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Moriel N, Memet E, Nitzan M. Optimal sequencing budget allocation for trajectory reconstruction of single cells. Bioinformatics 2024; 40:i446-i452. [PMID: 38940162 PMCID: PMC11211845 DOI: 10.1093/bioinformatics/btae258] [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: 06/29/2024] Open
Abstract
BACKGROUND Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations. RESULTS Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.
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Affiliation(s)
- Noa Moriel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Edvin Memet
- Department of Physics, Harvard University, Cambridge, MA 02138, United States
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel
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2
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Chai YC, To SK, Simorgh S, Zaunz S, Zhu Y, Ahuja K, Lemaitre A, Ramezankhani R, van der Veer BK, Wierda K, Verhulst S, van Grunsven LA, Pasque V, Verfaillie C. Spatially Self-Organized Three-Dimensional Neural Concentroid as a Novel Reductionist Humanized Model to Study Neurovascular Development. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304421. [PMID: 38037510 PMCID: PMC10837345 DOI: 10.1002/advs.202304421] [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: 07/02/2023] [Revised: 10/15/2023] [Indexed: 12/02/2023]
Abstract
Although human pluripotent stem cell (PSC)-derived brain organoids have enabled researchers to gain insight into human brain development and disease, these organoids contain solely ectodermal cells and are not vascularized as occurs during brain development. Here it is created less complex and more homogenous large neural constructs starting from PSC-derived neuroprogenitor cells (NPC), by fusing small NPC spheroids into so-called concentroids. Such concentroids consisted of a pro-angiogenic core, containing neuronal and outer radial glia cells, surrounded by an astroglia-dense outer layer. Incorporating PSC-derived endothelial cells (EC) around and/or in the concentroids promoted vascularization, accompanied by differential outgrowth and differentiation of neuronal and astroglia cells, as well as the development of ectodermal-derived pericyte-like mural cells co-localizing with EC networks. Single nucleus transcriptomic analysis revealed an enhanced neural cell subtype maturation and diversity in EC-containing concentroids, which better resemble the fetal human brain compared to classical organoids or NPC-only concentroids. This PSC-derived "vascularized" concentroid brain model will facilitate the study of neurovascular/blood-brain barrier development, neural cell migration, and the development of effective in vitro vascularization strategies of brain mimics.
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Affiliation(s)
- Yoke Chin Chai
- Stem Cell Institute LeuvenDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - San Kit To
- Stem Cell Institute LeuvenDepartment of Development and RegenerationLeuven Institute for Single Cell Omics (LISCO)KU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - Susan Simorgh
- Stem Cell Institute LeuvenDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - Samantha Zaunz
- Stem Cell Institute LeuvenDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - YingLi Zhu
- Stem Cell Institute LeuvenDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - Karan Ahuja
- Stem Cell Institute LeuvenDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - Alix Lemaitre
- Stem Cell Institute LeuvenDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - Roya Ramezankhani
- Stem Cell Institute LeuvenDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - Bernard K. van der Veer
- Laboratory for Stem Cell and Developmental EpigeneticsDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - Keimpe Wierda
- Electrophysiology Expert UnitVIB‐KU Leuven Center for Brain & Disease ResearchLeuven3000Belgium
| | - Stefaan Verhulst
- Liver Cell Biology Research GroupVrije Universiteit Brussel (VUB)Brussels1090Belgium
| | - Leo A. van Grunsven
- Liver Cell Biology Research GroupVrije Universiteit Brussel (VUB)Brussels1090Belgium
| | - Vincent Pasque
- Stem Cell Institute LeuvenDepartment of Development and RegenerationLeuven Institute for Single Cell Omics (LISCO)KU Leuven, O&N4, Herestraat 49Leuven3000Belgium
| | - Catherine Verfaillie
- Stem Cell Institute LeuvenDepartment of Development and RegenerationKU Leuven, O&N4, Herestraat 49Leuven3000Belgium
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3
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Sousa AGG, Smolander J, Junttila S, Elo LL. Inferring Tree-Shaped Single-Cell Trajectories with Totem. Methods Mol Biol 2024; 2812:169-191. [PMID: 39068362 DOI: 10.1007/978-1-0716-3886-6_9] [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: 07/30/2024]
Abstract
Single-cell transcriptomics allows unbiased characterization of cell heterogeneity in a sample by profiling gene expression at single-cell level. These profiles capture snapshots of transient or steady states in dynamic processes, such as cell cycle, activation, or differentiation, which can be computationally ordered into a "flip-book" of cell development using trajectory inference methods. However, prediction of more complex topology structures, such as multifurcations or trees, remains challenging. In this chapter, we present two user-friendly protocols for inferring tree-shaped single-cell trajectories and pseudotime from single-cell transcriptomics data with Totem. Totem is a trajectory inference method that offers flexibility in inferring both nonlinear and linear trajectories and usability by avoiding the cumbersome fine-tuning of parameters. The QuickStart protocol provides a simple and practical example, whereas the GuidedStart protocol details the analysis step-by-step. Both protocols are demonstrated using a case study of human bone marrow CD34+ cells, allowing the study of the branching of three lineages: erythroid, lymphoid, and myeloid. All the analyses can be fully reproduced in Linux, macOS, and Windows operating systems (amd64 architecture) with >8 Gb of RAM using the provided docker image distributed with notebooks, scripts, and data in Docker Hub (elolab/repro-totem-ti). These materials are shared online under open-source license at https://elolab.github.io/Totem-protocol .
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Affiliation(s)
- António G G Sousa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Johannes Smolander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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4
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Leary JR, Bacher R. Interpretable trajectory inference with single-cell Linear Adaptive Negative-binomial Expression (scLANE) testing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572477. [PMID: 38187622 PMCID: PMC10769309 DOI: 10.1101/2023.12.19.572477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The rapid proliferation of trajectory inference methods for single-cell RNA-seq data has allowed researchers to investigate complex biological processes by examining underlying gene expression dynamics. After estimating a latent cell ordering, statistical models are used to determine which genes exhibit changes in expression that are significantly associated with progression through the biological trajectory. While a few techniques for performing trajectory differential expression exist, most rely on the flexibility of generalized additive models in order to account for the inherent nonlinearity of changes in gene expression. As such, the results can be difficult to interpret, and biological conclusions often rest on subjective visual inspections of the most dynamic genes. To address this challenge, we propose scLANE testing, which is built around an interpretable generalized linear model and handles nonlinearity with basis splines chosen empirically for each gene. In addition, extensions to estimating equations and mixed models allow for reliable trajectory testing under complex experimental designs. After validating the accuracy of scLANE under several different simulation scenarios, we apply it to a set of diverse biological datasets and display its ability to provide novel biological information when used downstream of both pseudotime and RNA velocity estimation methods.
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Affiliation(s)
- Jack R. Leary
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA
| | - Rhonda Bacher
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA
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5
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Tsamir-Rimon M, Borenstein E. A manifold-based framework for studying the dynamics of the vaginal microbiome. NPJ Biofilms Microbiomes 2023; 9:102. [PMID: 38102172 PMCID: PMC10724123 DOI: 10.1038/s41522-023-00471-8] [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: 08/24/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
The vaginal microbiome plays a crucial role in our health. The composition of this community can be classified into five community state types (CSTs), four of which are primarily consisted of Lactobacillus species and considered healthy, while the fifth features non-Lactobacillus populations and signifies a disease state termed Bacterial vaginosis (BV), which is associated with various symptoms and increased susceptibility to diseases. Importantly, however, the exact mechanisms and dynamics underlying BV development are not yet fully understood, including specifically possible routes from a healthy to a BV state. To address this gap, this study set out to characterize the progression from healthy- to BV-associated compositions by analyzing 8026 vaginal samples and using a manifold-detection framework. This approach, inspired by single-cell analysis, aims to identify low-dimensional trajectories in the high-dimensional composition space. It further orders samples along these trajectories and assigns a score (pseudo-time) to each analyzed or new sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV states for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. Several BV indicators can also be successfully predicted based on pseudo-time scores, and key taxa involved in BV development can be identified using this approach. Taken together, these findings demonstrate how manifold detection can be used to successfully characterize the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.
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Affiliation(s)
| | - Elhanan Borenstein
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
- Santa Fe Institute, Santa Fe, NM, USA.
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6
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Zhang M, Chen Y, Yu D, Zhong W, Zhang J, Ma P. Elucidating dynamic cell lineages and gene networks in time-course single cell differentiation. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2023; 3:100068. [PMID: 37426065 PMCID: PMC10328540 DOI: 10.1016/j.ailsci.2023.100068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Single cell RNA sequencing (scRNA-seq) technologies provide researchers with an unprecedented opportunity to exploit cell heterogeneity. For example, the sequenced cells belong to various cell lineages, which may have different cell fates in stem and progenitor cells. Those cells may differentiate into various mature cell types in a cell differentiation process. To trace the behavior of cell differentiation, researchers reconstruct cell lineages and predict cell fates by ordering cells chronologically into a trajectory with a pseudo-time. However, in scRNA-seq experiments, there are no cell-to-cell correspondences along with the time to reconstruct the cell lineages, which creates a significant challenge for cell lineage tracing and cell fate prediction. Therefore, methods that can accurately reconstruct the dynamic cell lineages and predict cell fates are highly desirable. In this article, we develop an innovative machine-learning framework called Cell Smoothing Transformation (CellST) to elucidate the dynamic cell fate paths and construct gene networks in cell differentiation processes. Unlike the existing methods that construct one single bulk cell trajectory, CellST builds cell trajectories and tracks behaviors for each individual cell. Additionally, CellST can predict cell fates even for less frequent cell types. Based on the individual cell fate trajectories, CellST can further construct dynamic gene networks to model gene-gene relationships along the cell differentiation process and discover critical genes that potentially regulate cells into various mature cell types.
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Affiliation(s)
| | - Yongkai Chen
- Department of Statistics, University of Georgia, Athens, GA 30602, United Stated
| | - Dingyi Yu
- Department of Industrial Engineering, Center for Statistical Science, Tsinghua University, Beijing, China
| | - Wenxuan Zhong
- Department of Statistics, University of Georgia, Athens, GA 30602, United Stated
| | - Jingyi Zhang
- Department of Industrial Engineering, Center for Statistical Science, Tsinghua University, Beijing, China
| | - Ping Ma
- Department of Statistics, University of Georgia, Athens, GA 30602, United Stated
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7
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Shao N, Ren C, Hu T, Wang D, Zhu X, Li M, Cheng T, Zhang Y, Zhang XE. Detection of continuous hierarchical heterogeneity by single-cell surface antigen analysis in the prognosis evaluation of acute myeloid leukaemia. BMC Bioinformatics 2023; 24:450. [PMID: 38017410 PMCID: PMC10683216 DOI: 10.1186/s12859-023-05561-0] [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: 12/21/2022] [Accepted: 11/06/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Acute myeloid leukaemia (AML) is characterised by the malignant accumulation of myeloid progenitors with a high recurrence rate after chemotherapy. Blasts (leukaemia cells) exhibit a complete myeloid differentiation hierarchy hiding a wide range of temporal information from initial to mature clones, including genesis, phenotypic transformation, and cell fate decisions, which might contribute to relapse in AML patients. METHODS Based on the landscape of AML surface antigens generated by mass cytometry (CyTOF), we combined manifold analysis and principal curve-based trajectory inference algorithm to align myelocytes on a single-linear evolution axis by considering their phenotype continuum that correlated with differentiation order. Backtracking the trajectory from mature clusters located automatically at the terminal, we recurred the molecular dynamics during AML progression and confirmed the evolution stage of single cells. We also designed a 'dispersive antigens in neighbouring clusters exhibition (DANCE)' feature selection method to simplify and unify trajectories, which enabled the exploration and comparison of relapse-related traits among 43 paediatric AML bone marrow specimens. RESULTS The feasibility of the proposed trajectory analysis method was verified with public datasets. After aligning single cells on the pseudotime axis, primitive clones were recognized precisely from AML blasts, and the expression of the inner molecules before and after drug stimulation was accurately plotted on the trajectory. Applying DANCE to 43 clinical samples with different responses for chemotherapy, we selected 12 antigens as a general panel for myeloblast differentiation performance, and obtain trajectories to those patients. For the trajectories with unified molecular dynamics, CD11c overexpression in the primitive stage indicated a good chemotherapy outcome. Moreover, a later initial peak of stemness heterogeneity tended to be associated with a higher risk of relapse compared with complete remission. CONCLUSIONS In this study, pseudotime was generated as a new single-cell feature. Minute differences in temporal traits among samples could be exhibited on a trajectory, thus providing a new strategy for predicting AML relapse and monitoring drug responses over time scale.
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Affiliation(s)
- Nan Shao
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chenshuo Ren
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tianyuan Hu
- State Key Laboratory of Experimental Haematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Haematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China
| | - Dianbing Wang
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xiaofan Zhu
- State Key Laboratory of Experimental Haematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Haematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China
| | - Min Li
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Tao Cheng
- State Key Laboratory of Experimental Haematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Haematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China
| | - Yingchi Zhang
- State Key Laboratory of Experimental Haematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Haematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China.
| | - Xian-En Zhang
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Faculty of Synthetic Biology, University of Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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8
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Hou W, Ji Z, Chen Z, Wherry EJ, Hicks SC, Ji H. A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples. Nat Commun 2023; 14:7286. [PMID: 37949861 PMCID: PMC10638410 DOI: 10.1038/s41467-023-42841-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.
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Affiliation(s)
- Wenpin Hou
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Zhicheng Ji
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Zeyu Chen
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - E John Wherry
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stephanie C Hicks
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
| | - Hongkai Ji
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
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9
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Wang S, Sontag ED, Lauffenburger DA. What cannot be seen correctly in 2D visualizations of single-cell 'omics data? Cell Syst 2023; 14:723-731. [PMID: 37734322 PMCID: PMC10863674 DOI: 10.1016/j.cels.2023.07.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/07/2023] [Accepted: 07/14/2023] [Indexed: 09/23/2023]
Abstract
A common strategy for exploring single-cell 'omics data is visualizing 2D nonlinear projections that aim to preserve high-dimensional data properties such as neighborhoods. Alternatively, mathematical theory and other computational tools can directly describe data geometry, while also showing that neighborhoods and other properties cannot be well-preserved in any 2D projection.
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Affiliation(s)
- Shu Wang
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Eduardo D Sontag
- Departments of Bioengineering and Electrical & Computer Engineering, Northeastern University, Boston, MA 02115, USA.
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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10
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Tsamir-Rimon M, Borenstein E. A Manifold-Based Framework for Studying the Dynamics of the Vaginal Microbiome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.556518. [PMID: 37732273 PMCID: PMC10508760 DOI: 10.1101/2023.09.06.556518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The vaginal bacterial community plays a crucial role in preventing infections. The composition of this community can be classified into five main groups, termed community state types (CSTs). Four of these CSTs, which are primarily consisted of Lactobacillus species, are considered healthy, while the fifth, which is composed of non-Lactobacillus populations, is considered less protective. This latter CST is often considered to represent a state termed Bacterial vaginosis (BV) - a common disease condition associated with unpleasant symptoms and increased susceptibility to sexually transmitted diseases. However, the exact mechanisms underlying BV development are not yet fully understood, including specifically, the dynamics of the vaginal microbiome in BV, and the possible routes it may take from a healthy to a BV state. This study aims to identify the progression from healthy Lactobacillus-dominant populations to symptomatic BV by analyzing 8,026 vaginal samples and using a manifold-detection framework. This approach is inspired by single-cell analysis and aims to identify low-dimensional trajectories in the high-dimensional composition space. This framework further order samples along these trajectories and assign a score (pseudo-time) to each sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV state for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. BV indicators, including Nugent score, positive Amsel's test, and several Amsel's criteria, can also be successfully predicted based on pseudo-time scores. Additionally, Gardnerella vaginalis can be identified as a key taxon in BV development using this approach, with increased abundance in samples with high pseudo-time, indicating an unhealthier state across all BV-development routes on the manifold. Taken together, these findings demonstrate how manifold detection can be used to successfully characterizes the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.
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Affiliation(s)
| | - Elhanan Borenstein
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Santa Fe Institute, Santa Fe, NM, USA
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11
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Tu W, Ling G, Liu F, Hu F, Song X. GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2945-2958. [PMID: 37037234 DOI: 10.1109/tcbb.2023.3266109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's "internal clock". To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.
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12
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Vonk AC, Zhao X, Pan Z, Hudnall ML, Oakes CG, Lopez GA, Hasel-Kolossa SC, Kuncz AWC, Sengelmann SB, Gamble DJ, Lozito TP. Single-cell analysis of lizard blastema fibroblasts reveals phagocyte-dependent activation of Hedgehog-responsive chondrogenesis. Nat Commun 2023; 14:4489. [PMID: 37563130 PMCID: PMC10415409 DOI: 10.1038/s41467-023-40206-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: 12/09/2022] [Accepted: 07/18/2023] [Indexed: 08/12/2023] Open
Abstract
Lizards cannot naturally regenerate limbs but are the closest known relatives of mammals capable of epimorphic tail regrowth. However, the mechanisms regulating lizard blastema formation and chondrogenesis remain unclear. Here, single-cell RNA sequencing analysis of regenerating lizard tails identifies fibroblast and phagocyte populations linked to cartilage formation. Pseudotime trajectory analyses suggest spp1+-activated fibroblasts as blastema cell sources, with subsets exhibiting sulf1 expression and chondrogenic potential. Tail blastema, but not limb, fibroblasts express sulf1 and form cartilage under Hedgehog signaling regulation. Depletion of phagocytes inhibits blastema formation, but treatment with pericytic phagocyte-conditioned media rescues blastema chondrogenesis and cartilage formation in amputated limbs. The results indicate a hierarchy of phagocyte-induced fibroblast gene activations during lizard blastema formation, culminating in sulf1+ pro-chondrogenic populations singularly responsive to Hedgehog signaling. These properties distinguish lizard blastema cells from homeostatic and injury-stimulated fibroblasts and indicate potential actionable targets for inducing regeneration in other species, including humans.
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Affiliation(s)
- Ariel C Vonk
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, 1425 San Pablo St, Los Angeles, CA, 90033, USA
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1540 Alcazar St, Los Angeles, CA, 90033, USA
| | - Xiaofan Zhao
- Molecular Genomics Core, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA, 90033, USA
| | - Zheyu Pan
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, 1425 San Pablo St, Los Angeles, CA, 90033, USA
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1540 Alcazar St, Los Angeles, CA, 90033, USA
| | - Megan L Hudnall
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1540 Alcazar St, Los Angeles, CA, 90033, USA
| | - Conrad G Oakes
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1540 Alcazar St, Los Angeles, CA, 90033, USA
| | - Gabriela A Lopez
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, 1425 San Pablo St, Los Angeles, CA, 90033, USA
| | - Sarah C Hasel-Kolossa
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, 1425 San Pablo St, Los Angeles, CA, 90033, USA
| | - Alexander W C Kuncz
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1540 Alcazar St, Los Angeles, CA, 90033, USA
| | - Sasha B Sengelmann
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1540 Alcazar St, Los Angeles, CA, 90033, USA
| | - Darian J Gamble
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, 1425 San Pablo St, Los Angeles, CA, 90033, USA
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1540 Alcazar St, Los Angeles, CA, 90033, USA
| | - Thomas P Lozito
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, 1425 San Pablo St, Los Angeles, CA, 90033, USA.
- Department of Orthopaedic Surgery, Keck School of Medicine, University of Southern California, 1540 Alcazar St, Los Angeles, CA, 90033, USA.
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13
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Flynn E, Almonte-Loya A, Fragiadakis GK. Single-Cell Multiomics. Annu Rev Biomed Data Sci 2023; 6:313-337. [PMID: 37159875 PMCID: PMC11146013 DOI: 10.1146/annurev-biodatasci-020422-050645] [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: 05/11/2023]
Abstract
Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.
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Affiliation(s)
- Emily Flynn
- CoLabs, University of California, San Francisco, California, USA;
| | - Ana Almonte-Loya
- CoLabs, University of California, San Francisco, California, USA;
- Biomedical Informatics Program, University of California, San Francisco, California, USA
| | - Gabriela K Fragiadakis
- CoLabs, University of California, San Francisco, California, USA;
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
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14
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Baranpouyan M, Mohammadi H. HDSCC: A robust clustering approach for Single Cell RNA-seq data using Hyperdimensional Encoding. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38082926 DOI: 10.1109/embc40787.2023.10341176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Significant improvement in Single Cell technologies has given a hand to researchers to measure RNA expression of considerable number of Single Cells simultaneously, resulting in noticeable progress in our knowledge of cellular structure. Microfluidics-based sequencing protocols employing unique molecular identifiers (UMIs) lead to not only high-quality processing but also screening of thousands of cells. However, analysis of said data has caused challenges when it comes to processing time and computational resources as well as analyzing noisy and highly sparse data. Addressing these issues, we proposed a new method to cluster large RNA-seq datasets effectively. In our proposed approach, for having a noise robust clustering, we employed Hyper Dimensional Computing (HDC) approach to analyze Single Cell RNA sequencing data for the first time as best of our knowledge. We compared our results with state-of-the-art works on single-cell clustering and it shows promising performance and robustness in comparison to them. We performed our experiments with a 3.2-GHz CPU and 32 GB of RAM laptop.
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15
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Gromova T, Gehred ND, Vondriska TM. Single-cell transcriptomes in the heart: when every epigenome counts. Cardiovasc Res 2023; 119:64-78. [PMID: 35325060 DOI: 10.1093/cvr/cvac040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/03/2022] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
The response of an organ to stimuli emerges from the actions of individual cells. Recent cardiac single-cell RNA-sequencing studies of development, injury, and reprogramming have uncovered heterogeneous populations even among previously well-defined cell types, raising questions about what level of experimental resolution corresponds to disease-relevant, tissue-level phenotypes. In this review, we explore the biological meaning behind this cellular heterogeneity by undertaking an exhaustive analysis of single-cell transcriptomics in the heart (including a comprehensive, annotated compendium of studies published to date) and evaluating new models for the cardiac function that have emerged from these studies (including discussion and schematics that depict new hypotheses in the field). We evaluate the evidence to support the biological actions of newly identified cell populations and debate questions related to the role of cell-to-cell variability in development and disease. Finally, we present emerging epigenomic approaches that, when combined with single-cell RNA-sequencing, can resolve basic mechanisms of gene regulation and variability in cell phenotype.
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Affiliation(s)
- Tatiana Gromova
- Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Medicine/Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Natalie D Gehred
- Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Medicine/Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Thomas M Vondriska
- Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Medicine/Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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16
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Li X, Lin Y, Xie C, Li Z, Chen M, Wang P, Zhou J. A Clustering Method Unifying Cell-Type Recognition and Subtype Identification for Tumor Heterogeneity Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:822-832. [PMID: 36044493 DOI: 10.1109/tcbb.2022.3203185] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The rapid development of single-cell technology has opened up a whole new perspective for identifying cell types in multicellular organisms and understanding the relationships between them. Distinguishing different cell types and subtypes can identify the components of different immune cells and different tumor clones in the tumor microenvironment, which is the basic work of tumor heterogeneity analysis and can help researchers understand the mechanism of tumor immune escape. Existing algorithms treat both cell types and subtypes as populations of cells with specific gene expression patterns, which is not conducive to accurate cell typing. For that, we proposed a cell similarity metric that unifies cell type recognition and subtype identification (UCRSI), with the assumption that selectively expressed genes represent differences in underlying cell type with on/off manner, while differences in expression level represent different cell subtype with more/less manner. Our method calculates these two kinds of differences separately, and then combines them using a consensus adjacency matrix, and finally cell typing is completed using spectral clustering algorithm. The results show that UCRSI can reconstruct expert annotation of single-cell RNA sequencing datasets more robustly than existing methods. And, UCRSI is useful for analyzing tumor heterogeneity and improving visualization of large-scale cell clustering.
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17
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Choi Y, Li R, Quon G. siVAE: interpretable deep generative models for single-cell transcriptomes. Genome Biol 2023; 24:29. [PMID: 36803416 PMCID: PMC9940350 DOI: 10.1186/s13059-023-02850-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 01/06/2023] [Indexed: 02/22/2023] Open
Abstract
Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.
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Affiliation(s)
- Yongin Choi
- Graduate Group in Biomedical Engineering, University of California, Davis, Davis, CA, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | - Ruoxin Li
- Genome Center, University of California, Davis, Davis, CA, USA
- Graduate Group in Biostatistics, University of California, Davis, Davis, CA, USA
| | - Gerald Quon
- Graduate Group in Biomedical Engineering, University of California, Davis, Davis, CA, USA.
- Genome Center, University of California, Davis, Davis, CA, USA.
- Department of Molecular and Cellular Biology, University of California, Davis, Davis, CA, USA.
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18
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Wang X, Liu Q, Li J, Zhou L, Wang T, Zhao N. Dynamic cellular and molecular characteristics of spermatogenesis in the viviparous marine teleost Sebastes schlegelii†. Biol Reprod 2023; 108:338-352. [PMID: 36401879 DOI: 10.1093/biolre/ioac203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 07/13/2022] [Accepted: 11/02/2022] [Indexed: 11/21/2022] Open
Abstract
Spermatogenesis is a dynamic cell developmental process that is essential for reproductive success. Vertebrates utilize a variety of reproductive strategies, including sperm diversity, and internal and external fertilization. Research on the cellular and molecular dynamic changes involved in viviparous teleost spermatogenesis, however, is currently lacking. Here, we combined cytohistology, 10 × genomic single-cell RNA-seq, and transcriptome technology to determine the dynamic development characteristics of the spermatogenesis of Sebastes schlegelii. The expressions of lhcgr (Luteinizing hormone/Choriogonadotropin receptor), fshr (follicle-stimulating hormone receptor), ar (androgen receptor), pgr (progesterone receptor), and cox (cyclo-oxygen-ase), as well as the prostaglandin E and F levels peaked during the maturation period, indicating that they were important for sperm maturation and mating. Fifteen clusters were identified based on the 10 × genomic single-cell results. The cell markers of the sub-cluster were identified by their upregulation; piwil, dazl, and dmrt1 were upregulated and identified as spermatogonium markers, and sycp1/3 and spo11 were identified as spermatocyte markers. For S. schlegelii, the sperm head nucleus was elongated (spherical to streamlined in shape), which is a typical characteristic for sperm involved in internal fertilization. We also identified a series of crucial genes associated with spermiogenesis, such as spata6, spag16, kif20a, trip10, and klf10, while kif2c, kifap3, fez2, and spaca6 were found to be involved in nucleus elongation. The results of this study will enrich our cellular and molecular knowledge of spermatogenesis and spermiogenesis in fish that undergo internal fertilization.
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Affiliation(s)
- Xueying Wang
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.,Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
| | - Qinghua Liu
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.,Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
| | - Jun Li
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.,Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
| | - Li Zhou
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.,Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China.,College of Marine Science, University of Chinese Academy of Sciences, Beijing, China
| | - Tao Wang
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.,Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China.,School of Marine Science and Engineering, Qingdao Agricultural University, Qingdao, China
| | - Ning Zhao
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.,Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China.,College of Marine Science, University of Chinese Academy of Sciences, Beijing, China
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19
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Reconstruction of Single-Cell Trajectories Using Stochastic Tree Search. Genes (Basel) 2023; 14:genes14020318. [PMID: 36833245 PMCID: PMC9957497 DOI: 10.3390/genes14020318] [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: 12/05/2022] [Revised: 01/18/2023] [Accepted: 01/21/2023] [Indexed: 01/28/2023] Open
Abstract
The recent advancement in single-cell RNA sequencing technologies enables the understanding of dynamic cellular processes at the single-cell level. Using trajectory inference methods, pseudotimes can be estimated based on reconstructed single-cell trajectories which can be further used to gain biological knowledge. Existing methods for modeling cell trajectories, such as minimal spanning tree or k-nearest neighbor graph, often lead to locally optimal solutions. In this paper, we propose a penalized likelihood-based framework and introduce a stochastic tree search (STS) algorithm aiming at the global solution in a large and non-convex tree space. Both simulated and real data experiments show that our approach is more accurate and robust than other existing methods in terms of cell ordering and pseudotime estimation.
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20
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Singh A, Hermann BP. Bulk and Single-Cell RNA-Seq Analyses for Studies of Spermatogonia. Methods Mol Biol 2023; 2656:37-70. [PMID: 37249866 DOI: 10.1007/978-1-0716-3139-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Robust methods have been developed that leverage next-generation sequencing (NGS) to measure abundance of all mRNAs (RNA-seq) in samples as small as individual cells in order to study the testicular transcriptome in mammals. In this chapter, we present robust options for implementing bioinformatics workflows for the analysis of bulk RNA-seq from aggregate samples of hundreds to millions of cells and single-cell RNA-seq from individual cells. We also provide detailed protocols for using the R packages DESeq2 and Seurat, important parameters for successful implementation, and considerations for drawing conclusions from the results.
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Affiliation(s)
- Anukriti Singh
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Brian P Hermann
- Department of Neuroscience, Developmental and Regenerative Biology, University of Texas at San Antonio, San Antonio, TX, USA.
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21
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Gavish A, Chain B, Salame TM, Antebi YE, Nevo S, Reich-Zeliger S, Friedman N. From pseudo to real-time dynamics of T cell thymic differentiation. iScience 2022; 26:105826. [PMID: 36624839 PMCID: PMC9823121 DOI: 10.1016/j.isci.2022.105826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/14/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022] Open
Abstract
Numerous methods have recently emerged for ordering single cells along developmental trajectories. However, accurate depiction of developmental dynamics can only be achieved after rescaling the trajectory according to the relative time spent at each developmental point. We formulate a model which estimates local cell densities and fluxes, and incorporates cell division and apoptosis rates, to infer the real-time dimension of the developmental trajectory. We validate the model using mathematical simulations and apply it to experimental high dimensional cytometry data obtained from the mouse thymus to construct the true time profile of the thymocyte developmental process. Our method can easily be implemented in any of the existing tools for trajectory inference.
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Affiliation(s)
- Avishai Gavish
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel,Corresponding author
| | - Benny Chain
- Division of Infection and Immunity, University College London, London, UK
| | - Tomer M. Salame
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Yaron E. Antebi
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Shir Nevo
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Shlomit Reich-Zeliger
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel,Corresponding author
| | - Nir Friedman
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
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22
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Iturria-Medina Y, Adewale Q, Khan AF, Ducharme S, Rosa-Neto P, O’Donnell K, Petyuk VA, Gauthier S, De Jager PL, Breitner J, Bennett DA. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity. SCIENCE ADVANCES 2022; 8:eabo6764. [PMID: 36399579 PMCID: PMC9674284 DOI: 10.1126/sciadv.abo6764] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.
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Affiliation(s)
- Yasser Iturria-Medina
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Quadri Adewale
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Ahmed F. Khan
- Neurology and Neurosurgery Department, Montreal Neurological Institute, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada
| | - Pedro Rosa-Neto
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, Canada
| | - Kieran O’Donnell
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
- Yale School of Medicine, New Haven, CT 06519, USA
| | - Vladislav A. Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Serge Gauthier
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, Canada
| | - Philip L. De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - John Breitner
- Centre for Studies on Prevention of Alzheimer’s Disease (StoP-AD), Douglas Research Centre, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
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23
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Zeng Z, Zhao S, Peng Y, Hu X, Yin Z. Cascade Forest-Based Model for Prediction of RNA Velocity. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27227873. [PMID: 36431973 PMCID: PMC9698518 DOI: 10.3390/molecules27227873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022]
Abstract
In recent years, single-cell RNA sequencing technology (scRNA-seq) has developed rapidly and has been widely used in biological and medical research, such as in expression heterogeneity and transcriptome dynamics of single cells. The investigation of RNA velocity is a new topic in the study of cellular dynamics using single-cell RNA sequencing data. It can recover directional dynamic information from single-cell transcriptomics by linking measurements to the underlying dynamics of gene expression. Predicting the RNA velocity vector of each cell based on its gene expression data and formulating RNA velocity prediction as a classification problem is a new research direction. In this paper, we develop a cascade forest model to predict RNA velocity. Compared with other popular ensemble classifiers, such as XGBoost, RandomForest, LightGBM, NGBoost, and TabNet, it performs better in predicting RNA velocity. This paper provides guidance for researchers in selecting and applying appropriate classification tools in their analytical work and suggests some possible directions for future improvement of classification tools.
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24
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Cuevas-Diaz Duran R, González-Orozco JC, Velasco I, Wu JQ. Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases. Front Cell Dev Biol 2022; 10:884748. [PMID: 36353512 PMCID: PMC9637968 DOI: 10.3389/fcell.2022.884748] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 10/06/2022] [Indexed: 08/10/2023] Open
Abstract
Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.
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Affiliation(s)
| | | | - Iván Velasco
- Instituto de Fisiología Celular—Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Laboratorio de Reprogramación Celular, Instituto Nacional de Neurología y Neurocirugía “Manuel Velasco Suárez”, Mexico City, Mexico
| | - Jia Qian Wu
- The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Center for Stem Cell and Regenerative Medicine, UT Brown Foundation Institute of Molecular Medicine, Houston, TX, United States
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States
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25
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Sugihara R, Kato Y, Mori T, Kawahara Y. Alignment of single-cell trajectory trees with CAPITAL. Nat Commun 2022; 13:5972. [PMID: 36241645 PMCID: PMC9568509 DOI: 10.1038/s41467-022-33681-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 09/26/2022] [Indexed: 12/03/2022] Open
Abstract
Global alignment of complex pseudotime trajectories between different single-cell RNA-seq datasets is challenging, as existing tools mainly focus on linear alignment of single-cell trajectories. Here we present CAPITAL (comparative analysis of pseudotime trajectory inference with tree alignment), a method for comparing single-cell trajectories with tree alignment whereby branching trajectories can be automatically compared. Computational tests on synthetic datasets and authentic bone marrow cells datasets indicate that CAPITAL has achieved accurate and robust alignments of trajectory trees, revealing various gene expression dynamics including gene–gene correlation conservation between different species. Global alignment of complex cell state trajectories between single-cell datasets remains challenging. Here, the authors present a computational method called CAPITAL to compare branching trajectories, and demonstrate that this method achieves accurate and robust alignments.
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Affiliation(s)
- Reiichi Sugihara
- Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan
| | - Yuki Kato
- Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan. .,Integrated Frontier Research for Medical Science Division, and RNA Frontier Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan.
| | - Tomoya Mori
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan
| | - Yukio Kawahara
- Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan.,Integrated Frontier Research for Medical Science Division, and RNA Frontier Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan
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Schnell J, Achieng M, Lindström NO. Principles of human and mouse nephron development. Nat Rev Nephrol 2022; 18:628-642. [PMID: 35869368 DOI: 10.1038/s41581-022-00598-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/10/2022] [Indexed: 12/17/2022]
Abstract
The mechanisms underlying kidney development in mice and humans is an area of intense study. Insights into kidney organogenesis have the potential to guide our understanding of the origin of congenital anomalies and enable the assembly of genetic diagnostic tools. A number of studies have delineated signalling nodes that regulate positional identities and cell fates of nephron progenitor and precursor cells, whereas cross-species comparisons have markedly enhanced our understanding of conserved and divergent features of mammalian kidney organogenesis. Greater insights into the complex cellular movements that occur as the proximal-distal axis is established have challenged our understanding of nephron patterning and provided important clues to the elaborate developmental context in which human kidney diseases can arise. Studies of kidney development in vivo have also facilitated efforts to recapitulate nephrogenesis in kidney organoids in vitro, by providing a detailed blueprint of signalling events, cell movements and patterning mechanisms that are required for the formation of correctly patterned nephrons and maturation of physiologically functional apparatus that are responsible for maintaining human health.
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Affiliation(s)
- Jack Schnell
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at University of Southern California, Los Angeles, CA, USA
| | - MaryAnne Achieng
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at University of Southern California, Los Angeles, CA, USA
| | - Nils Olof Lindström
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at University of Southern California, Los Angeles, CA, USA.
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Martinelli AL, Wagner J, Bodenmiller B, Rapsomaniki MA. scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data. STAR Protoc 2022; 3:101578. [PMID: 35880127 PMCID: PMC9307583 DOI: 10.1016/j.xpro.2022.101578] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
| | - Johanna Wagner
- Division of Translational Medical Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland; Institute of Molecular Health Sciences, ETH Zurich, Otto-Stern-Weg 7, 8093 Zurich, Switzerland
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Lee H, Kim H, Choi YS, Pyo HR, Ahn MJ, Choi JY. Prognostic Significance of Pseudotime from Texture Parameters of FDG PET/CT in Locally Advanced Non-Small-Cell Lung Cancer with Tri-Modality Therapy. Cancers (Basel) 2022; 14:cancers14153809. [PMID: 35954472 PMCID: PMC9367384 DOI: 10.3390/cancers14153809] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Although texture parameters of F-18 fluorodeoxyglucose positron emission tomography/computed tomography images were known to associate tumor biology and clinical features, the types and implications of parameters are too various and complicated. To overcome the limitation of texture parameter, we attempted to produce a new simplified parameter from texture parameters of F-18 fluorodeoxyglucose positron emission tomography/computed tomography images in lung cancer patients using pseudotime analysis. Pseudotime analysis is a recently developed method to explore changes in cell or tissue characteristics based on transcriptomic expression. It is the first study to apply pseudotime analysis into radiomics dataset other than transcriptomics data. Herein, we demonstrated that pseudotime can be successfully estimated from texture parameters. In the aspect of prognostic prediction, pseudotime was an independent prognostic factor for overall survival in contrast to conventional parameters such as metabolic tumor volume and total lesion glycolysis. This study showed possibility of integrating various texture parameters into single parameter which reflects disease progression status. Pseudotime, as a concrete value of disease progression, is expected to be used in clinical field to evaluate disease and predict prognosis. Abstract Texture analysis provides image parameters from F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). Although some parameters are associated with tumor biology and clinical features, the types and implications of these parameters are complicated. We applied pseudotime analysis, which has recently been used to estimate changes in individual sample characteristics, to texture parameters from FDG PET/CT images of locally advanced non-small-cell lung cancer (NSCLC) patients undergoing neoadjuvant concurrent chemoradiation therapy (CCRT) followed by surgery. Our subjects were 303 NSCLC patients who underwent pretherapeutic FDG PET/CT and tri-modality therapy. Texture parameters of the primary tumor were calculated from FDG PET/CT images acquired before neoadjuvant CCRT. Pseudotime analysis was performed using the PhenoPath tool. Clinicopathologic features including survival data were collected and survival analysis was performed to compare the prognostic significances of pseudotime parameters with those of conventional PET parameters. Pseudotime was successfully estimated from texture parameters. Normalized co-occurrence homogeneity, normalized co-occurrence inverse difference moment, and black–white symmetry showed positive correlations with pseudotime, short run emphasis, normalized co-occurrence dissimilarity, and short zone emphasis negative correlation. The maximum standardized uptake value (SUV) and mean SUV were not associated with overall survival. Pseudotime, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) showed significant associations with overall survival. In contrast to MTV and TLG, pseudotime was an independent prognostic factor for overall survival. Various metabolic texture parameters can be integrated into a single parameter using pseudotime analysis. Pseudotime of the primary tumor, estimated from FDG PET/CT images, better predicts overall survival in locally advanced NSCLC patients treated with tri-modality therapy than conventional PET parameters.
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Affiliation(s)
- Hyunjong Lee
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Hojoong Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Yong Soo Choi
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Hong Ryul Pyo
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
- Correspondence: ; Tel.: +82-2-3410-2648; Fax: +82-2-3410-2639
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Hoft SG, Pherson MD, DiPaolo RJ. Discovering Immune-Mediated Mechanisms of Gastric Carcinogenesis Through Single-Cell RNA Sequencing. Front Immunol 2022; 13:902017. [PMID: 35757757 PMCID: PMC9231461 DOI: 10.3389/fimmu.2022.902017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/27/2022] [Indexed: 12/17/2022] Open
Abstract
Single-cell RNA sequencing (scRNAseq) technology is still relatively new in the field of gastric cancer immunology but gaining significant traction. This technology now provides unprecedented insights into the intratumoral and intertumoral heterogeneities at the immunological, cellular, and molecular levels. Within the last few years, a volume of publications reported the usefulness of scRNAseq technology in identifying thus far elusive immunological mechanisms that may promote and impede gastric cancer development. These studies analyzed datasets generated from primary human gastric cancer tissues, metastatic ascites fluid from gastric cancer patients, and laboratory-generated data from in vitro and in vivo models of gastric diseases. In this review, we overview the exciting findings from scRNAseq datasets that uncovered the role of critical immune cells, including T cells, B cells, myeloid cells, mast cells, ILC2s, and other inflammatory stromal cells, like fibroblasts and endothelial cells. In addition, we also provide a synopsis of the initial scRNAseq findings on the interesting epithelial cell responses to inflammation. In summary, these new studies have implicated roles for T and B cells and subsets like NKT cells in tumor development and progression. The current studies identified diverse subsets of macrophages and mast cells in the tumor microenvironment, however, additional studies to determine their roles in promoting cancer growth are needed. Some groups specifically focus on the less prevalent ILC2 cell type that may contribute to early cancer development. ScRNAseq analysis also reveals that stromal cells, e.g., fibroblasts and endothelial cells, regulate inflammation and promote metastasis, making them key targets for future investigations. While evaluating the outcomes, we also highlight the gaps in the current findings and provide an assessment of what this technology holds for gastric cancer research in the coming years. With scRNAseq technology expanding rapidly, we stress the need for periodic review of the findings and assess the available scRNAseq analytical tools to guide future work on immunological mechanisms of gastric carcinogenesis.
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Affiliation(s)
- Stella G Hoft
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Michelle D Pherson
- Department of Biochemistry and Molecular Biology, Saint Louis University School of Medicine, St. Louis, MO, United States.,Genomics Core Facility, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Richard J DiPaolo
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, United States
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Shi J, Aihara K, Li T, Chen L. Energy landscape decomposition for cell differentiation with proliferation effect. Natl Sci Rev 2022; 9:nwac116. [PMID: 35992240 PMCID: PMC9385468 DOI: 10.1093/nsr/nwac116] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 05/22/2022] [Accepted: 05/25/2022] [Indexed: 11/16/2022] Open
Abstract
Complex interactions between genes determine the development and differentiation of cells. We establish a landscape theory for cell differentiation with proliferation effect, in which the developmental process is modeled as a stochastic dynamical system with a birth-death term. We find that two different energy landscapes, denoted U and V, collectively contribute to the establishment of non-equilibrium steady differentiation. The potential U is known as the energy landscape leading to the steady distribution, whose metastable states stand for cell types, while V indicates the differentiation direction from pluripotent to differentiated cells. This interpretation of cell differentiation is different from the previous landscape theory without the proliferation effect. We propose feasible numerical methods and a mean-field approximation for constructing landscapes U and V. Successful applications to typical biological models demonstrate the energy landscape decomposition's validity and reveal biological insights into the considered processes.
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Affiliation(s)
- Jifan Shi
- Research Institute of Intelligent Complex Systems, Fudan University , Shanghai 200433, China
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study , The University of Tokyo, Tokyo 113-0033 , Japan
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study , The University of Tokyo, Tokyo 113-0033 , Japan
| | - Tiejun Li
- LMAM and School of Mathematical Sciences, Peking University , Beijing 100871, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , Shanghai 200031, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Hangzhou 310024, China
- School of Life Science and Technology, ShanghaiTech University , Shanghai 201210, China
- Guangdong Institute of Intelligence Science and Technology , Zhuhai 519031, China
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31
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Lee H, Choi H. Investigating the Clinico-Molecular and Immunological Evolution of Lung Adenocarcinoma Using Pseudotime Analysis. Front Oncol 2022; 12:828505. [PMID: 35311086 PMCID: PMC8931203 DOI: 10.3389/fonc.2022.828505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/07/2022] [Indexed: 11/22/2022] Open
Abstract
Introduction As the molecular features of lung adenocarcinoma (LUAD) have been evaluated as a cross-sectional study, the course of tumor characteristics has not been modeled. The temporal evolution of the tumor immune microenvironment (TIME), as well as the clinico-molecular features of LUAD, could provide a precise strategy for immunotherapy and surrogate biomarkers for the course of LUAD. Methods A pseudotime trajectory was constructed in patients with LUAD from the Cancer Genome Atlas and non-small cell lung cancer radiogenomics datasets. Correlation analyses were performed between clinical features and pseudotime. Genes associated with pseudotime were selected, and gene ontology analysis was performed. F-18 fluorodeoxyglucose positron emission tomography images of subjects were collected, and imaging parameters, including standardized uptake value (SUV), were obtained. Correlation analyses were performed between imaging parameters and pseudotime. Correlation analyses were performed between the enrichment scores of various immune cell types and pseudotime. In addition, correlation analyses were performed between the expression of PD-L1, tumor mutation burden, and pseudotime. Results Pseudotime trajectories of LUAD corresponded to clinical stages. Molecular profiles related to cell division and natural killer cell activity were changed along the pseudotime. The maximal SUV of LUAD tumors showed a positive correlation with pseudotime. Type 1 helper T (Th1) cells showed a positive correlation, whereas M2 macrophages showed a negative correlation with pseudotime. PD-L1 expression showed a negative correlation, whereas tumor mutation burden showed a positive correlation with pseudotime. Conclusion The estimated pseudotime associated with the stage suggested that it could reflect the clinico-molecular evolution of LUAD. Specific immune cell types in the TIME as well as cell division and glucose metabolism were dynamically changed according to the progression of the pseudotime. As a molecular progression of LUAD, different cellular targets should be considered for immunotherapy.
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Affiliation(s)
- Hyunjong Lee
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, South Korea
- *Correspondence: Hongyoon Choi,
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales. Cells 2022; 11:cells11050767. [PMID: 35269389 PMCID: PMC8909670 DOI: 10.3390/cells11050767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/08/2022] [Accepted: 02/14/2022] [Indexed: 01/27/2023] Open
Abstract
The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow with age. In this study, we use pseudotime analysis to determine the functional form of these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path, based on similarities in a latent dimension, to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain tissue and find that it is highly correlated with the epigenetic states described by the Epigenetic Pacemaker. Moreover, we show that the pseudotime trajectory can be modeled with respect to time, using a sum of two exponentials, with coefficients that are close to the timescales of human age-associated mortality. Thus, for the first time, we can identify age-associated molecular changes that appear to track the exponential dynamics of mortality risk.
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Baskar R, Kimmey SC, Bendall SC. Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts. Trends Cell Biol 2022; 32:501-512. [PMID: 35181197 DOI: 10.1016/j.tcb.2022.01.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 11/26/2022]
Abstract
Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.
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Affiliation(s)
- Reema Baskar
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sam C Kimmey
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sean C Bendall
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA.
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Wen ZH, Langsam JL, Zhang L, Shen W, Zhou X. A Bayesian factorization method to recover single-cell RNA sequencing data. CELL REPORTS METHODS 2022; 2:100133. [PMID: 35474868 PMCID: PMC9017157 DOI: 10.1016/j.crmeth.2021.100133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/11/2021] [Accepted: 11/24/2021] [Indexed: 02/05/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. In this article, we introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute the final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than ten other publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene- or cell-related information that users provide to increase performance.
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Affiliation(s)
- Zi-Hang Wen
- School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Jeremy L. Langsam
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Lu Zhang
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Wenjun Shen
- Department of Bioinformatics, Shantou University Medical College, Shantou 515041, China
| | - Xin Zhou
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA
- Data Science Institute, Nashville, TN 37212, USA
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Rams M, Conrad TOF. Dictionary learning allows model-free pseudotime estimation of transcriptomic data. BMC Genomics 2022; 23:56. [PMID: 35033004 PMCID: PMC8760643 DOI: 10.1186/s12864-021-08276-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 12/22/2021] [Indexed: 11/10/2022] Open
Abstract
Background Pseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the derived dimensions, which can result in important dataset properties being missed. In this paper, we suggest a new dictionary learning based approach, dynDLT, for dimension reduction and pseudotime estimation of dynamic transcriptomic data. Dictionary learning is a matrix factorisation approach that does not restrict the dependence of the derived dimensions. To evaluate the performance, we conduct a large simulation study and analyse 8 real-world datasets. Results The simulation studies reveal that firstly, dynDLT preserves the simulated patterns in low-dimension and the pseudotimes can be derived from the low-dimensional representation. Secondly, the results show that dynDLT is suitable for the detection of genes exhibiting the simulated dynamic patterns, thereby facilitating the interpretation of the compressed representation and thus the dynamic processes. For the real-world data analysis, we select datasets with samples that are taken at different time points throughout an experiment. The pseudotimes found by dynDLT have high correlations with the experimental times. We compare the results to other approaches used in pseudotime estimation, or those that are method-wise closely connected to dictionary learning: ICA, NMF, PCA, t-SNE, and UMAP. DynDLT has the best overall performance for the simulated and real-world datasets. Conclusions We introduce dynDLT, a method that is suitable for pseudotime estimation. Its main advantages are: (1) It presents a model-free approach, meaning that it does not restrict the dependence of the derived dimensions; (2) Genes that are relevant in the detected dynamic processes can be identified from the dictionary matrix; (3) By a restriction of the dictionary entries to positive values, the dictionary atoms are highly interpretable. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-08276-9).
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Affiliation(s)
- Mona Rams
- Freie Universitaet Berlin, Arnimallee 6, Berlin, 14195, Germany.
| | - Tim O F Conrad
- Konrad-Zuse-Zentrum für Informationstechnik Berlin, Takustraße 7, Berlin, 14195, Germany
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Hasan H, Nasirudeen NA, Ruzlan MAF, Mohd Jamil MA, Ismail NAS, Wahab AA, Ali A. Acute Infectious Gastroenteritis: The Causative Agents, Omics-Based Detection of Antigens and Novel Biomarkers. CHILDREN (BASEL, SWITZERLAND) 2021; 8:1112. [PMID: 34943308 PMCID: PMC8700514 DOI: 10.3390/children8121112] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 12/25/2022]
Abstract
Acute infectious gastroenteritis (AGE) is among the leading causes of mortality in children less than 5 years of age worldwide. There are many causative agents that lead to this infection, with rotavirus being the commonest pathogen in the past decade. However, this trend is now being progressively replaced by another agent, which is the norovirus. Apart from the viruses, bacteria such as Salmonella and Escherichia coli and parasites such as Entamoeba histolytica also contribute to AGE. These agents can be recognised by their respective biological markers, which are mainly the specific antigens or genes to determine the causative pathogen. In conjunction to that, omics technologies are currently providing crucial insights into the diagnosis of acute infectious gastroenteritis at the molecular level. Recent advancement in omics technologies could be an important tool to further elucidate the potential causative agents for AGE. This review will explore the current available biomarkers and antigens available for the diagnosis and management of the different causative agents of AGE. Despite the high-priced multi-omics approaches, the idea for utilization of these technologies is to allow more robust discovery of novel antigens and biomarkers related to management AGE, which eventually can be developed using easier and cheaper detection methods for future clinical setting. Thus, prediction of prognosis, virulence and drug susceptibility for active infections can be obtained. Case management, risk prediction for hospital-acquired infections, outbreak detection, and antimicrobial accountability are aimed for further improvement by integrating these capabilities into a new clinical workflow.
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Affiliation(s)
- Haziqah Hasan
- Department of Pediatric, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Kuala Lumpur 56000, Malaysia; (H.H.); (N.A.N.); (M.A.F.R.); (M.A.M.J.)
| | - Nor Ashika Nasirudeen
- Department of Pediatric, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Kuala Lumpur 56000, Malaysia; (H.H.); (N.A.N.); (M.A.F.R.); (M.A.M.J.)
| | - Muhammad Alif Farhan Ruzlan
- Department of Pediatric, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Kuala Lumpur 56000, Malaysia; (H.H.); (N.A.N.); (M.A.F.R.); (M.A.M.J.)
| | - Muhammad Aiman Mohd Jamil
- Department of Pediatric, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Kuala Lumpur 56000, Malaysia; (H.H.); (N.A.N.); (M.A.F.R.); (M.A.M.J.)
| | - Noor Akmal Shareela Ismail
- Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Kuala Lumpur 56000, Malaysia;
| | - Asrul Abdul Wahab
- Department of Medical Microbiology and Immunology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Kuala Lumpur 56000, Malaysia;
| | - Adli Ali
- Department of Pediatric, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Kuala Lumpur 56000, Malaysia; (H.H.); (N.A.N.); (M.A.F.R.); (M.A.M.J.)
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Ou-Yang L, Lu F, Zhang ZC, Wu M. Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. Brief Bioinform 2021; 23:6447434. [PMID: 34864871 DOI: 10.1093/bib/bbab479] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/25/2021] [Accepted: 10/18/2021] [Indexed: 02/02/2023] Open
Abstract
Advances in high-throughput experimental technologies promote the accumulation of vast number of biomedical data. Biomedical link prediction and single-cell RNA-sequencing (scRNA-seq) data imputation are two essential tasks in biomedical data analyses, which can facilitate various downstream studies and gain insights into the mechanisms of complex diseases. Both tasks can be transformed into matrix completion problems. For a variety of matrix completion tasks, matrix factorization has shown promising performance. However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such matrix factorization methods and their usage in biomedical link prediction and scRNA-seq data imputation. Moreover, we select representative matrix factorization methods and conduct a systematic empirical comparison on 15 real data sets to evaluate their performance under different scenarios. By summarizing the experimental results, we provide general guidelines for selecting matrix factorization methods for different biomedical matrix completion tasks and point out some future directions to further improve the performance for biomedical link prediction and scRNA-seq data imputation.
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Affiliation(s)
- Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China.,Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen,518172, China
| | - Fan Lu
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zi-Chao Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Min Wu
- Institute for Infocomm Research (I2R), A*STAR, 138632, Singapore
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Li Y, LeMaire SA, Shen YH. Molecular and Cellular Dynamics of Aortic Aneurysms Revealed by Single-Cell Transcriptomics. Arterioscler Thromb Vasc Biol 2021; 41:2671-2680. [PMID: 34615376 PMCID: PMC8556647 DOI: 10.1161/atvbaha.121.315852] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 09/20/2021] [Indexed: 11/16/2022]
Abstract
The aorta is highly heterogeneous, containing many different types of cells that perform sophisticated functions to maintain aortic homeostasis. Recently, single-cell RNA sequencing studies have provided substantial new insight into the heterogeneity of vascular cell types, the comprehensive molecular features of each cell type, and the phenotypic interrelationship between these cell populations. This new information has significantly improved our understanding of aortic biology and aneurysms at the molecular and cellular level. Here, we summarize these findings, with a focus on what single-cell RNA sequencing analysis has revealed about cellular heterogeneity, cellular transitions, communications among cell populations, and critical transcription factors in the vascular wall. We also review the information learned from single-cell RNA sequencing that has contributed to our understanding of the pathogenesis of vascular disease, such as the identification of cell types in which aneurysm-related genes and genetic variants function. Finally, we discuss the challenges and future directions of single-cell RNA sequencing applications in studies of aortic biology and diseases.
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Affiliation(s)
- Yanming Li
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX
| | - Scott A LeMaire
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX
| | - Ying H Shen
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX
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40
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Shadel GS, Adams PD, Berggren WT, Diedrich JK, Diffenderfer KE, Gage FH, Hah N, Hansen M, Hetzer MW, Molina AJA, Manor U, Marek K, O'Keefe DD, Pinto AFM, Sacco A, Sharpee TO, Shokriev MN, Zambetti S. The San Diego Nathan Shock Center: tackling the heterogeneity of aging. GeroScience 2021; 43:2139-2148. [PMID: 34370163 PMCID: PMC8599742 DOI: 10.1007/s11357-021-00426-x] [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: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 11/26/2022] Open
Abstract
Understanding basic mechanisms of aging holds great promise for developing interventions that prevent or delay many age-related declines and diseases simultaneously to increase human healthspan. However, a major confounding factor in aging research is the heterogeneity of the aging process itself. At the organismal level, it is clear that chronological age does not always predict biological age or susceptibility to frailty or pathology. While genetics and environment are major factors driving variable rates of aging, additional complexity arises because different organs, tissues, and cell types are intrinsically heterogeneous and exhibit different aging trajectories normally or in response to the stresses of the aging process (e.g., damage accumulation). Tackling the heterogeneity of aging requires new and specialized tools (e.g., single-cell analyses, mass spectrometry-based approaches, and advanced imaging) to identify novel signatures of aging across scales. Cutting-edge computational approaches are then needed to integrate these disparate datasets and elucidate network interactions between known aging hallmarks. There is also a need for improved, human cell-based models of aging to ensure that basic research findings are relevant to human aging and healthspan interventions. The San Diego Nathan Shock Center (SD-NSC) provides access to cutting-edge scientific resources to facilitate the study of the heterogeneity of aging in general and to promote the use of novel human cell models of aging. The center also has a robust Research Development Core that funds pilot projects on the heterogeneity of aging and organizes innovative training activities, including workshops and a personalized mentoring program, to help investigators new to the aging field succeed. Finally, the SD-NSC participates in outreach activities to educate the general community about the importance of aging research and promote the need for basic biology of aging research in particular.
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Affiliation(s)
- Gerald S Shadel
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA.
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - W Travis Berggren
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Jolene K Diedrich
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Kenneth E Diffenderfer
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Fred H Gage
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Nasun Hah
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Malene Hansen
- Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Martin W Hetzer
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Anthony J A Molina
- Divison of Geriatrics, Gerontology and Palliative Care, Department of Medicine, University of California, San Diego, 9500 Gilman Dr, San Diego, CA, 92093, USA
| | - Uri Manor
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Kurt Marek
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - David D O'Keefe
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | | | - Alessandra Sacco
- Sanford Burnham Prebys Medical Discovery Institute, 10901 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Tatyana O Sharpee
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Maxim N Shokriev
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Stefania Zambetti
- The Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA, 92037, USA
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41
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Jeong HY, Ham IH, Lee SH, Ryu D, Son SY, Han SU, Kim TM, Hur H. Spatially distinct reprogramming of the tumor microenvironment based on tumor invasion in diffuse-type gastric cancers. Clin Cancer Res 2021; 27:6529-6542. [PMID: 34385296 DOI: 10.1158/1078-0432.ccr-21-0792] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/14/2021] [Accepted: 08/10/2021] [Indexed: 12/09/2022]
Abstract
PURPOSE Histological features of diffuse-type gastric cancer (GC) indicate that the tumor microenvironment (TME) may substantially impact tumor invasiveness. However, cellular components and molecular features associated with cancer invasiveness in the TME of diffuse-type GCs are poorly understood. EXPERIMENTAL DESIGN We performed single-cell RNA-sequencing (scRNA-seq) using tissue samples from superficial and deep invasive layers of cancerous and paired normal tissues freshly harvested from five patients with diffuse-type GC. The scRNA-seq results were validated by immunohistochemistry and duplex in situ hybridization (ISH) in formalin-fixed paraffin-embedded tissues. RESULTS Seven major cell types were identified. Fibroblasts, endothelial cells, and myeloid cells were categorised as being enriched in the deep layers. Cell type-specific clustering further revealed that the superficial-to-deep layer transition is associated with enrichment in inflammatory endothelial cells and fibroblasts with upregulated CCL2 transcripts. Immunohistochemistry and duplex ISH revealed the distribution of the major cell types and CCL2-expressing endothelial cells and fibroblasts, indicating tumor invasion. Elevation of CCL2 levels along the superficial-to-deep layer axis revealed the immunosuppressive immune cell sub-types that may contribute to tumor cell aggressiveness in the deep invasive layers of diffuse-type GC. The analyses of public datasets revealed the high-level co-expression of stromal cell-specific genes and that CCL2 correlated with poor survival outcomes in GC patients. CONCLUSIONS This study reveals the spatial reprogramming of the TME that may underlie invasive tumor potential in diffuse-type GC. This TME profiling across tumor layers suggests new targets, such as CCL2, that can modify the TME to inhibit tumor progression in diffuse-type GC.
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Affiliation(s)
- Hye Young Jeong
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea
| | - In-Hye Ham
- Department of Surgery, Ajou University School of Medicine
| | - Sung Hak Lee
- Department of Hospital Pathology, Catholic University of Korea
| | - Daeun Ryu
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea
| | - Sang-Yong Son
- Department of Surgery, Ajou University School of Medicine
| | - Sang-Uk Han
- Department of Surgery, Ajou University School of Medicine
| | - Tae-Min Kim
- Department of Medical Informatics, Catholic University of Korea
| | - Hoon Hur
- Department of Surgery, Ajou University School of Medicine
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Moriel N, Senel E, Friedman N, Rajewsky N, Karaiskos N, Nitzan M. NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport. Nat Protoc 2021; 16:4177-4200. [PMID: 34349282 DOI: 10.1038/s41596-021-00573-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 05/17/2021] [Indexed: 11/09/2022]
Abstract
Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detailed protocol for using novoSpaRc, a computational framework that probabilistically assigns cells to tissue locations. At the core of this framework lies a structural correspondence hypothesis, that cells in physical proximity share similar gene expression profiles. Given scRNA-seq data, novoSpaRc spatially reconstructs tissues based on this hypothesis, and optionally, by including a reference atlas of marker genes to improve reconstruction. We describe the novoSpaRc algorithm, and its implementation in an open-source Python package ( https://pypi.org/project/novosparc ). NovoSpaRc maps a scRNA-seq dataset of 10,000 cells onto 1,000 locations in <5 min. We describe results obtained using novoSpaRc to reconstruct the mouse organ of Corti de novo based on the structural correspondence assumption and human osteosarcoma cultured cells based on marker gene information, and provide a step-by-step guide to Drosophila embryo reconstruction in the Procedure to demonstrate how these two strategies can be combined.
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Affiliation(s)
- Noa Moriel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Enes Senel
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Nir Friedman
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.,Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nikolaus Rajewsky
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Nikos Karaiskos
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. .,Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel. .,Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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43
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Weiskittel TM, Correia C, Yu GT, Ung CY, Kaufmann SH, Billadeau DD, Li H. The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches. Genes (Basel) 2021; 12:1098. [PMID: 34356114 PMCID: PMC8306972 DOI: 10.3390/genes12071098] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/13/2021] [Accepted: 07/18/2021] [Indexed: 12/18/2022] Open
Abstract
Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.
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Affiliation(s)
- Taylor M. Weiskittel
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First, Street SW, Rochester, MN 55905, USA; (T.M.W.); (C.C.); (G.T.Y.); (C.Y.U.); (S.H.K.)
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First, Street SW, Rochester, MN 55905, USA; (T.M.W.); (C.C.); (G.T.Y.); (C.Y.U.); (S.H.K.)
| | - Grace T. Yu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First, Street SW, Rochester, MN 55905, USA; (T.M.W.); (C.C.); (G.T.Y.); (C.Y.U.); (S.H.K.)
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First, Street SW, Rochester, MN 55905, USA; (T.M.W.); (C.C.); (G.T.Y.); (C.Y.U.); (S.H.K.)
| | - Scott H. Kaufmann
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First, Street SW, Rochester, MN 55905, USA; (T.M.W.); (C.C.); (G.T.Y.); (C.Y.U.); (S.H.K.)
| | - Daniel D. Billadeau
- Department of Immunology, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First, Street SW, Rochester, MN 55905, USA;
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Mayo Clinic, 200 First, Street SW, Rochester, MN 55905, USA; (T.M.W.); (C.C.); (G.T.Y.); (C.Y.U.); (S.H.K.)
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Thompson M, Matsumoto M, Ma T, Senabouth A, Palpant NJ, Powell JE, Nguyen Q. scGPS: Determining Cell States and Global Fate Potential of Subpopulations. Front Genet 2021; 12:666771. [PMID: 34349778 PMCID: PMC8326972 DOI: 10.3389/fgene.2021.666771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 06/04/2021] [Indexed: 12/20/2022] Open
Abstract
Finding cell states and their transcriptional relatedness is a main outcome from analysing single-cell data. In developmental biology, determining whether cells are related in a differentiation lineage remains a major challenge. A seamless analysis pipeline from cell clustering to estimating the probability of transitions between cell clusters is lacking. Here, we present Single Cell Global fate Potential of Subpopulations (scGPS) to characterise transcriptional relationship between cell states. scGPS decomposes mixed cell populations in one or more samples into clusters (SCORE algorithm) and estimates pairwise transitioning potential (scGPS algorithm) of any pair of clusters. SCORE allows for the assessment and selection of stable clustering results, a major challenge in clustering analysis. scGPS implements a novel approach, with machine learning classification, to flexibly construct trajectory connections between clusters. scGPS also has a feature selection functionality by network and modelling approaches to find biological processes and driver genes that connect cell populations. We applied scGPS in diverse developmental contexts and show superior results compared to a range of clustering and trajectory analysis methods. scGPS is able to identify the dynamics of cellular plasticity in a user-friendly workflow, that is fast and memory efficient. scGPS is implemented in R with optimised functions using C++ and is publicly available in Bioconductor.
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Affiliation(s)
- Michael Thompson
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Maika Matsumoto
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Tianqi Ma
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia.,UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
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Hou W, Ji Z, Chen Z, Wherry EJ, Hicks SC, Ji H. A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.07.10.451910. [PMID: 34282418 PMCID: PMC8288148 DOI: 10.1101/2021.07.10.451910] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many computational methods have been developed to infer the pseudo-temporal trajectories of cells within a biological sample, methods that compare pseudo-temporal patterns with multiple samples (or replicates) across different experimental conditions are lacking. Lamian is a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. It can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions, and also to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both simulations and real scRNA-seq data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.
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Affiliation(s)
- Wenpin Hou
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA
| | - Zeyu Chen
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA 19104, USA
| | - E. John Wherry
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Hongkai Ji
- Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
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Mondal PK, Saha US, Mukhopadhyay I. PseudoGA: cell pseudotime reconstruction based on genetic algorithm. Nucleic Acids Res 2021; 49:7909-7924. [PMID: 34244782 PMCID: PMC8661435 DOI: 10.1093/nar/gkab457] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 05/03/2021] [Accepted: 07/07/2021] [Indexed: 01/05/2023] Open
Abstract
Dynamic regulation of gene expression is often governed by progression through transient cell states. Bulk RNA-seq analysis can only detect average change in expression levels and is unable to identify this dynamics. Single cell RNA-seq presents an unprecedented opportunity that helps in placing the cells on a hypothetical time trajectory that reflects gradual transition of their transcriptomes. This continuum trajectory or ‘pseudotime’, may reveal the developmental pathway and provide us with information on dynamic transcriptomic changes and other biological processes. Existing approaches to build pseudotime heavily depend on reducing huge dimension to extremely low dimensional subspaces and may lead to loss of information. We propose PseudoGA, a genetic algorithm based approach to order cells assuming that gene expressions vary according to a smooth curve along the pseudotime trajectory. We observe superior accuracy of our method in simulated as well as benchmarking real datasets. Generality of the assumption behind PseudoGA and no dependence on dimensionality reduction technique make it a robust choice for pseudotime estimation from single cell transcriptome data. PseudoGA is also time efficient when applied to a large single cell RNA-seq data and adaptable to parallel computing. R code for PseudoGA is freely available at https://github.com/indranillab/pseudoga.
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Affiliation(s)
- Pronoy Kanti Mondal
- Human Genetics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, West Bengal, India
| | - Udit Surya Saha
- Human Genetics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, West Bengal, India
| | - Indranil Mukhopadhyay
- Human Genetics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, West Bengal, India
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47
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Ahn JH, Kim J, Hong SP, Choi SY, Yang MJ, Ju YS, Kim YT, Kim HM, Rahman MDT, Chung MK, Hong SD, Bae H, Lee CS, Koh GY. Nasal ciliated cells are primary targets for SARS-CoV-2 replication in the early stage of COVID-19. J Clin Invest 2021; 131:148517. [PMID: 34003804 PMCID: PMC8245175 DOI: 10.1172/jci148517] [Citation(s) in RCA: 141] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 05/05/2021] [Indexed: 12/12/2022] Open
Abstract
The upper respiratory tract is compromised in the early period of COVID-19, but SARS-CoV-2 tropism at the cellular level is not fully defined. Unlike recent single-cell RNA-Seq analyses indicating uniformly low mRNA expression of SARS-CoV-2 entry-related host molecules in all nasal epithelial cells, we show that the protein levels are relatively high and that their localizations are restricted to the apical side of multiciliated epithelial cells. In addition, we provide evidence in patients with COVID-19 that SARS-CoV-2 is massively detected and replicated within the multiciliated cells. We observed these findings during the early stage of COVID-19, when infected ciliated cells were rapidly replaced by differentiating precursor cells. Moreover, our analyses revealed that SARS-CoV-2 cellular tropism was restricted to the nasal ciliated versus oral squamous epithelium. These results imply that targeting ciliated cells of the nasal epithelium during the early stage of COVID-19 could be an ideal strategy to prevent SARS-CoV-2 propagation.
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Affiliation(s)
- Ji Hoon Ahn
- Center for Vascular Research, Institute for Basic Science (IBS), Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - JungMo Kim
- Center for Vascular Research, Institute for Basic Science (IBS), Daejeon, Republic of Korea
| | - Seon Pyo Hong
- Center for Vascular Research, Institute for Basic Science (IBS), Daejeon, Republic of Korea
| | - Sung Yong Choi
- Department of Otorhinolaryngology – Head and Neck Surgery, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Daejeon, Republic of Korea
| | - Myung Jin Yang
- Center for Vascular Research, Institute for Basic Science (IBS), Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Young Seok Ju
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Young Tae Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Ho Min Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- Center for Biomolecular and Cellular Structure, IBS, Daejeon, Republic of Korea
| | - MD Tazikur Rahman
- Department of Medical Science, Jeonbuk National University Medical School, Jeonju, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University – Biomedical Research Institute of Jeonbuk, National University Hospital, Jeonju, Republic of Korea
| | - Man Ki Chung
- Department of Otorhinolaryngology – Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang Duk Hong
- Department of Otorhinolaryngology – Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hosung Bae
- Center for Vascular Research, Institute for Basic Science (IBS), Daejeon, Republic of Korea
| | - Chang-Seop Lee
- Research Institute of Clinical Medicine of Jeonbuk National University – Biomedical Research Institute of Jeonbuk, National University Hospital, Jeonju, Republic of Korea
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
| | - Gou Young Koh
- Center for Vascular Research, Institute for Basic Science (IBS), Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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48
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Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box. Commun Biol 2021; 4:614. [PMID: 34021244 PMCID: PMC8140107 DOI: 10.1038/s42003-021-02133-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/22/2021] [Indexed: 02/04/2023] Open
Abstract
Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.
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49
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Szlachcic WJ, Ziojla N, Kizewska DK, Kempa M, Borowiak M. Endocrine Pancreas Development and Dysfunction Through the Lens of Single-Cell RNA-Sequencing. Front Cell Dev Biol 2021; 9:629212. [PMID: 33996792 PMCID: PMC8116659 DOI: 10.3389/fcell.2021.629212] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 04/06/2021] [Indexed: 12/16/2022] Open
Abstract
A chronic inability to maintain blood glucose homeostasis leads to diabetes, which can damage multiple organs. The pancreatic islets regulate blood glucose levels through the coordinated action of islet cell-secreted hormones, with the insulin released by β-cells playing a crucial role in this process. Diabetes is caused by insufficient insulin secretion due to β-cell loss, or a pancreatic dysfunction. The restoration of a functional β-cell mass might, therefore, offer a cure. To this end, major efforts are underway to generate human β-cells de novo, in vitro, or in vivo. The efficient generation of functional β-cells requires a comprehensive knowledge of pancreas development, including the mechanisms driving cell fate decisions or endocrine cell maturation. Rapid progress in single-cell RNA sequencing (scRNA-Seq) technologies has brought a new dimension to pancreas development research. These methods can capture the transcriptomes of thousands of individual cells, including rare cell types, subtypes, and transient states. With such massive datasets, it is possible to infer the developmental trajectories of cell transitions and gene regulatory pathways. Here, we summarize recent advances in our understanding of endocrine pancreas development and function from scRNA-Seq studies on developing and adult pancreas and human endocrine differentiation models. We also discuss recent scRNA-Seq findings for the pathological pancreas in diabetes, and their implications for better treatment.
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Affiliation(s)
- Wojciech J. Szlachcic
- Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań, Poland
| | - Natalia Ziojla
- Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań, Poland
| | - Dorota K. Kizewska
- Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań, Poland
| | - Marcelina Kempa
- Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań, Poland
| | - Malgorzata Borowiak
- Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Poznań, Poland
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, United States
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50
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Peterson JC, Kelder TP, Goumans MJTH, Jongbloed MRM, DeRuiter MC. The Role of Cell Tracing and Fate Mapping Experiments in Cardiac Outflow Tract Development, New Opportunities through Emerging Technologies. J Cardiovasc Dev Dis 2021; 8:47. [PMID: 33925811 PMCID: PMC8146276 DOI: 10.3390/jcdd8050047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/18/2021] [Accepted: 04/22/2021] [Indexed: 02/07/2023] Open
Abstract
Whilst knowledge regarding the pathophysiology of congenital heart disease (CHDs) has advanced greatly in recent years, the underlying developmental processes affecting the cardiac outflow tract (OFT) such as bicuspid aortic valve, tetralogy of Fallot and transposition of the great arteries remain poorly understood. Common among CHDs affecting the OFT, is a large variation in disease phenotypes. Even though the different cell lineages contributing to OFT development have been studied for many decades, it remains challenging to relate cell lineage dynamics to the morphologic variation observed in OFT pathologies. We postulate that the variation observed in cellular contribution in these congenital heart diseases might be related to underlying cell lineage dynamics of which little is known. We believe this gap in knowledge is mainly the result of technical limitations in experimental methods used for cell lineage analysis. The aim of this review is to provide an overview of historical fate mapping and cell tracing techniques used to study OFT development and introduce emerging technologies which provide new opportunities that will aid our understanding of the cellular dynamics underlying OFT pathology.
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Affiliation(s)
- Joshua C. Peterson
- Department Anatomy & Embryology, Leiden University Medical Center, 2300RC Leiden, The Netherlands; (J.C.P.); (T.P.K.); (M.R.M.J.)
| | - Tim P. Kelder
- Department Anatomy & Embryology, Leiden University Medical Center, 2300RC Leiden, The Netherlands; (J.C.P.); (T.P.K.); (M.R.M.J.)
| | - Marie José T. H. Goumans
- Department Cellular and Chemical Biology, Leiden University Medical Center, 2300RC Leiden, The Netherlands;
| | - Monique R. M. Jongbloed
- Department Anatomy & Embryology, Leiden University Medical Center, 2300RC Leiden, The Netherlands; (J.C.P.); (T.P.K.); (M.R.M.J.)
- Department of Cardiology, Leiden University Medical Center, 2300RC Leiden, The Netherlands
| | - Marco C. DeRuiter
- Department Anatomy & Embryology, Leiden University Medical Center, 2300RC Leiden, The Netherlands; (J.C.P.); (T.P.K.); (M.R.M.J.)
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