1
|
Hatai D, Levenson MT, Rehan VK, Allard P. Inter- and trans-generational impacts of environmental exposures on the germline resolved at the single-cell level. CURRENT OPINION IN TOXICOLOGY 2024; 38:100465. [PMID: 38586548 PMCID: PMC10993723 DOI: 10.1016/j.cotox.2024.100465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Reproduction is a remarkably intricate process involving the interaction of multiple cell types and organ systems unfolding over long periods of time and that culminates with the production of gametes. The initiation of germ cell development takes place during embryogenesis but only completes decades later in humans. The complexity inherent to reproduction and its study has long hampered our ability to decipher how environmental agents disrupt this process. Single-cell approaches provide an opportunity for a deeper understanding of the action of toxicants on germline function and analyze how the response to their exposure is differentially distributed across tissues and cell types. In addition to single-cell RNA sequencing, other single-cell or nucleus level approaches such as ATAC-sequencing and multi-omics have expanded the strategies that can be implemented in reproductive toxicological studies to include epigenomic and the nuclear transcriptomic data. Here we will discuss the current state of single-cell technologies and how they can best be utilized to advance reproductive toxicological studies. We will then discuss case studies in two model organisms (Caenorhabditis elegans and mouse) studying different environmental exposures (alcohol and e-cigarettes respectively) to highlight the value of single-cell and single-nucleus approaches for reproductive biology and reproductive toxicology.
Collapse
Affiliation(s)
- Dylan Hatai
- UCLA Molecular Toxicology Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Max T. Levenson
- UCLA Molecular Toxicology Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Virender K. Rehan
- UCLA Molecular Toxicology Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Patrick Allard
- UCLA Molecular Toxicology Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Institute for Society and Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, USA
| |
Collapse
|
2
|
Ranek JS, Stallaert W, Milner JJ, Redick M, Wolff SC, Beltran AS, Stanley N, Purvis JE. DELVE: feature selection for preserving biological trajectories in single-cell data. Nat Commun 2024; 15:2765. [PMID: 38553455 PMCID: PMC10980758 DOI: 10.1038/s41467-024-46773-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/07/2024] [Indexed: 04/02/2024] Open
Abstract
Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .
Collapse
Affiliation(s)
- Jolene S Ranek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wayne Stallaert
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Justin Milner
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Margaret Redick
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel C Wolff
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adriana S Beltran
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Human Pluripotent Cell Core, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jeremy E Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| |
Collapse
|
3
|
Zhuang H, Ji Z. PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585926. [PMID: 38585819 PMCID: PMC10996487 DOI: 10.1101/2024.03.20.585926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Modeling temporal and spatial gene expression patterns in large-scale single-cell and spatial transcriptomics data is a computationally intensive task. We present PreTSA, a method that offers computational efficiency in modeling these patterns and is applicable to single-cell and spatial transcriptomics data comprising millions of cells. PreTSA consistently matches the results of state-of-the-art methods while significantly reducing computational time. PreTSA provides a unique solution for studying gene expression patterns in extremely large datasets.
Collapse
Affiliation(s)
- Haotian Zhuang
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| |
Collapse
|
4
|
Roux de Bézieux H, Van den Berge K, Street K, Dudoit S. Trajectory inference across multiple conditions with condiments. Nat Commun 2024; 15:833. [PMID: 38280860 PMCID: PMC10821945 DOI: 10.1038/s41467-024-44823-0] [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: 10/13/2023] [Accepted: 01/08/2024] [Indexed: 01/29/2024] Open
Abstract
In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell's conditions along the trajectory's path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.
Collapse
Affiliation(s)
- Hector Roux de Bézieux
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Koen Van den Berge
- Department of Statistics, University of California, Berkeley, CA, USA
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Statistics and Decision Sciences, J&J Innovative Medicine, Beerse, Belgium
| | - Kelly Street
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA.
| | - Sandrine Dudoit
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
- Center for Computational Biology, University of California, Berkeley, CA, USA.
- Department of Statistics, University of California, Berkeley, CA, USA.
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|