51
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Trapnell C. Revealing gene function with statistical inference at single-cell resolution. Nat Rev Genet 2024; 25:623-638. [PMID: 38951690 DOI: 10.1038/s41576-024-00750-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 07/03/2024]
Abstract
Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.
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Affiliation(s)
- Cole Trapnell
- Department of Genome Sciences, University of Washington, 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.
- Seattle Hub for Synthetic Biology, Seattle, WA, USA.
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52
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Jang H, Yim SS. Toward DNA-Based Recording of Biological Processes. Int J Mol Sci 2024; 25:9233. [PMID: 39273181 PMCID: PMC11394691 DOI: 10.3390/ijms25179233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/21/2024] [Accepted: 08/24/2024] [Indexed: 09/15/2024] Open
Abstract
Exploiting the inherent compatibility of DNA-based data storage with living cells, various cellular recording approaches have been developed for recording and retrieving biologically relevant signals in otherwise inaccessible locations, such as inside the body. This review provides an overview of the current state of engineered cellular memory systems, highlighting their design principles, advantages, and limitations. We examine various technologies, including CRISPR-Cas systems, recombinases, retrons, and DNA methylation, that enable these recording systems. Additionally, we discuss potential strategies for improving recording accuracy, scalability, and durability to address current limitations in the field. This emerging modality of biological measurement will be key to gaining novel insights into diverse biological processes and fostering the development of various biotechnological applications, from environmental sensing to disease monitoring and beyond.
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Affiliation(s)
- Hyeri Jang
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Sung Sun Yim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- Graduate School of Engineering Biology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
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53
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Rood JE, Hupalowska A, Regev A. Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas. Cell 2024; 187:4520-4545. [PMID: 39178831 DOI: 10.1016/j.cell.2024.07.035] [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: 05/03/2024] [Revised: 07/15/2024] [Accepted: 07/21/2024] [Indexed: 08/26/2024]
Abstract
Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.
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Affiliation(s)
| | | | - Aviv Regev
- Genentech, South San Francisco, CA, USA.
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54
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Gao Y, Feder AF. Detecting branching rate heterogeneity in multifurcating trees with applications in lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.601073. [PMID: 39005367 PMCID: PMC11244928 DOI: 10.1101/2024.06.27.601073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Understanding cellular birth rate differences is crucial for predicting cancer progression and interpreting tumor-derived genetic data. Lineage tracing experiments enable detailed reconstruction of cellular genealogies, offering new opportunities to measure branching rate heterogeneity. However, the lineage tracing process can introduce complex tree features that complicate this effort. Here, we examine tree characteristics in lineage tracing-derived genealogies and find that editing window placement leads to multifurcations at a tree's root or tips. We propose several ways in which existing tree topology-based metrics can be extended to test for rate heterogeneity on trees even in the presence of lineage-tracing associated distortions. Although these methods vary in power and robustness, a test based on theJ 1 statistic effectively detects branching rate heterogeneity in simulated lineage tracing data. Tests based on other common statistics ( s ^ and the Sackin index) show interior performance toJ 1 . We apply our validated methods to xenograft experimental data and find widespread rate heterogeneity across multiple study systems. Our results demonstrate the potential of tree topology statistics in analyzing lineage tracing data, and highlight the challenges associated with adapting phylogenetic methods to these systems.
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Affiliation(s)
- Yingnan Gao
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - Alison F Feder
- Department of Genome Sciences, University of Washington, Seattle, WA
- Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA
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55
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Sande-Melon M, Bergemann D, Fernández-Lajarín M, González-Rosa JM, Cox AG. Development of a hepatic cryoinjury model to study liver regeneration. Development 2024; 151:dev203124. [PMID: 38975841 PMCID: PMC11318111 DOI: 10.1242/dev.203124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 06/20/2024] [Indexed: 07/09/2024]
Abstract
The liver is a remarkable organ that can regenerate in response to injury. Depending on the extent of injury, the liver can undergo compensatory hyperplasia or fibrosis. Despite decades of research, the molecular mechanisms underlying these processes are poorly understood. Here, we developed a new model to study liver regeneration based on cryoinjury. To visualise liver regeneration at cellular resolution, we adapted the CUBIC tissue-clearing approach. Hepatic cryoinjury induced a localised necrotic and apoptotic lesion characterised by inflammation and infiltration of innate immune cells. After this initial phase, we observed fibrosis, which resolved as regeneration re-established homeostasis in 30 days. Importantly, this approach enables the comparison of healthy and injured parenchyma within an individual animal, providing unique advantages to previous models. In summary, the hepatic cryoinjury model provides a fast and reproducible method for studying the cellular and molecular pathways underpinning fibrosis and liver regeneration.
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Affiliation(s)
- Marcos Sande-Melon
- Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria 3000, Australia
| | - David Bergemann
- Cardiovascular Research Centre, Massachusetts General Hospital Research Institute, Charlestown Navy Yard Campus, 149, 13th Street, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Miriam Fernández-Lajarín
- Cardiovascular Research Centre, Massachusetts General Hospital Research Institute, Charlestown Navy Yard Campus, 149, 13th Street, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
- Biology Department, Morrissey College of Arts and Sciences, Boston College, Chestnut Hill, MA 02467, USA
| | - Juan Manuel González-Rosa
- Cardiovascular Research Centre, Massachusetts General Hospital Research Institute, Charlestown Navy Yard Campus, 149, 13th Street, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
- Biology Department, Morrissey College of Arts and Sciences, Boston College, Chestnut Hill, MA 02467, USA
| | - Andrew G. Cox
- Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria 3000, Australia
- Department of Biochemistry and Pharmacology, The University of Melbourne, Melbourne, Victoria 3000, Australia
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56
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Liberali P, Schier AF. The evolution of developmental biology through conceptual and technological revolutions. Cell 2024; 187:3461-3495. [PMID: 38906136 DOI: 10.1016/j.cell.2024.05.053] [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: 04/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/23/2024]
Abstract
Developmental biology-the study of the processes by which cells, tissues, and organisms develop and change over time-has entered a new golden age. After the molecular genetics revolution in the 80s and 90s and the diversification of the field in the early 21st century, we have entered a phase when powerful technologies provide new approaches and open unexplored avenues. Progress in the field has been accelerated by advances in genomics, imaging, engineering, and computational biology and by emerging model systems ranging from tardigrades to organoids. We summarize how revolutionary technologies have led to remarkable progress in understanding animal development. We describe how classic questions in gene regulation, pattern formation, morphogenesis, organogenesis, and stem cell biology are being revisited. We discuss the connections of development with evolution, self-organization, metabolism, time, and ecology. We speculate how developmental biology might evolve in an era of synthetic biology, artificial intelligence, and human engineering.
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Affiliation(s)
- Prisca Liberali
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland; University of Basel, Basel, Switzerland.
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57
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Sun K, Liu X, Xu R, Liu C, Meng A, Lan X. Mapping the chromatin accessibility landscape of zebrafish embryogenesis at single-cell resolution by SPATAC-seq. Nat Cell Biol 2024; 26:1187-1199. [PMID: 38977847 DOI: 10.1038/s41556-024-01449-0] [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: 12/15/2022] [Accepted: 05/30/2024] [Indexed: 07/10/2024]
Abstract
Currently, the dynamic accessible elements that determine regulatory programs responsible for the unique identity and function of each cell type during zebrafish embryogenesis lack detailed study. Here we present SPATAC-seq: a split-pool ligation-based assay for transposase-accessible chromatin using sequencing. Using SPATAC-seq, we profiled chromatin accessibility in more than 800,000 individual nuclei across 20 developmental stages spanning the sphere stage to the early larval protruding mouth stage. Using this chromatin accessibility map, we identified 604 cell states and inferred their developmental relationships. We also identified 959,040 candidate cis-regulatory elements (cCREs) and delineated development-specific cCREs, as well as transcription factors defining diverse cell identities. Importantly, enhancer reporter assays confirmed that the majority of tested cCREs exhibited robust enhanced green fluorescent protein expression in restricted cell types or tissues. Finally, we explored gene regulatory programs that drive pigment and notochord cell differentiation. Our work provides a valuable open resource for exploring driver regulators of cell fate decisions in zebrafish embryogenesis.
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Affiliation(s)
- Keyong Sun
- School of Medicine, Tsinghua University, Beijing, China
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Tsinghua University, Beijing, China
| | - Xin Liu
- School of Life Sciences, Tsinghua University, Beijing, China
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China
| | - Runda Xu
- School of Medicine, Tsinghua University, Beijing, China
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China
| | - Chang Liu
- School of Medicine, Tsinghua University, Beijing, China
| | - Anming Meng
- School of Life Sciences, Tsinghua University, Beijing, China.
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China.
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing, China.
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Tsinghua University, Beijing, China.
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China.
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China.
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58
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Sashittal P, Chen V, Pasarkar A, Raphael BJ. Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data. Bioinformatics 2024; 40:i218-i227. [PMID: 38940122 PMCID: PMC11211840 DOI: 10.1093/bioinformatics/btae231] [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 Eukaryotic cells contain organelles called mitochondria that have their own genome. Most cells contain thousands of mitochondria which replicate, even in nondividing cells, by means of a relatively error-prone process resulting in somatic mutations in their genome. Because of the higher mutation rate compared to the nuclear genome, mitochondrial mutations have been used to track cellular lineage, particularly using single-cell sequencing that measures mitochondrial mutations in individual cells. However, existing methods to infer the cell lineage tree from mitochondrial mutations do not model "heteroplasmy," which is the presence of multiple mitochondrial clones with distinct sets of mutations in an individual cell. Single-cell sequencing data thus provide a mixture of the mitochondrial clones in individual cells, with the ancestral relationships between these clones described by a mitochondrial clone tree. While deconvolution of somatic mutations from a mixture of evolutionarily related genomes has been extensively studied in the context of bulk sequencing of cancer tumor samples, the problem of mitochondrial deconvolution has the additional constraint that the mitochondrial clone tree must be concordant with the cell lineage tree. RESULTS We formalize the problem of inferring a concordant pair of a mitochondrial clone tree and a cell lineage tree from single-cell sequencing data as the Nested Perfect Phylogeny Mixture (NPPM) problem. We derive a combinatorial characterization of the solutions to the NPPM problem, and formulate an algorithm, MERLIN, to solve this problem exactly using a mixed integer linear program. We show on simulated data that MERLIN outperforms existing methods that do not model mitochondrial heteroplasmy nor the concordance between the mitochondrial clone tree and the cell lineage tree. We use MERLIN to analyze single-cell whole-genome sequencing data of 5220 cells of a gastric cancer cell line and show that MERLIN infers a more biologically plausible cell lineage tree and mitochondrial clone tree compared to existing methods. AVAILABILITY AND IMPLEMENTATION https://github.com/raphael-group/MERLIN.
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Affiliation(s)
- Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, NJ 08540, United States
| | - Viola Chen
- Department of Computer Science, Princeton University, Princeton, NJ 08540, United States
| | - Amey Pasarkar
- Department of Computer Science, Princeton University, Princeton, NJ 08540, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08540, United States
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59
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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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60
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Sande-Melon M, Bergemann D, Fernández-Lajarín M, González-Rosa JM, Cox AG. Development of a hepatic cryoinjury model to study liver regeneration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.24.550437. [PMID: 38948752 PMCID: PMC11212901 DOI: 10.1101/2023.07.24.550437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The liver is a remarkable organ that can regenerate in response to injury. Depending on the extent of injury, the liver can undergo compensatory hyperplasia or fibrosis. Despite decades of research, the molecular mechanisms underlying these processes are poorly understood. Here, we developed a new model to study liver regeneration based on cryoinjury. To visualise liver regeneration at cellular resolution, we adapted the CUBIC tissue-clearing approach. Hepatic cryoinjury induced a localised necrotic and apoptotic lesion characterised by inflammation and infiltration of innate immune cells. Following this initial phase, we observed fibrosis, which resolved as regeneration re-established homeostasis in 30 days. Importantly, this approach enables the comparison of healthy and injured parenchyma with an individual animal, providing unique advantages to previous models. In summary, the hepatic cryoinjury model provides a fast and reproducible method for studying the cellular and molecular pathways underpinning fibrosis and liver regeneration.
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Affiliation(s)
- Marcos Sande-Melon
- Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, 3000, Australia
| | - David Bergemann
- Cardiovascular Research Centre, Massachusetts General Hospital Research Institute, Charlestown Navy Yard Campus, 149, 13 Street, 02129 MA, USA
- Harvard Medical School
| | - Miriam Fernández-Lajarín
- Cardiovascular Research Centre, Massachusetts General Hospital Research Institute, Charlestown Navy Yard Campus, 149, 13 Street, 02129 MA, USA
- Harvard Medical School
- Biology Department, Morrissey College of Arts and Sciences, Boston College, Chestnut Hill, MA 02467
| | - Juan Manuel González-Rosa
- Cardiovascular Research Centre, Massachusetts General Hospital Research Institute, Charlestown Navy Yard Campus, 149, 13 Street, 02129 MA, USA
- Harvard Medical School
- Biology Department, Morrissey College of Arts and Sciences, Boston College, Chestnut Hill, MA 02467
| | - Andrew G. Cox
- Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, 3000, Australia
- Department of Biochemistry and Pharmacology, The University of Melbourne, Melbourne, Victoria, 3000, Australia
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61
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Graham JH, Schlachetzki JCM, Yang X, Breuss MW. Genomic Mosaicism of the Brain: Origin, Impact, and Utility. Neurosci Bull 2024; 40:759-776. [PMID: 37898991 PMCID: PMC11178748 DOI: 10.1007/s12264-023-01124-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/16/2023] [Indexed: 10/31/2023] Open
Abstract
Genomic mosaicism describes the phenomenon where some but not all cells within a tissue harbor unique genetic mutations. Traditionally, research focused on the impact of genomic mosaicism on clinical phenotype-motivated by its involvement in cancers and overgrowth syndromes. More recently, we increasingly shifted towards the plethora of neutral mosaic variants that can act as recorders of cellular lineage and environmental exposures. Here, we summarize the current state of the field of genomic mosaicism research with a special emphasis on our current understanding of this phenomenon in brain development and homeostasis. Although the field of genomic mosaicism has a rich history, technological advances in the last decade have changed our approaches and greatly improved our knowledge. We will provide current definitions and an overview of contemporary detection approaches for genomic mosaicism. Finally, we will discuss the impact and utility of genomic mosaicism.
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Affiliation(s)
- Jared H Graham
- Department of Pediatrics, Section of Clinical Genetics and Metabolism, University of Colorado School of Medicine, Aurora, 80045-2581, CO, USA
| | - Johannes C M Schlachetzki
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, 92093-0021, San Diego, CA, USA
| | - Xiaoxu Yang
- Department of Neurosciences, University of California San Diego, La Jolla, 92093-0021, San Diego, CA, USA
- Rady Children's Institute for Genomic Medicine, San Diego, 92123, CA, USA
| | - Martin W Breuss
- Department of Pediatrics, Section of Clinical Genetics and Metabolism, University of Colorado School of Medicine, Aurora, 80045-2581, CO, USA.
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62
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Li L, Xie W, Zhan L, Wen S, Luo X, Xu S, Cai Y, Tang W, Wang Q, Li M, Xie Z, Deng L, Zhu H, Yu G. Resolving tumor evolution: a phylogenetic approach. JOURNAL OF THE NATIONAL CANCER CENTER 2024; 4:97-106. [PMID: 39282584 PMCID: PMC11390690 DOI: 10.1016/j.jncc.2024.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 02/28/2024] [Accepted: 03/20/2024] [Indexed: 09/19/2024] Open
Abstract
The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.
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Affiliation(s)
- Lin Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenqin Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shaodi Wen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Department of Oncology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital, Nanjing, China
| | - Xiao Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Division of Laboratory Medicine, Microbiome Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yantong Cai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ming Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Zijing Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lin Deng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Hongyuan Zhu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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63
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Aalam SMM, Nguyen LV, Ritting ML, Kannan N. Clonal tracking in cancer and metastasis. Cancer Metastasis Rev 2024; 43:639-656. [PMID: 37910295 PMCID: PMC11500829 DOI: 10.1007/s10555-023-10149-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023]
Abstract
The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.
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Affiliation(s)
| | - Long Viet Nguyen
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Megan L Ritting
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
- Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA.
- Center for Regenerative Biotherapeutics, Mayo Clinic, Rochester, MN, USA.
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64
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Yoon B, Kim H, Jung SW, Park J. Single-cell lineage tracing approaches to track kidney cell development and maintenance. Kidney Int 2024; 105:1186-1199. [PMID: 38554991 DOI: 10.1016/j.kint.2024.01.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/06/2023] [Accepted: 01/09/2024] [Indexed: 04/02/2024]
Abstract
The kidney is a complex organ consisting of various cell types. Previous studies have aimed to elucidate the cellular relationships among these cell types in developing and mature kidneys using Cre-loxP-based lineage tracing. However, this methodology falls short of fully capturing the heterogeneous nature of the kidney, making it less than ideal for comprehensively tracing cellular progression during kidney development and maintenance. Recent technological advancements in single-cell genomics have revolutionized lineage tracing methods. Single-cell lineage tracing enables the simultaneous tracing of multiple cell types within complex tissues and their transcriptomic profiles, thereby allowing the reconstruction of their lineage tree with cell state information. Although single-cell lineage tracing has been successfully applied to investigate cellular hierarchies in various organs and tissues, its application in kidney research is currently lacking. This review comprehensively consolidates the single-cell lineage tracing methods, divided into 4 categories (clustered regularly interspaced short palindromic repeat [CRISPR]/CRISPR-associated protein 9 [Cas9]-based, transposon-based, Polylox-based, and native barcoding methods), and outlines their technical advantages and disadvantages. Furthermore, we propose potential future research topics in kidney research that could benefit from single-cell lineage tracing and suggest suitable technical strategies to apply to these topics.
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Affiliation(s)
- Baul Yoon
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Hayoung Kim
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Su Woong Jung
- Division of Nephrology, Department of Internal Medicine, College of Medicine, Kyung Hee University, Seoul, Republic of Korea; Division of Nephrology, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea.
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea.
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65
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Quek YJ, Tay A. Nanoscale Methods for Longitudinal Extraction of Intracellular Contents. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2314184. [PMID: 38459829 DOI: 10.1002/adma.202314184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/04/2024] [Indexed: 03/10/2024]
Abstract
Longitudinal analysis of intracellular contents including gene and protein expression is crucial for deciphering the fundamentally dynamic nature of cells. This offers invaluable insights into complex tissue composition and behavior, and drives progress in disease diagnosis, biomarker discovery, and drug development. Traditional longitudinal analysis workflows, involving the destruction of cells at various timepoints, limit insights to singular moments and fail to account for cellular heterogeneity. Current non-destructive approaches, like temporal modeling with single-cell ribonucleic acid sequencing (RNA-seq) and live-cell fluorescence imaging, either rely on biological assumptions or possess the risk of cellular perturbation. Recent advances in nanoscale technologies for non-destructive intracellular content extraction offer a promising solution to these challenges. These novel methods work at the nanoscale to non-destructively access cellular membranes and can be broadly classified into three mechanisms: tip-facilitated aspiration, membrane-based, and probe-based methods. This perspective focuses on these emerging nanotechnologies for repeated intracellular content extraction. Their potential in longitudinal analysis is discussed, the critical requirements for effective repeated sampling are addressed, and the suitability of each technique for various applications is explored. Furthermore, unresolved challenges in repeated sampling are highlighted to encourage further research in this growing field.
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Affiliation(s)
- Ying Jie Quek
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, 138648, Singapore
| | - Andy Tay
- Department of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, Singapore, 117599, Singapore
- Tissue Engineering Programme, National University of Singapore, Singapore, 117510, Singapore
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66
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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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67
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Holze H, Talarmain L, Fennell KA, Lam EY, Dawson MA, Vassiliadis D. Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools. CELL REPORTS METHODS 2024; 4:100763. [PMID: 38670101 PMCID: PMC11133760 DOI: 10.1016/j.crmeth.2024.100763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/25/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024]
Abstract
Cellular barcoding is a lineage-tracing methodology that couples heritable synthetic barcodes to high-throughput sequencing, enabling the accurate tracing of cell lineages across a range of biological contexts. Recent studies have extended these methods by incorporating lineage information into single-cell or spatial transcriptomics readouts. Leveraging the rich biological information within these datasets requires dedicated computational tools for dataset pre-processing and analysis. Here, we present BARtab, a portable and scalable Nextflow pipeline, and bartools, an open-source R package, designed to provide an integrated end-to-end cellular barcoding analysis toolkit. BARtab and bartools contain methods to simplify the extraction, quality control, analysis, and visualization of lineage barcodes from population-level, single-cell, and spatial transcriptomics experiments. We showcase the utility of our integrated BARtab and bartools workflow via the analysis of exemplar bulk, single-cell, and spatial transcriptomics experiments containing cellular barcoding information.
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Affiliation(s)
- Henrietta Holze
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Laure Talarmain
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Katie A Fennell
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Enid Y Lam
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Mark A Dawson
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia; The University of Melbourne Centre for Cancer Research, The University of Melbourne, Melbourne, VIC 3000, Australia.
| | - Dane Vassiliadis
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia.
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Siniscalco A, Perera RP, Greenslade JE, Masters A, Doll H, Raj B. Barcoding Notch signaling in the developing brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593533. [PMID: 38766256 PMCID: PMC11100830 DOI: 10.1101/2024.05.10.593533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Developmental signaling inputs are fundamental for shaping cell fates and behavior. However, traditional fluorescent-based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER-seq, a CRISPR-Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single-cell transcriptomics. We applied SABER-seq to record Notch signaling in developing zebrafish brains. SABER-seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch-dependent manner with inducible control while the recorder accumulates mutations that represent Notch activity in founder cells. We combine SABER-seq with an expanded juvenile brain atlas to define cell types whose fates are determined downstream of Notch signaling. We identified examples wherein Notch signaling may have differential impact on terminal cell fates. SABER-seq is a novel platform for rapid, scalable and high-resolution mapping of signaling activity during development.
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Affiliation(s)
- Abigail Siniscalco
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Roshan Priyarangana Perera
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jessie E. Greenslade
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Aiden Masters
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Hannah Doll
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Bushra Raj
- Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Institute for Regenerative Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
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69
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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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70
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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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71
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Wang K, Hou L, Wang X, Zhai X, Lu Z, Zi Z, Zhai W, He X, Curtis C, Zhou D, Hu Z. PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes. Nat Biotechnol 2024; 42:778-789. [PMID: 37524958 DOI: 10.1038/s41587-023-01887-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/28/2023] [Indexed: 08/02/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.
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Affiliation(s)
- Kun Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- School of Mathematical Sciences, Xiamen University, Xiamen, China
| | - Liangzhen Hou
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Xin Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiangwei Zhai
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Zhaolian Lu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhike Zi
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weiwei Zhai
- CAS Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Xionglei He
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Christina Curtis
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
| | - Zheng Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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72
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Wang Z, Wang Y, Zhou T, Chen S, Morris D, Magalhães RDM, Li M, Wang S, Wang H, Xie Y, McSwiggin H, Oliver D, Yuan S, Zheng H, Mohammed J, Lai EC, McCarrey JR, Yan W. The rapidly evolving X-linked MIR-506 family fine-tunes spermatogenesis to enhance sperm competition. eLife 2024; 13:RP90203. [PMID: 38639482 PMCID: PMC11031087 DOI: 10.7554/elife.90203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024] Open
Abstract
Despite rapid evolution across eutherian mammals, the X-linked MIR-506 family miRNAs are located in a region flanked by two highly conserved protein-coding genes (SLITRK2 and FMR1) on the X chromosome. Intriguingly, these miRNAs are predominantly expressed in the testis, suggesting a potential role in spermatogenesis and male fertility. Here, we report that the X-linked MIR-506 family miRNAs were derived from the MER91C DNA transposons. Selective inactivation of individual miRNAs or clusters caused no discernible defects, but simultaneous ablation of five clusters containing 19 members of the MIR-506 family led to reduced male fertility in mice. Despite normal sperm counts, motility, and morphology, the KO sperm were less competitive than wild-type sperm when subjected to a polyandrous mating scheme. Transcriptomic and bioinformatic analyses revealed that these X-linked MIR-506 family miRNAs, in addition to targeting a set of conserved genes, have more targets that are critical for spermatogenesis and embryonic development during evolution. Our data suggest that the MIR-506 family miRNAs function to enhance sperm competitiveness and reproductive fitness of the male by finetuning gene expression during spermatogenesis.
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Affiliation(s)
- Zhuqing Wang
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterTorranceUnited States
| | - Yue Wang
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterTorranceUnited States
| | - Tong Zhou
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
| | - Sheng Chen
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterTorranceUnited States
| | - Dayton Morris
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterTorranceUnited States
| | | | - Musheng Li
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
| | - Shawn Wang
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
| | - Hetan Wang
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterTorranceUnited States
| | - Yeming Xie
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
| | - Hayden McSwiggin
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterTorranceUnited States
| | - Daniel Oliver
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
| | - Shuiqiao Yuan
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
| | - Huili Zheng
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterTorranceUnited States
| | - Jaaved Mohammed
- Developmental Biology Program, Sloan Kettering InstituteNew YorkUnited States
| | - Eric C Lai
- Developmental Biology Program, Sloan Kettering InstituteNew YorkUnited States
| | - John R McCarrey
- Department of Neuroscience, Developmental and Regenerative Biology, University of Texas at San AntonioSan AntonioUnited States
| | - Wei Yan
- Department of Physiology and Cell Biology, University of Nevada, Reno School of MedicineRenoUnited States
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical CenterTorranceUnited States
- Department of Medicine, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
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73
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Manso BA, Rodriguez y Baena A, Forsberg EC. From Hematopoietic Stem Cells to Platelets: Unifying Differentiation Pathways Identified by Lineage Tracing Mouse Models. Cells 2024; 13:704. [PMID: 38667319 PMCID: PMC11048769 DOI: 10.3390/cells13080704] [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: 03/29/2024] [Revised: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Platelets are the terminal progeny of megakaryocytes, primarily produced in the bone marrow, and play critical roles in blood homeostasis, clotting, and wound healing. Traditionally, megakaryocytes and platelets are thought to arise from multipotent hematopoietic stem cells (HSCs) via multiple discrete progenitor populations with successive, lineage-restricting differentiation steps. However, this view has recently been challenged by studies suggesting that (1) some HSC clones are biased and/or restricted to the platelet lineage, (2) not all platelet generation follows the "canonical" megakaryocytic differentiation path of hematopoiesis, and (3) platelet output is the default program of steady-state hematopoiesis. Here, we specifically investigate the evidence that in vivo lineage tracing studies provide for the route(s) of platelet generation and investigate the involvement of various intermediate progenitor cell populations. We further identify the challenges that need to be overcome that are required to determine the presence, role, and kinetics of these possible alternate pathways.
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Affiliation(s)
- Bryce A. Manso
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alessandra Rodriguez y Baena
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Program in Biomedical Sciences and Engineering, Department of Molecular, Cell, and Developmental Biology, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
| | - E. Camilla Forsberg
- Institute for the Biology of Stem Cells, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, University of California-Santa Cruz, Santa Cruz, CA 95064, USA
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74
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Feng DC, Zhu WZ, Wang J, Li DX, Shi X, Xiong Q, You J, Han P, Qiu S, Wei Q, Yang L. The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy. Mil Med Res 2024; 11:21. [PMID: 38605399 PMCID: PMC11007901 DOI: 10.1186/s40779-024-00526-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 03/25/2024] [Indexed: 04/13/2024] Open
Abstract
In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.
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Affiliation(s)
- De-Chao Feng
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Division of Surgery & Interventional Science, University College London, London, WC1E 6BT, UK.
| | - Wei-Zhen Zhu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jie Wang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Deng-Xiong Li
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xu Shi
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiao Xiong
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jia You
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ping Han
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Shi Qiu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qiang Wei
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Lu Yang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041, China.
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75
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Butler G, Amend SR, Axelrod R, Venditti C, Pienta KJ. Punctuational evolution is pervasive in distal site metastatic colonization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588529. [PMID: 38645078 PMCID: PMC11030309 DOI: 10.1101/2024.04.08.588529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The evolution of metastasis represents a lethal stage of cancer progression. Yet, the evolutionary kinetics of metastatic disease remain unresolved. Here, using single cell CRISPR-Cas9 lineage tracing data, we show that in metastatic disease, gradual molecular evolution is punctuated by episodes of rapid evolutionary change associated with lineage divergence. By measuring punctuational effects across the metastatic cascade, we show that punctuational effects contribute more to the molecular diversity at distal site metastases compared to the paired primary tumor, suggesting qualitatively different modes of evolution may drive primary and metastatic tumor progression. This is the first empirical evidence for distinct patterns of molecular evolution at early and late stages of metastasis and demonstrates the complex interplay of cell intrinsic and extrinsic factors that shape lethal cancer.
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Affiliation(s)
- George Butler
- Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Sarah R. Amend
- Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Robert Axelrod
- Geral R. Ford School of Public Policy, University of Michigan, Ann Arbor, MI, USA
| | - Chris Venditti
- School of Biological Sciences, University of Reading, Reading, UK
| | - Kenneth J. Pienta
- Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA
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76
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Deng S, Gong H, Zhang D, Zhang M, He X. A statistical method for quantifying progenitor cells reveals incipient cell fate commitments. Nat Methods 2024; 21:597-608. [PMID: 38379073 DOI: 10.1038/s41592-024-02189-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 01/19/2024] [Indexed: 02/22/2024]
Abstract
Quantifying the number of progenitor cells that found an organ, tissue or cell population is of fundamental importance for understanding the development and homeostasis of a multicellular organism. Previous efforts rely on marker genes that are specifically expressed in progenitors. This strategy is, however, often hindered by the lack of ideal markers. Here we propose a general statistical method to quantify the progenitors of any tissues or cell populations in an organism, even in the absence of progenitor-specific markers, by exploring the cell phylogenetic tree that records the cell division history during development. The method, termed targeting coalescent analysis (TarCA), computes the probability that two randomly sampled cells of a tissue coalesce within the tissue-specific monophyletic clades. The inverse of this probability then serves as a measure of the progenitor number of the tissue. Both mathematic modeling and computer simulations demonstrated the high accuracy of TarCA, which was then validated using real data from nematode, fruit fly and mouse, all with related cell phylogenetic trees. We further showed that TarCA can be used to identify lineage-specific upregulated genes during embryogenesis, revealing incipient cell fate commitments in mouse embryos.
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Affiliation(s)
- Shanjun Deng
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Han Gong
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Di Zhang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Mengdong Zhang
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Xionglei He
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.
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77
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Abdullah L, Emiliani FE, Vaidya CM, Stuart H, Kolling FW, Ackerman ME, Song L, McKenna A, Huang YH. Hierarchal single-cell lineage tracing reveals differential fate commitment of CD8 T-cell clones in response to acute infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.586160. [PMID: 38585810 PMCID: PMC10996474 DOI: 10.1101/2024.03.21.586160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Generating balanced populations of CD8 effector and memory T cells is necessary for immediate and durable immunity to infections and cancer. Yet, a definitive understanding of CD8 differentiation remains unclear. We used CARLIN, a processive lineage recording mouse model with single-cell RNA-seq and TCR-seq to track endogenous antigen-specific CD8 T cells during acute viral infection. We identified a diverse repertoire of expanded T-cell clones represented by seven transcriptional states. TCR enrichment analysis revealed differential memory- or effector-fate biases within clonal populations. Shared Vb segments and amino acid motifs were found within biased categories despite high TCR diversity. Using single-cell CARLIN barcode-seq we tracked multi-generational clones and found that unlike unbiased or memory-biased clones, which stably retain their fate profiles, effector-biased clones could adopt memory- or effector-bias within subclones. Collectively, our study demonstrates that a heterogenous T-cell repertoire specific for a shared antigen is composed of clones with distinct TCR-intrinsic fate-biases.
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Affiliation(s)
- Leena Abdullah
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Francesco E. Emiliani
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | - Chinmay M. Vaidya
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
| | - Hannah Stuart
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
| | | | - Margaret E. Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
- Dartmouth Cancer Center, Lebanon, NH 03756, USA
| | - Li Song
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
- Dartmouth Cancer Center, Lebanon, NH 03756, USA
| | - Aaron McKenna
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
- Dartmouth Cancer Center, Lebanon, NH 03756, USA
| | - Yina H. Huang
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA
- Dartmouth Cancer Center, Lebanon, NH 03756, USA
- Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH 03756, USA
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78
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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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79
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Kang M, Armenteros JJA, Gulati GS, Gleyzer R, Avagyan S, Brown EL, Zhang W, Usmani A, Earland N, Wu Z, Zou J, Fields RC, Chen DY, Chaudhuri AA, Newman AM. Mapping single-cell developmental potential in health and disease with interpretable deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585637. [PMID: 38562882 PMCID: PMC10983880 DOI: 10.1101/2024.03.19.585637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.
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80
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Shlyakhtina Y, Bloechl B, Moran KL, Portal MM. Protocol to study the inheritance and propagation of non-genetically encoded states using barcode decay lineage tracing. STAR Protoc 2024; 5:102809. [PMID: 38180835 PMCID: PMC10801334 DOI: 10.1016/j.xpro.2023.102809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/21/2023] [Accepted: 12/15/2023] [Indexed: 01/07/2024] Open
Abstract
Here, we present a protocol to perform barcode decay lineage tracing followed by single-cell transcriptome analysis (BdLT-Seq). We describe steps for BdLT-Seq experimental design, building barcoded episome reporters, performing episome transfection, and barcode retrieval. We then describe procedures for sequencing library construction while providing options for sample multiplexing and data analysis. This BdLT-Seq technique enables the assessment of clonal evolution in a directional manner while preserving isogeneity, thus allowing the comparison of non-genetic molecular features between isogenic cell lineages. For complete details on the use and execution of this protocol, please refer to Shlyakhtina et al. (2023).1.
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Affiliation(s)
- Yelyzaveta Shlyakhtina
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK
| | - Bianca Bloechl
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK
| | - Katherine L Moran
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Maximiliano M Portal
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK.
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81
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Zhang M, Lui KO, Zhou B. Application of New Lineage Tracing Techniques in Cardiovascular Development and Physiology. Circ Res 2024; 134:445-458. [PMID: 38359092 DOI: 10.1161/circresaha.123.323179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Cardiovascular disease has been the leading cause of mortality and morbidity worldwide in the past 3 decades. Multiple cell lineages undergo dynamic alternations in gene expression, cell state determination, and cell fate conversion to contribute, adapt, and even modulate the pathophysiological processes during disease progression. There is an urgent need to understand the intricate cellular and molecular underpinnings of cardiovascular cell development in homeostasis and pathogenesis. Recent strides in lineage tracing methodologies have revolutionized our understanding of cardiovascular biology with the identification of new cellular origins, fates, plasticity, and heterogeneity within the cardiomyocyte, endothelial, and mesenchymal cell populations. In this review, we introduce the new technologies for lineage tracing of cardiovascular cells and summarize their applications in studying cardiovascular development, diseases, repair, and regeneration.
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Affiliation(s)
- MingJun Zhang
- New Cornerstone Investigator Institute, State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, China (M.J., B.Z.)
| | - Kathy O Lui
- Department of Chemical Pathology, Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, China (K.O.L.)
| | - Bin Zhou
- New Cornerstone Investigator Institute, State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, China (M.J., B.Z.)
- School of Life Science and Technology, ShanghaiTech University, China (B.Z.)
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, China (B.Z.)
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82
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Sun W, Perkins M, Huyghe M, Faraldo MM, Fre S, Perié L, Lyne AM. Extracting, filtering and simulating cellular barcodes using CellBarcode tools. NATURE COMPUTATIONAL SCIENCE 2024; 4:128-143. [PMID: 38374363 PMCID: PMC10899113 DOI: 10.1038/s43588-024-00595-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/16/2024] [Indexed: 02/21/2024]
Abstract
Identifying true DNA cellular barcodes among polymerase chain reaction and sequencing errors is challenging. Current tools are restricted in the diversity of barcode types supported or the analysis strategies implemented. As such, there is a need for more versatile and efficient tools for barcode extraction, as well as for tools to investigate which factors impact barcode detection and which filtering strategies to best apply. Here we introduce the package CellBarcode and its barcode simulation kit, CellBarcodeSim, that allows efficient and versatile barcode extraction and filtering for a range of barcode types from bulk or single-cell sequencing data using a variety of filtering strategies. Using the barcode simulation kit and biological data, we explore the technical and biological factors influencing barcode identification and provide a decision tree on how to optimize barcode identification for different barcode settings. We believe that CellBarcode and CellBarcodeSim have the capability to enhance the reproducibility and interpretation of barcode results across studies.
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Affiliation(s)
- Wenjie Sun
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
| | - Meghan Perkins
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Mathilde Huyghe
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Marisa M Faraldo
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Silvia Fre
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Leïla Perié
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
| | - Anne-Marie Lyne
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
- INSERM U900, Paris, France.
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France.
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83
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Wang S, Mao X, Wang F, Zuo X, Fan C. Data Storage Using DNA. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307499. [PMID: 37800877 DOI: 10.1002/adma.202307499] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/01/2023] [Indexed: 10/07/2023]
Abstract
The exponential growth of global data has outpaced the storage capacities of current technologies, necessitating innovative storage strategies. DNA, as a natural medium for preserving genetic information, has emerged as a highly promising candidate for next-generation storage medium. Storing data in DNA offers several advantages, including ultrahigh physical density and exceptional durability. Facilitated by significant advancements in various technologies, such as DNA synthesis, DNA sequencing, and DNA nanotechnology, remarkable progress has been made in the field of DNA data storage over the past decade. However, several challenges still need to be addressed to realize practical applications of DNA data storage. In this review, the processes and strategies of in vitro DNA data storage are first introduced, highlighting recent advancements. Next, a brief overview of in vivo DNA data storage is provided, with a focus on the various writing strategies developed to date. At last, the challenges encountered in each step of DNA data storage are summarized and promising techniques are discussed that hold great promise in overcoming these obstacles.
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Affiliation(s)
- Shaopeng Wang
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Xiuhai Mao
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Fei Wang
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaolei Zuo
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chunhai Fan
- Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
- School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhangjiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China
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84
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Zhu J, Wang Y, Chang WY, Malewska A, Napolitano F, Gahan JC, Unni N, Zhao M, Yuan R, Wu F, Yue L, Guo L, Zhao Z, Chen DZ, Hannan R, Zhang S, Xiao G, Mu P, Hanker AB, Strand D, Arteaga CL, Desai N, Wang X, Xie Y, Wang T. Mapping Cellular Interactions from Spatially Resolved Transcriptomics Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.558298. [PMID: 37781617 PMCID: PMC10541142 DOI: 10.1101/2023.09.18.558298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between γδ T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.
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Affiliation(s)
- James Zhu
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Yunguan Wang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA
- Department of Pediatrics, University of Cincinnati, OH, 45221, USA
| | - Woo Yong Chang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Alicia Malewska
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Fabiana Napolitano
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jeffrey C. Gahan
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Nisha Unni
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Min Zhao
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Rongqing Yuan
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Fangjiang Wu
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Lauren Yue
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Lei Guo
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Zhuo Zhao
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Danny Z. Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Raquibul Hannan
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Siyuan Zhang
- Department of Pathology, 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
| | - Ping Mu
- Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
- Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ariella B. Hanker
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Douglas Strand
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Carlos L. Arteaga
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Neil Desai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Xinlei Wang
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, 76019, USA
- Center for Data Science Research and Education, College of Science, University of Texas at Arlington, Arlington, TX, 76019, USA
| | - Yang Xie
- 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
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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85
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Wang Z, Wang Y, Zhou T, Chen S, Morris D, Magalhães RDM, Li M, Wang S, Wang H, Xie Y, McSwiggin H, Oliver D, Yuan S, Zheng H, Mohammed J, Lai EC, McCarrey JR, Yan W. The Rapidly Evolving X-linked miR-506 Family Finetunes Spermatogenesis to Enhance Sperm Competition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.14.544876. [PMID: 37398484 PMCID: PMC10312769 DOI: 10.1101/2023.06.14.544876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Despite rapid evolution across eutherian mammals, the X-linked miR-506 family miRNAs are located in a region flanked by two highly conserved protein-coding genes (Slitrk2 and Fmr1) on the X chromosome. Intriguingly, these miRNAs are predominantly expressed in the testis, suggesting a potential role in spermatogenesis and male fertility. Here, we report that the X-linked miR-506 family miRNAs were derived from the MER91C DNA transposons. Selective inactivation of individual miRNAs or clusters caused no discernable defects, but simultaneous ablation of five clusters containing nineteen members of the miR-506 family led to reduced male fertility in mice. Despite normal sperm counts, motility and morphology, the KO sperm were less competitive than wild-type sperm when subjected to a polyandrous mating scheme. Transcriptomic and bioinformatic analyses revealed that these X-linked miR-506 family miRNAs, in addition to targeting a set of conserved genes, have more targets that are critical for spermatogenesis and embryonic development during evolution. Our data suggest that the miR-506 family miRNAs function to enhance sperm competitiveness and reproductive fitness of the male by finetuning gene expression during spermatogenesis.
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Affiliation(s)
- Zhuqing Wang
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Yue Wang
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Tong Zhou
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
| | - Sheng Chen
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Dayton Morris
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | | | - Musheng Li
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
| | - Shawn Wang
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
| | - Hetan Wang
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Yeming Xie
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
| | - Hayden McSwiggin
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Daniel Oliver
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
| | - Shuiqiao Yuan
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
| | - Huili Zheng
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Jaaved Mohammed
- Department of Developmental Biology, Memorial Sloan-Kettering Institute, 1275 York Ave, Box 252, New York, NY 10065, USA
| | - Eric C. Lai
- Department of Developmental Biology, Memorial Sloan-Kettering Institute, 1275 York Ave, Box 252, New York, NY 10065, USA
| | - John R. McCarrey
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, USA
| | - Wei Yan
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557, USA
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
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86
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Wang L, Dong W, Yin Z, Sheng J, Ezeana CF, Yang L, Yu X, Wong SSY, Wan Z, Danforth RL, Han K, Gao D, Wong STC. Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574236. [PMID: 38260351 PMCID: PMC10802354 DOI: 10.1101/2024.01.05.574236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model's attributes, reconstructs cellular lineage pathways, shedding light on the cell's evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.
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Affiliation(s)
- Lin Wang
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Wenjuan Dong
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Zheng Yin
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
- Biostatistics and Bioinformatics Shared Resource, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Jianting Sheng
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Chika F. Ezeana
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Li Yang
- T.T. and W. F. Chao Center for BRAIN, Houston Methodist Research Institute, Houston, Texas 77030
| | - Xiaohui Yu
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | | | - Zhihao Wan
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Rebecca L. Danforth
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Kun Han
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Dingcheng Gao
- Department of Cell & Development Biology, Weill Cornell Medical College, New York, NY 10065
| | - Stephen T. C. Wong
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
- Departments of Radiology, Pathology and Genomic Medicine, Houston Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030
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87
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Beumer J, Clevers H. Hallmarks of stemness in mammalian tissues. Cell Stem Cell 2024; 31:7-24. [PMID: 38181752 PMCID: PMC10769195 DOI: 10.1016/j.stem.2023.12.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/03/2023] [Accepted: 12/08/2023] [Indexed: 01/07/2024]
Abstract
All adult tissues experience wear and tear. Most tissues can compensate for cell loss through the activity of resident stem cells. Although the cellular maintenance strategies vary greatly between different adult (read: postnatal) tissues, the function of stem cells is best defined by their capacity to replace lost tissue through division. We discuss a set of six complementary hallmarks that are key enabling features of this basic function. These include longevity and self-renewal, multipotency, transplantability, plasticity, dependence on niche signals, and maintenance of genome integrity. We discuss these hallmarks in the context of some of the best-understood adult stem cell niches.
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Affiliation(s)
- Joep Beumer
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Basel, Switzerland.
| | - Hans Clevers
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Basel, Switzerland.
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88
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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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89
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Dylus D, Altenhoff A, Majidian S, Sedlazeck FJ, Dessimoz C. Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree. Nat Biotechnol 2024; 42:139-147. [PMID: 37081138 PMCID: PMC10791578 DOI: 10.1038/s41587-023-01753-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023]
Abstract
Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy. In a benchmark encompassing a broad variety of datasets, Read2Tree is 10-100 times faster than assembly-based approaches and in most cases more accurate-the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale.
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Affiliation(s)
- David Dylus
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- F. Hoffmann-La Roche Ltd, Immunology, Infectious Disease, and Ophthalmology (I2O), Roche Pharmaceutical Research and Early Development (pRED), Basel, Switzerland
| | - Adrian Altenhoff
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computer Science, ETH, Zurich, Switzerland
| | - Sina Majidian
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
| | - Christophe Dessimoz
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Computer Science, University College London, London, UK.
- Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, UK.
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90
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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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91
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Sashittal P, Schmidt H, Chan M, Raphael BJ. Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing. Cell Syst 2023; 14:1113-1121.e9. [PMID: 38128483 PMCID: PMC11257033 DOI: 10.1016/j.cels.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 10/31/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023]
Abstract
CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, "Startle", that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.
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Affiliation(s)
- Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Michelle Chan
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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92
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Sur A, Wang Y, Capar P, Margolin G, Prochaska MK, Farrell JA. Single-cell analysis of shared signatures and transcriptional diversity during zebrafish development. Dev Cell 2023; 58:3028-3047.e12. [PMID: 37995681 PMCID: PMC11181902 DOI: 10.1016/j.devcel.2023.11.001] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/24/2023] [Accepted: 11/01/2023] [Indexed: 11/25/2023]
Abstract
During development, animals generate distinct cell populations with specific identities, functions, and morphologies. We mapped transcriptionally distinct populations across 489,686 cells from 62 stages during wild-type zebrafish embryogenesis and early larval development (3-120 h post-fertilization). Using these data, we identified the limited catalog of gene expression programs reused across multiple tissues and their cell-type-specific adaptations. We also determined the duration each transcriptional state is present during development and identify unexpected long-term cycling populations. Focused clustering and transcriptional trajectory analyses of non-skeletal muscle and endoderm identified transcriptional profiles and candidate transcriptional regulators of understudied cell types and subpopulations, including the pneumatic duct, individual intestinal smooth muscle layers, spatially distinct pericyte subpopulations, and recently discovered best4+ cells. To enable additional discoveries, we make this comprehensive transcriptional atlas of early zebrafish development available through our website, Daniocell.
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Affiliation(s)
- Abhinav Sur
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - Yiqun Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Paulina Capar
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - Gennady Margolin
- Bioinformatics and Scientific Programming Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - Morgan Kathleen Prochaska
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA
| | - Jeffrey A Farrell
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20814, USA.
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93
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Pan X, Li H, Putta P, Zhang X. LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data. Nat Commun 2023; 14:8388. [PMID: 38104156 PMCID: PMC10725445 DOI: 10.1038/s41467-023-44173-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 12/03/2023] [Indexed: 12/19/2023] Open
Abstract
Lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for inference of cell lineage and cell types at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expressions are emerging. Effectively incorporating the gene expression data requires a reasonable model of how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), which integrates lineage barcode and gene expression data using asymmetric cell division model and infers cell lineages and ancestral cell states using Neighbor-Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. With inferred ancestral states, LinRace can also show how a progenitor cell generates a large population of cells with various functionalities.
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Affiliation(s)
- Xinhai Pan
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Hechen Li
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Pranav Putta
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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94
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Prillo S, Ravoor A, Yosef N, Song YS. ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.03.569785. [PMID: 38076815 PMCID: PMC10705529 DOI: 10.1101/2023.12.03.569785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
CRISPR/Cas9 gene editing technology has enabled lineage tracing for thousands of cells in vivo. However, most of the analysis of CRISPR/Cas9 lineage tracing data has so far been limited to the reconstruction of single-cell tree topologies, which depict lineage relationships between cells, but not the amount of time that has passed between ancestral cell states and the present. Time-resolved trees, known as chronograms, would allow one to study the evolutionary dynamics of cell populations at an unprecedented level of resolution. Indeed, time-resolved trees would reveal the timing of events on the tree, the relative fitness of subclones, and the dynamics underlying phenotypic changes in the cell population - among other important applications. In this work, we introduce the first scalable and accurate method to refine any given single-cell tree topology into a single-cell chronogram by estimating its branch lengths. To do this, we leverage a statistical model of CRISPR/Cas9 cutting with missing data, paired with a conservative version of maximum parsimony that reconstructs only the ancestral states that we are confident about. As part of our method, we propose a novel approach to represent and handle missing data - specifically, double-resection events - which greatly simplifies and speeds up branch length estimation without compromising quality. All this leads to a convex maximum likelihood estimation (MLE) problem that can be readily solved in seconds with off-the-shelf convex optimization solvers. To stabilize estimates in low-information regimes, we propose a simple penalized version of MLE using a minimum branch length and pseudocounts. We benchmark our method using simulations and show that it performs well on several tasks, outperforming more naive baselines. Our method, which we name 'ConvexML', is available through the cassiopeia open source Python package.
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Affiliation(s)
| | - Akshay Ravoor
- Computer Science Division, University of California, Berkeley
| | - Nir Yosef
- Computer Science Division, University of California, Berkeley
- Department of Systems Immunology, Weizmann Institute of Science
| | - Yun S. Song
- Computer Science Division, University of California, Berkeley
- Department of Statistics, University of California, Berkeley
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95
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May DA, Taha F, Child MA, Ewald SE. How colonization bottlenecks, tissue niches, and transmission strategies shape protozoan infections. Trends Parasitol 2023; 39:1074-1086. [PMID: 37839913 DOI: 10.1016/j.pt.2023.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/25/2023] [Accepted: 09/25/2023] [Indexed: 10/17/2023]
Abstract
Protozoan pathogens such as Plasmodium spp., Leishmania spp., Toxoplasma gondii, and Trypanosoma spp. are often associated with high-mortality, acute and chronic diseases of global health concern. For transmission and immune evasion, protozoans have evolved diverse strategies to interact with a range of host tissue environments. These interactions are linked to disease pathology, yet our understanding of the association between parasite colonization and host homeostatic disruption is limited. Recently developed techniques for cellular barcoding have the potential to uncover the biology regulating parasite transmission, dissemination, and the stability of infection. Understanding bottlenecks to infection and the in vivo tissue niches that facilitate chronic infection and spread has the potential to reveal new aspects of parasite biology.
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Affiliation(s)
- Dana A May
- Department of Microbiology, Immunology, and Cancer Biology at the Carter Immunology Center, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Fatima Taha
- Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
| | - Matthew A Child
- Department of Life Sciences, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
| | - Sarah E Ewald
- Department of Microbiology, Immunology, and Cancer Biology at the Carter Immunology Center, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
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96
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Cui Z, Wei H, Goding C, Cui R. Stem cell heterogeneity, plasticity, and regulation. Life Sci 2023; 334:122240. [PMID: 37925141 DOI: 10.1016/j.lfs.2023.122240] [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: 09/08/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 11/06/2023]
Abstract
As a population of homogeneous cells with both self-renewal and differentiation potential, stem cell pools are highly compartmentalized and contain distinct subsets that exhibit stable but limited heterogeneity during homeostasis. However, their striking plasticity is showcased under natural or artificial stress, such as injury, transplantation, cancer, and aging, leading to changes in their phenotype, constitution, metabolism, and function. The complex and diverse network of cell-extrinsic niches and signaling pathways, together with cell-intrinsic genetic and epigenetic regulators, tightly regulate both the heterogeneity during homeostasis and the plasticity under perturbation. Manipulating these factors offers better control of stem cell behavior and a potential revolution in the current state of regenerative medicine. However, disruptions of normal regulation by genetic mutation or excessive plasticity acquisition may contribute to the formation of tumors. By harnessing innovative techniques that enhance our understanding of stem cell heterogeneity and employing novel approaches to maximize the utilization of stem cell plasticity, stem cell therapy holds immense promise for revolutionizing the future of medicine.
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Affiliation(s)
- Ziyang Cui
- Department of Dermatology and Venerology, Peking University First Hospital, Beijing 100034, China.
| | - Hope Wei
- Department of Biology, Boston University, 5 Cummington Mall, Boston, MA 02215, United States of America
| | - Colin Goding
- Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Headington, Oxford OX37DQ, UK
| | - Rutao Cui
- Skin Disease Research Institute, The 2nd Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
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97
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Affiliation(s)
- Bushra Raj
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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98
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Li L, Bowling S, McGeary SE, Yu Q, Lemke B, Alcedo K, Jia Y, Liu X, Ferreira M, Klein AM, Wang SW, Camargo FD. A mouse model with high clonal barcode diversity for joint lineage, transcriptomic, and epigenomic profiling in single cells. Cell 2023; 186:5183-5199.e22. [PMID: 37852258 DOI: 10.1016/j.cell.2023.09.019] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 07/11/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023]
Abstract
Cellular lineage histories and their molecular states encode fundamental principles of tissue development and homeostasis. Current lineage-recording mouse models have insufficient barcode diversity and single-cell lineage coverage for profiling tissues composed of millions of cells. Here, we developed DARLIN, an inducible Cas9 barcoding mouse line that utilizes terminal deoxynucleotidyl transferase (TdT) and 30 CRISPR target sites. DARLIN is inducible, generates massive lineage barcodes across tissues, and enables the detection of edited barcodes in ∼70% of profiled single cells. Using DARLIN, we examined fate bias within developing hematopoietic stem cells (HSCs) and revealed unique features of HSC migration. Additionally, we established a protocol for joint transcriptomic and epigenomic single-cell measurements with DARLIN and found that cellular clonal memory is associated with genome-wide DNA methylation rather than gene expression or chromatin accessibility. DARLIN will enable the high-resolution study of lineage relationships and their molecular signatures in diverse tissues and physiological contexts.
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Affiliation(s)
- Li Li
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sarah Bowling
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Sean E McGeary
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Qi Yu
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Bianca Lemke
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Karel Alcedo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Yuemeng Jia
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Xugeng Liu
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Mark Ferreira
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Allon M Klein
- Department of Systems Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Shou-Wen Wang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; School of Science, Westlake University, Hangzhou, Zhejiang 310024, China.
| | - Fernando D Camargo
- Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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99
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Ballouz S, Kawaguchi RK, Pena MT, Fischer S, Crow M, French L, Knight FM, Adams LB, Gillis J. The transcriptional legacy of developmental stochasticity. Nat Commun 2023; 14:7226. [PMID: 37940702 PMCID: PMC10632366 DOI: 10.1038/s41467-023-43024-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 10/30/2023] [Indexed: 11/10/2023] Open
Abstract
Genetic and environmental variation are key contributors during organism development, but the influence of minor perturbations or noise is difficult to assess. This study focuses on the stochastic variation in allele-specific expression that persists through cell divisions in the nine-banded armadillo (Dasypus novemcinctus). We investigated the blood transcriptome of five wild monozygotic quadruplets over time to explore the influence of developmental stochasticity on gene expression. We identify an enduring signal of autosomal allelic variability that distinguishes individuals within a quadruplet despite their genetic similarity. This stochastic allelic variation, akin to X-inactivation but broader, provides insight into non-genetic influences on phenotype. The presence of stochastically canalized allelic signatures represents a novel axis for characterizing organismal variability, complementing traditional approaches based on genetic and environmental factors. We also developed a model to explain the inconsistent penetrance associated with these stochastically canalized allelic expressions. By elucidating mechanisms underlying the persistence of allele-specific expression, we enhance understanding of development's role in shaping organismal diversity.
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Affiliation(s)
- Sara Ballouz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales Sydney, Sydney, NSW, Australia
| | - Risa Karakida Kawaguchi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- Center for iPS Cell Research and Application, Kyoto University, Kyoto, Japan
| | - Maria T Pena
- US Department of Health and Human Services, Health Resources and Services Administration, Healthcare System Bureau, National Hansen's Disease Program, Baton Rouge, LA, 70803, USA
| | - Stephan Fischer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, F-75015, France
| | - Megan Crow
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
- Genentech, Inc., South San Francisco, CA, USA
| | - Leon French
- Physiology Department and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | | | - Linda B Adams
- US Department of Health and Human Services, Health Resources and Services Administration, Healthcare System Bureau, National Hansen's Disease Program, Baton Rouge, LA, 70803, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
- Physiology Department and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
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100
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Han X, Xu X, Yang C, Liu G. Microfluidic design in single-cell sequencing and application to cancer precision medicine. CELL REPORTS METHODS 2023; 3:100591. [PMID: 37725985 PMCID: PMC10545941 DOI: 10.1016/j.crmeth.2023.100591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/01/2023] [Accepted: 08/24/2023] [Indexed: 09/21/2023]
Abstract
Single-cell sequencing (SCS) is a crucial tool to reveal the genetic and functional heterogeneity of tumors, providing unique insights into the clonal evolution, microenvironment, drug resistance, and metastatic progression of cancers. Microfluidics is a critical component of many SCS technologies and workflows, conferring advantages in throughput, economy, and automation. Here, we review the current landscape of microfluidic architectures and sequencing techniques for single-cell omics analysis and highlight how these have enabled recent applications in oncology research. We also discuss the challenges and the promise of microfluidics-based single-cell analysis in the future of precision oncology.
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Affiliation(s)
- Xin Han
- CUHK(SZ)-Boyalife Joint Laboratory of Regenerative Medicine Engineering, Biomedical Engineering Programme, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Xing Xu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory for Chemical Biology of Fujian Province, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P.R. China; Institute of Molecular Medicine, State Key Laboratory of Oncogenes and Related 12 Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Chaoyang Yang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory for Chemical Biology of Fujian Province, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P.R. China; Institute of Molecular Medicine, State Key Laboratory of Oncogenes and Related 12 Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
| | - Guozhen Liu
- CUHK(SZ)-Boyalife Joint Laboratory of Regenerative Medicine Engineering, Biomedical Engineering Programme, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
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