1
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Chen R, Nie P, Wang J, Wang GZ. Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1865. [PMID: 38972934 DOI: 10.1002/wrna.1865] [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: 01/31/2024] [Revised: 05/05/2024] [Accepted: 05/14/2024] [Indexed: 07/09/2024]
Abstract
The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.
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Affiliation(s)
- Renrui Chen
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Pengxing Nie
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jing Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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2
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Sang-aram C, Browaeys R, Seurinck R, Saeys Y. Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics. eLife 2024; 12:RP88431. [PMID: 38787371 PMCID: PMC11126312 DOI: 10.7554/elife.88431] [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] [Indexed: 05/25/2024] Open
Abstract
Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).
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Affiliation(s)
- Chananchida Sang-aram
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Robin Browaeys
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Ruth Seurinck
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
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3
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Konecny AJ, Huang Y, Setty M, Prlic M. Signals that control MAIT cell function in healthy and inflamed human tissues. Immunol Rev 2024; 323:138-149. [PMID: 38520075 DOI: 10.1111/imr.13325] [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] [Indexed: 03/25/2024]
Abstract
Mucosal-associated invariant T (MAIT) cells have a semi-invariant T-cell receptor that allows recognition of antigen in the context of the MHC class I-related (MR1) protein. Metabolic intermediates of the riboflavin synthesis pathway have been identified as MR1-restricted antigens with agonist properties. As riboflavin synthesis occurs in many bacterial species, but not human cells, it has been proposed that the main purpose of MAIT cells is antibacterial surveillance and protection. The majority of human MAIT cells secrete interferon-gamma (IFNg) upon activation, while some MAIT cells in tissues can also express IL-17. Given that MAIT cells are present in human barrier tissues colonized by a microbiome, MAIT cells must somehow be able to distinguish colonization from infection to ensure effector functions are only elicited when necessary. Importantly, MAIT cells have additional functional properties, including the potential to contribute to restoring tissue homeostasis by expression of CTLA-4 and secretion of the cytokine IL-22. A recent study provided compelling data indicating that the range of human MAIT cell functional properties is explained by plasticity rather than distinct lineages. This further underscores the necessity to better understand how different signals regulate MAIT cell function. In this review, we highlight what is known in regards to activating and inhibitory signals for MAIT cells with a specific focus on signals relevant to healthy and inflamed tissues. We consider the quantity, quality, and the temporal order of these signals on MAIT cell function and discuss the current limitations of computational tools to extrapolate which signals are received by MAIT cells in human tissues. Using lessons learned from conventional CD8 T cells, we also discuss how TCR signals may integrate with cytokine signals in MAIT cells to elicit distinct functional states.
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Affiliation(s)
- Andrew J Konecny
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Immunology, University of Washington, Seattle, Washington, USA
| | - Yin Huang
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Herbold Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, USA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Herbold Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Martin Prlic
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Immunology, University of Washington, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
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4
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Navikas V, Kowal J, Rodriguez D, Rivest F, Brajkovic S, Cassano M, Dupouy D. Semi-automated approaches for interrogating spatial heterogeneity of tissue samples. Sci Rep 2024; 14:5025. [PMID: 38424144 PMCID: PMC10904364 DOI: 10.1038/s41598-024-55387-w] [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: 10/13/2023] [Accepted: 02/22/2024] [Indexed: 03/02/2024] Open
Abstract
Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components' interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues' composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.
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Affiliation(s)
| | - Joanna Kowal
- Lunaphore Technologies SA, Tolochenaz, Switzerland
| | | | | | | | | | - Diego Dupouy
- Lunaphore Technologies SA, Tolochenaz, Switzerland.
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5
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Lafzi A, Borrelli C, Baghai Sain S, Bach K, Kretz JA, Handler K, Regan-Komito D, Ficht X, Frei A, Moor A. Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA). Mol Syst Biol 2024; 20:98-119. [PMID: 38225383 PMCID: PMC10897385 DOI: 10.1038/s44320-023-00006-5] [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: 09/27/2023] [Revised: 11/28/2023] [Accepted: 12/08/2023] [Indexed: 01/17/2024] Open
Abstract
Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.
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Affiliation(s)
- Atefeh Lafzi
- Roche Pharma Research and Early Development, Immunology Infectious Diseases and Ophthalmology Discovery and Translational Area, Grenzacherstrasse 124, 4070, Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Costanza Borrelli
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Simona Baghai Sain
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Karsten Bach
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Jonas A Kretz
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Kristina Handler
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Daniel Regan-Komito
- Roche Pharma Research and Early Development, Immunology Infectious Diseases and Ophthalmology Discovery and Translational Area, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Xenia Ficht
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland
| | - Andreas Frei
- Roche Pharma Research and Early Development, Immunology Infectious Diseases and Ophthalmology Discovery and Translational Area, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Andreas Moor
- Department of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, 4058, Basel, Switzerland.
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6
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Ma W, Song X, Yuan GC, Wang P. RECCIPE: A new framework assessing localized cell-cell interaction on gene expression in multicellular ST data. Front Genet 2024; 15:1322886. [PMID: 38327830 PMCID: PMC10847567 DOI: 10.3389/fgene.2024.1322886] [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: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 02/09/2024] Open
Abstract
Cell-cell interaction (CCI) plays a pivotal role in cellular communication within the tissue microenvironment. The recent development of spatial transcriptomics (ST) technology and associated data analysis methods has empowered researchers to systematically investigate CCI. However, existing methods are tailored to single-cell resolution datasets, whereas the majority of ST platforms lack such resolution. Additionally, the detection of CCI through association screening based on ST data, which has complicated dependence structure, necessitates proper control of false discovery rates due to the multiple hypothesis testing issue in high dimensional spaces. To address these challenges, we introduce RECCIPE, a novel method designed for identifying cell signaling interactions across multiple cell types in spatial transcriptomic data. RECCIPE integrates gene expression data, spatial information and cell type composition in a multivariate regression framework, enabling genome-wide screening for changes in gene expression levels attributed to CCIs. We show that RECCIPE not only achieves high accuracy in simulated datasets but also provides new biological insights from real data obtained from a mouse model of Alzheimer's disease (AD). Overall, our framework provides a useful tool for studying impact of cell-cell interactions on gene expression in multicellular systems.
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Affiliation(s)
- Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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7
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Cao X, Ma T, Fan R, Yuan GC. Broad H3K4me3 Domain Is Associated with Spatial Coherence during Mammalian Embryonic Development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.11.570452. [PMID: 38168252 PMCID: PMC10760050 DOI: 10.1101/2023.12.11.570452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
It is well known that the chromatin states play a major role in cell-fate decision and cell-identity maintenance; however, the spatial variation of chromatin states in situ remains poorly characterized. Here, by leveraging recently available spatial-CUT&Tag data, we systematically characterized the global spatial organization of the H3K4me3 profiles in a mouse embryo. Our analysis identified a subset of genes with spatially coherent H3K4me3 patterns, which together delineate the tissue boundaries. The spatially coherent genes are strongly enriched with tissue-specific transcriptional regulators. Remarkably, their corresponding genomic loci are marked by broad H3K4me3 domains, which is distinct from the typical H3K4me3 signature. Spatial transition across tissue boundaries is associated with continuous shortening of the broad H3K4me3 domains as well as expansion of H3K27me3 domains. Our analysis reveals a strong connection between the genomic and spatial variation of chromatin states, which may play an important role in embryonic development.
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Affiliation(s)
- Xuan Cao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Terry Ma
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Havens, CT, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
- Lead contact
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8
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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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9
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 161] [Impact Index Per Article: 161.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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10
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Shiau C, Cao J, Gregory MT, Gong D, Yin X, Cho JW, Wang PL, Su J, Wang S, Reeves JW, Kim TK, Kim Y, Guo JA, Lester NA, Schurman N, Barth JL, Weissleder R, Jacks T, Qadan M, Hong TS, Wo JY, Roberts H, Beechem JM, Castillo CFD, Mino-Kenudson M, Ting DT, Hemberg M, Hwang WL. Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.28.546848. [PMID: 37425692 PMCID: PMC10327107 DOI: 10.1101/2023.06.28.546848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.
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Affiliation(s)
- Carina Shiau
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jingyi Cao
- Evergrande Center for Immunologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Dennis Gong
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard-MIT Health Sciences and Technology Program, Cambridge, MA, USA
| | - Xunqin Yin
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jae-Won Cho
- Evergrande Center for Immunologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter L Wang
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jennifer Su
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven Wang
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | | | - Jimmy A Guo
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Biological and Biomedical Sciences Program, Harvard Medical School, Boston, MA, USA
| | - Nicole A Lester
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Jamie L Barth
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Tyler Jacks
- Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Motaz Qadan
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Theodore S Hong
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jennifer Y Wo
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Hannah Roberts
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Mari Mino-Kenudson
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David T Ting
- Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Martin Hemberg
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Evergrande Center for Immunologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - William L Hwang
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
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Badve SS, Gökmen-Polar Y. Targeting the Tumor-Tumor Microenvironment Crosstalk. Expert Opin Ther Targets 2023; 27:447-457. [PMID: 37395003 DOI: 10.1080/14728222.2023.2230362] [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: 04/01/2023] [Accepted: 06/23/2023] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Cancer development and progression is a complex process influenced by co-evolution of the cancer cells and their microenvironment. However, traditional anti-cancer therapy is mostly targeted toward cancer cells. To improve the efficacy of cancer drugs, the complex interactions between the tumor (T) and the tumor microenvironment (TME) should be considered while developing therapeutics. AREAS COVERED The present review article will discuss the components of T-TME as well as the potential to co-target these two distinct elements. We document that these approaches have resulted in success in preventing tumor progression and metastasis, albeit in animal models in some cases. Lastly, it is important to consider the tissue context and tumor type as these could significantly modify the role of these molecules/pathways and hence the overall likelihood of response. Furthermore, we discuss the potential strategies to target the components of tumor microenvironment in anti-cancer therapy. PubMed and ClinicalTrials.gov was searched through May 2023. EXPERT OPINION The tumor-tumor microenvironment cross talk and heterogeneity are major mechanisms conferring resistance to standard of care. Better understanding of the tissue specific T-TME interactions and dual targeting has the promise of improving cancer control and clinical outcomes.
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Affiliation(s)
- Sunil S Badve
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Yesim Gökmen-Polar
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
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