In the era towards precision medicine, we here present the individual specific autoantibody signatures of 193 healthy individuals. The self-reactive IgG signatures are stable over time in a way that each individual profile is recognized in longitudinal sampling. The IgG autoantibody reactivity towards an antigen array comprising 335 protein fragments, representing 204 human proteins with potential relevance to autoimmune disorders, was measured in longitudinal plasma samples from 193 healthy individuals. This analysis resulted in unique autoantibody barcodes for each individual that were maintained over one year's time. The reactivity profiles, or signatures, are person specific in regards to the number of reactivities and antigen specificity. Two independent data sets were consistent in that each healthy individual displayed reactivity towards 0-16 antigens, with a median of six. Subsequently, four selected individuals were profiled on in-house produced high-density protein arrays containing 23,000 protein fragments representing 14,000 unique protein coding genes. Based on a unique, broad and deep longitudinal profiling of autoantibody reactivities, our results demonstrate a unique autoreactive profile in each analyzed healthy individual. The need and interest for broad-ranged and high-resolution molecular profiling of healthy individuals is rising. We have here generated and assessed an initial perspective on the global distribution of the self-reactive IgG repertoire in healthy individuals, by investigating 193 well-characterized healthy individuals.

The plasma proteome offers a clinically useful window into human health. Recent advances from highly multiplexed assays now call for appropriate pipelines to validate individual candidates. Here, a workflow is developed to build dual binder sandwich immunoassays (SIA) and for proteins predicted to be secreted into plasma. Utilizing suspension bead arrays, ≈1800 unique antibody pairs are first screened against 209 proteins with recombinant proteins as well as EDTA plasma. Employing 624 unique antibodies, dilution-dependent curves in plasma and concentration-dependent curves of full-length proteins for 102 (49%) of the targets are obtained. For 22 protein assays, the longitudinal, interindividual, and technical performance is determined in a set of plasma samples collected from 18 healthy subjects every third month over 1 year. Finally, 14 of these assays are compared with with SIAs composed of other binders, proximity extension assays, and affinity-free targeted mass spectrometry. The workflow provides a multiplexed approach to screen for SIA pairs that suggests using at least three antibodies per target. This design is applicable for a wider range of targets of the plasma proteome, and the assays can be applied for discovery but also to validate emerging candidates derived from other platforms.

The intermediate filament protein nestin is expressed during embryonic development, but considered largely restricted to areas of regeneration in the adult. Here, we perform a body-wide transcriptome and protein-profiling analysis to reveal that nestin is constitutively, and highly-selectively, expressed in adult human endothelial cells (EC), independent of proliferative status. Correspondingly, we demonstrate that it is not a marker for tumour EC in multiple malignancy types. Imaging of EC from different vascular beds reveals nestin subcellular distribution is shear-modulated. siRNA inhibition of nestin increases EC proliferation, and nestin expression is reduced in atherosclerotic plaque neovessels. eQTL analysis reveals an association between SNPs linked to cardiovascular disease and reduced aortic EC nestin mRNA expression. Our study challenges the dogma that nestin is a marker of proliferation, and provides insight into its regulation and function in EC. Furthermore, our systems-based approach can be applied to investigate body-wide expression profiles of any candidate protein. 

There is a need for standardized validation methods for antibody specificity and selectivity. Recently, five alternative validation pillars were proposed to explore the specificity of research antibodies using methods with no need for prior knowledge about the protein target. Here, we show that these principles can be used in a streamlined manner for enhanced validation of research antibodies in Western blot applications. More than 6,000 antibodies were validated with at least one of these strategies involving orthogonal methods, genetic knockdown, recombinant expression, independent antibodies, and capture mass spectrometry analysis. The results show a path forward for efforts to validate antibodies in an application-specific manner suitable for both providers and users.

Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.

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.

Cancer testis antigens (CTAs) are of clinical interest as biomarkers and present valuable targets for immunotherapy. To comprehensively characterize the CTA landscape of non-small-cell lung cancer (NSCLC), we compared RNAseq data from 199 NSCLC tissues to the normal transcriptome of 142 samples from 32 different normal organs. Of 232 CTAs currently annotated in the Caner Testis Database (CTdatabase), 96 were confirmed in NSCLC. To obtain an unbiased CTA profile of NSCLC, we applied stringent criteria on our RNAseq data set and defined 90 genes as CTAs, of which 55 genes were not annotated in the CTdatabase, thus representing potential new CTAs. Cluster analysis revealed that CTA expression is histology dependent and concurrent expression is common. IHC confirmed tissue-specific protein expression of selected new CTAs (TKTL1, TGIF2LX, VCX, and CXORF67). Furthermore, methylation was identified as a regulatory mechanism of CTA expression based on independent data from The Cancer Genome Atlas. The proposed prognostic impact of CTAs in lung cancer was not confirmed, neither in our RNAseq cohort nor in an independent meta-analysis of 1,117 NSCLC cases. In summary, we defined a set of 90 reliable CTAs, including information on protein expression, methylation, and survival association. The detailed RNAseq catalog can guide biomarker studies and efforts to identify targets for immunotherapeutic strategies.

To better understand prostate function and disease, it is important to define and explore the molecular constituents that signify the prostate gland. The aim of this study was to define the prostate specific transcriptome and proteome, in comparison to 26 other human tissues. Deep sequencing of mRNA (RNA-seq) and immunohistochemistry-based protein profiling were combined to identify prostate specific gene expression patterns and to explore tissue biomarkers for potential clinical use in prostate cancer diagnostics. We identified 203 genes with elevated expression in the prostate, 22 of which showed more than five-fold higher expression levels compared to all other tissue types. In addition to previously well-known proteins we identified two poorly characterized proteins, TMEM79 and ACOXL, with potential to differentiate between benign and cancerous prostatic glands in tissue biopsies. In conclusion, we have applied a genome-wide analysis to identify the prostate specific proteome using transcriptomics and antibody-based protein profiling to identify genes with elevated expression in the prostate. Our data provides a starting point for further functional studies to explore the molecular repertoire of normal and diseased prostate including potential prostate cancer markers such as TMEM79 and ACOXL.

Understanding the normal state of human tissue transcriptome profiles is essential for recognizing tissue disease states and identifying disease markers. Recently, the Human Protein Atlas and the FANTOM5 consortium have each published extensive transcriptome data for human samples using Illumina-sequenced RNA-Seq and Heliscope-sequenced CAGE. Here, we report on the first large-scale complex tissue transcriptome comparison between full-length versus 5'-capped mRNA sequencing data. Overall gene expression correlation was high between the 22 corresponding tissues analyzed (R > 0.8). For genes ubiquitously expressed across all tissues, the two data sets showed high genome-wide correlation (91% agreement), with differences observed for a small number of individual genes indicating the need to update their gene models. Among the identified single-tissue enriched genes, up to 75% showed consensus of 7-fold enrichment in the same tissue in both methods, while another 17% exhibited multiple tissue enrichment and/or high expression variety in the other data set, likely dependent on the cell type proportions included in each tissue sample. Our results show that RNA-Seq and CAGE tissue transcriptome data sets are highly complementary for improving gene model annotations and highlight biological complexities within tissue transcriptomes. Furthermore, integration with image-based protein expression data is highly advantageous for understanding expression specificities for many genes.