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1.
Nature ; 2021 Nov 24.
Artigo em Inglês | MEDLINE | ID: mdl-34819669

RESUMO

The cell is a multi-scale structure with modular organization across at least four orders of magnitude1. Two central approaches for mapping this structure-protein fluorescent imaging and protein biophysical association-each generate extensive datasets, but of distinct qualities and resolutions that are typically treated separately2,3. Here we integrate immunofluorescence images in the Human Protein Atlas4 with affinity purifications in BioPlex5 to create a unified hierarchical map of human cell architecture. Integration is achieved by configuring each approach as a general measure of protein distance, then calibrating the two measures using machine learning. The map, known as the multi-scale integrated cell (MuSIC 1.0), resolves 69 subcellular systems, of which approximately half are to our knowledge undocumented. Accordingly, we perform 134 additional affinity purifications and validate subunit associations for the majority of systems. The map reveals a pre-ribosomal RNA processing assembly and accessory factors, which we show govern rRNA maturation, and functional roles for SRRM1 and FAM120C in chromatin and RPS3A in splicing. By integration across scales, MuSIC increases the resolution of imaging while giving protein interactions a spatial dimension, paving the way to incorporate diverse types of data in proteome-wide cell maps.

2.
Nat Methods ; 2021 Nov 22.
Artigo em Inglês | MEDLINE | ID: mdl-34811556

RESUMO

Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.

3.
Nat Methods ; 18(10): 1192-1195, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34594030

RESUMO

DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state of the art solutions and it is equipped with utility tools for developers to include new models. Very recently, several training frameworks have adopted the deepImageJ format to deploy their work in one of the most used softwares in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of deep learning models in life sciences applications and bioimage informatics.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Software , Disciplinas das Ciências Biológicas , Redes Neurais de Computação
6.
Nature ; 590(7847): 649-654, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33627808

RESUMO

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.


Assuntos
Ciclo Celular , Proteogenômica/métodos , Análise de Célula Única/métodos , Transcriptoma , Proteínas de Ciclo Celular/metabolismo , Linhagem Celular Tumoral , Linhagem da Célula , Proliferação de Células , Humanos , Interfase , Mitose , Proteínas Oncogênicas/metabolismo , Fosforilação , Proteínas Quinases/metabolismo , Proteoma/metabolismo , Fatores de Tempo
7.
Trends Cancer ; 7(4): 278-282, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33436349

RESUMO

Cellular heterogeneity is an important biological phenomenon observed across space and time in human tissues. Imaging-based spatial proteomic technologies can provide fruitful new readouts of phenotypic states for individual cells at subcellular resolution, which may help unravel the roles of non-genetic cellular heterogeneity in tumorigenesis and drug resistance.

8.
Nat Commun ; 11(1): 5301, 2020 10 16.
Artigo em Inglês | MEDLINE | ID: mdl-33067450

RESUMO

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.


Assuntos
Doença/genética , Proteoma/genética , Projeto Genoma Humano , Humanos , Proteoma/química , Proteoma/metabolismo , Proteômica
9.
Mol Syst Biol ; 16(8): e9469, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32744794

RESUMO

The nucleolus is essential for ribosome biogenesis and is involved in many other cellular functions. We performed a systematic spatiotemporal dissection of the human nucleolar proteome using confocal microscopy. In total, 1,318 nucleolar proteins were identified; 287 were localized to fibrillar components, and 157 were enriched along the nucleoplasmic border, indicating a potential fourth nucleolar subcompartment: the nucleoli rim. We found 65 nucleolar proteins (36 uncharacterized) to relocate to the chromosomal periphery during mitosis. Interestingly, we observed temporal partitioning into two recruitment phenotypes: early (prometaphase) and late (after metaphase), suggesting phase-specific functions. We further show that the expression of MKI67 is critical for this temporal partitioning. We provide the first proteome-wide analysis of intrinsic protein disorder for the human nucleolus and show that nucleolar proteins in general, and mitotic chromosome proteins in particular, have significantly higher intrinsic disorder level compared to cytosolic proteins. In summary, this study provides a comprehensive and essential resource of spatiotemporal expression data for the nucleolar proteome as part of the Human Protein Atlas.


Assuntos
Nucléolo Celular/metabolismo , Antígeno Ki-67/metabolismo , Proteínas Nucleares/metabolismo , Proteômica/métodos , Cromossomos Humanos/metabolismo , Células HEK293 , Humanos , Microscopia Confocal , Mitose , Fenótipo , Análise de Célula Única
11.
Proteomics ; 20(23): e1900361, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32558245

RESUMO

After a century of research, the human centrosome continues to fascinate. Based on immunofluorescence and confocal microscopy, an extensive inventory of the protein components of the human centrosome, and the centriolar satellites, with the important contribution of over 300 novel proteins localizing to these compartments is presented. A network of candidate centrosome proteins involved in ubiquitination, including six interaction partners of the Kelch-like protein 21, and an additional network of protein phosphatases, together supporting the suggested role of the centrosome as an interactive hub for cell signaling, is identified. Analysis of multi-localization across cellular organelles analyzed within the Human Protein Atlas (HPA) project shows how multi-localizing proteins are particularly overrepresented in centriolar satellites, supporting the dynamic nature and wide range of functions for this compartment. In summary, the spatial dissection of the human centrosome and centriolar satellites described here provides a comprehensive knowledgebase for further exploration of their proteomes.


Assuntos
Centrossomo , Proteoma , Proteínas de Ciclo Celular/genética , Centríolos/metabolismo , Centrossomo/metabolismo , Humanos , Organelas/metabolismo , Proteoma/metabolismo , Ubiquitinação
12.
J Histochem Cytochem ; 68(7): 473-489, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32564662

RESUMO

Imaging is a powerful approach for studying protein expression and has the advantage over other methodologies in providing spatial information in situ at single cell level. Using immunofluorescence and confocal microscopy, detailed information of subcellular distribution of proteins can be obtained. While adherent cells of different tissue origin are relatively easy to prepare for imaging applications, non-adherent cells from hematopoietic origin, present a challenge due to their poor attachment to surfaces and subsequent loss of a substantial fraction of the cells. Still, these cell types represent an important part of the human proteome and express genes that are not expressed in adherent cell types. In the era of cell mapping efforts, overcoming the challenge with suspension cells for imaging applications would enable systematic profiling of hematopoietic cells. In this work, we successfully established an immunofluorescence protocol for preparation of suspension cell lines, peripheral blood mononucleated cells (PBMC) and human platelets on an adherent surface. The protocol is based on a multi-well plate format with automated sample preparation, allowing for robust high throughput imaging applications. In combination with confocal microscopy, the protocol enables systematic exploration of protein localization to all major subcellular structures.


Assuntos
Métodos Analíticos de Preparação de Amostras/métodos , Imunofluorescência/métodos , Animais , Adesão Celular , Humanos , Células Jurkat , Robótica , Propriedades de Superfície , Suspensões
16.
Nat Methods ; 16(12): 1254-1261, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31780840

RESUMO

Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Microscopia de Fluorescência/métodos , Proteínas/análise , Humanos
17.
Sci Signal ; 12(609)2019 11 26.
Artigo em Inglês | MEDLINE | ID: mdl-31772123

RESUMO

The proteins secreted by human cells (collectively referred to as the secretome) are important not only for the basic understanding of human biology but also for the identification of potential targets for future diagnostics and therapies. Here, we present a comprehensive analysis of proteins predicted to be secreted in human cells, which provides information about their final localization in the human body, including the proteins actively secreted to peripheral blood. The analysis suggests that a large number of the proteins of the secretome are not secreted out of the cell, but instead are retained intracellularly, whereas another large group of proteins were identified that are predicted to be retained locally at the tissue of expression and not secreted into the blood. Proteins detected in the human blood by mass spectrometry-based proteomics and antibody-based immunoassays are also presented with estimates of their concentrations in the blood. The results are presented in an updated version 19 of the Human Protein Atlas in which each gene encoding a secretome protein is annotated to provide an open-access knowledge resource of the human secretome, including body-wide expression data, spatial localization data down to the single-cell and subcellular levels, and data about the presence of proteins that are detectable in the blood.


Assuntos
Bases de Dados de Proteínas , Proteoma/metabolismo , Proteômica , Humanos
19.
Nat Rev Mol Cell Biol ; 20(5): 285-302, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-30659282

RESUMO

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.


Assuntos
Espectrometria de Massas , Proteoma/metabolismo , Proteômica , Animais , Humanos , Transporte Proteico/fisiologia , Proteoma/genética
20.
Nat Biotechnol ; 36(9): 820-828, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30125267

RESUMO

Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Humanos , Microscopia de Fluorescência , Frações Subcelulares/metabolismo
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