 (PRC2) and is responsible for repressing target gene expression through methylation of histone H3 on lysine 27 (H3K27). Over-expression of EZH2 is implicated in tumorigenesis and correlates with poor prognosis in multiple tumor types. Recent reports have identified somatic heterozygous mutations of Y641 and A677 residues within the catalytic SET domain of EZH2 in diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). The Y641 residue is the most frequently mutated residue, with 22% of GCB (Germinal Cell B-cell) DLBCL and FL harboring mutations at this site. These lymphomas exhibit increased H3K27 tri-methylation (H3K27me3) due to altered substrate preferences of the mutant enzymes. However, it is unknown whether direct inhibition of EZH2 methyltransferase activity alone will be effective in treating lymphomas carrying activating EZH2 mutations. Herein, we demonstrate that GSK126, a potent, highly-selective, SAM-competitive, small molecule inhibitor of EZH2 methyltransferase activity, decreases global H3K27me3 levels and reactivates silenced PRC2 target genes. GSK126 effectively inhibits the proliferation of EZH2 mutant DLBCL cell lines and dramatically inhibits the growth of EZH2 mutant DLBCL xenografts in mice. Together, these data demonstrate that pharmacological inhibition of EZH2 activity may provide a promising treatment for EZH2 mutant lymphoma.
10 DLBCL cell lines (7 mutant and 3 wild type EZH2), that were differentially sensitive to GSK126 in proliferation assays, were treated for 72 hours, in duplicate (n=2), with either DMSO (vehicle) or 500nM of GSK126, a potent selective EZH2 inhibitor. EZH2 mutant cell lines are Pfeiffer, KARPAS-422, WSU-DLCL2, SU-DHL-10, SU-DHL-6, DB and SU-DHL-4. EZH2 wildtype cell lines are HT, OCI-LY-19 and Toledo.
10 DLBCL cell lines (7 mutant and 3 wild type EZH2), that were differentially sensitive to GSK126 in proliferation assays, were treated for 72 hours, in duplicate (n=2), with either DMSO (vehicle) or 500nM of GSK126, a potent selective EZH2 inhibitor. EZH2 mutant cell lines are Pfeiffer, KARPAS-422, WSU-DLCL2, SU-DHL-10, SU-DHL-6, DB and SU-DHL-4. EZH2 wildtype cell lines are HT, OCI-LY-19 and Toledo."

The Genomics of Drug Sensitivity in Cancer (GDSC) dataset contains gene expression data for 700 cancer cell lines and 138 drugs. The Cancer Cell Line Encyclopedia (CCLE) dataset contains gene expression data for over 1000 cell lines and 24 drugs. The L1000 perturbations dataset contains gene expression data for 1000 genes in 19 drugs.

Table of cell line and primary neutrophil gene expression data. This comma separated value file contains the final averaged log10 normalized gene expression values for undifferentiated and differentiated cell lines, as well as primary human and mouse neutrophils. This file contains all of the data included in our online searchable neutrophil gene expression database. (CSV 2970 kb)

Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1708 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response. Overall design: lncRNA expression profiling of 60 cancer cell lines

Data set dimensions: 57 rows x 54357 columns

This is a data set contains RMA normalized log expression values for 54356 genes identified with their ENSEMBL ID (columns 1-54356) for 57 cancer cell lines and their respective proliferation rates (column 54357).

Gene expression was obtained from the GSE29682 GEO HuEx 1.0 ST microarray data.

Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype-phenotype relationships in human cells. These cell lines have been used to search for genetic variants that are associated with drug response as well as with more basic cellular traits such as RNA levels. In setting out to extend such studies by searching for genetic variants contributing to drug response, we observed that phenotypes in LCLs were, in our lab and others, significantly affected by experimental confounders (i.e. in vitro growth rate, metabolic state, and relative levels of the Epstein-Barr virus used to transform the cells). As we did not find any SNPs associated with genome-wide significance to drug response, we evaluated whether incorporating RNA expression levels (and eQTLs) in the analysis could increase power to detect such effects. As previously shown, cis-acting eQTLs were detectable for a sizeable fraction of RNAs and baseline levels of many RNAs predicted response to several drugs. However, we found only limited evidence that SNPs influenced drug response through their effect on expression of RNA. Efforts to use LCLs to map genes underlying cellular traits will require great care to control experimental confounders, unbiased methods for integrating and interpreting such multi-dimensional data, and much larger sample sizes than have been applied to date.Keywords: baseline RNA expression We studied 269 cell lines densely genotyped by the International HapMap Project [31]. Cell lines were cultured and characterized at baseline for a variety of cellular phenotypes including growth rate, ATP levels, mitochondrial DNA copy number, EBV copy number, and measures of B-cell relevant cell surface receptors and cytokine levels. Each cell line was exposed in 384-well plates to a range of doses for each of seven drugs selected based on their divergent mechanisms of action and importance in clinical use for treatment of B-cell diseases, focusing on anti-cancer agents: 5-fluorouracil (5FU), methotrexate (MTX), simvastatin, SAHA, 6-mercaptopurine (6MP), rapamycin, and bortezomib. Drug response was measured using Celltiter Glo, an ATP-activated intracellular luminescent marker that, when compared to mock-treated control wells, can represent relative levels of cellular viability and metabolic activity. RNA was collected at baseline and RNA transcript levels were measured genome-wide on the Affymetrix platform. Baseline characterization and plating for drug response experiments was performed using batches of 90 cell lines from each HapMap analysis panel (CEU, JPT / CHB, and YRI) on each of three experiment days. The order of cell lines within each panel was randomized to avoid inducing artificial intra-familial correlation. Each drug was tested at a range of doses around the expected IC50 as reported for the drug by the NCI DTP; each dose of drug was tested in two wells per plate and on two separate plates. These replicate measurements for each cell line allowed assessment of intra-experimental variation. To evaluate day-to-day (i.e. inter-experimental) variation in all traits, a subset of 90 cell lines (30 from each of the three HapMap panels) was grown from a fresh aliquot and the entire experiment was repeated. To evaluate the effect of technical error on measured RNA levels, a set of 22 RNAs previously expression profiled (using Illumina HumanChip) at Wellcome Trust Sanger Institute (WTSI) (generously provided by Emmanouil T. Dermatsakis) was included in expression profiling at the Broad on Affymetrix arrays. Data can be downloaded from the Broad Institute web site: (http://www.broad.mit.edu/~yelensky/cell_lines_paper/). Please see Materials and Methods for details of QC, normalization, etc.Note - Roman Yelensky (12/22/2009): A recent application of novel micro-array experiment QC methods by the scientific community (Lude Franke) has revealed the possibility of a mixup of a handful of RNA samples in the CHB/JPT population subset. Specifically, it has been suggested that RNA data for sample pairs:- NA18609 and NA18971- NA18592 and NA18605- NA18637 and NA18966- NA18550 and NA18635- NA18994 and NA18524may have been accidentally swapped. While we cannot verify whether a swap indeed occurred, users of the dataset are encouraged to also consider this alternate sample mapping (e.g. the array for NA18609 is actually NA18971 and vice versa) in their analyses.

Gene expression (counts) scRNA-seq of co-cultured cancer- and immune cells treated with trifluridine and DMSO control assayed at two time-points (12h and 72h).

HCT116 were seeded in 6-well Nunc plates (50,000 cells/3mL/well) and precultured for 24 h before PBMCs were added at a 1:8 ratio. Co-cultures were treated with DMSO vehicle (0.1%) or FTD (3mM) for 12 h or 72 h. MACS Dead Cell Removal Kit (Miltenyi Biotec, Gladbach, DEU) was performed according to the manufacturer’s instructions on cells treated for 72 h to increase the viability of the samples before RNA-sequencing. The viability of the samples treated for 12 h was not subjected to Dead Cell Removal as the viability was already sufficient. All samples were washed in PBS with 0.04% BSA (2x1mL). Chromium Next GEM Single Cell 3’ library preparation and RNA-sequencing were performed by the SNP&SEQ Technology Platform (National Genomics Infrastructure (NGI), Science for Life Laboratory, Uppsala University, Sweden).

This data set contains processed data using Cell Ranger toolkit version 5.0.1 provided by 10x Genomics, for demultiplexing, aligning reads to the human reference genome GRCh38, and generating gene-cell unique molecular identifiers

Omega-3 polyunsaturated fatty acids are normal constituents of the diet and have an essential role in maintaining important cellular functions. Docosahexaenoic acid (DHA) have demonstrated anticancer activities in several in vitro and in vivo studies, and in some clinical studies. The mechanism by which n-3 PUFAs reduce tumor growth probably involves the inhibition of cell proliferation, induction of cell death, or a combination of both. There are differences in sensitivity towards DHA treatment among colorectal cell lines, although the reason why is unclear. 10 human colorectal cell lines, representing 5 different subtypes of colorectal cancer, were included in gene expression analysis. (Pleae refer to Sadanandam A, Lyssiotis CA, Homicsko K, Collisson EA, Gibb WJ, Wullschleger S, Ostos LC, Lannon WA, Grotzinger C, Del Rio M, et al. “A colorectal cancer classification system that associates cellular phenotype and responses to therapy”. Nat Med. 2013;19:619-625. doi:10.1038/nm.3175 for details on cancer subtype classifications.) Our aim is to identify different levels of genes and pathways correlating with differences in sensitivity towards DHA between the colorectal cell lines for future classification of patients that could benefit from omega-3 PUFA treatment in addition to conventional treatment.

We generated RNA's and measured expression of 40000 genes using spotted cDNA microarrays from the fifty nine publicly available cell lines of the NCI Developmental Therapeutics Program's NCI60 studies and an additional set of seven cell lines for which GI50 compound sensitivity data were available. All cell lines were grown to 80% confluence in RPMI 1640 supplemented with phenol red, glutamine (2 mM) and 5% fetal calf serum. This expression data, in conjunction with the compound sensitivity data sets available from the DTP, were used to empirically determine whether gene-compound correlates of a sufficiently high correlation coefficient would have a suitable low false discovery rate to support the use of a correlative approach and these datasets for early discovery approaches for new targeted therapies. A cell type comparison design experiment design type compares cells of different type for example different cell lines. Sixty six cell lines were analysed, with no repetitions. Twelve cell lines were pooled for a common reference.

Datasets containing the results of gene expression analysis using GEO2R for different HPV cancer cell lines and normal samples.

It is a comprehensive database of Gene Expression Profiles, which enable to compare the transcriptome of various tissues, organs and experiments. mRNA expression levels of thousands of genes are measured with oligo-nucleotide DNA microarray "GeneChip". All gene expression data in this database is produced by LSBM (Laboratory for Systems Biology and Medicine) and the collaborators. SBM DB provides two different databases: A reference database for fur expression analysis (RefEXA) and LSMB GeNet, a database of various organisms, tissues, and experiences. RefEXA provides a comprehensive gene expression database of Human normal tissues, normal cultured cells and cancer cell lines with GeneChip HG-U133A, can help investigation of Human disease. LSMB provides

Table S1: A panel of target-specific compounds for chemosensitivity study in the Asian gastric cell lines. Table S2: Putative driver genes selected using correlated genes between copy number aberration and mRNA gene expression (Mann Whitney test, p0.6). Table S3: Integrative cluster signature (Copy number and mRNA correlated genes). Table S4: Integrative cluster-specific pathway. Table S5: Compounds showing significant differences in sensitivity between the two integrative clusters of Asian gastric cell lines. Figure S1: A schematic diagram of the analysis workflow. (XLSX)

Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention