1
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Chen W, Choi J. Molecular circuits for genomic recording of cellular events. Trends Genet 2025:S0168-9525(25)00079-4. [PMID: 40335327 DOI: 10.1016/j.tig.2025.04.004] [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: 02/18/2025] [Revised: 04/07/2025] [Accepted: 04/10/2025] [Indexed: 05/09/2025]
Abstract
Advances in precise genome editing are enabling genomic recordings of cellular events. Since the initial demonstration of CRISPR-based genome editing, the field of genomic recording has witnessed key strides in lineage recording, where clonal lineage relationships among cells are indirectly recorded as synthetic mutations. However, methods for directly recording and reconstructing past cellular events are still limited, and their potential for revealing new insights into cell fate decisions has yet to be realized. The field needs new sensing modules and genetic circuit architectures that faithfully encode past cellular states into genomic DNA recordings to achieve such goals. Here we review recently developed strategies to construct diverse sensors and explore how emerging synthetic biology tools may help to build molecular circuits for genomic recording of diverse cellular events.
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Affiliation(s)
- Wei Chen
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
| | - Junhong Choi
- Developmental Biology Program, Memorial Sloan Kettering Cancer Center, NY 10065, USA.
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2
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Reynolds DE, Roh YH, Oh D, Vallapureddy P, Fan R, Ko J. Temporal and spatial omics technologies for 4D profiling. Nat Methods 2025:10.1038/s41592-025-02683-6. [PMID: 40263585 DOI: 10.1038/s41592-025-02683-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/26/2025] [Indexed: 04/24/2025]
Abstract
Cells have distinct molecular repertoires on their surfaces and unique intracellular biomolecular profiles that play pivotal roles in orchestrating a myriad of biological responses in the context of growth, development and disease. A persistent challenge in the deep exploration of these cues has been in our inability to effectively and precisely capture the temporal and spatial characteristics of living cells. In this Perspective, we delve into techniques for temporal and two- and three-dimensional spatial omics analyses and underscore how their harmonious fusion promises to unlock insights into the dynamics and diversity of individual cells within biological systems such as tissues and organoids. We then explore four-dimensional profiling, a nascent but promising frontier that adds a temporal (fourth-dimension) component to three-dimensional omics; highlight the advancements, challenges and gaps in the field; and discuss potential strategies for further technological development.
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Affiliation(s)
- David E Reynolds
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Yoon Ho Roh
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Energy and Chemical Engineering, Incheon National University, Incheon, Republic of Korea
| | - Daniel Oh
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Phoebe Vallapureddy
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, USA
| | - Jina Ko
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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3
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Winter E, Emiliani F, Cook A, Abderrahim A, McKenna AH. BASELINE: A CRISPR Base Editing Platform for Mammalian-Scale Single-Cell Lineage Tracing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.19.644238. [PMID: 40166145 PMCID: PMC11957144 DOI: 10.1101/2025.03.19.644238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
A cells fate is shaped by its inherited state, or lineage, and the ever-shifting context of its environment. CRISPR-based recording technologies are a promising solution to map the lineage of a developing system, yet challenges remain regarding single-cell recovery, engineering complexity, and scale. Here, we introduce BASELINE, which uses base editing to generate high-resolution lineage trees in conjunction with single-cell profiling. BASELINE uses the Cas12a adenine base editor to irreversibly edit nucleotides within 50 synthetic target sites, which are integrated multiple times into a cells genome. We show that BASELINE accumulates lineage-specific marks over a wide range of biologically relevant intervals, recording more than 4300 bits of information in a model of pancreatic cancer, a 50X increase over existing technologies. Single-cell sequencing reveals high-fidelity capture of these recorders, recovering lineage reconstructions up to 40 cell divisions deep, within the estimated range of mammalian development. We expect BASELINE to apply to a wide range of lineage-tracing projects in development and disease, especially in which cellular engineering makes small, more distributed systems challenging.
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4
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Loveless TB, Carlson CK, Dentzel Helmy CA, Hu VJ, Ross SK, Demelo MC, Murtaza A, Liang G, Ficht M, Singhai A, Pajoh-Casco MJ, Liu CC. Open-ended molecular recording of sequential cellular events into DNA. Nat Chem Biol 2025; 21:512-521. [PMID: 39543397 PMCID: PMC11952980 DOI: 10.1038/s41589-024-01764-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 09/29/2024] [Indexed: 11/17/2024]
Abstract
Genetically encoded DNA recorders noninvasively convert transient biological events into durable mutations in a cell's genome, allowing for the later reconstruction of cellular experiences by DNA sequencing. We present a DNA recorder, peCHYRON, that achieves high-information, durable, and temporally resolved multiplexed recording of multiple cellular signals in mammalian cells. In each step of recording, prime editor, a Cas9-reverse transcriptase fusion protein, inserts a variable triplet DNA sequence alongside a constant propagator sequence that deactivates the previous and activates the next step of insertion. Insertions accumulate sequentially in a unidirectional order, editing can continue indefinitely, and high information is achieved by coexpressing a variety of prime editing guide RNAs (pegRNAs), each harboring unique triplet DNA sequences. We demonstrate that the constitutive expression of pegRNA collections generates insertion patterns for the straightforward reconstruction of cell lineage relationships and that the inducible expression of specific pegRNAs results in the accurate recording of exposures to biological stimuli.
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Affiliation(s)
- Theresa B Loveless
- Department of Biomedical Engineering, University of California, Irvine, CA, USA.
- Center for Synthetic Biology, University of California, Irvine, CA, USA.
- NSF-Simons Center for Multiscale Cell Fate, University of California, Irvine, CA, USA.
- Department of BioSciences, Rice University, Houston, TX, USA.
| | - Courtney K Carlson
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Synthetic Biology, University of California, Irvine, CA, USA
| | - Catalina A Dentzel Helmy
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Synthetic Biology, University of California, Irvine, CA, USA
- Department of BioSciences, Rice University, Houston, TX, USA
| | - Vincent J Hu
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Synthetic Biology, University of California, Irvine, CA, USA
- Graduate Program in Mathematical, Computational and Systems Biology, University of California, Irvine, CA, USA
| | - Sara K Ross
- Department of BioSciences, Rice University, Houston, TX, USA
| | - Matt C Demelo
- Department of BioSciences, Rice University, Houston, TX, USA
| | - Ali Murtaza
- Department of BioSciences, Rice University, Houston, TX, USA
| | - Guohao Liang
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Synthetic Biology, University of California, Irvine, CA, USA
| | - Michelle Ficht
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Synthetic Biology, University of California, Irvine, CA, USA
| | - Arushi Singhai
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Synthetic Biology, University of California, Irvine, CA, USA
| | - Marcello J Pajoh-Casco
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Synthetic Biology, University of California, Irvine, CA, USA
| | - Chang C Liu
- Department of Biomedical Engineering, University of California, Irvine, CA, USA.
- Center for Synthetic Biology, University of California, Irvine, CA, USA.
- NSF-Simons Center for Multiscale Cell Fate, University of California, Irvine, CA, USA.
- Department of Chemistry, University of California, Irvine, CA, USA.
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, USA.
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5
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Liu C, Li X, Hu Q, Jia Z, Ye Q, Wang X, Zhao K, Liu L, Wang M. Decoding the blueprints of embryo development with single-cell and spatial omics. Semin Cell Dev Biol 2025; 167:22-39. [PMID: 39889540 DOI: 10.1016/j.semcdb.2025.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/18/2025] [Accepted: 01/18/2025] [Indexed: 02/03/2025]
Abstract
Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.
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Affiliation(s)
- Chang Liu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China; Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China; Shenzhen Proof-of-Concept Center of Digital Cytopathology, BGI Research, Shenzhen 518083, China
| | | | - Qinan Hu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518005, China; Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen 518005, China
| | - Zihan Jia
- BGI Research, Hangzhou 310030, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qing Ye
- BGI Research, Hangzhou 310030, China; China Jiliang University, Hangzhou 310018, China
| | | | - Kaichen Zhao
- College of Biomedicine and Health, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Longqi Liu
- BGI Research, Hangzhou 310030, China; Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China.
| | - Mingyue Wang
- BGI Research, Hangzhou 310030, China; Key Laboratory of Spatial Omics of Zhejiang Province, BGI Research, Hangzhou 310030, China.
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6
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Askary A, Chen W, Choi J, Du LY, Elowitz MB, Gagnon JA, Schier AF, Seidel S, Shendure J, Stadler T, Tran M. The lives of cells, recorded. Nat Rev Genet 2025; 26:203-222. [PMID: 39587306 DOI: 10.1038/s41576-024-00788-w] [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] [Accepted: 09/26/2024] [Indexed: 11/27/2024]
Abstract
A paradigm for biology is emerging in which cells can be genetically programmed to write their histories into their own genomes. These records can subsequently be read, and the cellular histories reconstructed, which for each cell could include a record of its lineage relationships, extrinsic influences, internal states and physical locations, over time. DNA recording has the potential to transform the way that we study developmental and disease processes. Recent advances in genome engineering are driving the development of systems for DNA recording, and meanwhile single-cell and spatial omics technologies increasingly enable the recovery of the recorded information. Combined with advances in computational and phylogenetic inference algorithms, the DNA recording paradigm is beginning to bear fruit. In this Perspective, we explore the rationale and technical basis of DNA recording, what aspects of cellular biology might be recorded and how, and the types of discovery that we anticipate this paradigm will enable.
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Affiliation(s)
- Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Wei Chen
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Junhong Choi
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucia Y Du
- Biozentrum, University of Basel, Basel, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - Michael B Elowitz
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA, USA.
| | - James A Gagnon
- School of Biological Sciences, University of Utah, Salt Lake City, UT, USA.
| | - Alexander F Schier
- Biozentrum, University of Basel, Basel, Switzerland.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
| | - Sophie Seidel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA.
- Seattle Hub for Synthetic Biology, Seattle, WA, USA.
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Martin Tran
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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7
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Zwaans A, Seidel S, Manceau M, Stadler T. A Bayesian phylodynamic inference framework for single-cell CRISPR/Cas9 lineage tracing barcode data with dependent target sites. Philos Trans R Soc Lond B Biol Sci 2025; 380:20230318. [PMID: 39976408 PMCID: PMC11867110 DOI: 10.1098/rstb.2023.0318] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/03/2024] [Accepted: 08/05/2024] [Indexed: 02/21/2025] Open
Abstract
Analysing single-cell lineage relationships of an organism is crucial towards understanding the fundamental cellular dynamics that drive development. Clustered regularly interspaced short palindromic repeats (CRISPR)-based dynamic lineage tracing relies on recent advances in genome editing and sequencing technologies to generate inheritable, evolving genetic barcode sequences that enable reconstruction of such cell lineage trees, also referred to as phylogenetic trees. Recent work generated custom computational strategies to produce robust tree estimates from such data. We further capitalize on these advancements and introduce GESTALT analysis using Bayesian inference (GABI), which extends the analysis of genome editing of synthetic target arrays for lineage tracing (GESTALT) data to a fully integrated Bayesian phylogenetic inference framework in software BEAST 2. This implementation allows users to represent the uncertainty in reconstructed trees and enables their scaling in absolute time. Furthermore, based on such time-scaled lineage trees, the underlying processes of growth, differentiation and apoptosis are quantified through so-called phylodynamic inference, typically relying on a birth-death or coalescent model. After validating its implementation, we demonstrate that our methodology results in robust estimates of growth dynamics characteristic of early Danio rerio development. GABI's codebase is publicly available at https://github.com/azwaans/GABI.This article is part of the theme issue '"A mathematical theory of evolution": phylogenetic models dating back 100 years'.
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Affiliation(s)
- A. Zwaans
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - S. Seidel
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - M. Manceau
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - T. Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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8
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Zhou Y, Tang C, Xiao X, Zhan X, Wang T, Xiao G, Xu L. Dimensionality reduction for visualizing spatially resolved profiling data using SpaSNE. Gigascience 2025; 14:giaf002. [PMID: 39960663 PMCID: PMC11831803 DOI: 10.1093/gigascience/giaf002] [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: 05/03/2024] [Revised: 11/05/2024] [Accepted: 01/06/2025] [Indexed: 02/20/2025] Open
Abstract
BACKGROUND Spatially resolved profiling technologies to quantify transcriptomes, epigenomes, and proteomes have been emerging as groundbreaking methods for comprehensive molecular characterizations. Dimensionality reduction and visualization is an essential step to analyze and interpret spatially resolved profiling data. However, state-of-the-art dimensionality reduction methods for single-cell sequencing data, such as the t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), were not tailored for spatially resolved profiling data. RESULTS Here we developed a spatially resolved t-SNE (SpaSNE) method to integrate both spatial and molecular information. We applied it to a variety of public spatially resolved profiling datasets that were generated from 3 experimental platforms and consisted of cells from different diseases, tissues, and cell types. To compare the performances of SpaSNE, t-SNE, and UMAP, we applied them to 4 spatially resolved profiling datasets obtained from 3 distinct experimental platforms (Visium, STARmap, and MERFISH) on both diseased and normal tissues. Comparisons between SpaSNE and these state-of-the-art approaches reveal that SpaSNE achieves more accurate and meaningful visualization that better elucidates the underlying spatial and molecular data structures. CONCLUSIONS This work demonstrates the broad application of SpaSNE for reliable and robust interpretation of cell types based on both molecular and spatial information, which can set the foundation for many subsequent analysis steps, such as differential gene expression and trajectory or pseudotime analysis on the spatially resolved profiling data.
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Affiliation(s)
- Yuansheng Zhou
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Chen Tang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xue Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Lin Xu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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9
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Maranas CJ, George W, Scallon SK, VanGilder S, Nemhauser JL, Guiziou S. A history-dependent integrase recorder of plant gene expression with single-cell resolution. Nat Commun 2024; 15:9362. [PMID: 39472426 PMCID: PMC11522408 DOI: 10.1038/s41467-024-53716-1] [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: 06/05/2024] [Accepted: 10/18/2024] [Indexed: 11/02/2024] Open
Abstract
During development, most cells experience a progressive restriction of fate that ultimately results in a fully differentiated mature state. Understanding more about the gene expression patterns that underlie developmental programs can inform engineering efforts for new or optimized forms. Here, we present a four-state integrase-based recorder of gene expression history and demonstrate its use in tracking gene expression events in Arabidopsis thaliana in two developmental contexts: lateral root initiation and stomatal differentiation. The recorder uses two serine integrases to mediate sequential DNA recombination events, resulting in step-wise, history-dependent switching between expression of fluorescent reporters. By using promoters that express at different times along each of the two differentiation pathways to drive integrase expression, we tie fluorescent status to an ordered progression of gene expression along the developmental trajectory. In one snapshot of a mature tissue, our recorder is able to reveal past gene expression with single cell resolution. In this way, we are able to capture heterogeneity in stomatal development, confirming the existence of two alternate paths of differentiation.
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Affiliation(s)
| | - Wesley George
- Department of Biology, University of Washington, Seattle, WA, USA
| | - Sarah K Scallon
- Department of Biology, University of Washington, Seattle, WA, USA
| | - Sydney VanGilder
- Department of Biology, University of Washington, Seattle, WA, USA
| | | | - Sarah Guiziou
- Engineering Biology, Earlham Institute, Norwich, UK.
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10
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Jones MG, Sun D, Min KH(J, Colgan WN, Tian L, Weir JA, Chen VZ, Koblan LW, Yost KE, Mathey-Andrews N, Russell AJ, Stickels RR, Balderrama KS, Rideout WM, Chang HY, Jacks T, Chen F, Weissman JS, Yosef N, Yang D. Spatiotemporal lineage tracing reveals the dynamic spatial architecture of tumor growth and metastasis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.21.619529. [PMID: 39484491 PMCID: PMC11526908 DOI: 10.1101/2024.10.21.619529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/03/2024]
Abstract
Tumor progression is driven by dynamic interactions between cancer cells and their surrounding microenvironment. Investigating the spatiotemporal evolution of tumors can provide crucial insights into how intrinsic changes within cancer cells and extrinsic alterations in the microenvironment cooperate to drive different stages of tumor progression. Here, we integrate high-resolution spatial transcriptomics and evolving lineage tracing technologies to elucidate how tumor expansion, plasticity, and metastasis co-evolve with microenvironmental remodeling in a Kras;p53-driven mouse model of lung adenocarcinoma. We find that rapid tumor expansion contributes to a hypoxic, immunosuppressive, and fibrotic microenvironment that is associated with the emergence of pro-metastatic cancer cell states. Furthermore, metastases arise from spatially-confined subclones of primary tumors and remodel the distant metastatic niche into a fibrotic, collagen-rich microenvironment. Together, we present a comprehensive dataset integrating spatial assays and lineage tracing to elucidate how sequential changes in cancer cell state and microenvironmental structures cooperate to promote tumor progression.
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Affiliation(s)
- Matthew G. Jones
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- These authors contributed equally
| | - Dawei Sun
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- These authors contributed equally
| | - Kyung Hoi (Joseph) Min
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - William N. Colgan
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Luyi Tian
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jackson A. Weir
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Biological and Biomedical Sciences Program, Harvard University, Cambridge, MA, USA
| | - Victor Z. Chen
- Department of Molecular Pharmacology and Therapeutics, Columbia University, New York City, NY, USA
- Department of Systems Biology, Columbia University, New York City, NY, USA
| | - Luke W. Koblan
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kathryn E. Yost
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicolas Mathey-Andrews
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Andrew J.C. Russell
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | | | | | - William M. Rideout
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Howard Y. Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Tyler Jacks
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Jonathan S. Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nir Yosef
- Department of Systems Immunology, Weizmann Institute of Science, 234 Herzl Street, Rehovot 7610001, Israel
| | - Dian Yang
- Department of Molecular Pharmacology and Therapeutics, Columbia University, New York City, NY, USA
- Department of Systems Biology, Columbia University, New York City, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York City, NY, USA
- Lead Contact
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11
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Lange M, Piran Z, Klein M, Spanjaard B, Klein D, Junker JP, Theis FJ, Nitzan M. Mapping lineage-traced cells across time points with moslin. Genome Biol 2024; 25:277. [PMID: 39434128 PMCID: PMC11492637 DOI: 10.1186/s13059-024-03422-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 10/10/2024] [Indexed: 10/23/2024] Open
Abstract
Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information. We validate our approach in simulations and demonstrate on Caenorhabditis elegans embryonic development how moslin predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration.
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Affiliation(s)
- Marius Lange
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Department of Mathematics, Technical University of Munich, Munich, Germany
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
| | - Zoe Piran
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | | | - Bastiaan Spanjaard
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
- Department of Paediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Dominik Klein
- Department of Mathematics, Technical University of Munich, Munich, Germany
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
| | - Jan Philipp Junker
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Fabian J Theis
- Department of Mathematics, Technical University of Munich, Munich, Germany.
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, 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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12
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Hao K, Barrett M, Samadi Z, Zarezadeh A, McGrath Y, Askary A. Reconstructing signaling history of single cells with imaging-based molecular recording. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.11.617908. [PMID: 39416000 PMCID: PMC11482953 DOI: 10.1101/2024.10.11.617908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
The intensity and duration of biological signals encode information that allows a few pathways to regulate a wide array of cellular behaviors. Despite the central importance of signaling in biomedical research, our ability to quantify it in individual cells over time remains limited. Here, we introduce INSCRIBE, an approach for reconstructing signaling history in single cells using endpoint fluorescence images. By regulating a CRISPR base editor, INSCRIBE generates mutations in genomic target sequences, at a rate proportional to signaling activity. The number of edits is then recovered through a novel ratiometric readout strategy, from images of two fluorescence channels. We engineered human cell lines for recording WNT and BMP pathway activity, and demonstrated that INSCRIBE faithfully recovers both the intensity and duration of signaling. Further, we used INSCRIBE to study the variability of cellular response to WNT and BMP stimulation, and test whether the magnitude of response is a stable, heritable trait. We found a persistent memory in the BMP pathway. Progeny of cells with higher BMP response levels are likely to respond more strongly to a second BMP stimulation, up to 3 weeks later. Together, our results establish a scalable platform for genetic recording and in situ readout of signaling history in single cells, advancing quantitative analysis of cell-cell communication during development and disease.
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Affiliation(s)
- Kai Hao
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Mykel Barrett
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Zainalabedin Samadi
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Amirhossein Zarezadeh
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Yuka McGrath
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
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13
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Qin X, Tape CJ. Functional analysis of cell plasticity using single-cell technologies. Trends Cell Biol 2024; 34:854-864. [PMID: 38355348 DOI: 10.1016/j.tcb.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.
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Affiliation(s)
- Xiao Qin
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK.
| | - Christopher J Tape
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, 72 Huntley Street, London, WC1E 6DD, UK.
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14
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Zhang Y, Ba F, Huang S, Liu WQ, Li J. Orthogonal Serine Integrases Enable Scalable Gene Storage Cascades in Bacterial Genome. ACS Synth Biol 2024; 13:3022-3031. [PMID: 39238421 DOI: 10.1021/acssynbio.4c00505] [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: 09/07/2024]
Abstract
Genome integration enables host organisms to stably carry heterologous DNA messages, introducing new genotypes and phenotypes for expanded applications. While several genome integration approaches have been reported, a scalable tool for DNA message storage within site-specific genome landing pads is still lacking. Here, we introduce an iterative genome integration method utilizing orthogonal serine integrases, enabling the stable storage of multiple heterologous genes in the chromosome of Escherichia coli MG1655. By leveraging serine integrases TP901-1, Bxb1, and PhiC31, along with engineered integration vectors, we demonstrate high-efficiency, marker-free integration of DNA fragments up to 13 kb in length. To further simplify the procedure, we then develop a streamlined integration method and showcase the system's versatility by constructing an engineered E. coli strain capable of storing and expressing multiple genes from diverse species. Additionally, we illustrate the potential utility of these engineered strains for synthetic biology applications, including in vivo and in vitro protein expression. Our work extends the application scope of serine integrases for scalable gene integration cascades, with implications for genome manipulation and gene storage applications in synthetic biology.
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Affiliation(s)
- Yufei Zhang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Fang Ba
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shuhui Huang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Wan-Qiu Liu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jian Li
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
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15
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Sashittal P, Zhang RY, Law BK, Strzalkowski A, Schmidt H, Bolondi A, Chan MM, Raphael BJ. Inferring cell differentiation maps from lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.09.611835. [PMID: 39314473 PMCID: PMC11419031 DOI: 10.1101/2024.09.09.611835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, Carta, that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that Carta more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors.
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Affiliation(s)
- Palash Sashittal
- Dept. of Computer Science, Princeton University, Princeton; 08544 NJ, USA
| | - Richard Y. Zhang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton; 08544 NJ, USA
| | - Benjamin K. Law
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton; 08544 NJ, USA
- Dept. of Molecular Biology, Princeton University, Princeton; 08544 NJ, USA
| | | | - Henri Schmidt
- Dept. of Computer Science, Princeton University, Princeton; 08544 NJ, USA
| | - Adriano Bolondi
- Dept. of Genome Regulation, Max Planck Institute for Molecular Genetics; 14195 Berlin, Germany
| | - Michelle M. Chan
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton; 08544 NJ, USA
- Dept. of Molecular Biology, Princeton University, Princeton; 08544 NJ, USA
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16
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Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
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Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
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17
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Chen W, Choi J, Li X, Nathans JF, Martin B, Yang W, Hamazaki N, Qiu C, Lalanne JB, Regalado S, Kim H, Agarwal V, Nichols E, Leith A, Lee C, Shendure J. Symbolic recording of signalling and cis-regulatory element activity to DNA. Nature 2024; 632:1073-1081. [PMID: 39020177 PMCID: PMC11357993 DOI: 10.1038/s41586-024-07706-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 06/12/2024] [Indexed: 07/19/2024]
Abstract
Measurements of gene expression or signal transduction activity are conventionally performed using methods that require either the destruction or live imaging of a biological sample within the timeframe of interest. Here we demonstrate an alternative paradigm in which such biological activities are stably recorded to the genome. Enhancer-driven genomic recording of transcriptional activity in multiplex (ENGRAM) is based on the signal-dependent production of prime editing guide RNAs that mediate the insertion of signal-specific barcodes (symbols) into a genomically encoded recording unit. We show how this strategy can be used for multiplex recording of the cell-type-specific activities of dozens to hundreds of cis-regulatory elements with high fidelity, sensitivity and reproducibility. Leveraging signal transduction pathway-responsive cis-regulatory elements, we also demonstrate time- and concentration-dependent genomic recording of WNT, NF-κB and Tet-On activities. By coupling ENGRAM to sequential genome editing via DNA Typewriter1, we stably record information about the temporal dynamics of two orthogonal signalling pathways to genomic DNA. Finally we apply ENGRAM to integratively record the transient activity of nearly 100 transcription factor consensus motifs across daily windows spanning the differentiation of mouse embryonic stem cells into gastruloids, an in vitro model of early mammalian development. Although these are proof-of-concept experiments and much work remains to fully realize the possibilities, the symbolic recording of biological signals or states within cells, to the genome and over time, has broad potential to complement contemporary paradigms for how we make measurements in biological systems.
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Affiliation(s)
- Wei Chen
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA.
| | - Junhong Choi
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
- Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | - Xiaoyi Li
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | - Jenny F Nathans
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Beth Martin
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | - Wei Yang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | - Nobuhiko Hamazaki
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
- Department of Obstetrics & Gynecology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
| | - Chengxiang Qiu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | | | - Samuel Regalado
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | - Haedong Kim
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | - Vikram Agarwal
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Eva Nichols
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Anh Leith
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Choli Lee
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
- Seattle Hub for Synthetic Biology, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
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18
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Liao X, Scheidereit E, Kuppe C. New tools to study renal fibrogenesis. Curr Opin Nephrol Hypertens 2024; 33:420-426. [PMID: 38587103 PMCID: PMC11139246 DOI: 10.1097/mnh.0000000000000988] [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: 04/09/2024]
Abstract
PURPOSE OF REVIEW Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development. RECENT FINDINGS We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1] . We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRβ+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis. SUMMARY As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.
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Affiliation(s)
- Xian Liao
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
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19
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Mai U, Hu G, Raphael BJ. Maximum likelihood phylogeographic inference of cell motility and cell division from spatial lineage tracing data. Bioinformatics 2024; 40:i228-i236. [PMID: 38940146 PMCID: PMC11211844 DOI: 10.1093/bioinformatics/btae221] [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
MOTIVATION Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these mutations in the sampled cells along with the physical locations of the cells. These technologies enable high-throughput studies of developmental processes over space and time. However, these applications rely on accurate reconstruction of a spatial cell lineage tree describing both past cell divisions and cell locations. Spatial lineage trees are related to phylogeographic models that have been well-studied in the phylogenetics literature. We demonstrate that standard phylogeographic models based on Brownian motion are inadequate to describe the spatial symmetric displacement (SD) of cells during cell division. RESULTS We introduce a new model-the SD model for cell motility that includes symmetric displacements of daughter cells from the parental cell followed by independent diffusion of daughter cells. We show that this model more accurately describes the locations of cells in a real spatial lineage tracing of mouse embryonic stem cells. Combining the spatial SD model with an evolutionary model of DNA mutations, we obtain a phylogeographic model for spatial lineage tracing. Using this model, we devise a maximum likelihood framework-MOLLUSC (Maximum Likelihood Estimation Of Lineage and Location Using Single-Cell Spatial Lineage tracing Data)-to co-estimate time-resolved branch lengths, spatial diffusion rate, and mutation rate. On both simulated and real data, we show that MOLLUSC accurately estimates all parameters. In contrast, the Brownian motion model overestimates spatial diffusion rate in all test cases. In addition, the inclusion of spatial information improves accuracy of branch length estimation compared to sequence data alone. On real data, we show that spatial information has more signal than sequence data for branch length estimation, suggesting augmenting lineage tracing technologies with spatial information is useful to overcome the limitations of genome-editing in developmental systems. AVAILABILITY AND IMPLEMENTATION The python implementation of MOLLUSC is available at https://github.com/raphael-group/MOLLUSC.
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Affiliation(s)
- Uyen Mai
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
| | - Gary Hu
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
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20
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Guiziou S. Biocomputing in plants, from proof of concept to application. Curr Opin Biotechnol 2024; 87:103146. [PMID: 38781700 DOI: 10.1016/j.copbio.2024.103146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/30/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024]
Abstract
In response to the challenges of climate change and the transition toward sustainability, synthetic biology offers innovative solutions. Most current plant synthetic biology applications rely on the constitutive expression of enzymes and regulators. To engineer plant phenotypes tuneable to environmental conditions and plant cellular states, the integration of multiple signals in synthetic circuits is required. While most circuits are developed in model organisms, numerous tools were recently developed to implement biocomputation in plant synthetic circuits. I presented in this review the tools and design methods for logic circuit implementation in plants. I highlighted recent and potential applications of those circuits to understand and engineer plant interaction with the environment, development, and metabolic pathways.
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Affiliation(s)
- Sarah Guiziou
- Engineering Biology, Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
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21
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Rafelski SM, Theriot JA. Establishing a conceptual framework for holistic cell states and state transitions. Cell 2024; 187:2633-2651. [PMID: 38788687 DOI: 10.1016/j.cell.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/10/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024]
Abstract
Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.
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Affiliation(s)
- Susanne M Rafelski
- Allen Institute for Cell Science, 615 Westlake Avenue N, Seattle, WA 98125, USA.
| | - Julie A Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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22
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Saunders SH, Ahmed AM. ORBIT for E. coli: kilobase-scale oligonucleotide recombineering at high throughput and high efficiency. Nucleic Acids Res 2024; 52:e43. [PMID: 38587185 PMCID: PMC11077079 DOI: 10.1093/nar/gkae227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 02/28/2024] [Accepted: 03/19/2024] [Indexed: 04/09/2024] Open
Abstract
Microbiology and synthetic biology depend on reverse genetic approaches to manipulate bacterial genomes; however, existing methods require molecular biology to generate genomic homology, suffer from low efficiency, and are not easily scaled to high throughput. To overcome these limitations, we developed a system for creating kilobase-scale genomic modifications that uses DNA oligonucleotides to direct the integration of a non-replicating plasmid. This method, Oligonucleotide Recombineering followed by Bxb-1 Integrase Targeting (ORBIT) was pioneered in Mycobacteria, and here we adapt and expand it for Escherichia coli. Our redesigned plasmid toolkit for oligonucleotide recombineering achieved significantly higher efficiency than λ Red double-stranded DNA recombineering and enabled precise, stable knockouts (≤134 kb) and integrations (≤11 kb) of various sizes. Additionally, we constructed multi-mutants in a single transformation, using orthogonal attachment sites. At high throughput, we used pools of targeting oligonucleotides to knock out nearly all known transcription factor and small RNA genes, yielding accurate, genome-wide, single mutant libraries. By counting genomic barcodes, we also show ORBIT libraries can scale to thousands of unique members (>30k). This work demonstrates that ORBIT for E. coli is a flexible reverse genetic system that facilitates rapid construction of complex strains and readily scales to create sophisticated mutant libraries.
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Affiliation(s)
- Scott H Saunders
- Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75320, USA
| | - Ayesha M Ahmed
- Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75320, USA
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23
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Sánchez Rivera FJ, Dow LE. How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research. Cold Spring Harb Perspect Med 2024; 14:a041384. [PMID: 37487630 PMCID: PMC11065179 DOI: 10.1101/cshperspect.a041384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.
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Affiliation(s)
- Francisco J Sánchez Rivera
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Lukas E Dow
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10065, USA
- Department of Biochemistry, Weill Cornell Medicine, New York, New York 10065, USA
- Department of Medicine, Weill Cornell Medicine, New York, New York 10065, USA
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24
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Nathans JF, Ayers JL, Shendure J, Simpson CL. Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin. J Invest Dermatol 2024; 144:936-949. [PMID: 38643988 PMCID: PMC11034889 DOI: 10.1016/j.jid.2024.02.006] [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: 12/19/2023] [Revised: 01/28/2024] [Accepted: 02/08/2024] [Indexed: 04/23/2024]
Abstract
The epidermis is the body's first line of protection against dehydration and pathogens, continually regenerating the outermost protective skin layers throughout life. During both embryonic development and wound healing, epidermal stem and progenitor cells must respond to external stimuli and insults to build, maintain, and repair the cutaneous barrier. Recent advances in CRISPR-based methods for cell lineage tracing have remarkably expanded the potential for experiments that track stem and progenitor cell proliferation and differentiation over the course of tissue and even organismal development. Additional tools for DNA-based recording of cellular signaling cues promise to deepen our understanding of the mechanisms driving normal skin morphogenesis and response to stressors as well as the dysregulation of cell proliferation and differentiation in skin diseases and cancer. In this review, we highlight cutting-edge methods for cell lineage tracing, including in organoids and model organisms, and explore how cutaneous biology researchers might leverage these techniques to elucidate the developmental programs that support the regenerative capacity and plasticity of the skin.
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Affiliation(s)
- Jenny F Nathans
- Medical Scientist Training Program, University of Washington, Seattle, Washington, USA; Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Jessica L Ayers
- Molecular Medicine and Mechanisms of Disease PhD Program, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA; Department of Dermatology, University of Washington, Seattle, Washington, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA; Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, Washington, USA
| | - Cory L Simpson
- Department of Dermatology, University of Washington, Seattle, Washington, USA; Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, Washington, USA.
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25
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Maizels RJ. A dynamical perspective: moving towards mechanism in single-cell transcriptomics. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230049. [PMID: 38432314 PMCID: PMC10909508 DOI: 10.1098/rstb.2023.0049] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/31/2023] [Indexed: 03/05/2024] Open
Abstract
As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Rory J. Maizels
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- University College London, London WC1E 6BT, UK
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26
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Tansley C, Patron NJ, Guiziou S. Engineering Plant Cell Fates and Functions for Agriculture and Industry. ACS Synth Biol 2024; 13:998-1005. [PMID: 38573786 PMCID: PMC11036505 DOI: 10.1021/acssynbio.4c00047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/06/2024]
Abstract
Many plant species are grown to enable access to specific organs or tissues, such as seeds, fruits, or stems. In some cases, a value is associated with a molecule that accumulates in a single type of cell. Domestication and subsequent breeding have often increased the yields of these target products by increasing the size, number, and quality of harvested organs and tissues but also via changes to overall plant growth architecture to suit large-scale cultivation. Many of the mutations that underlie these changes have been identified in key regulators of cellular identity and function. As key determinants of yield, these regulators are key targets for synthetic biology approaches to engineer new forms and functions. However, our understanding of many plant developmental programs and cell-type specific functions is still incomplete. In this Perspective, we discuss how advances in cellular genomics together with synthetic biology tools such as biosensors and DNA-recording devices are advancing our understanding of cell-specific programs and cell fates. We then discuss advances and emerging opportunities for cell-type-specific engineering to optimize plant morphology, responses to the environment, and the production of valuable compounds.
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Affiliation(s)
- Connor Tansley
- Engineering
Biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ United Kingdom
- Department
of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA United
Kingdom
| | - Nicola J. Patron
- Engineering
Biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ United Kingdom
- Department
of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA United
Kingdom
| | - Sarah Guiziou
- Engineering
Biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ United Kingdom
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27
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Tran M, Askary A, Elowitz MB. Lineage motifs as developmental modules for control of cell type proportions. Dev Cell 2024; 59:812-826.e3. [PMID: 38359830 DOI: 10.1016/j.devcel.2024.01.017] [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: 05/19/2023] [Revised: 10/10/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells or extrinsic interactions that produce specific patterns of descendant cell types on lineage trees. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer these dynamics and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states or extrinsic interactions. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryonic development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.
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Affiliation(s)
- Martin Tran
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Michael B Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
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28
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Mai U, Chu G, Raphael BJ. Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583638. [PMID: 38496496 PMCID: PMC10942411 DOI: 10.1101/2024.03.05.583638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequencing to track cell divisions during development. A key computational problem in dynamic lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Three features of dynamic lineage tracing data distinguish this problem from standard phylogenetic tree inference. First, the CRISPR-editing process modifies a genomic location exactly once. This non-modifiable property is not well described by the time-reversible models commonly used in phylogenetics. Second, as a consequence of non-modifiability, the number of mutations per time unit decreases over time. Third, CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to search for the maximum likelihood (ML) tree under the PMM model. LAML combines an Expectation Maximization (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters. We derive a closed-form solution for the M-step in the case of no heritable missing data, and a block coordinate ascent approach in the general case which is more efficient than the standard General Time Reversible (GTR) phylogenetic model. On simulated data, LAML infers more accurate tree topologies and branch lengths than existing methods, with greater advantages on datasets with higher ratios of heritable to non-heritable missing data. We show that LAML provides unbiased time-scaled estimates of branch lengths. In contrast, we demonstrate that maximum parsimony methods for lineage tracing data not only underestimate branch lengths, but also yield branch lengths which are not proportional to time, due to the nonlinear decay in the number of mutations on branches further from the root. On lineage tracing data from a mouse model of lung adenocarcinoma, we show that LAML infers phylogenetic distances that are more concordant with gene expression data compared to distances derived from maximum parsimony. The LAML tree topology is more plausible than existing published trees, with fewer total cell migrations between distant metastases and fewer reseeding events where cells migrate back to the primary tumor. Crucially, we identify three distinct time epochs of metastasis progression, which includes a burst of metastasis events to various anatomical sites during a single month.
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Affiliation(s)
| | | | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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29
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Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med 2024; 16:14. [PMID: 38238823 PMCID: PMC10795303 DOI: 10.1186/s13073-024-01282-y] [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: 08/15/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
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Affiliation(s)
- Paul Kiessling
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany.
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30
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Li Z, Yang W, Wu P, Shan Y, Zhang X, Chen F, Yang J, Yang JR. Reconstructing cell lineage trees with genomic barcoding: approaches and applications. J Genet Genomics 2024; 51:35-47. [PMID: 37269980 DOI: 10.1016/j.jgg.2023.05.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.
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Affiliation(s)
- Zizhang Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenjing Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Peng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuyan Shan
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xiaoyu Zhang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Feng Chen
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junnan Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian-Rong Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
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31
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Kim IS. DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination. Cells 2023; 13:27. [PMID: 38201231 PMCID: PMC10778210 DOI: 10.3390/cells13010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
In various biological contexts, cells receive signals and stimuli that prompt them to change their current state, leading to transitions into a future state. This change underlies the processes of development, tissue maintenance, immune response, and the pathogenesis of various diseases. Following the path of cells from their initial identity to their current state reveals how cells adapt to their surroundings and undergo transformations to attain adjusted cellular states. DNA-based molecular barcoding technology enables the documentation of a phylogenetic tree and the deterministic events of cell lineages, providing the mechanisms and timing of cell lineage commitment that can either promote homeostasis or lead to cellular dysregulation. This review comprehensively presents recently emerging molecular recording technologies that utilize CRISPR/Cas systems, base editing, recombination, and innate variable sequences in the genome. Detailing their underlying principles, applications, and constraints paves the way for the lineage tracing of every cell within complex biological systems, encompassing the hidden steps and intermediate states of organism development and disease progression.
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Affiliation(s)
- Ik Soo Kim
- Department of Microbiology, Gachon University College of Medicine, Incheon 21999, Republic of Korea
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32
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Walsh LA, Quail DF. Decoding the tumor microenvironment with spatial technologies. Nat Immunol 2023; 24:1982-1993. [PMID: 38012408 DOI: 10.1038/s41590-023-01678-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023]
Abstract
Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.
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Affiliation(s)
- Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
| | - Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
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33
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Jang MJ, Coughlin GM, Jackson CR, Chen X, Chuapoco MR, Vendemiatti JL, Wang AZ, Gradinaru V. Spatial transcriptomics for profiling the tropism of viral vectors in tissues. Nat Biotechnol 2023; 41:1272-1286. [PMID: 36702899 PMCID: PMC10443732 DOI: 10.1038/s41587-022-01648-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 12/15/2022] [Indexed: 01/27/2023]
Abstract
A barrier to advancing engineered adeno-associated viral vectors (AAVs) for precision access to cell subtypes is a lack of high-throughput, high-resolution assays to characterize in vivo transduction profiles. In this study, we developed an ultrasensitive, sequential fluorescence in situ hybridization (USeqFISH) method for spatial transcriptomic profiling of endogenous and viral RNA with a short barcode in intact tissue volumes by integrating hydrogel-based tissue clearing, enhanced signal amplification and multiplexing using sequential labeling. Using USeqFISH, we investigated the transduction and cell subtype tropisms across mouse brain regions of six systemic AAVs, including AAV-PHP.AX, a new variant that transduces robustly and efficiently across neurons and astrocytes. Here we reveal distinct cell subtype biases of each AAV variant, including a bias of AAV-PHP.N toward excitatory neurons. USeqFISH also enables profiling of pooled regulatory cargos, as we show for a 13-variant pool of microRNA target sites in AAV genomes. Lastly, we demonstrate potential applications of USeqFISH for in situ AAV profiling and multimodal single-cell analysis in non-human primates.
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Affiliation(s)
- Min J Jang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Gerard M Coughlin
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Cameron R Jackson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Xinhong Chen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Miguel R Chuapoco
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Julia L Vendemiatti
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Alexander Z Wang
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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34
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Prusokiene A, Prusokas A, Retkute R. Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes. NAR Genom Bioinform 2023; 5:lqad077. [PMID: 37608801 PMCID: PMC10440785 DOI: 10.1093/nargab/lqad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/26/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023] Open
Abstract
Tracking cells as they divide and progress through differentiation is a fundamental step in understanding many biological processes, such as the development of organisms and progression of diseases. In this study, we investigate a machine learning approach to reconstruct lineage trees in experimental systems based on mutating synthetic genomic barcodes. We refine previously proposed methodology by embedding information of higher level relationships between cells and single-cell barcode values into a feature space. We test performance of the algorithm on shallow trees (up to 100 cells) and deep trees (up to 10 000 cells). Our proposed algorithm can improve tree reconstruction accuracy in comparison to reconstructions based on a maximum parsimony method, but this comes at a higher computational time requirement.
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Affiliation(s)
- Alisa Prusokiene
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | | | - Renata Retkute
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
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35
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Tran M, Askary A, Elowitz MB. Lineage motifs: developmental modules for control of cell type proportions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543925. [PMID: 37333085 PMCID: PMC10274800 DOI: 10.1101/2023.06.06.543925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells that produce specific sets of descendant cell types. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer progenitor states and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryo development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.
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Affiliation(s)
- Martin Tran
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Michael B. Elowitz
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
- Lead contact
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36
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Tyser RCV. Formation of the Heart: Defining Cardiomyocyte Progenitors at Single-Cell Resolution. Curr Cardiol Rep 2023; 25:495-503. [PMID: 37119451 PMCID: PMC10188409 DOI: 10.1007/s11886-023-01880-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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/04/2023] [Indexed: 05/01/2023]
Abstract
PURPOSE OF REVIEW Formation of the heart requires the coordinated addition of multiple progenitor sources which have undergone different pathways of specification and differentiation. In this review, I aim to put into context how recent studies defining cardiac progenitor heterogeneity build on our understanding of early heart development and also discuss the questions raised by this new insight. RECENT FINDINGS With the development of sequencing technologies and imaging approaches, it has been possible to define, at high temporal resolution, the molecular profile and anatomical location of cardiac progenitors at the single-cell level, during the formation of the mammalian heart. Given the recent progress in our understanding of early heart development and technical advances in high-resolution time-lapse imaging and lineage analysis, we are now in a position of great potential, allowing us to resolve heart formation at previously impossible levels of detail. Understanding how this essential organ forms not only addresses questions of fundamental biological significance but also provides a blueprint for strategies to both treat and model heart disease.
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Affiliation(s)
- Richard C V Tyser
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Jeffrey Cheah Biomedical Centre, Cambridge, CB2 0AW, UK.
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37
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McNamara HM, Ramm B, Toettcher JE. Synthetic developmental biology: New tools to deconstruct and rebuild developmental systems. Semin Cell Dev Biol 2023; 141:33-42. [PMID: 35484026 PMCID: PMC10332110 DOI: 10.1016/j.semcdb.2022.04.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/13/2022] [Indexed: 11/16/2022]
Abstract
Technological advances have driven many recent advances in developmental biology. Light sheet imaging can reveal single-cell dynamics in living three-dimensional tissues, whereas single-cell genomic methods open the door to a complete catalogue of cell types and gene expression states. An equally powerful but complementary set of approaches are also becoming available to define development processes from the bottom up. These synthetic approaches aim to reconstruct the minimal developmental patterns, signaling processes, and gene networks that produce the basic set of developmental operations: spatial polarization, morphogen interpretation, tissue movement, and cellular memory. In this review we discuss recent approaches at the intersection of synthetic biology and development, including synthetic circuits to deliver and record signaling stimuli and synthetic reconstitution of pattern formation on multicellular scales.
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Affiliation(s)
- Harold M McNamara
- Lewis Sigler Institute, Princeton University, Princeton, NJ 08544, USA; Department of Physics, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - Beatrice Ramm
- Department of Physics, Princeton University, Princeton, NJ 08544, USA; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - Jared E Toettcher
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
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38
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Fang Z, Ford AJ, Hu T, Zhang N, Mantalaris A, Coskun AF. Subcellular spatially resolved gene neighborhood networks in single cells. CELL REPORTS METHODS 2023; 3:100476. [PMID: 37323566 PMCID: PMC10261906 DOI: 10.1016/j.crmeth.2023.100476] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 02/18/2023] [Accepted: 04/18/2023] [Indexed: 06/17/2023]
Abstract
Image-based spatial omics methods such as fluorescence in situ hybridization (FISH) generate molecular profiles of single cells at single-molecule resolution. Current spatial transcriptomics methods focus on the distribution of single genes. However, the spatial proximity of RNA transcripts can play an important role in cellular function. We demonstrate a spatially resolved gene neighborhood network (spaGNN) pipeline for the analysis of subcellular gene proximity relationships. In spaGNN, machine-learning-based clustering of subcellular spatial transcriptomics data yields subcellular density classes of multiplexed transcript features. The nearest-neighbor analysis produces heterogeneous gene proximity maps in distinct subcellular regions. We illustrate the cell-type-distinguishing capability of spaGNN using multiplexed error-robust FISH data of fibroblast and U2-OS cells and sequential FISH data of mesenchymal stem cells (MSCs), revealing tissue-source-specific MSC transcriptomics and spatial distribution characteristics. Overall, the spaGNN approach expands the spatial features that can be used for cell-type classification tasks.
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Affiliation(s)
- Zhou Fang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Machine Learning Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA
| | - Adam J. Ford
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Thomas Hu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Nicholas Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Interdisciplinary Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA
| | - Athanasios Mantalaris
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Ahmet F. Coskun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Interdisciplinary Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
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39
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Kalamakis G, Platt RJ. CRISPR for neuroscientists. Neuron 2023:S0896-6273(23)00306-9. [PMID: 37201524 DOI: 10.1016/j.neuron.2023.04.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/14/2023] [Accepted: 04/18/2023] [Indexed: 05/20/2023]
Abstract
Genome engineering technologies provide an entry point into understanding and controlling the function of genetic elements in health and disease. The discovery and development of the microbial defense system CRISPR-Cas yielded a treasure trove of genome engineering technologies and revolutionized the biomedical sciences. Comprising diverse RNA-guided enzymes and effector proteins that evolved or were engineered to manipulate nucleic acids and cellular processes, the CRISPR toolbox provides precise control over biology. Virtually all biological systems are amenable to genome engineering-from cancer cells to the brains of model organisms to human patients-galvanizing research and innovation and giving rise to fundamental insights into health and powerful strategies for detecting and correcting disease. In the field of neuroscience, these tools are being leveraged across a wide range of applications, including engineering traditional and non-traditional transgenic animal models, modeling disease, testing genomic therapies, unbiased screening, programming cell states, and recording cellular lineages and other biological processes. In this primer, we describe the development and applications of CRISPR technologies while highlighting outstanding limitations and opportunities.
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Affiliation(s)
- Georgios Kalamakis
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland; Novartis Institutes for BioMedical Research, 4056 Basel, Switzerland
| | - Randall J Platt
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland; Department of Chemistry, University of Basel, Petersplatz 1, 4003 Basel, Switzerland; NCCR MSE, Mattenstrasse 24a, 4058 Basel, Switzerland; Botnar Research Center for Child Health, Mattenstrasse 24a, 4058 Basel, Switzerland.
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40
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Guiziou S, Maranas CJ, Chu JC, Nemhauser JL. An integrase toolbox to record gene-expression during plant development. Nat Commun 2023; 14:1844. [PMID: 37012288 PMCID: PMC10070421 DOI: 10.1038/s41467-023-37607-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 03/23/2023] [Indexed: 04/05/2023] Open
Abstract
There are many open questions about the mechanisms that coordinate the dynamic, multicellular behaviors required for organogenesis. Synthetic circuits that can record in vivo signaling networks have been critical in elucidating animal development. Here, we report on the transfer of this technology to plants using orthogonal serine integrases to mediate site-specific and irreversible DNA recombination visualized by switching between fluorescent reporters. When combined with promoters expressed during lateral root initiation, integrases amplify reporter signal and permanently mark all descendants. In addition, we present a suite of methods to tune the threshold for integrase switching, including: RNA/protein degradation tags, a nuclear localization signal, and a split-intein system. These tools improve the robustness of integrase-mediated switching with different promoters and the stability of switching behavior over multiple generations. Although each promoter requires tuning for optimal performance, this integrase toolbox can be used to build history-dependent circuits to decode the order of expression during organogenesis in many contexts.
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Affiliation(s)
- Sarah Guiziou
- Department of Biology, University of Washington, Seattle, WA, 98195, USA
| | | | - Jonah C Chu
- Department of Biology, University of Washington, Seattle, WA, 98195, USA
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41
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Durrant MG, Fanton A, Tycko J, Hinks M, Chandrasekaran SS, Perry NT, Schaepe J, Du PP, Lotfy P, Bassik MC, Bintu L, Bhatt AS, Hsu PD. Systematic discovery of recombinases for efficient integration of large DNA sequences into the human genome. Nat Biotechnol 2023; 41:488-499. [PMID: 36217031 PMCID: PMC10083194 DOI: 10.1038/s41587-022-01494-w] [Citation(s) in RCA: 86] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 09/01/2022] [Indexed: 11/09/2022]
Abstract
Large serine recombinases (LSRs) are DNA integrases that facilitate the site-specific integration of mobile genetic elements into bacterial genomes. Only a few LSRs, such as Bxb1 and PhiC31, have been characterized to date, with limited efficiency as tools for DNA integration in human cells. In this study, we developed a computational approach to identify thousands of LSRs and their DNA attachment sites, expanding known LSR diversity by >100-fold and enabling the prediction of their insertion site specificities. We tested their recombination activity in human cells, classifying them as landing pad, genome-targeting or multi-targeting LSRs. Overall, we achieved up to seven-fold higher recombination than Bxb1 and genome integration efficiencies of 40-75% with cargo sizes over 7 kb. We also demonstrate virus-free, direct integration of plasmid or amplicon libraries for improved functional genomics applications. This systematic discovery of recombinases directly from microbial sequencing data provides a resource of over 60 LSRs experimentally characterized in human cells for large-payload genome insertion without exposed DNA double-stranded breaks.
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Affiliation(s)
- Matthew G Durrant
- Arc Institute, Palo Alto, CA, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Alison Fanton
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- University of California, Berkeley-University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Josh Tycko
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Michaela Hinks
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Sita S Chandrasekaran
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- University of California, Berkeley-University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Nicholas T Perry
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- University of California, Berkeley-University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Julia Schaepe
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Peter P Du
- Department of Genetics, Stanford University, Stanford, CA, USA
- Cancer Biology Program, Stanford University, Stanford, CA, USA
| | - Peter Lotfy
- Laboratory of Molecular and Cell Biology, Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Lacramioara Bintu
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
| | - Ami S Bhatt
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Medicine (Hematology), Stanford University, Stanford, CA, USA.
| | - Patrick D Hsu
- Arc Institute, Palo Alto, CA, USA.
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA.
- Laboratory of Molecular and Cell Biology, Salk Institute for Biological Studies, La Jolla, CA, USA.
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA.
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
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42
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Richman LP, Goyal Y, Jiang CL, Raj A. ClonoCluster: A method for using clonal origin to inform transcriptome clustering. CELL GENOMICS 2023; 3:100247. [PMID: 36819662 PMCID: PMC9932990 DOI: 10.1016/j.xgen.2022.100247] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 09/22/2022] [Accepted: 12/16/2022] [Indexed: 01/13/2023]
Abstract
Clustering cells based on their high-dimensional profiles is an important data reduction process by which researchers infer distinct cellular states. The advent of cellular barcoding, however, provides an alternative means by which to group cells: by their clonal origin. We developed ClonoCluster, a computational method that combines both clone and transcriptome information to create hybrid clusters that weight both kinds of data with a tunable parameter. We generated hybrid clusters across six independent datasets and found that ClonoCluster generated qualitatively different clusters in all cases. The markers of these hybrid clusters were different but had equivalent fidelity to transcriptome-only clusters. The genes most strongly associated with the rearrangements in hybrid clusters were ribosomal function and extracellular matrix genes. We also developed the complementary tool Warp Factor that incorporates clone information in popular 2D visualization techniques like UMAP. Integrating ClonoCluster and Warp Factor revealed biologically relevant markers of cell identity.
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Affiliation(s)
- Lee P. Richman
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogesh Goyal
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
| | - Connie L. Jiang
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arjun Raj
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
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43
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Espinosa-Medina I, Feliciano D, Belmonte-Mateos C, Linda Miyares R, Garcia-Marques J, Foster B, Lindo S, Pujades C, Koyama M, Lee T. TEMPO enables sequential genetic labeling and manipulation of vertebrate cell lineages. Neuron 2023; 111:345-361.e10. [PMID: 36417906 DOI: 10.1016/j.neuron.2022.10.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/15/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022]
Abstract
During development, regulatory factors appear in a precise order to determine cell fates over time. Consequently, to investigate complex tissue development, it is necessary to visualize and manipulate cell lineages with temporal control. Current strategies for tracing vertebrate cell lineages lack genetic access to sequentially produced cells. Here, we present TEMPO (Temporal Encoding and Manipulation in a Predefined Order), an imaging-readable genetic tool allowing differential labeling and manipulation of consecutive cell generations in vertebrates. TEMPO is based on CRISPR and powered by a cascade of gRNAs that drive orderly activation and inactivation of reporters and/or effectors. Using TEMPO to visualize zebrafish and mouse neurogenesis, we recapitulated birth-order-dependent neuronal fates. Temporally manipulating cell-cycle regulators in mouse cortex progenitors altered the proportion and distribution of neurons and glia, revealing the effects of temporal gene perturbation on serial cell fates. Thus, TEMPO enables sequential manipulation of molecular factors, crucial to study cell-type specification.
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Affiliation(s)
| | - Daniel Feliciano
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Carla Belmonte-Mateos
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, PRBB, Barcelona 08003, Spain
| | - Rosa Linda Miyares
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Jorge Garcia-Marques
- Centro Nacional de Biotecnologia, Consejo Superior de Investigaciones Cientificas, Madrid 28049, Spain
| | - Benjamin Foster
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Sarah Lindo
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Cristina Pujades
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, PRBB, Barcelona 08003, Spain
| | - Minoru Koyama
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada
| | - Tzumin Lee
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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44
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Haghverdi L, Ludwig LS. Single-cell multi-omics and lineage tracing to dissect cell fate decision-making. Stem Cell Reports 2023; 18:13-25. [PMID: 36630900 PMCID: PMC9860164 DOI: 10.1016/j.stemcr.2022.12.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 01/12/2023] Open
Abstract
The concept of cell fate relates to the future identity of a cell, and its daughters, which is obtained via cell differentiation and division. Understanding, predicting, and manipulating cell fate has been a long-sought goal of developmental and regenerative biology. Recent insights obtained from single-cell genomic and integrative lineage-tracing approaches have further aided to identify molecular features predictive of cell fate. In this perspective, we discuss these approaches with a focus on theoretical concepts and future directions of the field to dissect molecular mechanisms underlying cell fate.
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Affiliation(s)
- Laleh Haghverdi
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany.
| | - Leif S Ludwig
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
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45
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Rovira-Clavé X, Drainas AP, Jiang S, Bai Y, Baron M, Zhu B, Dallas AE, Lee MC, Chu TP, Holzem A, Ayyagari R, Bhattacharya D, McCaffrey EF, Greenwald NF, Markovic M, Coles GL, Angelo M, Bassik MC, Sage J, Nolan GP. Spatial epitope barcoding reveals clonal tumor patch behaviors. Cancer Cell 2022; 40:1423-1439.e11. [PMID: 36240778 PMCID: PMC9673683 DOI: 10.1016/j.ccell.2022.09.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 07/22/2022] [Accepted: 09/21/2022] [Indexed: 01/09/2023]
Abstract
Intratumoral heterogeneity is a seminal feature of human tumors contributing to tumor progression and response to treatment. Current technologies are still largely unsuitable to accurately track phenotypes and clonal evolution within tumors, especially in response to genetic manipulations. Here, we developed epitopes for imaging using combinatorial tagging (EpicTags), which we coupled to multiplexed ion beam imaging (EpicMIBI) for in situ tracking of barcodes within tissue microenvironments. Using EpicMIBI, we dissected the spatial component of cell lineages and phenotypes in xenograft models of small cell lung cancer. We observed emergent properties from mixed clones leading to the preferential expansion of clonal patches for both neuroendocrine and non-neuroendocrine cancer cell states in these models. In a tumor model harboring a fraction of PTEN-deficient cancer cells, we observed a non-autonomous increase of clonal patch size in PTEN wild-type cancer cells. EpicMIBI facilitates in situ interrogation of cell-intrinsic and cell-extrinsic processes involved in intratumoral heterogeneity.
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Affiliation(s)
- Xavier Rovira-Clavé
- Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA
| | - Alexandros P Drainas
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Maya Baron
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA
| | - Alec E Dallas
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Myung Chang Lee
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Theresa P Chu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Alessandra Holzem
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Ramya Ayyagari
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Debadrita Bhattacharya
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Erin F McCaffrey
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Noah F Greenwald
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Maxim Markovic
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Garry L Coles
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Michael C Bassik
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Julien Sage
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA 94305, USA.
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46
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Seidel S, Stadler T. TiDeTree: a Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data. Proc Biol Sci 2022; 289:20221844. [PMID: 36350216 PMCID: PMC9653226 DOI: 10.1098/rspb.2022.1844] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
The development of organisms and tissues is dictated by an elaborate balance between cell division, apoptosis and differentiation: the cell population dynamics. To quantify these dynamics, we propose a phylodynamic inference approach based on single-cell lineage recorder data. We developed a Bayesian phylogenetic framework-time-scaled developmental trees (TiDeTree)-that uses lineage recorder data to estimate time-scaled single-cell trees. By implementing TiDeTree within BEAST 2, we enable joint inference of the time-scaled trees and the cell population dynamics. We validated TiDeTree using simulations and showed that performance further improves when including multiple independent sources of information into the inference, such as frequencies of editing outcomes or experimental replicates. We benchmarked TiDeTree against state-of-the-art methods and show comparable performance in terms of tree topology, plus direct assessment of uncertainty and co-estimation of additional parameters. To demonstrate TiDeTree's use in practice, we analysed a public dataset containing lineage data from approximately 100 stem cell colonies. We estimated a time-scaled phylogeny for each colony; as well as the cell division and apoptosis rates underlying the growth dynamics of all colonies. We envision that TiDeTree will find broad application in the analysis of single-cell lineage tracing data, which will improve our understanding of cellular processes during development.
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Affiliation(s)
- Sophie Seidel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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47
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Chen C, Liao Y, Peng G. Connecting past and present: single-cell lineage tracing. Protein Cell 2022; 13:790-807. [PMID: 35441356 PMCID: PMC9237189 DOI: 10.1007/s13238-022-00913-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/06/2022] [Indexed: 01/16/2023] Open
Abstract
Central to the core principle of cell theory, depicting cells' history, state and fate is a fundamental goal in modern biology. By leveraging clonal analysis and single-cell RNA-seq technologies, single-cell lineage tracing provides new opportunities to interrogate both cell states and lineage histories. During the past few years, many strategies to achieve lineage tracing at single-cell resolution have been developed, and three of them (integration barcodes, polylox barcodes, and CRISPR barcodes) are noteworthy as they are amenable in experimentally tractable systems. Although the above strategies have been demonstrated in animal development and stem cell research, much care and effort are still required to implement these methods. Here we review the development of single-cell lineage tracing, major characteristics of the cell barcoding strategies, applications, as well as technical considerations and limitations, providing a guide to choose or improve the single-cell barcoding lineage tracing.
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Affiliation(s)
- Cheng Chen
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Yuanxin Liao
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guangdun Peng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Center for Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
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48
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Tyser RCV, Srinivas S. Recent advances in understanding cell types during human gastrulation. Semin Cell Dev Biol 2022; 131:35-43. [PMID: 35606274 PMCID: PMC7615356 DOI: 10.1016/j.semcdb.2022.05.004] [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: 12/13/2021] [Revised: 04/20/2022] [Accepted: 05/04/2022] [Indexed: 12/14/2022]
Abstract
Gastrulation is a fundamental process during embryonic development, conserved across all multicellular animals [1]. In the majority of metazoans, gastrulation is characterised by large scale morphogenetic remodeling, leading to the conversion of an early pluripotent embryonic cell layer into the three primary 'germ layers': an outer ectoderm, inner endoderm and intervening mesoderm layer. The morphogenesis of these three layers of cells is closely coordinated with cellular diversification, laying the foundation for the generation of the hundreds of distinct specialized cell types in the animal body. The process of gastrulation has for a long time attracted tremendous attention in a broad range of experimental systems ranging from sponges to mice. In humans the process of gastrulation starts approximately 14 days after fertilization and continues for slightly over a week. However our understanding of this important process, as it pertains to human, is limited. Donations of human fetal material at these early stages are exceptionally rare, making it nearly impossible to study human gastrulation directly. Therefore, our understanding of human gastrulation is predominantly derived from animal models such as the mouse [2,3] and from studies of limited collections of fixed whole samples and histological sections of human gastrulae [4-7], some of which date back to over a century ago. More recently we have been gaining valuable molecular insights into human gastrulation using in vitro models of hESCs [8-12] and increasingly, in vitro cultured human and non-human primate embryos [13-16]. However, while methods have been developed to culture human embryos into this stage (and probably beyond), current ethical standards prohibit the culture of human embryos past 14 days again limiting our ability to experimentally probe human gastrulation. This review discusses recent molecular insights from the study of a rare CS 7 human gastrula obtained as a live sample and raises several questions arising from this recent study that it will be interesting to address in the future using emerging models of human gastrulation.
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Affiliation(s)
- Richard C V Tyser
- Department of Physiology, Anatomy and Genetics, South Parks Road, University of Oxford , Oxford OX1 3QX, UK
| | - Shankar Srinivas
- Department of Physiology, Anatomy and Genetics, South Parks Road, University of Oxford , Oxford OX1 3QX, UK
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49
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Sankaran VG, Weissman JS, Zon LI. Cellular barcoding to decipher clonal dynamics in disease. Science 2022; 378:eabm5874. [PMID: 36227997 PMCID: PMC10111813 DOI: 10.1126/science.abm5874] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Cellular barcodes are distinct DNA sequences that enable one to track specific cells across time or space. Recent advances in our ability to detect natural or synthetic cellular barcodes, paired with single-cell readouts of cell state, have markedly increased our knowledge of clonal dynamics and genealogies of the cells that compose a variety of tissues and organs. These advances hold promise to redefine our view of human disease. Here, we provide an overview of cellular barcoding approaches, discuss applications to gain new insights into disease mechanisms, and provide an outlook on future applications. We discuss unanticipated insights gained through barcoding in studies of cancer and blood cell production and describe how barcoding can be applied to a growing array of medical fields, particularly with the increasing recognition of clonal contributions in human diseases.
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Affiliation(s)
- Vijay G Sankaran
- Division of Hematology and Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Harvard Stem Cell Institute, Cambridge, MA 02138, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.,David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Leonard I Zon
- Division of Hematology and Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Harvard Stem Cell Institute, Cambridge, MA 02138, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.,Stem Cell Program, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Harvard Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
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50
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Altshuler A, Wickström SA, Shalom-Feuerstein R. Spotlighting adult stem cells: advances, pitfalls, and challenges. Trends Cell Biol 2022; 33:477-494. [PMID: 36270939 DOI: 10.1016/j.tcb.2022.09.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/09/2022] [Accepted: 09/16/2022] [Indexed: 11/06/2022]
Abstract
The existence of stem cells (SCs) at the tip of the cellular differentiation hierarchy has fascinated the scientific community ever since their discovery in the early 1950s to 1960s. Despite the remarkable success of the SC theory and the development of SC-based treatments, fundamental features of SCs remain enigmatic. Recent advances in single-cell lineage tracing, live imaging, and genomic technologies have allowed capture of life histories and transcriptional signatures of individual cells, leaving SCs much less space to 'hide'. Focusing on epithelial SCs and comparing them to other SCs, we discuss new paradigms of the SC niche, dynamics, and pathology, highlighting key open questions in SC biology that need to be resolved for harnessing SC potential in regenerative medicine.
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