{"entity": "journal", "iuid": "3bc94cb61ab9428a8eb67d0b1bcb605a", "timestamp": "2026-08-15T12:58:57.023Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Cell%20Systems.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Cell%20Systems"}}, "title": "Cell Systems", "issn": "2639-5460", "issn-l": "2405-4712", "publications_count": 10, "publications": [{"entity": "publication", "iuid": "f1c4be6840db4ebfb8707f35ac4a02ea", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f1c4be6840db4ebfb8707f35ac4a02ea.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f1c4be6840db4ebfb8707f35ac4a02ea"}}, "title": "Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays.", "authors": [{"family": "Smith", "given": "Kevin", "initials": "K"}, {"family": "Piccinini", "given": "Filippo", "initials": "F"}, {"family": "Balassa", "given": "Tamas", "initials": "T"}, {"family": "Koos", "given": "Krisztian", "initials": "K"}, {"family": "Danka", "given": "Tivadar", "initials": "T"}, {"family": "Azizpour", "given": "Hossein", "initials": "H"}, {"family": "Horvath", "given": "Peter", "initials": "P"}], "type": "journal article", "published": "2018-06-27", "journal": {"title": "Cell Systems", "issn": "2405-4712", "volume": "6", "issue": "6", "pages": "636-653", "issn-l": null}, "abstract": "Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.", "doi": "10.1016/j.cels.2018.06.001", "pmid": "29953863", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(18)30241-2"}], "notes": [], "created": "2019-01-17T13:52:08.594Z", "modified": "2019-01-17T13:52:08.614Z"}, {"entity": "publication", "iuid": "58a5b53ab434402b8c3669b167c28a0f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/58a5b53ab434402b8c3669b167c28a0f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/58a5b53ab434402b8c3669b167c28a0f"}}, "title": "Network Visualization and Analysis of Spatially Aware Gene Expression Data with InsituNet.", "authors": [{"family": "Salamon", "given": "John", "initials": "J"}, {"family": "Qian", "given": "Xiaoyan", "initials": "X"}, {"family": "Nilsson", "given": "Mats", "initials": "M"}, {"family": "Lynn", "given": "David John", "initials": "DJ"}], "type": "journal article", "published": "2018-05-23", "journal": {"title": "Cell Systems", "issn": "2405-4712", "volume": "6", "issue": "5", "pages": "626-630.e3", "issn-l": null}, "abstract": "In situ sequencing methods generate spatially resolved RNA localization and expression data at an almost single-cell resolution. Few methods, however, currently exist to analyze and visualize the complex data that is produced, which can encode the localization and expression of a million or more individual transcripts in a tissue section. Here, we present InsituNet, an application that converts in situ sequencing data into interactive network-based visualizations, where each unique transcript is a node in the network and edges represent the spatial co-expression relationships between transcripts. InsituNet is available as an app for the Cytoscape platform at http://apps.cytoscape.org/apps/insitunet. InsituNet enables the analysis of the relationships that exist\u00a0between these transcripts and can uncover how spatial co-expression profiles change in different regions of the tissue or across different tissue sections.", "doi": "10.1016/j.cels.2018.03.010", "pmid": "29753646", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(18)30107-8"}], "notes": [], "created": "2018-12-05T12:39:06.384Z", "modified": "2018-12-05T12:39:06.404Z"}, {"entity": "publication", "iuid": "54014750038f4ec5843fe57c2a8c0b46", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/54014750038f4ec5843fe57c2a8c0b46.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/54014750038f4ec5843fe57c2a8c0b46"}}, "title": "GeneGini: Assessment via the Gini Coefficient of Reference \"Housekeeping\" Genes and Diverse Human Transporter Expression Profiles.", "authors": [{"family": "O'Hagan", "given": "Steve", "initials": "S"}, {"family": "Wright Muelas", "given": "Marina", "initials": "M"}, {"family": "Day", "given": "Philip J", "initials": "PJ"}, {"family": "Lundberg", "given": "Emma", "initials": "E"}, {"family": "Kell", "given": "Douglas B", "initials": "DB"}], "type": "journal article", "published": "2018-02-28", "journal": {"title": "Cell Systems", "issn": "2405-4712", "volume": "6", "issue": "2", "pages": "230-244.e1", "issn-l": null}, "abstract": "The expression levels of SLC or ABC membrane transporter transcripts typically differ 100- to 10,000-fold between different tissues. The Gini coefficient characterizes such inequalities and here is used to describe the distribution of the expression of each transporter among different human tissues and cell lines. Many transporters exhibit extremely high Gini coefficients even for common substrates, indicating considerable specialization consistent with divergent evolution. The expression profiles of SLC transporters in different cell lines behave similarly, although Gini coefficients for ABC transporters tend to be larger in cell lines than in tissues, implying selection. Transporter genes are significantly more heterogeneously expressed than the members of most non-transporter gene classes. Transcripts with the stablest expression have a low Gini index and often differ significantly from the \"housekeeping\" genes commonly used for normalization in transcriptomics/qPCR studies. PCBP1 has a low Gini coefficient, is reasonably expressed, and is an\u00a0excellent novel reference gene. The approach, referred to as GeneGini, provides rapid and simple characterization of expression-profile distributions and improved normalization of genome-wide expression-profiling data.", "doi": "10.1016/j.cels.2018.01.003", "pmid": "29428416", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(18)30003-6"}, {"db": "pmc", "key": "PMC5840522"}], "notes": [], "created": "2018-12-05T12:46:06.983Z", "modified": "2018-12-05T12:46:07.002Z"}, {"entity": "publication", "iuid": "dc8c3d0f8a6b4333bb4f6b60431470f3", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/dc8c3d0f8a6b4333bb4f6b60431470f3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/dc8c3d0f8a6b4333bb4f6b60431470f3"}}, "title": "Integrative Personal Omics Profiles during Periods of Weight Gain and Loss.", "authors": [{"family": "Piening", "given": "Brian D", "initials": "BD"}, {"family": "Zhou", "given": "Wenyu", "initials": "W"}, {"family": "Contrepois", "given": "K\u00e9vin", "initials": "K"}, {"family": "R\u00f6st", "given": "Hannes", "initials": "H"}, {"family": "Gu Urban", "given": "Gucci Jijuan", "initials": "GJ"}, {"family": "Mishra", "given": "Tejaswini", "initials": "T"}, {"family": "Hanson", "given": "Blake M", "initials": "BM"}, {"family": "Bautista", "given": "Eddy J", "initials": "EJ"}, {"family": "Leopold", "given": "Shana", "initials": "S"}, {"family": "Yeh", "given": "Christine Y", "initials": "CY"}, {"family": "Spakowicz", "given": "Daniel", "initials": "D"}, {"family": "Banerjee", "given": "Imon", "initials": "I"}, {"family": "Chen", "given": "Cynthia", "initials": "C"}, {"family": "Kukurba", "given": "Kimberly", "initials": "K"}, {"family": "Perelman", "given": "Dalia", "initials": "D"}, {"family": "Craig", "given": "Colleen", "initials": "C"}, {"family": "Colbert", "given": "Elizabeth", "initials": "E"}, {"family": "Salins", "given": "Denis", "initials": "D"}, {"family": "Rego", "given": "Shannon", "initials": "S"}, {"family": "Lee", "given": "Sunjae", "initials": "S"}, {"family": "Zhang", "given": "Cheng", "initials": "C"}, {"family": "Wheeler", "given": "Jessica", "initials": "J"}, {"family": "Sailani", "given": "M Reza", "initials": "MR"}, {"family": "Liang", "given": "Liang", "initials": "L"}, {"family": "Abbott", "given": "Charles", "initials": "C"}, {"family": "Gerstein", "given": "Mark", "initials": "M"}, {"family": "Mardinoglu", "given": "Adil", "initials": "A"}, {"family": "Smith", "given": "Ulf", "initials": "U"}, {"family": "Rubin", "given": "Daniel L", "initials": "DL"}, {"family": "Pitteri", "given": "Sharon", "initials": "S"}, {"family": "Sodergren", "given": "Erica", "initials": "E"}, {"family": "McLaughlin", "given": "Tracey L", "initials": "TL"}, {"family": "Weinstock", "given": "George M", "initials": "GM"}, {"family": "Snyder", "given": "Michael P", "initials": "MP"}], "type": "journal article", "published": "2018-02-28", "journal": {"title": "Cell Systems", "issn": "2405-4712", "issn-l": null, "volume": "6", "issue": "2", "pages": "157-170.e8"}, "abstract": "Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.", "doi": "10.1016/j.cels.2017.12.013", "pmid": "29361466", "labels": {"Affiliated researcher": null, "Adil Mardinoglu": null, "SciLifeLab Fellow": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(17)30555-0"}, {"db": "pmc", "key": "PMC6021558"}, {"db": "mid", "key": "NIHMS929035"}], "notes": [], "created": "2018-12-03T14:45:34.099Z", "modified": "2022-11-04T11:32:17.911Z"}, {"entity": "publication", "iuid": "9bc5bcfa4dcc4fab8ade7b88faf40154", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9bc5bcfa4dcc4fab8ade7b88faf40154.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9bc5bcfa4dcc4fab8ade7b88faf40154"}}, "title": "Broad Views of Non-alcoholic Fatty Liver Disease.", "authors": [{"family": "Mardinoglu", "given": "Adil", "initials": "A"}, {"family": "Uhlen", "given": "Mathias", "initials": "M"}, {"family": "Bor\u00e9n", "given": "Jan", "initials": "J"}], "type": "journal article", "published": "2018-01-24", "journal": {"title": "Cell Systems", "issn": "2405-4712", "issn-l": null, "volume": "6", "issue": "1", "pages": "7-9"}, "abstract": "Multi-omics multi-tissue data are used to interpret genome-wide association study results from mice to identify key driver genes of non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease (NAFLD) is the accumulation of fat (steatosis) in the liver due to causes other than excessive alcohol consumption. The disease may progress to more severe forms of liver diseases, including non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. In this issue of Cell Systems, Krishnan et al. (2018) reveal mechanisms underlying NAFLD by generating multi-omics data using liver and adipose tissues obtained from the Hybrid Mouse Diversity Panel, consisting of 113 mouse strains with various degrees of NAFLD. The study identified key driver genes of NAFLD that can be used in the development of efficient treatment strategies and illustrates the potential utility of systematic analysis of multi-layer biological networks.", "doi": "10.1016/j.cels.2018.01.004", "pmid": "29401451", "labels": {"Adil Mardinoglu": null, "SciLifeLab Fellow": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(18)30004-8"}], "notes": [], "created": "2020-09-25T13:34:41.466Z", "modified": "2022-11-04T11:32:18.012Z"}, {"entity": "publication", "iuid": "d90e8d5ac14a4580872688e5ccee81c1", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d90e8d5ac14a4580872688e5ccee81c1.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d90e8d5ac14a4580872688e5ccee81c1"}}, "title": "Metabolic Models of Protein Allocation Call for the Kinetome.", "authors": [{"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f2e21dbc1c624f6a841c59e959e948e4.json"}}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/33f2b49a39ed4e54ba77dfe397ed3087.json"}}, {"family": "Palsson", "given": "Bernhard O", "initials": "BO", "orcid": "0000-0003-2357-6785", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fd602b497a2f48f39452f55f1fc28ac9.json"}}], "type": "journal article", "published": "2017-12-27", "journal": {"title": "Cell Systems", "issn": "2405-4712", "issn-l": null, "volume": "5", "issue": "6", "pages": "538-541"}, "abstract": "The flux of metabolites in the living cell depend on enzyme activities. Recently, many metabolic phenotypes have been explained by computer models that incorporate enzyme activity data. To move further, the scientific community needs to measure the kinetics of all enzymes in a systematic way.", "doi": "10.1016/j.cels.2017.11.013", "pmid": "29284126", "labels": {"Avlant Nilsson": null, "DDLS Fellow": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(17)30541-0"}], "notes": [], "created": "2025-03-20T11:00:14.681Z", "modified": "2025-03-21T13:14:43.640Z"}, {"entity": "publication", "iuid": "e26fde20e7eb48c59d341d54844973fc", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/e26fde20e7eb48c59d341d54844973fc.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/e26fde20e7eb48c59d341d54844973fc"}}, "title": "Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.", "authors": [{"family": "Piccinini", "given": "Filippo", "initials": "F"}, {"family": "Balassa", "given": "Tamas", "initials": "T"}, {"family": "Szkalisity", "given": "Abel", "initials": "A"}, {"family": "Molnar", "given": "Csaba", "initials": "C"}, {"family": "Paavolainen", "given": "Lassi", "initials": "L"}, {"family": "Kujala", "given": "Kaisa", "initials": "K"}, {"family": "Buzas", "given": "Krisztina", "initials": "K"}, {"family": "Sarazova", "given": "Marie", "initials": "M"}, {"family": "Pietiainen", "given": "Vilja", "initials": "V"}, {"family": "Kutay", "given": "Ulrike", "initials": "U"}, {"family": "Smith", "given": "Kevin", "initials": "K"}, {"family": "Horvath", "given": "Peter", "initials": "P"}], "type": "journal article", "published": "2017-06-28", "journal": {"title": "Cell Systems", "issn": "2405-4712", "volume": "4", "issue": "6", "pages": "651-655.e5", "issn-l": null}, "abstract": "High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.", "doi": "10.1016/j.cels.2017.05.012", "pmid": "28647475", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(17)30227-2"}], "notes": [], "created": "2018-12-05T11:57:08.479Z", "modified": "2018-12-05T11:57:08.496Z"}, {"entity": "publication", "iuid": "f4700c79c189490eb64ed499be66397d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f4700c79c189490eb64ed499be66397d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f4700c79c189490eb64ed499be66397d"}}, "title": "Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast.", "authors": [{"family": "Lahtvee", "given": "Petri-Jaan", "initials": "PJ"}, {"family": "S\u00e1nchez", "given": "Benjam\u00edn J", "initials": "BJ"}, {"family": "Smialowska", "given": "Agata", "initials": "A"}, {"family": "Kasvandik", "given": "Sergo", "initials": "S"}, {"family": "Elsemman", "given": "Ibrahim E", "initials": "IE"}, {"family": "Gatto", "given": "Francesco", "initials": "F"}, {"family": "Nielsen", "given": "Jens", "initials": "J"}], "type": "journal article", "published": "2017-05-24", "journal": {"title": "Cell Systems", "issn": "2405-4712", "volume": "4", "issue": "5", "pages": "495-504.e5", "issn-l": null}, "abstract": "Protein synthesis is the most energy-consuming process in a proliferating cell, and understanding what controls protein abundances represents a key question in biology and biotechnology. We quantified absolute abundances of 5,354 mRNAs and 2,198 proteins in Saccharomyces cerevisiae under ten environmental conditions and protein turnover for 1,384 proteins under a reference condition. The overall correlation between mRNA and protein abundances across all conditions was low (0.46), but for differentially expressed proteins (n\u00a0= 202), the median mRNA-protein correlation was 0.88. We used these data to model translation efficiencies and found that they vary more than 400-fold between genes. Non-linear regression analysis detected that mRNA abundance and translation elongation were the dominant factors controlling protein synthesis, explaining 61% and 15% of its variance. Metabolic flux balance analysis further showed that only mitochondrial fluxes were positively associated with changes at the transcript level. The present dataset represents a crucial expansion to the current resources for future studies on yeast physiology.", "doi": "10.1016/j.cels.2017.03.003", "pmid": "28365149", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(17)30088-1"}], "notes": [], "created": "2018-12-05T10:22:33.961Z", "modified": "2018-12-05T10:22:33.986Z"}, {"entity": "publication", "iuid": "d292e3a63176465c85eb567d5a98ef9f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d292e3a63176465c85eb567d5a98ef9f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d292e3a63176465c85eb567d5a98ef9f"}}, "title": "Analysis of Body-wide Unfractionated Tissue Data to Identify a Core Human Endothelial Transcriptome.", "authors": [{"family": "Butler", "given": "Lynn Marie", "initials": "LM"}, {"family": "Hallstr\u00f6m", "given": "Bj\u00f6rn Mikael", "initials": "BM"}, {"family": "Fagerberg", "given": "Linn", "initials": "L"}, {"family": "Pont\u00e9n", "given": "Fredrik", "initials": "F"}, {"family": "Uhl\u00e9n", "given": "Mathias", "initials": "M"}, {"family": "Renn\u00e9", "given": "Thomas", "initials": "T"}, {"family": "Odeberg", "given": "Jacob", "initials": "J"}], "type": "journal article", "published": "2016-09-28", "journal": {"title": "Cell Systems", "issn": "2405-4712", "volume": "3", "issue": "3", "pages": "287-301.e3", "issn-l": null}, "abstract": "Endothelial cells line blood vessels and regulate hemostasis, inflammation, and blood pressure. Proteins critical for these specialized functions tend to be predominantly expressed in endothelial cells across vascular beds. Here, we present a systems approach to identify a panel of human endothelial-enriched genes using global, body-wide transcriptomics data from 124 tissue samples from 32 organs. We identified known and unknown endothelial-enriched gene transcripts and used antibody-based profiling to confirm expression across vascular beds. The majority of identified transcripts could be detected in cultured endothelial cells from various vascular beds, and we observed maintenance of relative expression in early passage cells. In summary, we describe a widely applicable method to determine cell-type-specific transcriptome profiles in a whole-organism context, based on differential abundance across tissues. We identify potential vascular drug targets or endothelial biomarkers and highlight candidates for functional studies to increase understanding of the endothelium in health and disease.", "doi": "10.1016/j.cels.2016.08.001", "pmid": "27641958", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(16)30256-3"}], "notes": [], "created": "2018-12-05T11:59:05.576Z", "modified": "2018-12-05T11:59:05.602Z"}, {"entity": "publication", "iuid": "8a918813cd8a47ba8ec7cbfc50a804ec", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/8a918813cd8a47ba8ec7cbfc50a804ec.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/8a918813cd8a47ba8ec7cbfc50a804ec"}}, "title": "System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis.", "authors": [{"family": "Pozniak", "given": "Yair", "initials": "Y"}, {"family": "Balint-Lahat", "given": "Nora", "initials": "N"}, {"family": "Rudolph", "given": "Jan Daniel", "initials": "JD"}, {"family": "Lindskog", "given": "Cecilia", "initials": "C"}, {"family": "Katzir", "given": "Rotem", "initials": "R"}, {"family": "Avivi", "given": "Camilla", "initials": "C"}, {"family": "Pont\u00e9n", "given": "Fredrik", "initials": "F"}, {"family": "Ruppin", "given": "Eytan", "initials": "E"}, {"family": "Barshack", "given": "Iris", "initials": "I"}, {"family": "Geiger", "given": "Tamar", "initials": "T"}], "type": "journal article", "published": "2016-03-23", "journal": {"title": "Cell Systems", "issn": "2405-4712", "volume": "2", "issue": "3", "pages": "172-184", "issn-l": null}, "abstract": "The genomic and transcriptomic landscapes of breast cancer have been extensively studied, but the proteomes of breast tumors are far less characterized. Here, we use high-resolution, high-accuracy mass spectrometry to perform a deep analysis of luminal-type breast cancer progression using clinical breast samples from primary tumors, matched lymph node metastases, and healthy breast epithelia. We used a super-SILAC mix to quantify over 10,000 proteins with high accuracy, enabling us to identify key proteins and pathways associated with tumorigenesis and metastatic spread. We found high expression levels of proteins associated with protein synthesis and degradation in cancer tissues, accompanied by metabolic alterations that may facilitate energy production in cancer cells within their natural environment. In addition, we found proteomic differences between breast cancer stages and minor differences between primary tumors and their matched lymph node metastases. These results highlight the potential of proteomic technology in the elucidation of clinically relevant cancer signatures.", "doi": "10.1016/j.cels.2016.02.001", "pmid": "27135363", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pii", "key": "S2405-4712(16)30031-X"}], "notes": [], "created": "2018-12-05T09:37:35.538Z", "modified": "2018-12-05T09:37:35.557Z"}], "created": "2018-12-03T14:45:34.112Z", "modified": "2020-11-27T13:13:00.363Z"}