51
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Ghazanfar S, Guibentif C, Marioni JC. Stabilized mosaic single-cell data integration using unshared features. Nat Biotechnol 2024; 42:284-292. [PMID: 37231260 PMCID: PMC10869270 DOI: 10.1038/s41587-023-01766-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/28/2023] [Indexed: 05/27/2023]
Abstract
Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.
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Affiliation(s)
- Shila Ghazanfar
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
- School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia.
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia.
| | - Carolina Guibentif
- Sahlgrenska Center for Cancer Research, Inst. Biomedicine, Dept. Microbiology and Immunology, University of Gothenburg, Gothenburg, Sweden
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
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52
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Sun H, Li J, Murphy RF. Expanding the coverage of spatial proteomics: a machine learning approach. Bioinformatics 2024; 40:btae062. [PMID: 38310340 PMCID: PMC10873576 DOI: 10.1093/bioinformatics/btae062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/05/2024] Open
Abstract
MOTIVATION Multiplexed protein imaging methods use a chosen set of markers and provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is inherently limited. RESULTS In this paper, we present an efficient method to choose a minimal predictive subset of markers that for the first time allows the prediction of full images for a much larger set of markers. We demonstrate that our approach also outperforms previous methods for predicting cell-level protein composition. Most importantly, we demonstrate that our approach can be used to select a marker set that enables prediction of a much larger set than could be measured concurrently. AVAILABILITY AND IMPLEMENTATION All code and intermediate results are available in a Reproducible Research Archive at https://github.com/murphygroup/CODEXPanelOptimization.
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Affiliation(s)
- Huangqingbo Sun
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Jiayi Li
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Robert F Murphy
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
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53
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Zhao C, Xu Z, Wang X, Tao S, MacDonald WA, He K, Poholek AC, Chen K, Huang H, Chen W. Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression. Brief Bioinform 2024; 25:bbae052. [PMID: 38436557 PMCID: PMC10939304 DOI: 10.1093/bib/bbae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 03/05/2024] Open
Abstract
Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model's accuracy, unveiling complex structure-functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.
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Affiliation(s)
- Chongyue Zhao
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
| | - Zhongli Xu
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- School of Medicine, Tsinghua University, Beijing, 100084, Beijing, China
| | - Xinjun Wang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
| | - Shiyue Tao
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, 15261, Pennsylvania, USA
| | - William A MacDonald
- Health Sciences Sequencing Core at UPMC Children’s Hospital of Pittsburgh, Department of Pediatrics , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
| | - Kun He
- Division of Pediatric Rheumatology, Department of Pediatrics , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
| | - Amanda C Poholek
- Division of Pediatric Rheumatology, Department of Pediatrics , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- Department of Immunology , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- Health Sciences Sequencing Core at UPMC Children’s Hospital of Pittsburgh, Department of Pediatrics , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
| | - Kong Chen
- Department of Medicine, University of Pittsburgh, Pittsburgh, 15213, Pennsylvania, USA
| | - Heng Huang
- Department of Computer Science, University of Maryland, College Park, 20742, Maryland, USA
| | - Wei Chen
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, 15261, Pennsylvania, USA
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54
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Gisch DL, Brennan M, Lake BB, Basta J, Keller MS, Melo Ferreira R, Akilesh S, Ghag R, Lu C, Cheng YH, Collins KS, Parikh SV, Rovin BH, Robbins L, Stout L, Conklin KY, Diep D, Zhang B, Knoten A, Barwinska D, Asghari M, Sabo AR, Ferkowicz MJ, Sutton TA, Kelly KJ, De Boer IH, Rosas SE, Kiryluk K, Hodgin JB, Alakwaa F, Winfree S, Jefferson N, Türkmen A, Gaut JP, Gehlenborg N, Phillips CL, El-Achkar TM, Dagher PC, Hato T, Zhang K, Himmelfarb J, Kretzler M, Mollah S, Jain S, Rauchman M, Eadon MT. The chromatin landscape of healthy and injured cell types in the human kidney. Nat Commun 2024; 15:433. [PMID: 38199997 PMCID: PMC10781985 DOI: 10.1038/s41467-023-44467-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024] Open
Abstract
There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.
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Affiliation(s)
- Debora L Gisch
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | - Blue B Lake
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Jeannine Basta
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | | | | | | | - Reetika Ghag
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Charles Lu
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Ying-Hua Cheng
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | - Samir V Parikh
- Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Brad H Rovin
- Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Lynn Robbins
- St. Louis Veteran Affairs Medical Center, St. Louis, MO, 63106, USA
| | - Lisa Stout
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Kimberly Y Conklin
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Dinh Diep
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bo Zhang
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Amanda Knoten
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Daria Barwinska
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Mahla Asghari
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Angela R Sabo
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | - Timothy A Sutton
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | | | - Sylvia E Rosas
- Joslin Diabetes Center, Harvard Medical School, Boston, MA, 02215, USA
| | | | | | | | - Seth Winfree
- University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Nichole Jefferson
- Kidney Precision Medicine Project Community Engagement Committee, Dallas, TX, USA
| | - Aydın Türkmen
- Istanbul School of Medicine, Division of Nephrology, Istanbul, Turkey
| | - Joseph P Gaut
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | | | | | - Pierre C Dagher
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Takashi Hato
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Shamim Mollah
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Sanjay Jain
- Washington University in Saint Louis, St. Louis, MO, 63103, USA.
| | - Michael Rauchman
- Washington University in Saint Louis, St. Louis, MO, 63103, USA.
| | - Michael T Eadon
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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55
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Li Z, Yang W, Wu P, Shan Y, Zhang X, Chen F, Yang J, Yang JR. Reconstructing cell lineage trees with genomic barcoding: approaches and applications. J Genet Genomics 2024; 51:35-47. [PMID: 37269980 DOI: 10.1016/j.jgg.2023.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.
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Affiliation(s)
- Zizhang Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenjing Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Peng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuyan Shan
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xiaoyu Zhang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Feng Chen
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junnan Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian-Rong Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
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56
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Smith KD, Prince DK, MacDonald JW, Bammler TK, Akilesh S. Challenges and Opportunities for the Clinical Translation of Spatial Transcriptomics Technologies. GLOMERULAR DISEASES 2024; 4:49-63. [PMID: 38600956 PMCID: PMC11006413 DOI: 10.1159/000538344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 03/10/2024] [Indexed: 04/12/2024]
Abstract
Background The first spatially resolved transcriptomics platforms, GeoMx (Nanostring) and Visium (10x Genomics) were launched in 2019 and were recognized as the method of the year by Nature Methods in 2020. The subsequent refinement and expansion of these and other technologies to increase -plex, work with formalin-fixed paraffin-embedded tissue, and analyze protein in addition to gene expression have only added to their significance and impact on the biomedical sciences. In this perspective, we focus on two platforms for spatial transcriptomics, GeoMx and Visium, and how these platforms have been used to provide novel insight into kidney disease. The choice of platform will depend largely on experimental questions and design. The application of these technologies to clinically sourced biopsies presents the opportunity to identify specific tissue biomarkers that help define disease etiology and more precisely target therapeutic interventions in the future. Summary In this review, we provide a description of the existing and emerging technologies that can be used to capture spatially resolved gene and protein expression data from tissue. These technologies have provided new insight into the spatial heterogeneity of diseases, how reactions to disease are distributed within a tissue, which cells are affected, and molecular pathways that predict disease and response to therapy. Key Message The upcoming years will see intense use of spatial transcriptomics technologies to better define the pathophysiology of kidney diseases and develop novel diagnostic tests to guide personalized treatments for patients.
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Affiliation(s)
- Kelly D. Smith
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - James W. MacDonald
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Theo K. Bammler
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Shreeram Akilesh
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, Seattle, WA, USA
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57
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Antolović V, Chubb JR. Single Cell Transcriptome Analysis During Development in Dictyostelium. Methods Mol Biol 2024; 2814:223-245. [PMID: 38954209 DOI: 10.1007/978-1-0716-3894-1_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Dictyostelium represents a stripped-down model for understanding how cells make decisions during development. The complete life cycle takes around a day and the fully differentiated structure is composed of only two major cell types. With this apparent reduction in "complexity," single cell transcriptomics has proven to be a valuable tool in defining the features of developmental transitions and cell fate separation events, even providing causal information on how mechanisms of gene expression can feed into cell decision-making. These scientific outputs have been strongly facilitated by the ease of non-disruptive single cell isolation-allowing access to more physiological measures of transcript levels. In addition, the limited number of cell states during development allows the use of more straightforward analysis tools for handling the ensuing large datasets, which provides enhanced confidence in inferences made from the data. In this chapter, we will outline the approaches we have used for handling Dictyostelium single cell transcriptomic data, illustrating how these approaches have contributed to our understanding of cell decision-making during development.
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Affiliation(s)
- Vlatka Antolović
- UCL Laboratory for Molecular Cell Biology, University College London, London, UK.
| | - Jonathan R Chubb
- UCL Laboratory for Molecular Cell Biology, University College London, London, UK
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58
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Xu C, Prete M, Webb S, Jardine L, Stewart BJ, Hoo R, He P, Meyer KB, Teichmann SA. Automatic cell-type harmonization and integration across Human Cell Atlas datasets. Cell 2023; 186:5876-5891.e20. [PMID: 38134877 DOI: 10.1016/j.cell.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/24/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023]
Abstract
Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.
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Affiliation(s)
- Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Martin Prete
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Simone Webb
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Laura Jardine
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Benjamin J Stewart
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK; Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 0QQ, UK
| | - Regina Hoo
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Peng He
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Kerstin B Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Theory of Condensed Matter Group, Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK.
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59
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Hou W, Ji Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.16.537094. [PMID: 37131626 PMCID: PMC10153208 DOI: 10.1101/2023.04.16.537094] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines. We assessed the performance of GPT-4, a highly potent large language model, for cell type annotation, and demonstrated that it can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations and has the potential to considerably reduce the effort and expertise needed in cell type annotation. We also developed GPTCelltype, an open-source R software package to facilitate cell type annotation by GPT-4.
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Affiliation(s)
- Wenpin Hou
- Department of Biostatistics, The Mailman School of Public Health, Columbia University, New York City, NY, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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60
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Hickey JW, Agmon E, Horowitz N, Lamore M, Sunwoo J, Covert M, Nolan GP. Integrating Multiplexed Imaging and Multiscale Modeling Identifies Tumor Phenotype Transformation as a Critical Component of Therapeutic T Cell Efficacy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570168. [PMID: 38106218 PMCID: PMC10723382 DOI: 10.1101/2023.12.06.570168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here we integrated CODEX multiplexed tissue imaging with multiscale modeling software, to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface.
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Affiliation(s)
- John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Nina Horowitz
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Matthew Lamore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - John Sunwoo
- Department of Otolaryngology, Head and Neck Surgery, Stanford Cancer Institute, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305
| | - Markus Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
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61
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Fogo AB. Learning from deep learning and pathomics. Kidney Int 2023; 104:1050-1053. [PMID: 37336291 DOI: 10.1016/j.kint.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 06/06/2023] [Indexed: 06/21/2023]
Affiliation(s)
- Agnes B Fogo
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
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62
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Djambazova KV, van Ardenne JM, Spraggins JM. Advances in Imaging Mass Spectrometry for Biomedical and Clinical Research. Trends Analyt Chem 2023; 169:117344. [PMID: 38045023 PMCID: PMC10688507 DOI: 10.1016/j.trac.2023.117344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Imaging mass spectrometry (IMS) allows for the untargeted mapping of biomolecules directly from tissue sections. This technology is increasingly integrated into biomedical and clinical research environments to supplement traditional microscopy and provide molecular context for tissue imaging. IMS has widespread clinical applicability in the fields of oncology, dermatology, microbiology, and others. This review summarizes the two most widely employed IMS technologies, matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), and covers technological advancements, including efforts to increase spatial resolution, specificity, and throughput. We also highlight recent biomedical applications of IMS, primarily focusing on disease diagnosis, classification, and subtyping.
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Affiliation(s)
- Katerina V. Djambazova
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
| | - Jacqueline M. van Ardenne
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Jeffrey M. Spraggins
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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Yang E, Shen XE, West‐Foyle H, Hahm T, Siegler MA, Brown DR, Johnson CC, Kim JH, Roker LA, Tressler CM, Barman I, Kuo SC, Glunde K. FluoMALDI Microscopy: Matrix Co-Crystallization Simultaneously Enhances Fluorescence and MALDI Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304343. [PMID: 37908150 PMCID: PMC10724403 DOI: 10.1002/advs.202304343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/15/2023] [Indexed: 11/02/2023]
Abstract
Here, the authors report that co-crystallization of fluorophores with matrix-assisted laser desorption/ionization (MALDI) imaging matrices significantly enhances fluorophore brightness up to 79-fold, enabling the amplification of innate tissue autofluorescence. This discovery facilitates FluoMALDI, the imaging of the same biological sample by both fluorescence microscopy and MALDI imaging. The approach combines the high spatial resolution and specific labeling capabilities of fluorescence microscopy with the inherently multiplexed, versatile imaging capabilities of MALDI imaging. This new paradigm simplifies registration by avoiding physical changes between fluorescence and MALDI imaging, allowing to image the exact same cells in tissues with both modalities. Matrix-fluorophore co-crystallization also facilitates applications with insufficient fluorescence brightness. The authors demonstrate feasibility of FluoMALDI imaging with endogenous and exogenous fluorophores and autofluorescence-based FluoMALDI of brain and kidney tissue sections. FluoMALDI will advance structural-functional microscopic imaging in cell biology, biomedicine, and pathology.
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Affiliation(s)
- Ethan Yang
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Xinyi Elaine Shen
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Hoku West‐Foyle
- Microscope FacilityJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Cell BiologyJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Tae‐Hun Hahm
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | | | - Dalton R. Brown
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Cole C. Johnson
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Jeong Hee Kim
- Department of Mechanical EngineeringJohns Hopkins UniversityBaltimoreMD21218USA
| | - LaToya Ann Roker
- Microscope FacilityJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Cell BiologyJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Caitlin M. Tressler
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Ishan Barman
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Department of Mechanical EngineeringJohns Hopkins UniversityBaltimoreMD21218USA
- Sidney Kimmel Comprehensive Cancer CancerJohns Hopkins University School of MedicineBaltimoreMD21231USA
| | - Scot C. Kuo
- Microscope FacilityJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Cell BiologyJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Biomedical EngineeringJohns Hopkins University School of MedicineBaltimoreMD21218USA
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Sidney Kimmel Comprehensive Cancer CancerJohns Hopkins University School of MedicineBaltimoreMD21231USA
- Department of Biological ChemistryJohns Hopkins University School of MedicineBaltimoreMD21205USA
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64
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Shree A, Pavan MK, Zafar H. scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier. Nat Commun 2023; 14:7781. [PMID: 38012145 PMCID: PMC10682386 DOI: 10.1038/s41467-023-43590-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/14/2023] [Indexed: 11/29/2023] Open
Abstract
Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time, and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER ( https://github.com/Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and adversarial training for both unsupervised and supervised (scDREAMER-Sup) integration of multiple batches. Using six real benchmarking datasets, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches, nested batch-effects, large number of batches and conservation of development trajectory across batches. Our experiments also show that scDREAMER and scDREAMER-Sup outperform state-of-the-art unsupervised and supervised integration methods respectively in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we demonstrate that scDREAMER is scalable and can perform atlas-level cross-species (e.g., human and mouse) integration while being faster than other deep-learning-based methods.
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Affiliation(s)
- Ajita Shree
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Musale Krushna Pavan
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Hamim Zafar
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India.
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, India.
- Mehta Family Centre for Engineering in Medicine, Indian Institute of Technology Kanpur, Kanpur, India.
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Chen JG, Chávez-Fuentes JC, O'Brien M, Xu J, Ruiz E, Wang W, Amin I, Sarfraz I, Guckhool P, Sistig A, Yuan GC, Dries R. Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.26.568752. [PMID: 38077085 PMCID: PMC10705291 DOI: 10.1101/2023.11.26.568752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Emerging spatial omics technologies continue to advance the molecular mapping of tissue architecture and the investigation of gene regulation and cellular crosstalk, which in turn provide new mechanistic insights into a wide range of biological processes and diseases. Such technologies provide an increasingly large amount of information content at multiple spatial scales. However, representing and harmonizing diverse spatial datasets efficiently, including combining multiple modalities or spatial scales in a scalable and flexible manner, remains a substantial challenge. Here, we present Giotto Suite, a suite of open-source software packages that underlies a fully modular and integrated spatial data analysis toolbox. At its core, Giotto Suite is centered around an innovative and technology-agnostic data framework embedded in the R software environment, which allows the representation and integration of virtually any type of spatial omics data at any spatial resolution. In addition, Giotto Suite provides both scalable and extensible end-to-end solutions for data analysis, integration, and visualization. Giotto Suite integrates molecular, morphology, spatial, and annotated feature information to create a responsive and flexible workflow for multi-scale, multi-omic data analyses, as demonstrated here by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive ecosystem for spatial multi-omic data analysis.
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Affiliation(s)
- Jiaji George Chen
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | | | - Matthew O'Brien
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Junxiang Xu
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Edward Ruiz
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Wen Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Iqra Amin
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Irzam Sarfraz
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Pratishtha Guckhool
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ruben Dries
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA
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66
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Dong M, Kluger Y. Deep identifiable modeling of single-cell atlases enables zero-shot query of cellular states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.11.566161. [PMID: 38014345 PMCID: PMC10680588 DOI: 10.1101/2023.11.11.566161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
With the emerging single-cell RNA-seq datasets at atlas levels, the potential of a universal model built on existing atlas that can extrapolate to new data remains unclear. A fundamental yet challenging problem for such a model is to identify the underlying biological and batch variations in a zero-shot manner, which is crucial for characterizing scRNA-seq datasets with new biological states. In this work, we present scShift, a mechanistic model that learns batch and biological patterns from atlas-level scRNA-seq data as well as perturbation scRNA-seq data. scShift models genes as functions of latent biological processes, with sparse shifts induced by batch effects and biological perturbations, leveraging recent advances of causal representation learning. Through benchmarking in holdout real datasets, we show scShift reveals unified cell type representations as well as underlying biological variations for query data in zero-shot manners, outperforming widely-used atlas integration, batch correction, and perturbation modeling approaches. scShift enables mapping of gene expression profiles to perturbation labels, and predicts meaningful targets for exhausted T cells as well as a list of diseases in the CellxGene blood atlas.
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67
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Wan G, Maliga Z, Yan B, Vallius T, Shi Y, Khattab S, Chang C, Nirmal AJ, Yu KH, Liu D, Lian CG, DeSimone MS, Sorger PK, Semenov YR. SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566378. [PMID: 38014067 PMCID: PMC10680639 DOI: 10.1101/2023.11.10.566378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Background Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.
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Affiliation(s)
- Guihong Wan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Boshen Yan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tuulia Vallius
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
| | - Yingxiao Shi
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Khattab
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Crystal Chang
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ajit J. Nirmal
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - David Liu
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christine G. Lian
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Mia S. DeSimone
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Yevgeniy R. Semenov
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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Johnson JA, Stein-O’Brien GL, Booth M, Heiland R, Kurtoglu F, Bergman DR, Bucher E, Deshpande A, Forjaz A, Getz M, Godet I, Lyman M, Metzcar J, Mitchell J, Raddatz A, Rocha H, Solorzano J, Sundus A, Wang Y, Gilkes D, Kagohara LT, Kiemen AL, Thompson ED, Wirtz D, Wu PH, Zaidi N, Zheng L, Zimmerman JW, Jaffee EM, Hwan Chang Y, Coussens LM, Gray JW, Heiser LM, Fertig EJ, Macklin P. Digitize your Biology! Modeling multicellular systems through interpretable cell behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.17.557982. [PMID: 37745323 PMCID: PMC10516032 DOI: 10.1101/2023.09.17.557982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.
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Affiliation(s)
- Jeanette A.I. Johnson
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Genevieve L. Stein-O’Brien
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins University. Baltimore, MD USA
| | - Max Booth
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Randy Heiland
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Furkan Kurtoglu
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Daniel R. Bergman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elmar Bucher
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - André Forjaz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Michael Getz
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Ines Godet
- Memorial Sloan Kettering Cancer Center. New York, NY USA
| | - Melissa Lyman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - John Metzcar
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
- Department of Informatics, Indiana University. Bloomington, IN USA
| | - Jacob Mitchell
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Human Genetics, Johns Hopkins University. Baltimore, MD USA
| | - Andrew Raddatz
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University. Atlanta, GA USA
| | - Heber Rocha
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Jacobo Solorzano
- Centre de Recherches en Cancerologie de Toulouse. Toulouse, France
| | - Aneequa Sundus
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Yafei Wang
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Danielle Gilkes
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Luciane T. Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Ashley L. Kiemen
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
| | | | - Denis Wirtz
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
- Department of Materials Science and Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Pei-Hsun Wu
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Neeha Zaidi
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Lei Zheng
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Jacquelyn W. Zimmerman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elizabeth M. Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Lisa M. Coussens
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University. Portland, OR USA
| | - Joe W. Gray
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Laura M. Heiser
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Elana J. Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
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De Donno C, Hediyeh-Zadeh S, Moinfar AA, Wagenstetter M, Zappia L, Lotfollahi M, Theis FJ. Population-level integration of single-cell datasets enables multi-scale analysis across samples. Nat Methods 2023; 20:1683-1692. [PMID: 37813989 PMCID: PMC10630133 DOI: 10.1038/s41592-023-02035-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 09/05/2023] [Indexed: 10/11/2023]
Abstract
The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.
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Affiliation(s)
- Carlo De Donno
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | | | - Amir Ali Moinfar
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Marco Wagenstetter
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany.
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
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71
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Puhm F, Laroche A, Boilard E. Diversity of Megakaryocytes. Arterioscler Thromb Vasc Biol 2023; 43:2088-2098. [PMID: 37675634 DOI: 10.1161/atvbaha.123.318782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 08/14/2023] [Indexed: 09/08/2023]
Abstract
Megakaryocytes are commonly known as large, polyploid, bone marrow resident cells that contribute to hemostasis through the production of platelets. Soon after their discovery in the 19th century, megakaryocytes were described in tissue locations other than the bone marrow, specifically in the lungs and the blood circulation. However, the localization of megakaryocytes in the lungs and the contribution of lung megakaryocytes to the general platelet pool has only recently been appreciated. Moreover, the conception of megakaryocytes as uniform cells with the sole purpose of platelet production has been challenged. Here, we review the literature on megakaryocyte cell identity and location with a special focus on recent observations of megakaryocyte subpopulations identified by transcriptomic analyses.
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Affiliation(s)
- Florian Puhm
- Department of Infectious Diseases and Immunity, Centre de Recherche du Centre Hospitalier Universitaire de Québec - Université Laval, Canada (F.P., A.L., E.B.)
- Centre de Recherche ARThrite, Faculté de Médecine de l'Université Laval, Québec, Canada (F.P., A.L., E.B.)
| | - Audrée Laroche
- Department of Infectious Diseases and Immunity, Centre de Recherche du Centre Hospitalier Universitaire de Québec - Université Laval, Canada (F.P., A.L., E.B.)
- Centre de Recherche ARThrite, Faculté de Médecine de l'Université Laval, Québec, Canada (F.P., A.L., E.B.)
| | - Eric Boilard
- Department of Infectious Diseases and Immunity, Centre de Recherche du Centre Hospitalier Universitaire de Québec - Université Laval, Canada (F.P., A.L., E.B.)
- Centre de Recherche ARThrite, Faculté de Médecine de l'Université Laval, Québec, Canada (F.P., A.L., E.B.)
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Badia-I-Mompel P, Wessels L, Müller-Dott S, Trimbour R, Ramirez Flores RO, Argelaguet R, Saez-Rodriguez J. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet 2023; 24:739-754. [PMID: 37365273 DOI: 10.1038/s41576-023-00618-5] [Citation(s) in RCA: 66] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/28/2023]
Abstract
The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.
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Affiliation(s)
- Pau Badia-I-Mompel
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Lorna Wessels
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Department of Vascular Biology and Tumor Angiogenesis, European Center for Angioscience, Medical Faculty, MannHeim Heidelberg University, Mannheim, Germany
| | - Sophia Müller-Dott
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Rémi Trimbour
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France
| | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | | | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
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73
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Abstract
Endothelial cells are important constituents of blood vessels and play a critical role in vascular homeostasis. They do not only control the exchanges between the blood and the surrounding tissues, but are also essential in regulating blood flow, modulating immune-cell trafficking and controlling vascular growth and repair. Endothelial dysfunction leads to cardiovascular diseases and is characterized by deficiency in secretion of vasodilator molecules, elevated reactive oxygen species (ROS), expression of adhesion molecules and excretion of proinflammatory cytokines. The sex hormones, estrogens, androgens and progestogens, regulate endothelial functions. Because cardiovascular disease risk increases after menopause, it is believed that female hormones, estrogens and progestogens promote endothelial cell health and function whereas androgens, the male hormones, might be detrimental. However, as illustrated in the present review, the picture might not be that simple. In addition, sex influences endothelial cell physiology independently of sex hormones but at genetic level.
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Affiliation(s)
- Jerome Robert
- University Hospital of Zurich, Institute of Clinical Chemistry, Wagistrasse 14, 8952, Schlieren, Switzerland.
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74
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Yayon N, Kedlian VR, Boehme L, Suo C, Wachter B, Beuschel RT, Amsalem O, Polanski K, Koplev S, Tuck E, Dann E, Van Hulle J, Perera S, Putteman T, Predeus AV, Dabrowska M, Richardson L, Tudor C, Kreins AY, Engelbert J, Stephenson E, Kleshchevnikov V, De Rita F, Crossland D, Bosticardo M, Pala F, Prigmore E, Chipampe NJ, Prete M, Fei L, To K, Barker RA, He X, Van Nieuwerburgh F, Bayraktar O, Patel M, Davies GE, Haniffa MA, Uhlmann V, Notarangelo LD, Germain RN, Radtke AJ, Marioni JC, Taghon T, Teichmann SA. A spatial human thymus cell atlas mapped to a continuous tissue axis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.562925. [PMID: 37986877 PMCID: PMC10659407 DOI: 10.1101/2023.10.25.562925] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
T cells develop from circulating precursors, which enter the thymus and migrate throughout specialised sub-compartments to support maturation and selection. This process starts already in early fetal development and is highly active until the involution of the thymus in adolescence. To map the micro-anatomical underpinnings of this process in pre- vs. post-natal states, we undertook a spatially resolved analysis and established a new quantitative morphological framework for the thymus, the Cortico-Medullary Axis. Using this axis in conjunction with the curation of a multimodal single-cell, spatial transcriptomics and high-resolution multiplex imaging atlas, we show that canonical thymocyte trajectories and thymic epithelial cells are highly organised and fully established by post-conception week 12, pinpoint TEC progenitor states, find that TEC subsets and peripheral tissue genes are associated with Hassall's Corpuscles and uncover divergence in the pace and drivers of medullary entry between CD4 vs. CD8 T cell lineages. These findings are complemented with a holistic toolkit for spatial analysis and annotation, providing a basis for a detailed understanding of T lymphocyte development.
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Affiliation(s)
- Nadav Yayon
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, United Kingdom
| | | | - Lena Boehme
- Ghent University, Department of Diagnostic Sciences, Ghent, Belgium
| | - Chenqu Suo
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Brianna Wachter
- National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, Bethesda, MD, United States
| | - Rebecca T Beuschel
- National Institute of Allergy and Infectious Diseases, NIH, Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Bethesda, MD, United States
| | - Oren Amsalem
- Beth Israel Deaconess Medical Center, Harvard Medical School, Division of Endocrinology, Diabetes and Metabolism, Boston, MA, United States
| | | | - Simon Koplev
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Elizabeth Tuck
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Emma Dann
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Jolien Van Hulle
- Ghent University, Department of Diagnostic Sciences, Ghent, Belgium
| | - Shani Perera
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Tom Putteman
- Ghent University, Department of Diagnostic Sciences, Ghent, Belgium
| | | | - Monika Dabrowska
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Laura Richardson
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Catherine Tudor
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Alexandra Y Kreins
- Great Ormond Street Hospital for Children NHS Foundation Trust, Department of Immunology and Gene Therapy, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, Infection, Immunity and Inflammation Research & Teaching Department, London, United Kingdom
| | - Justin Engelbert
- Newcastle University, Biosciences Institute, Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom
| | - Emily Stephenson
- Newcastle University, Biosciences Institute, Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom
| | | | - Fabrizio De Rita
- Freeman Hospital, Department of Adult Congenital Heart Disease and Paediatric Cardiology/Cardiothoracic Surgery, Newcastle upon Tyne, United Kingdom
| | - David Crossland
- Freeman Hospital, Department of Adult Congenital Heart Disease and Paediatric Cardiology/Cardiothoracic Surgery, Newcastle upon Tyne, United Kingdom
| | - Marita Bosticardo
- National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, Bethesda, MD, United States
| | - Francesca Pala
- National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, Bethesda, MD, United States
| | - Elena Prigmore
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | | | - Martin Prete
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Lijiang Fei
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Ken To
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Roger A Barker
- University of Cambridge, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences and Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, United Kingdom
| | - Xiaoling He
- University of Cambridge, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences and Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, United Kingdom
| | - Filip Van Nieuwerburgh
- Ghent University, Laboratory of Pharmaceutical Biotechnology, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Omer Bayraktar
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Minal Patel
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
| | - Graham E Davies
- Great Ormond Street Hospital for Children NHS Foundation Trust, Department of Immunology and Gene Therapy, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, Infection, Immunity and Inflammation Research & Teaching Department, London, United Kingdom
| | - Muzlifah A Haniffa
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
- Newcastle University, Biosciences Institute, Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom
| | - Virginie Uhlmann
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, United Kingdom
| | - Luigi D Notarangelo
- National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Laboratory of Clinical Immunology and Microbiology, Division of Intramural Research, Bethesda, MD, United States
| | - Ronald N Germain
- National Institute of Allergy and Infectious Diseases, NIH, Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Bethesda, MD, United States
| | - Andrea J Radtke
- National Institute of Allergy and Infectious Diseases, NIH, Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Bethesda, MD, United States
| | - John C Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK, Cambridge, United Kingdom
| | - Tom Taghon
- Ghent University, Department of Diagnostic Sciences, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cellular Genetics, Cambridge, United Kingdom
- University of Cambridge, Cavendish Laboratory, Cambridge, United Kingdom
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75
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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76
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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77
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Abstract
Multiplex imaging has emerged as an invaluable tool for immune-oncologists and translational researchers, enabling them to examine intricate interactions among immune cells, stroma, matrix, and malignant cells within the tumor microenvironment (TME). It holds significant promise in the quest to discover improved biomarkers for treatment stratification and identify novel therapeutic targets. Nonetheless, several challenges exist in the realms of study design, experiment optimization, and data analysis. In this review, our aim is to present an overview of the utilization of multiplex imaging in immuno-oncology studies and inform novice researchers about the fundamental principles at each stage of the imaging and analysis process.
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Affiliation(s)
- Chen Zhao
- Thoracic and GI Malignancies Branch, CCR, NCI, Bethesda, Maryland, USA
- Lymphocyte Biology Section, Laboratory of Immune System Biology, NIAID, Bethesda, Maryland, USA
| | - Ronald N Germain
- Lymphocyte Biology Section, Laboratory of Immune System Biology, NIAID, Bethesda, Maryland, USA
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78
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Moore J, Basurto-Lozada D, Besson S, Bogovic J, Bragantini J, Brown EM, Burel JM, Casas Moreno X, de Medeiros G, Diel EE, Gault D, Ghosh SS, Gold I, Halchenko YO, Hartley M, Horsfall D, Keller MS, Kittisopikul M, Kovacs G, Küpcü Yoldaş A, Kyoda K, le Tournoulx de la Villegeorges A, Li T, Liberali P, Lindner D, Linkert M, Lüthi J, Maitin-Shepard J, Manz T, Marconato L, McCormick M, Lange M, Mohamed K, Moore W, Norlin N, Ouyang W, Özdemir B, Palla G, Pape C, Pelkmans L, Pietzsch T, Preibisch S, Prete M, Rzepka N, Samee S, Schaub N, Sidky H, Solak AC, Stirling DR, Striebel J, Tischer C, Toloudis D, Virshup I, Walczysko P, Watson AM, Weisbart E, Wong F, Yamauchi KA, Bayraktar O, Cimini BA, Gehlenborg N, Haniffa M, Hotaling N, Onami S, Royer LA, Saalfeld S, Stegle O, Theis FJ, Swedlow JR. OME-Zarr: a cloud-optimized bioimaging file format with international community support. Histochem Cell Biol 2023; 160:223-251. [PMID: 37428210 PMCID: PMC10492740 DOI: 10.1007/s00418-023-02209-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2023] [Indexed: 07/11/2023]
Abstract
A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.
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Affiliation(s)
- Josh Moore
- German BioImaging-Gesellschaft für Mikroskopie und Bildanalyse e.V., Constance, Germany.
| | | | - Sébastien Besson
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - John Bogovic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Eva M Brown
- Allen Institute for Cell Science, Seattle, WA, USA
| | - Jean-Marie Burel
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Xavier Casas Moreno
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | | | - David Gault
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Ilan Gold
- Harvard Medical School, Boston, MA, USA
| | | | - Matthew Hartley
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK
| | - Dave Horsfall
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Mark Kittisopikul
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Gabor Kovacs
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Aybüke Küpcü Yoldaş
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK
| | - Koji Kyoda
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Tong Li
- Wellcome Sanger Institute, Hinxton, UK
| | - Prisca Liberali
- Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland
| | - Dominik Lindner
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Joel Lüthi
- Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland
| | | | | | - Luca Marconato
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | | | | | - Khaled Mohamed
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - William Moore
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Nils Norlin
- Department of Experimental Medical Science & Lund Bioimaging Centre, Lund University, Lund, Sweden
| | - Wei Ouyang
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | - Giovanni Palla
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | | | | | - Tobias Pietzsch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Stephan Preibisch
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | | | | | - Nicholas Schaub
- Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health, Bethesda, USA
| | | | | | | | | | | | | | - Isaac Virshup
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Petr Walczysko
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | | | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Frances Wong
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
| | - Kevin A Yamauchi
- Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland
| | | | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Nathan Hotaling
- Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health, Bethesda, USA
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Stephan Saalfeld
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jason R Swedlow
- Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK
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79
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Cantley J, Eizirik DL, Latres E, Dayan CM. Islet cells in human type 1 diabetes: from recent advances to novel therapies - a symposium-based roadmap for future research. J Endocrinol 2023; 259:e230082. [PMID: 37493471 PMCID: PMC10502961 DOI: 10.1530/joe-23-0082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/25/2023] [Indexed: 07/27/2023]
Abstract
There is a growing understanding that the early phases of type 1 diabetes (T1D) are characterised by a deleterious dialogue between the pancreatic beta cells and the immune system. This, combined with the urgent need to better translate this growing knowledge into novel therapies, provided the background for the JDRF-DiabetesUK-INNODIA-nPOD symposium entitled 'Islet cells in human T1D: from recent advances to novel therapies', which took place in Stockholm, Sweden, in September 2022. We provide in this article an overview of the main themes addressed in the symposium, pointing to both promising conclusions and key unmet needs that remain to be addressed in order to achieve better approaches to prevent or reverse T1D.
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Affiliation(s)
- J Cantley
- School of Medicine, University of Dundee, Dundee, United Kingdom of Great Britain and Northern Ireland
| | - D L Eizirik
- ULB Center for Diabetes Research, Université Libre de Bruxelles Faculté de Médecine, Bruxelles, Belgium
| | - E Latres
- JDRF International, New York, NY, USA
| | - C M Dayan
- Cardiff University School of Medicine, Cardiff, United Kingdom of Great Britain and Northern Ireland
| | - the JDRF-DiabetesUK-INNODIA-nPOD Stockholm Symposium 2022
- School of Medicine, University of Dundee, Dundee, United Kingdom of Great Britain and Northern Ireland
- ULB Center for Diabetes Research, Université Libre de Bruxelles Faculté de Médecine, Bruxelles, Belgium
- JDRF International, New York, NY, USA
- Cardiff University School of Medicine, Cardiff, United Kingdom of Great Britain and Northern Ireland
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80
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Kitata RB, Velickovic M, Xu Z, Zhao R, Scholten D, Chu RK, Orton DJ, Chrisler WB, Mathews JV, Piehowski PD, Liu T, Smith RD, Liu H, Wasserfall CH, Tsai CF, Shi T. Robust collection and processing for label-free single voxel proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.14.553333. [PMID: 37645907 PMCID: PMC10462033 DOI: 10.1101/2023.08.14.553333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures important tissue heterogeneity, which make it impossible for proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single tissue voxel and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single tissue voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of ∼900 and ∼4,600 proteins for single voxel from fresh frozen human spleen tissue at 20 μm × 20 μm × 10 μm (close to single cells) and 200 μm × 200 μm × 10 μm (∼100 cells), respectively. 100s-1000s of protein signatures with differential expression levels were identified to be spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. Antibody-based CODEX imaging was used to validate label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.
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81
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Wicks MN, Glinka M, Hill B, Houghton D, Sharghi M, Ferreira I, Adams D, Din S, Papatheodorou I, Kirkwood K, Cheeseman M, Burger A, Baldock RA, Arends MJ. The Comparative Pathology Workbench: Interactive visual analytics for biomedical data. J Pathol Inform 2023; 14:100328. [PMID: 37693862 PMCID: PMC10491844 DOI: 10.1016/j.jpi.2023.100328] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/07/2023] [Accepted: 08/04/2023] [Indexed: 09/12/2023] Open
Abstract
Pathologists need to compare histopathological images of normal and diseased tissues between different samples, cases, and species. We have designed an interactive system, termed Comparative Pathology Workbench (CPW), which allows direct and dynamic comparison of images at a variety of magnifications, selected regions of interest, as well as the results of image analysis or other data analyses such as scRNA-seq. This allows pathologists to indicate key diagnostic features, with a mechanism to allow discussion threads amongst expert groups of pathologists and other disciplines. The data and associated discussions can be accessed online from anywhere in the world. The Comparative Pathology Workbench (CPW) is a web-browser-based visual analytics platform providing shared access to an interactive "spreadsheet" style presentation of image and associated analysis data. The CPW provides a grid layout of rows and columns so that images that correspond to matching data can be organised in the form of an image-enabled "spreadsheet". An individual workbench can be shared with other users with read-only or full edit access as required. In addition, each workbench element or the whole bench itself has an associated discussion thread to allow collaborative analysis and consensual interpretation of the data. The CPW is a Django-based web-application that hosts the workbench data, manages users, and user-preferences. All image data are hosted by other resource applications such as OMERO or the Digital Slide Archive. Further resources can be added as required. The discussion threads are managed using WordPress and include additional graphical and image data. The CPW has been developed to allow integration of image analysis outputs from systems such as QuPath or ImageJ. All software is open-source and available from a GitHub repository.
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Affiliation(s)
- Michael N. Wicks
- Edinburgh Pathology & Centre for Comparative Pathology, Institute of Genetics & Cancer, University of Edinburgh, Crewe Road, Edinburgh EH4 2XR, UK
| | - Michael Glinka
- Edinburgh Pathology & Centre for Comparative Pathology, Institute of Genetics & Cancer, University of Edinburgh, Crewe Road, Edinburgh EH4 2XR, UK
| | - Bill Hill
- Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK
| | - Derek Houghton
- Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK
| | - Mehran Sharghi
- Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK
| | - Ingrid Ferreira
- Edinburgh Pathology & Centre for Comparative Pathology, Institute of Genetics & Cancer, University of Edinburgh, Crewe Road, Edinburgh EH4 2XR, UK
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - David Adams
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Shahida Din
- Edinburgh IBD Unit Western General Hospital, NHS Lothian, Edinburgh, UK
| | - Irene Papatheodorou
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Kathryn Kirkwood
- Pathology Department, Western General Hospital, NHS Lothian, Edinburgh, UK
| | - Michael Cheeseman
- Edinburgh Pathology & Centre for Comparative Pathology, Institute of Genetics & Cancer, University of Edinburgh, Crewe Road, Edinburgh EH4 2XR, UK
| | - Albert Burger
- Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK
| | - Richard A. Baldock
- Edinburgh Pathology & Centre for Comparative Pathology, Institute of Genetics & Cancer, University of Edinburgh, Crewe Road, Edinburgh EH4 2XR, UK
| | - Mark J. Arends
- Edinburgh Pathology & Centre for Comparative Pathology, Institute of Genetics & Cancer, University of Edinburgh, Crewe Road, Edinburgh EH4 2XR, UK
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82
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Jain Y, Godwin LL, Joshi S, Mandarapu S, Le T, Lindskog C, Lundberg E, Börner K. Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms. Nat Commun 2023; 14:4656. [PMID: 37537179 PMCID: PMC10400613 DOI: 10.1038/s41467-023-40291-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023] Open
Abstract
The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We create a dataset containing 880 histology images with 12,901 segmented structures, engaging 1175 teams from 78 countries in community-driven, open-science development of machine learning models. Tissue variations in the dataset pose a major challenge to the teams which they overcome by using color normalization techniques and combining vision transformers with convolutional models. The best model will be productized in the HuBMAP portal to process tissue image datasets at scale in support of Human Reference Atlas construction.
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Affiliation(s)
- Yashvardhan Jain
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
| | - Leah L Godwin
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Sripad Joshi
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Shriya Mandarapu
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Trang Le
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Division of Cancer Precision Medicine, Uppsala University, Uppsala, Sweden
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
- Chan Zuckerberg Biohub, San Francisco, CA, 94305, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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83
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Dekker J, Alber F, Aufmkolk S, Beliveau BJ, Bruneau BG, Belmont AS, Bintu L, Boettiger A, Calandrelli R, Disteche CM, Gilbert DM, Gregor T, Hansen AS, Huang B, Huangfu D, Kalhor R, Leslie CS, Li W, Li Y, Ma J, Noble WS, Park PJ, Phillips-Cremins JE, Pollard KS, Rafelski SM, Ren B, Ruan Y, Shav-Tal Y, Shen Y, Shendure J, Shu X, Strambio-De-Castillia C, Vertii A, Zhang H, Zhong S. Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project. Mol Cell 2023; 83:2624-2640. [PMID: 37419111 PMCID: PMC10528254 DOI: 10.1016/j.molcel.2023.06.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
The four-dimensional nucleome (4DN) consortium studies the architecture of the genome and the nucleus in space and time. We summarize progress by the consortium and highlight the development of technologies for (1) mapping genome folding and identifying roles of nuclear components and bodies, proteins, and RNA, (2) characterizing nuclear organization with time or single-cell resolution, and (3) imaging of nuclear organization. With these tools, the consortium has provided over 2,000 public datasets. Integrative computational models based on these data are starting to reveal connections between genome structure and function. We then present a forward-looking perspective and outline current aims to (1) delineate dynamics of nuclear architecture at different timescales, from minutes to weeks as cells differentiate, in populations and in single cells, (2) characterize cis-determinants and trans-modulators of genome organization, (3) test functional consequences of changes in cis- and trans-regulators, and (4) develop predictive models of genome structure and function.
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Affiliation(s)
- Job Dekker
- University of Massachusetts Chan Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA.
| | - Frank Alber
- University of California, Los Angeles, Los Angeles, CA, USA
| | | | | | - Benoit G Bruneau
- Gladstone Institutes, San Francisco, CA, USA; University of California, San Francisco, San Francisco, CA, USA
| | | | | | | | | | | | | | | | | | - Bo Huang
- University of California, San Francisco, San Francisco, CA, USA
| | - Danwei Huangfu
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Reza Kalhor
- Johns Hopkins University, Baltimore, MD, USA
| | | | - Wenbo Li
- University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yun Li
- University of North Carolina, Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Jian Ma
- Carnegie Mellon University, Pittsburgh, PA, USA
| | | | | | | | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA, USA; University of California, San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA
| | | | - Bing Ren
- University of California, San Diego, La Jolla, CA, USA
| | - Yijun Ruan
- Zhejiang University, Hangzhou, Zhejiang, China
| | | | - Yin Shen
- University of California, San Francisco, San Francisco, CA, USA
| | | | - Xiaokun Shu
- University of California, San Francisco, San Francisco, CA, USA
| | | | | | | | - Sheng Zhong
- University of California, San Diego, La Jolla, CA, USA.
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84
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Jain S, Pei L, Spraggins JM, Angelo M, Carson JP, Gehlenborg N, Ginty F, Gonçalves JP, Hagood JS, Hickey JW, Kelleher NL, Laurent LC, Lin S, Lin Y, Liu H, Naba A, Nakayasu ES, Qian WJ, Radtke A, Robson P, Stockwell BR, Van de Plas R, Vlachos IS, Zhou M, Börner K, Snyder MP. Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nat Cell Biol 2023; 25:1089-1100. [PMID: 37468756 PMCID: PMC10681365 DOI: 10.1038/s41556-023-01194-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/22/2023] [Indexed: 07/21/2023]
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.
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Affiliation(s)
- Sanjay Jain
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
| | - Liming Pei
- Center for Mitochondrial and Epigenomic Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jeffrey M Spraggins
- Department of Cell and Developmental Biology and the Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
| | - Michael Angelo
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - James P Carson
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Joana P Gonçalves
- Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands
| | - James S Hagood
- Department of Pediatrics (Pulmonology) and Program for Rare and Interstitial Lung Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John W Hickey
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Neil L Kelleher
- Departments of Medicine, Chemistry and Molecular Biosciences, Northwestern University, Evanston, IL, USA
| | - Louise C Laurent
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Shin Lin
- Division of Cardiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
| | - Huiping Liu
- Departments of Pharmacology, Medicine (Hematology and Oncology), Lurie Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alexandra Naba
- Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, IL, USA
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Andrea Radtke
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Paul Robson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Brent R Stockwell
- Department of Biological Sciences and Department of Chemistry, Columbia University, New York, NY, USA
| | - Raf Van de Plas
- Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
| | - Ioannis S Vlachos
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Spatial Technologies Unit, Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA.
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85
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Gosline SJC, Veličković M, Pino JC, Day LZ, Attah IK, Swensen AC, Danna V, Posso C, Rodland KD, Chen J, Matthews CE, Campbell-Thompson M, Laskin J, Burnum-Johnson K, Zhu Y, Piehowski PD. Proteome Mapping of the Human Pancreatic Islet Microenvironment Reveals Endocrine-Exocrine Signaling Sphere of Influence. Mol Cell Proteomics 2023; 22:100592. [PMID: 37328065 PMCID: PMC10460696 DOI: 10.1016/j.mcpro.2023.100592] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 04/24/2023] [Accepted: 06/05/2023] [Indexed: 06/18/2023] Open
Abstract
The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller pixel/voxel number and amount of tissue measured, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of cell types involved while maintaining spatial information and the degree of the islet's sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be detected in the adjacent tissue. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.
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Affiliation(s)
- Sara J C Gosline
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | | | - James C Pino
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Le Z Day
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Isaac K Attah
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Adam C Swensen
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Vincent Danna
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Camilo Posso
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Karin D Rodland
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Clayton E Matthews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana, USA
| | | | - Ying Zhu
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Paul D Piehowski
- Pacific Northwest National Laboratories, Richland, Washington, USA.
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86
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Quardokus EM, Saunders DC, McDonough E, Hickey JW, Werlein C, Surrette C, Rajbhandari P, Casals AM, Tian H, Lowery L, Neumann EK, Björklund F, Neelakantan TV, Croteau J, Wiblin AE, Fisher J, Livengood AJ, Dowell KG, Silverstein JC, Spraggins JM, Pryhuber GS, Deutsch G, Ginty F, Nolan GP, Melov S, Jonigk D, Caldwell MA, Vlachos IS, Muller W, Gehlenborg N, Stockwell BR, Lundberg E, Snyder MP, Germain RN, Camarillo JM, Kelleher NL, Börner K, Radtke AJ. Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging. Nat Methods 2023; 20:1174-1178. [PMID: 37468619 PMCID: PMC10406602 DOI: 10.1038/s41592-023-01846-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 03/14/2023] [Indexed: 07/21/2023]
Abstract
Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.
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Affiliation(s)
- Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Diane C Saunders
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Diabetes Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - John W Hickey
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Presha Rajbhandari
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Anna Martinez Casals
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Hua Tian
- Department of Chemistry, Pennsylvania State University, University Park, PA, USA
| | | | - Elizabeth K Neumann
- Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
- Department of Chemistry, University of California Davis, Davis, CA, USA
| | - Frida Björklund
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | - Josh Croteau
- Department of Business Development, BioLegend Inc., San Diego, CA, USA
| | - Anne E Wiblin
- Department of Research and Development, Abcam PLC, Discovery Drive, Cambridge Biomedical Campus, Cambridge, UK
| | - Jeremy Fisher
- Department of Research and Development, Cell Signaling Technology, Inc., Danvers, MA, USA
| | - April J Livengood
- Department of Protein and Cell Analysis, Thermo Fisher Scientific, Carlsbad, CA, USA
| | | | - Jonathan C Silverstein
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jeffrey M Spraggins
- Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | - Gloria S Pryhuber
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
| | - Gail Deutsch
- Department of Pathology, University of Washington Medical Center, Seattle, WA, USA
| | | | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Simon Melov
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Danny Jonigk
- Institute of Pathology, Hannover Medical School, Hannover, Germany
- German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Institute of Pathology, RWTH University of Aachen, Aachen, Germany
| | - Michael A Caldwell
- Departments of Chemistry, Molecular Biosciences and the Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
| | - Ioannis S Vlachos
- Spatial Technologies Unit, Harvard Medical School Initiative for RNA Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Werner Muller
- Miltenyi Biotec B.V. and Co. KG, Bergisch Gladbach, Germany
- Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Brent R Stockwell
- Department of Biological Sciences, Columbia University, New York, NY, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - Emma Lundberg
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald N Germain
- Laboratory of Immune System Biology, Lymphocyte Biology Section and Center for Advanced Tissue Imaging, NIAID, NIH, Bethesda, MD, USA
| | - Jeannie M Camarillo
- Departments of Chemistry, Molecular Biosciences and the Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
- Inotiv, Nashville, TN, USA
| | - Neil L Kelleher
- Departments of Chemistry, Molecular Biosciences and the Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Andrea J Radtke
- Laboratory of Immune System Biology, Lymphocyte Biology Section and Center for Advanced Tissue Imaging, NIAID, NIH, Bethesda, MD, USA.
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87
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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88
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Zidane M, Makky A, Bruhns M, Rochwarger A, Babaei S, Claassen M, Schürch CM. A review on deep learning applications in highly multiplexed tissue imaging data analysis. FRONTIERS IN BIOINFORMATICS 2023; 3:1159381. [PMID: 37564726 PMCID: PMC10410935 DOI: 10.3389/fbinf.2023.1159381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 07/12/2023] [Indexed: 08/12/2023] Open
Abstract
Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.
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Affiliation(s)
- Mohammed Zidane
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Ahmad Makky
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Matthias Bruhns
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sepideh Babaei
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
| | - Manfred Claassen
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Christian M. Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
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89
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The Impact of Genomic Variation on Function (IGVF) Consortium. ARXIV 2023:arXiv:2307.13708v1. [PMID: 37547663 PMCID: PMC10402186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations.
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90
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Prabhakaran S, Yapp C, Baker GJ, Beyer J, Chang YH, Creason AL, Krueger R, Muhlich J, Patterson NH, Sidak K, Sudar D, Taylor AJ, Ternes L, Troidl J, Xie Y, Sokolov A, Tyson DR. Addressing persistent challenges in digital image analysis of cancerous tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.548450. [PMID: 37547011 PMCID: PMC10401923 DOI: 10.1101/2023.07.21.548450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.
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91
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Arceneaux D, Chen Z, Simmons AJ, Heiser CN, Southard-Smith AN, Brenan MJ, Yang Y, Chen B, Xu Y, Choi E, Campbell JD, Liu Q, Lau KS. A contamination focused approach for optimizing the single-cell RNA-seq experiment. iScience 2023; 26:107242. [PMID: 37496679 PMCID: PMC10366499 DOI: 10.1016/j.isci.2023.107242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/10/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023] Open
Abstract
Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio. Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain. Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics. Through a series of controlled experiments, we report improvements that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality.
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Affiliation(s)
- Deronisha Arceneaux
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Zhengyi Chen
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alan J. Simmons
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Cody N. Heiser
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Austin N. Southard-Smith
- McDonnell Genome Institute and Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Yilin Yang
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bob Chen
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Yanwen Xu
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Eunyoung Choi
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joshua D. Campbell
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Qi Liu
- Department of Biostatistics and Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ken S. Lau
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
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92
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Jain Y, Godwin LL, Ju Y, Sood N, Quardokus EM, Bueckle A, Longacre T, Horning A, Lin Y, Esplin ED, Hickey JW, Snyder MP, Patterson NH, Spraggins JM, Börner K. Segmentation of human functional tissue units in support of a Human Reference Atlas. Commun Biol 2023; 6:717. [PMID: 37468557 PMCID: PMC10356924 DOI: 10.1038/s42003-023-04848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/17/2023] [Indexed: 07/21/2023] Open
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale-showcasing the value of Kaggle competitions for advancing research.
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Affiliation(s)
- Yashvardhan Jain
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
| | - Leah L Godwin
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Yingnan Ju
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Naveksha Sood
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Andreas Bueckle
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Teri Longacre
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Aaron Horning
- Thermo Fisher Scientific, South San Francisco, CA, 94080, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Edward D Esplin
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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93
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Ghose S, Ju Y, McDonough E, Ho J, Karunamurthy A, Chadwick C, Cho S, Rose R, Corwin A, Surrette C, Martinez J, Williams E, Sood A, Al-Kofahi Y, Falo LD, Börner K, Ginty F. 3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging. Commun Biol 2023; 6:718. [PMID: 37468758 PMCID: PMC10356782 DOI: 10.1038/s42003-023-04991-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/11/2023] [Indexed: 07/21/2023] Open
Abstract
Mapping the human body at single cell resolution in three dimensions (3D) is important for understanding cellular interactions in context of tissue and organ organization. 2D spatial cell analysis in a single tissue section may be limited by cell numbers and histology. Here we show a workflow for 3D reconstruction of multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). We demonstrate MATRICS-A in 26 serial sections of fixed skin (stained with 18 biomarkers) from 12 donors aged between 32-72 years. Comparing the 3D reconstructed cellular data with the 2D data, we show significantly shorter distances between immune cells and vascular endothelial cells (56 µm in 3D vs 108 µm in 2D). We also show 10-70% more T cells (total) within 30 µm of a neighboring T helper cell in 3D vs 2D. Distances of p53, DDB2 and Ki67 positive cells to the skin surface were consistent across all ages/sun exposure and largely localized to the lower stratum basale layer of the epidermis. MATRICS-A provides a framework for analysis of 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs.
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Affiliation(s)
- Soumya Ghose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yingnan Ju
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA
| | | | - Jonhan Ho
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | | | | | - Sanghee Cho
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Rachel Rose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Alex Corwin
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | | | - Jessica Martinez
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Eric Williams
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Anup Sood
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yousef Al-Kofahi
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Louis D Falo
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | - Katy Börner
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA.
| | - Fiona Ginty
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA.
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94
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Boppana A, Lee S, Malhotra R, Halushka M, Gustilo KS, Quardokus EM, Herr BW, Börner K, Weber GM. Anatomical structures, cell types, and biomarkers of the healthy human blood vasculature. Sci Data 2023; 10:452. [PMID: 37468503 PMCID: PMC10356915 DOI: 10.1038/s41597-023-02018-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 01/30/2023] [Indexed: 07/21/2023] Open
Abstract
More than 150 scientists from 17 consortia are collaborating on an international project to build a Human Reference Atlas, which maps all 37 trillion cells in the healthy adult human body. The initial release of this atlas provided hierarchical lists of the anatomical structures, cell types, and biomarkers in 11 organs. Here, we describe the methods we used as part of this initiative to build the first open, computer-readable, and comprehensive database of the adult human blood vasculature, called the Human Reference Atlas-Vasculature Common Coordinate Framework (HRA-VCCF). It includes 993 vessels and their branching connections, 10 cell types, and 10 biomarkers. With this paper we are releasing additional details on vessel types and subtypes, branching sequence, anastomoses, portal systems, microvasculature, functional tissue units, mappings to regions vessels supply or drain, geometric properties of vessels, and links to 3D reference objects. Future versions will add variants and connections to the lymph vasculature; and, it will iteratively expand and improve the database as additional experimental data become available through the participating consortia.
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Affiliation(s)
| | - Sujin Lee
- Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Rajeev Malhotra
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Marc Halushka
- Department of Pathology, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Katherine S Gustilo
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, USA
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, USA
| | - Bruce W Herr
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
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95
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Zhang Q, Jiang S, Schroeder A, Hu J, Li K, Zhang B, Dai D, Lee EB, Xiao R, Li M. Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry. Nat Commun 2023; 14:4050. [PMID: 37422469 PMCID: PMC10329686 DOI: 10.1038/s41467-023-39895-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 07/03/2023] [Indexed: 07/10/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method's robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.
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Affiliation(s)
- Qihuang Zhang
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montreal, QC, Canada.
| | - Shunzhou Jiang
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Amelia Schroeder
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jian Hu
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, 30322, USA
| | - Kejie Li
- Research Department, Biogen, Inc., 225 Binney St., Cambridge, MA, 02142, USA
| | - Baohong Zhang
- Research Department, Biogen, Inc., 225 Binney St., Cambridge, MA, 02142, USA
| | - David Dai
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rui Xiao
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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96
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Marino G, Ngai M, Clarke DB, Fleishman R, Deng E, Xie Z, Ahmed N, Ma’ayan A. GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery. Nucleic Acids Res 2023; 51:W213-W224. [PMID: 37166966 PMCID: PMC10320068 DOI: 10.1093/nar/gkad399] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/23/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.
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Affiliation(s)
- Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Ngai
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Reid H Fleishman
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nasheath Ahmed
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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97
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Evangelista JE, Xie Z, Marino GB, Nguyen N, Clarke DB, Ma’ayan A. Enrichr-KG: bridging enrichment analysis across multiple libraries. Nucleic Acids Res 2023; 51:W168-W179. [PMID: 37166973 PMCID: PMC10320098 DOI: 10.1093/nar/gkad393] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/23/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023] Open
Abstract
Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in providing enrichment analysis with many gene set libraries from different categories, integrating enrichment results across libraries and domains of knowledge can further hypothesis generation. To this end, Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genes to the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources. Enrichr-KG currently serves 26 gene set libraries from different categories that include transcription, pathways, ontologies, diseases/drugs, and cell types. To demonstrate the utility of Enrichr-KG we provide several case studies. Enrichr-KG is freely available at: https://maayanlab.cloud/enrichr-kg.
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Affiliation(s)
- John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Nhi Nguyen
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, NY, NY, USA
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98
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Vento-Tormo R, Vilarrasa-Blasi R. Cell-level reference maps for the human body take shape. Nature 2023; 619:467-468. [PMID: 37468591 DOI: 10.1038/d41586-023-01817-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
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99
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Lake BB, Menon R, Winfree S, Hu Q, Melo Ferreira R, Kalhor K, Barwinska D, Otto EA, Ferkowicz M, Diep D, Plongthongkum N, Knoten A, Urata S, Mariani LH, Naik AS, Eddy S, Zhang B, Wu Y, Salamon D, Williams JC, Wang X, Balderrama KS, Hoover PJ, Murray E, Marshall JL, Noel T, Vijayan A, Hartman A, Chen F, Waikar SS, Rosas SE, Wilson FP, Palevsky PM, Kiryluk K, Sedor JR, Toto RD, Parikh CR, Kim EH, Satija R, Greka A, Macosko EZ, Kharchenko PV, Gaut JP, Hodgin JB, Eadon MT, Dagher PC, El-Achkar TM, Zhang K, Kretzler M, Jain S. An atlas of healthy and injured cell states and niches in the human kidney. Nature 2023; 619:585-594. [PMID: 37468583 PMCID: PMC10356613 DOI: 10.1038/s41586-023-05769-3] [Citation(s) in RCA: 124] [Impact Index Per Article: 124.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 01/30/2023] [Indexed: 07/21/2023]
Abstract
Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.
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Affiliation(s)
- Blue B Lake
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Rajasree Menon
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Seth Winfree
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ricardo Melo Ferreira
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kian Kalhor
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Daria Barwinska
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Edgar A Otto
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Michael Ferkowicz
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Dinh Diep
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Nongluk Plongthongkum
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Amanda Knoten
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Sarah Urata
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Laura H Mariani
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Abhijit S Naik
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Sean Eddy
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA
| | - Bo Zhang
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Yan Wu
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Diane Salamon
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - James C Williams
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xin Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Paul J Hoover
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Evan Murray
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Teia Noel
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Anitha Vijayan
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | | | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sushrut S Waikar
- Section of Nephrology, Boston University School of Medicine and Boston Medical Center, Boston, MA, USA
| | - Sylvia E Rosas
- Kidney and Hypertension Unit, Joslin Diabetes Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Francis P Wilson
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Paul M Palevsky
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - John R Sedor
- Lerner Research and Glickman Urology and Kidney Institutes, Cleveland Clinic, Cleveland, OH, USA
| | - Robert D Toto
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Chirag R Parikh
- Division of Nephrology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Eric H Kim
- Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
| | | | - Anna Greka
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Joseph P Gaut
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA
| | - Jeffrey B Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael T Eadon
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Pierre C Dagher
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Tarek M El-Achkar
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA.
| | - Matthias Kretzler
- Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.
| | - Sanjay Jain
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA.
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Dayao MT, Trevino A, Kim H, Ruffalo M, D’Angio HB, Preska R, Duvvuri U, Mayer AT, Bar-Joseph Z. Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis. Bioinformatics 2023; 39:i140-i148. [PMID: 37387167 PMCID: PMC10311350 DOI: 10.1093/bioinformatics/btad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization. RESULTS Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival. AVAILABILITY AND IMPLEMENTATION The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.
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Affiliation(s)
- Monica T Dayao
- Joint Carnegie Mellon University—University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, PA 15213, United States
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | | | - Honesty Kim
- Enable Medicine, Menlo Park, CA 94025, United States
| | - Matthew Ruffalo
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | | | - Ryan Preska
- Enable Medicine, Menlo Park, CA 94025, United States
| | - Umamaheswar Duvvuri
- Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Aaron T Mayer
- Enable Medicine, Menlo Park, CA 94025, United States
| | - Ziv Bar-Joseph
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
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