| 1. |
- Adhikari, Subash, et al.
(författare)
-
A high-stringency blueprint of the human proteome
- 2020
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
-
Forskningsöversikt (refereegranskat)abstract
- The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP’s tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.
|
|
| 2. |
- Carapito, Christine, et al.
(författare)
-
Validating Missing Proteins in Human Sperm Cells by Targeted Mass-Spectrometry- and Antibody-based Methods
- 2017
-
Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 16:12, s. 4340-4351
-
Tidskriftsartikel (refereegranskat)abstract
- The present study is a contribution to the "neXt50 challenge", a coordinated effort across C-HPP teams to identify the SO most tractable missing proteins (MPs) on each chromosome. We report the targeted search of 38 theoretically detectable MPs from chromosomes 2 and 14 in Triton X-100 soluble and insoluble sperm fractions from a total of 15 healthy donors. A targeted mass spectrometry-based strategy consisting of the development of LC-PRM assays (with heavy labeled synthetic peptides) targeting 92 proteotypic peptides of the 38 selected MPs was used. Out of the 38 selected MPs, 12 were identified with two or more peptides and 3 with one peptide after extensive SDS-PAGE fractionation of the two samples and with overall low-intensity signals. The PRM data are available via ProteomeXchange in PASSEL (PASS01013). Further validation by immunohistochemistry on human testes sections and cytochemistry on sperm smears was performed for eight MPs with antibodies available from the Human Protein Atlas. Deep analysis of human sperm still allows the validation of MPs and therefore contributes to the C-HPP worldwide effort. We anticipate that our results will be of interest to the reproductive biology community because an in-depth analysis of these MPs may identify potential new candidates in the context of human idiopathic infertilities.
|
|
| 3. |
- Ghoshal, Biraja, et al.
(författare)
-
DeepHistoClass : A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning
- 2021
-
Ingår i: Molecular & Cellular Proteomics. - : Elsevier. - 1535-9476 .- 1535-9484. ; 20
-
Tidskriftsartikel (refereegranskat)abstract
- A multitude of efforts worldwide aim to create a single-cell reference map of the human body, for fundamental understanding of human health, molecular medicine, and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has proven to be an excellent technology for integration with large-scale single-cell transcriptomics datasets. The golden standard for evaluation of IHC staining patterns is manual annotation, which is expensive and may lead to subjective errors. Artificial intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. Here, the aim was to present a reliable and comprehensive framework for automated annotation of IHC images. We developed a multilabel classification of 7848 complex IHC images of human testis corresponding to 2794 unique proteins, generated as part of the Human Protein Atlas (HPA) project. Manual annotation data for eight different cell types was generated as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric, DeepHistoClass (DHC) Confidence Score, the average diagnostic performance improved from 86.9% to 96.3%. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project.
|
|
| 4. |
- Omenn, Gilbert S., et al.
(författare)
-
The 2022 Report on the Human Proteome from the HUPO Human Proteome Project
- 2023
-
Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 22:4, s. 1024-1042
-
Tidskriftsartikel (refereegranskat)abstract
- The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, “A Function for Each Protein”.
|
|
| 5. |
- Omenn, Gilbert S., et al.
(författare)
-
The 2023 Report on the Proteome from the HUPO Human Proteome Project
- 2024
-
Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 23:2, s. 532-549
-
Forskningsöversikt (refereegranskat)abstract
- Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1–4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.
|
|
| 6. |
- Vandenbrouck, Yves, et al.
(författare)
-
Looking for Missing Proteins in the Proteome of Human Spermatozoa : An Update
- 2016
-
Ingår i: Journal of Proteome Research. - : AMER CHEMICAL SOC. - 1535-3893 .- 1535-3907. ; 15:11, s. 3998-4019
-
Tidskriftsartikel (refereegranskat)abstract
- The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify "missing" proteins in the neXtProt knowledgebase. We present an in-depth proteomics analysis of the human sperm proteome to identify testis-enriched missing proteins. Using protein extraction procedures and LC-MS/MS analysis, we detected 235 proteins (PE2-PE4) for which no previous evidence of protein expression was annotated. Through LC-MS/MS and LC-PRM analysis, data mining, and immunohistochemistry, we confirmed the expression of 206 missing proteins (PE2-PE4) in line with current HPP guidelines (version 2.0). Parallel reaction monitoring acquisition and sythetic heavy labeled peptides targeted 36 "one-hit wonder" candidates selected based on prior peptide spectrum match assessment. 24 were validated with additional predicted and specifically targeted peptides. Evidence was found for 16 more missing proteins using immunohistochemistry on human testis sections. The expression pattern for some of these proteins was specific to the testis, and they could possibly be valuable markers with fertility assessment applications. Strong evidence was also found of four "uncertain" proteins (PE5); their status should be re-examined. We show how using a range of sample preparation techniques combined with MS-based analysis, expert knowledge, and complementary antibody-based techniques can produce data of interest to the community. All MS/MS data are available via ProteomeXchange under identifier PXD003947. In addition to contributing to the C-HPP, we hope these data will stimulate continued exploration of the sperm proteome.
|
|
