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Unterauer EM, Shetab Boushehri S, Jevdokimenko K, Masullo LA, Ganji M, Sograte-Idrissi S, Kowalewski R, Strauss S, Reinhardt SCM, Perovic A, Marr C, Opazo F, Fornasiero EF, Jungmann R. Spatial proteomics in neurons at single-protein resolution. Cell 2024; 187:1785-1800.e16. [PMID: 38552614 DOI: 10.1016/j.cell.2024.02.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/28/2023] [Accepted: 02/29/2024] [Indexed: 04/02/2024]
Abstract
To understand biological processes, it is necessary to reveal the molecular heterogeneity of cells by gaining access to the location and interaction of all biomolecules. Significant advances were achieved by super-resolution microscopy, but such methods are still far from reaching the multiplexing capacity of proteomics. Here, we introduce secondary label-based unlimited multiplexed DNA-PAINT (SUM-PAINT), a high-throughput imaging method that is capable of achieving virtually unlimited multiplexing at better than 15 nm resolution. Using SUM-PAINT, we generated 30-plex single-molecule resolved datasets in neurons and adapted omics-inspired analysis for data exploration. This allowed us to reveal the complexity of synaptic heterogeneity, leading to the discovery of a distinct synapse type. We not only provide a resource for researchers, but also an integrated acquisition and analysis workflow for comprehensive spatial proteomics at single-protein resolution.
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Affiliation(s)
- Eduard M Unterauer
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany
| | - Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Data & Analytics, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Kristina Jevdokimenko
- Institute of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany
| | | | - Mahipal Ganji
- Max Planck Institute of Biochemistry, Planegg, Germany; Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Shama Sograte-Idrissi
- Institute of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany; Center for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen, Göttingen, Germany
| | - Rafal Kowalewski
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany
| | - Sebastian Strauss
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany
| | - Susanne C M Reinhardt
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany
| | - Ana Perovic
- Max Planck Institute of Biochemistry, Planegg, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Felipe Opazo
- Institute of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany; Center for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen, Göttingen, Germany; NanoTag Biotechnologies GmbH, Göttingen, Germany
| | - Eugenio F Fornasiero
- Institute of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany; Department of Life Sciences, University of Trieste, Trieste, Italy.
| | - Ralf Jungmann
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany.
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Shetab Boushehri S, Kornivetc A, Winter DJE, Kazeminia S, Essig K, Schmich F, Marr C. PXPermute reveals staining importance in multichannel imaging flow cytometry. Cell Rep Methods 2024; 4:100715. [PMID: 38412831 PMCID: PMC10921034 DOI: 10.1016/j.crmeth.2024.100715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 11/08/2023] [Accepted: 01/29/2024] [Indexed: 02/29/2024]
Abstract
Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibodies for IFC analysis is time consuming, expensive, and potentially harmful to cell viability. To streamline experimental workflows and reduce costs, it is crucial to identify the most relevant channels for downstream analysis. In this study, we introduce PXPermute, a user-friendly and powerful method for assessing the significance of IFC channels, particularly for cell profiling. Our approach evaluates channel importance by permuting pixel values within each channel and analyzing the resulting impact on machine learning or deep learning models. Through rigorous evaluation of three multichannel IFC image datasets, we demonstrate PXPermute's potential in accurately identifying the most informative channels, aligning with established biological knowledge. PXPermute can assist biologists with systematic channel analysis, experimental design optimization, and biomarker identification.
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Affiliation(s)
- Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Institute of Computational Biology, Helmholtz Zentrum MünchenMunich - Helmholtz Munich - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich, Department of Mathematics, 85748 Munich, Germany; Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Aleksandra Kornivetc
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Institute of Computational Biology, Helmholtz Zentrum MünchenMunich - Helmholtz Munich - German Research Center for Environmental Health, 85764 Neuherberg, Germany; University of Hamburg, Department of Informatics, 22527 Hamburg, Germany
| | - Domink J E Winter
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Institute of Computational Biology, Helmholtz Zentrum MünchenMunich - Helmholtz Munich - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich, School of Life Sciences, 85354 Weihenstephan, Germany
| | - Salome Kazeminia
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich, Department of Mathematics, 85748 Munich, Germany
| | - Katharina Essig
- Large Molecule Research (LMR), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Fabian Schmich
- Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.
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Wagner SJ, Matek C, Shetab Boushehri S, Boxberg M, Lamm L, Sadafi A, Winter DJE, Marr C, Peng T. Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology. Mod Pathol 2024; 37:100350. [PMID: 37827448 DOI: 10.1016/j.modpat.2023.100350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
Recent progress in computational pathology has been driven by deep learning. While code and data availability are essential to reproduce findings from preceding publications, ensuring a deep learning model's reusability is more challenging. For that, the codebase should be well-documented and easy to integrate into existing workflows and models should be robust toward noise and generalizable toward data from different sources. Strikingly, only a few computational pathology algorithms have been reused by other researchers so far, let alone employed in a clinical setting. To assess the current state of reproducibility and reusability of computational pathology algorithms, we evaluated peer-reviewed articles available in PubMed, published between January 2019 and March 2021, in 5 use cases: stain normalization; tissue type segmentation; evaluation of cell-level features; genetic alteration prediction; and inference of grading, staging, and prognostic information. We compiled criteria for data and code availability and statistical result analysis and assessed them in 160 publications. We found that only one-quarter (41 of 160 publications) made code publicly available. Among these 41 studies, three-quarters (30 of 41) analyzed their results statistically, half of them (20 of 41) released their trained model weights, and approximately a third (16 of 41) used an independent cohort for evaluation. Our review is intended for both pathologists interested in deep learning and researchers applying algorithms to computational pathology challenges. We provide a detailed overview of publications with published code in the field, list reusable data handling tools, and provide criteria for reproducibility and reusability.
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Affiliation(s)
- Sophia J Wagner
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Christian Matek
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Pathology, University Hospital Erlangen, Erlangen, Germany
| | - Sayedali Shetab Boushehri
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany; Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Germany
| | - Melanie Boxberg
- Institute of Pathology, Technical University Munich, Munich, Germany; Institute of Pathology Munich-North, Munich, Germany
| | - Lorenz Lamm
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Helmholtz Pioneer Campus, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany
| | - Ario Sadafi
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany; Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany
| | - Dominik J E Winter
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany.
| | - Tingying Peng
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany.
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Shetab Boushehri S, Essig K, Chlis NK, Herter S, Bacac M, Theis FJ, Glasmacher E, Marr C, Schmich F. Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies. Nat Commun 2023; 14:7888. [PMID: 38036503 PMCID: PMC10689847 DOI: 10.1038/s41467-023-43429-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 11/09/2023] [Indexed: 12/02/2023] Open
Abstract
Therapeutic antibodies are widely used to treat severe diseases. Most of them alter immune cells and act within the immunological synapse; an essential cell-to-cell interaction to direct the humoral immune response. Although many antibody designs are generated and evaluated, a high-throughput tool for systematic antibody characterization and prediction of function is lacking. Here, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI), for preprocessing, feature engineering, and explainable, predictive machine learning on imaging flow cytometry (IFC) data. Additionally, we generate the largest publicly available IFC dataset of the human immunological synapse containing over 2.8 million images. Using scifAI, we analyze class frequency and morphological changes under different immune stimulation. T cell cytokine production across multiple donors and therapeutic antibodies is quantitatively predicted in vitro, linking morphological features with function and demonstrating the potential to significantly impact antibody design. scifAI is universally applicable to IFC data. Given its modular architecture, it is straightforward to incorporate into existing workflows and analysis pipelines, e.g., for rapid antibody screening and functional characterization.
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Affiliation(s)
- Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich, Department of Mathematics, Munich, Germany
- Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Munich, Germany
| | - Katharina Essig
- Large Molecule Research (LMR), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Munich, Germany
| | - Nikolaos-Kosmas Chlis
- Large Molecule Research (LMR), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Munich, Germany
| | - Sylvia Herter
- Roche Innovation Center Zurich, Roche Pharma Research and Early Development (pRED), Zurich, Switzerland
| | - Marina Bacac
- Roche Innovation Center Zurich, Roche Pharma Research and Early Development (pRED), Zurich, Switzerland
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich, Department of Mathematics, Munich, Germany
| | - Elke Glasmacher
- Research and Early Development (RED), Roche Diagnostics Solutions, Roche Innovation Center Munich, Munich, Germany.
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Fabian Schmich
- Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Munich, Germany.
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Atwell S, Waibel DJE, Boushehri SS, Wiedenmann S, Marr C, Meier M. Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip. Cell Rep Methods 2023; 3:100523. [PMID: 37533640 PMCID: PMC10391578 DOI: 10.1016/j.crmeth.2023.100523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 05/09/2023] [Accepted: 06/15/2023] [Indexed: 08/04/2023]
Abstract
Massive, parallelized 3D stem cell cultures for engineering in vitro human cell types require imaging methods with high time and spatial resolution to fully exploit technological advances in cell culture technologies. Here, we introduce a large-scale integrated microfluidic chip platform for automated 3D stem cell differentiation. To fully enable dynamic high-content imaging on the chip platform, we developed a label-free deep learning method called Bright2Nuc to predict in silico nuclear staining in 3D from confocal microscopy bright-field images. Bright2Nuc was trained and applied to hundreds of 3D human induced pluripotent stem cell cultures differentiating toward definitive endoderm on a microfluidic platform. Combined with existing image analysis tools, Bright2Nuc segmented individual nuclei from bright-field images, quantified their morphological properties, predicted stem cell differentiation state, and tracked the cells over time. Our methods are available in an open-source pipeline, enabling researchers to upscale image acquisition and phenotyping of 3D cell culture.
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Affiliation(s)
- Scott Atwell
- Helmholtz Pioneer Campus, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Dominik Jens Elias Waibel
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
| | - Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
- Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Munich (RICM), Penzberg, Germany
| | - Sandra Wiedenmann
- Helmholtz Pioneer Campus, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Meier
- Helmholtz Pioneer Campus, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Center for Biotechnology and Biomedicine, University of Leipzig, Leipzig, Germany
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Wagner SJ, Matek C, Shetab Boushehri S, Boxberg M, Lamm L, Sadafi A, Waibel DJE, Marr C, Peng T. Make deep learning algorithms in computational pathology more reproducible and reusable. Nat Med 2022; 28:1744-1746. [PMID: 35941376 DOI: 10.1038/s41591-022-01905-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Sophia J Wagner
- Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,Department of Informatics, Technical University of Munich, Garching, Germany
| | - Christian Matek
- Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Pathology, University Hospital Erlangen, Erlangen, Germany
| | - Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany.,Data Science, Pharmaceutical Research and Early Development Informatics (pREDi), Roche Innovation Center Munich (RICM), Penzberg, Germany
| | - Melanie Boxberg
- Institute of Pathology, Technical University Munich, Munich, Germany.,Institute of Pathology Munich-North, Munich, Germany
| | - Lorenz Lamm
- Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,Department of Informatics, Technical University of Munich, Garching, Germany.,Helmholtz Pioneer Campus, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Ario Sadafi
- Department of Informatics, Technical University of Munich, Garching, Germany.,Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Dominik J E Waibel
- Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
| | - Carsten Marr
- Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany. .,Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
| | - Tingying Peng
- Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany. .,Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
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Waibel DJE, Shetab Boushehri S, Marr C. InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification. BMC Bioinformatics 2021; 22:103. [PMID: 33653266 PMCID: PMC7971147 DOI: 10.1186/s12859-021-04037-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/21/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. RESULTS We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. CONCLUSIONS With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.
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Affiliation(s)
- Dominik Jens Elias Waibel
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
| | - Sayedali Shetab Boushehri
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
- Roche Innovation Center Munich, Roche Diagnostics GmbH, Penzberg, Germany
| | - Carsten Marr
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
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