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Plants are essential for life and are extremely diverse organisms with unique molecular capabilities 1 . Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana . Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions. A quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana provides a valuable resource for plant research.

Genome‐, transcriptome‐ and proteome‐wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein‐level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue‐specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation. Synopsis Proteome and transcriptome quantification across tissues reveals which human genes exist as transcripts and proteins, where they are expressed and in which approximate quantities. Tissue‐specific protein expression is found to be a rare and quantitative rather than qualitative characteristic. The study presents the most comprehensive atlas of protein expression to date, across 29 healthy human tissues. Protein level evidence is provided for 13,640 genes and 15,257 isoforms, including 37 missing proteins. Tissue‐specific protein expression is rare and quantitative rather than qualitative characteristic. Proteogenomics is still challenging and needs rigorous validation by synthetic peptides. Graphical Abstract Proteome and transcriptome quantification across tissues reveals which human genes exist as transcripts and proteins, where they are expressed and in which approximate quantities. Tissue‐specific protein expression is found to be a rare and quantitative rather than qualitative characteristic.

Integrated analysis of genomes, transcriptomes, proteomes and drug responses of cancer cell lines (CCLs) is an emerging approach to uncover molecular mechanisms of drug action. We extend this paradigm to measuring proteome activity landscapes by acquiring and integrating quantitative data for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 CCLs. These data are used to contextualize proteins and p-sites and predict drug sensitivity. For example, we find that Progesterone Receptor (PGR) phosphorylation is associated with sensitivity to drugs modulating estrogen signaling such as Raloxifene. We also demonstrate that Adenylate kinase isoenzyme 1 (AK1) inactivates antimetabolites like Cytarabine. Consequently, high AK1 levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients, qualifying AK1 as a patient stratification marker and possibly as a drug target. We provide an interactive web application termed ATLANTiC ( http://atlantic.proteomics.wzw.tum.de ), which enables the community to explore the thousands of novel functional associations generated by this work. Proteome activity has a major role in cancer progression and response to drugs. Here, the authors use comprehensive proteomic and phosphoproteomic data, in conjunction with drug-sensitivity screens, to generate a community resource consisting of landscapes of pathway and kinase activity across different cell lines

The laboratory mouse ranks among the most important experimental systems for biomedical research and molecular reference maps of such models are essential informational tools. Here, we present a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues and several lines of analyses exemplify which insights can be gleaned from the data. For instance, tissue- and cell-type resolved profiles provide protein evidence for the expression of 17,000 genes, thousands of isoforms and 50,000 phosphorylation sites in vivo. Proteogenomic comparison of mouse, human and Arabidopsis reveal common and distinct mechanisms of gene expression regulation and, despite many similarities, numerous differentially abundant orthologs that likely serve species-specific functions. We leverage the mouse proteome by integrating phenotypic drug ( n  > 400) and radiation response data with the proteomes of 66 pancreatic ductal adenocarcinoma (PDAC) cell lines to reveal molecular markers for sensitivity and resistance. This unique atlas complements other molecular resources for the mouse and can be explored online via ProteomicsDB and PACiFIC. This work presents a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues covering 15 major anatomical systems and 66 cell lines.

Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome‐guided pre‐clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials. Synopsis Deep proteome profiling of colorectal cancer (CRC) cell lines is used to identify cell lines matching to molecular subtypes of CRC patients. The subsequent identification of molecular signatures predicting the response to specific drugs is a useful resource for clinical trial design. The proteomes of 65 CRC cell lines are characterized by quantitative mass spectrometry to a depth of > 10,000 proteins. A combined analysis of proteomes and transcriptomes of cell lines and patients reveals integrated CRC subtypes. Integration with phenotypic drug sensitivity data predicts subtype‐specific drug response. MERTK protein levels in patients predict response to MEK inhibitors and patient survival. Graphical Abstract Deep proteome profiling of colorectal cancer (CRC) cell lines is used to identify cell lines matching to molecular subtypes of CRC patients. The subsequent identification of molecular signatures predicting the response to specific drugs is a useful resource for clinical trial design.

Background RNAi technology by feeding of E. coli containing dsRNA in C. elegans has significantly contributed to further our understanding of many different fields, including genetics, molecular biology, developmental biology and functional genomics. Most of this research has been carried out in a single genotype or genetic background. However, RNAi effects in one genotype do not reveal the allelic effects that segregate in natural populations and contribute to phenotypic variation. Results Here we present a method that allows for rapidly comparing RNAi effects among diverse genotypes at an improved high throughput rate. It is based on assessing the fitness of a population of worms by measuring the rate at which E. coli is consumed. Critically, we demonstrate the analytical power of this method by QTL mapping the loss of RNAi sensitivity (in the germline) in a recombinant inbred population derived from a cross between Bristol and a natural isolate from Hawaii. Hawaii has lost RNAi sensitivity in the germline. We found that polymorphisms in ppw-1 contribute to this loss of RNAi sensitivity, but that other loci are also likely to be important. Conclusions In summary, we have established a fast method that improves the throughput of RNAi in liquid, that generates quantitative data, that is easy to implement in most laboratories, and importantly that enables QTL mapping using RNAi.

Plant growth and development are regulated by a tightly controlled interplay between cell division, cell expansion and cell differentiation during the entire plant life cycle from seed germination to maturity and seed propagation. To explore some of the underlying molecular mechanisms in more detail, we selected different aerial tissue types of the model plant Arabidopsis thaliana , namely rosette leaf, flower and silique/seed and performed proteomic, phosphoproteomic and transcriptomic analyses of sequential growth stages using tandem mass tag-based mass spectrometry and RNA sequencing. With this exploratory multi-omics dataset, development dynamics of photosynthetic tissues can be investigated from different angles. As expected, we found progressive global expression changes between growth stages for all three omics types and often but not always corresponding expression patterns for individual genes on transcript, protein and phosphorylation site level. The biggest difference between proteomic- and transcriptomic-based expression information could be observed for seed samples. Proteomic and transcriptomic data is available via ProteomeXchange and ArrayExpress with the respective identifiers PXD018814 and E-MTAB-7978. Measurement(s) transcriptome • Proteome • Phosphoproteome Technology Type(s) RNA sequencing • liquid chromatography-tandem mass spectrometry Factor Type(s) organism part • growth stage Sample Characteristic - Organism Arabidopsis thaliana Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12998993

Colorectal cancer (CRC) is one of the top three most common cancers and among the top four causes of cancer-related deaths worldwide (Torre et al., 2015). CRC patients are well characterised on the transcriptome and proteome level, but proteomics data on representative cell lines as model systems for pre-clinical drug sensitivity studies lag behind. Here, label-free quantitative mass spectrometry was used to characterise the kinomes and full proteomes of 65 CRC cell lines, collectively termed the CRC65 cell line panel. This data was integrated with proteomics data on patient samples, as well as public transcriptome and drug sensitivity datasets, which were reanalysed from raw data in order to unify and streamline the data processing. Protein/mRNA ratios were constant across these datasets, enabling linear prediction of protein abundance from mRNA abundance after appropriate adjustment, which was used for mRNA-guided missing value imputation. An exploration of secondary imputation methods prompted the development of a complementary method for minimum-guided missing value imputation. Combining the proteomics datasets on cell lines and patients led to the discovery of integrated proteomic subtypes of CRC and enabled the identification of representative cell lines for each of them. Modelling publicly available dose-response data generated by four large-scale drug sensitivity studies as a function of kinome/full proteome profiles fuelled the prediction of drug sensitivity for cell lines and patients, allowed the identification of drugs differentially effective between the different integrated proteomic subtypes and revealed MERTK as a predictive biomarker for resistance towards MEK1/2 inhibitors. This predictive role of MERTK was subsequently confirmed using in vitro experiments, while immunohistochemistry of TMAs from 1,074 tumours generated as part of the QUASAR2 clinical trial unveiled that MERTK is also a prognostic biomarker in CRC. This dataset will be made available to the scientific community to facilitate the design of prospective clinical studies.

Proteomic workflows generate vastly complex peptide mixtures that are analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), creating thousands of spectra, most of which are chimeric and contain fragment ions from more than one peptide. Because of differences in data acquisition strategies such as data-dependent (DDA), data-independent (DIA) or parallel reaction monitoring (PRM), separate software packages employing different analysis concepts are used for peptide identification and quantification, even though the underlying information is principally the same. Here, we introduce CHIMERYS, a novel, spectrum-centric search algorithm designed for the deconvolution of chimeric spectra that unifies proteomic data analysis. Using accurate predictions of peptide retention time, fragment ion intensities and applying regularized linear regression, it explains as much fragment ion intensity as possible with as few peptides as possible. Together with rigorous false discovery rate control, CHIMERYS accurately identifies and quantifies multiple peptides per tandem mass spectrum in DDA, DIA and PRM experiments.

Genome-, transcriptome- and proteome-wide measurements provide valuable insights into how biological systems are regulated. However, even fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we have generated a systematic, quantitative and deep proteome and transcriptome abundance atlas from 29 paired healthy human tissues from the Human Protein Atlas Project and representing human genes by 17,615 transcripts and 13,664 proteins. The analysis revealed that few proteins show truly tissue-specific expression, that vast differences between mRNA and protein quantities within and across tissues exist and that the expression levels of proteins are often more stable across tissues than those of transcripts. In addition, only ~2% of all exome and ~7% of all mRNA variants could be confidently detected at the protein level showing that proteogenomics remains challenging, requires rigorous validation using synthetic peptides and needs more sophisticated computational methods. Many uses of this resource can be envisaged ranging from the study of gene/protein expression regulation to protein biomarker specificity evaluation to name a few.